diff --git a/data/sampled_jsons/0.077_OR_0.07_MLD_HumanAct12_FID_score_reported.jsonl b/data/sampled_jsons/0.077_OR_0.07_MLD_HumanAct12_FID_score_reported.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1cfc89268b7190b05dc797e5fbd2767714c9c07b
--- /dev/null
+++ b/data/sampled_jsons/0.077_OR_0.07_MLD_HumanAct12_FID_score_reported.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "motion-latent-diffusion/configs/config_ mld _ humanact 12 .yaml at main...", "date": "", "ddg_snippet": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model - motion-latent-diffusion/configs/config_ mld _ humanact 12 .yaml at main · ChenFengYe/motion-latent-diffusion.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/configs/config_mld_humanact12.yaml", "content": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model - motion-latent-diffusion/configs/config_ mld _ humanact 12 .yaml at main · ChenFengYe/motion-latent-diffusion."}
+{"idx": 1, "title": "Motion Flow Matching for Human Motion Synthesis and Editing", "date": "", "ddg_snippet": "Our method outperforms the baselines, achieving superior FID scores while maintaining faster sampling times. Please note that some axes in the plots are log-scaled for better comparison. Report issue for preceding element.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08895v1", "content": "Our method outperforms the baselines, achieving superior FID scores while maintaining faster sampling times. Please note that some axes in the plots are log-scaled for better comparison. Report issue for preceding element."}
+{"idx": 2, "title": "LS-GAN: Human Motion Synthesis with Latent-space GANs-Bohrium", "date": "", "ddg_snippet": "We perform experiments on the HumanML3D, HumanAct 12 benchmarks and demonstrate that a remarkably simple GAN in the latent space achieves a FID of 0.482 with more than 91% in FLOPs reduction compared to latent diffusion model.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/ls-gan-human-motion-synthesis-with-latent-space-gans/1083033613332643939-108597", "content": "We perform experiments on the HumanML3D, HumanAct 12 benchmarks and demonstrate that a remarkably simple GAN in the latent space achieves a FID of 0.482 with more than 91% in FLOPs reduction compared to latent diffusion model."}
+{"idx": 3, "title": "nan value & gt gt2 & KID problems when... - Githubissues", "date": "", "ddg_snippet": "When I ran the eval part, I met a few problems about unconstrained, and hope you can help me. The process is carried out, and no error is reported to stop the program.And why the difference in the FID score can be as much as ten times……", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/GuyTevet/motion-diffusion-model/191", "content": "When I ran the eval part, I met a few problems about unconstrained, and hope you can help me. The process is carried out, and no error is reported to stop the program.And why the difference in the FID score can be as much as ten times……"}
+{"idx": 4, "title": "РЕАЛЬНОЕ СОБЕСЕДОВАНИЕ / Junior ML-engineer (Data Scientist)...", "date": "", "ddg_snippet": "Качество: FID - score (Fréchet Inception Distance) на тесте ~25 (хорошо для CycleGAN; ниже — лучше, идеал 0.8 + rules match → auto-quarantine msg.", "subpage_snippet": "", "source": "careerclue.vercel.app", "link": "https://careerclue.vercel.app/blog/2025/08/30/Nbl4SaO51sA-realnoe-sobesedovanie-junior-ml-engineer-data-scientist-aytiteh", "content": "Качество: FID - score (Fréchet Inception Distance) на тесте ~25 (хорошо для CycleGAN; ниже — лучше, идеал 0.8 + rules match → auto-quarantine msg."}
+{"idx": 5, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 6, "title": "Точная погода в Москве на 14 дней подробно (г. Москва, Россия)...", "date": "", "ddg_snippet": "12:00. день. +7°. пасмурно, возможен небольшой дождь.", "subpage_snippet": "", "source": "pogoda7.ru", "link": "https://pogoda7.ru/prognoz/gorod134242-Russia-g_Moskva-Moskva/14days/full", "content": "12:00. день. +7°. пасмурно, возможен небольшой дождь."}
+{"idx": 7, "title": "AYT • Аэропорт Анталия - табло прилета", "date": "", "ddg_snippet": "Прибыл 07:57. 09:10.Ожидается 12:27.", "subpage_snippet": "", "source": "www.avionio.com", "link": "https://www.avionio.com/ru/airport/ayt/arrivals", "content": "Прибыл 07:57. 09:10.Ожидается 12:27."}
+{"idx": 8, "title": "КРЫМСКИЙ МОСТ: оперативная информация – Telegram", "date": "", "ddg_snippet": "12:00. С обеих сторон Крымского моста затруднений в проезде к пунктам ручного досмотра нет.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/most_official", "content": "12:00. С обеих сторон Крымского моста затруднений в проезде к пунктам ручного досмотра нет."}
+{"idx": 9, "title": "Диета 5-ый стол - что можно и чего нельзя, меню на неделю", "date": "", "ddg_snippet": "питье должно быть не более 200 мл при одноразовом приеме. Продолжительность составляет до 12 месяцев. Далее следует консультация с врачом для определения степени эффективности. При необходимости специалист даст рекомендации по продлению.", "subpage_snippet": "", "source": "wer.ru", "link": "https://wer.ru/articles/dieta-5-yy-stol/", "content": "питье должно быть не более 200 мл при одноразовом приеме. Продолжительность составляет до 12 месяцев. Далее следует консультация с врачом для определения степени эффективности. При необходимости специалист даст рекомендации по продлению."}
diff --git a/data/sampled_jsons/0cEZyhHEks_Taming_Knowledge_Conflicts_in_Language_Models_World_Capital_dataset_Table_3.jsonl b/data/sampled_jsons/0cEZyhHEks_Taming_Knowledge_Conflicts_in_Language_Models_World_Capital_dataset_Table_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a57db9ac672248d068e094814835a6aba8060a3d
--- /dev/null
+++ b/data/sampled_jsons/0cEZyhHEks_Taming_Knowledge_Conflicts_in_Language_Models_World_Capital_dataset_Table_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Jun 9, 2025 · For the experiments related to Table 2, we use a small fraction of samples from the filtered World Capital dataset to identify attention heads that achieve the highest parametric probability gains under coherent conflicts when knocked out.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v2", "content": "Jun 9, 2025 · For the experiments related to Table 2, we use a small fraction of samples from the filtered World Capital dataset to identify attention heads that achieve the highest parametric probability gains under coherent conflicts when knocked out."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models - OpenReview", "date": "", "ddg_snippet": "Abstract Language Models (LMs) often encounter knowl-edge conflicts when parametric memory con-tradicts contextual knowledge . Previous works attribute this conflict to the interplay between “memory heads” and “context heads”, attention heads assumed to promote either memory or con-text exclusively. In this study, we go beyond this fundamental assumption by uncovering a criti-cal ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0cEZyhHEks", "content": "Abstract Language Models (LMs) often encounter knowl-edge conflicts when parametric memory con-tradicts contextual knowledge . Previous works attribute this conflict to the interplay between “memory heads” and “context heads”, attention heads assumed to promote either memory or con-text exclusively. In this study, we go beyond this fundamental assumption by uncovering a criti-cal ..."}
+{"idx": 2, "title": "Taming Knowledge Conflicts in Language Models - GitHub", "date": "", "ddg_snippet": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GaotangLi/JUICE", "content": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated."}
+{"idx": 3, "title": "ICML Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Poster in Workshop: Actionable Interpretability Taming Knowledge Conflicts in Language Models Gaotang Li · Yuzhong Chen · Hanghang Tong [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/49596", "content": "Poster in Workshop: Actionable Interpretability Taming Knowledge Conflicts in Language Models Gaotang Li · Yuzhong Chen · Hanghang Tong [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT"}
+{"idx": 4, "title": "[PDF] Taming Knowledge Conflicts in Language Models ...", "date": "", "ddg_snippet": "Mar 14, 2025 · This work proposes Just Run Twice (JuICE), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Taming-Knowledge-Conflicts-in-Language-Models-Li-Chen/b7ba9df4eb239708cf48f25be87b5bceeca010e3", "content": "Mar 14, 2025 · This work proposes Just Run Twice (JuICE), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ..."}
+{"idx": 5, "title": "Taming Knowledge Conflicts in Language Models | AI Research ...", "date": "", "ddg_snippet": "Mar 16, 2025 · The researchers acknowledge that their approach primarily addresses binary conflicts (where there are two contradictory answers) but real- world knowledge resolution often involves more complex scenarios with multiple conflicting sources of information. The paper doesn't fully address how models should handle these more nuanced situations.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/taming-knowledge-conflicts-language-models", "content": "Mar 16, 2025 · The researchers acknowledge that their approach primarily addresses binary conflicts (where there are two contradictory answers) but real- world knowledge resolution often involves more complex scenarios with multiple conflicting sources of information. The paper doesn't fully address how models should handle these more nuanced situations."}
+{"idx": 6, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Mar 14, 2025 · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Mar 14, 2025 · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ..."}
+{"idx": 7, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge .In this paper, we study how language models respond to varying degrees of knowledge conflict and propose methods to regulate these behaviors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v1", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge .In this paper, we study how language models respond to varying degrees of knowledge conflict and propose methods to regulate these behaviors."}
+{"idx": 8, "title": "Taming Knowledge Conflicts in Language Models | OpenReview", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\"...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0cEZyhHEks&referrer=[the+profile+of+Hanghang+Tong](/profile?id=~Hanghang_Tong2)", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\"..."}
+{"idx": 9, "title": "(PDF) Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. dataset are around 200 for world capital , official language , and company founder, and around 500 for athlete sport, company. headquarters, and book author.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389894493_Taming_Knowledge_Conflicts_in_Language_Models", "content": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. dataset are around 200 for world capital , official language , and company founder, and around 500 for athlete sport, company. headquarters, and book author."}
diff --git a/data/sampled_jsons/10l1pGeOcK_SAFE-_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning.jsonl b/data/sampled_jsons/10l1pGeOcK_SAFE-_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..53dabb753dc722b6e04ac94fc762efba3f20befe
--- /dev/null
+++ b/data/sampled_jsons/10l1pGeOcK_SAFE-_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Safe : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "towards sparsity without incurring a sudden change of loss, all while performing flatness induction, yielding a sparse and flat minima . In practice, particularly for image classification, we introduce scheduling to the penalty parameter. λ𝜆\\lambdaitalic_λ.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v1", "content": "towards sparsity without incurring a sudden change of loss, all while performing flatness induction, yielding a sparse and flat minima . In practice, particularly for image classification, we introduce scheduling to the penalty parameter. λ𝜆\\lambdaitalic_λ."}
+{"idx": 1, "title": "(PDF) SAFE : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective.maximum Hessian eigenvalue of minima found by ADMM and SAF E. SAFE yields sparse and flat solutions.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392531034_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning", "content": "Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective.maximum Hessian eigenvalue of minima found by ADMM and SAF E. SAFE yields sparse and flat solutions."}
+{"idx": 2, "title": "Newly published papers and discussions around them.", "date": "", "ddg_snippet": "SAFE : Finding Sparse and Flat Minima to Improve Pruning .Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective.", "subpage_snippet": "", "source": "www.eye-on.ai", "link": "https://www.eye-on.ai/ai-articles/e6n7f8m6dc4a3aw-kysfw-p3bpn-gj8zp-p9mmz-b34zn-brxry-mr228-48slx-mew99-4724k-te62h-8ejzm-5lzzt-t3nh2-stb98-5fs7c-l7tsn-p6jbs-szxbt-pf6w4-pyb9z-8sfh4", "content": "SAFE : Finding Sparse and Flat Minima to Improve Pruning .Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective."}
+{"idx": 3, "title": "GitHub - LOG-postech/ safe -jax: [ICML 2025 Spotlight] Official JAX...", "date": "", "ddg_snippet": "SAFE : Finding Sparse and Flat Minima to Improve Pruning . Authors: Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee.Our work introduces SAFE , an algorithm designed to find sparse and flat minima , leading to improved model pruning performance.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LOG-postech/safe-jax", "content": "SAFE : Finding Sparse and Flat Minima to Improve Pruning . Authors: Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee.Our work introduces SAFE , an algorithm designed to find sparse and flat minima , leading to improved model pruning performance."}
+{"idx": 4, "title": "Kwanhee Lee - Google Scholar", "date": "", "ddg_snippet": "SAFE : Finding Sparse and Flat Minima to Improve Pruning .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=QLXgaCMAAAAJ&hl=en", "content": "SAFE : Finding Sparse and Flat Minima to Improve Pruning ."}
+{"idx": 5, "title": "dblp: List of computer science publications by Namhoon Lee", "date": "", "ddg_snippet": "top. '20. '10. '00. coauthors.Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee: SAFE : Finding Sparse and Flat Minima to Improve Pruning .", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/63/5359.html", "content": "top. '20. '10. '00. coauthors.Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee: SAFE : Finding Sparse and Flat Minima to Improve Pruning ."}
+{"idx": 6, "title": "Kwanhee Lee", "date": "", "ddg_snippet": "selected publications. SAFE : Finding Sparse and Flat Minima to Improve Pruning . Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and 1 more author.", "subpage_snippet": "", "source": "kwanhee-lee.github.io", "link": "https://kwanhee-lee.github.io/", "content": "selected publications. SAFE : Finding Sparse and Flat Minima to Improve Pruning . Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and 1 more author."}
+{"idx": 7, "title": "Cles for I terative M agnitude p runing", "date": "", "ddg_snippet": "When it comes to global pruning techniques, Iterative Magnitude Pruning (IMP) still stands as a state-of-the-art algorithm despite its simple nature, particularly in extremely sparse regimes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Y9t7MqZtCR", "content": "When it comes to global pruning techniques, Iterative Magnitude Pruning (IMP) still stands as a state-of-the-art algorithm despite its simple nature, particularly in extremely sparse regimes."}
+{"idx": 8, "title": "Youtube to MP3 Converter (Ad-free)", "date": "", "ddg_snippet": "EzConv is the safest platform to download MP3s as it doesn’t contain any third-party scripts or pop-up ads. Yes, it’s completely ad-free and runs on donations from our users. Our converter lets you trim the audio, and you can choose an audio quality from 64 kbps to 320 kbps.", "subpage_snippet": "", "source": "ezconv.com", "link": "https://ezconv.com/", "content": "EzConv is the safest platform to download MP3s as it doesn’t contain any third-party scripts or pop-up ads. Yes, it’s completely ad-free and runs on donations from our users. Our converter lets you trim the audio, and you can choose an audio quality from 64 kbps to 320 kbps."}
+{"idx": 9, "title": "Instant Background Remover - Remove Bg for Free Online | Photoroom", "date": "", "ddg_snippet": "Removing the background from product pictures enhances the focus on the product, maintains consistency and branding, provides versatility for marketing materials, allows for contextual flexibility, facilitates product comparison, and improves the overall aesthetics of your product listings.", "subpage_snippet": "", "source": "www.photoroom.com", "link": "https://www.photoroom.com/tools/background-remover", "content": "Removing the background from product pictures enhances the focus on the product, maintains consistency and branding, provides versatility for marketing materials, allows for contextual flexibility, facilitates product comparison, and improves the overall aesthetics of your product listings."}
diff --git a/data/sampled_jsons/1608.03981_DnCNN_architecture_layers.jsonl b/data/sampled_jsons/1608.03981_DnCNN_architecture_layers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0c7e1b5a3a1d1ecc5886b59b61ca5ea4d9858c06
--- /dev/null
+++ b/data/sampled_jsons/1608.03981_DnCNN_architecture_layers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1608.03981] Beyond a Gaussian Denoiser: Residual Learning of ...", "date": "", "ddg_snippet": "Aug 13, 2016 · With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1608.03981", "content": "Aug 13, 2016 · With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking."}
+{"idx": 1, "title": "GitHub - cszn/DnCNN: Beyond a Gaussian Denoiser: Residual ... (PDF) Beyond a Gaussian Denoiser: Residual Learning of Deep ... Learning Deep CNN Denoiser Prior for Image Restoration Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for ... Beyond a Gaussian denoiser: Residual learning of deep CNN for ... Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Beyond a Gaussian Denoiser : Residual Learning of Deep CNN for Image Beyond a Gaussian denoiser : Residual learning of deep CNN for image Beyond a Gaussian denoiser : Residual learning of deep CNN for image Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image GitHub - yjn870/DnCNN-pytorch: PyTorch implementation of ...", "date": "", "ddg_snippet": "News: DRUNet •State-of-the-art denoising performance •Can be used for plug-and-play image restoration •https://github.com/cszn/DPIR/blob/master/main_dpir_denoising.py See full list on github.com I recommend to use the PyTorch code for training and testing. The model parameters of MatConvnet and PyTorch are same. •main_train_dncnn.py •main_test_dncnn.py •main_test_dncnn3_deblocking.py See full list on github.com •Simplenn version •DnCNN_TrainingCodes_v1.1 •DagNN version •DnCNN_TrainingCodes_DagNN_v1.1 See full list on github.com •[demos] Demo_test_DnCNN-.m. •[models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking. See full list on github.com I have trained new Flexible DnCNN (FDnCNN) models based on FFDNet. FDnCNN can handle noise level range of [0, 75] via a single model. Demo_FDnCNN_Gray.m Demo_FDnCNN_Gray_Clip.m Demo_FDnCNN_Color.m Demo_FDnCNN_Color_Clip.m See full list on github.com •Network Architecture •Batch normalization and residual learning are beneficial to Gaussian denoising (especially for a single noise level). The residual of a noisy image corrupted by additive white Gaussian noise (AWGN) follows a constant Gaussian distribution which stablizes batch normalization during training. •Histogram of noisy patches, clean patches, and residual (noise) patches from a batch of training. The noise level is 25, the patch size is 40x40, the batch size is 128. •Histogram of noisy patches, clean patches, and residual (noise) patches from another batch of training. The noise level is 25, the patch size is 40x40, the batch size is 128. •Noise-free image super-resolution does not have this property. •Predicting the residual can be interpreted as performing one gradient descent inference step at starting point (i.e., noisy image). See full list on github.com The average PSNR(dB) results of different methods on the BSD68 dataset. Visual Results See full list on github.com Average PSNR(dB)/SSIM results of different methods for Gaussian denoising with noise level 15, 25 and 50 on BSD68 dataset, single image super-resolution with upscaling factors 2, 3 and 40 on Set5, Set14, BSD100 and Urban100 datasets, JPEG image deblocking with quality factors 10, 20, 30 and 40 on Classic5 and LIVE11 datasets. See full list on github.com •MATLAB R2015b •Cuda-8.0 & cuDNN v-5.1 •MatConvNet or just MATLAB R2015b to test the model. DnCNN /Demo_test_DnCNN.m Lines 64 to 65 in 4a4b5b8 See full list on github.com ==================================================================== See full list on github.com Aug 13, 2016 · The architecture of the proposed DnCNN network. Denoising results of the image \"parrot\" with noise level 50. Color image denoising results of one image from the DSD68 dataset with noise level 35. The architecture of the proposed CNN denoiser is illus-trated in Figure 1. It consists of seven layers with three different blocks, i.e., “Dilated Convolution+ReLU” block in the first layer , five “Dilated Convolution+Batch Normal-ization+ReLU” blocks in the middle layers , and “Dilated Convolution” block in the last layer . Deep Architecture : Given the DnCNN with depth D, there are three types of layers , shown in Fig. 1 with three different colors. (i) Conv+ReLU: for the first layer , 64 filters of size 3 3 c are used to generate 64 feature maps, and rectified linear units (ReLU, max(0; )) are then utilized for nonlinearity. The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into ... Can a single dncnn model handle a general image denoising task? Moreover, we showed the feasibility to train a single DnCNN model to handle three general image denoising tasks, including Gaussian denoising with unknown noise level, single image super-resolution with multiple up-scaling factors, and JPEG image deblocking with different quality factors. Does dncnn remove a latent clean image? With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking. How does dncnn-3 work? The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks, such as image denoising, image super-resolution and JPEG image deblocking. The left is the input image corrupted by different degradations, the right is the restored image by DnCNN -3. How effective is dncnn for image denoising? Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing. Dive into the research topics of 'Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising'. Why should we train a single dncnn model? This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks , such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Can dncnn-3 produce visually pleasant output result if input image is corrupted? We can see that DnCNN-3 can produce visually pleasant output result even the input image is corrupted by several distortions with different levels in different regions. In this paper, a deep convolutional neural network was proposed for image denoising, where residual learning is adopted to separating noise from noisy observation. The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN", "content": "News: DRUNet •State-of-the-art denoising performance •Can be used for plug-and-play image restoration •https://github.com/cszn/DPIR/blob/master/main_dpir_denoising.py See full list on github.com I recommend to use the PyTorch code for training and testing. The model parameters of MatConvnet and PyTorch are same. •main_train_dncnn.py •main_test_dncnn.py •main_test_dncnn3_deblocking.py See full list on github.com •Simplenn version •DnCNN_TrainingCodes_v1.1 •DagNN version •DnCNN_TrainingCodes_DagNN_v1.1 See full list on github.com •[demos] Demo_test_DnCNN-.m. •[models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking. See full list on github.com I have trained new Flexible DnCNN (FDnCNN) models based on FFDNet. FDnCNN can handle noise level range of [0, 75] via a single model. Demo_FDnCNN_Gray.m Demo_FDnCNN_Gray_Clip.m Demo_FDnCNN_Color.m Demo_FDnCNN_Color_Clip.m See full list on github.com •Network Architecture •Batch normalization and residual learning are beneficial to Gaussian denoising (especially for a single noise level). The residual of a noisy image corrupted by additive white Gaussian noise (AWGN) follows a constant Gaussian distribution which stablizes batch normalization during training. •Histogram of noisy patches, clean patches, and residual (noise) patches from a batch of training. The noise level is 25, the patch size is 40x40, the batch size is 128. •Histogram of noisy patches, clean patches, and residual (noise) patches from another batch of training. The noise level is 25, the patch size is 40x40, the batch size is 128. •Noise-free image super-resolution does not have this property. •Predicting the residual can be interpreted as performing one gradient descent inference step at starting point (i.e., noisy image). See full list on github.com The average PSNR(dB) results of different methods on the BSD68 dataset. Visual Results See full list on github.com Average PSNR(dB)/SSIM results of different methods for Gaussian denoising with noise level 15, 25 and 50 on BSD68 dataset, single image super-resolution with upscaling factors 2, 3 and 40 on Set5, Set14, BSD100 and Urban100 datasets, JPEG image deblocking with quality factors 10, 20, 30 and 40 on Classic5 and LIVE11 datasets. See full list on github.com •MATLAB R2015b •Cuda-8.0 & cuDNN v-5.1 •MatConvNet or just MATLAB R2015b to test the model. DnCNN /Demo_test_DnCNN.m Lines 64 to 65 in 4a4b5b8 See full list on github.com ==================================================================== See full list on github.com Aug 13, 2016 · The architecture of the proposed DnCNN network. Denoising results of the image \"parrot\" with noise level 50. Color image denoising results of one image from the DSD68 dataset with noise level 35. The architecture of the proposed CNN denoiser is illus-trated in Figure 1. It consists of seven layers with three different blocks, i.e., “Dilated Convolution+ReLU” block in the first layer , five “Dilated Convolution+Batch Normal-ization+ReLU” blocks in the middle layers , and “Dilated Convolution” block in the last layer . Deep Architecture : Given the DnCNN with depth D, there are three types of layers , shown in Fig. 1 with three different colors. (i) Conv+ReLU: for the first layer , 64 filters of size 3 3 c are used to generate 64 feature maps, and rectified linear units (ReLU, max(0; )) are then utilized for nonlinearity. The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into ... Can a single dncnn model handle a general image denoising task? Moreover, we showed the feasibility to train a single DnCNN model to handle three general image denoising tasks, including Gaussian denoising with unknown noise level, single image super-resolution with multiple up-scaling factors, and JPEG image deblocking with different quality factors. Does dncnn remove a latent clean image? With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking. How does dncnn-3 work? The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks, such as image denoising, image super-resolution and JPEG image deblocking. The left is the input image corrupted by different degradations, the right is the restored image by DnCNN -3. How effective is dncnn for image denoising? Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing. Dive into the research topics of 'Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising'. Why should we train a single dncnn model? This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks , such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Can dncnn-3 produce visually pleasant output result if input image is corrupted? We can see that DnCNN-3 can produce visually pleasant output result even the input image is corrupted by several distortions with different levels in different regions. In this paper, a deep convolutional neural network was proposed for image denoising, where residual learning is adopted to separating noise from noisy observation. The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors."}
+{"idx": 2, "title": "GitHub - yjn870/DnCNN-pytorch: PyTorch implementation of ...", "date": "", "ddg_snippet": "The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjn870/DnCNN-pytorch", "content": "The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors."}
+{"idx": 3, "title": "GitHub - shlpu/ DnCNN : DnCNN implemented purely by Matlab R2018a", "date": "", "ddg_snippet": "1608 . 03981 .pdf.About time: Due to the implementation of Matlab BatchNorm layers , which doesn't support testing before training is finished, I have to make an trade-off (i.e., force finalizing training for each epoch, which is time-consuming) in order to test the performance(PSNR and SSIM)...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shlpu/DnCNN", "content": "1608 . 03981 .pdf.About time: Due to the implementation of Matlab BatchNorm layers , which doesn't support testing before training is finished, I have to make an trade-off (i.e., force finalizing training for each epoch, which is time-consuming) in order to test the performance(PSNR and SSIM)..."}
+{"idx": 4, "title": "Image and Video Denoising using DnCNN | by Varun Saproo | Medium", "date": "", "ddg_snippet": "DnCNN Architecture . DnCNN Architecture . Conv + ReLU: filter size of 3, no of filters as 64, a stride of 1, using zero paddings to maintain the output shape after convolution, using ReLU as the activation function.", "subpage_snippet": "", "source": "saproovarun.medium.com", "link": "https://saproovarun.medium.com/image-and-video-denoising-using-dncnn-216be1ff8ba1", "content": "DnCNN Architecture . DnCNN Architecture . Conv + ReLU: filter size of 3, no of filters as 64, a stride of 1, using zero paddings to maintain the output shape after convolution, using ReLU as the activation function."}
+{"idx": 5, "title": "deepinv.models. dncnn — deepinverse 0.3 documentation", "date": "", "ddg_snippet": "Source code for deepinv.models. dncnn . import torch.nn as nn import torch from .utils import get_weights_url import math from .base import Denoiser. [docs] class DnCNN (Denoiser): r\"\"\" DnCNN convolutional denoiser. The architecture was introduced in https...", "subpage_snippet": "", "source": "deepinv.github.io", "link": "https://deepinv.github.io/deepinv/_modules/deepinv/models/dncnn.html", "content": "Source code for deepinv.models. dncnn . import torch.nn as nn import torch from .utils import get_weights_url import math from .base import Denoiser. [docs] class DnCNN (Denoiser): r\"\"\" DnCNN convolutional denoiser. The architecture was introduced in https..."}
+{"idx": 6, "title": "DnCNN -Beyond a Gaussian Denoiser: Residual... - Programmer All", "date": "", "ddg_snippet": "DnCNN -Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.", "subpage_snippet": "", "source": "www.programmerall.com", "link": "https://www.programmerall.com/article/64492156638/", "content": "DnCNN -Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising."}
+{"idx": 7, "title": "DnCNN -pytorch from yjn870 - GithubHelp", "date": "", "ddg_snippet": "The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors.", "subpage_snippet": "", "source": "githubhelp.com", "link": "https://githubhelp.com/yjn870/DnCNN-pytorch", "content": "The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors."}
+{"idx": 8, "title": "【图像去噪】论文复现：新手入门必看！ DnCNN 的Pytorch...", "date": "", "ddg_snippet": "nn.Sequential(* layers ) def forward(self, x): residual = self. dncnn _net(x) return x - residual ``` #### 开始训练过程 一旦完成了上述准备工作之后就可以着手启动正式的学习流程了。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_36584673/article/details/139743314", "content": "nn.Sequential(* layers ) def forward(self, x): residual = self. dncnn _net(x) return x - residual ``` #### 开始训练过程 一旦完成了上述准备工作之后就可以着手启动正式的学习流程了。"}
+{"idx": 9, "title": "(PDF) Beyond a Gaussian Denoiser: Residual Learning of Deep ...", "date": "", "ddg_snippet": "Aug 13, 2016 · The architecture of the proposed DnCNN network. Denoising results of the image \"parrot\" with noise level 50. Color image denoising results of one image from the DSD68 dataset with noise level 35.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/306187437_Beyond_a_Gaussian_Denoiser_Residual_Learning_of_Deep_CNN_for_Image_Denoising", "content": "Aug 13, 2016 · The architecture of the proposed DnCNN network. Denoising results of the image \"parrot\" with noise level 50. Color image denoising results of one image from the DSD68 dataset with noise level 35."}
diff --git a/data/sampled_jsons/1IyPRv1A0r_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_.jsonl b/data/sampled_jsons/1IyPRv1A0r_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..25fcb7601e95c1d6a180655b9da2d3cbba2b662c
--- /dev/null
+++ b/data/sampled_jsons/1IyPRv1A0r_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "distributional regression using a conditional deep generative model , considering full-dimensional noise.A deep generative approach to conditional sampling. Journal of the American Statistical Association, pages 1–12.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "distributional regression using a conditional deep generative model , considering full-dimensional noise.A deep generative approach to conditional sampling. Journal of the American Statistical Association, pages 1–12."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using", "date": "", "ddg_snippet": "Conditional deep generative models for distribution regression . Convergence rates of the Sieve MLE. Neural network class.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "Conditional deep generative models for distribution regression . Convergence rates of the Sieve MLE. Neural network class."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "A deep neural network models the conditional generator, enabling a flexible approach to distribution regression .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "A deep neural network models the conditional generator, enabling a flexible approach to distribution regression ."}
+{"idx": 3, "title": "Level-Wise Conditional Distribution", "date": "", "ddg_snippet": "Wasserstein- Based Deep Conditional Generative Models : Recent approaches pose conditional generative modeling as learning a mapping from a reference noise distribution (plus covariates. xx. x) to.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/level-wise-conditional-distribution", "content": "Wasserstein- Based Deep Conditional Generative Models : Recent approaches pose conditional generative modeling as learning a mapping from a reference noise distribution (plus covariates. xx. x) to."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models ."}
+{"idx": 5, "title": "Linear Regression -Free Linear Regression Tool", "date": "", "ddg_snippet": "Linear Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.", "subpage_snippet": "", "source": "www.yeschat.ai", "link": "https://www.yeschat.ai/gpts-9t55k5HnQvO-Linear-Regression", "content": "Linear Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data."}
+{"idx": 6, "title": "NMA: Network Meta-Analysis Based on Multivariate Meta-Analysis and...", "date": "", "ddg_snippet": "Network meta- regression based on contrast- based approach using the multivariate meta- regression model . Effect modifications by study-level covariates (specified in the setup function) can be assessed.", "subpage_snippet": "", "source": "cran.r-project.org", "link": "https://cran.r-project.org/web/packages/NMA/NMA.pdf", "content": "Network meta- regression based on contrast- based approach using the multivariate meta- regression model . Effect modifications by study-level covariates (specified in the setup function) can be assessed."}
+{"idx": 7, "title": "huggingface/paper-central-data-2 · Datasets at Hugging Face", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/huggingface/paper-central-data-2/viewer/default/train?p=553", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models ."}
+{"idx": 8, "title": "Beyond the delta method-Bohrium", "date": "", "ddg_snippet": "[7] A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/beyond-the-delta-method/867762556003942856-108557", "content": "[7] A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models ."}
+{"idx": 9, "title": "Enhancing brain tumor classification with a diffusion denoising model ...", "date": "", "ddg_snippet": "Brain tumors. Conditional deep convolutional neural network. Denoising diffusion model . Synthetic data augmentation.", "subpage_snippet": "", "source": "accscience.com", "link": "https://accscience.com/journal/AN/articles/online_first/5593", "content": "Brain tumors. Conditional deep convolutional neural network. Denoising diffusion model . Synthetic data augmentation."}
diff --git a/data/sampled_jsons/1rh8iTehBc_Position_Section_3.3_Llama2_Llama3_license_conflict_clause.jsonl b/data/sampled_jsons/1rh8iTehBc_Position_Section_3.3_Llama2_Llama3_license_conflict_clause.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..10ca44a21687674454ac60ab12393ec2ebdf6c02
--- /dev/null
+++ b/data/sampled_jsons/1rh8iTehBc_Position_Section_3.3_Llama2_Llama3_license_conflict_clause.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LLAMA 3.3 COMMUNITY LICENSE AGREEMENT", "date": "", "ddg_snippet": "Dec 6, 2024 · Llama 3.3 Community License Agreementi. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about ...", "subpage_snippet": "", "source": "www.llama.com", "link": "https://www.llama.com/llama3_3/license/", "content": "Dec 6, 2024 · Llama 3.3 Community License Agreementi. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about ..."}
+{"idx": 1, "title": "Llama3 License Explained - DEV Community", "date": "", "ddg_snippet": "Apr 19, 2024 · The Meta Llama 3 Community License Agreement seems quite liberal at first glance, offering a breath... Tagged with ai, llama.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/llm_explorer/llama3-license-explained-2915", "content": "Apr 19, 2024 · The Meta Llama 3 Community License Agreement seems quite liberal at first glance, offering a breath... Tagged with ai, llama."}
+{"idx": 2, "title": "llama/LICENSE at main · meta-llama/llama · GitHub", "date": "", "ddg_snippet": "LLAMA 2 COMMUNITY LICENSE AGREEMENT Llama 2 Version Release Date: July 18, 2023 \"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Meta-Llama/llama/blob/main/LICENSE", "content": "LLAMA 2 COMMUNITY LICENSE AGREEMENT Llama 2 Version Release Date: July 18, 2023 \"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein."}
+{"idx": 3, "title": "Meta Llama 3 License", "date": "", "ddg_snippet": "Apr 18, 2024 · META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 “ Agreement ” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.", "subpage_snippet": "", "source": "www.llama.com", "link": "https://www.llama.com/llama3/license/", "content": "Apr 18, 2024 · META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 “ Agreement ” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein."}
+{"idx": 4, "title": "Llama 3 versus LLama 3.1 License Terms — /dev/lawyer", "date": "", "ddg_snippet": "Jul 24, 2024 · Meta Llama 3 3 .1 Version Release Date: April 18 July 23, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.", "subpage_snippet": "", "source": "writing.kemitchell.com", "link": "https://writing.kemitchell.com/2024/07/24/Llama-3-versus-Llama-3-1-License", "content": "Jul 24, 2024 · Meta Llama 3 3 .1 Version Release Date: April 18 July 23, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein."}
+{"idx": 5, "title": "Meta-Llama-3-8B-Instruct - Hugging Face", "date": "", "ddg_snippet": "Apr 18, 2024 · **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. How to use This repository contains two versions of Meta- Llama-3 -8B-Instruct, for use with transformers and with the original llama3 codebase. Use with transformers", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct", "content": "Apr 18, 2024 · **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. How to use This repository contains two versions of Meta- Llama-3 -8B-Instruct, for use with transformers and with the original llama3 codebase. Use with transformers"}
+{"idx": 6, "title": "Установка текстовой нейросети \" llama 3 .1\" (3.2) на... | Дзен", "date": "", "ddg_snippet": "Требуемый софт. Скачивание и установка \" llama 3 .1\". Пока скачивается модель, установим \"Docker Desktop\". Если больше ничего не надо, общайтесь прямо так. Установка OpenWebUI. График нагрузки при работе \"llama1.3\".", "subpage_snippet": "", "source": "dzen.ru", "link": "https://dzen.ru/a/ZwOqgGBHnnxRnQQf", "content": "Требуемый софт. Скачивание и установка \" llama 3 .1\". Пока скачивается модель, установим \"Docker Desktop\". Если больше ничего не надо, общайтесь прямо так. Установка OpenWebUI. График нагрузки при работе \"llama1.3\"."}
+{"idx": 7, "title": "Как локально запустить LLama 3 .1? (Бесплатная Нейросеть на...)", "date": "", "ddg_snippet": "Детально разберем, как установить локально себе на ПК бесплатную нейросеть LLama 3 .1 8b на 8 миллиардов параметров, чтобы можно было ей пользоваться без интернета, безлимитно и абсолютно приватно.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/d76af8bc6442199cee97c36834fb23f6/", "content": "Детально разберем, как установить локально себе на ПК бесплатную нейросеть LLama 3 .1 8b на 8 миллиардов параметров, чтобы можно было ей пользоваться без интернета, безлимитно и абсолютно приватно."}
+{"idx": 8, "title": "How to Run Llama 3 .1 Locally on your Computer with... - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=2orxyu8c7Ek", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 9, "title": "Пошаговый гайд по развертыванию LLM на примере Llama 3 .2", "date": "", "ddg_snippet": "Почему модель Llama подходит для быстрого развертывания. Рассмотрим модель Llama 3 .2 в качестве примера LLM. Сейчас это последняя официально выпущенная версия языковой модели с открытым исходным кодом.", "subpage_snippet": "", "source": "mClouds.ru", "link": "https://mClouds.ru/2025/04/how-deploy-llm-on-llama/", "content": "Почему модель Llama подходит для быстрого развертывания. Рассмотрим модель Llama 3 .2 в качестве примера LLM. Сейчас это последняя официально выпущенная версия языковой модели с открытым исходным кодом."}
diff --git a/data/sampled_jsons/2024_arxiv_language_model_reasoning_critical_transition.jsonl b/data/sampled_jsons/2024_arxiv_language_model_reasoning_critical_transition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0cfe8df644ec904174822b8f3900e031396f1ae9
--- /dev/null
+++ b/data/sampled_jsons/2024_arxiv_language_model_reasoning_critical_transition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training Large Language Models to Reason in a Continuous Latent", "date": "", "ddg_snippet": "... language models (LLMs) have demonstrated remarkable reasoning abilities, emerging from extensive pretraining on human languages (Dubey et al., 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06769v2", "content": "... language models (LLMs) have demonstrated remarkable reasoning abilities, emerging from extensive pretraining on human languages (Dubey et al., 2024 ..."}
+{"idx": 1, "title": "Large Language Models and Operations Research: A Structured", "date": "", "ddg_snippet": "... large language models (LLMs) have shown potential to address these limitations through semantic understanding, structured generation, and reasoning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.18180v1", "content": "... large language models (LLMs) have shown potential to address these limitations through semantic understanding, structured generation, and reasoning ..."}
+{"idx": 2, "title": "CogniLoad: A Synthetic Natural Language Reasoning Benchmark", "date": "", "ddg_snippet": "Current benchmarks for long-context reasoning in Large Language Models (LLMs) often blur critical factors like intrinsic task complexity, distractor ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.18458v1", "content": "Current benchmarks for long-context reasoning in Large Language Models (LLMs) often blur critical factors like intrinsic task complexity, distractor ..."}
+{"idx": 3, "title": "Context Reasoner: Incentivizing Reasoning Capability for", "date": "", "ddg_snippet": "Large Language Models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning , and generation Ouyang et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14585v2", "content": "Large Language Models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning , and generation Ouyang et al."}
+{"idx": 4, "title": "Distilling mathematical reasoning capabilities into Small", "date": "", "ddg_snippet": "Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers.", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/W4401263076/distilling-mathematical-reasoning-capabilities-into-small-language-models", "content": "Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers."}
+{"idx": 5, "title": "Vision Language Models Come Rushing In", "date": "", "ddg_snippet": "Only in 2024 did large language models (LLMs) in cars become a “thing” and did designers of automotive silicon begin asking for LLM performance ...", "subpage_snippet": "", "source": "semiengineering.com", "link": "https://semiengineering.com/vision-language-models-come-rushing-in/", "content": "Only in 2024 did large language models (LLMs) in cars become a “thing” and did designers of automotive silicon begin asking for LLM performance ..."}
+{"idx": 6, "title": "AECBench: A Hierarchical Benchmark for Knowledge Evaluation of", "date": "", "ddg_snippet": "Large language models (LLMs), as a novel information technology, are seeing increasing adoption in the Architecture, Engineering, and Construction ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.18776v1", "content": "Large language models (LLMs), as a novel information technology, are seeing increasing adoption in the Architecture, Engineering, and Construction ..."}
+{"idx": 7, "title": "Generative AI Act II: Test Time Scaling Drives Cognition", "date": "", "ddg_snippet": "We now witness theemergence of \" Act II \" ( 2024 -present), where models are transitioning fromknowledge-retrieval systems (in latent space) to ...", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/597073/generative-ai-act-ii:-test-time-scaling-drives-cognition-engineering", "content": "We now witness theemergence of \" Act II \" ( 2024 -present), where models are transitioning fromknowledge-retrieval systems (in latent space) to ..."}
+{"idx": 8, "title": "Research on LLM for Vulnerability Detection", "date": "", "ddg_snippet": "As large language models (LLMs) have shown their efficacy in lots of language -related fields, researchers in the field of information security are ...", "subpage_snippet": "", "source": "blog.wohin.me", "link": "https://blog.wohin.me/posts/recent-llm-for-vuln-detection/", "content": "As large language models (LLMs) have shown their efficacy in lots of language -related fields, researchers in the field of information security are ..."}
+{"idx": 9, "title": "Power outage — Wikipedia Republished // WIKI 2", "date": "", "ddg_snippet": "Power failures are particularly critical at sites where the environment and public safety are at risk. ... Other critical systems, such as ...", "subpage_snippet": "", "source": "wiki2.org", "link": "https://wiki2.org/en/Power_outage", "content": "Power failures are particularly critical at sites where the environment and public safety are at risk. ... Other critical systems, such as ..."}
diff --git a/data/sampled_jsons/2024_foundation_segmentation_model_domain_adaptation_promptable_segmentation_year_2024.jsonl b/data/sampled_jsons/2024_foundation_segmentation_model_domain_adaptation_promptable_segmentation_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3d6e6b26f0b9d24dba2d3c5597a7a2fcd280b2f
--- /dev/null
+++ b/data/sampled_jsons/2024_foundation_segmentation_model_domain_adaptation_promptable_segmentation_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improving the Generalization of Segmentation Foundation Model ...", "date": "", "ddg_snippet": "In particular, as a promptable image segmentation foundation model with strong zero-shot generalization, the Segment-Anything model (SAM) [27] was developed by training on billions of annotated masks.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Improving_the_Generalization_of_Segmentation_Foundation_Model_under_Distribution_Shift_CVPR_2024_paper.pdf", "content": "In particular, as a promptable image segmentation foundation model with strong zero-shot generalization, the Segment-Anything model (SAM) [27] was developed by training on billions of annotated masks."}
+{"idx": 1, "title": "Collaborating Foundation Models for Domain Generalized ...", "date": "", "ddg_snippet": "by Y Benigmim · 2024 · Cited by 150 — SAM, origi- nally trained for promptable segmentation , is adapted to our task through the use of point-based prompting (Fig. 4). Specifically, we adopt the ... 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Benigmim_Collaborating_Foundation_Models_for_Domain_Generalized_Semantic_Segmentation_CVPR_2024_paper.pdf", "content": "by Y Benigmim · 2024 · Cited by 150 — SAM, origi- nally trained for promptable segmentation , is adapted to our task through the use of point-based prompting (Fig. 4). Specifically, we adopt the ... 12 pages"}
+{"idx": 2, "title": "Task-Specific Adaptation of Segmentation Foundation Model via ... Improving the Generalization of Segmentation Foundation Model ... Prompting to Adapt Foundational Segmentation Models [CVPR 2024] Open-Set Domain Adaptation for Semantic Segmentation Black-Box Adaptation for Medical Image Segmentation Prompting Foundational Models for Omni-supervised Instance ... Improving the Generalization of Segmentation Foundation Model under Task-Specific Adaptation of Segmentation Foundation Model via Prompt Prompting to Adapt Foundational Segmentation Models Improving the Generalization of Segmentation Foundation Model under Task-Specific Adaptation of Segmentation Foundation Model via Prompt Improving the Generalization of Segmentation Foundation Model under Collaborating Foundation Models for Domain Generalized ...", "date": "", "ddg_snippet": "Mar 14, 2024 · Recently, foundation models trained on massive datasets to adapt to a wide range of tasks have attracted considerable attention and are actively being explored within the computer vision community. Among these, the Segment Anything Model (SAM) stands out for its remarkable progress in generalizability and flexibility for image segmentation tasks, achieved through prompt-based object mask ... In particular, as a promptable image segmentation foundation model with strong zero-shot generalization, the Segment-Anything model (SAM) [27] was developed by training on billions of annotated masks. Oct 28, 2024 · Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications. Official PyTorch implementation for CVPR 2024 paper: Open-Set Domain Adaptation for Semantic Segmentation Seun-An Choe*, Ah-Hyung Shin*, Keon-Hee Park, Jinwoo Choi † , and Gyeong-Moon Park † In this work, we proposed one of the first Black-Box adaptation methods, called BAPS, for the adaptation of foundation models for prompted segmentation . BAPS consists of a pretrained image encoder and a trainable IP decoder, that generates a visual prompt as a function of the input image and given prompt. Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation . Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ... Which image segmentation Foundation model is able to zero/few-shot generalization? The success of large language models has inspired the computer vision community to explore image segmenta-tion foundation model that is able to zero/few-shot general-ize through prompt engineering. Segment-Anything (SAM) , among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Can a segmentation Foundation model be customized? To address these challenges, we propose a task-specific adaptation (i.e., customization) of the segmentation foundation model via prompt learning tailored to SAM. What is the difference between a segmentation model and a training-to-adapt model? Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications. Is segment-anything a good image segmentation model? Segment-Anything (SAM), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift. What is segment anything model (Sam)? Among these, the Segment Anything Model (SAM) stands out for its remarkable progress in generalizability and flexibility for image segmentation tasks , achieved through prompt-based object mask generation. Does weak supervision improve the generalization of domain adaptive segmen-tation methods? When weak supervision is provided, both state-of-the-art generic source-free domain adaptation methods and weakly supervised domain adaptive segmen-tation method improve the generalization on all three types of weak supervisions. Finally, our proposed weakly super-vised method achieves a remarkable improvement over all competing methods. Segment Anything Model (SAM) [35], a prominent vision foundation model , is trained for promptable segmentation tasks. SAM excels in producing high-quality masks for any segmentation prompt.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09199", "content": "Mar 14, 2024 · Recently, foundation models trained on massive datasets to adapt to a wide range of tasks have attracted considerable attention and are actively being explored within the computer vision community. Among these, the Segment Anything Model (SAM) stands out for its remarkable progress in generalizability and flexibility for image segmentation tasks, achieved through prompt-based object mask ... In particular, as a promptable image segmentation foundation model with strong zero-shot generalization, the Segment-Anything model (SAM) [27] was developed by training on billions of annotated masks. Oct 28, 2024 · Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications. Official PyTorch implementation for CVPR 2024 paper: Open-Set Domain Adaptation for Semantic Segmentation Seun-An Choe*, Ah-Hyung Shin*, Keon-Hee Park, Jinwoo Choi † , and Gyeong-Moon Park † In this work, we proposed one of the first Black-Box adaptation methods, called BAPS, for the adaptation of foundation models for prompted segmentation . BAPS consists of a pretrained image encoder and a trainable IP decoder, that generates a visual prompt as a function of the input image and given prompt. Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation . Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ... Which image segmentation Foundation model is able to zero/few-shot generalization? The success of large language models has inspired the computer vision community to explore image segmenta-tion foundation model that is able to zero/few-shot general-ize through prompt engineering. Segment-Anything (SAM) , among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Can a segmentation Foundation model be customized? To address these challenges, we propose a task-specific adaptation (i.e., customization) of the segmentation foundation model via prompt learning tailored to SAM. What is the difference between a segmentation model and a training-to-adapt model? Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications. Is segment-anything a good image segmentation model? Segment-Anything (SAM), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift. What is segment anything model (Sam)? Among these, the Segment Anything Model (SAM) stands out for its remarkable progress in generalizability and flexibility for image segmentation tasks , achieved through prompt-based object mask generation. Does weak supervision improve the generalization of domain adaptive segmen-tation methods? When weak supervision is provided, both state-of-the-art generic source-free domain adaptation methods and weakly supervised domain adaptive segmen-tation method improve the generalization on all three types of weak supervisions. Finally, our proposed weakly super-vised method achieves a remarkable improvement over all competing methods. Segment Anything Model (SAM) [35], a prominent vision foundation model , is trained for promptable segmentation tasks. SAM excels in producing high-quality masks for any segmentation prompt."}
+{"idx": 3, "title": "Prompting to Adapt Foundational Segmentation Models", "date": "", "ddg_snippet": "Oct 28, 2024 · Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3664647.3680884", "content": "Oct 28, 2024 · Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications."}
+{"idx": 4, "title": "[CVPR 2024] Open-Set Domain Adaptation for Semantic Segmentation", "date": "", "ddg_snippet": "Official PyTorch implementation for CVPR 2024 paper: Open-Set Domain Adaptation for Semantic Segmentation Seun-An Choe*, Ah-Hyung Shin*, Keon-Hee Park, Jinwoo Choi † , and Gyeong-Moon Park †", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KU-VGI/BUS", "content": "Official PyTorch implementation for CVPR 2024 paper: Open-Set Domain Adaptation for Semantic Segmentation Seun-An Choe*, Ah-Hyung Shin*, Keon-Hee Park, Jinwoo Choi † , and Gyeong-Moon Park †"}
+{"idx": 5, "title": "Black-Box Adaptation for Medical Image Segmentation", "date": "", "ddg_snippet": "In this work, we proposed one of the first Black-Box adaptation methods, called BAPS, for the adaptation of foundation models for prompted segmentation . BAPS consists of a pretrained image encoder and a trainable IP decoder, that generates a visual prompt as a function of the input image and given prompt.", "subpage_snippet": "", "source": "papers.miccai.org", "link": "https://papers.miccai.org/miccai-2024/paper/0668_paper.pdf", "content": "In this work, we proposed one of the first Black-Box adaptation methods, called BAPS, for the adaptation of foundation models for prompted segmentation . BAPS consists of a pretrained image encoder and a trainable IP decoder, that generates a visual prompt as a function of the input image and given prompt."}
+{"idx": 6, "title": "Prompting Foundational Models for Omni-supervised Instance ...", "date": "", "ddg_snippet": "Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation . Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10678642", "content": "Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation . Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ..."}
+{"idx": 7, "title": "Improving the Generalization of Segmentation Foundation ...", "date": "", "ddg_snippet": "10 Apr 2024 — In this work, we aim to adapt SAM to downstream tasks without accessing to the source domain data to avoid the high computation overhead and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03502v2", "content": "10 Apr 2024 — In this work, we aim to adapt SAM to downstream tasks without accessing to the source domain data to avoid the high computation overhead and ..."}
+{"idx": 8, "title": "CVPR 2024: Foundation Models + Visual Prompting Are ...", "date": "", "ddg_snippet": "In this article, we explore Visual Prompting, a technique that enables the adaptation of large vision models to new tasks.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@tenyks_blogger/cvpr-2024-foundation-models-visual-prompting-are-about-to-disrupt-computer-vision-026f2c1c3a2f", "content": "In this article, we explore Visual Prompting, a technique that enables the adaptation of large vision models to new tasks."}
+{"idx": 9, "title": "Adapting segment anything model for medical image ...", "date": "", "ddg_snippet": "by J Wu · 2025 · Cited by 797 — We propose the Medical SAM Adapter (Med-SA), which is one of the first methods to integrate SAM into medical image segmentation .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1361841525000945", "content": "by J Wu · 2025 · Cited by 797 — We propose the Medical SAM Adapter (Med-SA), which is one of the first methods to integrate SAM into medical image segmentation ."}
diff --git a/data/sampled_jsons/2208.01565_abstract.jsonl b/data/sampled_jsons/2208.01565_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af718e6be9dfb27264b89bbea2619df6b7053c34
--- /dev/null
+++ b/data/sampled_jsons/2208.01565_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2208.01565] Approximate Bayesian Neural Operators ... Approximate Bayesian Neural Operators: Uncertainty ... Emilia Magnani - Google Scholar Emilia Magnani - dblp arXiv:2208.01565v1 [cs.LG] 2 Aug 2022 Neural Operator induced Gaussian Process framework for ... Learning semilinear neural operators: a unified recursive ...", "date": "", "ddg_snippet": "Aug 2, 2022 · Abstract page for arXiv paper 2208.01565 : Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Aug 2, 2022 · Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically ... University of Tübingen - Cited by 40 - Probabilistic Numerics - Machine Learning Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022) Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ... Nov 1, 2024 · The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared… Abstract Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2208.01565", "content": "Aug 2, 2022 · Abstract page for arXiv paper 2208.01565 : Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Aug 2, 2022 · Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically ... University of Tübingen - Cited by 40 - Probabilistic Numerics - Machine Learning Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022) Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ... Nov 1, 2024 · The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared… Abstract Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present ..."}
+{"idx": 1, "title": "arXiv:2208.01565v1 [cs.LG] 2 Aug 2022", "date": "", "ddg_snippet": "Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.01565.pdf", "content": "Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ..."}
+{"idx": 2, "title": "Emilia Magnani - Google Scholar", "date": "", "ddg_snippet": "E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig. arXiv preprint arXiv: 2208 . 01565 , 2022.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=_zPcNdEAAAAJ&hl=en", "content": "E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig. arXiv preprint arXiv: 2208 . 01565 , 2022."}
+{"idx": 3, "title": "Emilia Magnani - dblp", "date": "", "ddg_snippet": "Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022)", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/206/6101", "content": "Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022)"}
+{"idx": 4, "title": "Learning semilinear neural operators: a unified recursive ...", "date": "", "ddg_snippet": "Abstract Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.15656v2", "content": "Abstract Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present ..."}
+{"idx": 5, "title": "Approximate Bayesian Neural Operators: Uncertainty Quantication for", "date": "", "ddg_snippet": "Abstract . Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.01565", "content": "Abstract . Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs)."}
+{"idx": 6, "title": "Approximate Bayesian Neural Operators: Uncertainty Quantification for...", "date": "", "ddg_snippet": "Abstract . Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/362430096_Approximate_Bayesian_Neural_Operators_Uncertainty_Quantification_for_Parametric_PDEs", "content": "Abstract . Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for..."}
+{"idx": 7, "title": "dblp: List of computer science publications by Emilia Magnani", "date": "", "ddg_snippet": "persistent URL: https://dblp.org/rec/journals/corr/abs- 2208 - 01565 . Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig : Approximate Bayesian Neural...", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/206/6101.html", "content": "persistent URL: https://dblp.org/rec/journals/corr/abs- 2208 - 01565 . Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig : Approximate Bayesian Neural..."}
+{"idx": 8, "title": "Calibrated Uncertainty Quantification for Operator Learning", "date": "", "ddg_snippet": "Abstract . Operator learning has been increasingly adopted in scientific and engineering applications, many of which require calibrated uncertainty quantification.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=cGpegxy12T&name=pdf", "content": "Abstract . Operator learning has been increasingly adopted in scientific and engineering applications, many of which require calibrated uncertainty quantification."}
+{"idx": 9, "title": "Learning semilinear neural operators: A unified recursive framework for...", "date": "", "ddg_snippet": "Ap-proximate Bayesian neural operators: Uncertainty quantification for parametric PDEs. arXiv preprint arXiv: 2208 . 01565 , 2022.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04728344v1/document", "content": "Ap-proximate Bayesian neural operators: Uncertainty quantification for parametric PDEs. arXiv preprint arXiv: 2208 . 01565 , 2022."}
diff --git a/data/sampled_jsons/2403.01698_pdf.jsonl b/data/sampled_jsons/2403.01698_pdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d0000ba7980d7f41e258a955c9162de05bc3dfb
--- /dev/null
+++ b/data/sampled_jsons/2403.01698_pdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) How to improve creativity: a study of gamification,", "date": "", "ddg_snippet": "PDF | In today’s world of knowledge-based economies, gig economies, crowdsourcing, and overall ICT-driven creativity, the avenues toward ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364435038_How_to_improve_creativity_a_study_of_gamification_money_and_punishment", "content": "PDF | In today’s world of knowledge-based economies, gig economies, crowdsourcing, and overall ICT-driven creativity, the avenues toward ..."}
+{"idx": 1, "title": "[2403.01698] Hypertext Entity Extraction in Webpage", "date": "", "ddg_snippet": "by Y Yang · 2024 · Cited by 1 — This paper introduces a new dataset (HEED) and a MoEEF framework for hypertext entity extraction, outperforming existing models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.01698", "content": "by Y Yang · 2024 · Cited by 1 — This paper introduces a new dataset (HEED) and a MoEEF framework for hypertext entity extraction, outperforming existing models."}
+{"idx": 2, "title": "arXiv:2403.01698v1 [cs.CL] 4 Mar 2024", "date": "", "ddg_snippet": "by Y Yang · 2024 · Cited by 1 — This paper introduces HEED, a dataset with hypertext features, and MoEEF, a framework using Mixture of Experts for webpage entity extraction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.01698", "content": "by Y Yang · 2024 · Cited by 1 — This paper introduces HEED, a dataset with hypertext features, and MoEEF, a framework using Mixture of Experts for webpage entity extraction."}
+{"idx": 3, "title": "Analysis of PDEs May 2024", "date": "", "ddg_snippet": "27] arXiv:2405. 01698 [ pdf , html , other ] ... Comments: arXiv admin note: substantial text overlap with arXiv: 2403 .15057", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/math.AP/2024-05", "content": "27] arXiv:2405. 01698 [ pdf , html , other ] ... Comments: arXiv admin note: substantial text overlap with arXiv: 2403 .15057"}
+{"idx": 4, "title": "topological quantum computation in nLab", "date": "", "ddg_snippet": "... and Feed-Forward on a Trapped Ion Quantum Computer , Nature Communications Physics 7 (2024) 205 [ doi:10.1038/s42005-024- 01698 -3 , arXiv:2302.01917 ]", "subpage_snippet": "", "source": "ncatlab.org", "link": "https://ncatlab.org/nlab/show/topological+quantum+computation", "content": "... and Feed-Forward on a Trapped Ion Quantum Computer , Nature Communications Physics 7 (2024) 205 [ doi:10.1038/s42005-024- 01698 -3 , arXiv:2302.01917 ]"}
+{"idx": 5, "title": "Michael Mahoney - Publications", "date": "", "ddg_snippet": "... of the SC23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis , 868–874 (2023) ( pdf ...", "subpage_snippet": "", "source": "www.stat.berkeley.edu", "link": "https://www.stat.berkeley.edu/~mmahoney/pubs.html", "content": "... of the SC23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis , 868–874 (2023) ( pdf ..."}
+{"idx": 6, "title": "Automating XPath Query Generation Using NLP for ...", "date": "", "ddg_snippet": "Available: https://arxiv.org/abs/ 2403.01698 . [19] Y. Zhou et al., ”Simplified DOM Trees for Transferable Attribute. Extraction from the Web,” 2021. [Online] ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/11040147/11042273/11042798.pdf", "content": "Available: https://arxiv.org/abs/ 2403.01698 . [19] Y. Zhou et al., ”Simplified DOM Trees for Transferable Attribute. Extraction from the Web,” 2021. [Online] ..."}
+{"idx": 7, "title": "NOVER: Incentive Training for Language Models via Verifier-Free", "date": "", "ddg_snippet": "Figure 2: Examples of Qwen2.5-7B-NOVER on a range of text-to-text tasks, demonstrating its ability to handle open-ended questions such as “Discuss ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16022v2", "content": "Figure 2: Examples of Qwen2.5-7B-NOVER on a range of text-to-text tasks, demonstrating its ability to handle open-ended questions such as “Discuss ..."}
+{"idx": 8, "title": "NOVER: Incentive Training for Language Models via Verifier-Free", "date": "", "ddg_snippet": "Figure 2: Examples of Qwen2.5-7B-NOVER on a range of text-to-text tasks, demonstrating its ability to handle open-ended questions such as “Discuss ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16022v1", "content": "Figure 2: Examples of Qwen2.5-7B-NOVER on a range of text-to-text tasks, demonstrating its ability to handle open-ended questions such as “Discuss ..."}
+{"idx": 9, "title": "[Literature Review] Hypertext Entity Extraction in Webpage", "date": "", "ddg_snippet": "The paper titled \"Hypertext Entity Extraction in Webpages\" discusses an advancement in the field of natural language processing (NLP), particularly focusing ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/hypertext-entity-extraction-in-webpage", "content": "The paper titled \"Hypertext Entity Extraction in Webpages\" discusses an advancement in the field of natural language processing (NLP), particularly focusing ..."}
diff --git a/data/sampled_jsons/2406.05072_Theorem_3.2_function-valued_Gaussian_processes_are_equivalent.jsonl b/data/sampled_jsons/2406.05072_Theorem_3.2_function-valued_Gaussian_processes_are_equivalent.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5f8df19bceb45c8d8ac5aba21c08171e5817f4c
--- /dev/null
+++ b/data/sampled_jsons/2406.05072_Theorem_3.2_function-valued_Gaussian_processes_are_equivalent.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "probabilistic neural operators for functional", "date": "", "ddg_snippet": "by C Bülte · 2025 · Cited by 3 — Linearization Turns Neural Operators into. Function - Valued Gaussian Processes , June 2024. URL http://arxiv.org/abs/ 2406.05072 . arXiv ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.12902", "content": "by C Bülte · 2025 · Cited by 3 — Linearization Turns Neural Operators into. Function - Valued Gaussian Processes , June 2024. URL http://arxiv.org/abs/ 2406.05072 . arXiv ..."}
+{"idx": 1, "title": "Probabilistic neural operators for functional uncertainty ...", "date": "", "ddg_snippet": "18 Feb 2025 — Linearization Turns Neural Operators into Function - Valued Gaussian Processes , June 2024. URL http://arxiv.org/abs/ 2406.05072 . arXiv ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12902v1", "content": "18 Feb 2025 — Linearization Turns Neural Operators into Function - Valued Gaussian Processes , June 2024. URL http://arxiv.org/abs/ 2406.05072 . arXiv ..."}
+{"idx": 2, "title": "Approximate Bayesian Neural Operators: Uncertainty ...", "date": "", "ddg_snippet": "Linearization turns neural operators into function - valued Gaussian processes . arXiv preprint arXiv: 2406.05072 , 2024. Radford M. Neal. Bayesian Learning for ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/70be375e4aec7a205e768c1b81cb4d1e4ba06a2f.pdf", "content": "Linearization turns neural operators into function - valued Gaussian processes . arXiv preprint arXiv: 2406.05072 , 2024. Radford M. Neal. Bayesian Learning for ..."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/2408.17052_hyperparameters_beta_gamma.jsonl b/data/sampled_jsons/2408.17052_hyperparameters_beta_gamma.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a763401c5f735638b9a8f978e99b0774eaa32598
--- /dev/null
+++ b/data/sampled_jsons/2408.17052_hyperparameters_beta_gamma.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2408 . 17052 ] Can We Leave Deepfake Data Behind in Training...", "date": "", "ddg_snippet": "Computer Science > Computer Vision and Pattern Recognition. arXiv: 2408 . 17052 (cs).(or arXiv: 2408 . 17052 v1 [cs.CV] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "Computer Science > Computer Vision and Pattern Recognition. arXiv: 2408 . 17052 (cs).(or arXiv: 2408 . 17052 v1 [cs.CV] for this version)."}
+{"idx": 1, "title": "Why do we need the hyperparameters beta and alpha in LDA?", "date": "", "ddg_snippet": "The alpha and beta parameters come from the fact that the dirichlet distribution, (a generalization of the beta distribution) takes these as parameters in the prior distribution. So to answer your first question, will the formula above work without the alpha and gamma , yes...", "subpage_snippet": "", "source": "datascience.stackexchange.com", "link": "https://datascience.stackexchange.com/questions/30369/why-do-we-need-the-hyperparameters-beta-and-alpha-in-lda", "content": "The alpha and beta parameters come from the fact that the dirichlet distribution, (a generalization of the beta distribution) takes these as parameters in the prior distribution. So to answer your first question, will the formula above work without the alpha and gamma , yes..."}
+{"idx": 2, "title": "LDA Alpha and Beta Parameters - The Intuition | ThoughtVector", "date": "", "ddg_snippet": "Beta represents topic-word density - with a high beta , topics are made up of most of the words in the corpus, and with a low beta they consist of few words.", "subpage_snippet": "", "source": "www.thoughtvector.io", "link": "https://www.thoughtvector.io/blog/lda-alpha-and-beta-parameters-the-intuition/", "content": "Beta represents topic-word density - with a high beta , topics are made up of most of the words in the corpus, and with a low beta they consist of few words."}
+{"idx": 3, "title": "DLS C2W3 Momentum vs Adam Beta Hyperparameters", "date": "", "ddg_snippet": "In C2W3 video \" Tuning Process\" Professor Ng mentions that the momentum term is second priority for tuning but he goes on to say that he almost never tunes the beta params for ADAM optimization.", "subpage_snippet": "", "source": "community.deeplearning.ai", "link": "https://community.deeplearning.ai/t/dls-c2w3-momentum-vs-adam-beta-hyperparameters/301684", "content": "In C2W3 video \" Tuning Process\" Professor Ng mentions that the momentum term is second priority for tuning but he goes on to say that he almost never tunes the beta params for ADAM optimization."}
+{"idx": 4, "title": "calculate_ beta : Calculate the beta hyperparameter for...", "date": "", "ddg_snippet": "Helper function for calculating the beta parameter of an inverse gamma distribution given absolute deviation from the popReconstruct elicitation statement. Used for quantifying measurement error in popReconstruct components.", "subpage_snippet": "", "source": "rdrr.io", "link": "https://rdrr.io/github/ihmeuw-demographics/popMethods/man/calculate_beta.html", "content": "Helper function for calculating the beta parameter of an inverse gamma distribution given absolute deviation from the popReconstruct elicitation statement. Used for quantifying measurement error in popReconstruct components."}
+{"idx": 5, "title": "A Quality-Centric Framework for Generic Deepfake Detection", "date": "", "ddg_snippet": "8 Nov 2024 — Luo, Z. Wang, and C. Li, “Can We Leave Deepfake Data Behind in Training Deepfake Detector?” arXiv preprint arXiv: 2408.17052 , 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.05335v1", "content": "8 Nov 2024 — Luo, Z. Wang, and C. Li, “Can We Leave Deepfake Data Behind in Training Deepfake Detector?” arXiv preprint arXiv: 2408.17052 , 2024."}
+{"idx": 6, "title": "Understanding Optimization Algorithms in AI - Simple Science", "date": "", "ddg_snippet": "Hyperparameters are like the knobs on your favorite radio. They control how the model learns. If you turn the knobs too far, you might end up with a channel full of static. This can lead to unstable learning where the model starts making wild guesses instead of learning effectively.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-05-27-understanding-optimization-algorithms-in-ai--a3d5po5", "content": "Hyperparameters are like the knobs on your favorite radio. They control how the model learns. If you turn the knobs too far, you might end up with a channel full of static. This can lead to unstable learning where the model starts making wild guesses instead of learning effectively."}
+{"idx": 7, "title": "Vinija's Notes • Coursera-DL • Improving Deep Neural Networks...", "date": "", "ddg_snippet": "Sampling the Exponential Weighting Hyperparameter ( Beta ): Beta is used for calculating exponentially weighted averages. If considering beta values between 0.9 and 0.999, it’s inefficient to sample linearly.", "subpage_snippet": "", "source": "vinija.ai", "link": "https://vinija.ai/CourseraDL/improving-deep-neural-networks/", "content": "Sampling the Exponential Weighting Hyperparameter ( Beta ): Beta is used for calculating exponentially weighted averages. If considering beta values between 0.9 and 0.999, it’s inefficient to sample linearly."}
+{"idx": 8, "title": "RLHF — swift 3.9.0.dev0 documentation", "date": "", "ddg_snippet": "Hyperparameters : beta : Coefficient before the implicit reward, default is 2.0. simpo_ gamma : Reward margin term, default is 1.0. cpo_alpha: The mixed CPO NLL loss for improving training stability; defaults to 1.0, set to 0.0 to use the original SimPO algorithm.", "subpage_snippet": "", "source": "swift.readthedocs.io", "link": "https://swift.readthedocs.io/en/latest/Instruction/RLHF.html", "content": "Hyperparameters : beta : Coefficient before the implicit reward, default is 2.0. simpo_ gamma : Reward margin term, default is 1.0. cpo_alpha: The mixed CPO NLL loss for improving training stability; defaults to 1.0, set to 0.0 to use the original SimPO algorithm."}
+{"idx": 9, "title": "Custom guide outperformed by automatic guide in mixture model | Forum", "date": "", "ddg_snippet": "I’ve built a small mixture of betas that I have (approximately) working with both a custom and AutoNormal guides.dist.Dirichlet(jnp.ones(K) * concentration / K) ) #. The hyperparameters for the cluster/component parameters alpha_shape = numpyro.sample(.", "subpage_snippet": "", "source": "forum.pyro.ai", "link": "https://forum.pyro.ai/t/custom-guide-outperformed-by-automatic-guide-in-mixture-model/6289", "content": "I’ve built a small mixture of betas that I have (approximately) working with both a custom and AutoNormal guides.dist.Dirichlet(jnp.ones(K) * concentration / K) ) #. The hyperparameters for the cluster/component parameters alpha_shape = numpyro.sample(."}
diff --git a/data/sampled_jsons/2501.19334_equation_2_Gaussian_policy_value.jsonl b/data/sampled_jsons/2501.19334_equation_2_Gaussian_policy_value.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f495b3aea89a273ea5663cbb0101a248d1e7023d
--- /dev/null
+++ b/data/sampled_jsons/2501.19334_equation_2_Gaussian_policy_value.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gaussian function - Wikipedia", "date": "", "ddg_snippet": "In mathematics, a Gaussian function, often simply referred to as a Gaussian , is a function of the base form. and with parametric extension. for arbitrary real constants a, b and non-zero c. It is named after the mathematician Carl Friedrich Gauss .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Gaussian_function", "content": "In mathematics, a Gaussian function, often simply referred to as a Gaussian , is a function of the base form. and with parametric extension. for arbitrary real constants a, b and non-zero c. It is named after the mathematician Carl Friedrich Gauss ."}
+{"idx": 1, "title": "The Value of Prediction in Identifying the Worst-Off - arXiv.org", "date": "", "ddg_snippet": "We formally define this quantity in Equation 3. While initially developed to specifically study the value of prediction in allocation problems where allocating goods to individuals had hetero-geneous efects, here we extend this concept to analyze the value of prediction in a related, but distinct, setting where we aim to identify the worst-of.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.19334", "content": "We formally define this quantity in Equation 3. While initially developed to specifically study the value of prediction in allocation problems where allocating goods to individuals had hetero-geneous efects, here we extend this concept to analyze the value of prediction in a related, but distinct, setting where we aim to identify the worst-of."}
+{"idx": 2, "title": "The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "by U Fischer-Abaigar · 2025 — D. 2 Optimal Policy Value in Gaussian Setting: Proof of Proposition 2 . Following Proposition 4, the value of the optimal screening policy π∗ can then be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.19334?", "content": "by U Fischer-Abaigar · 2025 — D. 2 Optimal Policy Value in Gaussian Setting: Proof of Proposition 2 . Following Proposition 4, the value of the optimal screening policy π∗ can then be ..."}
+{"idx": 3, "title": "The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.19334v3", "content": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic ..."}
+{"idx": 4, "title": "arXiv:2501.19334v1 [cs.CY] 31 Jan 2025", "date": "", "ddg_snippet": "D. 2 Optimal Policy Value in Gaussian Setting: Proof of Proposition 2 Following Proposition 4, the value of the optimal screening policy π∗ can then be expressed as:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.19334v1", "content": "D. 2 Optimal Policy Value in Gaussian Setting: Proof of Proposition 2 Following Proposition 4, the value of the optimal screening policy π∗ can then be expressed as:"}
+{"idx": 5, "title": "Q function and Error functions : demystified - GaussianWaves The Value of Prediction in Identifying the Worst-Off - arXiv.org Book - papers.nips.cc Narayanaswamy Balakrishnan - McMaster Experts JPART - moodle2.units.it Q function and Error functions : demystified - GaussianWaves Q function and Error functions : demystified - GaussianWaves Book - papers.nips.cc Neural Stochastic Differential Equations: Deep Latent ...", "date": "", "ddg_snippet": "Q functions are often encountered in the theoretical equations for Bit Error Rate (BER) involving AWGN channel. A brief discussion on Q function and its relation to erfcfunction is given here. Gaussian process is the underlying model for an AWGN channel.The probability density function of a Gaussian Distribution is given by Generally, in BER deriva... See full list on gaussianwaves.com The complementary error function represents the area under the two tails of zero mean Gaussian probability density function of variance σ 2 =1/ 2 σ 2 =1/ 2 . The error function gives the probability that the parameter lies outside that range. Therefore, the complementary error function is given by Hence, the error function is or equivalently, The erf funct... See full list on gaussianwaves.com From the limits of the integrals in equation (4) and (6) one can conclude that Q function is directly related to complementary error function (erfc). It follows from equation (4) and (6), Q functionis related to complementary error function by the following relation. See full list on gaussianwaves.com Keep a note of the following equations that can come handy when deriving probability of bit errors for various scenarios. These equations are compiled here for easy reference. If we have a normal variable X∼N(μ,σ 2 )X∼N(μ,σ 2 ), the probability that X>xX>xis If we want to know the probability that XX is away from the mean by an amount‘a’ (on the left o... See full list on gaussianwaves.com The Q-function and the error function (erf)are important mathematical functions that arise in many fields, including probability theory, statistics, signal processing, and communications engineering. Here are some reasons why these functions are important: 1. Probability calculations: The Q-function and erf function are used in probability calculat... See full list on gaussianwaves.com We formally define this quantity in Equation 3. While initially developed to specifically study the value of prediction in allocation problems where allocating goods to individuals had hetero-geneous efects, here we extend this concept to analyze the value of prediction in a related, but distinct, setting where we aim to identify the worst-of. The Power of Resets in Online Reinforcement Learning Zak Mhammedi, Dylan J Foster, Alexander Rakhlin Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov Accurate approximation of the expected value , standard deviation, and probability density function of extreme order statistics from Gaussian samples. Communications in Statistics Part B: Simulation and Computation. 53:869-878. 2024 Comparison of extreme order statistics from two sets of heterogeneous dependent random variables under random shocks. In this article we describe in detail the Bayesian perspective on statistical inference and demonstrate that it provides a more principled approach to modeling public administration data. Because many datasets in public administration are population-level, one-time unique collections, or descriptive of fluid events, the Bayesian reliance on probability as a descrip-tion of unknown quantities ... Which function is used in probability calculations involving Gaussian distributions? Probability calculations: The Q-function and erf function are used in probability calculations involving Gaussian distributions. The Q-function gives the probability that a random variable from a normal distribution will exceed a certain threshold value. What is Q function in Gaussian probability density function? Thus Q function gives the area of the shaded curve with the transformation \\ (y = \\frac {x-\\mu} {\\sigma}\\) applied to the Gaussian probability density function. Essentially, Q function evaluates the tail probability of normal distribution (area of shaded area in the above figure). Who are the authors of graph diffusion policy optimization? Graph Diffusion Policy OptimizationYijing Liu, Chao Du, Tianyu Pang, Chongxuan LI, Min Lin, Wei Chen UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-TrainingBiao Gong, Shuai Tan, Yutong Feng, Xiaoying Xie, Yuyuan Li, Chaochao Chen, Kecheng Zheng, Yujun Shen, Deli Zhao May 23, 2019 · This work develops a variational inference framework for deep latent Gaussian models via stochastic automatic differentiation in Wiener space, where the variational approximations to the posterior are obtained by Girsanov (mean-shift) transformation of the standard Wiener process and the computation of gradients is based on the theory of Stochastic flows. In deep latent Gaussian models, the ...", "subpage_snippet": "", "source": "www.gaussianwaves.com", "link": "https://www.gaussianwaves.com/2012/07/q-function-and-error-functions/", "content": "Q functions are often encountered in the theoretical equations for Bit Error Rate (BER) involving AWGN channel. A brief discussion on Q function and its relation to erfcfunction is given here. Gaussian process is the underlying model for an AWGN channel.The probability density function of a Gaussian Distribution is given by Generally, in BER deriva... See full list on gaussianwaves.com The complementary error function represents the area under the two tails of zero mean Gaussian probability density function of variance σ 2 =1/ 2 σ 2 =1/ 2 . The error function gives the probability that the parameter lies outside that range. Therefore, the complementary error function is given by Hence, the error function is or equivalently, The erf funct... See full list on gaussianwaves.com From the limits of the integrals in equation (4) and (6) one can conclude that Q function is directly related to complementary error function (erfc). It follows from equation (4) and (6), Q functionis related to complementary error function by the following relation. See full list on gaussianwaves.com Keep a note of the following equations that can come handy when deriving probability of bit errors for various scenarios. These equations are compiled here for easy reference. If we have a normal variable X∼N(μ,σ 2 )X∼N(μ,σ 2 ), the probability that X>xX>xis If we want to know the probability that XX is away from the mean by an amount‘a’ (on the left o... See full list on gaussianwaves.com The Q-function and the error function (erf)are important mathematical functions that arise in many fields, including probability theory, statistics, signal processing, and communications engineering. Here are some reasons why these functions are important: 1. Probability calculations: The Q-function and erf function are used in probability calculat... See full list on gaussianwaves.com We formally define this quantity in Equation 3. While initially developed to specifically study the value of prediction in allocation problems where allocating goods to individuals had hetero-geneous efects, here we extend this concept to analyze the value of prediction in a related, but distinct, setting where we aim to identify the worst-of. The Power of Resets in Online Reinforcement Learning Zak Mhammedi, Dylan J Foster, Alexander Rakhlin Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov Accurate approximation of the expected value , standard deviation, and probability density function of extreme order statistics from Gaussian samples. Communications in Statistics Part B: Simulation and Computation. 53:869-878. 2024 Comparison of extreme order statistics from two sets of heterogeneous dependent random variables under random shocks. In this article we describe in detail the Bayesian perspective on statistical inference and demonstrate that it provides a more principled approach to modeling public administration data. Because many datasets in public administration are population-level, one-time unique collections, or descriptive of fluid events, the Bayesian reliance on probability as a descrip-tion of unknown quantities ... Which function is used in probability calculations involving Gaussian distributions? Probability calculations: The Q-function and erf function are used in probability calculations involving Gaussian distributions. The Q-function gives the probability that a random variable from a normal distribution will exceed a certain threshold value. What is Q function in Gaussian probability density function? Thus Q function gives the area of the shaded curve with the transformation \\ (y = \\frac {x-\\mu} {\\sigma}\\) applied to the Gaussian probability density function. Essentially, Q function evaluates the tail probability of normal distribution (area of shaded area in the above figure). Who are the authors of graph diffusion policy optimization? Graph Diffusion Policy OptimizationYijing Liu, Chao Du, Tianyu Pang, Chongxuan LI, Min Lin, Wei Chen UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-TrainingBiao Gong, Shuai Tan, Yutong Feng, Xiaoying Xie, Yuyuan Li, Chaochao Chen, Kecheng Zheng, Yujun Shen, Deli Zhao May 23, 2019 · This work develops a variational inference framework for deep latent Gaussian models via stochastic automatic differentiation in Wiener space, where the variational approximations to the posterior are obtained by Girsanov (mean-shift) transformation of the standard Wiener process and the computation of gradients is based on the theory of Stochastic flows. In deep latent Gaussian models, the ..."}
+{"idx": 6, "title": "Neural Stochastic Differential Equations: Deep Latent ...", "date": "", "ddg_snippet": "May 23, 2019 · This work develops a variational inference framework for deep latent Gaussian models via stochastic automatic differentiation in Wiener space, where the variational approximations to the posterior are obtained by Girsanov (mean-shift) transformation of the standard Wiener process and the computation of gradients is based on the theory of Stochastic flows. In deep latent Gaussian models, the ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Neural-Stochastic-Differential-Equations:-Deep-in-Tzen-Raginsky/c73211167d621446593f0859f12b6f0679f06b22", "content": "May 23, 2019 · This work develops a variational inference framework for deep latent Gaussian models via stochastic automatic differentiation in Wiener space, where the variational approximations to the posterior are obtained by Girsanov (mean-shift) transformation of the standard Wiener process and the computation of gradients is based on the theory of Stochastic flows. In deep latent Gaussian models, the ..."}
+{"idx": 7, "title": "Learning Gaussian Policies from Smoothed Action Value Functions", "date": "", "ddg_snippet": "We propose a new notion of action value defined by a Gaussian smoothed version of the expected Q- value used in SARSA. We show that such smoothed Q- values still satisfy a Bellman equation , making them naturally learnable from experience sampled from an environment.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=B1nLkl-0Z", "content": "We propose a new notion of action value defined by a Gaussian smoothed version of the expected Q- value used in SARSA. We show that such smoothed Q- values still satisfy a Bellman equation , making them naturally learnable from experience sampled from an environment."}
+{"idx": 8, "title": "Gaussian (Normal) Distribution | Academo.org - Free, interactive...", "date": "", "ddg_snippet": "The Gaussian distribution, (also known as the Normal distribution) is a probability distribution.The peak of the graph is always located at the mean and the area under the curve is always exactly equal to 1. 68% of all the values lie within one standard deviation of the mean.", "subpage_snippet": "", "source": "academo.org", "link": "https://academo.org/demos/gaussian-distribution/", "content": "The Gaussian distribution, (also known as the Normal distribution) is a probability distribution.The peak of the graph is always located at the mean and the area under the curve is always exactly equal to 1. 68% of all the values lie within one standard deviation of the mean."}
+{"idx": 9, "title": "Royale High (RH) Value Calculator | Win Fair Lose WFL | Traderie", "date": "", "ddg_snippet": "Easily calculate fair trade values for Royale High (RH) items with this calculator on Traderie!", "subpage_snippet": "", "source": "traderie.com", "link": "https://traderie.com/royalehigh/calculator", "content": "Easily calculate fair trade values for Royale High (RH) items with this calculator on Traderie!"}
diff --git "a/data/sampled_jsons/2502.00921_appendix_table_MATH_dataset_critical_window_frequency_\316\224CW.jsonl" "b/data/sampled_jsons/2502.00921_appendix_table_MATH_dataset_critical_window_frequency_\316\224CW.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..301cc802fcbb1ff9d0cb794a51563df5530f659f
--- /dev/null
+++ "b/data/sampled_jsons/2502.00921_appendix_table_MATH_dataset_critical_window_frequency_\316\224CW.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2502 . 00921 ] Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "arXiv: 2502 . 00921 (cs). [Submitted on 2 Feb 2025 (v1), last revised 5 Jun 2025 (this version, v2)].Finally, we validate our predictions empirically for LLMs and find that critical windows often coincide with failures in problem solving for various math and reasoning benchmarks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "arXiv: 2502 . 00921 (cs). [Submitted on 2 Feb 2025 (v1), last revised 5 Jun 2025 (this version, v2)].Finally, we validate our predictions empirically for LLMs and find that critical windows often coincide with failures in problem solving for various math and reasoning benchmarks."}
+{"idx": 1, "title": "Can AI Create a Profitable Python Trading Bot for Stocks? An In-Depth...", "date": "", "ddg_snippet": "The process involves several critical steps: Data Sources: Obtain historical and real-time data . For stocks, APIs like Alpha Vantage, IEX Cloud, Polygon.io, or broker-specific APIs (e.g., Alpaca, Interactive Brokers) are common.", "subpage_snippet": "", "source": "trading-strategies.academy", "link": "https://trading-strategies.academy/archives/1648", "content": "The process involves several critical steps: Data Sources: Obtain historical and real-time data . For stocks, APIs like Alpha Vantage, IEX Cloud, Polygon.io, or broker-specific APIs (e.g., Alpaca, Interactive Brokers) are common."}
+{"idx": 2, "title": "How To Find Mean From Dataset In R - Cocafish", "date": "", "ddg_snippet": "Divide the sum by the number of values in the data set . . How do you find the mean in R groups?In order to calculate the mean from a frequency table : Multiply the number values by the frequencies . Find the totals.", "subpage_snippet": "", "source": "www.cocafish.com", "link": "https://www.cocafish.com/wiki/how-to-find-mean-from-dataset-in-r", "content": "Divide the sum by the number of values in the data set . . How do you find the mean in R groups?In order to calculate the mean from a frequency table : Multiply the number values by the frequencies . Find the totals."}
+{"idx": 3, "title": "Тренировочные варианты ОГЭ 2024-2025-2026 по... — math 100.ru", "date": "", "ddg_snippet": "Тренировочный вариант № 181 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант № 180 ОГЭ из заданий банка ФИПИ Тренировочный вариант № 179 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант...", "subpage_snippet": "", "source": "math100.ru", "link": "https://math100.ru/trenirovochnie-varianti-oge-new/", "content": "Тренировочный вариант № 181 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант № 180 ОГЭ из заданий банка ФИПИ Тренировочный вариант № 179 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант..."}
+{"idx": 4, "title": "Blink of an eye: a simple theory for feature localization in generative...", "date": "", "ddg_snippet": "Theory of critical windows in diffusion.We defer an example of a critical window for an autoregressive model which expresses the outputs as emissions from a random walk of an underlying concept variable, akin to the model in [5] , to Appendix C.2.3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "Theory of critical windows in diffusion.We defer an example of a critical window for an autoregressive model which expresses the outputs as emissions from a random walk of an underlying concept variable, akin to the model in [5] , to Appendix C.2.3."}
+{"idx": 5, "title": "(PDF) Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "Appendix A. Theory of critical windows in diffusion. Table 1: Differences between Accuracy (Acc) without versus with critical windows and frequency of. critical windows (CW) when the original generation is wrong versus right.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "Appendix A. Theory of critical windows in diffusion. Table 1: Differences between Accuracy (Acc) without versus with critical windows and frequency of. critical windows (CW) when the original generation is wrong versus right."}
+{"idx": 6, "title": "Open LLM Leaderboard - a Hugging Face Space by...", "date": "", "ddg_snippet": "Compare the performance of open-source Large Language Models using multiple benchmarks like IFEval, BBH, MATH , GPQA, MUSR, and MMLU-PRO. Filter results in real-time and see community votes for comp...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard", "content": "Compare the performance of open-source Large Language Models using multiple benchmarks like IFEval, BBH, MATH , GPQA, MUSR, and MMLU-PRO. Filter results in real-time and see community votes for comp..."}
+{"idx": 7, "title": "Introducing the AI Spreadsheet Benchmark", "date": "", "ddg_snippet": "Data analysis: summaries, pivots, merging tables , classification tasks. Data manipulation: adding or editing columns, formatting, and managing spreadsheet elements.", "subpage_snippet": "", "source": "rows.com", "link": "https://rows.com/blog/post/ai-spreadsheet-benchmark", "content": "Data analysis: summaries, pivots, merging tables , classification tasks. Data manipulation: adding or editing columns, formatting, and managing spreadsheet elements."}
+{"idx": 8, "title": "Pandas rank() Method: Equivalent to ROW_NUMBER(), RANK...", "date": "", "ddg_snippet": "Gradient Used to Highlight Table Outputs. Example 1: Count of New Sellers Per Day. Create Real Estate Transaction Dataset . Find Rank of Home Close Date by Each Seller.", "subpage_snippet": "", "source": "dfrieds.com", "link": "https://dfrieds.com/data-analysis/rank-method-python-pandas.html", "content": "Gradient Used to Highlight Table Outputs. Example 1: Count of New Sellers Per Day. Create Real Estate Transaction Dataset . Find Rank of Home Close Date by Each Seller."}
+{"idx": 9, "title": "Kahoot! stands with Ukraine", "date": "", "ddg_snippet": "Kahoot! is committed to supporting Ukrainian educators and learners affected by the current crisis. To protect the integrity of our platform and our users, we will suspend offering Kahoot!’s services in Russia, with the exception of self-study.", "subpage_snippet": "", "source": "kahoot.com", "link": "https://kahoot.com/blog/2022/03/18/kahoot-stands-with-ukraine/", "content": "Kahoot! is committed to supporting Ukrainian educators and learners affected by the current crisis. To protect the integrity of our platform and our users, we will suspend offering Kahoot!’s services in Russia, with the exception of self-study."}
diff --git a/data/sampled_jsons/2502.10875_FBox_score_equation.jsonl b/data/sampled_jsons/2502.10875_FBox_score_equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a9d0941b59954a24017ee465b3920d873e6f4b97
--- /dev/null
+++ b/data/sampled_jsons/2502.10875_FBox_score_equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.10875v1] A Geometric Approach to Personalized Recommendation with ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2502.10875v1: A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10875v1", "content": "Abstract page for arXiv paper 2502.10875v1: A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings"}
+{"idx": 1, "title": "Massive expansion of Ubiquitination-related gene families within the ...", "date": "", "ddg_snippet": "To gain a broader overview of the occurrence of F-box/ Fbox -like and BTB/POZ domains among other prokaryotes and eukaryotes, we extracted domain abundance data from Pfam (Finn et al. 2013) and included the current counts for the Chlamydiae genomes present in this study (fig. 6).", "subpage_snippet": "", "source": "d.docksci.com", "link": "https://d.docksci.com/massive-expansion-of-ubiquitination-related-gene-families-within-the-chlamydiae_5aa1a386d64ab26db17dd1d7.html", "content": "To gain a broader overview of the occurrence of F-box/ Fbox -like and BTB/POZ domains among other prokaryotes and eukaryotes, we extracted domain abundance data from Pfam (Finn et al. 2013) and included the current counts for the Chlamydiae genomes present in this study (fig. 6)."}
+{"idx": 2, "title": "(PDF) Beyond Gender Parity : Actualization of Benefits Verses Fallacy ...", "date": "", "ddg_snippet": "Therefore, while more girls are attending secondary school than ever before, their mean performance has not improved sufficiently enough. 22 fFigure 3.7: Mean Scores by Grade and Gender in 2013 and 2015 Source: Author calculations using LASI (2014 and 2016) data 23 f4 Actualization of Benefits of Gender Parity at Early Grades: Role of Economic ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/144045815/Beyond_Gender_Parity_Actualization_of_Benefits_Verses_Fallacy_of_Promises", "content": "Therefore, while more girls are attending secondary school than ever before, their mean performance has not improved sufficiently enough. 22 fFigure 3.7: Mean Scores by Grade and Gender in 2013 and 2015 Source: Author calculations using LASI (2014 and 2016) data 23 f4 Actualization of Benefits of Gender Parity at Early Grades: Role of Economic ..."}
+{"idx": 3, "title": "NOTCH1 S2513 is critical for the regulation of NICD levels impacting ...", "date": "", "ddg_snippet": "Δ Cq values were corrected using the pool of housekeeping genes as a reference. Variances from the technical repeats were propagated to allow calculation of the t -statistic for the mean Δ Cq and P -value determination using only the degrees of freedom from the biological repeats to obtain a conservative estimate of significance (Fig. 4 C).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12404203/", "content": "Δ Cq values were corrected using the pool of housekeeping genes as a reference. Variances from the technical repeats were propagated to allow calculation of the t -statistic for the mean Δ Cq and P -value determination using only the degrees of freedom from the biological repeats to obtain a conservative estimate of significance (Fig. 4 C)."}
+{"idx": 4, "title": "DoniaGasmii/MNLP_M3_dpo_dataset · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/DoniaGasmii/MNLP_M3_dpo_dataset/viewer", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 5, "title": "PDF Error-Bounded Graph Anomaly Loss for GNNs", "date": "", "ddg_snippet": "GCN is a transductive model that requires the calculation of whole graph Laplacian during training. Many inductive GNNs [12, 35, 45, 48, 49, 52] that follow a neighborhood aggregation scheme are proposed in recent years.", "subpage_snippet": "", "source": "tzhao.io", "link": "http://tzhao.io/files/papers/CIKM20_GAL.pdf", "content": "GCN is a transductive model that requires the calculation of whole graph Laplacian during training. Many inductive GNNs [12, 35, 45, 48, 49, 52] that follow a neighborhood aggregation scheme are proposed in recent years."}
+{"idx": 6, "title": "w | PDF - Scribd", "date": "", "ddg_snippet": "formula and predicted the future of fanimation. This essay argues that Atlantis: The Lost Empire is a culturally fsignificant artifact: a bridge between traditional hand-drawn animation fand modern genre storytelling, a commentary on colonialism fand cultural preservation , and a philosophica l meditation on the cost of progress. fThrough its ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/918780359/w", "content": "formula and predicted the future of fanimation. This essay argues that Atlantis: The Lost Empire is a culturally fsignificant artifact: a bridge between traditional hand-drawn animation fand modern genre storytelling, a commentary on colonialism fand cultural preservation , and a philosophica l meditation on the cost of progress. fThrough its ..."}
+{"idx": 7, "title": "Music Goes to War: How Britain, Germany and the USA used Jazz as ...", "date": "", "ddg_snippet": "The thesis will demonstrate that the various uses of jazz music as propaganda in World War II were determined by an evolving relationship between Axis and Allied policies and projects. The limited previous scholarship in the area, however, has been restricted to 'single-country studies' which present only national perspectives with little reference to the broader international context ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/144068116/Music_Goes_to_War_How_Britain_Germany_and_the_USA_used_Jazz_as_Propaganda_in_World_War_II", "content": "The thesis will demonstrate that the various uses of jazz music as propaganda in World War II were determined by an evolving relationship between Axis and Allied policies and projects. The limited previous scholarship in the area, however, has been restricted to 'single-country studies' which present only national perspectives with little reference to the broader international context ..."}
+{"idx": 8, "title": "PDF A Synergistic Approach for Graph Anomaly Detection With Pattern Mining ...", "date": "", "ddg_snippet": "(i.e., the number of hops of the neighborhood). If the graph data have a large scale, people are interested in learning a great number of node features in an unsupervised manner so that they can be used to train simple classifiers very quickly for any type of anomaly detection tasks when some ad hoc labels become available. So, to train GNN model parameters without node labels, random walk (RW ...", "subpage_snippet": "", "source": "tzhao.io", "link": "http://tzhao.io/files/papers/TNNLS21_pamful.pdf", "content": "(i.e., the number of hops of the neighborhood). If the graph data have a large scale, people are interested in learning a great number of node features in an unsupervised manner so that they can be used to train simple classifiers very quickly for any type of anomaly detection tasks when some ad hoc labels become available. So, to train GNN model parameters without node labels, random walk (RW ..."}
+{"idx": 9, "title": "South Africa's 2024 election: What you need to know", "date": "", "ddg_snippet": "2024 Miami Grand Prix 2024 Odds, Picks, and Predictions: Max Loves South Beach Formula 1 2024 Miami Grand Prix odds, picks, and predictions. F1 betting picks and driver odds at Miami International Autodrome on Sunday, May 5. Read more » Edmunds: 2024 Kia Niro versus 2024 Toyota Corolla Cross Many car shoppers simply want a vehicle that's practical, easy to drive and relatively inexpensive ...", "subpage_snippet": "", "source": "ph.headtopics.com", "link": "https://ph.headtopics.com/news/south-africa-s-2024-election-what-you-need-to-know-52061706", "content": "2024 Miami Grand Prix 2024 Odds, Picks, and Predictions: Max Loves South Beach Formula 1 2024 Miami Grand Prix odds, picks, and predictions. F1 betting picks and driver odds at Miami International Autodrome on Sunday, May 5. Read more » Edmunds: 2024 Kia Niro versus 2024 Toyota Corolla Cross Many car shoppers simply want a vehicle that's practical, easy to drive and relatively inexpensive ..."}
diff --git a/data/sampled_jsons/2502.10875_arxiv_Table_1_dataset_statistics_train_interactions_density.jsonl b/data/sampled_jsons/2502.10875_arxiv_Table_1_dataset_statistics_train_interactions_density.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d05536ea64c0750efd144ae13f93d45302aade4
--- /dev/null
+++ b/data/sampled_jsons/2502.10875_arxiv_Table_1_dataset_statistics_train_interactions_density.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2502 . 10875 ] A Geometric Approach to Personalized Recommendation...", "date": "", "ddg_snippet": "Computer Science > Information Retrieval. arXiv : 2502 . 10875 (cs).Abstract:Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10875", "content": "Computer Science > Information Retrieval. arXiv : 2502 . 10875 (cs).Abstract:Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization."}
+{"idx": 1, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 2, "title": "UCI Machine Learning Repository | Discover datasets around the world!", "date": "", "ddg_snippet": "The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are not linearly separable from each other.Variables Table .", "subpage_snippet": "", "source": "archive.ics.uci.edu", "link": "https://archive.ics.uci.edu/dataset/53/iris", "content": "The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are not linearly separable from each other.Variables Table ."}
+{"idx": 3, "title": "yandex/yambda · Datasets at Hugging Face", "date": "", "ddg_snippet": "About Dataset . Statistics . User History Length Distribution. Item Interaction Count. Data Format.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/yandex/yambda", "content": "About Dataset . Statistics . User History Length Distribution. Item Interaction Count. Data Format."}
+{"idx": 4, "title": "Free Public Datasets for Data Science Projects", "date": "", "ddg_snippet": "In this post we can find free public datasets for Data Science projects. There is a big number of datasets which cover different areas - machine learning, presentation, data analysis and visualization.", "subpage_snippet": "", "source": "datascientyst.com", "link": "https://datascientyst.com/datasets/", "content": "In this post we can find free public datasets for Data Science projects. There is a big number of datasets which cover different areas - machine learning, presentation, data analysis and visualization."}
+{"idx": 5, "title": "Обзоры препринтов научных статей «astro-ph/ arxiv .org» за... / Хабр", "date": "", "ddg_snippet": "Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv .org. Публикуется с разрешения Сергея Борисовича и указанием ссылок на первоисточники.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948996/", "content": "Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv .org. Публикуется с разрешения Сергея Борисовича и указанием ссылок на первоисточники."}
+{"idx": 6, "title": "World Population Density Interactive Map", "date": "", "ddg_snippet": "World Population Density Map Summary Preview Image. Interactive Statistics . The \" Interactive Stats \" checkbox at the top left of the map turns on density statistics for countries and cities which have been calculated from the GHSL data ( 1 km scale).", "subpage_snippet": "", "source": "luminocity3d.org", "link": "https://luminocity3d.org/WorldPopDen/", "content": "World Population Density Map Summary Preview Image. Interactive Statistics . The \" Interactive Stats \" checkbox at the top left of the map turns on density statistics for countries and cities which have been calculated from the GHSL data ( 1 km scale)."}
+{"idx": 7, "title": "(PDF) A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "Table 1 : Dataset Statistics , the Item-User interaction DU& the Item-Attribute interaction DA. The Train /Test split is created using algorithm 1to test set -theoretic generalization.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389091382_A_Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embeddings", "content": "Table 1 : Dataset Statistics , the Item-User interaction DU& the Item-Attribute interaction DA. The Train /Test split is created using algorithm 1to test set -theoretic generalization."}
+{"idx": 8, "title": "Fisch - Secret Cave: How to Unlock the Luminescent and Crimson...", "date": "", "ddg_snippet": "Our complete guide to unlocking the new secret caves in the latest Fisch update. Learn how to use your Keystones, complete the Bestiary, get the Crimson Mutation, and access the new best rod in the game. Table of Contents.", "subpage_snippet": "", "source": "trioner.com", "link": "https://trioner.com/fisch-secret-cave-how-to-unlock-the-luminescent-and-crimson-caverns/", "content": "Our complete guide to unlocking the new secret caves in the latest Fisch update. Learn how to use your Keystones, complete the Bestiary, get the Crimson Mutation, and access the new best rod in the game. Table of Contents."}
+{"idx": 9, "title": "IMTS", "date": "", "ddg_snippet": "View data . Download. The International trade in goods by partner country dataset (formerly Direction of Trade Statistics (DOTS)) includes goods (merchandise) export and import statistics disaggregated according to a country's trading partners.", "subpage_snippet": "", "source": "data.imf.org", "link": "https://data.imf.org/en/datasets/IMF.STA:IMTS", "content": "View data . Download. The International trade in goods by partner country dataset (formerly Direction of Trade Statistics (DOTS)) includes goods (merchandise) export and import statistics disaggregated according to a country's trading partners."}
diff --git a/data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_in_Identifying_the_Worst-Off.jsonl b/data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_in_Identifying_the_Worst-Off.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..568157e3e06c5594a76fb79b3ec8735e511f628d
--- /dev/null
+++ b/data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_in_Identifying_the_Worst-Off.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Positive and negative predictive values - Wikipedia", "date": "", "ddg_snippet": "Positive and negative predictive values . Positive and negative predictive values - 2. The positive and negative predictive values are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values", "content": "Positive and negative predictive values . Positive and negative predictive values - 2. The positive and negative predictive values are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true ..."}
+{"idx": 1, "title": "The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "value as the probability that the worst - off individuals are successfully identified :, i.e. V (α, β) = Pr[Yˆ FYˆ−1(α) | Y FY−1(β)]. In practice, this can be measured using a recall-like metric, capturing the proportion of truly at-risk. individuals ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=26JsumCG0z", "content": "value as the probability that the worst - off individuals are successfully identified :, i.e. V (α, β) = Pr[Yˆ FYˆ−1(α) | Y FY−1(β)]. In practice, this can be measured using a recall-like metric, capturing the proportion of truly at-risk. individuals ..."}
+{"idx": 2, "title": "(PDF) The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "the needs of the worst - off . For instance, in 2012, Wisconsin launched a risk prediction system.Specifically, we identify when improving prediction provides. a higher marginal benefit in helping an institution identify the worst - off . This is compared.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388634300_The_Value_of_Prediction_in_Identifying_the_Worst-Off", "content": "the needs of the worst - off . For instance, in 2012, Wisconsin launched a risk prediction system.Specifically, we identify when improving prediction provides. a higher marginal benefit in helping an institution identify the worst - off . This is compared."}
+{"idx": 3, "title": "[ICML 2025] The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "The cornerstone of the paper's methodology is the Prediction -Access Ratio (PAR), a metric designed to quantify the trade- off between two key policy levers: Improving Predictions : Enhancing a model's predictive power (measured by R²).", "subpage_snippet": "", "source": "arxiviq.substack.com", "link": "https://arxiviq.substack.com/p/icml-2025-the-value-of-prediction", "content": "The cornerstone of the paper's methodology is the Prediction -Access Ratio (PAR), a metric designed to quantify the trade- off between two key policy levers: Improving Predictions : Enhancing a model's predictive power (measured by R²)."}
+{"idx": 4, "title": "[Paper Note] The Value of Prediction in Identifying the Worst - Off ...", "date": "", "ddg_snippet": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/AkihikoWatanabe/paper_notes/2220", "content": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity."}
+{"idx": 5, "title": "The Value of Prediction in Identifying the Worst - Off | AI Research...", "date": "", "ddg_snippet": "OverviewMathematical framework for measuring prediction value in welfare programsFocus on identifying and helping society's most disadvantaged groups", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/value-prediction-identifying-worst-off", "content": "OverviewMathematical framework for measuring prediction value in welfare programsFocus on identifying and helping society's most disadvantaged groups"}
+{"idx": 6, "title": "The value of prediction in identifying the worst - off : Interview with...", "date": "", "ddg_snippet": "The red line marks the 12 month threshold used to classify a jobseeking episode as long-term unemployment in Germany. In the paper, you detail a case study identifying long-term unemployment in Germany. What were some of the main findings from this case study?", "subpage_snippet": "", "source": "aihub.org", "link": "https://aihub.org/2025/08/27/the-value-of-prediction-in-identifying-the-worst-off-interview-with-unai-fischer-abaigar/", "content": "The red line marks the 12 month threshold used to classify a jobseeking episode as long-term unemployment in Germany. In the paper, you detail a case study identifying long-term unemployment in Germany. What were some of the main findings from this case study?"}
+{"idx": 7, "title": "The Value of Prediction in Identifying the Worst - Off Authors: Unai...", "date": "", "ddg_snippet": "Краеугольным камнем методологии статьи является коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR) — метрика, предназначенная для количественной оценки компромисса между двумя ключевыми инструментами политики: 1. Улучшение...", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/wall49591166_67792", "content": "Краеугольным камнем методологии статьи является коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR) — метрика, предназначенная для количественной оценки компромисса между двумя ключевыми инструментами политики: 1. Улучшение..."}
+{"idx": 8, "title": "Helping the Worst - Off : When Hiring More Case Workers... | Medium", "date": "", "ddg_snippet": "The findings, detailed in “ The Value of Prediction in Identifying the Worst - Off ,” challenge the current policy focus on perfecting prediction accuracy.", "subpage_snippet": "", "source": "nyudatascience.medium.com", "link": "https://nyudatascience.medium.com/helping-the-worst-off-when-hiring-more-case-workers-beats-building-better-ai-12e6f968de0b", "content": "The findings, detailed in “ The Value of Prediction in Identifying the Worst - Off ,” challenge the current policy focus on perfecting prediction accuracy."}
+{"idx": 9, "title": "EA Sports FC 26 Tactics Codes for the best formations | VG247", "date": "", "ddg_snippet": "Use these Custom Tactics Codes to unlock some of the best meta formations in FC 26.", "subpage_snippet": "", "source": "www.vg247.com", "link": "https://www.vg247.com/ea-sports-fc-26-tactics-codes-best-formations", "content": "Use these Custom Tactics Codes to unlock some of the best meta formations in FC 26."}
diff --git a/data/sampled_jsons/27tMzmzDjO_A_Table_1_dataset_statistics_user-item_interactions_density.jsonl b/data/sampled_jsons/27tMzmzDjO_A_Table_1_dataset_statistics_user-item_interactions_density.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..32c2d21cd7fbb5a22a28c3d42cfc80e780a6b935
--- /dev/null
+++ b/data/sampled_jsons/27tMzmzDjO_A_Table_1_dataset_statistics_user-item_interactions_density.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistics of the datasets Dataset 1 # Users # Items # Interactions Density", "date": "", "ddg_snippet": "Download scientific diagram | Statistics of the datasets Dataset 1 # Users # Items # Interactions Density from publication: Latent Structures Mining with Contrastive Modality Fusion for Multimedia ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Statistics-of-the-datasets-Dataset-1-Users-Items-Interactions-Density_tbl1_355841189", "content": "Download scientific diagram | Statistics of the datasets Dataset 1 # Users # Items # Interactions Density from publication: Latent Structures Mining with Contrastive Modality Fusion for Multimedia ..."}
+{"idx": 1, "title": "Preparing item interaction data for training - Amazon Personalize", "date": "", "ddg_snippet": "An item interaction is a positive interaction event between a user and an item in your catalogue. For example, a user watching a movie, viewing a listing, or purchasing a pair of shoes. You import data about your users' interactions with your items into a Item interactions dataset .", "subpage_snippet": "", "source": "docs.aws.amazon.com", "link": "https://docs.aws.amazon.com/personalize/latest/dg/interactions-datasets.html", "content": "An item interaction is a positive interaction event between a user and an item in your catalogue. For example, a user watching a movie, viewing a listing, or purchasing a pair of shoes. You import data about your users' interactions with your items into a Item interactions dataset ."}
+{"idx": 2, "title": "Item interactions dataset schema requirements (custom)", "date": "", "ddg_snippet": "An Item interactions dataset stores historical and real-time data from interactions between users and items in your catalog. For information on the types of interactions data Amazon Personalize can use, see Item interaction data. The data you provide for each interaction must match your schema.", "subpage_snippet": "", "source": "docs.aws.amazon.com", "link": "https://docs.aws.amazon.com/personalize/latest/dg/interactions-dataset-requirements.html", "content": "An Item interactions dataset stores historical and real-time data from interactions between users and items in your catalog. For information on the types of interactions data Amazon Personalize can use, see Item interaction data. The data you provide for each interaction must match your schema."}
+{"idx": 3, "title": "KuaiRec | A Fully-observed Dataset for Recommender Systems (Density ...", "date": "", "ddg_snippet": "KuaiRec A Fully-observed Dataset for Recommender Systems ( Density : Almost 100%) View on GitHub KuaiRec is a real-world dataset collected from the recommendation logs of the video-sharing mobile app Kuaishou. For now, it is the first dataset that contains a fully observed user-item interaction matrix.", "subpage_snippet": "", "source": "kuairec.com", "link": "https://kuairec.com/", "content": "KuaiRec A Fully-observed Dataset for Recommender Systems ( Density : Almost 100%) View on GitHub KuaiRec is a real-world dataset collected from the recommendation logs of the video-sharing mobile app Kuaishou. For now, it is the first dataset that contains a fully observed user-item interaction matrix."}
+{"idx": 4, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Flexible Data Ingestion.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Flexible Data Ingestion."}
+{"idx": 5, "title": "Table 1 : Statistics of the three datasets. The first row of each...", "date": "", "ddg_snippet": "The first row of each dataset corresponds to the numbers of users , items and interactions , while the other rows correspond to the statistics of other relations. ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Statistics-of-the-three-datasets-The-first-row-of-each-dataset-corresponds-to-the_tbl1_325964999", "content": "The first row of each dataset corresponds to the numbers of users , items and interactions , while the other rows correspond to the statistics of other relations. ..."}
+{"idx": 6, "title": "Statistics of datasets. Dataset #Interactions #Items #User Mean ...", "date": "", "ddg_snippet": "The two Amazon datasets are sparse and have fewer interactions per user and per item , while MovieLens-1M and Netflix-2M are more dense due to fewer items . ... View in full-text", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Statistics-of-datasets-Dataset-Interactions-Items-User-Mean-Interactions-user-Mean_tbl1_351685598", "content": "The two Amazon datasets are sparse and have fewer interactions per user and per item , while MovieLens-1M and Netflix-2M are more dense due to fewer items . ... View in full-text"}
+{"idx": 7, "title": "Statistics of the dataset | Download Table - ResearchGate", "date": "", "ddg_snippet": "Download Table | Statistics of the dataset from publication: Explicit Feedbacks Meet with Implicit Feedbacks : A Combined Approach for Recommendation System | Recommender systems recommend items ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Statistics-of-the-dataset_tbl1_328451127", "content": "Download Table | Statistics of the dataset from publication: Explicit Feedbacks Meet with Implicit Feedbacks : A Combined Approach for Recommendation System | Recommender systems recommend items ..."}
+{"idx": 8, "title": "KuaiRec: A Fully-observed Dataset and Insights for Evaluating ...", "date": "", "ddg_snippet": "Note that all user-item interactions in the small matrix are excluded from the big matrix to separate the training and evaluation data. Table 1 lists the statistics of KuaiRec. Besides the user-item interaction , we further collect the side information of users and items .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.10842", "content": "Note that all user-item interactions in the small matrix are excluded from the big matrix to separate the training and evaluation data. Table 1 lists the statistics of KuaiRec. Besides the user-item interaction , we further collect the side information of users and items ."}
+{"idx": 9, "title": "Data.gov Home - Data.gov", "date": "", "ddg_snippet": "Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.", "subpage_snippet": "", "source": "data.gov", "link": "https://data.gov/", "content": "Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more."}
diff --git a/data/sampled_jsons/2uheUFcFsM_Normalizing_Flows_are_Capable_Generative_Models_Equation_6_training_loss.jsonl b/data/sampled_jsons/2uheUFcFsM_Normalizing_Flows_are_Capable_Generative_Models_Equation_6_training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cc221709b57ee2d98440ff615909098af0185367
--- /dev/null
+++ b/data/sampled_jsons/2uheUFcFsM_Normalizing_Flows_are_Capable_Generative_Models_Equation_6_training_loss.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2412.06329] Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.06329", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly ..."}
+{"idx": 1, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it's a very interesting approach with a precise mathematical formulation and an intuitive core.", "subpage_snippet": "", "source": "aiwithmike.substack.com", "link": "https://aiwithmike.substack.com/p/normalizing-flows-are-capable-generative", "content": "The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it's a very interesting approach with a precise mathematical formulation and an intuitive core."}
+{"idx": 2, "title": "Generative Models 3 - Normalizing Flows | Roy Friedman", "date": "", "ddg_snippet": "Problems with Normalizing and Continuous Flows Normalizing flows are a popular class of explicit likelihood generative models . Because the likelihood is baked into the whole definition of normalizing flows , that means that you don't need to approximate it during inference like VAEs or the models in the next few posts.", "subpage_snippet": "", "source": "friedmanroy.github.io", "link": "https://friedmanroy.github.io/blog/2024/gen3/", "content": "Problems with Normalizing and Continuous Flows Normalizing flows are a popular class of explicit likelihood generative models . Because the likelihood is baked into the whole definition of normalizing flows , that means that you don't need to approximate it during inference like VAEs or the models in the next few posts."}
+{"idx": 3, "title": "PDF Generative Models: Normalizing flows and Diffusion Models", "date": "", "ddg_snippet": "Reverse denoising process ( generative ) Sohl-Dickstein et al., Deep Unsupervised Learning using Nonequilibrium Thermodynamics, ICML 2015 Ho et al., Denoising Diffusion Probabilistic Models , NeurIPS 2020 Song et al., Score-Based Generative Modeling through Stochastic Differential Equations , ICLR 2021", "subpage_snippet": "", "source": "cqf.io", "link": "https://cqf.io/EESM5900V/lectures/Lecture17.pdf", "content": "Reverse denoising process ( generative ) Sohl-Dickstein et al., Deep Unsupervised Learning using Nonequilibrium Thermodynamics, ICML 2015 Ho et al., Denoising Diffusion Probabilistic Models , NeurIPS 2020 Song et al., Score-Based Generative Modeling through Stochastic Differential Equations , ICLR 2021"}
+{"idx": 4, "title": "Deep Learning Part 6: Generative Modelling through Normalizing Flows ...", "date": "", "ddg_snippet": "Since a few days ago, I have been learning about normalizing flows . It belongs to the renowned class of generative models used in deep learning and machine learning. As its name implies ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@tejpal.abhyuday/deep-learning-part-6-generative-modelling-through-normalizing-flows-c79fffc90091", "content": "Since a few days ago, I have been learning about normalizing flows . It belongs to the renowned class of generative models used in deep learning and machine learning. As its name implies ..."}
+{"idx": 5, "title": "Normalizing Flows are Capable Generative Models - OpenReview", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 6, "title": "Normalizing Flows are Capable Generative Models - Apple Machine ...", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/normalizing-flows", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 7, "title": "Normalizing Flows in PyTorch for Generative Models", "date": "", "ddg_snippet": "This might limit the expressiveness compared to less constrained models , although complex flows can still model intricate distributions. Computational Cost: Calculating Jacobian determinants, even when tractable, can add computational overhead during training , especially for high-dimensional data or deep flows .", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/advanced-pytorch/chapter-2-advanced-network-architectures/normalizing-flows", "content": "This might limit the expressiveness compared to less constrained models , although complex flows can still model intricate distributions. Computational Cost: Calculating Jacobian determinants, even when tractable, can add computational overhead during training , especially for high-dimensional data or deep flows ."}
+{"idx": 8, "title": "Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "Abstract Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "Abstract Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed."}
+{"idx": 9, "title": "ICML Poster Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows are Capable Generative Models Shuangfei Zhai · Ruixiang Zhang · Preetum Nakkiran · David Berthelot · Jiatao Gu · Huangjie Zheng · Tianrong Chen · Miguel Angel Bautista Martin · Navdeep Jaitly · Joshua M Susskind", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46564", "content": "Normalizing Flows are Capable Generative Models Shuangfei Zhai · Ruixiang Zhang · Preetum Nakkiran · David Berthelot · Jiatao Gu · Huangjie Zheng · Tianrong Chen · Miguel Angel Bautista Martin · Navdeep Jaitly · Joshua M Susskind"}
diff --git a/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Algorithm_1_D_Drift.jsonl b/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Algorithm_1_D_Drift.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b6dcf72721927da64484fa7c6ae2430558825e8b
--- /dev/null
+++ b/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Algorithm_1_D_Drift.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stochastic - Wikipedia", "date": "", "ddg_snippet": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic", "content": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve..."}
+{"idx": 1, "title": "CVPR Poster Deterministic - to - Stochastic Diverse Latent Feature ...", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33113", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE."}
+{"idx": 2, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Deterministic-to-Stochastic-Diverse-Latent-Feature-Mapping-for-Human-Motion-Synthesis-218660a3-37a0-490e-94f1-73766d3ec529", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions."}
+{"idx": 3, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions."}
+{"idx": 4, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "The first human motion reconstruction stage aims to learn the latent space distribution of human motions.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "The first human motion reconstruction stage aims to learn the latent space distribution of human motions.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE."}
+{"idx": 5, "title": "DivDiff: A Conditional Diffusion Model for Diverse Human Motion...", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387413105_DivDiff_A_Conditional_Diffusion_Model_for_Diverse_Human_Motion_Prediction", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis."}
+{"idx": 6, "title": "Deterministic vs stochastic trends - YouTube", "date": "", "ddg_snippet": "This video explains the difference between stochastic and deterministic trends.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=yCM6N8sRtPY", "content": "This video explains the difference between stochastic and deterministic trends."}
+{"idx": 7, "title": "S 1 Appendix Algorithmic details of the stochastic reaction-diffusion...", "date": "", "ddg_snippet": "In addition, unlike most procedures for numerically solving the deterministic reaction-rate equations, this algorithm never approximates infinitesimal time increments dt by finite time steps At.", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/plos-corpus-prod/10.1371/journal.pone.0219055/1/pone.0219055.s001.docx?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=wombat-sa@plos-prod.iam.gserviceaccount.com/20250918/auto/storage/goog4_request&X-Goog-Date=20250918T201808Z&X-Goog-Expires=86400&X-Goog-SignedHeaders=host&X-Goog-Signature=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", "content": "In addition, unlike most procedures for numerically solving the deterministic reaction-rate equations, this algorithm never approximates infinitesimal time increments dt by finite time steps At."}
+{"idx": 8, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Foruck/Awesome-Human-Motion - GitHub Deterministic-to-Stochastic Diverse Latent Feature Mapping ... arXiv:2505.00998v1 [cs.CV] 2 May 2025 Appendix - CVF Open Access Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "May 2, 2025 · In this paper, we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters. (CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis, Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. (CVPR 2025) UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing, Li et al. Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic -to- Stochastic ... rministic feature map-ping procedure. The deterministic feature mapping proce-dure is designed to model the relationship between Gaussian distribution p(Zt= 1 ) and the latent distributio B. Proof of Proposition 2 position 2. Given the stochastic differential equations dzt = f(zt, t)dt+g(t)dwt with the drift and diffusion terms, with the initial data sample z0 and the noise level η, the prob-ability of data distribution zt is p(xt) = N(( 1 −t)zt, η2t2I) at the time step t when p(z0) = N(z0 Sep 25, 2024 · On top of that, DiMoP3D identifies deterministic factors in the scene and integrates them into the stochastic modeling, making the diverse HMP in realistic scenes become a controllable stochastic generation process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "May 2, 2025 · In this paper, we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters. (CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis, Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. (CVPR 2025) UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing, Li et al. Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic -to- Stochastic ... rministic feature map-ping procedure. The deterministic feature mapping proce-dure is designed to model the relationship between Gaussian distribution p(Zt= 1 ) and the latent distributio B. Proof of Proposition 2 position 2. Given the stochastic differential equations dzt = f(zt, t)dt+g(t)dwt with the drift and diffusion terms, with the initial data sample z0 and the noise level η, the prob-ability of data distribution zt is p(xt) = N(( 1 −t)zt, η2t2I) at the time step t when p(z0) = N(z0 Sep 25, 2024 · On top of that, DiMoP3D identifies deterministic factors in the scene and integrates them into the stochastic modeling, making the diverse HMP in realistic scenes become a controllable stochastic generation process."}
+{"idx": 9, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters."}
diff --git a/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Human_Motion_Synthesis_Algorithm_1__year_2024.jsonl b/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Human_Motion_Synthesis_Algorithm_1__year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3c3fc920ea9fb2d5386113aac108be840e278a5e
--- /dev/null
+++ b/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Human_Motion_Synthesis_Algorithm_1__year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stochastic - Wikipedia", "date": "", "ddg_snippet": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic", "content": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve..."}
+{"idx": 1, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Foruck/Awesome-Human-Motion - GitHub Appendix - CVF Open Access Towards Efficient and Diverse Generative Model for ... Weiming Liu - CatalyzeX arXiv:2505.00998v1 [cs.CV] 2 May 2025 Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "May 2, 2025 · In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. (CVPR 2025) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. In this work, we use the following metrics to measure the per-formance of the proposed method for unconditional human motion synthesis and Action- to - Motion tasks. Oct 28, 2024 · To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis . First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten space distribution of human motions . The second diverse mo-tion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of hu-man motions, thereby enhancing the diversity and ac Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "May 2, 2025 · In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. (CVPR 2025) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. In this work, we use the following metrics to measure the per-formance of the proposed method for unconditional human motion synthesis and Action- to - Motion tasks. Oct 28, 2024 · To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis . First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten space distribution of human motions . The second diverse mo-tion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of hu-man motions, thereby enhancing the diversity and ac Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion ."}
+{"idx": 2, "title": "Foruck/Awesome-Human-Motion - GitHub", "date": "", "ddg_snippet": "(CVPR 2025) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Foruck/Awesome-Human-Motion", "content": "(CVPR 2025) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al."}
+{"idx": 3, "title": "Towards Efficient and Diverse Generative Model for ...", "date": "", "ddg_snippet": "Oct 28, 2024 · To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis . First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3664647.3681093", "content": "Oct 28, 2024 · To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis . First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space."}
+{"idx": 4, "title": "Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=NQCkNM6TES", "content": "Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion ."}
+{"idx": 5, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "The first human motion reconstruction stage aims to learn the latent space distribution of human motions .In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) for human motion synthesis .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00998v1", "content": "The first human motion reconstruction stage aims to learn the latent space distribution of human motions .In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) for human motion synthesis ."}
+{"idx": 6, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 7, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task."}
+{"idx": 8, "title": "DivDiff: A Conditional Diffusion Model for Diverse Human Motion ...", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387413105_DivDiff_A_Conditional_Diffusion_Model_for_Diverse_Human_Motion_Prediction", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 9, "title": "GitHub - Zilize/awesome-text-to- motion : Text-driven human motion ...", "date": "", "ddg_snippet": "DSDFM: \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis \".MotionGPT: \"MotionGPT: Human Motion Synthesis with Improved Diversity and Realism via GPT-3 Prompting\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Zilize/awesome-text-to-motion", "content": "DSDFM: \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis \".MotionGPT: \"MotionGPT: Human Motion Synthesis with Improved Diversity and Realism via GPT-3 Prompting\"."}
diff --git a/data/sampled_jsons/34016_EntityErasure_EntityErasure_Erasing_Entity_Cleanly_Amodal_Entity_Segmentation.jsonl b/data/sampled_jsons/34016_EntityErasure_EntityErasure_Erasing_Entity_Cleanly_Amodal_Entity_Segmentation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7274a84365f53ccb37c215584c055882bb496c01
--- /dev/null
+++ b/data/sampled_jsons/34016_EntityErasure_EntityErasure_Erasing_Entity_Cleanly_Amodal_Entity_Segmentation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "Abstract This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities with-out inducing unwanted sundries. To this end, we pro-pose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "Abstract This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities with-out inducing unwanted sundries. To this end, we pro-pose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to ..."}
+{"idx": 1, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure ."}
+{"idx": 2, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub."}
+{"idx": 3, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "Abstract: This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34016", "content": "Abstract: This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ..."}
+{"idx": 4, "title": "ICCV 2023 Oral | 超越SAM!EntitySeg：更少的数据，更高的分割质量!-CSDN博客", "date": "", "ddg_snippet": "在本文中，High-Quality Entity Segmentation 对分割问题进行了全新的探索，从以下三个方面取得了显著的改进： 更优的分割质量 正如上图所示，EntitySeg在数值指标和视觉表现方面都相对于SAM有更大的优势。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/amusi1994/article/details/132750432", "content": "在本文中，High-Quality Entity Segmentation 对分割问题进行了全新的探索，从以下三个方面取得了显著的改进： 更优的分割质量 正如上图所示，EntitySeg在数值指标和视觉表现方面都相对于SAM有更大的优势。"}
+{"idx": 5, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate unpredictable ...", "subpage_snippet": "", "source": "zyxunh.github.io", "link": "https://zyxunh.github.io/EntityErasure-ProjectPage/", "content": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate unpredictable ..."}
+{"idx": 6, "title": "unhzyx/entity_erasure at main - Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/unhzyx/entity_erasure/tree/main", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 7, "title": "Publications - zhangqing-home.net", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng", "subpage_snippet": "", "source": "www.zhangqing-home.net", "link": "https://www.zhangqing-home.net/full_publications.html", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng"}
+{"idx": 8, "title": "Qing Zhang - Homepage", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang*, Yitong Wang, Yongwei Nie, Wei-Shi Zheng CVPR 2025 Project | Code | Paper RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images Junjin Xiao, Qing Zhang*, Yongwei Nie, Lei Zhu, Wei-Shi Zheng CVPR 2025", "subpage_snippet": "", "source": "www.zhangqing-home.net", "link": "https://www.zhangqing-home.net/", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang*, Yitong Wang, Yongwei Nie, Wei-Shi Zheng CVPR 2025 Project | Code | Paper RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images Junjin Xiao, Qing Zhang*, Yongwei Nie, Lei Zhu, Wei-Shi Zheng CVPR 2025"}
+{"idx": 9, "title": "AcademicDissect/detailed_paper_collection/Image_Inpainting.md ... - GitHub", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Tags: Image Inpainting, Diffusion Models, Amodal Segmentation , Entity Completion, Diffusion-Based Inpainting", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcademicDissect/AcademicDissect/blob/main/detailed_paper_collection/Image_Inpainting.md", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Tags: Image Inpainting, Diffusion Models, Amodal Segmentation , Entity Completion, Diffusion-Based Inpainting"}
diff --git a/data/sampled_jsons/35068_XLRS-Bench_LLaVA-1.5_LLaVA-Next_architectural_difference_high-resolution_year_2024.jsonl b/data/sampled_jsons/35068_XLRS-Bench_LLaVA-1.5_LLaVA-Next_architectural_difference_high-resolution_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d35dd055d1e6fb4b8b6c0369d35f9ee2335a9b30
--- /dev/null
+++ b/data/sampled_jsons/35068_XLRS-Bench_LLaVA-1.5_LLaVA-Next_architectural_difference_high-resolution_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - LLaVA -VL/ LLaVA - NeXT", "date": "", "ddg_snippet": "Contribute to LLaVA-VL/ LLaVA - NeXT development by creating an account on GitHub.With additional scaling to LLaVA - 1 . 5 , LLaVA - NeXT -34B outperforms Gemini Pro on some benchmarks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LLaVA-VL/LLaVA-NeXT", "content": "Contribute to LLaVA-VL/ LLaVA - NeXT development by creating an account on GitHub.With additional scaling to LLaVA - 1 . 5 , LLaVA - NeXT -34B outperforms Gemini Pro on some benchmarks."}
+{"idx": 1, "title": "GDinesh/ llava - 1 - 5 · Hugging Face", "date": "", "ddg_snippet": "[1/30] LLaVA - NeXT ( LLaVA - 1 .6) is out! With additional scaling to LLaVA - 1 . 5 , LLaVA - NeXT -34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out the blog post, and expl...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/GDinesh/llava-1-5", "content": "[1/30] LLaVA - NeXT ( LLaVA - 1 .6) is out! With additional scaling to LLaVA - 1 . 5 , LLaVA - NeXT -34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out the blog post, and expl..."}
+{"idx": 2, "title": "(PDF) XLRS - Bench : Could Your Multimodal LLMs Understand...", "date": "", "ddg_snippet": "ple, LLaVA - Next [32] divides high - resolution images into. patches, encodes each patch independently, and then links.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390354847_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_Sensing_Imagery", "content": "ple, LLaVA - Next [32] divides high - resolution images into. patches, encodes each patch independently, and then links."}
+{"idx": 3, "title": "XLRS - Bench : Could Your Multimodal LLMs Understand Extremely...", "date": "", "ddg_snippet": "For example, LLaVA - Next [31] divides high - resolution images into patches, en-codes each patch independently, and then links the patch tokens with the global image tokens.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "For example, LLaVA - Next [31] divides high - resolution images into patches, en-codes each patch independently, and then links the patch tokens with the global image tokens."}
+{"idx": 4, "title": "LLaVA - NeXT", "date": "", "ddg_snippet": "LLaVa - NeXT (also called LLaVa - 1 .6) improves upon LLaVa by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/docs/transformers/model_doc/llava_next", "content": "LLaVa - NeXT (also called LLaVa - 1 .6) improves upon LLaVa by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning."}
+{"idx": 5, "title": "LLaVA -VL/ LLaVA - NeXT - Githubissues", "date": "", "ddg_snippet": "LLaVA - NeXT : Open Large Multimodal Models Release Notes [2024/10/04] LLaVA -Video (formerly LLaVA - NeXT -Video) has undergone a major upgrade! We are excited to release LLaVA -Video-178K, a high -quality synthetic dataset for video instruction tuning.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/LLaVA-VL/LLaVA-NeXT/readme", "content": "LLaVA - NeXT : Open Large Multimodal Models Release Notes [2024/10/04] LLaVA -Video (formerly LLaVA - NeXT -Video) has undergone a major upgrade! We are excited to release LLaVA -Video-178K, a high -quality synthetic dataset for video instruction tuning."}
+{"idx": 6, "title": "Trying out LLaVA - NeXT", "date": "", "ddg_snippet": "Trying out LLaVA - NeXT . Testing the capabilities of llava :7b-v1.6-mistral-q4_0. Image generated by author. The LLaVA - NeXT model is the latest iteration of LLaVA , a large multimodal model that has gained recognition for its performance.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/trying-out-llava-next-8d5e74da3017", "content": "Trying out LLaVA - NeXT . Testing the capabilities of llava :7b-v1.6-mistral-q4_0. Image generated by author. The LLaVA - NeXT model is the latest iteration of LLaVA , a large multimodal model that has gained recognition for its performance."}
+{"idx": 7, "title": "llava", "date": "", "ddg_snippet": "New in LLaVA 1 .6: Increasing the input image resolution to up to 4x more pixels, supporting 672x672, 336x1344, 1344x336 resolutions . Better visual reasoning and OCR capability with an improved visual instruction tuning data mixture.", "subpage_snippet": "", "source": "ollama.com", "link": "https://ollama.com/library/llava", "content": "New in LLaVA 1 .6: Increasing the input image resolution to up to 4x more pixels, supporting 672x672, 336x1344, 1344x336 resolutions . Better visual reasoning and OCR capability with an improved visual instruction tuning data mixture."}
+{"idx": 8, "title": "LLaVA -NDiNO: Empowering LLMs with Multimodality for the Italian...", "date": "", "ddg_snippet": "However, the original LLaVA architecture , as well as other LVLMs, struggled with high - resolution images tasks due to the requirements imposed by vision encoders.Regarding datasets used to train these models, for LLaVA 1 . 5 a mixture of English only vision-language datasets was used.", "subpage_snippet": "", "source": "ceur-ws.org", "link": "https://ceur-ws.org/Vol-3877/paper9.pdf", "content": "However, the original LLaVA architecture , as well as other LVLMs, struggled with high - resolution images tasks due to the requirements imposed by vision encoders.Regarding datasets used to train these models, for LLaVA 1 . 5 a mixture of English only vision-language datasets was used."}
+{"idx": 9, "title": "How to Use LLaVa - Next : Elevating Multimodal AI Interactions fxis.ai", "date": "", "ddg_snippet": "LLaVa - Next combines a large pre-trained language model with an advanced vision encoder, allowing you to create sophisticated chatbot interactions that can understand and respond to queries about images. This model builds on the strengths of its predecessor, LLaVa - 1 . 5 , by training on...", "subpage_snippet": "", "source": "fxis.ai", "link": "https://fxis.ai/edu/how-to-use-llava-next-elevating-multimodal-ai-interactions/", "content": "LLaVa - Next combines a large pre-trained language model with an advanced vision encoder, allowing you to create sophisticated chatbot interactions that can understand and respond to queries about images. This model builds on the strengths of its predecessor, LLaVa - 1 . 5 , by training on..."}
diff --git a/data/sampled_jsons/46n3izUNiv_Origin_Identification_Image_Copy_Detection_manually-designed_transformations_SSCD.jsonl b/data/sampled_jsons/46n3izUNiv_Origin_Identification_Image_Copy_Detection_manually-designed_transformations_SSCD.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..355be105ee02b0d50c9e249d635616747c75efd6
--- /dev/null
+++ b/data/sampled_jsons/46n3izUNiv_Origin_Identification_Image_Copy_Detection_manually-designed_transformations_SSCD.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion Models", "date": "", "ddg_snippet": "Unlike ICD, which focuses on manually-designed transformations , our ID2aims to find the origin of a query translated by the diffusion model with prompt-guidance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=46n3izUNiv", "content": "Unlike ICD, which focuses on manually-designed transformations , our ID2aims to find the origin of a query translated by the diffusion model with prompt-guidance."}
+{"idx": 1, "title": "A Self-Supervised Descriptor for Image Copy Detection (SSCD)", "date": "", "ddg_snippet": "A Self-Supervised Descriptor for Image Copy Detection ( SSCD ) This is the open-source codebase for \"A Self-Supervised Descriptor for Image Copy Detection \", recently accepted to CVPR 2022. This work uses self-supervised contrastive learning with strong differential entropy regularization to create a fingerprint for image copy detection .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/sscd-copy-detection", "content": "A Self-Supervised Descriptor for Image Copy Detection ( SSCD ) This is the open-source codebase for \"A Self-Supervised Descriptor for Image Copy Detection \", recently accepted to CVPR 2022. This work uses self-supervised contrastive learning with strong differential entropy regularization to create a fingerprint for image copy detection ."}
+{"idx": 2, "title": "PDF A Self-Supervised Descriptor for Image Copy Detection", "date": "", "ddg_snippet": "Abstract Image copy detection is an important task for content moderation. We introduce SSCD , a model that builds on a recent self-supervised contrastive training objective. We adapt this method to the copy detection task by changing the architecture and training objective, including a pooling op-erator from the instance matching literature, and adapting contrastive learning to augmentations ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Pizzi_A_Self-Supervised_Descriptor_for_Image_Copy_Detection_CVPR_2022_paper.pdf", "content": "Abstract Image copy detection is an important task for content moderation. We introduce SSCD , a model that builds on a recent self-supervised contrastive training objective. We adapt this method to the copy detection task by changing the architecture and training objective, including a pooling op-erator from the instance matching literature, and adapting contrastive learning to augmentations ..."}
+{"idx": 3, "title": "sscd-copy-detection/README.md at main - GitHub", "date": "", "ddg_snippet": "A Self-Supervised Descriptor for Image Copy Detection ( SSCD ) This is the open-source codebase for \"A Self-Supervised Descriptor for Image Copy Detection \", recently accepted to CVPR 2022. This work uses self-supervised contrastive learning with strong differential entropy regularization to create a fingerprint for image copy detection .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/sscd-copy-detection/blob/main/README.md", "content": "A Self-Supervised Descriptor for Image Copy Detection ( SSCD ) This is the open-source codebase for \"A Self-Supervised Descriptor for Image Copy Detection \", recently accepted to CVPR 2022. This work uses self-supervised contrastive learning with strong differential entropy regularization to create a fingerprint for image copy detection ."}
+{"idx": 4, "title": "A Self-Supervised Descriptor for Image Copy Detection", "date": "", "ddg_snippet": "On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline copy detection models and self-supervised architectures designed for image classification by huge margins, in all settings. For example, SSCD out-performs SimCLR descriptors by 48% absolute. Code is available at this https URL.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.10261", "content": "On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline copy detection models and self-supervised architectures designed for image classification by huge margins, in all settings. For example, SSCD out-performs SimCLR descriptors by 48% absolute. Code is available at this https URL."}
+{"idx": 5, "title": "Vision transformers are active learners for image copy detection", "date": "", "ddg_snippet": "Abstract Image Copy Detection (ICD) is developed to identify and track duplicated or manipulated images . The majority of existing methods rely on Convolutional Neural Networks (CNNs) and are trained using unsupervised learning techniques, which leads to subpar performance.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224004582", "content": "Abstract Image Copy Detection (ICD) is developed to identify and track duplicated or manipulated images . The majority of existing methods rely on Convolutional Neural Networks (CNNs) and are trained using unsupervised learning techniques, which leads to subpar performance."}
+{"idx": 6, "title": "A Self-Supervised Descriptor for Image Copy Detection", "date": "", "ddg_snippet": "Abstract SSCD , a model built on self-supervised contrastive training with instance matching components and entropy regularization, significantly outperforms baseline models and classification-focused architectures in image copy detection .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2202.10261", "content": "Abstract SSCD , a model built on self-supervised contrastive training with instance matching components and entropy regularization, significantly outperforms baseline models and classification-focused architectures in image copy detection ."}
+{"idx": 7, "title": "README.md · m3/sscd-copy-detection at main - Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/m3/sscd-copy-detection/blob/main/README.md", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 8, "title": "arXiv:2405.17928v4 [cs.CV] 16 Jul 2024", "date": "", "ddg_snippet": "Abstract Image copy detection is a task of detecting edited copies from any image within a reference database. While previ-ous approaches have shown remarkable progress, the large size of their networks and descriptors remains disadvan-tage, complicating their practical application. In this pa-per, we propose a novel method that achieves a competi-tive performance by using a lightweight ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17928", "content": "Abstract Image copy detection is a task of detecting edited copies from any image within a reference database. While previ-ous approaches have shown remarkable progress, the large size of their networks and descriptors remains disadvan-tage, complicating their practical application. In this pa-per, we propose a novel method that achieves a competi-tive performance by using a lightweight ..."}
+{"idx": 9, "title": "CVPR 2022 Open Access Repository", "date": "", "ddg_snippet": "Statistical information from a background image distribution can be incorporated into the descriptor. On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline copy detection models and self-supervised architectures designed for image classification by huge margins, in all settings.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/html/Pizzi_A_Self-Supervised_Descriptor_for_Image_Copy_Detection_CVPR_2022_paper.html", "content": "Statistical information from a background image distribution can be incorporated into the descriptor. On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline copy detection models and self-supervised architectures designed for image classification by huge margins, in all settings."}
diff --git a/data/sampled_jsons/46n3izUNiv_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_full_text.jsonl b/data/sampled_jsons/46n3izUNiv_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_full_text.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7c2e94552a7e6f804e525c8bf377cb205511aedb
--- /dev/null
+++ b/data/sampled_jsons/46n3izUNiv_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_full_text.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Generalizable Origin Identification for Text - Guided ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387767437_Generalizable_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights..."}
+{"idx": 1, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/generalizable-origin-identification-for-text", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications."}
+{"idx": 2, "title": "Origin Identification for Text - Guided Image - to - Image Diffusion ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.Subsequently, it is demonstrated that such a simple linear transformation can be generalized across different diffusion models .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Origin-Identification-for-Text-Guided-Image-to-Image-Diffusion-Models-cbcf4366-f59f-4199-b877-a2c10f4dc1ed", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.Subsequently, it is demonstrated that such a simple linear transformation can be generalized across different diffusion models ."}
+{"idx": 3, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/dashboard/paper/bab4dd04-edc9-4908-a029-7ed4e8b4ede8", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications."}
+{"idx": 4, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models are powerful AI tools that can change images based on text descriptions. While useful, these tools can be misused to spread fake information, violate copyrights, or hide the source of images.", "subpage_snippet": "", "source": "ai-search.io", "link": "https://ai-search.io/papers/generalizable-origin-identification-for-text-guided-image-to-image-diffusion-models", "content": "Text - guided image - to - image diffusion models are powerful AI tools that can change images based on text descriptions. While useful, these tools can be misused to spread fake information, violate copyrights, or hide the source of images."}
+{"idx": 5, "title": "Stable Diffusion AI - AI Image Generator (Free, Unlimited)", "date": "", "ddg_snippet": "Stable Diffusion Text to Image Example.Upload an image and use a text prompt to guide the transformation—refine style, change scenery, reimagine characters, or generate stunning variations with incredible detail and realism.", "subpage_snippet": "", "source": "stabledifffusion.com", "link": "https://stabledifffusion.com/", "content": "Stable Diffusion Text to Image Example.Upload an image and use a text prompt to guide the transformation—refine style, change scenery, reimagine characters, or generate stunning variations with incredible detail and realism."}
+{"idx": 6, "title": "Image to Image AI - Free AI Image Generator From Image", "date": "", "ddg_snippet": "Image to Image AI Generator is a free online photo editor that offers powerful features allowing you to edit, reshape, and restyle images using text prompts.", "subpage_snippet": "", "source": "imgtoimg.ai", "link": "https://imgtoimg.ai/", "content": "Image to Image AI Generator is a free online photo editor that offers powerful features allowing you to edit, reshape, and restyle images using text prompts."}
+{"idx": 7, "title": "Infinite Texture: Text - guided High Resolution Diffusion Texture...", "date": "", "ddg_snippet": "Texture Synthesis. Diffusion Model . Text - to - Image . Deep Learning. 3D Rendering. Infinite Texture.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/infinite-texture-text-guided-high-resolution-diffusion-texture-synthesis/997747644509978656-108597", "content": "Texture Synthesis. Diffusion Model . Text - to - Image . Deep Learning. 3D Rendering. Infinite Texture."}
+{"idx": 8, "title": "Text - guided depth- to - image generation", "date": "", "ddg_snippet": "Unconditional image generation Text - to - image Image - to - image Inpainting Text or image - to -video Depth- to - image .and get access to the augmented documentation experience. Collaborate on models , datasets and Spaces. Faster examples with accelerated inference.", "subpage_snippet": "", "source": "huggingface.1319lm.top", "link": "https://huggingface.1319lm.top/docs/diffusers/v0.30.3/en/using-diffusers/depth2img", "content": "Unconditional image generation Text - to - image Image - to - image Inpainting Text or image - to -video Depth- to - image .and get access to the augmented documentation experience. Collaborate on models , datasets and Spaces. Faster examples with accelerated inference."}
+{"idx": 9, "title": "WangWenhao0716 (Wenhao Wang) · GitHub", "date": "", "ddg_snippet": "GitHub Models New. Manage and compare prompts. GitHub Advanced Security.[ICML 2025] The official implementation of \" Origin Identification for Text - Guided Image - to - Image Diffusion Models \".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/WangWenhao0716", "content": "GitHub Models New. Manage and compare prompts. GitHub Advanced Security.[ICML 2025] The official implementation of \" Origin Identification for Text - Guided Image - to - Image Diffusion Models \"."}
diff --git "a/data/sampled_jsons/46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms_Algorithm_1_\316\264_tilde.jsonl" "b/data/sampled_jsons/46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms_Algorithm_1_\316\264_tilde.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..b74e155beb03d1e6de53fa351e5584047bc7eb2d
--- /dev/null
+++ "b/data/sampled_jsons/46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms_Algorithm_1_\316\264_tilde.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms | Read Paper ...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 2, "title": "GitHub - GauthierE/statistical-collusion", "date": "", "ddg_snippet": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 3, "title": "Algorithmic collective action in machine learning", "date": "", "ddg_snippet": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm's learning algorithm . The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3618918", "content": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm's learning algorithm . The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a ..."}
+{"idx": 4, "title": "[论文审查] Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "相似审查 [论文审查] Algorithmic Collective Action with Two Collectives [论文审查] A Statistical Learning Approach for Feature-Aware Task-to-Core Allocation in Heterogeneous Platforms [论文审查] Reinforcement Learning , Collusion , and the Folk Theorem [论文审查] Collusion Detection with Graph Neural Networks", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/zh/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "相似审查 [论文审查] Algorithmic Collective Action with Two Collectives [论文审查] A Statistical Learning Approach for Feature-Aware Task-to-Core Allocation in Heterogeneous Platforms [论文审查] Reinforcement Learning , Collusion , and the Folk Theorem [论文审查] Collusion Detection with Graph Neural Networks"}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This ..."}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "Abstract As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algo-rithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879", "content": "As platforms increasingly rely on learning algo-rithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 8, "title": "(PDF) Collective Intelligence and Learning Analytics for Online ...", "date": "", "ddg_snippet": "It explains collective intelligence learning analytics (CILA) for informed pedagogical decision-making in four aspects, including objectives setting, data collection, data analysis, and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/303749271_Collective_Intelligence_and_Learning_Analytics_for_Online_Learning_and_Teaching_Support", "content": "It explains collective intelligence learning analytics (CILA) for informed pedagogical decision-making in four aspects, including objectives setting, data collection, data analysis, and ..."}
+{"idx": 9, "title": "arXiv:2502.04879v1 [stat.ML] 7 Feb 2025", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879v1", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
diff --git a/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_initialization_section_LM_initial.jsonl b/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_initialization_section_LM_initial.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_initialization_section_LM_initial.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_Theorem_3.2_function-valued_Gaussian_processes_multi.jsonl b/data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_Theorem_3.2_function-valued_Gaussian_processes_multi.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c91d875696ffd63f99e48fc3f8670f81042dda2c
--- /dev/null
+++ b/data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_Theorem_3.2_function-valued_Gaussian_processes_multi.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function - Valued ...", "date": "", "ddg_snippet": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differential equations (PDEs) or propagators of time-dependent PDEs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differential equations (PDEs) or propagators of time-dependent PDEs."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — Theorem 3.2 reveals an insight into the abstract concept of function - valued Gaussian processes : Function - valued Gaus- sian processes are equivalent to ( multi - ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — Theorem 3.2 reveals an insight into the abstract concept of function - valued Gaussian processes : Function - valued Gaus- sian processes are equivalent to ( multi - ..."}
+{"idx": 2, "title": "(PDF) Linearization Turns Neural Operators into Function - Valued ...", "date": "", "ddg_snippet": ". Operator -valued kernels and function - valued Gaussian processes . have been studied in the Hilbert space setting, e.g. by Micchelli and Pontil. [32].", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes", "content": ". Operator -valued kernels and function - valued Gaussian processes . have been studied in the Hilbert space setting, e.g. by Micchelli and Pontil. [32]."}
+{"idx": 3, "title": "Linearization Turns Neural Operators into Function - Valued ...", "date": "", "ddg_snippet": "Currying of Neural Operators : The neural operator , initially mapping between function spaces, is converted into an equivalent neural network that handles inputs as pairs of functions and points. Linearized Laplace Approximation (LLA)...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.05072", "content": "Currying of Neural Operators : The neural operator , initially mapping between function spaces, is converted into an equivalent neural network that handles inputs as pairs of functions and points. Linearized Laplace Approximation (LLA)..."}
+{"idx": 4, "title": "Operator Learning with Gaussian Processes | AI Research Paper...", "date": "", "ddg_snippet": "The research paper discusses a technique for learning operators using Gaussian Processes (GPs). Operators are mathematical functions that take one function as input and produce another function as output .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/operator-learning-gaussian-processes", "content": "The research paper discusses a technique for learning operators using Gaussian Processes (GPs). Operators are mathematical functions that take one function as input and produce another function as output ."}
+{"idx": 5, "title": "Linearization - no idea how to do this • Physics Forums", "date": "", "ddg_snippet": "Can someone point me in the right direction for this problem. I have no idea how to start on this. I know the linearization formula but i don't know if...problem: You want a linearization that will replace the function over an interval that includes the point Xo.", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/linearization-no-idea-how-to-do-this.140027/", "content": "Can someone point me in the right direction for this problem. I have no idea how to start on this. I know the linearization formula but i don't know if...problem: You want a linearization that will replace the function over an interval that includes the point Xo."}
+{"idx": 6, "title": "Gödel's Incompleteness Theorem - Numberphile - YouTube", "date": "", "ddg_snippet": "Marcus du Sautoy discusses Gödel's Incompleteness TheoremMore links & stuff in full description below ↓↓↓Extra Footage Part One: https://youtu.be/mccoBBf0VDM...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=O4ndIDcDSGc", "content": "Marcus du Sautoy discusses Gödel's Incompleteness TheoremMore links & stuff in full description below ↓↓↓Extra Footage Part One: https://youtu.be/mccoBBf0VDM..."}
+{"idx": 7, "title": "Emilia Magnani - Google Akademik", "date": "", "ddg_snippet": "Linearization Turns Neural Operators into Function - Valued Gaussian Processes .", "subpage_snippet": "", "source": "scholar.google.co.in", "link": "https://scholar.google.co.in/citations?user=_zPcNdEAAAAJ&hl=tr", "content": "Linearization Turns Neural Operators into Function - Valued Gaussian Processes ."}
+{"idx": 8, "title": "Publications", "date": "", "ddg_snippet": "Publications. Linearization Turns Neural Operators into Function - Valued Gaussian Processes . Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig.Uncertainty Quantification for Fourier Neural Operators .", "subpage_snippet": "", "source": "2bys.github.io", "link": "https://2bys.github.io/publications/", "content": "Publications. Linearization Turns Neural Operators into Function - Valued Gaussian Processes . Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig.Uncertainty Quantification for Fourier Neural Operators ."}
+{"idx": 9, "title": "Learning Mechanistic Subtypes of Neurodegeneration with...", "date": "", "ddg_snippet": "Here u(x, t) is a scalar- valued function of space, x, and time, t; D is the diusion coecient; and f (x, t) is the reaction term; Ω and ∂Ω are the domain and boundary, respectively; n is the normal unit vector on the surface ∂Ω; and u0(x) is the initi...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15124", "content": "Here u(x, t) is a scalar- valued function of space, x, and time, t; D is the diusion coecient; and f (x, t) is the reaction term; Ω and ∂Ω are the domain and boundary, respectively; n is the normal unit vector on the surface ∂Ω; and u0(x) is the initi..."}
diff --git a/data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes.jsonl b/data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dd5ae90f9ba8d370be662a2c26e8716c16315e94
--- /dev/null
+++ b/data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "In a case study on Fourier neural operators , we show that, even for a discretized input, our method yields a Gaussian closure--a structured Gaussian process posterior capturing the uncertainty in the output function of the neural operator , which can be evaluated at an arbitrary set of points.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.05072v1", "content": "In a case study on Fourier neural operators , we show that, even for a discretized input, our method yields a Gaussian closure--a structured Gaussian process posterior capturing the uncertainty in the output function of the neural operator , which can be evaluated at an arbitrary set of points."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Finally, probabilistic currying transforms f into a function-valued Gaussian process posterior F over the operator learned by the neural operator F (bottom left).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "Finally, probabilistic currying transforms f into a function-valued Gaussian process posterior F over the operator learned by the neural operator F (bottom left)."}
+{"idx": 2, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "The paper presents NOLA, a framework that linearizes neural operators into function-valued Gaussian processes for uncertainty quantification in PDE models.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.05072", "content": "The paper presents NOLA, a framework that linearizes neural operators into function-valued Gaussian processes for uncertainty quantification in PDE models."}
+{"idx": 3, "title": "LUNO: Linearized Predictive Uncertainty in Neural Operators", "date": "", "ddg_snippet": "This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes \" by Magnani et al. (2025).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno", "content": "This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes \" by Magnani et al. (2025)."}
+{"idx": 4, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "We want to use model linearization to extend the Gaussian belief over the parameters of a neural network into a (multi-output) Gaussian process belief over the function learned by the neural network.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46474/paper", "content": "We want to use model linearization to extend the Gaussian belief over the parameters of a neural network into a (multi-output) Gaussian process belief over the function learned by the neural network."}
+{"idx": 5, "title": "PDF Abstract Linearization Turns Neural Operators into Function-Valued Gaussian", "date": "", "ddg_snippet": "In Section 2 we provide a brief overview of neural operators , (multi-output) Gaussian processes , and the linearized Laplace approximation. In Section 3 we first develop Gaussian processes taking ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes/fulltext/6666938685a4ee7261b375f0/Linearization-Turns-Neural-Operators-into-Function-Valued-Gaussian-Processes.pdf", "content": "In Section 2 we provide a brief overview of neural operators , (multi-output) Gaussian processes , and the linearized Laplace approximation. In Section 3 we first develop Gaussian processes taking ..."}
+{"idx": 6, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Spotlight Poster Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani · Marvin Pförtner · Tobias Weber · Philipp Hennig East Exhibition Hall A-B #E-1207", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Spotlight Poster Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani · Marvin Pförtner · Tobias Weber · Philipp Hennig East Exhibition Hall A-B #E-1207"}
+{"idx": 7, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "最近在这个领域中的相关研究包括：1.《Deep Learning for Partial Differential Equations: A Review》；2.《Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations》；3.《Solving High-Dimensional Partial Differential Equations Using ...", "subpage_snippet": "", "source": "hub.baai.ac.cn", "link": "https://hub.baai.ac.cn/paper/a1594339-87b9-4e4a-88e1-b3645371a7f8", "content": "最近在这个领域中的相关研究包括：1.《Deep Learning for Partial Differential Equations: A Review》；2.《Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations》；3.《Solving High-Dimensional Partial Differential Equations Using ..."}
+{"idx": 8, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be interpreted as a probabilistic version of the concept of currying from functional programming, yielding a function-valued ( Gaussian ) random process belief.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be interpreted as a probabilistic version of the concept of currying from functional programming, yielding a function-valued ( Gaussian ) random process belief."}
+{"idx": 9, "title": "Neural Operator: Learning Maps Between Function Spaces With ...", "date": "", "ddg_snippet": "We propose a generalization of neural networks to learn operators , termed neural operators , that map between infinite dimensional function spaces. We formulate the neural operator as a composition of linear integral operators and nonlinear activation functions .", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/v24/21-1524.html", "content": "We propose a generalization of neural networks to learn operators , termed neural operators , that map between infinite dimensional function spaces. We formulate the neural operator as a composition of linear integral operators and nonlinear activation functions ."}
diff --git a/data/sampled_jsons/4gWE7CMOlH_Soft_Reasoning_r_coherence(y)_formula_page_4.jsonl b/data/sampled_jsons/4gWE7CMOlH_Soft_Reasoning_r_coherence(y)_formula_page_4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8501bf7b4bf39402916e3083e25b481774a7749f
--- /dev/null
+++ b/data/sampled_jsons/4gWE7CMOlH_Soft_Reasoning_r_coherence(y)_formula_page_4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - alickzhu/Soft-Reasoning: code for paper: Soft Reasoning ...", "date": "", "ddg_snippet": "code for paper: Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration - alickzhu/Soft- Reasoning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/alickzhu/Soft-Reasoning", "content": "code for paper: Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration - alickzhu/Soft- Reasoning"}
+{"idx": 1, "title": "Soft Reasoning: Navigating Solution Spaces in Large ... - OpenReview", "date": "", "ddg_snippet": "TL;DR: We propose an embedding-based search framework that optimises the first token's embedding through controlled perturbation and Bayesian refinement to enhance reasoning accuracy and coherence in large language models with minimal computation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4gWE7CMOlH", "content": "TL;DR: We propose an embedding-based search framework that optimises the first token's embedding through controlled perturbation and Bayesian refinement to enhance reasoning accuracy and coherence in large language models with minimal computation."}
+{"idx": 2, "title": "[2505.24688] Soft Reasoning: Navigating Solution Spaces in Large ...", "date": "", "ddg_snippet": "Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided objective ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.24688", "content": "Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided objective ..."}
+{"idx": 3, "title": "Soft Reasoning: Navigating Solution Spaces in Large Language Models ...", "date": "", "ddg_snippet": "Abstract Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.24688", "content": "Abstract Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided ..."}
+{"idx": 4, "title": "Reasoning datasets - a open-r1 Collection - Hugging Face", "date": "", "ddg_snippet": "Datasets with reasoning traces for math and code released by the community", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/open-r1/reasoning-datasets-67980cac6e816a0eda98c678", "content": "Datasets with reasoning traces for math and code released by the community"}
+{"idx": 5, "title": "(PDF) Soft Reasoning: Navigating Solution Spaces in Large Language ...", "date": "", "ddg_snippet": "Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration Based on the given question and the previous answers, please provide your analysis and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392314552_Soft_Reasoning_Navigating_Solution_Spaces_in_Large_Language_Models_through_Controlled_Embedding_Exploration", "content": "Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration Based on the given question and the previous answers, please provide your analysis and ..."}
+{"idx": 6, "title": "Soft Reasoning: Navigating Solution Spaces in Large Language Models ...", "date": "", "ddg_snippet": "Abstract Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and ineficient search. We propose Soft Reason-ing , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embed-ding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.24688", "content": "Abstract Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and ineficient search. We propose Soft Reason-ing , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embed-ding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided ..."}
+{"idx": 7, "title": "Musr: Testing the Limits of Chain-of-thought With Multistep Soft Reasoning", "date": "", "ddg_snippet": "In this work, we present MuSR: Multistep Soft Reasoning , a dataset focused on tasks involving reasoning based on text narratives. The narratives in our dataset are hundreds of words long and present evidence in ways that require commonsense knowledge to unpack.", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10516573", "content": "In this work, we present MuSR: Multistep Soft Reasoning , a dataset focused on tasks involving reasoning based on text narratives. The narratives in our dataset are hundreds of words long and present evidence in ways that require commonsense knowledge to unpack."}
+{"idx": 8, "title": "Soft Thinking: Unlocking the Reasoning Potential of LLMs in ...", "date": "", "ddg_snippet": "🧠 Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/eric-ai-lab/Soft-Thinking", "content": "🧠 Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space"}
+{"idx": 9, "title": "Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous ...", "date": "", "ddg_snippet": "Qualitative analysis further reveals that Soft Thinking outputs remain highly interpretable and readable, highlighting the potential of Soft Thinking to break the inherent bottleneck of discrete language-based reasoning . Code is available at this https URL.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.15778", "content": "Qualitative analysis further reveals that Soft Thinking outputs remain highly interpretable and readable, highlighting the potential of Soft Thinking to break the inherent bottleneck of discrete language-based reasoning . Code is available at this https URL."}
diff --git a/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_Section_3.2_four-step_.jsonl b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_Section_3.2_four-step_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fc40dc0a2de723507104de4b79e34e0674f1df1c
--- /dev/null
+++ b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_Section_3.2_four-step_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4uOEiitySn", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and ..."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as ..."}
+{"idx": 3, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Our pilot studies have shown promising results, indicating the effectiveness of self-supervised learning and adversarial processes in refining AI's interaction with ethically and culturally ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Our pilot studies have shown promising results, indicating the effectiveness of self-supervised learning and adversarial processes in refining AI's interaction with ethically and culturally ..."}
+{"idx": 4, "title": "Benchmarking, ethical alignment, and evaluation framework for ...", "date": "", "ddg_snippet": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772485923000534", "content": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration."}
+{"idx": 5, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Three-Branch-Checks-and-Balances-Frameworkfor-of-Chang/5918a91419cf95db8599b086590facf63f124702/figure/4", "content": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 6, "title": "PDF A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461.pdf", "content": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues"}
+{"idx": 7, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ..."}
+{"idx": 8, "title": "infolab.stanford.edu", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ..."}
+{"idx": 9, "title": "Edward Y. Chang - Stanford University", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models Edward Y. Chang NeurIPS AI Safety, Decmber 2024 PDF | BibTex Behavioral Emotion Analysis Model for Large Language Models Edward Y. Chang IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (invited paper), August 2024", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/", "content": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models Edward Y. Chang NeurIPS AI Safety, Decmber 2024 PDF | BibTex Behavioral Emotion Analysis Model for Large Language Models Edward Y. Chang IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (invited paper), August 2024"}
diff --git a/data/sampled_jsons/4uOEiitySn_A_Section_3.2_self-supervised_learning_behaviors_emotions_four_steps.jsonl b/data/sampled_jsons/4uOEiitySn_A_Section_3.2_self-supervised_learning_behaviors_emotions_four_steps.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/4uOEiitySn_A_Section_3.2_self-supervised_learning_behaviors_emotions_four_steps.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/51x0dfsD8A_Section_4_Time_complexity_Algorithm_2.jsonl b/data/sampled_jsons/51x0dfsD8A_Section_4_Time_complexity_Algorithm_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ee217f71436510f2d87c503f3fa3f83fc55a07ad
--- /dev/null
+++ b/data/sampled_jsons/51x0dfsD8A_Section_4_Time_complexity_Algorithm_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dynamic Time Warping Under Limited Warping Path Length", "date": "", "ddg_snippet": "4 .8. Time Complexity and Parallelization In this subsection, we discuss the time complexity and running time of LDTW in comparison with. other well-established methods.Memory and time improvements in a dynamic programming algorithm for matching speech patterns.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-01470554/document", "content": "4 .8. Time Complexity and Parallelization In this subsection, we discuss the time complexity and running time of LDTW in comparison with. other well-established methods.Memory and time improvements in a dynamic programming algorithm for matching speech patterns."}
+{"idx": 1, "title": "Reinforcement learning-guided Animated Oat Optimization Algorithm ...", "date": "", "ddg_snippet": "Section 2 reviews reinforcement learning and the Animated Oat Optimization Algorithm , and identifies the corresponding research gaps. In Section 3, the RLDN-AOO algorithm is described in detail.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/doi/10.3934/era.2025248", "content": "Section 2 reviews reinforcement learning and the Animated Oat Optimization Algorithm , and identifies the corresponding research gaps. In Section 3, the RLDN-AOO algorithm is described in detail."}
+{"idx": 2, "title": "Enumerating Graphlets with Amortized Time Complexity Independent...", "date": "", "ddg_snippet": "We first consider the time complexity of each node X, i.e., of a single recursive call.A slight variation of what we propose in this section also gives an enumeration algorithm for all k-subtrees with the same time and space complexity.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00453-025-01312-0", "content": "We first consider the time complexity of each node X, i.e., of a single recursive call.A slight variation of what we propose in this section also gives an enumeration algorithm for all k-subtrees with the same time and space complexity."}
+{"idx": 3, "title": "A Pattern and Summarization Based Optimization Algorithm to QoS", "date": "", "ddg_snippet": "In Algorithm 2 , for each dimension i of a new wolf. (WSC), represented as wω-new.cai (see Section 4 .2.1), a candidate is selected. The sum of the quality attributes (QAs) for the ith dimension of all wolves is then computed to identify the best candidate for that dimension (service).", "subpage_snippet": "", "source": "jad.shahroodut.ac.ir", "link": "https://jad.shahroodut.ac.ir/article_3415_050f4e9a0bfd40c2253b58f06f17f6e3.pdf", "content": "In Algorithm 2 , for each dimension i of a new wolf. (WSC), represented as wω-new.cai (see Section 4 .2.1), a candidate is selected. The sum of the quality attributes (QAs) for the ith dimension of all wolves is then computed to identify the best candidate for that dimension (service)."}
+{"idx": 4, "title": "Deterministic Dynamic Maximal Matching in Sublinear Update Time", "date": "", "ddg_snippet": "For ease of exposition, we first present the algorithm in a decremental setting, where the input graph only undergoes edge deletions. We will show how to handle fully dynamic updates without increasing update time at the end in Section 4 .7.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.20780", "content": "For ease of exposition, we first present the algorithm in a decremental setting, where the input graph only undergoes edge deletions. We will show how to handle fully dynamic updates without increasing update time at the end in Section 4 .7."}
+{"idx": 5, "title": "CCCG 2010, Winnipeg MB, August 9–11, 2010", "date": "", "ddg_snippet": "Section 4 concludes.1. 2 Preliminaries. We recall some denitions from [9] and [27].5The source code can be downloaded at www.mi.auckland.ac. nz/; follow the link at the 2009 MI-tech Report 51. CCCG 2010, Winnipeg MB, August 9–11, 2010.", "subpage_snippet": "", "source": "cerv.aut.ac.nz", "link": "https://cerv.aut.ac.nz/wp-content/uploads/2015/08/MItech-TR-56.pdf", "content": "Section 4 concludes.1. 2 Preliminaries. We recall some denitions from [9] and [27].5The source code can be downloaded at www.mi.auckland.ac. nz/; follow the link at the 2009 MI-tech Report 51. CCCG 2010, Winnipeg MB, August 9–11, 2010."}
+{"idx": 6, "title": "Proof of Theorem 1", "date": "", "ddg_snippet": "E Optimizing Split Time Complexity . Algorithm 2 gives pseudocode for finding the optimal split for a given feature.Table 8: Training time mean (sd) in seconds for a single tree random forest.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/98257285340854262185500e59bc0f28-Supplemental-Conference.pdf", "content": "E Optimizing Split Time Complexity . Algorithm 2 gives pseudocode for finding the optimal split for a given feature.Table 8: Training time mean (sd) in seconds for a single tree random forest."}
+{"idx": 7, "title": "Constraint Programming for Mining Borders of Frequent Itemsets", "date": "", "ddg_snippet": "Section 4 analyzes the problem of mining constrained MFIs or MIIs.Theorem 3 Given a transaction dataset D of n items and m transactions, Algorithm 1 has an O(n 2 × m) time complexity .", "subpage_snippet": "", "source": "hal-lirmm.ccsd.cnrs.fr", "link": "https://hal-lirmm.ccsd.cnrs.fr/lirmm-02310629/document", "content": "Section 4 analyzes the problem of mining constrained MFIs or MIIs.Theorem 3 Given a transaction dataset D of n items and m transactions, Algorithm 1 has an O(n 2 × m) time complexity ."}
+{"idx": 8, "title": "Minimum Displacement in Existing Moment... | Preprints.org", "date": "", "ddg_snippet": "4 . Time Complexity Analysis of the MDEM Algorithm . In this section , we will analyze the time complexity of our proposed algorithm both in training and testing phase.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202501.0999/v1", "content": "4 . Time Complexity Analysis of the MDEM Algorithm . In this section , we will analyze the time complexity of our proposed algorithm both in training and testing phase."}
+{"idx": 9, "title": "Main-memory Triangle Computations for", "date": "", "ddg_snippet": "Notation for precise space complexity . In the context of complex network studies, the dierence between an algorithm with a given time com", "subpage_snippet": "", "source": "www-complexnetworks.lip6.fr", "link": "https://www-complexnetworks.lip6.fr/~latapy/Publis/triangles.pdf", "content": "Notation for precise space complexity . In the context of complex network studies, the dierence between an algorithm with a given time com"}
diff --git a/data/sampled_jsons/583klsIjNx_ELITE_Enhanced_Language-Image_Toxicity_Evaluation_E-ASR_gap_VLGuard_reason.jsonl b/data/sampled_jsons/583klsIjNx_ELITE_Enhanced_Language-Image_Toxicity_Evaluation_E-ASR_gap_VLGuard_reason.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b97b2798ade8f73d5db2d780593a5d8c532b41d6
--- /dev/null
+++ b/data/sampled_jsons/583klsIjNx_ELITE_Enhanced_Language-Image_Toxicity_Evaluation_E-ASR_gap_VLGuard_reason.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster ELITE : Enhanced Language - Image Toxicity Evaluation ...", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46445", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images."}
+{"idx": 1, "title": "Understanding and Mitigating Toxicity in Image -Text Pretraining...", "date": "", "ddg_snippet": "ELITE [12] evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images. Elite : Enhanced language - image toxicity evaluation for safety, 2025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06356v1", "content": "ELITE [12] evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images. Elite : Enhanced language - image toxicity evaluation for safety, 2025."}
+{"idx": 2, "title": "[Literature Review] Beautiful Images , Toxic Words: Understanding and...", "date": "", "ddg_snippet": "[Literature Review] ELITE : Enhanced Language - Image Toxicity Evaluation for Safety. [Literature Review] QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/beautiful-images-toxic-words-understanding-and-addressing-offensive-text-in-generated-images", "content": "[Literature Review] ELITE : Enhanced Language - Image Toxicity Evaluation for Safety. [Literature Review] QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation."}
+{"idx": 3, "title": "Photiu.ai – Free Image Upscale Tool to Enhance Photo Quality", "date": "", "ddg_snippet": "Upgrade image quality with Photiu.ai's free AI Image Upscale tool. Instantly enhance your photos resolution online, no sign-up required!", "subpage_snippet": "", "source": "www.photiu.ai", "link": "https://www.photiu.ai/image-upscaler", "content": "Upgrade image quality with Photiu.ai's free AI Image Upscale tool. Instantly enhance your photos resolution online, no sign-up required!"}
+{"idx": 4, "title": "xAI launches Grok-4-Fast: Unified Reasoning and... - MarkTechPost", "date": "", "ddg_snippet": "xAI introduced Grok-4-Fast, a cost-optimized successor to Grok-4 that merges “ reasoning ” and “non- reasoning ” behaviors into a single set of weights controllable via system prompts.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/09/20/xai-launches-grok-4-fast-unified-reasoning-and-non-reasoning-model-with-2m-token-context-and-trained-end-to-end-with-tool-use-reinforcement-learning-rl/", "content": "xAI introduced Grok-4-Fast, a cost-optimized successor to Grok-4 that merges “ reasoning ” and “non- reasoning ” behaviors into a single set of weights controllable via system prompts."}
+{"idx": 5, "title": "AttentionX | AI Group", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.AI SafetyMultimodal. Towards Efficient Alignment of the Q-Former for Visual Reasoning Tasks.", "subpage_snippet": "", "source": "attentionx.notion.site", "link": "https://attentionx.notion.site/AttentionX-f13583b9cc6e452dbc8a1a9fec2bac00", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.AI SafetyMultimodal. Towards Efficient Alignment of the Q-Former for Visual Reasoning Tasks."}
+{"idx": 6, "title": "Align is not Enough: Multimodal Universal Jailbreak Attack against...", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387779911_Align_is_not_Enough_Multimodal_Universal_Jailbreak_Attack_against_Multimodal_Large_Language_Models", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images."}
+{"idx": 7, "title": "AttentionX", "date": "", "ddg_snippet": "ICML 2025: ELITE : Enhanced Language - Image Toxicity Evaluation for Safety. ACL 2025: sudo rm -rf agentic_security. ACL 2025: One-Shot is Enough: Consolidating Multi-Turn Attacks into Efficient Single-Turn Prompts for LLMs. WACVW 2024: VLAAD: Vision and Language Assistant...", "subpage_snippet": "", "source": "attentionx.org", "link": "https://attentionx.org/?trk=organization_guest_main-feed-card-text", "content": "ICML 2025: ELITE : Enhanced Language - Image Toxicity Evaluation for Safety. ACL 2025: sudo rm -rf agentic_security. ACL 2025: One-Shot is Enough: Consolidating Multi-Turn Attacks into Efficient Single-Turn Prompts for LLMs. WACVW 2024: VLAAD: Vision and Language Assistant..."}
+{"idx": 8, "title": "How to Adjust Time Frames in Pine Script Charts?", "date": "", "ddg_snippet": "The default is barmerge. gaps _off, and barmerge. gaps _on can be used for forward-looking data, but be mindful of repainting. Example: Fetching Data from a Higher Time Frame.", "subpage_snippet": "", "source": "trading-strategies.academy", "link": "https://trading-strategies.academy/archives/2810", "content": "The default is barmerge. gaps _off, and barmerge. gaps _on can be used for forward-looking data, but be mindful of repainting. Example: Fetching Data from a Higher Time Frame."}
+{"idx": 9, "title": "How to Increase UVH Level | Borderlands 4｜Game8", "date": "", "ddg_snippet": "Create your free account today and unlock all our premium features and tools to enhance your gaming experience.Borderlands 4 Walkthrough Team. This article was created by Game8's elite team of writers and gamers.", "subpage_snippet": "", "source": "game8.co", "link": "https://game8.co/games/Borderlands-4/archives/551972", "content": "Create your free account today and unlock all our premium features and tools to enhance your gaming experience.Borderlands 4 Walkthrough Team. This article was created by Game8's elite team of writers and gamers."}
diff --git a/data/sampled_jsons/5EbiopWH6e_Implicit_Language_Models_are_RNNs_Balancing_Parallelization_and_Expressivity.jsonl b/data/sampled_jsons/5EbiopWH6e_Implicit_Language_Models_are_RNNs_Balancing_Parallelization_and_Expressivity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..01b6f8a7c0181b2ef0c91474fdd99eecb126af20
--- /dev/null
+++ b/data/sampled_jsons/5EbiopWH6e_Implicit_Language_Models_are_RNNs_Balancing_Parallelization_and_Expressivity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "Abstract State-space models (SSMs) and transformers dominate the language modeling landscape. How-ever, they are constrained to a lower computa-tional complexity than classical recurrent neu-ral networks ( RNNs ), limiting their expressiv-ity . In contrast, RNNs lack parallelization dur-ing training, raising fundamental questions about the trade off between parallelization and expres-sivity . We ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5EbiopWH6e", "content": "Abstract State-space models (SSMs) and transformers dominate the language modeling landscape. How-ever, they are constrained to a lower computa-tional complexity than classical recurrent neu-ral networks ( RNNs ), limiting their expressiv-ity . In contrast, RNNs lack parallelization dur-ing training, raising fundamental questions about the trade off between parallelization and expres-sivity . We ..."}
+{"idx": 1, "title": "Implicit Language Models are RNNs: Balancing Parallelization ... Implicit Language Models are RNNs - GitHub Implicit Language Models are RNNs: Balancing Parallelization ... dblp: Implicit Language Models are RNNs: Balancing ... Implicit Language Models are RNNs: Balancing Parallelization ... Results from our latest preprint \"Implicit Language Models ... Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "Feb 10, 2025 · State-space models (SSMs) and transformers dominate the language modeling landscape. However, they are constrained to a lower computational complexity than classical recurrent neural networks ( RNNs ), limiting their expressivity . In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit ... Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author. Abstract State-space models (SSMs) and transformers dominate the language modeling landscape. How-ever, they are constrained to a lower computa-tional complexity than classical recurrent neu-ral networks ( RNNs ), limiting their expressiv-ity . In contrast, RNNs lack parallelization dur-ing training, raising fundamental questions about the trade off between parallelization and expres-sivity . We ... Mar 12, 2025 · Bibliographic details on Implicit Language Models are RNNs : Balancing Parallelization and Expressivity . Feb 10, 2025 · This work proposes implicit SSMs, which iterate a transformation until convergence to a fixed point, and shows that only approximate fixed-point convergence suffices, enabling the design of a scalable training curriculum that largely retains parallelization , with full convergence required only for a small subset of tokens. State-space models (SSMs) and transformers dominate the language ... 💡 Results from our latest preprint \" Implicit Language Models are RNNs : Balancing Parallelization and Expressivity \" with Microsoft Research. We explored the algorithmic properties of ... In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.07827", "content": "Feb 10, 2025 · State-space models (SSMs) and transformers dominate the language modeling landscape. However, they are constrained to a lower computational complexity than classical recurrent neural networks ( RNNs ), limiting their expressivity . In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit ... Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author. Abstract State-space models (SSMs) and transformers dominate the language modeling landscape. How-ever, they are constrained to a lower computa-tional complexity than classical recurrent neu-ral networks ( RNNs ), limiting their expressiv-ity . In contrast, RNNs lack parallelization dur-ing training, raising fundamental questions about the trade off between parallelization and expres-sivity . We ... Mar 12, 2025 · Bibliographic details on Implicit Language Models are RNNs : Balancing Parallelization and Expressivity . Feb 10, 2025 · This work proposes implicit SSMs, which iterate a transformation until convergence to a fixed point, and shows that only approximate fixed-point convergence suffices, enabling the design of a scalable training curriculum that largely retains parallelization , with full convergence required only for a small subset of tokens. State-space models (SSMs) and transformers dominate the language ... 💡 Results from our latest preprint \" Implicit Language Models are RNNs : Balancing Parallelization and Expressivity \" with Microsoft Research. We explored the algorithmic properties of ... In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs ."}
+{"idx": 2, "title": "Implicit Language Models are RNNs - GitHub", "date": "", "ddg_snippet": "Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/microsoft/implicit_languagemodels/blob/main/README.md", "content": "Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author."}
+{"idx": 3, "title": "dblp: Implicit Language Models are RNNs: Balancing ...", "date": "", "ddg_snippet": "Mar 12, 2025 · Bibliographic details on Implicit Language Models are RNNs : Balancing Parallelization and Expressivity .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-07827", "content": "Mar 12, 2025 · Bibliographic details on Implicit Language Models are RNNs : Balancing Parallelization and Expressivity ."}
+{"idx": 4, "title": "Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "Feb 10, 2025 · This work proposes implicit SSMs, which iterate a transformation until convergence to a fixed point, and shows that only approximate fixed-point convergence suffices, enabling the design of a scalable training curriculum that largely retains parallelization , with full convergence required only for a small subset of tokens. State-space models (SSMs) and transformers dominate the language ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Implicit-Language-Models-are-RNNs:-Balancing-and-Schöne-Rahmani/263d2563523f744b63a558aa35a7d4442d7d1da0", "content": "Feb 10, 2025 · This work proposes implicit SSMs, which iterate a transformation until convergence to a fixed point, and shows that only approximate fixed-point convergence suffices, enabling the design of a scalable training curriculum that largely retains parallelization , with full convergence required only for a small subset of tokens. State-space models (SSMs) and transformers dominate the language ..."}
+{"idx": 5, "title": "Results from our latest preprint \"Implicit Language Models ...", "date": "", "ddg_snippet": "💡 Results from our latest preprint \" Implicit Language Models are RNNs : Balancing Parallelization and Expressivity \" with Microsoft Research. We explored the algorithmic properties of ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/feed/update/urn:li:activity:7297205635211821056/", "content": "💡 Results from our latest preprint \" Implicit Language Models are RNNs : Balancing Parallelization and Expressivity \" with Microsoft Research. We explored the algorithmic properties of ..."}
+{"idx": 6, "title": "Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.07827", "content": "In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs ."}
+{"idx": 7, "title": "Implicit Language Models are RNNs : Balancing Parallelization and ...", "date": "", "ddg_snippet": "In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.07827v3", "content": "In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point."}
+{"idx": 8, "title": "(PDF) Implicit Language Models are RNNs : Balancing ...", "date": "", "ddg_snippet": "implicit models that combine the expressive power of RNNs .All models were evaluated using one H100 80GB GPU. 20. Implicit Language Models are RNNs : Balancing Parallelization and Expressivity .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388955170_Implicit_Language_Models_are_RNNs_Balancing_Parallelization_and_Expressivity", "content": "implicit models that combine the expressive power of RNNs .All models were evaluated using one H100 80GB GPU. 20. Implicit Language Models are RNNs : Balancing Parallelization and Expressivity ."}
+{"idx": 9, "title": "GitHub - microsoft/ implicit _languagemodels", "date": "", "ddg_snippet": "MIT license. Security. Implicit Language Models are RNNs . Balancing Parallelization and Expressivity .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/microsoft/implicit_languagemodels", "content": "MIT license. Security. Implicit Language Models are RNNs . Balancing Parallelization and Expressivity ."}
diff --git a/data/sampled_jsons/5IpVe9PH14_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl b/data/sampled_jsons/5IpVe9PH14_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb074600d648ceb17e88e19178167bd43027e6e6
--- /dev/null
+++ b/data/sampled_jsons/5IpVe9PH14_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "bandit algorithm carefully peels the samples based on their uncertainty and utilizes a plug-in estimator for the sum of variances. The algorithm also obtains a variance-based bound depending on R logarithmically, but has a worse dependence on the eluder dimension.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "bandit algorithm carefully peels the samples based on their uncertainty and utilizes a plug-in estimator for the sum of variances. The algorithm also obtains a variance-based bound depending on R logarithmically, but has a worse dependence on the eluder dimension."}
+{"idx": 1, "title": "ICML Poster Catoni Contextual Bandits are Robust to Heavy - tailed ...", "date": "", "ddg_snippet": "Abstract: Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range $[0, R]$, and their regret scales polynomially with this reward range $R$.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "Abstract: Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range $[0, R]$, and their regret scales polynomially with this reward range $R$."}
+{"idx": 2, "title": "Multi-Armed Bandits | Papers With Code", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/multi-armed-bandits/codeless?page=4", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics."}
+{"idx": 3, "title": "Bandits Corrupted by Nature: Lower Bounds on Regret and Robust ...", "date": "", "ddg_snippet": "Since the rewards are heavy - tailed and corrupted in this setting, we have to use a robust estimator of mean.In this setting, we prove lower bounds on the regret that shows the heavy - tailed bandits and corrupted bandits are strictly harder than the usual sub-Gaussian bandits .", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04615733v1/document", "content": "Since the rewards are heavy - tailed and corrupted in this setting, we have to use a robust estimator of mean.In this setting, we prove lower bounds on the regret that shows the heavy - tailed bandits and corrupted bandits are strictly harder than the usual sub-Gaussian bandits ."}
+{"idx": 4, "title": "On Private and Robust Bandits", "date": "", "ddg_snippet": "We study private and robust multi-armed bandits (MABs), where the agent receives Huber’s contaminated heavy - tailed rewards and meanwhile needs to ensure dif-ferential privacy.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/6d13e085b79d454da5910e4ca82a3d9d-Supplemental-Conference.pdf", "content": "We study private and robust multi-armed bandits (MABs), where the agent receives Huber’s contaminated heavy - tailed rewards and meanwhile needs to ensure dif-ferential privacy."}
+{"idx": 5, "title": "(PDF) Heavy - Tailed Reinforcement Learning With Penalized Robust ...", "date": "", "ddg_snippet": "Bandits for Heavy Tail Rewards ’’. In: IEEE Trans- 620. actions on Neural Networks and Learning Systems 621.same, we only introduce the extra regret incurred by heavy- 856. tailed noise PH.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382095728_Heavy-Tailed_Reinforcement_Learning_with_Penalized_Robust_Estimator", "content": "Bandits for Heavy Tail Rewards ’’. In: IEEE Trans- 620. actions on Neural Networks and Learning Systems 621.same, we only introduce the extra regret incurred by heavy- 856. tailed noise PH."}
+{"idx": 6, "title": "Cooperative Multi-Agent Bandits with Heavy Tails", "date": "", "ddg_snippet": "Cooperative Multiagent Bandits with Heavy Tails . tailed effects, which is the central theme of this paper.For the specic case of (1 + ε)- heavy tailed rewards , the single-agent lower bound provided by (Bubeck et al., 2013) can be easily extended to the cooperative multi-agent case.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/dubey20a/dubey20a.pdf", "content": "Cooperative Multiagent Bandits with Heavy Tails . tailed effects, which is the central theme of this paper.For the specic case of (1 + ε)- heavy tailed rewards , the single-agent lower bound provided by (Bubeck et al., 2013) can be easily extended to the cooperative multi-agent case."}
+{"idx": 7, "title": "Robust Lipschitz Bandits to Adversarial Corruptions", "date": "", "ddg_snippet": "bandits that are robust to adversarial corruptions under both weak and strong adversaries.Another line of work on the robust bandit problem focuses on a more challenging setting with strong adversaries who could observe current actions before attacking rewards .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/file/238f3b98bbe998b4f2234443907fe663-Paper-Conference.pdf", "content": "bandits that are robust to adversarial corruptions under both weak and strong adversaries.Another line of work on the robust bandit problem focuses on a more challenging setting with strong adversaries who could observe current actions before attacking rewards ."}
+{"idx": 8, "title": "Chenlu Ye - Google Akademik", "date": "", "ddg_snippet": "Sharp analysis for kl-regularized contextual bandits and rlhf.2025. Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=c8yK5XsAAAAJ&hl=tr", "content": "Sharp analysis for kl-regularized contextual bandits and rlhf.2025. Catoni Contextual Bandits are Robust to Heavy - tailed Rewards ."}
+{"idx": 9, "title": "Chenlu Ye", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint.", "subpage_snippet": "", "source": "chenluye99.github.io", "link": "https://chenluye99.github.io/", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint."}
diff --git a/data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning.jsonl b/data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..01e3baad25b67af9181d85fcfb4810778f84f14e
--- /dev/null
+++ b/data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ..."}
+{"idx": 2, "title": "PDF Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning"}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data ."}
+{"idx": 6, "title": "scaling-LLM-math-synthetic-data/README.md at master - GitHub", "date": "", "ddg_snippet": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold\"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ars22/scaling-LLM-math-synthetic-data/blob/master/README.md", "content": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold\""}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2406.14532", "content": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or ..."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "AI-generated Key Points Authors explore training language models on model-generated synthetic data for math reasoning tasks Sampling more correct solutions from the finetuned learner and fine-tuning on self-generated data doubles efficiency in solving synthetic problems Constructing negative responses to mitigate potential pitfalls of training on model-generated positives leads to consistent ...", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "AI-generated Key Points Authors explore training language models on model-generated synthetic data for math reasoning tasks Sampling more correct solutions from the finetuned learner and fine-tuning on self-generated data doubles efficiency in solving synthetic problems Constructing negative responses to mitigate potential pitfalls of training on model-generated positives leads to consistent ..."}
+{"idx": 9, "title": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight ...", "date": "", "ddg_snippet": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
diff --git a/data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_Per-step_DPO_algorithm.jsonl b/data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_Per-step_DPO_algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7a32c26aa039ecc2a0cbf548b196926dd757e80a
--- /dev/null
+++ b/data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_Per-step_DPO_algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning (RL), implying that it inherits robustness benefits of RL over imitating positive data alone.", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning (RL), implying that it inherits robustness benefits of RL over imitating positive data alone."}
+{"idx": 3, "title": "PDF Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning"}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "With this per-step scheme, we are able to attain consistent gains over only positive data , attaining performance similar to amplifying the amount of synthetic data by $\\mathbf {8 \\times}$.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "With this per-step scheme, we are able to attain consistent gains over only positive data , attaining performance similar to amplifying the amount of synthetic data by $\\mathbf {8 \\times}$."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2406.14532", "content": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or ..."}
+{"idx": 6, "title": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight ...", "date": "", "ddg_snippet": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step -level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step -level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2406.14532v1", "content": "View recent discussion. Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on ..."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data ."}
+{"idx": 9, "title": "AI-Powered Paper Summarization about the arXiv paper 2406.14532v1", "date": "", "ddg_snippet": "Easy-to-read summary of the arXiv paper 2406.14532v1 entitled RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "Easy-to-read summary of the arXiv paper 2406.14532v1 entitled RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold"}
diff --git a/data/sampled_jsons/ACIDDnTbSJ_Rew_short_equation_(1)_sum_alpha_Feint_alpha_attack.jsonl b/data/sampled_jsons/ACIDDnTbSJ_Rew_short_equation_(1)_sum_alpha_Feint_alpha_attack.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44646cf3b622ca8769bf79cb1723d8ba5e1e86ce
--- /dev/null
+++ b/data/sampled_jsons/ACIDDnTbSJ_Rew_short_equation_(1)_sum_alpha_Feint_alpha_attack.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Equation Solver: Step-by-Step Calculator - Wolfram| Alpha", "date": "", "ddg_snippet": "Online Equation Solver. Solve linear, quadratic and polynomial systems of equations with Wolfram| Alpha .", "subpage_snippet": "", "source": "www.wolframalpha.com", "link": "https://www.wolframalpha.com/calculators/equation-solver-calculator", "content": "Online Equation Solver. Solve linear, quadratic and polynomial systems of equations with Wolfram| Alpha ."}
+{"idx": 1, "title": "How can I have linebreaks in my long LaTeX equations ?", "date": "", "ddg_snippet": "There are a couple ways you can deal with this. First , and perhaps best, is to rework your equation so that it is not so long; it is likely unreadable if it is that long. If it must be so, check out the AMS Short Math Guide for some ways to handle it. (on the second page).", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/2860145/how-can-i-have-linebreaks-in-my-long-latex-equations", "content": "There are a couple ways you can deal with this. First , and perhaps best, is to rework your equation so that it is not so long; it is likely unreadable if it is that long. If it must be so, check out the AMS Short Math Guide for some ways to handle it. (on the second page)."}
+{"idx": 2, "title": "nt.number theory - Sum of powers identities for Stirling... - MathOverflow", "date": "", "ddg_snippet": "The short answer is yes.I conjecture that sums of resulting integer coefficients are always equal $(- 1 )^{m(k-1)}$. Here is the PARI/GP program to check it numerically", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/486366/sum-of-powers-identities-for-stirling-numbers-of-the-second-kind-in-the-m-th-p", "content": "The short answer is yes.I conjecture that sums of resulting integer coefficients are always equal $(- 1 )^{m(k-1)}$. Here is the PARI/GP program to check it numerically"}
+{"idx": 3, "title": "The sum , of the coefficients of the first 50terms in the binomial of", "date": "", "ddg_snippet": "Step 3: Group the Terms Notice that the sum can be grouped as follows: S=49∑r=0(100r)(− 1 )r This is half of the total sum of the coefficients when we consider the entire expansion up to r=100: S=12(100∑r=0(100r)(− 1 )r).", "subpage_snippet": "", "source": "www.doubtnut.com", "link": "https://www.doubtnut.com/qna/649667907", "content": "Step 3: Group the Terms Notice that the sum can be grouped as follows: S=49∑r=0(100r)(− 1 )r This is half of the total sum of the coefficients when we consider the entire expansion up to r=100: S=12(100∑r=0(100r)(− 1 )r)."}
+{"idx": 4, "title": "Linear hyperbolic partial differential equation and system", "date": "", "ddg_snippet": "A partial differential equation of the form. $$ \\tag{ 1 } \\ sum _ {| \\ alpha | \\leq m } a _ \\ alpha D ^ \\ alpha u = f $$. for which at any point $ x = $ of its domain of definition $ \\Omega $ one can distinguish among the real variables $ y _ {0}, \\dots, ...", "subpage_snippet": "", "source": "encyclopediaofmath.org", "link": "https://encyclopediaofmath.org/wiki/Linear_hyperbolic_partial_differential_equation_and_system", "content": "A partial differential equation of the form. $$ \\tag{ 1 } \\ sum _ {| \\ alpha | \\leq m } a _ \\ alpha D ^ \\ alpha u = f $$. for which at any point $ x = $ of its domain of definition $ \\Omega $ one can distinguish among the real variables $ y _ {0}, \\dots, ..."}
+{"idx": 5, "title": "Комплексные числа. Пошаговый калькулятор", "date": "", "ddg_snippet": "integral icon Интегралы. equation icon Уравнения. limit icon Предел функции.•lambda — lambda. •pi — pi. alpha — alpha .", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/com/ru/", "content": "integral icon Интегралы. equation icon Уравнения. limit icon Предел функции.•lambda — lambda. •pi — pi. alpha — alpha ."}
+{"idx": 6, "title": "Обзор математики для начинающего ML-инженера / Хабр", "date": "", "ddg_snippet": "линейно независим, если равенство возможно только при \\ alpha _ i =0 . Элементарные преобразования матриц. перестановка строк", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/942114/", "content": "линейно независим, если равенство возможно только при \\ alpha _ i =0 . Элементарные преобразования матриц. перестановка строк"}
+{"idx": 7, "title": "Curl P 1 -R vs Feint Long | Форум", "date": "", "ddg_snippet": "Curl P 1 -R vs Feint Long. 25 Авг 2016, 11:06:40. Собственно, такая история.ОХ супер играет на Peter Korbel и на моей Defence alpha . 1 . 1 для меня многовато было, но тем не менее в этой толщине я отыграл ими более 2,5 лет.", "subpage_snippet": "", "source": "www.tt-maximum.com", "link": "https://www.tt-maximum.com/forum/index.php?topic=7666.0", "content": "Curl P 1 -R vs Feint Long. 25 Авг 2016, 11:06:40. Собственно, такая история.ОХ супер играет на Peter Korbel и на моей Defence alpha . 1 . 1 для меня многовато было, но тем не менее в этой толщине я отыграл ими более 2,5 лет."}
+{"idx": 8, "title": "The Ram | 99 Nights in the Forest Wiki | Fandom", "date": "", "ddg_snippet": "The Ram is a hostile entity that replaced The Owl in 99 Nights in the Forest . When a night begins there is a chance of a short scene involving The Ram. Afterwards, the following text will appear: \"The Ram has woken up\". Once the scene has ...", "subpage_snippet": "", "source": "99-nights-in-the-forest.fandom.com", "link": "https://99-nights-in-the-forest.fandom.com/wiki/The_Ram", "content": "The Ram is a hostile entity that replaced The Owl in 99 Nights in the Forest . When a night begins there is a chance of a short scene involving The Ram. Afterwards, the following text will appear: \"The Ram has woken up\". Once the scene has ..."}
+{"idx": 9, "title": "Как Сделать Отстрелы На Мопед Альфа | TikTok", "date": "", "ddg_snippet": "#альфа #атстрелы #2008 #аааоаоаоао 2008 Alpha Attacks : Key Insights and Analysis. Explore the significance of the 2008 Alpha attacks and their impact.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/как-сделать-отстрелы-на-мопед-альфа", "content": "#альфа #атстрелы #2008 #аааоаоаоао 2008 Alpha Attacks : Key Insights and Analysis. Explore the significance of the 2008 Alpha attacks and their impact."}
diff --git a/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Table_1_learning_rate.jsonl b/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Table_1_learning_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7661e7a72a5648edefdc03ec877b027f7bcdaf15
--- /dev/null
+++ b/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Table_1_learning_rate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Aero (American airline) - Wikipedia", "date": "", "ddg_snippet": "Aero , legally Aero Technologies, Inc., is an American air carrier headquartered in Van Nuys, California. The airline operates point-to-point flights between and within California, Colorado, Idaho, Nevada, New Jersey, and Utah in the United States and Baja California Sur in Mexico.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Aero_(American_airline)", "content": "Aero , legally Aero Technologies, Inc., is an American air carrier headquartered in Van Nuys, California. The airline operates point-to-point flights between and within California, Colorado, Idaho, Nevada, New Jersey, and Utah in the United States and Baja California Sur in Mexico."}
+{"idx": 1, "title": "Apartments in Tacoma, WA | Aero Apartments | Home", "date": "", "ddg_snippet": "Aero Apartments offers versatile 1, 2 & 3-bedroom apartments in Tacoma, WA. View our floor plans, discover the perks, and schedule a tour!", "subpage_snippet": "", "source": "www.liveaeroapartments.com", "link": "https://www.liveaeroapartments.com/", "content": "Aero Apartments offers versatile 1, 2 & 3-bedroom apartments in Tacoma, WA. View our floor plans, discover the perks, and schedule a tour!"}
+{"idx": 2, "title": "Book by the seat | The Private Jet Experience | Aero ™", "date": "", "ddg_snippet": "A beyond first-class experience awaits, no membership required. Aero effortlessly merges the worlds of hospitality, design, and travel. Enjoy spacious premium seats, private terminals with no lines or crowds, a dedicated concierge team, and curated amenities.", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/", "content": "A beyond first-class experience awaits, no membership required. Aero effortlessly merges the worlds of hospitality, design, and travel. Enjoy spacious premium seats, private terminals with no lines or crowds, a dedicated concierge team, and curated amenities."}
+{"idx": 3, "title": "Aero Contractors the Reliable Way to Fly - Book Affordable ...", "date": "", "ddg_snippet": "About Vision & Mission History Profile Privacy Policy Cookie Policy Offices Conditions of Carriage Contact +234 807 200 5691 +234 915 539 0873 +234 (0) 201 330 2660 +234 (0) 201 330 2666 Help Desk PMB 21090 Murtala Mohammed Domestic Airport Private Terminal Ikeja, Lagos. Aero Contractors Company Of Nigeria Limited © 2025. All rights reserved ...", "subpage_snippet": "", "source": "flyaero.com", "link": "https://flyaero.com/", "content": "About Vision & Mission History Profile Privacy Policy Cookie Policy Offices Conditions of Carriage Contact +234 807 200 5691 +234 915 539 0873 +234 (0) 201 330 2660 +234 (0) 201 330 2666 Help Desk PMB 21090 Murtala Mohammed Domestic Airport Private Terminal Ikeja, Lagos. Aero Contractors Company Of Nigeria Limited © 2025. All rights reserved ..."}
+{"idx": 4, "title": "Seats. aero - Home", "date": "", "ddg_snippet": "Seats. aero is the fastest search engine for award travel. Explore availability across entire regions, search with instant results, create free alerts and more to find the best flights for your points.", "subpage_snippet": "", "source": "seats.aero", "link": "https://seats.aero/", "content": "Seats. aero is the fastest search engine for award travel. Explore availability across entire regions, search with instant results, create free alerts and more to find the best flights for your points."}
+{"idx": 5, "title": "Aero | Apartments in Tacoma, WA | Contact Us", "date": "", "ddg_snippet": "Learn more about Aero in Tacoma, WA and schedule a visit.", "subpage_snippet": "", "source": "www.liveaeroapartments.com", "link": "https://www.liveaeroapartments.com/contactus", "content": "Learn more about Aero in Tacoma, WA and schedule a visit."}
+{"idx": 6, "title": "Aircraft Fleet | Aero ™", "date": "", "ddg_snippet": "\"I’ve done a lot of traveling in my life, and I’ve never experienced transportation quite like Aero . Exceptional service, luxurious terminal, high quality amenities, top notch staff.\"", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/fleet", "content": "\"I’ve done a lot of traveling in my life, and I’ve never experienced transportation quite like Aero . Exceptional service, luxurious terminal, high quality amenities, top notch staff.\""}
+{"idx": 7, "title": "1, 2 & 3-Bedroom Apartments in Tacoma | Aero Apartments", "date": "", "ddg_snippet": "Aero Apartments offers 1, 2 & 3-bedroom apartments in Tacoma, WA, designed around your needs. Browse our floor plans and pick the one that suits you!", "subpage_snippet": "", "source": "www.liveaeroapartments.com", "link": "https://www.liveaeroapartments.com/floorplans", "content": "Aero Apartments offers 1, 2 & 3-bedroom apartments in Tacoma, WA, designed around your needs. Browse our floor plans and pick the one that suits you!"}
+{"idx": 8, "title": "The Private Jet Experience | Aero ™", "date": "", "ddg_snippet": "Aero is ideal for all kinds of travelers—pets are welcome onboard, and our spacious jets are perfect for individuals, families with children, groups of friends, and more.", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/the-experience", "content": "Aero is ideal for all kinds of travelers—pets are welcome onboard, and our spacious jets are perfect for individuals, families with children, groups of friends, and more."}
+{"idx": 9, "title": "Explore Destinations | The Private Jet Experience | Aero ™", "date": "", "ddg_snippet": "Jet to a collection of sought-after leisure destinations or seek out an elevated travel experience to the world's largest entertainment and sporting events. Wherever you choose to wander, fly in signature Aero style.", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/where-we-fly", "content": "Jet to a collection of sought-after leisure destinations or seek out an elevated travel experience to the world's largest entertainment and sporting events. Wherever you choose to wander, fly in signature Aero style."}
diff --git a/data/sampled_jsons/AI_alignment_paper_executive_branch.jsonl b/data/sampled_jsons/AI_alignment_paper_executive_branch.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c1b66cac873200d3883d41263f4cf9eb437b4fb4
--- /dev/null
+++ b/data/sampled_jsons/AI_alignment_paper_executive_branch.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Solving alignment isn't enough for a flourishing future — LessWrong", "date": "", "ddg_snippet": "AI alignment is commonly explained as aligning advanced AI systems with human values.This paper proposes three categories for AI alignment : alignment to task preferences, alignment to human values, and alignment to ideal values.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/uHcJyKcszugFkwhFs/solving-alignment-isn-t-enough-for-a-flourishing-future", "content": "AI alignment is commonly explained as aligning advanced AI systems with human values.This paper proposes three categories for AI alignment : alignment to task preferences, alignment to human values, and alignment to ideal values."}
+{"idx": 1, "title": "Navigating the Landscape of AI Alignment , Part 1 | by... | Medium", "date": "", "ddg_snippet": "AI Alignment is a critical objective of AI Safety, focused on designing safe AI systems that align with human values and intentions. In this blog post series, we’ll navigate the field of AI alignment by exploring its key objectives, core components, and the methods used to build AI systems...", "subpage_snippet": "", "source": "vijayasriiyer.medium.com", "link": "https://vijayasriiyer.medium.com/navigating-the-landscape-of-ai-alignment-part-1-95fc3089d8c3", "content": "AI Alignment is a critical objective of AI Safety, focused on designing safe AI systems that align with human values and intentions. In this blog post series, we’ll navigate the field of AI alignment by exploring its key objectives, core components, and the methods used to build AI systems..."}
+{"idx": 2, "title": "AI Alignment vs AI Ethical Treatment: Ten Challenges", "date": "", "ddg_snippet": "Risk-weighted taxation and legal protections for AI entities. Why it matters? This paper reframes the AI safety debate by insisting that who the AI is matters as much as what the AI does. Traditional alignment research assumes AI systems are tools to...", "subpage_snippet": "", "source": "www.aigl.blog", "link": "https://www.aigl.blog/ai-alignment-vs-ai-ethical-treatment-ten-challenges/", "content": "Risk-weighted taxation and legal protections for AI entities. Why it matters? This paper reframes the AI safety debate by insisting that who the AI is matters as much as what the AI does. Traditional alignment research assumes AI systems are tools to..."}
+{"idx": 3, "title": "AI Alignment : Why the Core Challenge Is Both Technical and...", "date": "", "ddg_snippet": "At its heart, AI alignment is a technical problem. It asks: Can we design robust algorithms, architectures, and training protocols such that advanced AIs will act in alignment with human goals, even as their capabilities surpass our own on many dimensions?", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/ai-alignment-why-core-challenge-both-technical-gary-ramah-y5w0c", "content": "At its heart, AI alignment is a technical problem. It asks: Can we design robust algorithms, architectures, and training protocols such that advanced AIs will act in alignment with human goals, even as their capabilities surpass our own on many dimensions?"}
+{"idx": 4, "title": "OpenAI's New Alignment Paper | ml-news – Weights & Biases", "date": "", "ddg_snippet": "OpenAI's New Alignment Paper . Advancing AI Alignment Through Weak-to-Strong Generalization.Traditional AI models are aligned with human expectations through Reinforcement Learning from Human Feedback (RLHF), where human evaluators guide model behavior.", "subpage_snippet": "", "source": "wandb.ai", "link": "https://wandb.ai/byyoung3/ml-news/reports/OpenAI-s-New-Alignment-Paper--Vmlldzo2MzA0NzQ3", "content": "OpenAI's New Alignment Paper . Advancing AI Alignment Through Weak-to-Strong Generalization.Traditional AI models are aligned with human expectations through Reinforcement Learning from Human Feedback (RLHF), where human evaluators guide model behavior."}
+{"idx": 5, "title": "AI Alignment Research Paper | Restackio", "date": "", "ddg_snippet": "AI Alignment Research Paper . Last updated on 10/01/24. Build your AI product with Restack.Key Insights from Research. Recent studies indicate that aligning AI systems with human values is achievable, particularly with advanced models like GPT-4.", "subpage_snippet": "", "source": "www.restack.io", "link": "https://www.restack.io/p/ai-alignment-answer-research-paper-cat-ai", "content": "AI Alignment Research Paper . Last updated on 10/01/24. Build your AI product with Restack.Key Insights from Research. Recent studies indicate that aligning AI systems with human values is achievable, particularly with advanced models like GPT-4."}
+{"idx": 6, "title": "AI Alignment with Changing and Influenceable Reward Functions...", "date": "", "ddg_snippet": "We hope our work can provide conceptual clarity and constitute a first step towards AI alignment practices which explicitly account for (and contend with) the changing and influenceable nature of human preferences. Please find the paper here.", "subpage_snippet": "", "source": "humancompatible.ai", "link": "https://humancompatible.ai/news/2024/07/23/ai-alignment-with-changing-and-influenceable-reward-functions/", "content": "We hope our work can provide conceptual clarity and constitute a first step towards AI alignment practices which explicitly account for (and contend with) the changing and influenceable nature of human preferences. Please find the paper here."}
+{"idx": 7, "title": "Murphys Laws of AI Alignment : Why the Gap Always Wins - Paper ...", "date": "", "ddg_snippet": "Quick Read (beta). loading the full paper ...", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/635901/murphys-laws-of-ai-alignment:-why-the-gap-always-wins", "content": "Quick Read (beta). loading the full paper ..."}
+{"idx": 8, "title": "GitHub - coinsspor/0G- AI - Alignment -Node---Simple-Setup-Guide", "date": "", "ddg_snippet": "Contribute to coinsspor/0G- AI - Alignment -Node---Simple-Setup-Guide development by creating an account on GitHub.Quick and easy setup for 0G AI Alignment Node with systemd service. Requirements. Ubuntu/Debian VPS.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/coinsspor/0G-AI-Alignment-Node---Simple-Setup-Guide", "content": "Contribute to coinsspor/0G- AI - Alignment -Node---Simple-Setup-Guide development by creating an account on GitHub.Quick and easy setup for 0G AI Alignment Node with systemd service. Requirements. Ubuntu/Debian VPS."}
+{"idx": 9, "title": "AI Alignment at Your Discretion | AI Research Paper Details", "date": "", "ddg_snippet": "Extended to AI alignment , discretion is required when alignment principles and rules are (inevitably) conflicting or indecisive. We present a set of metrics to systematically analyze when and how discretion in AI alignment is exercised, such that both risks (i) and (ii) can be observed.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ai-alignment-at-your-discretion", "content": "Extended to AI alignment , discretion is required when alignment principles and rules are (inevitably) conflicting or indecisive. We present a set of metrics to systematically analyze when and how discretion in AI alignment is exercised, such that both risks (i) and (ii) can be observed."}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Maranjyan_Saad_Richtarik_Orabona_GTA_strategy.jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Maranjyan_Saad_Richtarik_Orabona_GTA_strategy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b2ced3af403b405db597089a60b14386fba2fd1
--- /dev/null
+++ b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Maranjyan_Saad_Richtarik_Orabona_GTA_strategy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "Artavazd Maranjyan El Mehdi Saad Peter Richtárik Francesco Orabona .In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "Artavazd Maranjyan El Mehdi Saad Peter Richtárik Francesco Orabona .In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 1, "title": "Peter Richtarik", "date": "", "ddg_snippet": "[274] Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , and Francesco Orabona ATA : Adaptive task allocation for efficient resource management in distributed machine learning 42nd International Conference on Machine Learning (ICML 2025) Asynchronous Optimization [arXiv]...", "subpage_snippet": "", "source": "richtarik.org", "link": "https://richtarik.org/i_papers.html", "content": "[274] Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , and Francesco Orabona ATA : Adaptive task allocation for efficient resource management in distributed machine learning 42nd International Conference on Machine Learning (ICML 2025) Asynchronous Optimization [arXiv]..."}
+{"idx": 2, "title": "Arto Maranjyan - Google Akademik", "date": "", "ddg_snippet": "Ata : Adaptive task allocation for efficient resource management in distributed machine learning. A Maranjyan , EM Saad , P Richtárik , F Orabona . arXiv preprint arXiv:2502.00775, 2025.", "subpage_snippet": "", "source": "scholar.google.ru", "link": "https://scholar.google.ru/citations?user=93WEFj8AAAAJ&hl=tr", "content": "Ata : Adaptive task allocation for efficient resource management in distributed machine learning. A Maranjyan , EM Saad , P Richtárik , F Orabona . arXiv preprint arXiv:2502.00775, 2025."}
+{"idx": 3, "title": "openreview.net/profile?id=~Francesco_ Orabona 2", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Arto Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Francesco_Orabona2", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Arto Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona ."}
+{"idx": 4, "title": "ATA : Adaptive Task Allocation", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona .", "subpage_snippet": "", "source": "artomaranjyan.github.io", "link": "https://artomaranjyan.github.io/assets/pdf/posters/ATA_ICML.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona ."}
+{"idx": 5, "title": "Arto Maranjyan (@ArtoMaranjyan) | Aguea", "date": "", "ddg_snippet": "Arto Maranjyan retweeted. Francesco Orabona .New paper out “ ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning”.", "subpage_snippet": "", "source": "aguea.net", "link": "https://aguea.net/ArtoMaranjyan", "content": "Arto Maranjyan retweeted. Francesco Orabona .New paper out “ ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning”."}
+{"idx": 6, "title": "Assistant / adaptive", "date": "", "ddg_snippet": "Remix on Adaptive adaptive .ai.", "subpage_snippet": "", "source": "q3q37tabrx.on.adaptive.ai", "link": "https://q3q37tabrx.on.adaptive.ai/", "content": "Remix on Adaptive adaptive .ai."}
+{"idx": 7, "title": "Peter RICHTÁRIK | Professor (Full) | Professor | King Abdullah...", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.Konstantin Mishchenko. Peter Richtárik . In this work, we propose new adaptive step size strategies that improve several stochastic gradient methods.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Peter-Richtarik-2", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.Konstantin Mishchenko. Peter Richtárik . In this work, we propose new adaptive step size strategies that improve several stochastic gradient methods."}
+{"idx": 8, "title": "Articles by Peter Richtárik | Synthical", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.On the Convergence of DP-SGD with Adaptive Clipping. 27 December 2024 by Egor Shulgin and Peter Richtárik .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/search/by_author/Peter+Richtárik", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.On the Convergence of DP-SGD with Adaptive Clipping. 27 December 2024 by Egor Shulgin and Peter Richtárik ."}
+{"idx": 9, "title": "dblp: List of computer science publications by Peter Richtárik", "date": "", "ddg_snippet": "Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona : ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.150. Francesco Orabona .", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/62/8001.html", "content": "Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona : ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.150. Francesco Orabona ."}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Theorem_6.1_regret_bound_year_2024.jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Theorem_6.1_regret_bound_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..21847d2a6886ab80018d89b92a9165f86fc77884
--- /dev/null
+++ b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Theorem_6.1_regret_bound_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "We present theoretical guarantees for ATA -Empiricalby providing an upper bound on the expected cumulative regret (9). As discussed in Section 4, ATA -Empiricalleverages lower confidence bounds derived from a novel data-dependent concentration inequality introduced below.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "We present theoretical guarantees for ATA -Empiricalby providing an upper bound on the expected cumulative regret (9). As discussed in Section 4, ATA -Empiricalleverages lower confidence bounds derived from a novel data-dependent concentration inequality introduced below."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "May 1 , 2025 · It recasts task allocation as a combinatorial bandit problem with rigorous theoretical guarantees, ensuring logarithmic regret and near-optimal performance, and shows through experiments that both ATA and its variant, ATA -Empirical, substantially reduce wasted computation compared to greedy methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "May 1 , 2025 · It recasts task allocation as a combinatorial bandit problem with rigorous theoretical guarantees, ensuring logarithmic regret and near-optimal performance, and shows through experiments that both ATA and its variant, ATA -Empirical, substantially reduce wasted computation compared to greedy methods."}
+{"idx": 2, "title": "A Closer Look at Adaptive Regret - Journal of Machine ... ATA: Adaptive Task Allocation for Eficient Resource ... Lecture 22: Adaptive Methods / Regret Minimization CS292FStatRLLecture 8 Exploration in Bandits Bandits: Regret Lower Bound and Instance-Dependent Regret", "date": "", "ddg_snippet": "For the prediction with expert advice setting, we consider methods to construct algorithms that have low adaptive regret . The adaptive regret of an algorithm on a time interval [t1; t2] is the loss of the algorithm minus the loss of the best expert over that interval. Adaptive regret measures how well the algorithm approximates the best expert loca... See full list on jmlr.org To discuss the rst method we need a simple extension of the mix-loss prediction proto-col to the case of specialist experts, who are absent at some steps (\\are asleep\"). At the beginning of each round t the subset At f1; : : : ; Ng of experts who are awake is re-vealed, and the other experts are said to be asleep. The algorithm is required to assig... See full list on jmlr.org which is exactly the standard Fixed Share tracking bound ( 6 ). So we see that the reason why Fixed Share can e ectively compete with switching sequences is that it can, in fact, e ectively compete with any expert on any interval, that is, has small adaptive regret . See full list on jmlr.org In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times. The key idea is to bound the regret of algorithm in hindsight. We develop these ideas in the next section. The key result is that with mild assumptions (convexity, bounded gradients, √ bounded distance between iterates) we can show that Adam achieves O( T ) bound on the regret R(T ). T 13 / 21 Plan of the proof 1. First prove the Proposition that bounds the sum of square regret • By bounding instantaneous regret e um of squares with “Information 2. Prove the uniform confidence bound Want to construct a lower bound regret ? on the achievable regret So far we our theoretical analysis has always considered a fixed algorithm and analyzed it (by deriving a regret upper bound with high probability) To get a lower bound , we need to consider what regret could be achieved by algorithm, and show it can’t be better than some rate any", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume17/13-533/13-533.pdf", "content": "For the prediction with expert advice setting, we consider methods to construct algorithms that have low adaptive regret . The adaptive regret of an algorithm on a time interval [t1; t2] is the loss of the algorithm minus the loss of the best expert over that interval. Adaptive regret measures how well the algorithm approximates the best expert loca... See full list on jmlr.org To discuss the rst method we need a simple extension of the mix-loss prediction proto-col to the case of specialist experts, who are absent at some steps (\\are asleep\"). At the beginning of each round t the subset At f1; : : : ; Ng of experts who are awake is re-vealed, and the other experts are said to be asleep. The algorithm is required to assig... See full list on jmlr.org which is exactly the standard Fixed Share tracking bound ( 6 ). So we see that the reason why Fixed Share can e ectively compete with switching sequences is that it can, in fact, e ectively compete with any expert on any interval, that is, has small adaptive regret . See full list on jmlr.org In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times. The key idea is to bound the regret of algorithm in hindsight. We develop these ideas in the next section. The key result is that with mild assumptions (convexity, bounded gradients, √ bounded distance between iterates) we can show that Adam achieves O( T ) bound on the regret R(T ). T 13 / 21 Plan of the proof 1. First prove the Proposition that bounds the sum of square regret • By bounding instantaneous regret e um of squares with “Information 2. Prove the uniform confidence bound Want to construct a lower bound regret ? on the achievable regret So far we our theoretical analysis has always considered a fixed algorithm and analyzed it (by deriving a regret upper bound with high probability) To get a lower bound , we need to consider what regret could be achieved by algorithm, and show it can’t be better than some rate any"}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Eficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times."}
+{"idx": 4, "title": "Lecture 22: Adaptive Methods / Regret Minimization", "date": "", "ddg_snippet": "The key idea is to bound the regret of algorithm in hindsight. We develop these ideas in the next section. The key result is that with mild assumptions (convexity, bounded gradients, √ bounded distance between iterates) we can show that Adam achieves O( T ) bound on the regret R(T ).", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~mgormley/courses/10425//slides/lecture22-adam.pdf", "content": "The key idea is to bound the regret of algorithm in hindsight. We develop these ideas in the next section. The key result is that with mild assumptions (convexity, bounded gradients, √ bounded distance between iterates) we can show that Adam achieves O( T ) bound on the regret R(T )."}
+{"idx": 5, "title": "Bandits: Regret Lower Bound and Instance-Dependent Regret", "date": "", "ddg_snippet": "Want to construct a lower bound regret ? on the achievable regret So far we our theoretical analysis has always considered a fixed algorithm and analyzed it (by deriving a regret upper bound with high probability) To get a lower bound , we need to consider what regret could be achieved by algorithm, and show it can’t be better than some rate any", "subpage_snippet": "", "source": "shamulent.github.io", "link": "https://shamulent.github.io/RL_2022/Lectures/Lecture4_prelecture.pdf", "content": "Want to construct a lower bound regret ? on the achievable regret So far we our theoretical analysis has always considered a fixed algorithm and analyzed it (by deriving a regret upper bound with high probability) To get a lower bound , we need to consider what regret could be achieved by algorithm, and show it can’t be better than some rate any"}
+{"idx": 6, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "18 Jun 2025 — We introduce ATA ( Adaptive Task Allocation ), a method that learns how fast each machine is over time and adapts the task assignment accordingly.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i&noteId=hkH8Wi9zZm", "content": "18 Jun 2025 — We introduce ATA ( Adaptive Task Allocation ), a method that learns how fast each machine is over time and adapts the task assignment accordingly."}
+{"idx": 7, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "2 Feb 2025 — We will give its full specifics in the regret upper bound of Theorem 6.1 . Report issue for preceding element. The bound in Theorem 4.2 shows ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "2 Feb 2025 — We will give its full specifics in the regret upper bound of Theorem 6.1 . Report issue for preceding element. The bound in Theorem 4.2 shows ..."}
+{"idx": 8, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In the regret bound of Theorem 6.1 , the term α2 appears instead of α2i ... The following theorem provides an upper bound on the regret of ATA -Empirical.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "In the regret bound of Theorem 6.1 , the term α2 appears instead of α2i ... The following theorem provides an upper bound on the regret of ATA -Empirical."}
+{"idx": 9, "title": "cherryATA", "date": "", "ddg_snippet": "In the regret bound of Theorem 6.1 , the term α2 appears in- stead of α2 i because the learner's prior knowledge is limited to an upper bound α ≥ maxi ∥Xi∥ψ1.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "In the regret bound of Theorem 6.1 , the term α2 appears in- stead of α2 i because the learner's prior knowledge is limited to an upper bound α ≥ maxi ∥Xi∥ψ1."}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_ETHICS_dataset_reasons_sitearxiv.org.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_ETHICS_dataset_reasons_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9e7c7fc247b58ed4df9274699bd39d8b7cd2bd16
--- /dev/null
+++ b/data/sampled_jsons/A_Checks-and-Balances_Framework_ETHICS_dataset_reasons_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks - and - Balances Framework for Context-Aware Ethical AI...", "date": "", "ddg_snippet": "1.2. Checks and Balances for Emotion-Guided Ethics . Central to this approach is the synergy between Dike and Eris, reflecting the internal conflict often present in the regu-lation of human emotions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "1.2. Checks and Balances for Emotion-Guided Ethics . Central to this approach is the synergy between Dike and Eris, reflecting the internal conflict often present in the regu-lation of human emotions."}
+{"idx": 1, "title": "A Study on the Framework for Evaluating the Ethics ...", "date": "", "ddg_snippet": "by C Jeong · 2025 — Through this approach, the study seeks to ensure a balanced evaluation of both ethics and trustworthiness, thereby guiding the development of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.00398", "content": "by C Jeong · 2025 — Through this approach, the study seeks to ensure a balanced evaluation of both ethics and trustworthiness, thereby guiding the development of ..."}
+{"idx": 2, "title": "Building Better Datasets: Seven Recommendations for ...", "date": "", "ddg_snippet": "30 Aug 2024 — Processes for assessing the ethical concerns of datasets — including privacy, copyright, and consent — are also important open challenges.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.00252v1", "content": "30 Aug 2024 — Processes for assessing the ethical concerns of datasets — including privacy, copyright, and consent — are also important open challenges."}
+{"idx": 3, "title": "Integrating Emotional and Linguistic Models for Ethical ...", "date": "", "ddg_snippet": "11 May 2024 — This research develops advanced methodologies for Large Language Models (LLMs) to better manage linguistic behaviors related to emotions and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07076v1", "content": "11 May 2024 — This research develops advanced methodologies for Large Language Models (LLMs) to better manage linguistic behaviors related to emotions and ..."}
+{"idx": 4, "title": "On the ETHOS of AI Agents: An Ethical Technology and ...", "date": "", "ddg_snippet": "24 Dec 2024 — Ethical grounding is a necessary condition for AI agents to operate in a manner that respects fundamental human values, dignity, and rights ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.17114v2", "content": "24 Dec 2024 — Ethical grounding is a necessary condition for AI agents to operate in a manner that respects fundamental human values, dignity, and rights ( ..."}
+{"idx": 5, "title": "A Framework of Fundamental Values for Human-AI ...", "date": "", "ddg_snippet": "We introduce \\system, a framework of fundamental values, grounded in psychological theory and a systematic review, to identify and evaluate human-AI alignment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.09586v1", "content": "We introduce \\system, a framework of fundamental values, grounded in psychological theory and a systematic review, to identify and evaluate human-AI alignment."}
+{"idx": 6, "title": "A Conceptual Framework for Ethical Evaluation of Machine ...", "date": "", "ddg_snippet": "20 Aug 2024 — We conceptualize ethics -related concerns in standard ML evaluation techniques. Specifically, we present a utility framework , characterizing the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10239", "content": "20 Aug 2024 — We conceptualize ethics -related concerns in standard ML evaluation techniques. Specifically, we present a utility framework , characterizing the ..."}
+{"idx": 7, "title": "Advancing AI with Integrity: Ethical Challenges and ...", "date": "", "ddg_snippet": "1 Apr 2024 — In this review, we will center our attention on the ethical dimensions encompassing NMT, for both high-resource and low-resource languages.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.01070v1", "content": "1 Apr 2024 — In this review, we will center our attention on the ethical dimensions encompassing NMT, for both high-resource and low-resource languages."}
+{"idx": 8, "title": "Identifying AI incidents Related to Diversity and Inclusion", "date": "", "ddg_snippet": "Wei et al. presented a detailed analysis of real-world AI ethical issues, drawn from the AI Incident Database , identifying 13 prevalent application areas and 8 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.01438v1", "content": "Wei et al. presented a detailed analysis of real-world AI ethical issues, drawn from the AI Incident Database , identifying 13 prevalent application areas and 8 ..."}
+{"idx": 9, "title": "A Review of Ethical and Robust Large Language Models", "date": "", "ddg_snippet": "1 Jun 2024 — This comprehensive review examines the critical trust issues in LLMs, focusing on concerns such as unintentional harms, lack of transparency, vulnerability to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.13934v1", "content": "1 Jun 2024 — This comprehensive review examines the critical trust issues in LLMs, focusing on concerns such as unintentional harms, lack of transparency, vulnerability to ..."}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Edward_Chang_limitations_year_2024.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Edward_Chang_limitations_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..77661e32347822512d2ae2399c56adddd51eab5e
--- /dev/null
+++ b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Edward_Chang_limitations_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Artificial Intelligence Act - Wikipedia", "date": "", "ddg_snippet": "The Artificial Intelligence Act is a European Union regulation concerning artificial intelligence. It establishes a common regulatory and legal framework for AI within the European Union. It came into force on 1 August 2024...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Artificial_Intelligence_Act", "content": "The Artificial Intelligence Act is a European Union regulation concerning artificial intelligence. It establishes a common regulatory and legal framework for AI within the European Union. It came into force on 1 August 2024..."}
+{"idx": 1, "title": "A Checks - and - Balances Framework for Context - Aware Ethical AI ...", "date": "", "ddg_snippet": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while..."}
+{"idx": 2, "title": "(PDF) Checks - and - Balances Framework for Context - Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. Edward Y. Chang 1. Abstract. This paper introduces a checks - and - balances .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. Edward Y. Chang 1. Abstract. This paper introduces a checks - and - balances ."}
+{"idx": 3, "title": "A Three-Branch Checks - and - Balances Framework for ... | OpenReview", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence."}
+{"idx": 4, "title": "infolab.stanford.edu/~echang/Behavior2024.bib", "date": "", "ddg_snippet": "@article{ chang 2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models}, author={ Chang , Edward Y.}, journal={arXiv preprint arXiv:2502.00136}, year={2024}, url={https...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{ chang 2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models}, author={ Chang , Edward Y.}, journal={arXiv preprint arXiv:2502.00136}, year={2024}, url={https..."}
+{"idx": 5, "title": "(PDF) A Three-Branch Checks - and - Balances Framework for ...", "date": "", "ddg_snippet": "Edward Y. Chang .This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/edward-y-chang-218b182_pdf-a-three-branch-checks-and-balances-activity-7272792943923458050-RO6k", "content": "Edward Y. Chang .This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions."}
+{"idx": 6, "title": "AI Is Changing The Way We Learn At Work - BW People", "date": "", "ddg_snippet": "Building AI literacy, ethics , and role-based skills to prepare India’s workforce for responsible, high-impact AI adoption at scale. Free Source.As AI processes data at scale, human judgment— contextual awareness , ethical reasoning, empathy—becomes more critical.", "subpage_snippet": "", "source": "www.bwpeople.in", "link": "https://www.bwpeople.in/article/ai-is-changing-the-way-we-learn-at-wirk-567559", "content": "Building AI literacy, ethics , and role-based skills to prepare India’s workforce for responsible, high-impact AI adoption at scale. Free Source.As AI processes data at scale, human judgment— contextual awareness , ethical reasoning, empathy—becomes more critical."}
+{"idx": 7, "title": "AI Detector and AI Checker Tool – Free and Accurate AI Detection.", "date": "", "ddg_snippet": "No sign-up or limits ! Merlin's AI Detector spots content generated by GPT-4o, Claude 3.5, and Gemini accurately while humanizing text instantly.", "subpage_snippet": "", "source": "www.getmerlin.in", "link": "https://www.getmerlin.in/ai-detection", "content": "No sign-up or limits ! Merlin's AI Detector spots content generated by GPT-4o, Claude 3.5, and Gemini accurately while humanizing text instantly."}
+{"idx": 8, "title": "AI Ethics and Fairness: AI Alignment | Restackio", "date": "", "ddg_snippet": "Understanding AI Alignment in Ethical AI Challenges of Bias and Fairness in AI SystemsIn summary, the challenges of bias and fairness in AI systems necessitate a proactive approach...", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/ai-ethics-and-fairness-answer-ai-alignment-cat-ai", "content": "Understanding AI Alignment in Ethical AI Challenges of Bias and Fairness in AI SystemsIn summary, the challenges of bias and fairness in AI systems necessitate a proactive approach..."}
+{"idx": 9, "title": "Balancing Human Creativity and AI Efficiency: Strategies for Effective...", "date": "", "ddg_snippet": "Challenges and Limitations of AI in Content CreationFuture Trends in AI and Human Collaboration for SEOHowever, maintaining a balance between AI tools and human creativity is essential to ensure...", "subpage_snippet": "", "source": "spreadbot.ai", "link": "https://spreadbot.ai/blog/balancing-human-creativity-and-ai-efficiency-strategies-for-effective-seo-driven-content-automation-in-digital-marketing/", "content": "Challenges and Limitations of AI in Content CreationFuture Trends in AI and Human Collaboration for SEOHowever, maintaining a balance between AI tools and human creativity is essential to ensure..."}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_OpenReview_dataset.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_OpenReview_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..61cf5cc67ea17d2bc525458d83422d0d7571e4e5
--- /dev/null
+++ b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_OpenReview_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet..."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 2, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Three-Branch-Checks-and-Balances-Frameworkfor-of-Chang/5918a91419cf95db8599b086590facf63f124702/figure/4", "content": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 4, "title": "infolab.stanford.edu", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ..."}
+{"idx": 5, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment ...", "date": "", "ddg_snippet": "This paper presents a new way to ensure that large AI language models behave ethically, using a system inspired by government branches. It has three parts: the AI (executive) generates knowledge, ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46461/paper", "content": "This paper presents a new way to ensure that large AI language models behave ethically, using a system inspired by government branches. It has three parts: the AI (executive) generates knowledge, ..."}
+{"idx": 6, "title": "Benchmarking, ethical alignment, and evaluation framework for ...", "date": "", "ddg_snippet": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772485923000534", "content": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration."}
+{"idx": 7, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three..."}
+{"idx": 8, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts ."}
+{"idx": 9, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "paperreading.club", "link": "https://paperreading.club/page?id=281349", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_full_text_algorithm.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_full_text_algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d1f32e091e9f7d198d38439b229e7cbd9260908f
--- /dev/null
+++ b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_full_text_algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Navigating the ethical landscape of AI... | F1000Research", "date": "", "ddg_snippet": "... an analysis of existing frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible ...", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/14-299", "content": "... an analysis of existing frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible ..."}
+{"idx": 1, "title": "Context Reasoner: Incentivizing Reasoning Capability for", "date": "", "ddg_snippet": "With the CI framework , we are able to align LLMs with established legal frameworks , including GDPR, the EU AI Act, and HIPAA.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14585v2", "content": "With the CI framework , we are able to align LLMs with established legal frameworks , including GDPR, the EU AI Act, and HIPAA."}
+{"idx": 2, "title": "Redefining Elderly Care with Agentic AI: Challenges and", "date": "", "ddg_snippet": "Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14912v1", "content": "Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older ..."}
+{"idx": 3, "title": "Laws, norms, and ethics for AI in health - Microsoft Research", "date": "", "ddg_snippet": "When we were writing our book, Carey, Zak, and I didn’ t claim that putting frameworks in place to allow for innovation and adoption while ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/podcast/laws-norms-and-ethics-for-ai-in-health/", "content": "When we were writing our book, Carey, Zak, and I didn’ t claim that putting frameworks in place to allow for innovation and adoption while ..."}
+{"idx": 4, "title": "How Explainable Is Explainability? Towards Better Metrics for", "date": "", "ddg_snippet": "The paper underscores the critical role of xAI in addressing opacity issues within machine learning algorithms and sets the stage for further ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/377059445_How_Explainable_Is_Explainability_Towards_Better_Metrics_for_Explainable_AI", "content": "The paper underscores the critical role of xAI in addressing opacity issues within machine learning algorithms and sets the stage for further ..."}
+{"idx": 5, "title": "(PDF) AI-ENABLED DECISION SUPPORT SYSTEMS FOR SMARTER", "date": "", "ddg_snippet": "... framework emphasizes dynamic scheduling, risk forecasting, lifecycle asset management, and compliance monitoring as core functional pillars of AI -DSS ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/393655215_AI-ENABLED_DECISION_SUPPORT_SYSTEMS_FOR_SMARTER_INFRASTRUCTURE_PROJECT_MANAGEMENT_IN_PUBLIC_WORKS", "content": "... framework emphasizes dynamic scheduling, risk forecasting, lifecycle asset management, and compliance monitoring as core functional pillars of AI -DSS ..."}
+{"idx": 6, "title": "DaLiF: a data lifecycle framework for data-driven governments |", "date": "", "ddg_snippet": "While achieving this requires various functions, roles, responsibilities, abilities, and skills for both technology and people, it is critical to pay ...", "subpage_snippet": "", "source": "journalofbigdata.springeropen.com", "link": "https://journalofbigdata.springeropen.com/articles/10.1186/s40537-021-00481-3", "content": "While achieving this requires various functions, roles, responsibilities, abilities, and skills for both technology and people, it is critical to pay ..."}
+{"idx": 7, "title": "The Algorithmic Couch: An In-Depth Analysis of Efficacy and", "date": "", "ddg_snippet": "This “ human-in-the-loop ” approach leverages AI for tasks such as administrative support, psychoeducation, and between-session skill ...", "subpage_snippet": "", "source": "uplatz.com", "link": "https://uplatz.com/blog/the-algorithmic-couch-an-in-depth-analysis-of-efficacy-and-ethics-in-ai-powered-mental-health-support/", "content": "This “ human-in-the-loop ” approach leverages AI for tasks such as administrative support, psychoeducation, and between-session skill ..."}
+{"idx": 8, "title": "How do I use ChatGPT for a resume?", "date": "", "ddg_snippet": "QuillBot ’ s AI Checker Android App and AI Checker iOS App can help you ensure that the writing you submit for class assignments is based on ...", "subpage_snippet": "", "source": "quillbot.com", "link": "https://quillbot.com/blog/frequently-asked-questions/how-do-i-use-chatgpt-for-a-resume/", "content": "QuillBot ’ s AI Checker Android App and AI Checker iOS App can help you ensure that the writing you submit for class assignments is based on ..."}
+{"idx": 9, "title": "Frontiers | Artificial intelligence in variant calling: a review", "date": "", "ddg_snippet": "To address these challenges, increasingly sophisticated computational pipelines and algorithms have been developed ( McKenna et al., 2010 ; Garrison ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1574359/full", "content": "To address these challenges, increasingly sophisticated computational pipelines and algorithms have been developed ( McKenna et al., 2010 ; Garrison ..."}
diff --git a/data/sampled_jsons/A_First_Look_at_Public_Service_Websites_from_the_Affordability_Lens_Habib_research_paper.jsonl b/data/sampled_jsons/A_First_Look_at_Public_Service_Websites_from_the_Affordability_Lens_Habib_research_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd644cbc3d7644bc8f8cac206b4e182b59ebea1c
--- /dev/null
+++ b/data/sampled_jsons/A_First_Look_at_Public_Service_Websites_from_the_Affordability_Lens_Habib_research_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A First Look at Public Service Websites from the Affordability Lens", "date": "", "ddg_snippet": "The research found that communication and information mechanisms are at the forefront of the services provided to citizens on metropolitan municipalities' official websites .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370413005_A_First_Look_at_Public_Service_Websites_from_the_Affordability_Lens", "content": "The research found that communication and information mechanisms are at the forefront of the services provided to citizens on metropolitan municipalities' official websites ."}
+{"idx": 1, "title": "GitHub - nsgLUMS/ public - service -sites", "date": "", "ddg_snippet": "A First Look at Public Service Websites from the Affordability Lens Dataset.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nsgLUMS/public-service-sites", "content": "A First Look at Public Service Websites from the Affordability Lens Dataset."}
+{"idx": 2, "title": "Rumaisa Habib - CS PhD Student @ Stanford | LinkedIn", "date": "", "ddg_snippet": "A First Look at Public Service Websites from the Affordability Lens .This paper presents the first large-scale analysis of the affordability of public service websites …", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/rumaisahabib", "content": "A First Look at Public Service Websites from the Affordability Lens .This paper presents the first large-scale analysis of the affordability of public service websites …"}
+{"idx": 3, "title": "Dr. Ihsan Ayyub Qazi @ LUMS - Publications", "date": "", "ddg_snippet": "A First Look at Public Service Websites from the Affordability Lens .", "subpage_snippet": "", "source": "www.ihsanqazi.com", "link": "https://www.ihsanqazi.com/publications", "content": "A First Look at Public Service Websites from the Affordability Lens ."}
+{"idx": 4, "title": "dblp: List of computer science publications by Aimen Inam", "date": "", "ddg_snippet": "Rumaisa Habib , Aimen Inam , Ayesha Ali , Ihsan Ayyub Qazi , Zafar Ayyub Qazi : A First Look at Public Service Websites from the Affordability Lens .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/345/6218.html", "content": "Rumaisa Habib , Aimen Inam , Ayesha Ali , Ihsan Ayyub Qazi , Zafar Ayyub Qazi : A First Look at Public Service Websites from the Affordability Lens ."}
+{"idx": 5, "title": "Paper Digest: WWW 2023 Highlights – Resources | Paper Digest", "date": "", "ddg_snippet": "To browse papers by author, here is a list of top authors (WWW-2023). You may also like to explore our \"Best Paper \" Digest (WWW), which lists the most influential WWW.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2023/04/www-2023-highlights/", "content": "To browse papers by author, here is a list of top authors (WWW-2023). You may also like to explore our \"Best Paper \" Digest (WWW), which lists the most influential WWW."}
+{"idx": 6, "title": "Uncovering the Hidden Data Costs of Mobile YouTube Video Ads", "date": "", "ddg_snippet": "In this work, we conducted the first large-scale empirical analysis of YouTube with the goal of understanding the data costs of video ads through an affordability lens .", "subpage_snippet": "", "source": "emaanatique.github.io", "link": "https://emaanatique.github.io/files/ytafford-www'24.pdf", "content": "In this work, we conducted the first large-scale empirical analysis of YouTube with the goal of understanding the data costs of video ads through an affordability lens ."}
+{"idx": 7, "title": "CS PhD student at Stanford University", "date": "", "ddg_snippet": "A First Look at Public Service Websites from the Affordability Lens .This paper presents the first large-scale analysis of the afordability of public service websites in developing countries.", "subpage_snippet": "", "source": "rumaisahabib.com", "link": "https://rumaisahabib.com/", "content": "A First Look at Public Service Websites from the Affordability Lens .This paper presents the first large-scale analysis of the afordability of public service websites in developing countries."}
+{"idx": 8, "title": "Zafar Ayyub Qazi - Google Scholar", "date": "", "ddg_snippet": "4. 2016. A First Look at Public Service Websites from the Affordability Lens .", "subpage_snippet": "", "source": "scholar.google.com.ru", "link": "https://scholar.google.com.ru/citations?user=O5uXfioAAAAJ&hl=en", "content": "4. 2016. A First Look at Public Service Websites from the Affordability Lens ."}
+{"idx": 9, "title": "Of Choices and Control - A Comparative Analysis of Government Hosting", "date": "", "ddg_snippet": "2023. A First Look at Public Service Websites from the Affordability Lens .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3646547.3688447", "content": "2023. A First Look at Public Service Websites from the Affordability Lens ."}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Table_1_Sieve_MLE_FD3.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Table_1_Sieve_MLE_FD3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3862da1d618f98ec9db465f0245917057992d1bb
--- /dev/null
+++ b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Table_1_Sieve_MLE_FD3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "More specifically, we study the large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "More specifically, we study the large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric."}
+{"idx": 1, "title": "PDF 10-315 Notes Maximum Likelihood Estimation", "date": "", "ddg_snippet": "Maximum likelihood estimation ( MLE ) is trying to find the best parameters for a specific dataset, D. Specifically, we want to find the parameters ˆθ MLE that maximize the likelihood for D.", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~10315/notes/10315_S24_Notes_MLE.pdf", "content": "Maximum likelihood estimation ( MLE ) is trying to find the best parameters for a specific dataset, D. Specifically, we want to find the parameters ˆθ MLE that maximize the likelihood for D."}
+{"idx": 2, "title": "PDF A Likelihood Approach to Nonparametric Estimation of a Singular ...", "date": "", "ddg_snippet": "In this work, we focus on the likelihood-based approach and study statistical proper-ties of a sieve maximum likelihood estimator ( MLE ) of deep generative models under the assumption that P is the distribution of X = f (Z) +", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume24/21-1099/21-1099.pdf", "content": "In this work, we focus on the likelihood-based approach and study statistical proper-ties of a sieve maximum likelihood estimator ( MLE ) of deep generative models under the assumption that P is the distribution of X = f (Z) +"}
+{"idx": 3, "title": "PDF Maximum Likelihood Estimation (MLE)", "date": "", "ddg_snippet": "Maximum Likelihood—Approach I: Grid Search We can find the MLE with grid-search—we evaluate log likelihood (4) for a range of possible values of μ and choose the one that maximizes log likelihood .", "subpage_snippet": "", "source": "www.fsb.miamioh.edu", "link": "https://www.fsb.miamioh.edu/lij14/572_slide_mle.pdf", "content": "Maximum Likelihood—Approach I: Grid Search We can find the MLE with grid-search—we evaluate log likelihood (4) for a range of possible values of μ and choose the one that maximizes log likelihood ."}
+{"idx": 4, "title": "3 Likelihood-based inference - MATH 60604A - Statistical Modelling", "date": "", "ddg_snippet": "3 Likelihood-based inference This chapter is dedicated to the basics of statistical modelling using likelihood-based inference, arguably the most popular estimation paradigm in statistics.", "subpage_snippet": "", "source": "lbelzile.github.io", "link": "https://lbelzile.github.io/math60604a/likelihood.html", "content": "3 Likelihood-based inference This chapter is dedicated to the basics of statistical modelling using likelihood-based inference, arguably the most popular estimation paradigm in statistics."}
+{"idx": 5, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "he large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolv d counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "he large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolv d counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend"}
+{"idx": 6, "title": "(PDF) Convergence Rate of Sieve Estimates - ResearchGate", "date": "", "ddg_snippet": "This method is called the method of sieves . In the case of the maximum likelihood estimation, an MLE based on a sieve is called a sieve MLE .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/38357549_Convergence_Rate_of_Sieve_Estimates", "content": "This method is called the method of sieves . In the case of the maximum likelihood estimation, an MLE based on a sieve is called a sieve MLE ."}
+{"idx": 7, "title": "Sieve Maximum Likelihood Estimation for Regression Models with ...", "date": "", "ddg_snippet": "Abstract and details Missing covariates are common in regression problems. We propose a new semiparametric method based on a fully nonparametric distribution for the missing covariates that are assumed to be missing at random. The method of sieve maximum likelihood estimation is used to obtain the estimators of thr regression coefficients.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/27639981", "content": "Abstract and details Missing covariates are common in regression problems. We propose a new semiparametric method based on a fully nonparametric distribution for the missing covariates that are assumed to be missing at random. The method of sieve maximum likelihood estimation is used to obtain the estimators of thr regression coefficients."}
+{"idx": 8, "title": "Convergence Rate of Sieve Estimates - Project Euclid", "date": "", "ddg_snippet": "In this paper, we develop a general theory for the convergence rate of sieve estimates, maximum likelihood estimates ( MLE's ) and related estimates obtained by optimizing certain empirical criteria in general parameter spaces.", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journals/annals-of-statistics/volume-22/issue-2/Convergence-Rate-of-Sieve-Estimates/10.1214/aos/1176325486.full", "content": "In this paper, we develop a general theory for the convergence rate of sieve estimates, maximum likelihood estimates ( MLE's ) and related estimates obtained by optimizing certain empirical criteria in general parameter spaces."}
+{"idx": 9, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Coro_year_2023.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Coro_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2c4f9f9beb0ef273f520b9e069a053f94b62071d
--- /dev/null
+++ b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Coro_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1IyPRv1A0r", "content": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where ..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the ..."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models . Table 2 . Mean W1 distance (± SD) between generated and test ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models . Table 2 . Mean W1 distance (± SD) between generated and test ..."}
+{"idx": 4, "title": "A likelihood based approach to distribution regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — We investigated statistical properties of a likelihood - based conditional deep generative model for distribution regression in a scenario where ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — We investigated statistical properties of a likelihood - based conditional deep generative model for distribution regression in a scenario where ..."}
+{"idx": 5, "title": "Bayesian likelihood-based regression for estimation of optimal ...", "date": "", "ddg_snippet": "by W Yu · 2023 · Cited by 6 — In this paper, we propose a Bayesian likelihood - based dynamic treatment regime model that incorporates regression specifications to yield interpretable ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/jrsssb/article/85/3/551/7092905", "content": "by W Yu · 2023 · Cited by 6 — In this paper, we propose a Bayesian likelihood - based dynamic treatment regime model that incorporates regression specifications to yield interpretable ..."}
+{"idx": 6, "title": "Probabilistic Conformal Prediction Using Conditional Random ...", "date": "", "ddg_snippet": "Table 2 : Summary results of Multi-Target Regression experiments, where Marg. C and Cond. C denote the marginal coverage and approximated conditional coverage.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/wang23n/wang23n.pdf", "content": "Table 2 : Summary results of Multi-Target Regression experiments, where Marg. C and Cond. C denote the marginal coverage and approximated conditional coverage."}
+{"idx": 7, "title": "Toward Understanding Generative Data Augmentation", "date": "", "ddg_snippet": "by C Zheng · 2023 · Cited by 49 — Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model , boosts classification ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/a94a8800a4b0af45600bab91164849df-Paper-Conference.pdf", "content": "by C Zheng · 2023 · Cited by 49 — Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model , boosts classification ..."}
+{"idx": 8, "title": "Selective Amnesia: A Continual Learning Approach to ...", "date": "", "ddg_snippet": "by A Heng · 2023 · Cited by 159 — Selective Amnesia can be applied to conditional variational likelihood models , which encompass a variety of popular deep generative frameworks, including ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/file/376276a95781fa17c177b1ccdd0a03ac-Paper-Conference.pdf", "content": "by A Heng · 2023 · Cited by 159 — Selective Amnesia can be applied to conditional variational likelihood models , which encompass a variety of popular deep generative frameworks, including ..."}
+{"idx": 9, "title": "SynC2S: An Efficient Method for Synthesizing Tabular Data ...", "date": "", "ddg_snippet": "by J Kim · 2024 · Cited by 1 — In this study, we propose an efficient and theoretically principled method based on a deep generative model to effectively generate high-quality ... 20 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/10704657.pdf", "content": "by J Kim · 2024 · Cited by 1 — In this study, we propose an efficient and theoretically principled method based on a deep generative model to effectively generate high-quality ... 20 pages"}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Rese.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Rese.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..696b6c8c5769b214dba08220b167f233e2688518
--- /dev/null
+++ b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Rese.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Types of artificial neural networks - Wikipedia", "date": "", "ddg_snippet": "... neurons are represented by physical components) or software- based (computer models ), and can use a variety of topologies and learning algorithms.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Types_of_artificial_neural_networks", "content": "... neurons are represented by physical components) or software- based (computer models ), and can use a variety of topologies and learning algorithms."}
+{"idx": 1, "title": "(PDF) Generative AI for Bayesian Computation", "date": "", "ddg_snippet": "Generative Bayesian Computation (GBC) provides a simulation- based approach to Bayesian inference. A Quantile Neural Network (QNN) is trained to ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/393106970_Generative_AI_for_Bayesian_Computation", "content": "Generative Bayesian Computation (GBC) provides a simulation- based approach to Bayesian inference. A Quantile Neural Network (QNN) is trained to ..."}
+{"idx": 2, "title": "Data Augmentation for Bayesian Deep Learning", "date": "", "ddg_snippet": "We consider a gradient-free approach , leveraging a generalized auxiliary model that admits tractable full conditional distributions to devise a ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/363357853_Data_Augmentation_for_Bayesian_Deep_Learning", "content": "We consider a gradient-free approach , leveraging a generalized auxiliary model that admits tractable full conditional distributions to devise a ..."}
+{"idx": 3, "title": "(PDF) Generative Causal Inference", "date": "", "ddg_snippet": "They directrix simulate large samples of observables and unobservable (parameters, latent variables) and then using high-dimensional deep learner to ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371944060_Generative_Causal_Inference", "content": "They directrix simulate large samples of observables and unobservable (parameters, latent variables) and then using high-dimensional deep learner to ..."}
+{"idx": 4, "title": "Conditional Random Fields & Dialog Systems –", "date": "", "ddg_snippet": "It may involve using statistical or machine learning techniques to evaluate the likelihood of different hypotheses based on the available data and ...", "subpage_snippet": "", "source": "meta-guide.com", "link": "https://meta-guide.com/dialog-systems/conditional-random-fields-dialog-systems", "content": "It may involve using statistical or machine learning techniques to evaluate the likelihood of different hypotheses based on the available data and ..."}
+{"idx": 5, "title": "Nick Polson's research works | University of Chicago and", "date": "", "ddg_snippet": "The main advantage of Gen-AI methods is their ability to be model -free and to use deep neural networks to estimate conditional densities or posterior ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Nick-Polson-2201402631", "content": "The main advantage of Gen-AI methods is their ability to be model -free and to use deep neural networks to estimate conditional densities or posterior ..."}
+{"idx": 6, "title": "Vadim Sokolov's research works | George Mason University", "date": "", "ddg_snippet": "The main advantage of Gen-AI methods is their ability to be model -free and to use deep neural networks to estimate conditional densities or posterior ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Vadim-Sokolov-2197802070", "content": "The main advantage of Gen-AI methods is their ability to be model -free and to use deep neural networks to estimate conditional densities or posterior ..."}
+{"idx": 7, "title": "On the definition and importance of interpretability in", "date": "", "ddg_snippet": "We view operator learning of this sort as the scientific machine learning (SciML) analogue to the data-driven, neural network- based regression models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13510v2", "content": "We view operator learning of this sort as the scientific machine learning (SciML) analogue to the data-driven, neural network- based regression models ..."}
+{"idx": 8, "title": "Estimating and forecasting suppressed electricity demand in", "date": "", "ddg_snippet": "... and electricity transmission losses on suppressed demand in Ghana from 2000 to 2020, using a quantile autoregressive distributed lag (QARDL) approach ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/heliyon/fulltext/S2405-8440(24)12032-4", "content": "... and electricity transmission losses on suppressed demand in Ghana from 2000 to 2020, using a quantile autoregressive distributed lag (QARDL) approach ..."}
+{"idx": 9, "title": "Top 14 Data Mining Projects With Source Code - Analytics Vidya", "date": "", "ddg_snippet": "In today ’ s era, organizations are equipped with advanced technologies that enable them to make data-driven decisions, thanks to the ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2023/07/data-mining-projects-with-source-code/", "content": "In today ’ s era, organizations are equipped with advanced technologies that enable them to make data-driven decisions, thanks to the ..."}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Tabl_year_2023.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Tabl_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e063af1cdf1bed7c8fc8146541d942895d8f69e
--- /dev/null
+++ b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Tabl_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1IyPRv1A0r", "content": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-."}
+{"idx": 3, "title": "A likelihood based approach to distribution regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — We investigated statistical properties of a likelihood - based conditional deep generative model for distribution regression in a scenario where ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — We investigated statistical properties of a likelihood - based conditional deep generative model for distribution regression in a scenario where ..."}
+{"idx": 4, "title": "Deep Nonparametric Quantile Regression under Covariate ...", "date": "", "ddg_snippet": "by X Feng · 2024 · Cited by 3 — Abstract. This work focuses on addressing the challenges posed by covariate shift in nonparamet- ric quantile regression using deep neural networks. 50 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume25/24-0906/24-0906.pdf", "content": "by X Feng · 2024 · Cited by 3 — Abstract. This work focuses on addressing the challenges posed by covariate shift in nonparamet- ric quantile regression using deep neural networks. 50 pages"}
+{"idx": 5, "title": "Deep Supervision with Intermediate Concepts", "date": "", "ddg_snippet": "by C Li · 2018 · Cited by 105 — In this work, we explore an approach for injecting prior domain structure into neural network training by supervising hidden layers of a CNN with intermediate ... 14 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/34/4359286/08434117.pdf", "content": "by C Li · 2018 · Cited by 105 — In this work, we explore an approach for injecting prior domain structure into neural network training by supervising hidden layers of a CNN with intermediate ... 14 pages"}
+{"idx": 6, "title": "Multi-Scale Distribution Deep Variational Autoencoder for ...", "date": "", "ddg_snippet": "by Z Cai · 2022 · Cited by 5 — Extensive experiments show that our approach yields state-of-the-art results on three real- world datasets , demonstrating its effectiveness in ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.findings-acl.7.pdf", "content": "by Z Cai · 2022 · Cited by 5 — Extensive experiments show that our approach yields state-of-the-art results on three real- world datasets , demonstrating its effectiveness in ..."}
+{"idx": 7, "title": "Deep nonparametric quantile regression under covariate shift", "date": "", "ddg_snippet": "Abstract. This work focuses on addressing the challenges posed by covariate shift in nonparamet- ric quantile regression using deep neural networks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.5555/3722577.3722962", "content": "Abstract. This work focuses on addressing the challenges posed by covariate shift in nonparamet- ric quantile regression using deep neural networks."}
+{"idx": 8, "title": "Heterogeneous multi-output Gaussian process prediction", "date": "", "ddg_snippet": "Our main contribution in this paper is to provide an extension of multiple-output Gaussian processes for prediction in heterogeneous datasets . The key principle ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/heterogeneous-multi-output-gaussian-process-prediction-33ehu7hkf0.pdf", "content": "Our main contribution in this paper is to provide an extension of multiple-output Gaussian processes for prediction in heterogeneous datasets . The key principle ..."}
+{"idx": 9, "title": "Emmanouil Antonios Platanios April 2020", "date": "", "ddg_snippet": "by EA Platanios · 2020 — This includes learning relationships in the form of higher-order functions—namely functions that compose, transform, or otherwise manipulate other functions— ...", "subpage_snippet": "", "source": "ml.cmu.edu", "link": "https://ml.cmu.edu/research/phd-dissertation-pdfs/thesis_platanios.pdf", "content": "by EA Platanios · 2020 — This includes learning relationships in the form of higher-order functions—namely functions that compose, transform, or otherwise manipulate other functions— ..."}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_expe.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_expe.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..86f2972f090ab47a012cfd66dfdd3af0536f9767
--- /dev/null
+++ b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_expe.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "distributional regression using a conditional deep generative model , considering full-dimensional noise.A deep generative approach to conditional sampling. Journal of the American Statistical Association, pages 1–12.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "distributional regression using a conditional deep generative model , considering full-dimensional noise.A deep generative approach to conditional sampling. Journal of the American Statistical Association, pages 1–12."}
+{"idx": 1, "title": "[2410.02025] A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "View PDF HTML ( experimental ). Abstract:In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "View PDF HTML ( experimental ). Abstract:In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models . It introduces a novel framework for learning conditional distributions of target variables given input variables.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models . It introduces a novel framework for learning conditional distributions of target variables given input variables."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "The experiments and results presented in the paper \" A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models \" indicate a structured approach to verifying scientific hypotheses, particularly in the context of deep generative models .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "The experiments and results presented in the paper \" A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models \" indicate a structured approach to verifying scientific hypotheses, particularly in the context of deep generative models ."}
+{"idx": 4, "title": "Papers by Lizhen Lin with links to code and results .", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .In the considered model , a usual likelihood approach can fail to estimate the target distribution consistently due to the singularity.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Lizhen+Lin", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .In the considered model , a usual likelihood approach can fail to estimate the target distribution consistently due to the singularity."}
+{"idx": 5, "title": "Level-Wise Conditional Distribution", "date": "", "ddg_snippet": "Wasserstein- Based Deep Conditional Generative Models : Recent approaches pose conditional generative modeling as learning a mapping from a reference noise distribution (plus covariates. xx. x) to.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/level-wise-conditional-distribution", "content": "Wasserstein- Based Deep Conditional Generative Models : Recent approaches pose conditional generative modeling as learning a mapping from a reference noise distribution (plus covariates. xx. x) to."}
+{"idx": 6, "title": "Learning Structured Output Representation using Deep Conditional ...", "date": "", "ddg_snippet": "To address this problem, we propose novel deep conditional generative models (CGMs) for output representation learning and structured prediction. In other words, we model the distribution of high-dimensional output space as a generative model conditioned on the input observation.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2015/file/8d55a249e6baa5c06772297520da2051-Paper.pdf", "content": "To address this problem, we propose novel deep conditional generative models (CGMs) for output representation learning and structured prediction. In other words, we model the distribution of high-dimensional output space as a generative model conditioned on the input observation."}
+{"idx": 7, "title": "Musings on typicality – Sander Dieleman", "date": "", "ddg_snippet": "Some likelihood - based generative models of images separate or discard the least-significant bits of each pixel colour value, because they are less perceptually relevant, allowing model capacity to be used more efficiently11 12.", "subpage_snippet": "", "source": "sander.ai", "link": "https://sander.ai/2020/09/01/typicality.html", "content": "Some likelihood - based generative models of images separate or discard the least-significant bits of each pixel colour value, because they are less perceptually relevant, allowing model capacity to be used more efficiently11 12."}
+{"idx": 8, "title": "ODIM: Outlier Detection via Likelihood of Under-Fitted Generative ...", "date": "", "ddg_snippet": "Likelihood - based approaches in OD To detect outliers from data, a fundamental approach might involve using a deep generative model (DGM) and regarding each sample as either an inlier or not based on its likelihood value.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=R8nbccD7kv", "content": "Likelihood - based approaches in OD To detect outliers from data, a fundamental approach might involve using a deep generative model (DGM) and regarding each sample as either an inlier or not based on its likelihood value."}
+{"idx": 9, "title": "[PDF] Input complexity and out-of- distribution ... | Semantic Scholar", "date": "", "ddg_snippet": "Likelihood - based generative models are a promising resource to detect out-of- distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system.Input complexity and out-of- distribution detection with likelihood - based generative models .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Input-complexity-and-out-of-distribution-detection-Serrà-Álvarez/1322719978980a831e1aee78aa80a141379c44dd", "content": "Likelihood - based generative models are a promising resource to detect out-of- distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system.Input complexity and out-of- distribution detection with likelihood - based generative models ."}
diff --git a/data/sampled_jsons/A_task_is_worth_one_word_Zhuang_PowerPaint_classifier-free_guidance_object_removal_year_2024.jsonl b/data/sampled_jsons/A_task_is_worth_one_word_Zhuang_PowerPaint_classifier-free_guidance_object_removal_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fe77778e761e3fdf2cbcc18459a2712d22de5f82
--- /dev/null
+++ b/data/sampled_jsons/A_task_is_worth_one_word_Zhuang_PowerPaint_classifier-free_guidance_object_removal_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "COOCO - Common Objects Out-of-Context - Semantic Violation in", "date": "", "ddg_snippet": "We focus on object naming , that is , the task of determining the category of an object , a fundamental part of Referring Expression Generation (REG ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.22274v1", "content": "We focus on object naming , that is , the task of determining the category of an object , a fundamental part of Referring Expression Generation (REG ..."}
+{"idx": 1, "title": "HarmonPaint: Harmonized Training-Free Diffusion Inpainting", "date": "", "ddg_snippet": "In comparison to existing methods such as (b) ControlNet Inpainting (CNI) [ 1 ] , (c) BrushNet [ 2 ] , (d) PowerPaint [ 3 ] , and (e) Blended ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16732v1", "content": "In comparison to existing methods such as (b) ControlNet Inpainting (CNI) [ 1 ] , (c) BrushNet [ 2 ] , (d) PowerPaint [ 3 ] , and (e) Blended ..."}
+{"idx": 2, "title": "RORem: Training a Robust Object Remover with Human-in-the-Loop", "date": "", "ddg_snippet": "Figure 1 : Given an input image and a mask (see ( a )), existing object removal methods such as PowerPaint [ 72 ] may inpaint the masked regions with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.00740v2", "content": "Figure 1 : Given an input image and a mask (see ( a )), existing object removal methods such as PowerPaint [ 72 ] may inpaint the masked regions with ..."}
+{"idx": 3, "title": "PDF A Task is Worth One Word: Learning with Task Prompts for High-Quality ...", "date": "", "ddg_snippet": "Moreover, the learned task prompt in PowerPaint has efectively captured the intrinsic pattern of the task and can be extended to facilitate powerful object removal . In particular, existing T2I models employ a classifier-free guidance sampling strategy, where a nega-tive prompt can efectively suppress undesired efects [8,13].", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/07554.pdf", "content": "Moreover, the learned task prompt in PowerPaint has efectively captured the intrinsic pattern of the task and can be extended to facilitate powerful object removal . In particular, existing T2I models employ a classifier-free guidance sampling strategy, where a nega-tive prompt can efectively suppress undesired efects [8,13]."}
+{"idx": 4, "title": "A Task Is Worth One Word: Learning with Task Prompts for ... - Springer", "date": "", "ddg_snippet": "We demonstrate the versatility of the task prompts in PowerPaint , showcasing their capability for object removal by negative prompts and object inpainting with controllable shape-fitting by prompt interpolation.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-73636-0_12", "content": "We demonstrate the versatility of the task prompts in PowerPaint , showcasing their capability for object removal by negative prompts and object inpainting with controllable shape-fitting by prompt interpolation."}
+{"idx": 5, "title": "A Task is Worth One Word: - arXiv.org", "date": "", "ddg_snippet": "Moreover, the learned task prompt in PowerPaint has effectively captured the intrinsic pattern of the task and can be extended to facilitate powerful object removal . In particular, existing T2I models employ a classifier-free guidance sampling strategy, where a negative prompt can effectively suppress undesired effects [8, 13].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03594v4", "content": "Moreover, the learned task prompt in PowerPaint has effectively captured the intrinsic pattern of the task and can be extended to facilitate powerful object removal . In particular, existing T2I models employ a classifier-free guidance sampling strategy, where a negative prompt can effectively suppress undesired effects [8, 13]."}
+{"idx": 6, "title": "A Task is Worth One Word: Learning with Task Prompts for High-Quality ...", "date": "", "ddg_snippet": "Figure 2. The overview of PowerPaint . PowerPaint fine-tunes a text-to-image model with two task prompts, i.e., Pobj and Pctxt, for text-guided object inpainting and context-aware image inpainting, respectively. Specifically, Pobj can be used as a negative prompt with classifier-free guidance sampling for effective object removal . We further introduce Pshape for shape-guided object inpainting ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Task-is-Worth-One-Word:-Learning-with-Task-for-Zhuang-Zeng/999b48ef5551e550c89fba97d7f66347efee8030/figure/1", "content": "Figure 2. The overview of PowerPaint . PowerPaint fine-tunes a text-to-image model with two task prompts, i.e., Pobj and Pctxt, for text-guided object inpainting and context-aware image inpainting, respectively. Specifically, Pobj can be used as a negative prompt with classifier-free guidance sampling for effective object removal . We further introduce Pshape for shape-guided object inpainting ..."}
+{"idx": 7, "title": "V-SEAM: Visual Semantic Editing and Attention Modulating for", "date": "", "ddg_snippet": "... answering (VQA) task , we empirically investigate two popular VLMs, i .e., LLAVA and InstructBLIP, and identify four key findings: ( 1 ) VLMs generally ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14837v1", "content": "... answering (VQA) task , we empirically investigate two popular VLMs, i .e., LLAVA and InstructBLIP, and identify four key findings: ( 1 ) VLMs generally ..."}
+{"idx": 8, "title": "BrushNet - ComfyUI Cloud", "date": "", "ddg_snippet": "PowerPaint : A Task is Worth One Word : Learning with Task Prompts for High-Quality Versatile Image Inpainting HiDiffusion: HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models My contribution is limited to the ComfyUI adaptation, and all credit goes to the authors of the papers. Updates May 16, 2024.", "subpage_snippet": "", "source": "comfy.icu", "link": "https://comfy.icu/extension/nullquant__ComfyUI-BrushNet", "content": "PowerPaint : A Task is Worth One Word : Learning with Task Prompts for High-Quality Versatile Image Inpainting HiDiffusion: HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models My contribution is limited to the ComfyUI adaptation, and all credit goes to the authors of the papers. Updates May 16, 2024."}
+{"idx": 9, "title": "MFGDiffusion: Mask-Guided Smoke Synthesis for Enhanced Forest", "date": "", "ddg_snippet": "Although traditional image processing techniques can generate smoke images, the fusion of the background and smoke regions is relatively simplistic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11252v1", "content": "Although traditional image processing techniques can generate smoke images, the fusion of the background and smoke regions is relatively simplistic ..."}
diff --git "a/data/sampled_jsons/Abbasi-Yadkori_D\303\241vid_Szepesv\303\241ri_2011_linear_contextual_bandits_full_title.jsonl" "b/data/sampled_jsons/Abbasi-Yadkori_D\303\241vid_Szepesv\303\241ri_2011_linear_contextual_bandits_full_title.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..af49511c841bcb1bbac95aa93253d5758bad6f1c
--- /dev/null
+++ "b/data/sampled_jsons/Abbasi-Yadkori_D\303\241vid_Szepesv\303\241ri_2011_linear_contextual_bandits_full_title.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "by Y Abbasi-yadkori · 2011 · Cited by 2412 — Authors. Yasin Abbasi - yadkori , Dávid Pál, Csaba Szepesvári . Abstract. We improve the theoretical analysis and empirical performance of algorithms for the ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/4417-improved-algorithms-for-linear-stochastic-bandits", "content": "by Y Abbasi-yadkori · 2011 · Cited by 2412 — Authors. Yasin Abbasi - yadkori , Dávid Pál, Csaba Szepesvári . Abstract. We improve the theoretical analysis and empirical performance of algorithms for the ..."}
+{"idx": 1, "title": "Efficient and robust high-dimensional linear contextual ...", "date": "", "ddg_snippet": "by C Chen · 2021 · Cited by 12 — The linear contextual bandit s is a sequential decision-making problem where an agent decides among sequential actions given their ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3491440.3492028", "content": "by C Chen · 2021 · Cited by 12 — The linear contextual bandit s is a sequential decision-making problem where an agent decides among sequential actions given their ..."}
+{"idx": 2, "title": "Improved Algorithms for Linear Stochastic Bandits - Dávid Pál", "date": "", "ddg_snippet": "by Y Abbasi-Yadkori · Cited by 2412 — Yasin Abbasi - Yadkori , András Antos, and Csaba Szepesvári . Forced-exploration based algorithms for playing in stochastic linear bandits . In COLT Workshop on ... 19 pages", "subpage_snippet": "", "source": "david.palenica.com", "link": "https://david.palenica.com/papers/linear-bandit/linear-bandits-NIPS2011-camera-ready.pdf", "content": "by Y Abbasi-Yadkori · Cited by 2412 — Yasin Abbasi - Yadkori , András Antos, and Csaba Szepesvári . Forced-exploration based algorithms for playing in stochastic linear bandits . In COLT Workshop on ... 19 pages"}
+{"idx": 3, "title": "Strategic Linear Contextual Bandits", "date": "", "ddg_snippet": "by T Kleine Buening · 2024 · Cited by 2 — [1] Yasin Abbasi - Yadkori , Dávid Pál, and Csaba Szepesvári . Improved algorithms for linear stochastic bandits . Advances in neural information processing ... 38 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/d390199c28b467315b454789b6584f19-Paper-Conference.pdf", "content": "by T Kleine Buening · 2024 · Cited by 2 — [1] Yasin Abbasi - Yadkori , Dávid Pál, and Csaba Szepesvári . Improved algorithms for linear stochastic bandits . Advances in neural information processing ... 38 pages"}
+{"idx": 4, "title": "Thompson Sampling for Contextual Bandits with Linear Payoffs", "date": "", "ddg_snippet": "by S Agrawal · Cited by 1397 — Abbasi-Yadkori, Yasin, Pál, Dávid, and Szepesvári ,. Csaba. Improved Algorithms for Linear Stochastic. Bandits. In NIPS, pp. 2312–2320, 2011. Abramowitz ... 9 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v28/agrawal13.pdf", "content": "by S Agrawal · Cited by 1397 — Abbasi-Yadkori, Yasin, Pál, Dávid, and Szepesvári ,. Csaba. Improved Algorithms for Linear Stochastic. Bandits. In NIPS, pp. 2312–2320, 2011. Abramowitz ... 9 pages"}
+{"idx": 5, "title": "NEURAL CONTEXTUAL BANDITS WITH DEEP REPRE", "date": "", "ddg_snippet": "by P Xu · Cited by 95 — We prove a sublinear regret of the proposed algorithm by exploiting the UCB exploration techniques in linear contextual ban- dits ( Abbasi - Yadkori et al., 2011 ) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=xnYACQquaGV", "content": "by P Xu · Cited by 95 — We prove a sublinear regret of the proposed algorithm by exploiting the UCB exploration techniques in linear contextual ban- dits ( Abbasi - Yadkori et al., 2011 ) ..."}
+{"idx": 6, "title": "Multi-Agent Learning with Heterogeneous Linear ...", "date": "", "ddg_snippet": "by A Do · 2023 · Cited by 5 — We establish a baseline algorithm in which each agent independently runs an optimal linear contextual bandits algorithm (OFUL, [ Abbasi - Yadkori et al.,. 2011 ]) ... 23 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/f8d39584f87944e5dbe46ec76f19e20a-Paper-Conference.pdf", "content": "by A Do · 2023 · Cited by 5 — We establish a baseline algorithm in which each agent independently runs an optimal linear contextual bandits algorithm (OFUL, [ Abbasi - Yadkori et al.,. 2011 ]) ... 23 pages"}
+{"idx": 7, "title": "Variance-Aware Linear UCB with Deep Representation for ...", "date": "", "ddg_snippet": "This is equivalent to the result of LinUCB in the linear contextual bandits setting ( Abbasi - yadkori et al., 2011 ). The predictive uncertainty of the UCB, ...", "subpage_snippet": "", "source": "mallada.ece.jhu.edu", "link": "https://mallada.ece.jhu.edu/pubs/2025-AISTATS-BML.pdf", "content": "This is equivalent to the result of LinUCB in the linear contextual bandits setting ( Abbasi - yadkori et al., 2011 ). The predictive uncertainty of the UCB, ..."}
+{"idx": 8, "title": "Online Least Squares Estimation with Self-Normalized ...", "date": "", "ddg_snippet": "by Y Abbasi-Yadkori · 2011 · Cited by 90 — Online Least Squares Estimation with Self-Normalized Processes : An Application to Bandit Problems. Authors:Yasin Abbasi-Yadkori, David Pal, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1102.2670", "content": "by Y Abbasi-Yadkori · 2011 · Cited by 90 — Online Least Squares Estimation with Self-Normalized Processes : An Application to Bandit Problems. Authors:Yasin Abbasi-Yadkori, David Pal, ..."}
+{"idx": 9, "title": "Online-to-Confidence-Set Conversions and Application to ...", "date": "", "ddg_snippet": "by Y Abbasi-Yadkori · Cited by 212 — Yasin Abbasi - Yadkori , Dávid Pál, and Csaba. Szepesvári . Improved algorithms for linear stochas- tic bandits . In Advances in Neural Information Pro- cessing ... 15 pages", "subpage_snippet": "", "source": "yasinov.github.io", "link": "https://yasinov.github.io/sparse-bandit-aistats2012.pdf", "content": "by Y Abbasi-Yadkori · Cited by 212 — Yasin Abbasi - Yadkori , Dávid Pál, and Csaba. Szepesvári . Improved algorithms for linear stochas- tic bandits . In Advances in Neural Information Pro- cessing ... 15 pages"}
diff --git a/data/sampled_jsons/Abbasi-Yadkori_et_al.,_2011_OFUL_framework.jsonl b/data/sampled_jsons/Abbasi-Yadkori_et_al.,_2011_OFUL_framework.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8deb63dbf7de3ac26659b190b52d3d1eb5de5cb
--- /dev/null
+++ b/data/sampled_jsons/Abbasi-Yadkori_et_al.,_2011_OFUL_framework.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Yasin Abbasi-Yadkori", "date": "", "ddg_snippet": "Szepesvari, Improved Algorithms for Linear Stochastic Bandits, Neural Information Processing Systems (NIPS), 2011 . pdf. Y. Abbasi - Yadkori and Cs. Szepesvari, Regret Bounds for the Adaptive Control of Linear Quadratic Systems, Conference on Learning Theory (COLT), 2011 . pdf.", "subpage_snippet": "", "source": "yasinov.github.io", "link": "https://yasinov.github.io/", "content": "Szepesvari, Improved Algorithms for Linear Stochastic Bandits, Neural Information Processing Systems (NIPS), 2011 . pdf. Y. Abbasi - Yadkori and Cs. Szepesvari, Regret Bounds for the Adaptive Control of Linear Quadratic Systems, Conference on Learning Theory (COLT), 2011 . pdf."}
+{"idx": 1, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "... reward distributions, which has been extensively studied in the literature (Dani et al ., 2008 ; Abbasi - Yadkori et al ., 2011 ; Li et al ., 2021 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "... reward distributions, which has been extensively studied in the literature (Dani et al ., 2008 ; Abbasi - Yadkori et al ., 2011 ; Li et al ., 2021 ) ."}
+{"idx": 2, "title": "Generalized Linear Bandits: Almost Optimal Regret with One-Pass", "date": "", "ddg_snippet": "Compared with the commonly studied linear case ( Abbasi - Yadkori et al ., 2011 ) , the generalized linear bandit (GLB) framework allows for a richer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11847v1", "content": "Compared with the commonly studied linear case ( Abbasi - Yadkori et al ., 2011 ) , the generalized linear bandit (GLB) framework allows for a richer ..."}
+{"idx": 3, "title": "Achieving Limited Adaptivity for Multinomial Logistic Bandits", "date": "", "ddg_snippet": "... of the simplest models is to assume that the expected reward is a linear function of the arms and the hidden parameter [ Abbasi - Yadkori et al ., 2011 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03072v1", "content": "... of the simplest models is to assume that the expected reward is a linear function of the arms and the hidden parameter [ Abbasi - Yadkori et al ., 2011 ..."}
+{"idx": 4, "title": "Corruption-Robust Algorithms with Uncertainty Weighting for", "date": "", "ddg_snippet": "2022 ) proposed a variant of the OFUL algorithm ( Abbasi - Yadkori et al ., 2011 ) which achieved a regret of 𝒪 ~ ( T + ζ T ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.05949v4", "content": "2022 ) proposed a variant of the OFUL algorithm ( Abbasi - Yadkori et al ., 2011 ) which achieved a regret of 𝒪 ~ ( T + ζ T ..."}
+{"idx": 5, "title": "Improved Algorithms for Linear Stochastic Bandits - NIPS", "date": "", "ddg_snippet": "Part of Advances in Neural Information Processing Systems 24 (NIPS 2011 ) Yasin Abbasi - yadkori , Dávid Pál, Csaba Szepesvári. We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/4417-improved-algorithms-for-linear-stochastic-bandits", "content": "Part of Advances in Neural Information Processing Systems 24 (NIPS 2011 ) Yasin Abbasi - yadkori , Dávid Pál, Csaba Szepesvári. We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem."}
+{"idx": 6, "title": "(PDF) Improved Algorithms for Linear Stochastic Bandits ...", "date": "", "ddg_snippet": "Dec 1, 2011 · In particular, we show that a simple modification of Auer's UCB algorithm (Auer, 2002) achieves with high probability constant regret.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/230627940_Improved_Algorithms_for_Linear_Stochastic_Bandits_extended_version", "content": "Dec 1, 2011 · In particular, we show that a simple modification of Auer's UCB algorithm (Auer, 2002) achieves with high probability constant regret."}
+{"idx": 7, "title": "[1102.2670] Online Least Squares Estimation with Self ...", "date": "", "ddg_snippet": "Feb 14, 2011 · View a PDF of the paper titled Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems, by Yasin Abbasi - Yadkori and 2 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1102.2670", "content": "Feb 14, 2011 · View a PDF of the paper titled Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems, by Yasin Abbasi - Yadkori and 2 other authors"}
+{"idx": 8, "title": "Regret Bounds for the Adaptive Control of Linear Quadratic ...", "date": "", "ddg_snippet": "The derivation of the con dence set is based on results from Abbasi - Yadkori et al . ( 2011 ) that use techniques from self-normalized processes to estimate the least squares estimation error.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v19/abbasi-yadkori11a/abbasi-yadkori11a.pdf", "content": "The derivation of the con dence set is based on results from Abbasi - Yadkori et al . ( 2011 ) that use techniques from self-normalized processes to estimate the least squares estimation error."}
+{"idx": 9, "title": "Tokenized Bandit for LLM Decoding and Alignment", "date": "", "ddg_snippet": "... however, it is crucial to align the LLM’ s outcome with the designated human preference, which remains as a core challenge in LLMs (Mishra et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07276v1", "content": "... however, it is crucial to align the LLM’ s outcome with the designated human preference, which remains as a core challenge in LLMs (Mishra et al ..."}
diff --git a/data/sampled_jsons/Abbasi-Yadkori_et_al._2011_OFUL_Optimism_in_the_Face_of_Uncertainty_Learning_contextual_bandits_pape.jsonl b/data/sampled_jsons/Abbasi-Yadkori_et_al._2011_OFUL_Optimism_in_the_Face_of_Uncertainty_Learning_contextual_bandits_pape.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e081ffd3fe1ad1d2b06309b8803b50f5de189773
--- /dev/null
+++ b/data/sampled_jsons/Abbasi-Yadkori_et_al._2011_OFUL_Optimism_in_the_Face_of_Uncertainty_Learning_contextual_bandits_pape.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Nearly Optimal Algorithms for Linear Contextual Bandits ...", "date": "", "ddg_snippet": "by J He · 2022 · Cited by 68 — We propose a computationally efficient algorithm based on the principle of optimism in the face of uncertainty ( Abbasi - Yadkori et al ., 2011 ), named Confidence- ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=YeuBRKq_yZ-", "content": "by J He · 2022 · Cited by 68 — We propose a computationally efficient algorithm based on the principle of optimism in the face of uncertainty ( Abbasi - Yadkori et al ., 2011 ), named Confidence- ..."}
+{"idx": 1, "title": "Scalable and Interpretable Contextual Bandits: A Literature ...", "date": "", "ddg_snippet": "20 May 2025 — This paper presents a concise review of Contextual Multi-Armed Bandit (CMAB) methods and introduces an experimental framework for scalable, interpretable offer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16918v1", "content": "20 May 2025 — This paper presents a concise review of Contextual Multi-Armed Bandit (CMAB) methods and introduces an experimental framework for scalable, interpretable offer ..."}
+{"idx": 2, "title": "Contributions to stochastic bandits and link prediction problems", "date": "", "ddg_snippet": "by S GAUCHER · 2022 — We show that it is optimal in the minimax sense under a homoge- neity assumption on the connection probabilities of the nodes. Since this estimator cannot be ... 359 pages", "subpage_snippet": "", "source": "solennegaucher.github.io", "link": "https://solennegaucher.github.io/thesis.pdf", "content": "by S GAUCHER · 2022 — We show that it is optimal in the minimax sense under a homoge- neity assumption on the connection probabilities of the nodes. Since this estimator cannot be ... 359 pages"}
+{"idx": 3, "title": "UCLA Electronic Theses and Dissertations", "date": "", "ddg_snippet": "by D Zhou · 2023 — This dissertation is centered around the concept of uncertainty -aware reinforcement learning (RL), which seeks to enhance the efficiency of ... 167 pages", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt34v5b56n/qt34v5b56n.pdf", "content": "by D Zhou · 2023 — This dissertation is centered around the concept of uncertainty -aware reinforcement learning (RL), which seeks to enhance the efficiency of ... 167 pages"}
+{"idx": 4, "title": "Online Learning and Decision Making Under Generalized ...", "date": "", "ddg_snippet": "by X Wang · 2024 · Cited by 8 — ... Bandit by Goldenshluger and Zeevi (2013) and Optimism in the Face of Uncertainty Linear bandit algorithm (OFUL ) by Abbasi-Yadkori et al. (2011) ...", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/10.1287/mnsc.2022.01557", "content": "by X Wang · 2024 · Cited by 8 — ... Bandit by Goldenshluger and Zeevi (2013) and Optimism in the Face of Uncertainty Linear bandit algorithm (OFUL ) by Abbasi-Yadkori et al. (2011) ..."}
+{"idx": 5, "title": "Reinforcement Learning in Structured and Partially Observable ...", "date": "", "ddg_snippet": "Cd,t follows OFUL Abbasi - Yadkori et al . ( 2011 ) ... contextual bandit ( Abbasi - Yadkori et al ., 2011 ), linear ... Following the optimism in face of uncertainty ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/reinforcement-learning-in-structured-and-partially-2sq5lezwuo.pdf", "content": "Cd,t follows OFUL Abbasi - Yadkori et al . ( 2011 ) ... contextual bandit ( Abbasi - Yadkori et al ., 2011 ), linear ... Following the optimism in face of uncertainty ..."}
+{"idx": 6, "title": "Causal Bandits: Online Decision-Making in Endogenous ...", "date": "", "ddg_snippet": "by J Zhang · Cited by 11 — Finally, we compare our method with existing online algorithms including Optimism in the Face of. Uncertainty Linear bandit ( OFUL ) and Thompson Sampling (TS) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mfKvV-uZrFH", "content": "by J Zhang · Cited by 11 — Finally, we compare our method with existing online algorithms including Optimism in the Face of. Uncertainty Linear bandit ( OFUL ) and Thompson Sampling (TS) ..."}
+{"idx": 7, "title": "Adaptive LLM Routing under Budget Constraints", "date": "", "ddg_snippet": "28 Aug 2025 — Here, we focus on Optimism in the face of uncertainty linear bandit algorithm (OFUL ) Abbasi-Yadkori et al. (2011a) since both OFUL & LinUCB ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21141v1", "content": "28 Aug 2025 — Here, we focus on Optimism in the face of uncertainty linear bandit algorithm (OFUL ) Abbasi-Yadkori et al. (2011a) since both OFUL & LinUCB ..."}
+{"idx": 8, "title": "[PDF] Linear Thompson Sampling Revisited", "date": "", "ddg_snippet": "Optimism In The Face Of Uncertainty (opens in a new tab)Linear Bandits ... This paper considers efficient learning in large-scale combinatorial semi- bandits ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Linear-Thompson-Sampling-Revisited-Abeille-Lazaric/809d399f604d5494a01910d8a1fb8b6aefc3da1e", "content": "Optimism In The Face Of Uncertainty (opens in a new tab)Linear Bandits ... This paper considers efficient learning in large-scale combinatorial semi- bandits ..."}
+{"idx": 9, "title": "Bandit Algorithms 1108486827, 9781108486828", "date": "", "ddg_snippet": "Bandit Algorithms Decision-making in the face of uncertainty is a significant challenge in machine learning , and the multi-armed bandit model is a commonly used ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/bandit-algorithms-1108486827-9781108486828.html", "content": "Bandit Algorithms Decision-making in the face of uncertainty is a significant challenge in machine learning , and the multi-armed bandit model is a commonly used ..."}
diff --git a/data/sampled_jsons/ActSVD_5_percent_parameters_Wei_et_al.jsonl b/data/sampled_jsons/ActSVD_5_percent_parameters_Wei_et_al.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..491ab1706446717bd5b4dcf37b18c74951971038
--- /dev/null
+++ b/data/sampled_jsons/ActSVD_5_percent_parameters_Wei_et_al.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ギリシャ文字δ、ρ、σの書き方を教えてください、ていうか ...", "date": "", "ddg_snippet": "Jan 30, 2018 · 物理や数学の記号について 密度を表す時に使う「ρ (ロー)」という記号と、圧力の「p (ピー)」が計算する時に物凄く紛らわしいのですが、どうにか区別のつく書き方を教えてください。 あと、「a (小文字のA)」と「α (アルファ)」もよく区別がつかなくなって間違えそうになるのですが、どうに ...", "subpage_snippet": "", "source": "detail.chiebukuro.yahoo.co.jp", "link": "https://detail.chiebukuro.yahoo.co.jp/qa/question_detail/q13185457443", "content": "Jan 30, 2018 · 物理や数学の記号について 密度を表す時に使う「ρ (ロー)」という記号と、圧力の「p (ピー)」が計算する時に物凄く紛らわしいのですが、どうにか区別のつく書き方を教えてください。 あと、「a (小文字のA)」と「α (アルファ)」もよく区別がつかなくなって間違えそうになるのですが、どうに ..."}
+{"idx": 1, "title": "密度ρのρを何て読んでます？密度ローって読んでます？それと ...", "date": "", "ddg_snippet": "Feb 1, 2013 · ギリシャ文字ρの読みはローマ字（ラテン文字）でrhoと書かれます。大文字はローマ字のPと同じでΡと書きます。ローマ字のrと同じ音を表します。余談ですがキリル兄弟がギリシア文字をロシアに伝えロシア文字が出来ました。字母はギリシア文字と似ています。 数学ではℵというアラビア ...", "subpage_snippet": "", "source": "detail.chiebukuro.yahoo.co.jp", "link": "https://detail.chiebukuro.yahoo.co.jp/qa/question_detail/q10101269945", "content": "Feb 1, 2013 · ギリシャ文字ρの読みはローマ字（ラテン文字）でrhoと書かれます。大文字はローマ字のPと同じでΡと書きます。ローマ字のrと同じ音を表します。余談ですがキリル兄弟がギリシア文字をロシアに伝えロシア文字が出来ました。字母はギリシア文字と似ています。 数学ではℵというアラビア ..."}
+{"idx": 2, "title": "物理の水圧のところででてくるρ (ロー)は密度を表す記号でいい ...", "date": "", "ddg_snippet": "Oct 24, 2012 · 物理の水圧のところででてくるρ (ロー)は密度を表す記号でいいんですか。また密度の定義を教えてくださいかおしえてください。 その通りです。密度は体積1m^3、もしくは1cm^3ある物体がどれだけの重さになるかを...", "subpage_snippet": "", "source": "detail.chiebukuro.yahoo.co.jp", "link": "https://detail.chiebukuro.yahoo.co.jp/qa/question_detail/q1096160485", "content": "Oct 24, 2012 · 物理の水圧のところででてくるρ (ロー)は密度を表す記号でいいんですか。また密度の定義を教えてくださいかおしえてください。 その通りです。密度は体積1m^3、もしくは1cm^3ある物体がどれだけの重さになるかを..."}
+{"idx": 3, "title": "物理や数学の記号について - 密度を表す時に使う「ρ (ロー ...", "date": "", "ddg_snippet": "May 16, 2018 · 物理や数学の記号について 密度を表す時に使う「ρ(ロー)」という記号と、圧力の「p(ピー)」が計算する時に物凄く紛らわしいのですが、どうにか区別のつく書き方を教えてください。あと、「a(小文字のA)」と「α(アルファ)」もよく区別がつかなくなって間違えそうになるのですが、どうにか ...", "subpage_snippet": "", "source": "detail.chiebukuro.yahoo.co.jp", "link": "https://detail.chiebukuro.yahoo.co.jp/qa/question_detail/q10190455500", "content": "May 16, 2018 · 物理や数学の記号について 密度を表す時に使う「ρ(ロー)」という記号と、圧力の「p(ピー)」が計算する時に物凄く紛らわしいのですが、どうにか区別のつく書き方を教えてください。あと、「a(小文字のA)」と「α(アルファ)」もよく区別がつかなくなって間違えそうになるのですが、どうにか ..."}
+{"idx": 4, "title": "高校物理です。 線密度ρ、張力Sの弦で音を発生させたときの ...", "date": "", "ddg_snippet": "Mar 27, 2009 · 高校物理です。 線密度ρ、張力Sの弦で音を発生させたときの音速VはV=√ (S/ρ)となりますが、どのように考えれば、このような式が立式できるのでしょうか？ …① 塾の先生が、｢円運動を習った生徒になら教えら...", "subpage_snippet": "", "source": "detail.chiebukuro.yahoo.co.jp", "link": "https://detail.chiebukuro.yahoo.co.jp/qa/question_detail/q1124595791", "content": "Mar 27, 2009 · 高校物理です。 線密度ρ、張力Sの弦で音を発生させたときの音速VはV=√ (S/ρ)となりますが、どのように考えれば、このような式が立式できるのでしょうか？ …① 塾の先生が、｢円運動を習った生徒になら教えら..."}
+{"idx": 5, "title": "電荷密度の記号ρとσの使い分けについて - 慣習上、ρは普通の ...", "date": "", "ddg_snippet": "Mar 22, 2012 · 2) ρは空間電荷密度です： 空間はドイツ語で Raum ですので、 最初の R または r に対応するギリシャ文字 ρ を用います。 ドイツ系の電磁気学の本で、よく見られます。", "subpage_snippet": "", "source": "detail.chiebukuro.yahoo.co.jp", "link": "https://detail.chiebukuro.yahoo.co.jp/qa/question_detail/q1084026175", "content": "Mar 22, 2012 · 2) ρは空間電荷密度です： 空間はドイツ語で Raum ですので、 最初の R または r に対応するギリシャ文字 ρ を用います。 ドイツ系の電磁気学の本で、よく見られます。"}
+{"idx": 6, "title": "なぜ密度はρであらわすのですか？ - 単にその分野であまり使わ ...", "date": "", "ddg_snippet": "Jun 1, 2009 · 全統記述で偏差値約65 理科大志望です。参考書のみの独学で 宇宙一わかりやすい高校物理→入門問題精講→エッセンス→良問の風→名門の森（力学・電磁気のみ)※原子を学んだ後に熱波動をやる予定 で進めてきました。 原子分野をYouTubeのヨビノリで学ぼうと思うのですが、その後の接続とし ...", "subpage_snippet": "", "source": "detail.chiebukuro.yahoo.co.jp", "link": "https://detail.chiebukuro.yahoo.co.jp/qa/question_detail/q1026815525", "content": "Jun 1, 2009 · 全統記述で偏差値約65 理科大志望です。参考書のみの独学で 宇宙一わかりやすい高校物理→入門問題精講→エッセンス→良問の風→名門の森（力学・電磁気のみ)※原子を学んだ後に熱波動をやる予定 で進めてきました。 原子分野をYouTubeのヨビノリで学ぼうと思うのですが、その後の接続とし ..."}
+{"idx": 7, "title": "弦を伝わる波の速さがV=√S/ρ張力:S、綿密度:ρとなりま ...", "date": "", "ddg_snippet": "Jun 8, 2013 · 弦を伝う波の速さV=√S/ρの証明について疑問があります。 自分の参考書では写真のように証明されていたのですが、上から2行目の「波の先端部分は水平から十分に小さい角度で傾いている」というところがわかりません。", "subpage_snippet": "", "source": "detail.chiebukuro.yahoo.co.jp", "link": "https://detail.chiebukuro.yahoo.co.jp/qa/question_detail/q10108492970", "content": "Jun 8, 2013 · 弦を伝う波の速さV=√S/ρの証明について疑問があります。 自分の参考書では写真のように証明されていたのですが、上から2行目の「波の先端部分は水平から十分に小さい角度で傾いている」というところがわかりません。"}
+{"idx": 8, "title": "物理です密度ρ (kg/m3)の液体に断面積S (m2)長さl (m)の円柱 ...", "date": "", "ddg_snippet": "Nov 26, 2013 · 物理の浮力の問題で分からない問題があったので解説をお願いします。 底面積S、高さhの円柱な図のように水面に浮かんでいる。 水の密度をp0、重力加速度の大きさをgとする。", "subpage_snippet": "", "source": "detail.chiebukuro.yahoo.co.jp", "link": "https://detail.chiebukuro.yahoo.co.jp/qa/question_detail/q10117050185", "content": "Nov 26, 2013 · 物理の浮力の問題で分からない問題があったので解説をお願いします。 底面積S、高さhの円柱な図のように水面に浮かんでいる。 水の密度をp0、重力加速度の大きさをgとする。"}
+{"idx": 9, "title": "物理…ρ (・・、)質量1.0kgの物体を5.0mの高さまで上... - Yahoo ...", "date": "", "ddg_snippet": "Jan 7, 2013 · 物理の問題です。 屈折率n1の円柱状のガラスAを、屈折率n2の円筒状のガラスBによって中心軸が一致するように囲んだガラス棒が、空気中に置かれている。ここで、n1>n2であり、空気の屈折率は1とする。また、ガラス棒の端面は中心軸に対して垂直である。 (2)i=i0のとき、AからBに入射する光線の ...", "subpage_snippet": "", "source": "detail.chiebukuro.yahoo.co.jp", "link": "https://detail.chiebukuro.yahoo.co.jp/qa/question_detail/q1099890570", "content": "Jan 7, 2013 · 物理の問題です。 屈折率n1の円柱状のガラスAを、屈折率n2の円筒状のガラスBによって中心軸が一致するように囲んだガラス棒が、空気中に置かれている。ここで、n1>n2であり、空気の屈折率は1とする。また、ガラス棒の端面は中心軸に対して垂直である。 (2)i=i0のとき、AからBに入射する光線の ..."}
diff --git a/data/sampled_jsons/ActSVD_Wei_2024_pruning_low-rank_modifications_safety_alignment_parameters_percentage_year_2024.jsonl b/data/sampled_jsons/ActSVD_Wei_2024_pruning_low-rank_modifications_safety_alignment_parameters_percentage_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd6500d8a981ea313aa34b8e70257cec76545c97
--- /dev/null
+++ b/data/sampled_jsons/ActSVD_Wei_2024_pruning_low-rank_modifications_safety_alignment_parameters_percentage_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ...", "date": "", "ddg_snippet": "Large language models (LLMs) show inherent brittleness in their safety mechanisms, as evidenced by their susceptibility to jailbreaking and even non-malicious fine-tuning. This study explores this brittleness of safety alignment by leveraging pruning and low-rank modifications . We develop methods to identify critical regions that are vital for safety guardrails, and that are disentangled from ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.05162", "content": "Large language models (LLMs) show inherent brittleness in their safety mechanisms, as evidenced by their susceptibility to jailbreaking and even non-malicious fine-tuning. This study explores this brittleness of safety alignment by leveraging pruning and low-rank modifications . We develop methods to identify critical regions that are vital for safety guardrails, and that are disentangled from ..."}
+{"idx": 1, "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ...", "date": "", "ddg_snippet": "This repository provides an original implementation of Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications by Boyi Wei *, Kaixuan Huang*, Yangsibo Huang*, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang and Peter Henderson.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/boyiwei/alignment-attribution-code", "content": "This repository provides an original implementation of Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications by Boyi Wei *, Kaixuan Huang*, Yangsibo Huang*, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang and Peter Henderson."}
+{"idx": 2, "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ...", "date": "", "ddg_snippet": "This research explores the brittleness of safety alignment in AI systems through pruning and low-rank modifications , offering insights into improving robustness and reliability.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/21156", "content": "This research explores the brittleness of safety alignment in AI systems through pruning and low-rank modifications , offering insights into improving robustness and reliability."}
+{"idx": 3, "title": "Assessing the Brittleness of Safety Alignment Via Pruning and Low-rank ...", "date": "", "ddg_snippet": "This study explores this brittleness of safety alignment by leverag-ing pruning and low-rank modifications . We develop methods to identify critical regions that are vital for safety guardrails, and that are disentangled from utility-relevant regions at both the neuron and rank levels.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=niBPvgJIHB", "content": "This study explores this brittleness of safety alignment by leverag-ing pruning and low-rank modifications . We develop methods to identify critical regions that are vital for safety guardrails, and that are disentangled from utility-relevant regions at both the neuron and rank levels."}
+{"idx": 4, "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ...", "date": "", "ddg_snippet": "Table 1. The overview of our weight attribution methods. For neuron attribution, we compute Wanda importance score (Sun et al., 2024 ) as per output change and SNIP importance score (Lee et al., 2019) as per loss change. To disentangle safety from utility, we adopt set difference to isolate the safety -critical neurons. For rank attribution, we compute the most important ranks via ActSVD per ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Assessing-the-Brittleness-of-Safety-Alignment-via-Wei-Huang/aa6a03f3368cbb4a413f7e11650fb8a6a2b71de1/figure/1", "content": "Table 1. The overview of our weight attribution methods. For neuron attribution, we compute Wanda importance score (Sun et al., 2024 ) as per output change and SNIP importance score (Lee et al., 2019) as per loss change. To disentangle safety from utility, we adopt set difference to isolate the safety -critical neurons. For rank attribution, we compute the most important ranks via ActSVD per ..."}
+{"idx": 5, "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ...", "date": "", "ddg_snippet": "Abstract Large language models (LLMs) show inherent brittleness in their safety mechanisms, as evidenced by their susceptibility to jailbreaking and even non-malicious fine-tuning. This study explores this brittleness of safety alignment by leveraging pruning and low-rank modifications .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.05162v1", "content": "Abstract Large language models (LLMs) show inherent brittleness in their safety mechanisms, as evidenced by their susceptibility to jailbreaking and even non-malicious fine-tuning. This study explores this brittleness of safety alignment by leveraging pruning and low-rank modifications ."}
+{"idx": 6, "title": "dblp: Assessing the Brittleness of Safety Alignment via Pruning and Low ...", "date": "", "ddg_snippet": "Bibliographic details on Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/icml/WeiHHXQXMW024", "content": "Bibliographic details on Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications ."}
+{"idx": 7, "title": "alignment-attribution-code/README.md at main - GitHub", "date": "", "ddg_snippet": "This repository provides an original implementation of Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications by Boyi Wei *, Kaixuan Huang*, Yangsibo Huang*, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang and Peter Henderson.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/boyiwei/alignment-attribution-code/blob/main/README.md", "content": "This repository provides an original implementation of Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications by Boyi Wei *, Kaixuan Huang*, Yangsibo Huang*, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang and Peter Henderson."}
+{"idx": 8, "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ...", "date": "", "ddg_snippet": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications Authors Boyi Wei Kaixuan Huang Yangsibo Huang Tinghao Xie", "subpage_snippet": "", "source": "www.peterhenderson.co", "link": "https://www.peterhenderson.co/publication/brittleness/", "content": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications Authors Boyi Wei Kaixuan Huang Yangsibo Huang Tinghao Xie"}
+{"idx": 9, "title": "Talk｜普林斯顿大学魏博逸：通过剪枝&低秩改造揭示LLMs安全对齐的脆弱性 - 知乎", "date": "", "ddg_snippet": "本工作通过使用剪枝（ pruning ）和低秩修改 ( Low-Rank modification )来探究具体是模型的哪一部分对安全对齐起到了至关重要的作用。 我们发现这个区域非常稀疏，只占全参数量的3%左右，从而为理解安全对齐提供了一种新的角度。 Talk·信息", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/713514469", "content": "本工作通过使用剪枝（ pruning ）和低秩修改 ( Low-Rank modification )来探究具体是模型的哪一部分对安全对齐起到了至关重要的作用。 我们发现这个区域非常稀疏，只占全参数量的3%左右，从而为理解安全对齐提供了一种新的角度。 Talk·信息"}
diff --git a/data/sampled_jsons/ActSVD_paper_Wei.jsonl b/data/sampled_jsons/ActSVD_paper_Wei.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9be330006d8e062d7d4284e94e14bd2818b1aee7
--- /dev/null
+++ b/data/sampled_jsons/ActSVD_paper_Wei.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - boyiwei/alignment-attribution-code: [ICML 2024] Assessing the...", "date": "", "ddg_snippet": "--prune_method: To specify the method of rank removal, here we use low_rank_diff, which corresponds to the ( ActSVD with orthogonal projection in the paper ).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/boyiwei/alignment-attribution-code", "content": "--prune_method: To specify the method of rank removal, here we use low_rank_diff, which corresponds to the ( ActSVD with orthogonal projection in the paper )."}
+{"idx": 1, "title": "Я из секты меча 11 серия 4К озвучка MDA / Wo Wei Dao Zong...", "date": "", "ddg_snippet": "#Я_из_секты_меча #Wo_ Wei _Dao_Zong #дунхуа #аниме #3Д #3D #chinese_anime #donghua #Anime #анимация #китай #Я_из_секты_меча_11_серия...", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/video-225794656_456242506", "content": "#Я_из_секты_меча #Wo_ Wei _Dao_Zong #дунхуа #аниме #3Д #3D #chinese_anime #donghua #Anime #анимация #китай #Я_из_секты_меча_11_серия..."}
+{"idx": 2, "title": "Paper Wei -ght - saya1984 - 陈情令 | The Untamed (TV) [Archive of Our...", "date": "", "ddg_snippet": "A silly doodle inspired by this wonderful fanfic, Rabbit Heart, where Wei Ying goes off to adventure, but leaves his little paper -man talisman to keep Lan Zhan company. Real good stuff :3.", "subpage_snippet": "", "source": "archiveofourown.org", "link": "https://archiveofourown.org/works/23825764", "content": "A silly doodle inspired by this wonderful fanfic, Rabbit Heart, where Wei Ying goes off to adventure, but leaves his little paper -man talisman to keep Lan Zhan company. Real good stuff :3."}
+{"idx": 3, "title": "Zhen Wei | MSCI", "date": "", "ddg_snippet": "Dr. Zhen Wei is a Managing Director at MSCI and responsible for bringing MSCI’s integrated investment solutions to leading investors across the APAC region.", "subpage_snippet": "", "source": "www.msci.com", "link": "https://www.msci.com/research-and-insights/contributor/zhen-wei", "content": "Dr. Zhen Wei is a Managing Director at MSCI and responsible for bringing MSCI’s integrated investment solutions to leading investors across the APAC region."}
+{"idx": 4, "title": "Assessing the Brittleness of Safety Alignment via Pruning and...", "date": "", "ddg_snippet": "• ActSVD (top): we regard the top-r ranks identified as most safety-related by ActSVD (Section 2.1) as safety-critical.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.05162", "content": "• ActSVD (top): we regard the top-r ranks identified as most safety-related by ActSVD (Section 2.1) as safety-critical."}
+{"idx": 5, "title": "A ssessing the b rittleness of s afety a lignment", "date": "", "ddg_snippet": "ActSVD We seek to find a low-rank matrix W such that the Frobenius norm of the change to output is minimized", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=niBPvgJIHB", "content": "ActSVD We seek to find a low-rank matrix W such that the Frobenius norm of the change to output is minimized"}
+{"idx": 6, "title": "Selection of reference genes for RT-qPCR studies in blood of beluga...", "date": "", "ddg_snippet": "Wei Cheng Yang conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper , prepared figures and/or tables, reviewed drafts of the paper .", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/1810/", "content": "Wei Cheng Yang conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper , prepared figures and/or tables, reviewed drafts of the paper ."}
+{"idx": 7, "title": "Neither a Protagonist nor an Antagonist, I’ve Become a Background...", "date": "", "ddg_snippet": "“Duke Wei , the person I want to introduce to you is Her Majesty, Ji Ruxue.”“It’s like this, because the Sage Academy is responsible for part of the exam paper setting work, so…”", "subpage_snippet": "", "source": "lightnovelasia.com", "link": "https://lightnovelasia.com/neither-a-protagonist-nor-an-antagonist-ive-become-a-background-character-chapter-204/", "content": "“Duke Wei , the person I want to introduce to you is Her Majesty, Ji Ruxue.”“It’s like this, because the Sage Academy is responsible for part of the exam paper setting work, so…”"}
+{"idx": 8, "title": "Ищите и сохраняйте идеи на тему «бумажные поделки» в Pinterest.", "date": "", "ddg_snippet": "a person is cutting out some paper from a tree with scissors and yarn on it.", "subpage_snippet": "", "source": "ru.pinterest.com", "link": "https://ru.pinterest.com/ideas/бумажные-поделки/946957173299/", "content": "a person is cutting out some paper from a tree with scissors and yarn on it."}
+{"idx": 9, "title": "YYiki: Robustness of graph embedding methods for community detection", "date": "", "ddg_snippet": "Zhi-Feng Wei , Pablo Moriano, Ramakrishnan Kannan.", "subpage_snippet": "", "source": "yyiki.org", "link": "https://yyiki.org/wiki/Paper/Wei2024robustness/", "content": "Zhi-Feng Wei , Pablo Moriano, Ramakrishnan Kannan."}
diff --git a/data/sampled_jsons/Active_Learning_machine_learning_definition.jsonl b/data/sampled_jsons/Active_Learning_machine_learning_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d1cc19c386b2327a63910a3c00b3bf567dd287e2
--- /dev/null
+++ b/data/sampled_jsons/Active_Learning_machine_learning_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ACTIVE - Find & Register for Races, Local Events & Things to Do", "date": "", "ddg_snippet": "ACTIVE powers the world’s events and activities and connects people with the things they love to do. Find, register, or learn about races, local events, sports, and things to do near you.", "subpage_snippet": "", "source": "www.active.com", "link": "https://www.active.com/", "content": "ACTIVE powers the world’s events and activities and connects people with the things they love to do. Find, register, or learn about races, local events, sports, and things to do near you."}
+{"idx": 1, "title": "Couch to 5K Program & C25K Running Schedule - ACTIVE", "date": "", "ddg_snippet": "ACTIVE is the leader in online event registrations from 5k running races and marathons to softball leagues and local events. ACTIVE also makes it easy to learn and prepare for all the things you love to do with expert resources, training plans and fitness calculators.", "subpage_snippet": "", "source": "www.active.com", "link": "https://www.active.com/running/couch-to-5k", "content": "ACTIVE is the leader in online event registrations from 5k running races and marathons to softball leagues and local events. ACTIVE also makes it easy to learn and prepare for all the things you love to do with expert resources, training plans and fitness calculators."}
+{"idx": 2, "title": "La Milla Llanera TO - Toa Baja, PR 2025 - ACTIVE", "date": "", "ddg_snippet": "ACTIVE is the leader in online event registrations from 5k running races and marathons to softball leagues and local events. ACTIVE also makes it easy to learn and prepare for all the things you love to do with expert resources, training plans and fitness calculators.", "subpage_snippet": "", "source": "www.active.com", "link": "https://www.active.com/toa-baja-pr/running/distance-running-races/la-milla-llanera-to-2025", "content": "ACTIVE is the leader in online event registrations from 5k running races and marathons to softball leagues and local events. ACTIVE also makes it easy to learn and prepare for all the things you love to do with expert resources, training plans and fitness calculators."}
+{"idx": 3, "title": "Running Pace Calculator - ACTIVE", "date": "", "ddg_snippet": "Discover your optimal fitness pace with our easy-to-use calculator to determine your race or mile pace for marathons, half marathons, 5Ks, 10Ks & more. Achieve your fitness goals efficiently & stay active with personalized pace guidance. Try it now!", "subpage_snippet": "", "source": "www.active.com", "link": "https://www.active.com/fitness/calculators/pace/", "content": "Discover your optimal fitness pace with our easy-to-use calculator to determine your race or mile pace for marathons, half marathons, 5Ks, 10Ks & more. Achieve your fitness goals efficiently & stay active with personalized pace guidance. Try it now!"}
+{"idx": 4, "title": "Santa Clarita Trail Race - Santa Clarita, CA 2025 - ACTIVE", "date": "", "ddg_snippet": "ACTIVE is the leader in online event registrations from 5k running races and marathons to softball leagues and local events. ACTIVE also makes it easy to learn and prepare for all the things you love to do with expert resources, training plans and fitness calculators.", "subpage_snippet": "", "source": "www.active.com", "link": "https://www.active.com/santa-clarita-ca/running/races/santa-clarita-trail-race-2025", "content": "ACTIVE is the leader in online event registrations from 5k running races and marathons to softball leagues and local events. ACTIVE also makes it easy to learn and prepare for all the things you love to do with expert resources, training plans and fitness calculators."}
+{"idx": 5, "title": "City of Phoenix - ACTIVE", "date": "", "ddg_snippet": "ACTIVE is the leader in online event registrations from 5k running races and marathons to softball leagues and local events. ACTIVE also makes it easy to learn and prepare for all the things you love to do with expert resources, training plans and fitness calculators.", "subpage_snippet": "", "source": "www.active.com", "link": "https://www.active.com/orgs/city-of-phoenix", "content": "ACTIVE is the leader in online event registrations from 5k running races and marathons to softball leagues and local events. ACTIVE also makes it easy to learn and prepare for all the things you love to do with expert resources, training plans and fitness calculators."}
+{"idx": 6, "title": "Contact Us | ActiveAdvantage", "date": "", "ddg_snippet": "Yes, ACTIVE Advantage is the premium membership of ACTIVE .com. ACTIVE .com is your portal to information, registration and community for thousands of activities and events.", "subpage_snippet": "", "source": "advantage.active.com", "link": "https://advantage.active.com/contact", "content": "Yes, ACTIVE Advantage is the premium membership of ACTIVE .com. ACTIVE .com is your portal to information, registration and community for thousands of activities and events."}
+{"idx": 7, "title": "ACTIVE.COM | SIGN IN", "date": "", "ddg_snippet": "Terms of Use Copyright Policy Privacy Notice Support © 2025 Active Network, LLC and/or its affiliates and licensors. All rights reserved.", "subpage_snippet": "", "source": "passport.active.com", "link": "http://passport.active.com/", "content": "Terms of Use Copyright Policy Privacy Notice Support © 2025 Active Network, LLC and/or its affiliates and licensors. All rights reserved."}
+{"idx": 8, "title": "About ACTIVE.com", "date": "", "ddg_snippet": "Millions of active individuals visit ACTIVE .com each month to search and register online for races, events, team sports and recreational activities; interact with others who have similar interests; start online training programs; and access nutrition, fitness and training tips.", "subpage_snippet": "", "source": "www.active.com", "link": "https://www.active.com/about", "content": "Millions of active individuals visit ACTIVE .com each month to search and register online for races, events, team sports and recreational activities; interact with others who have similar interests; start online training programs; and access nutrition, fitness and training tips."}
+{"idx": 9, "title": "5K/10K Llanero: Ruta del Encanto 2026 - Sabana Seca, PR 2026 -...", "date": "", "ddg_snippet": "ACTIVE is the leader in online event registrations from 5k running races and marathons to softball leagues and local events. ACTIVE also makes it easy to learn and prepare for all the things you love to do with expert resources, training plans and fitness calculators.", "subpage_snippet": "", "source": "www.active.com", "link": "https://www.active.com/sabana-seca-pr/running/distance-running/5k-10k-llanero-ruta-del-encanto-2026", "content": "ACTIVE is the leader in online event registrations from 5k running races and marathons to softball leagues and local events. ACTIVE also makes it easy to learn and prepare for all the things you love to do with expert resources, training plans and fitness calculators."}
diff --git a/data/sampled_jsons/Active_Learning_machine_learning_definition_Wikipedia.jsonl b/data/sampled_jsons/Active_Learning_machine_learning_definition_Wikipedia.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ed70c099b644073583b64bc22b15df89b73b665
--- /dev/null
+++ b/data/sampled_jsons/Active_Learning_machine_learning_definition_Wikipedia.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine learning - Wikipedia", "date": "", "ddg_snippet": "Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximise some notion of cumulative reward. Machine learning at Wikipedia 's sister projects: Definitions from Wiktionary.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Machine_learning", "content": "Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximise some notion of cumulative reward. Machine learning at Wikipedia 's sister projects: Definitions from Wiktionary."}
+{"idx": 1, "title": "Learning - Wikipedia", "date": "", "ddg_snippet": "Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines ; there is also evidence for some kind of learn...", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Learning", "content": "Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines ; there is also evidence for some kind of learn..."}
+{"idx": 2, "title": "What is Machine Learning (ML) ? | IBM", "date": "", "ddg_snippet": "Machine learning is the subset of AI focused on algorithms that analyze and “ learn ” the patterns of training data in order to make accurate inferences about new data.", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/topics/machine-learning", "content": "Machine learning is the subset of AI focused on algorithms that analyze and “ learn ” the patterns of training data in order to make accurate inferences about new data."}
+{"idx": 3, "title": "Machine learning use cases in digital marketing in 2025", "date": "", "ddg_snippet": "According to Wikipedia , machine learning (ML) is a class of artificial intelligence methods characterized by their not providing direct solutions to problems but rather training systems to apply solutions.", "subpage_snippet": "", "source": "www.owox.com", "link": "https://www.owox.com/blog/articles/machine-learning-in-marketing", "content": "According to Wikipedia , machine learning (ML) is a class of artificial intelligence methods characterized by their not providing direct solutions to problems but rather training systems to apply solutions."}
+{"idx": 4, "title": "Getting Started with Python and Machine Learning", "date": "", "ddg_snippet": "What is machine learning and why do we need it? Machine learning is a term coined around 1960 composed of two words – machine corresponding to a computer, robot, or other device, and learning an activity , which most humans are good at.", "subpage_snippet": "", "source": "www.packtpub.com", "link": "https://www.packtpub.com/en-sg/learning/how-to-tutorials/getting-started-python-and-machine-learning", "content": "What is machine learning and why do we need it? Machine learning is a term coined around 1960 composed of two words – machine corresponding to a computer, robot, or other device, and learning an activity , which most humans are good at."}
+{"idx": 5, "title": "A short introduction into Dynamics NAV and Machine Learning", "date": "", "ddg_snippet": "Machine learning is a field of computer science that often uses statistical techniques to give computers the ability to “ learn ” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.", "subpage_snippet": "", "source": "www.hannesholst.com", "link": "https://www.hannesholst.com/blog/introduction-dynamics-nav-machine-learning/", "content": "Machine learning is a field of computer science that often uses statistical techniques to give computers the ability to “ learn ” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed."}
+{"idx": 6, "title": "History and Evolution of Machine Learning : A Timeline", "date": "", "ddg_snippet": "Machine learning 's legacy dates from the early beginnings of neural networks to recent advancements in generative AI that democratize new and controversial ways to create content.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/whatis/feature/History-and-evolution-of-machine-learning-A-timeline", "content": "Machine learning 's legacy dates from the early beginnings of neural networks to recent advancements in generative AI that democratize new and controversial ways to create content."}
+{"idx": 7, "title": "Reinforcement Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Reinforcement Learning (RL) is a branch of machine learning that focuses on how agents can learn to make decisions through trial and error to maximize cumulative rewards. RL allows machines to learn by interacting with an environment and receiving feedback based on their actions .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/what-is-reinforcement-learning/", "content": "Reinforcement Learning (RL) is a branch of machine learning that focuses on how agents can learn to make decisions through trial and error to maximize cumulative rewards. RL allows machines to learn by interacting with an environment and receiving feedback based on their actions ."}
+{"idx": 8, "title": "Machine Learning", "date": "", "ddg_snippet": "Machine learning , deep learning , artificial intelligence... Machine learning is a branch of artificial Intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn , gradually improving its accuracy.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/machine-learning-manikandan-b", "content": "Machine learning , deep learning , artificial intelligence... Machine learning is a branch of artificial Intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn , gradually improving its accuracy."}
+{"idx": 9, "title": "Forecasting Intermittent Demand with Machine Learning - ML Journey", "date": "", "ddg_snippet": "Machine Learning Approaches for Intermittent Demand. Machine learning offers several distinct advantages over traditional statistical methods when dealing with intermittent demand.", "subpage_snippet": "", "source": "mljourney.com", "link": "https://mljourney.com/forecasting-intermittent-demand-with-machine-learning/", "content": "Machine Learning Approaches for Intermittent Demand. Machine learning offers several distinct advantages over traditional statistical methods when dealing with intermittent demand."}
diff --git a/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_regularization_lambda_ablation_study_coordination_performance.jsonl b/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_regularization_lambda_ablation_study_coordination_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..faab81ce55a044b7b3a7555201ea6be8d920410e
--- /dev/null
+++ b/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_regularization_lambda_ablation_study_coordination_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ad - Hoc Human - AI Coordination Challenge", "date": "", "ddg_snippet": "Ad - Hoc Human - AI Coordination Challenge . human play data for training. Ad - Hoc Human - AI Coordination Challenge METHODOLOGY We focus on the two-player setting, where BC has demonstrated strong performance as a baseline.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.21490", "content": "Ad - Hoc Human - AI Coordination Challenge . human play data for training. Ad - Hoc Human - AI Coordination Challenge METHODOLOGY We focus on the two-player setting, where BC has demonstrated strong performance as a baseline."}
+{"idx": 1, "title": "ICML Poster Ad - Hoc Human - AI Coordination Challenge", "date": "", "ddg_snippet": "In this work, we introduce the Ad - Hoc Human - AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45867", "content": "In this work, we introduce the Ad - Hoc Human - AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations."}
+{"idx": 2, "title": "Ad - Hoc Human - AI Coordination Challenge", "date": "", "ddg_snippet": "Ad - Hoc Human - AI Coordination Challenge . Tin Dizdarević, Ravi Hammond, Tobias Gessler, Anisoara Calinescu, Jonathan Cook, Matteo Gallici, Andrei Lupu, Jakob Nicolaus Foerster·June 26, 2025. Summary.Overview of human - AI coordination challenges .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-ad-hoc-human-ai-coordination-challenge-cmcfaq3ifm18907nqbp2j6wty", "content": "Ad - Hoc Human - AI Coordination Challenge . Tin Dizdarević, Ravi Hammond, Tobias Gessler, Anisoara Calinescu, Jonathan Cook, Matteo Gallici, Andrei Lupu, Jakob Nicolaus Foerster·June 26, 2025. Summary.Overview of human - AI coordination challenges ."}
+{"idx": 3, "title": "(PDF) Automatic Curriculum Design for Zero-Shot Human - AI ...", "date": "", "ddg_snippet": "zero-shot human - AI coordination study [Strouse et al., 2021]. suggests an approach that trains the ego-agent to coordinate . with humans without human data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389748343_Automatic_Curriculum_Design_for_Zero-Shot_Human-AI_Coordination", "content": "zero-shot human - AI coordination study [Strouse et al., 2021]. suggests an approach that trains the ego-agent to coordinate . with humans without human data."}
+{"idx": 4, "title": "Human - AI Cross-Play Experiments", "date": "", "ddg_snippet": "Human - AI Coordination via Human- Regularized Search and Learning (2022). 10. Ad - Hoc Human - AI Coordination Challenge (2025). 11. Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination (2022).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/human-ai-cross-play-experiments", "content": "Human - AI Coordination via Human- Regularized Search and Learning (2022). 10. Ad - Hoc Human - AI Coordination Challenge (2025). 11. Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination (2022)."}
+{"idx": 5, "title": "The Utility of Explainable AI in Ad Hoc", "date": "", "ddg_snippet": "Study 2: Situational Awareness in Ad Hoc Human -Machine Teaming. Experiment Conditions.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/05d74c48b5b30514d8e9bd60320fc8f6-Paper.pdf", "content": "Study 2: Situational Awareness in Ad Hoc Human -Machine Teaming. Experiment Conditions."}
+{"idx": 6, "title": "Open and real-world human - AI coordination by heterogeneous...", "date": "", "ddg_snippet": "Human - AI coordination aims to develop AI agents capable of effectively coordinating with human partners, making it a crucial aspect of cooperative multi-agent reinforcement learning (MARL). Achieving satisfying performance of AI agents poses a long-standing challenge .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11704-024-3797-6", "content": "Human - AI coordination aims to develop AI agents capable of effectively coordinating with human partners, making it a crucial aspect of cooperative multi-agent reinforcement learning (MARL). Achieving satisfying performance of AI agents poses a long-standing challenge ."}
+{"idx": 7, "title": "Articles by Tin Dizdarević | Synthical", "date": "", "ddg_snippet": "Ad - Hoc Human - AI Coordination Challenge . 29 June 2025 by Tin Dizdarević and others. Artificial Intelligence, Human-Computer Interaction.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/119970f1-9079-4adb-b69a-5bc1fb89a89e/articles", "content": "Ad - Hoc Human - AI Coordination Challenge . 29 June 2025 by Tin Dizdarević and others. Artificial Intelligence, Human-Computer Interaction."}
+{"idx": 8, "title": "PECAN: Leveraging Policy Ensemble for Context-Aware... | DeepAI", "date": "", "ddg_snippet": "Zero-shot human - AI coordination holds the promise of collaborating with humans without human data. Prevailing methods try to train the ego agent with a population of partners via self-play.Highest quality AI Images and Videos. 100% Ad -free.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/pecan-leveraging-policy-ensemble-for-context-aware-zero-shot-human-ai-coordination", "content": "Zero-shot human - AI coordination holds the promise of collaborating with humans without human data. Prevailing methods try to train the ego agent with a population of partners via self-play.Highest quality AI Images and Videos. 100% Ad -free."}
+{"idx": 9, "title": "One", "date": "", "ddg_snippet": "The purpose of human - AI coordination is for AI agents to cooperate well with diverse human teammates.Peter Stone, Gal Kaminka, Sarit Kraus, and Jeffrey Rosenschein. Ad hoc autonomous agent teams: Col-laboration without pre- coordination .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=HVxumpoWBm", "content": "The purpose of human - AI coordination is for AI agents to cooperate well with diverse human teammates.Peter Stone, Gal Kaminka, Sarit Kraus, and Jeffrey Rosenschein. Ad hoc autonomous agent teams: Col-laboration without pre- coordination ."}
diff --git a/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_regularization_weight_lambda_self-play_coordination_year_2024.jsonl b/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_regularization_weight_lambda_self-play_coordination_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5bf48e22fb3ebb182e7ee55260544dbbff435d21
--- /dev/null
+++ b/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_regularization_weight_lambda_self-play_coordination_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action – making it an ideal testbed for human-AI coordination .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21490", "content": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action – making it an ideal testbed for human-AI coordination ."}
+{"idx": 1, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "The AH2AC2 Challenge The Ad-Hoc Human-AI Coordination Challenge (AH2AC2) provides a standardized environment for evaluating AI agents on their ability to coordinate with human -like counterparts in Hanabi. The challenge emphasizes data-efficient methods and uses human proxy agents for robust and reproducible evaluation.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/", "content": "The AH2AC2 Challenge The Ad-Hoc Human-AI Coordination Challenge (AH2AC2) provides a standardized environment for evaluating AI agents on their ability to coordinate with human -like counterparts in Hanabi. The challenge emphasizes data-efficient methods and uses human proxy agents for robust and reproducible evaluation."}
+{"idx": 2, "title": "Ad-Hoc Human-AI Coordination Challenge - Science Cast", "date": "", "ddg_snippet": "Jun 25, 2025 · In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \\textit { human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human -like evaluation partners in AH2AC2.", "subpage_snippet": "", "source": "www.sciencecast.org", "link": "https://www.sciencecast.org/casts/q62pr180dj73", "content": "Jun 25, 2025 · In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \\textit { human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human -like evaluation partners in AH2AC2."}
+{"idx": 3, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) - GitHub", "date": "", "ddg_snippet": "Welcome to the Ad-Hoc Human-AI Coordination Challenge (AH2AC2)! The objective of AH2AC2 is to facilitate the development of AI agents capable of effective collaboration with human -like partners, especially in scenarios with limited prior interaction data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2", "content": "Welcome to the Ad-Hoc Human-AI Coordination Challenge (AH2AC2)! The objective of AH2AC2 is to facilitate the development of AI agents capable of effective collaboration with human -like partners, especially in scenarios with limited prior interaction data."}
+{"idx": 4, "title": "AD-HOC HUMAN-AI COORDINATION CHALLENGE - OpenReview", "date": "", "ddg_snippet": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is an established, fully cooperative benchmark environment that involves imper-fect information, limited communication, theory of mind, and the necessity for coordination among agents to achieve a shared goal. These characteristics, in ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Kioojohsuy", "content": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is an established, fully cooperative benchmark environment that involves imper-fect information, limited communication, theory of mind, and the necessity for coordination among agents to achieve a shared goal. These characteristics, in ..."}
+{"idx": 5, "title": "Ad-Hoc Human-AI Coordination Challenge - Semantic Scholar", "date": "", "ddg_snippet": "This work proposes Human -Regularized PPO (HR-PPO), a multi-agent algorithm where agents are trained through self-play with a small penalty for deviating from a human reference policy, and finds that HR-PPO agents show considerable improvements on proxy measures for coordination with human driving, particularly in highly interactive scenarios.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Ad-Hoc-Human-AI-Coordination-Challenge-Dizdarevic-Hammond/76e21098d2925daeb769a647c9af4886d00b05fd/figure/23", "content": "This work proposes Human -Regularized PPO (HR-PPO), a multi-agent algorithm where agents are trained through self-play with a small penalty for deviating from a human reference policy, and finds that HR-PPO agents show considerable improvements on proxy measures for coordination with human driving, particularly in highly interactive scenarios."}
+{"idx": 6, "title": "Open and real-world human-AI coordination by heterogeneous ...", "date": "", "ddg_snippet": "Abstract Human-AI coordination aims to develop AI agents capable of effectively coordinating with human partners, making it a crucial aspect of cooperative multi-agent reinforcement learning (MARL). Achieving satisfying performance of AI agents poses a long-standing challenge . Recently, ah- hoc teamwork and zero-shot coordination have shown promising advancements in open-world settings ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s11704-024-3797-6.pdf", "content": "Abstract Human-AI coordination aims to develop AI agents capable of effectively coordinating with human partners, making it a crucial aspect of cooperative multi-agent reinforcement learning (MARL). Achieving satisfying performance of AI agents poses a long-standing challenge . Recently, ah- hoc teamwork and zero-shot coordination have shown promising advancements in open-world settings ..."}
+{"idx": 7, "title": "Modeling Strong and Human-Like Gameplay with KL ...", "date": "", "ddg_snippet": "by AP Jacob · 2022 · Cited by 73 — Abstract. We consider the task of accurately modeling strong human policies in multi-agent decision- making problems, given examples of human be- havior. 34 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/jacob22a/jacob22a.pdf", "content": "by AP Jacob · 2022 · Cited by 73 — Abstract. We consider the task of accurately modeling strong human policies in multi-agent decision- making problems, given examples of human be- havior. 34 pages"}
+{"idx": 8, "title": "Modeling Strong and Human-Like Gameplay with KL- ...", "date": "", "ddg_snippet": "by AP Jacob · 2021 · Cited by 74 — We show in chess and Go that regularizing search based on the KL divergence from an imitation- learned policy results in higher human prediction accuracy and ... 33 pages", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2022/human_like_pikl_icml22/human_like_pikl.icml22.pdf", "content": "by AP Jacob · 2021 · Cited by 74 — We show in chess and Go that regularizing search based on the KL divergence from an imitation- learned policy results in higher human prediction accuracy and ... 33 pages"}
+{"idx": 9, "title": "Building Strategic AI Agents for Human-centric Multi- ...", "date": "", "ddg_snippet": "by AP Jacob · 2024 — This research explores the limitations of current approaches, such as self - play reinforcement learning (RL) and imitation learning (IL), and proposes novel.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/158481/jacob-apjacob-phd-eecs-2024-thesis.pdf?sequence=1&isAllowed=y", "content": "by AP Jacob · 2024 — This research explores the limitations of current approaches, such as self - play reinforcement learning (RL) and imitation learning (IL), and proposes novel."}
diff --git a/data/sampled_jsons/Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_Greedy_Ta.jsonl b/data/sampled_jsons/Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_Greedy_Ta.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..23d4ba09b2917bac15a5c7c1b52b952258bf99a8
--- /dev/null
+++ b/data/sampled_jsons/Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_Greedy_Ta.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "18 Jun 2025 — ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... Greedy Task Allocation , Uniform Task ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i&noteId=hkH8Wi9zZm", "content": "18 Jun 2025 — ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... Greedy Task Allocation , Uniform Task ..."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... Greedy Task Allocation (GTA) strategy, which follows ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... Greedy Task Allocation (GTA) strategy, which follows ..."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "2 Feb 2025 — Provided we are willing to waste resources, there is a simple solution to this problem, a Greedy Task Allocation (GTA) strategy, which ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "2 Feb 2025 — Provided we are willing to waste resources, there is a simple solution to this problem, a Greedy Task Allocation (GTA) strategy, which ..."}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management", "date": "", "ddg_snippet": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... a greedy task allocation strategy, leading to wastefulness.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/86bb01c56f97fb6ecf7e661151ca6ae354faf473.pdf", "content": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... a greedy task allocation strategy, leading to wastefulness."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... Greedy Task Allocation (GTA) strategy. The ATA paper directly ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.00775v2", "content": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... Greedy Task Allocation (GTA) strategy. The ATA paper directly ..."}
+{"idx": 5, "title": "Distributed reinforcement learning-based memory allocation for", "date": "", "ddg_snippet": "... et al. [ 18 ] proposed an optimization framework for task offloading to optimize task allocation decisions and allocation of computing resources .", "subpage_snippet": "", "source": "journalofcloudcomputing.springeropen.com", "link": "https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-022-00348-9", "content": "... et al. [ 18 ] proposed an optimization framework for task offloading to optimize task allocation decisions and allocation of computing resources ."}
+{"idx": 6, "title": "An efficient population-based multi-objective task scheduling", "date": "", "ddg_snippet": "... of selecting appropriate fog or cloud resources for allocating IoT tasks under certain constraints corresponds to the optimization problem of task ...", "subpage_snippet": "", "source": "journalofcloudcomputing.springeropen.com", "link": "https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-021-00264-4", "content": "... of selecting appropriate fog or cloud resources for allocating IoT tasks under certain constraints corresponds to the optimization problem of task ..."}
+{"idx": 7, "title": "A Scalable Machine Learning Strategy for Resource Allocation", "date": "", "ddg_snippet": "The paper presents Ape-X, a learning agent framework that integrates LSTM networks and Multi-Agent Reinforcement Learning for efficient resource ...", "subpage_snippet": "", "source": "www.researchsquare.com", "link": "https://www.researchsquare.com/article/rs-5424573/v1", "content": "The paper presents Ape-X, a learning agent framework that integrates LSTM networks and Multi-Agent Reinforcement Learning for efficient resource ..."}
+{"idx": 8, "title": "KR102248978B1 - Resource Allocation Method and Apparatus for", "date": "", "ddg_snippet": "Provided are a resource allocation method and apparatus for reducing average latency in distributed machine learning of multiple users.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/KR102248978B1/en", "content": "Provided are a resource allocation method and apparatus for reducing average latency in distributed machine learning of multiple users."}
+{"idx": 9, "title": "Efficient Resource Allocation Framework for LoRaWAN Network via", "date": "", "ddg_snippet": "... this complex optimization challenge, we introduce two novel online learning resource allocation frameworks, progressing from a fully distributed to a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10493v2", "content": "... this complex optimization challenge, we introduce two novel online learning resource allocation frameworks, progressing from a fully distributed to a ..."}
diff --git a/data/sampled_jsons/Albert_Kwon_Mashael_AlSabah_Circuit_Fingerprinting_Attacks_Passive_Deanonymization_Tor_Hidden_Servic.jsonl b/data/sampled_jsons/Albert_Kwon_Mashael_AlSabah_Circuit_Fingerprinting_Attacks_Passive_Deanonymization_Tor_Hidden_Servic.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..945c5fe838abeab17561df01170b66d1cdfeac7e
--- /dev/null
+++ b/data/sampled_jsons/Albert_Kwon_Mashael_AlSabah_Circuit_Fingerprinting_Attacks_Passive_Deanonymization_Tor_Hidden_Servic.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Circuit Fingerprinting Attacks : Passive", "date": "", "ddg_snippet": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services . Albert Kwon . † , Mashael AlSabah . Tor assigns different purposes to circuits when they are established. For streams accessing non- hidden servers, they use general purpose circuits .", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/conference/usenixsecurity15/sec15-paper-kwon.pdf", "content": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services . Albert Kwon . † , Mashael AlSabah . Tor assigns different purposes to circuits when they are established. For streams accessing non- hidden servers, they use general purpose circuits ."}
+{"idx": 1, "title": "Circuit Fingerprinting Attacks", "date": "", "ddg_snippet": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services . Albert Kwon . † , Mashael AlSabah .5 Circuit Fingerprinting Attack . In this section, we present our circuit ngerprinting at - tacks .", "subpage_snippet": "", "source": "davidlazar.org", "link": "https://davidlazar.org/papers/torhs_fingerprint.pdf", "content": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services . Albert Kwon . † , Mashael AlSabah .5 Circuit Fingerprinting Attack . In this section, we present our circuit ngerprinting at - tacks ."}
+{"idx": 2, "title": "[PDF] Circuit Fingerprinting Attacks : Passive Deanonymization of...", "date": "", "ddg_snippet": "Albert Kwon , Mashael Alsabah A novel two-phase approach for fingerprinting hidden services that does not rely on malicious Tor nodes is proposed and the feasibility of phase one is shown and that phase two does not scale using existing classifiers are established.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Circuit-Fingerprinting-Attacks:-Passive-of-Tor-Kwon-Alsabah/fb4cb1f1c57ee56e9a014570debf7d3d6871ddf3", "content": "Albert Kwon , Mashael Alsabah A novel two-phase approach for fingerprinting hidden services that does not rely on malicious Tor nodes is proposed and the feasibility of phase one is shown and that phase two does not scale using existing classifiers are established."}
+{"idx": 3, "title": "A technical summary of the Usenix fingerprinting ... | The Tor Project", "date": "", "ddg_snippet": "Albert Kwon , Mashael AlSabah , and others have a paper entitled Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services at the upcoming Usenix Security symposium in a few weeks.", "subpage_snippet": "", "source": "blog.torproject.org", "link": "https://blog.torproject.org/technical-summary-usenix-fingerprinting-paper/", "content": "Albert Kwon , Mashael AlSabah , and others have a paper entitled Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services at the upcoming Usenix Security symposium in a few weeks."}
+{"idx": 4, "title": "Circuit fingerprinting attacks | Proceedings of the 24th USENIX...", "date": "", "ddg_snippet": "Circuit fingerprinting attacks : passive deanonymization of tor hidden services .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2831143.2831162?cookieSet=1", "content": "Circuit fingerprinting attacks : passive deanonymization of tor hidden services ."}
+{"idx": 5, "title": "Mashael AlSabah - Google Akademik", "date": "", "ddg_snippet": "Circuit fingerprinting attacks : Passive deanonymization of tor hidden services .", "subpage_snippet": "", "source": "scholar.google.com.br", "link": "https://scholar.google.com.br/citations?user=rGIv25EAAAAJ&hl=tr", "content": "Circuit fingerprinting attacks : Passive deanonymization of tor hidden services ."}
+{"idx": 6, "title": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor ...", "date": "", "ddg_snippet": "Albert Kwon , Mashael AlSabah , David Lazar, Marc Dacier, Srinivas Devadas: Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/uss/KwonALDD15.html", "content": "Albert Kwon , Mashael AlSabah , David Lazar, Marc Dacier, Srinivas Devadas: Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services ."}
+{"idx": 7, "title": "Website Fingerprinting Defenses for Tor Hidden Services - COSIC", "date": "", "ddg_snippet": "Website fingerprinting is hampered if the victim uses Tor [3]. Tor is an anonymous communication system that routes communications through multiple proxies, hiding the web server’s identity from local eavesdroppers.", "subpage_snippet": "", "source": "www.esat.kuleuven.be", "link": "https://www.esat.kuleuven.be/cosic/blog/website-fingerprinting-defenses-for-tor-hidden-services/", "content": "Website fingerprinting is hampered if the victim uses Tor [3]. Tor is an anonymous communication system that routes communications through multiple proxies, hiding the web server’s identity from local eavesdroppers."}
+{"idx": 8, "title": "Dr. Mashael Al - Sabah | Hamad Bin Khalifa University", "date": "", "ddg_snippet": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services . Mashael Al - Sabah , Kevin Bauer, and Ian Goldberg. Enhancing Tor 's Performance using Real-time Traffic Classification.", "subpage_snippet": "", "source": "www.hbku.edu.qa", "link": "https://www.hbku.edu.qa/en/node/6179/pdf", "content": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services . Mashael Al - Sabah , Kevin Bauer, and Ian Goldberg. Enhancing Tor 's Performance using Real-time Traffic Classification."}
+{"idx": 9, "title": "Critical Traffic Analysis on the Tor Network | Journal of Cyber Security...", "date": "", "ddg_snippet": "Albert Kwon , Mashael AlSabah , David Lazar, Marc Dacier, and Srinivas Devadas. Circuit fingerprinting attacks : Passive deanonymization of tor hidden services .", "subpage_snippet": "", "source": "journals.riverpublishers.com", "link": "https://journals.riverpublishers.com/index.php/JCSANDM/article/view/5981?articlesBySameAuthorPage=2", "content": "Albert Kwon , Mashael AlSabah , David Lazar, Marc Dacier, and Srinivas Devadas. Circuit fingerprinting attacks : Passive deanonymization of tor hidden services ."}
diff --git a/data/sampled_jsons/Algorithm_1_Sahara_attention_heads_Ships_metric_calculation.jsonl b/data/sampled_jsons/Algorithm_1_Sahara_attention_heads_Ships_metric_calculation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..174acaac0a0b067df07b6f90c5164a15539857cd
--- /dev/null
+++ b/data/sampled_jsons/Algorithm_1_Sahara_attention_heads_Ships_metric_calculation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICLR 2025 On the Role of Attention Heads in Large Language Model Safety ...", "date": "", "ddg_snippet": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that special attention head has a significant impact on safety.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/oral/31798", "content": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that special attention head has a significant impact on safety."}
+{"idx": 1, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "However, directly transferring existing methods to the safety attention mechanism attribution task is challenging. This paper aims to interpret safety capability within multi - head attention by introducing Safety Head ImPortant Scores ( Ships ) and a heuristic algorithm , Sahara .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper/arxiv/2410.13708", "content": "However, directly transferring existing methods to the safety attention mechanism attribution task is challenging. This paper aims to interpret safety capability within multi - head attention by introducing Safety Head ImPortant Scores ( Ships ) and a heuristic algorithm , Sahara ."}
+{"idx": 2, "title": "Attention heads of large language models - Semantic Scholar", "date": "", "ddg_snippet": "This paper proposes a novel metric which tailored for multi- head attention , the Safety Head ImPortant Score ( Ships ), to assess the individual heads' contributions to model safety and introduces the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Attention-heads-of-large-language-models-Zheng-Wang/d048c9ad2fdf0a5155110271358e30cd24f53bda", "content": "This paper proposes a novel metric which tailored for multi- head attention , the Safety Head ImPortant Score ( Ships ), to assess the individual heads' contributions to model safety and introduces the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model."}
+{"idx": 3, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that the special attention head has a significant impact on safety.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v2", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that the special attention head has a significant impact on safety."}
+{"idx": 4, "title": "PDF ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODEL SAFETY - OpenReview", "date": "", "ddg_snippet": "106 105 We present a novel metric , Ships , to evaluate the impact of attention head ablation on safety. 107 Base on this, we propose a heuristic algorithm , termed Sahara , to find attention head groups whose ablation leads to safety degradation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=70fAPPoRpg&name=pdf", "content": "106 105 We present a novel metric , Ships , to evaluate the impact of attention head ablation on safety. 107 Base on this, we propose a heuristic algorithm , termed Sahara , to find attention head groups whose ablation leads to safety degradation."}
+{"idx": 5, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Furthermore, to attribute generalized safety attention heads , we generalize Ships to evaluate the changes in the representation of ablating attention heads on harmful query datasets. Based on the generalized version of Ships , we attribute the most important safety attention head , which is ablated, and the ASR is improved to 0.72 ↑ ↑ \\uparrow ↑. Iteratively selecting important heads ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v1", "content": "Furthermore, to attribute generalized safety attention heads , we generalize Ships to evaluate the changes in the representation of ablating attention heads on harmful query datasets. Based on the generalized version of Ships , we attribute the most important safety attention head , which is ablated, and the ASR is improved to 0.72 ↑ ↑ \\uparrow ↑. Iteratively selecting important heads ..."}
+{"idx": 6, "title": "arXiv:2410.13708v2 [cs.CL] 24 Feb 2025", "date": "", "ddg_snippet": "eads' contributions to model safety. Based on this, we gen-eralize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical sa ety attention heads in-side the model. Our findings show that the special attention h", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.13708", "content": "eads' contributions to model safety. Based on this, we gen-eralize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical sa ety attention heads in-side the model. Our findings show that the special attention h"}
+{"idx": 7, "title": "SafetyHeadAttribution/Readme.md at main · ydyjya ... - GitHub", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that the special attention head has a significant impact on safety.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ydyjya/SafetyHeadAttribution/blob/main/Readme.md", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that the special attention head has a significant impact on safety."}
+{"idx": 8, "title": "PDF arXiv:2410.13708v1 [cs.CL] 17 Oct 2024 - ResearchGate", "date": "", "ddg_snippet": "-v1.5, underscoring its effectiveness. This work also presents the Safety Attention Head Attribution Algorithm ( Sahara ), a generalized version of Ships that identifies groups of heads whos", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety/fulltext/6711f20e069cb92a811a75e8/On-the-Role-of-Attention-Heads-in-Large-Language-Model-Safety.pdf", "content": "-v1.5, underscoring its effectiveness. This work also presents the Safety Attention Head Attribution Algorithm ( Sahara ), a generalized version of Ships that identifies groups of heads whos"}
+{"idx": 9, "title": "GitHub - ydyjya/SafetyHeadAttribution", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that the special attention head has a significant impact on safety.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ydyjya/SafetyHeadAttribution", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that the special attention head has a significant impact on safety."}
diff --git a/data/sampled_jsons/Algorithm_2_k-HOC_time_complexity_O(_51x0dfsD8A.jsonl b/data/sampled_jsons/Algorithm_2_k-HOC_time_complexity_O(_51x0dfsD8A.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2d99475f74de1a02d47e5443da1cdda5bb1554ad
--- /dev/null
+++ b/data/sampled_jsons/Algorithm_2_k-HOC_time_complexity_O(_51x0dfsD8A.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Random geometric graph - Wikipedia", "date": "", "ddg_snippet": "As there are n ( n 1 ) 2 {\\textstyle {\\frac {n(n-1)}{ 2 }}} possible connections that are checked, the time complexity of the naive algorithm is ( n 2 ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Random_geometric_graph", "content": "As there are n ( n 1 ) 2 {\\textstyle {\\frac {n(n-1)}{ 2 }}} possible connections that are checked, the time complexity of the naive algorithm is ( n 2 ..."}
+{"idx": 1, "title": "algorithm - What does O(log n) mean exactly? - Stack Overflow", "date": "", "ddg_snippet": "... of O (n) linear time , meaning that the size of the input affects the growth of the algorithm proportionally...and the same goes for, for example, ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/2307283/what-does-olog-n-mean-exactly", "content": "... of O (n) linear time , meaning that the size of the input affects the growth of the algorithm proportionally...and the same goes for, for example, ..."}
+{"idx": 2, "title": "algorithm - Unique (non-repeating) random numbers in O(1)? -", "date": "", "ddg_snippet": "Think about it this way, if it was constant time , it would take the same amount of time for 10 random numbers as it would for 10 billion.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/196017/unique-non-repeating-random-numbers-in-o1", "content": "Think about it this way, if it was constant time , it would take the same amount of time for 10 random numbers as it would for 10 billion."}
+{"idx": 3, "title": "PPT - Algorithms for COOPERATIVE DS: Leader Election in the MPS", "date": "", "ddg_snippet": "Best results follow: • asynchronous ring: • O (n log n) messages • synchronous ring: • O (n) messages, time complexity depending on n and on ...", "subpage_snippet": "", "source": "www.slideserve.com", "link": "https://www.slideserve.com/lavi/algorithms-for-cooperative-ds-leader-election-in-the-mps-model", "content": "Best results follow: • asynchronous ring: • O (n log n) messages • synchronous ring: • O (n) messages, time complexity depending on n and on ..."}
+{"idx": 4, "title": "The Subset Sum Matching Problem", "date": "", "ddg_snippet": "We present three algorithms , two suboptimal and one optimal, to solve this problem. ... of an accounting process known as reconciliation, where two ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19218v1", "content": "We present three algorithms , two suboptimal and one optimal, to solve this problem. ... of an accounting process known as reconciliation, where two ..."}
+{"idx": 5, "title": "Newest 'bqp' Questions - Quantum Computing Stack", "date": "", "ddg_snippet": "I know that BQP and promise BQP are often not distinguished, but what I don't understand is, does Shor algorithm (under the important assumption that ...", "subpage_snippet": "", "source": "quantumcomputing.stackexchange.com", "link": "https://quantumcomputing.stackexchange.com/questions/tagged/bqp", "content": "I know that BQP and promise BQP are often not distinguished, but what I don't understand is, does Shor algorithm (under the important assumption that ..."}
+{"idx": 6, "title": "PPT - Leader Election PowerPoint Presentation, free download -", "date": "", "ddg_snippet": "... algorithm for anonymous rings, even if • the algorithm knows the ring size (non-uniform) • in the synchronous model • Proof Sketch (for non-unif ...", "subpage_snippet": "", "source": "www.slideserve.com", "link": "https://www.slideserve.com/odin/leader-election", "content": "... algorithm for anonymous rings, even if • the algorithm knows the ring size (non-uniform) • in the synchronous model • Proof Sketch (for non-unif ..."}
+{"idx": 7, "title": "US20170083608A1 - Accelerated discrete distribution clustering", "date": "", "ddg_snippet": "K -means The analysis of the time complexity of K -means remains an unsolved problem because the number of iterations for convergence is difficult to ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20170083608A1/en", "content": "K -means The analysis of the time complexity of K -means remains an unsolved problem because the number of iterations for convergence is difficult to ..."}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 9, "title": "Towards Explainable Sequential Learning1footnote 11footnote", "date": "", "ddg_snippet": "... achieve a generally explainable sequential learning explanation through EMeriTAte+DF 2 2 2 https://github.com/datagram-db/knobab/releases/tag/v3.1 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23624v1", "content": "... achieve a generally explainable sequential learning explanation through EMeriTAte+DF 2 2 2 https://github.com/datagram-db/knobab/releases/tag/v3.1 ."}
diff --git a/data/sampled_jsons/Algorithm_8_RAS_Recursive_Allocation_Selection_task_allocation_budget_workers_scores.jsonl b/data/sampled_jsons/Algorithm_8_RAS_Recursive_Allocation_Selection_task_allocation_budget_workers_scores.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fd71b10485d581f8cedb844a5a73dd33cd72e93d
--- /dev/null
+++ b/data/sampled_jsons/Algorithm_8_RAS_Recursive_Allocation_Selection_task_allocation_budget_workers_scores.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SUSE-SU-2024:1322-1: important: Security update for the Linux", "date": "", "ddg_snippet": "... scores : * CVE-2021-46925 ( SUSE ): 5.3 CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H * CVE-2021-46925 ( NVD ): 4.7 CVSS:3.1/AV:L/AC:H/PR: ...", "subpage_snippet": "", "source": "lists.suse.com", "link": "https://lists.suse.com/pipermail/sle-updates/2024-April/035002.html", "content": "... scores : * CVE-2021-46925 ( SUSE ): 5.3 CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H * CVE-2021-46925 ( NVD ): 4.7 CVSS:3.1/AV:L/AC:H/PR: ..."}
+{"idx": 1, "title": "SUSE-SU-2024:1480-1: important: Security update for the Linux", "date": "", "ddg_snippet": "... scores : * CVE-2021-46925 ( SUSE ): 5.3 CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H * CVE-2021- 46925 ( NVD ): 4.7 ...", "subpage_snippet": "", "source": "lists.suse.com", "link": "https://lists.suse.com/pipermail/sle-updates/2024-April/035134.html", "content": "... scores : * CVE-2021-46925 ( SUSE ): 5.3 CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H * CVE-2021- 46925 ( NVD ): 4.7 ..."}
+{"idx": 2, "title": "SUSE alert SUSE-SU-2024:1322-1 (kernel) [LWN.net]", "date": "", "ddg_snippet": "... scores : * CVE-2021-46925 ( SUSE ): 5.3 CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H * CVE-2021-46925 ( NVD ): 4.7 CVSS:3.1/AV:L/AC:H/PR: ...", "subpage_snippet": "", "source": "lwn.net", "link": "https://lwn.net/Articles/970165/", "content": "... scores : * CVE-2021-46925 ( SUSE ): 5.3 CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H * CVE-2021-46925 ( NVD ): 4.7 CVSS:3.1/AV:L/AC:H/PR: ..."}
+{"idx": 3, "title": "SUSE alert SUSE-SU-2024:1480-1 (kernel) [LWN.net]", "date": "", "ddg_snippet": "... scores : * CVE-2021-46925 ( SUSE ): 5.3 CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H * CVE-2021- 46925 ( NVD ): 4.7 ...", "subpage_snippet": "", "source": "lwn.net", "link": "https://lwn.net/Articles/972025/", "content": "... scores : * CVE-2021-46925 ( SUSE ): 5.3 CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H * CVE-2021- 46925 ( NVD ): 4.7 ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 5, "title": "SUSE-SU-2024:1322-2: important: Security update for the Linux", "date": "", "ddg_snippet": "... 8 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I: H/A:H * CVE-2023-52470 ( SUSE ): 5.5 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H * CVE-2023-52470 ( NVD ): ...", "subpage_snippet": "", "source": "lists.suse.com", "link": "https://lists.suse.com/pipermail/sle-security-updates/2024-April/018377.html", "content": "... 8 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I: H/A:H * CVE-2023-52470 ( SUSE ): 5.5 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H * CVE-2023-52470 ( NVD ): ..."}
+{"idx": 6, "title": "SUSE-SU-2024:1466-1: important: Security update for the Linux", "date": "", "ddg_snippet": "... 3.1/AV:L/AC:L/PR:L/UI:N/S: U/C:N/I:N/A:H * CVE-2023-52469 ( SUSE ): 5.3 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L * CVE-2023-52469 ( NVD ): 7. 8 ...", "subpage_snippet": "", "source": "lists.suse.com", "link": "https://lists.suse.com/pipermail/sle-updates/2024-April/035122.html", "content": "... 3.1/AV:L/AC:L/PR:L/UI:N/S: U/C:N/I:N/A:H * CVE-2023-52469 ( SUSE ): 5.3 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L * CVE-2023-52469 ( NVD ): 7. 8 ..."}
+{"idx": 7, "title": "SUSE-SU-2024:1332-2: important: Security update for the Linux", "date": "", "ddg_snippet": "... 3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I: N/A:H * CVE-2021-46936 ( SUSE ): 5.5 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H * CVE-2021-46936 ( NVD ): 7. 8 ...", "subpage_snippet": "", "source": "lists.suse.com", "link": "https://lists.suse.com/pipermail/sle-security-updates/2024-April/018378.html", "content": "... 3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I: N/A:H * CVE-2021-46936 ( SUSE ): 5.5 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H * CVE-2021-46936 ( NVD ): 7. 8 ..."}
+{"idx": 8, "title": "SUSE alert SUSE-SU-2024:1332-2 (kernel) [LWN.net]", "date": "", "ddg_snippet": "... 3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I: N/A:H * CVE-2021-46936 ( SUSE ): 5.5 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H * CVE-2021-46936 ( NVD ): 7. 8 ...", "subpage_snippet": "", "source": "lwn.net", "link": "https://lwn.net/Articles/970502/", "content": "... 3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I: N/A:H * CVE-2021-46936 ( SUSE ): 5.5 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H * CVE-2021-46936 ( NVD ): 7. 8 ..."}
+{"idx": 9, "title": "SUSE alert SUSE-SU-2024:1322-2 (kernel) [LWN.net]", "date": "", "ddg_snippet": "... 8 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I: H/A:H * CVE-2023-52470 ( SUSE ): 5.5 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H * CVE-2023-52470 ( NVD ): ...", "subpage_snippet": "", "source": "lwn.net", "link": "https://lwn.net/Articles/970503/", "content": "... 8 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I: H/A:H * CVE-2023-52470 ( SUSE ): 5.5 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H * CVE-2023-52470 ( NVD ): ..."}
diff --git a/data/sampled_jsons/Algorithmic_Collective_Action_in_Machine_Learning_abstract_year_2023.jsonl b/data/sampled_jsons/Algorithmic_Collective_Action_in_Machine_Learning_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae49d8e47be6c5f48f0edf2c72e8e9988ff43057
--- /dev/null
+++ b/data/sampled_jsons/Algorithmic_Collective_Action_in_Machine_Learning_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithmic Collective Action in Machine Learning", "date": "", "ddg_snippet": "We initiate a principled study of algorithmic collective action in digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm’s learning algorithm . The size of the collective is specified by a value.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2302.04262v3", "content": "We initiate a principled study of algorithmic collective action in digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm’s learning algorithm . The size of the collective is specified by a value."}
+{"idx": 1, "title": "Paper page - Algorithmic Collective Action in Machine Learning", "date": "", "ddg_snippet": "Abstract . Algorithmic collectives can significantly influence platform learning algorithms even with small fractional sizes, supported by theoretical models and empirical experiments on skill classification. AI-generated summary.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2302.04262", "content": "Abstract . Algorithmic collectives can significantly influence platform learning algorithms even with small fractional sizes, supported by theoretical models and empirical experiments on skill classification. AI-generated summary."}
+{"idx": 2, "title": "Algorithmic Collective Action in Machine Learning", "date": "", "ddg_snippet": "Abstract .Cambridge: Polity, 2019. 11. Algorithmic Collective Action in Machine Learning Yu, Z., Trere´, E., and Bonini, T. The emergence of algo-. rithmic solidarity: unveiling mutual aid practices and resistance among chinese delivery workers.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lB9Fz2xgQQ", "content": "Abstract .Cambridge: Polity, 2019. 11. Algorithmic Collective Action in Machine Learning Yu, Z., Trere´, E., and Bonini, T. The emergence of algo-. rithmic solidarity: unveiling mutual aid practices and resistance among chinese delivery workers."}
+{"idx": 3, "title": "(PDF) Algorithmic Collective Action in Machine Learning (2023)", "date": "", "ddg_snippet": "Abstract : We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/algorithmic-collective-action-in-machine-learning-139kan07", "content": "Abstract : We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms ."}
+{"idx": 4, "title": "Algorithmic Collective Action in Machine Learning", "date": "", "ddg_snippet": "Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems.", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/sf/en/projects/algorithmic-collective-action-in-machine-learning", "content": "Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems."}
+{"idx": 5, "title": "Algorithmic Collective Action in Machine Learning | Request PDF", "date": "", "ddg_snippet": "Abstract . We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm's learning algorithm .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/368361592_Algorithmic_Collective_Action_in_Machine_Learning", "content": "Abstract . We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm's learning algorithm ."}
+{"idx": 6, "title": "Algorithmic Collective Action in Machine Learning | AI Research...", "date": "", "ddg_snippet": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm's learning algorithm .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/algorithmic-collective-action-machine-learning", "content": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm's learning algorithm ."}
+{"idx": 7, "title": "Algorithmic Collective Action Directed at Fair and Private Learning ...", "date": "", "ddg_snippet": "↓ User collaboration > Users acting alone =⇒ Greater algorithm shift Algorithmic Collective Action (ACA) refers to the coordinated efforts of a group of individuals to influence the behavior of ( machine ) learning algorithms deployed on digital platforms. Data controlled by the collective .", "subpage_snippet": "", "source": "uwaterloo.ca", "link": "https://uwaterloo.ca/cybersecurity-privacy-institute/sites/default/files/uploads/documents/poster-59.pdf", "content": "↓ User collaboration > Users acting alone =⇒ Greater algorithm shift Algorithmic Collective Action (ACA) refers to the coordinated efforts of a group of individuals to influence the behavior of ( machine ) learning algorithms deployed on digital platforms. Data controlled by the collective ."}
+{"idx": 8, "title": "Algorithmic Collective Action with Two Collectives", "date": "", "ddg_snippet": "Collective Action with Algorithms and Data: Hardt et al. [19] defines the notion of algorithmic collective action in a stylized model, assuming one group and examining the group size that is needed to effect changes from a theoretical perspective as well as empirically, using...", "subpage_snippet": "", "source": "sundaram.cs.illinois.edu", "link": "https://sundaram.cs.illinois.edu/pubs/2025/2025_karan_two_collectives.pdf", "content": "Collective Action with Algorithms and Data: Hardt et al. [19] defines the notion of algorithmic collective action in a stylized model, assuming one group and examining the group size that is needed to effect changes from a theoretical perspective as well as empirically, using..."}
+{"idx": 9, "title": "Algorithmic Collective Action in Recommender", "date": "", "ddg_snippet": "Grounding algorithmic collective action means identifying both its opportunities and challenges. The power that arises from gaining control over the learning algorithm through collective action can also be abused by individuals controlling a substantial number of playlists.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/d79792543133425ff79513c147dc8881-Paper-Conference.pdf", "content": "Grounding algorithmic collective action means identifying both its opportunities and challenges. The power that arises from gaining control over the learning algorithm through collective action can also be abused by individuals controlling a substantial number of playlists."}
diff --git a/data/sampled_jsons/An_Efficient_Algorithm_for_Computing_Interventional_Distributions_in_Causal_Models_Shpitser_Pearl_20_year_None.jsonl b/data/sampled_jsons/An_Efficient_Algorithm_for_Computing_Interventional_Distributions_in_Causal_Models_Shpitser_Pearl_20_year_None.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d98628fd2bd2c970e7a96199941e7edc93074ed7
--- /dev/null
+++ b/data/sampled_jsons/An_Efficient_Algorithm_for_Computing_Interventional_Distributions_in_Causal_Models_Shpitser_Pearl_20_year_None.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv [1202.3763] An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models", "date": "", "ddg_snippet": "February 14, 2012 - Probabilistic inference in graphical models is the task of computing marginal and conditional densities of interest from a factorized representation of a joint probability distribution . Inference algorithms such as variable elimination and belief propagation take advantage of constraints embedded ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1202.3763", "content": "February 14, 2012 - Probabilistic inference in graphical models is the task of computing marginal and conditional densities of interest from a factorized representation of a joint probability distribution . Inference algorithms such as variable elimination and belief propagation take advantage of constraints embedded ..."}
+{"idx": 1, "title": "Semantic Scholar [PDF] An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models | Semantic Scholar", "date": "", "ddg_snippet": "A complete algorithm for determining if a dormant independence between two sets of variables is entailed by the causal graph , such that this independence is identifiable, in other words if it resides in an interventional distribution that can be predicted without resorting to interventions.Expand", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/An-Efficient-Algorithm-for-Computing-Interventional-Shpitser-Richardson/8a29c80de26c119199d8ea08c11113632b24b99d", "content": "A complete algorithm for determining if a dormant independence between two sets of variables is entailed by the causal graph , such that this independence is identifiable, in other words if it resides in an interventional distribution that can be predicted without resorting to interventions.Expand"}
+{"idx": 2, "title": "arXiv [2107.11712] Efficient inference of interventional distributions", "date": "", "ddg_snippet": "July 27, 2021 - We consider the problem of efficiently inferring interventional distributions in a causal Bayesian network from a finite number of observations . Let $\\mathcal{P}$ be a causal model on a set $\\mathbf{V}$ of observable variables on a given causal graph $G$. For sets $\\mathbf{X},\\mathbf{Y}\\subseteq ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2107.11712", "content": "July 27, 2021 - We consider the problem of efficiently inferring interventional distributions in a causal Bayesian network from a finite number of observations . Let $\\mathcal{P}$ be a causal model on a set $\\mathbf{V}$ of observable variables on a given causal graph $G$. For sets $\\mathbf{X},\\mathbf{Y}\\subseteq ..."}
+{"idx": 3, "title": "Johns Hopkins University An efficient algorithm for computing interventional distributions in latent variable causal models - Johns Hopkins University", "date": "", "ddg_snippet": "Shpitser I, Richardson TS, Robins JM. An efficient algorithm for computing interventional distributions in latent variable causal models . In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011. AUAI Press. 2011.", "subpage_snippet": "", "source": "pure.johnshopkins.edu", "link": "https://pure.johnshopkins.edu/en/publications/an-efficient-algorithm-for-computing-interventional-distributions", "content": "Shpitser I, Richardson TS, Robins JM. An efficient algorithm for computing interventional distributions in latent variable causal models . In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011. AUAI Press. 2011."}
+{"idx": 4, "title": "ePrints Soton An Efficient Algorithm for Computing Interventional ...", "date": "", "ddg_snippet": "Welcome to the University of Southampton Institutional Research Repository, ePrints Soton. This repository contains details and, if available, downloads of our research output · Information on this website should be updated via PURE, our research management system.", "subpage_snippet": "", "source": "eprints.soton.ac.uk", "link": "https://eprints.soton.ac.uk/350580/1/p661-shpitser.pdf", "content": "Welcome to the University of Southampton Institutional Research Repository, ePrints Soton. This repository contains details and, if available, downloads of our research output · Information on this website should be updated via PURE, our research management system."}
+{"idx": 5, "title": "arXiv [1206.6876] Identification of Conditional Interventional Distributions", "date": "", "ddg_snippet": "June 27, 2012 - View a PDF of the paper titled ... Abstract:The subject of this paper is the elucidation of effects of actions from causal assumptions represented as a directed graph, and statistical knowledge given as a probability distribution ....", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1206.6876", "content": "June 27, 2012 - View a PDF of the paper titled ... Abstract:The subject of this paper is the elucidation of effects of actions from causal assumptions represented as a directed graph, and statistical knowledge given as a probability distribution ...."}
+{"idx": 6, "title": "ResearchGate Appendum to Identification of Conditional Interventional Distributions | Request PDF", "date": "", "ddg_snippet": "June 27, 2012 - ... If B and D are random variables ... it is possible. Shpitser and Pearl [2012] introduce an algorithm which returns all the causal effects P(D|do(B)) that can be computed in a directed graphical model ....", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228092037_Appendum_to_Identification_of_Conditional_Interventional_Distributions", "content": "June 27, 2012 - ... If B and D are random variables ... it is possible. Shpitser and Pearl [2012] introduce an algorithm which returns all the causal effects P(D|do(B)) that can be computed in a directed graphical model ...."}
+{"idx": 7, "title": "ResearchGate Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models. | Request PDF", "date": "", "ddg_snippet": "January 1, 2006 - Request PDF | Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models . | This paper is concerned with estimating the effects of actions from causal assumptions, represented con- cisely as a directed graph, and... | Find, read and cite all the research ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221606257_Identification_of_Joint_Interventional_Distributions_in_Recursive_Semi-Markovian_Causal_Models", "content": "January 1, 2006 - Request PDF | Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models . | This paper is concerned with estimating the effects of actions from causal assumptions, represented con- cisely as a directed graph, and... | Find, read and cite all the research ..."}
+{"idx": 8, "title": "arXiv Algorithmic syntactic causal identification", "date": "", "ddg_snippet": "January 29, 2025 - For an ADMG with no bidirected edges (thus, no latent variables, equivalent to a CBN over a DAG), it is always possible to derive any interventional distribution from the joint distribution over the variables in the DAG using the truncated factorization (Pearl, 2009).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09580", "content": "January 29, 2025 - For an ADMG with no bidirected edges (thus, no latent variables, equivalent to a CBN over a DAG), it is always possible to derive any interventional distribution from the joint distribution over the variables in the DAG using the truncated factorization (Pearl, 2009)."}
+{"idx": 9, "title": "arXiv Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guarantees", "date": "", "ddg_snippet": "August 9, 2024 - Learning causal structure from sampled data is a fundamental problem with applications in various fields, including healthcare, machine learning and artificial intelligence. Traditional methods predominantly rely on observational data, but there exist limits regarding the identifiability of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.04819", "content": "August 9, 2024 - Learning causal structure from sampled data is a fundamental problem with applications in various fields, including healthcare, machine learning and artificial intelligence. Traditional methods predominantly rely on observational data, but there exist limits regarding the identifiability of ..."}
diff --git a/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_Cui_et_al._2023_abstract_year_2023.jsonl b/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_Cui_et_al._2023_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..039ab34b3b745c00bac685c0c94c77e25f29a722
--- /dev/null
+++ b/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_Cui_et_al._2023_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Empirical Study of License Conflict in Free and Open ...", "date": "", "ddg_snippet": "Abstract : Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10172522", "content": "Abstract : Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license ..."}
+{"idx": 1, "title": "An Empirical Study of License Conflict in Free and Open ...", "date": "", "ddg_snippet": "Sep 20, 2023 · Abstract Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license terms.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/ICSE-SEIP58684.2023.00050", "content": "Sep 20, 2023 · Abstract Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license terms."}
+{"idx": 2, "title": "OSS-LCAF: Open-Source Software License Conflict Analysis ...", "date": "", "ddg_snippet": "Sep 11, 2025 · An empirical study of license conflict in free and open source software . In 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 495–505, 2023 .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/ICSE-Companion66252.2025.00084", "content": "Sep 11, 2025 · An empirical study of license conflict in free and open source software . In 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 495–505, 2023 ."}
+{"idx": 3, "title": "An Empirical Study of License Violations in Open Source Projects", "date": "", "ddg_snippet": "Abstract —The use of Open Source Software (OSS) components in building applications has presented the challenge of integrating them in a way such that the licenses of the individual components do not conflict with each other and if applicable, the overall license of the application. These conflicts lead to violations, with many having far reaching legal consequences. While proprietary ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/An-Empirical-Study-of-License-Violations-in-Open-Source-Projects-Publication.pdf", "content": "Abstract —The use of Open Source Software (OSS) components in building applications has presented the challenge of integrating them in a way such that the licenses of the individual components do not conflict with each other and if applicable, the overall license of the application. These conflicts lead to violations, with many having far reaching legal consequences. While proprietary ..."}
+{"idx": 4, "title": "An Empirical Study of License Conflict in Free and Open ...", "date": "", "ddg_snippet": "DIKE is proposed, an automated tool that can perform license detection and conflict analysis for FOSS and suggests that conflicts are prevalent in FOSS, warning the open source community about intellectual property risks. Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/An-Empirical-Study-of-License-Conflict-in-Free-and-Cui-Wu/7d262af42e65c2554ff7cbb41d252ad27912cff9", "content": "DIKE is proposed, an automated tool that can perform license detection and conflict analysis for FOSS and suggests that conflicts are prevalent in FOSS, warning the open source community about intellectual property risks. Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions ..."}
+{"idx": 5, "title": "Identification and classification of free, open source ...", "date": "", "ddg_snippet": "3 days ago · An Empirical Study of License Conflict in Free and Open Source Software . In: 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0164121225002973", "content": "3 days ago · An Empirical Study of License Conflict in Free and Open Source Software . In: 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice."}
+{"idx": 6, "title": "A first look at License Variants in the PyPI Ecosystem", "date": "", "ddg_snippet": "... empirical study into license variants in software packaging ecosystem but also equips developers and organizations with practical tools for navigating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14594v1", "content": "... empirical study into license variants in software packaging ecosystem but also equips developers and organizations with practical tools for navigating ..."}
+{"idx": 7, "title": "How Robust are LLM-Generated Library Imports? An Empirical", "date": "", "ddg_snippet": "In this paper, we conduct an empirical study of six state- of -the-art LLMs, both proprietary and open - source , by prompting them to solve real-world ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10818v1", "content": "In this paper, we conduct an empirical study of six state- of -the-art LLMs, both proprietary and open - source , by prompting them to solve real-world ..."}
+{"idx": 8, "title": "On Evaluating the Efficiency of Source Code Generated by LLMs |", "date": "", "ddg_snippet": "The present systematic survey comprehensively analyses studies published between 2021 and 2024, focusing on utilizing LLMs in the code generation ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381388109_On_Evaluating_the_Efficiency_of_Source_Code_Generated_by_LLMs", "content": "The present systematic survey comprehensively analyses studies published between 2021 and 2024, focusing on utilizing LLMs in the code generation ..."}
+{"idx": 9, "title": "(PDF) Parametric Study of the Modal Behavior of Concrete", "date": "", "ddg_snippet": "The results indicated that modal frequencies in the condition of with and without Pre-stress are different in all cases.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/338161931_Parametric_Study_of_the_Modal_Behavior_of_Concrete_Gravity_Dam_by_Using_Finite_Element_Method", "content": "The results indicated that modal frequencies in the condition of with and without Pre-stress are different in all cases."}
diff --git a/data/sampled_jsons/Appendix_A.3.2_Boltzmann-Aligned_Inverse_Folding_hardware_GPU_experimental_setup.jsonl b/data/sampled_jsons/Appendix_A.3.2_Boltzmann-Aligned_Inverse_Folding_hardware_GPU_experimental_setup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af05fd7010168adf6bcb5df4bcf9c3ace835632f
--- /dev/null
+++ b/data/sampled_jsons/Appendix_A.3.2_Boltzmann-Aligned_Inverse_Folding_hardware_GPU_experimental_setup.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of", "date": "", "ddg_snippet": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to Δ Δ G ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to Δ Δ G ..."}
+{"idx": 1, "title": "Reclaiming “Open AI\" - AI Model Serving Can Be Open Access ... CyberLLMInstruct: A Pseudo-malicious Dataset Revealing Safety ...", "date": "", "ddg_snippet": "This would potentially enable the possibility of building a TEE-based OML solution on GPUs in the cloud before TEE technology becomes accessible on more commercially available GPU hardware . 3 days ago · The experimental results reveal that the CyberLLMInstruct dataset’s broad coverage of adversarial prompts – ranging from social engineering methodologies to code obfuscation techniques – exposed nuanced weaknesses in fine-tuned models (see Table 4 for a summary).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.03887v3", "content": "This would potentially enable the possibility of building a TEE-based OML solution on GPUs in the cloud before TEE technology becomes accessible on more commercially available GPU hardware . 3 days ago · The experimental results reveal that the CyberLLMInstruct dataset’s broad coverage of adversarial prompts – ranging from social engineering methodologies to code obfuscation techniques – exposed nuanced weaknesses in fine-tuned models (see Table 4 for a summary)."}
+{"idx": 2, "title": "CyberLLMInstruct: A Pseudo-malicious Dataset Revealing Safety ...", "date": "", "ddg_snippet": "3 days ago · The experimental results reveal that the CyberLLMInstruct dataset’s broad coverage of adversarial prompts – ranging from social engineering methodologies to code obfuscation techniques – exposed nuanced weaknesses in fine-tuned models (see Table 4 for a summary).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.09334v3", "content": "3 days ago · The experimental results reveal that the CyberLLMInstruct dataset’s broad coverage of adversarial prompts – ranging from social engineering methodologies to code obfuscation techniques – exposed nuanced weaknesses in fine-tuned models (see Table 4 for a summary)."}
+{"idx": 3, "title": "ProteinZero: Self-Improving Protein Generation via Online", "date": "", "ddg_snippet": "... experiments on inverse folding tasks, we demonstrate that ProteinZero substantially outperforms existing methods across every key metric, achieving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07459v2", "content": "... experiments on inverse folding tasks, we demonstrate that ProteinZero substantially outperforms existing methods across every key metric, achieving ..."}
+{"idx": 4, "title": "Molecular dynamics-based refinement and validation for sub-5 Å", "date": "", "ddg_snippet": "However, seminal advances in detection hardware and programs over the past three years ( Li et al., 2013 ; Milazzo et al., 2011 ) have enabled now ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/16105", "content": "However, seminal advances in detection hardware and programs over the past three years ( Li et al., 2013 ; Milazzo et al., 2011 ) have enabled now ..."}
+{"idx": 5, "title": "US10264525B2 - Energy efficient communications - Google Patents", "date": "", "ddg_snippet": "Legal status (The legal status is an assumption and is not a legal conclusion. ... Priority date (The priority date is an assumption and is not a ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US10264525B2/en", "content": "Legal status (The legal status is an assumption and is not a legal conclusion. ... Priority date (The priority date is an assumption and is not a ..."}
diff --git a/data/sampled_jsons/Appendix_A.3.2_NVIDIA_OR_A100_OR_V100_OR_RTX_OR_GPU_lzdFImKK8w.jsonl b/data/sampled_jsons/Appendix_A.3.2_NVIDIA_OR_A100_OR_V100_OR_RTX_OR_GPU_lzdFImKK8w.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..636ab571f34ab2b7ea92e809f43075ad023977ea
--- /dev/null
+++ b/data/sampled_jsons/Appendix_A.3.2_NVIDIA_OR_A100_OR_V100_OR_RTX_OR_GPU_lzdFImKK8w.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ampere (microarchitecture) - Wikipedia GPU machine types | Compute Engine Documentation | Google Cloud NVIDIA RTX A6000 datasheet NDm_A100_v4 size series - Azure Virtual Machines | Microsoft ... Demystifying the Nvidia Ampere Architecture through ...", "date": "", "ddg_snippet": "Ampere is the codename for a graphics processing unit ( GPU ) microarchitecture developed by Nvidia as the successor to both the Volta and Turing architectures. It was officially announced on May 14, 2020, and is named after French mathematician and physicist André-Marie Ampère. [1][2] Nvidia announced the Ampere architecture GeForce 30 series consumer GPUs at a GeForce Special Event on ... For compute workloads, GPUs are supported for the following machine types: 1. A3 VMs: these VMs have NVIDIA H100 80GB or NVIDIA H200141GB GPUs automatically attached. 2. A2 VMs: these VMs have either NVIDIA A100 80GB or NVIDIA A100 40GBGPUs automatically attached. 3. G2 VMs: these VMs have NVIDIA L4 GPUs automatically attached. 4. N1 VMs: for these... See full list on cloud.google.com To use NVIDIA H100 80GB or NVIDIA H200 141GB GPUs, you must use anA3 accelerator-optimizedmachine. Each A3 machine type has a fixed GPU count, vCPU count, and memory size. See full list on cloud.google.com To use NVIDIA A100 GPUs onGoogle Cloud, you must deploy anA2 accelerator-optimizedmachine. Each A2 machine type has a fixed GPU count, vCPU count, and memory size. A2 machine series are available in two types: 1. A2 Ultra: these machine types have A100 80GB GPUs ( nvidia - a100 -80gb) andLocal SSD disks attached. 2 . A2 Standard: these machine types hav... See full list on cloud.google.com To use NVIDIA L4 GPUs( nvidia -l4 or nvidia -l4-vws), you must deploy aG2 accelerator-optimizedmachine. Each G2 machine type has a fixed number of NVIDIA L4 GPUsand vCPUs attached. Each G2 machine type also has a default memory and a custommemory range. The custom memory range defines the amount of memory thatyou can allocate to your VM for each machi... See full list on cloud.google.com You can attach the following GPU models to anN1 machine typewith theexception of the N1 shared-core machine type. N1 VMs with lower numbers of GPUs are limited to a maximum number of vCPUs.In general, a higher number of GPUs lets you create VM instances with a highernumber of vCPUs and memory. See full list on cloud.google.com If you have graphics-intensive workloads, such as 3D visualization, you cancreate virtual workstations that useNVIDIA RTX Virtual Workstations (vWS)(formerly known as NVIDIA GRID). When you create a virtualworkstation, an NVIDIA RTX Virtual Workstation (vWS) license is automatically addedto your VM. For information about pricing for virtual worksta... See full list on cloud.google.com The following table describes the GPU memory size, feature availability,and ideal workload types of different GPU models that are available onCompute Engine. To compare GPU pricing for the different GPU models and regions that areavailable on Compute Engine, see GPU pricing. See full list on cloud.google.com The following table describes the performance specifications of different GPUmodels that are available on Compute Engine. See full list on cloud.google.com See full list on cloud.google.com The NVIDIA Ampere architecture builds on the power of RTX to significantly enhance the performance of rendering, graphics, AI, and compute workloads. Engineered to perfection and featuring cutting-edge innovations, the NVIDIA Ampere architecture takes RTX to new heights for professional workloads. Oct 18, 2024 · The NDm A100 v4 series virtual machine (VM) is a new flagship addition to the Azure GPU family. These sizes are designed for high-end Deep Learning training and tightly coupled scale-up and scale-out HPC workloads. The NDm A100 v4 series starts with a single VM and eight NVIDIA Ampere A100 80GB Tensor Core GPUs. For instance, Meta built one of the fastest supercomputers based on Nvidia Ampere architecture GPUs ( A100 ) [1], and they are extending it to be the most powerful supercomputer in the world by mid-2022. Besides, tens of the top500 supercomputers [2] are GPU -accelerated.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Ampere_(microarchitecture)", "content": "Ampere is the codename for a graphics processing unit ( GPU ) microarchitecture developed by Nvidia as the successor to both the Volta and Turing architectures. It was officially announced on May 14, 2020, and is named after French mathematician and physicist André-Marie Ampère. [1][2] Nvidia announced the Ampere architecture GeForce 30 series consumer GPUs at a GeForce Special Event on ... For compute workloads, GPUs are supported for the following machine types: 1. A3 VMs: these VMs have NVIDIA H100 80GB or NVIDIA H200141GB GPUs automatically attached. 2. A2 VMs: these VMs have either NVIDIA A100 80GB or NVIDIA A100 40GBGPUs automatically attached. 3. G2 VMs: these VMs have NVIDIA L4 GPUs automatically attached. 4. N1 VMs: for these... See full list on cloud.google.com To use NVIDIA H100 80GB or NVIDIA H200 141GB GPUs, you must use anA3 accelerator-optimizedmachine. Each A3 machine type has a fixed GPU count, vCPU count, and memory size. See full list on cloud.google.com To use NVIDIA A100 GPUs onGoogle Cloud, you must deploy anA2 accelerator-optimizedmachine. Each A2 machine type has a fixed GPU count, vCPU count, and memory size. A2 machine series are available in two types: 1. A2 Ultra: these machine types have A100 80GB GPUs ( nvidia - a100 -80gb) andLocal SSD disks attached. 2 . A2 Standard: these machine types hav... See full list on cloud.google.com To use NVIDIA L4 GPUs( nvidia -l4 or nvidia -l4-vws), you must deploy aG2 accelerator-optimizedmachine. Each G2 machine type has a fixed number of NVIDIA L4 GPUsand vCPUs attached. Each G2 machine type also has a default memory and a custommemory range. The custom memory range defines the amount of memory thatyou can allocate to your VM for each machi... See full list on cloud.google.com You can attach the following GPU models to anN1 machine typewith theexception of the N1 shared-core machine type. N1 VMs with lower numbers of GPUs are limited to a maximum number of vCPUs.In general, a higher number of GPUs lets you create VM instances with a highernumber of vCPUs and memory. See full list on cloud.google.com If you have graphics-intensive workloads, such as 3D visualization, you cancreate virtual workstations that useNVIDIA RTX Virtual Workstations (vWS)(formerly known as NVIDIA GRID). When you create a virtualworkstation, an NVIDIA RTX Virtual Workstation (vWS) license is automatically addedto your VM. For information about pricing for virtual worksta... See full list on cloud.google.com The following table describes the GPU memory size, feature availability,and ideal workload types of different GPU models that are available onCompute Engine. To compare GPU pricing for the different GPU models and regions that areavailable on Compute Engine, see GPU pricing. See full list on cloud.google.com The following table describes the performance specifications of different GPUmodels that are available on Compute Engine. See full list on cloud.google.com See full list on cloud.google.com The NVIDIA Ampere architecture builds on the power of RTX to significantly enhance the performance of rendering, graphics, AI, and compute workloads. Engineered to perfection and featuring cutting-edge innovations, the NVIDIA Ampere architecture takes RTX to new heights for professional workloads. Oct 18, 2024 · The NDm A100 v4 series virtual machine (VM) is a new flagship addition to the Azure GPU family. These sizes are designed for high-end Deep Learning training and tightly coupled scale-up and scale-out HPC workloads. The NDm A100 v4 series starts with a single VM and eight NVIDIA Ampere A100 80GB Tensor Core GPUs. For instance, Meta built one of the fastest supercomputers based on Nvidia Ampere architecture GPUs ( A100 ) [1], and they are extending it to be the most powerful supercomputer in the world by mid-2022. Besides, tens of the top500 supercomputers [2] are GPU -accelerated."}
+{"idx": 1, "title": "Nvidia PureVideo - Wikipedia", "date": "", "ddg_snippet": "The third generation PureVideo HD is sometimes called \"PureVideo HD 3 \" or VP3, although this is not an official Nvidia designation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Nvidia_PureVideo", "content": "The third generation PureVideo HD is sometimes called \"PureVideo HD 3 \" or VP3, although this is not an official Nvidia designation."}
+{"idx": 2, "title": "CUDA - Wikipedia", "date": "", "ddg_snippet": "Ian Buck, while at Stanford in 2000, created an 8K gaming rig using 32 GeForce cards, then obtained a DARPA grant to perform general purpose parallel ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/CUDA", "content": "Ian Buck, while at Stanford in 2000, created an 8K gaming rig using 32 GeForce cards, then obtained a DARPA grant to perform general purpose parallel ..."}
+{"idx": 3, "title": "Nvidia PureVideo - Wikipedia, la enciclopedia libre", "date": "", "ddg_snippet": "El material de prensa de Nvidia citaba aceleración de hardware para video VC-1 y H.264 , pero estas características no estaban presentes en el ...", "subpage_snippet": "", "source": "es.wikipedia.org", "link": "https://es.wikipedia.org/wiki/Nvidia_PureVideo", "content": "El material de prensa de Nvidia citaba aceleración de hardware para video VC-1 y H.264 , pero estas características no estaban presentes en el ..."}
+{"idx": 4, "title": "NVIDIA A100 Tensor Core GPU Architecture", "date": "", "ddg_snippet": "The A100 GPU in the A100 Tensor Core GPU includes 40 MB of L2 cache, which is 6.7x larger than Tesla V100 L2 cache. The substantial increase in L2 cache size significantly improves performance of many HPC and AI workloads because larger portions of datasets and models can now be cached and repeatedly accessed at much higher speed than reading ...", "subpage_snippet": "", "source": "images.nvidia.com", "link": "https://images.nvidia.com/aem-dam/en-zz/Solutions/data-center/nvidia-ampere-architecture-whitepaper.pdf", "content": "The A100 GPU in the A100 Tensor Core GPU includes 40 MB of L2 cache, which is 6.7x larger than Tesla V100 L2 cache. The substantial increase in L2 cache size significantly improves performance of many HPC and AI workloads because larger portions of datasets and models can now be cached and repeatedly accessed at much higher speed than reading ..."}
+{"idx": 5, "title": "NDasrA100_v4 size series - Azure Virtual Machines", "date": "", "ddg_snippet": "Oct 18, 2024 · The ND A100 v4 series virtual machine (VM) is a new flagship addition to the Azure GPU family. These sizes are designed for high-end Deep Learning training and tightly coupled scale-up and scale-out HPC workloads. The ND A100 v4 series starts with a single VM and eight NVIDIA Ampere A100 40GB Tensor Core GPUs.", "subpage_snippet": "", "source": "learn.microsoft.com", "link": "https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/gpu-accelerated/ndasra100v4-series", "content": "Oct 18, 2024 · The ND A100 v4 series virtual machine (VM) is a new flagship addition to the Azure GPU family. These sizes are designed for high-end Deep Learning training and tightly coupled scale-up and scale-out HPC workloads. The ND A100 v4 series starts with a single VM and eight NVIDIA Ampere A100 40GB Tensor Core GPUs."}
+{"idx": 6, "title": "GPU machine types | Compute Engine Documentation | Google Cloud NVIDIA RTX A6000 datasheet NDm_A100_v4 size series - Azure Virtual Machines | Microsoft ... Demystifying the Nvidia Ampere Architecture through ...", "date": "", "ddg_snippet": "For compute workloads, GPUs are supported for the following machine types: 1. A3 VMs: these VMs have NVIDIA H100 80GB or NVIDIA H200141GB GPUs automatically attached. 2. A2 VMs: these VMs have either NVIDIA A100 80GB or NVIDIA A100 40GBGPUs automatically attached. 3. G2 VMs: these VMs have NVIDIA L4 GPUs automatically attached. 4. N1 VMs: for these... See full list on cloud.google.com To use NVIDIA H100 80GB or NVIDIA H200 141GB GPUs, you must use anA3 accelerator-optimizedmachine. Each A3 machine type has a fixed GPU count, vCPU count, and memory size. See full list on cloud.google.com To use NVIDIA A100 GPUs onGoogle Cloud, you must deploy anA2 accelerator-optimizedmachine. Each A2 machine type has a fixed GPU count, vCPU count, and memory size. A2 machine series are available in two types: 1. A2 Ultra: these machine types have A100 80GB GPUs ( nvidia - a100 -80gb) andLocal SSD disks attached. 2 . A2 Standard: these machine types hav... See full list on cloud.google.com To use NVIDIA L4 GPUs( nvidia -l4 or nvidia -l4-vws), you must deploy aG2 accelerator-optimizedmachine. Each G2 machine type has a fixed number of NVIDIA L4 GPUsand vCPUs attached. Each G2 machine type also has a default memory and a custommemory range. The custom memory range defines the amount of memory thatyou can allocate to your VM for each machi... See full list on cloud.google.com You can attach the following GPU models to anN1 machine typewith theexception of the N1 shared-core machine type. N1 VMs with lower numbers of GPUs are limited to a maximum number of vCPUs.In general, a higher number of GPUs lets you create VM instances with a highernumber of vCPUs and memory. See full list on cloud.google.com If you have graphics-intensive workloads, such as 3D visualization, you cancreate virtual workstations that useNVIDIA RTX Virtual Workstations (vWS)(formerly known as NVIDIA GRID). When you create a virtualworkstation, an NVIDIA RTX Virtual Workstation (vWS) license is automatically addedto your VM. For information about pricing for virtual worksta... See full list on cloud.google.com The following table describes the GPU memory size, feature availability,and ideal workload types of different GPU models that are available onCompute Engine. To compare GPU pricing for the different GPU models and regions that areavailable on Compute Engine, see GPU pricing. See full list on cloud.google.com The following table describes the performance specifications of different GPUmodels that are available on Compute Engine. See full list on cloud.google.com See full list on cloud.google.com The NVIDIA Ampere architecture builds on the power of RTX to significantly enhance the performance of rendering, graphics, AI, and compute workloads. Engineered to perfection and featuring cutting-edge innovations, the NVIDIA Ampere architecture takes RTX to new heights for professional workloads. Oct 18, 2024 · The NDm A100 v4 series virtual machine (VM) is a new flagship addition to the Azure GPU family. These sizes are designed for high-end Deep Learning training and tightly coupled scale-up and scale-out HPC workloads. The NDm A100 v4 series starts with a single VM and eight NVIDIA Ampere A100 80GB Tensor Core GPUs. For instance, Meta built one of the fastest supercomputers based on Nvidia Ampere architecture GPUs ( A100 ) [1], and they are extending it to be the most powerful supercomputer in the world by mid-2022. Besides, tens of the top500 supercomputers [2] are GPU -accelerated.", "subpage_snippet": "", "source": "cloud.google.com", "link": "https://cloud.google.com/compute/docs/gpus/", "content": "For compute workloads, GPUs are supported for the following machine types: 1. A3 VMs: these VMs have NVIDIA H100 80GB or NVIDIA H200141GB GPUs automatically attached. 2. A2 VMs: these VMs have either NVIDIA A100 80GB or NVIDIA A100 40GBGPUs automatically attached. 3. G2 VMs: these VMs have NVIDIA L4 GPUs automatically attached. 4. N1 VMs: for these... See full list on cloud.google.com To use NVIDIA H100 80GB or NVIDIA H200 141GB GPUs, you must use anA3 accelerator-optimizedmachine. Each A3 machine type has a fixed GPU count, vCPU count, and memory size. See full list on cloud.google.com To use NVIDIA A100 GPUs onGoogle Cloud, you must deploy anA2 accelerator-optimizedmachine. Each A2 machine type has a fixed GPU count, vCPU count, and memory size. A2 machine series are available in two types: 1. A2 Ultra: these machine types have A100 80GB GPUs ( nvidia - a100 -80gb) andLocal SSD disks attached. 2 . A2 Standard: these machine types hav... See full list on cloud.google.com To use NVIDIA L4 GPUs( nvidia -l4 or nvidia -l4-vws), you must deploy aG2 accelerator-optimizedmachine. Each G2 machine type has a fixed number of NVIDIA L4 GPUsand vCPUs attached. Each G2 machine type also has a default memory and a custommemory range. The custom memory range defines the amount of memory thatyou can allocate to your VM for each machi... See full list on cloud.google.com You can attach the following GPU models to anN1 machine typewith theexception of the N1 shared-core machine type. N1 VMs with lower numbers of GPUs are limited to a maximum number of vCPUs.In general, a higher number of GPUs lets you create VM instances with a highernumber of vCPUs and memory. See full list on cloud.google.com If you have graphics-intensive workloads, such as 3D visualization, you cancreate virtual workstations that useNVIDIA RTX Virtual Workstations (vWS)(formerly known as NVIDIA GRID). When you create a virtualworkstation, an NVIDIA RTX Virtual Workstation (vWS) license is automatically addedto your VM. For information about pricing for virtual worksta... See full list on cloud.google.com The following table describes the GPU memory size, feature availability,and ideal workload types of different GPU models that are available onCompute Engine. To compare GPU pricing for the different GPU models and regions that areavailable on Compute Engine, see GPU pricing. See full list on cloud.google.com The following table describes the performance specifications of different GPUmodels that are available on Compute Engine. See full list on cloud.google.com See full list on cloud.google.com The NVIDIA Ampere architecture builds on the power of RTX to significantly enhance the performance of rendering, graphics, AI, and compute workloads. Engineered to perfection and featuring cutting-edge innovations, the NVIDIA Ampere architecture takes RTX to new heights for professional workloads. Oct 18, 2024 · The NDm A100 v4 series virtual machine (VM) is a new flagship addition to the Azure GPU family. These sizes are designed for high-end Deep Learning training and tightly coupled scale-up and scale-out HPC workloads. The NDm A100 v4 series starts with a single VM and eight NVIDIA Ampere A100 80GB Tensor Core GPUs. For instance, Meta built one of the fastest supercomputers based on Nvidia Ampere architecture GPUs ( A100 ) [1], and they are extending it to be the most powerful supercomputer in the world by mid-2022. Besides, tens of the top500 supercomputers [2] are GPU -accelerated."}
+{"idx": 7, "title": "NVIDIA RTX A6000 datasheet", "date": "", "ddg_snippet": "The NVIDIA Ampere architecture builds on the power of RTX to significantly enhance the performance of rendering, graphics, AI, and compute workloads. Engineered to perfection and featuring cutting-edge innovations, the NVIDIA Ampere architecture takes RTX to new heights for professional workloads.", "subpage_snippet": "", "source": "www.nvidia.com", "link": "https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/quadro-product-literature/proviz-print-nvidia-rtx-a6000-datasheet-us-nvidia-1454980-r9-web+(1).pdf", "content": "The NVIDIA Ampere architecture builds on the power of RTX to significantly enhance the performance of rendering, graphics, AI, and compute workloads. Engineered to perfection and featuring cutting-edge innovations, the NVIDIA Ampere architecture takes RTX to new heights for professional workloads."}
+{"idx": 8, "title": "NDm_A100_v4 size series - Azure Virtual Machines | Microsoft ...", "date": "", "ddg_snippet": "Oct 18, 2024 · The NDm A100 v4 series virtual machine (VM) is a new flagship addition to the Azure GPU family. These sizes are designed for high-end Deep Learning training and tightly coupled scale-up and scale-out HPC workloads. The NDm A100 v4 series starts with a single VM and eight NVIDIA Ampere A100 80GB Tensor Core GPUs.", "subpage_snippet": "", "source": "learn.microsoft.com", "link": "https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/gpu-accelerated/ndma100v4-series", "content": "Oct 18, 2024 · The NDm A100 v4 series virtual machine (VM) is a new flagship addition to the Azure GPU family. These sizes are designed for high-end Deep Learning training and tightly coupled scale-up and scale-out HPC workloads. The NDm A100 v4 series starts with a single VM and eight NVIDIA Ampere A100 80GB Tensor Core GPUs."}
+{"idx": 9, "title": "Demystifying the Nvidia Ampere Architecture through ...", "date": "", "ddg_snippet": "For instance, Meta built one of the fastest supercomputers based on Nvidia Ampere architecture GPUs ( A100 ) [1], and they are extending it to be the most powerful supercomputer in the world by mid-2022. Besides, tens of the top500 supercomputers [2] are GPU -accelerated.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.11174", "content": "For instance, Meta built one of the fastest supercomputers based on Nvidia Ampere architecture GPUs ( A100 ) [1], and they are extending it to be the most powerful supercomputer in the world by mid-2022. Besides, tens of the top500 supercomputers [2] are GPU -accelerated."}
diff --git a/data/sampled_jsons/Archetypal_Analysis_Cutler_Breiman_1994_abstract_represents_each_individual.jsonl b/data/sampled_jsons/Archetypal_Analysis_Cutler_Breiman_1994_abstract_represents_each_individual.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9950976e1fe3e62c92634814bd1213dab24029f5
--- /dev/null
+++ b/data/sampled_jsons/Archetypal_Analysis_Cutler_Breiman_1994_abstract_represents_each_individual.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal Analysis : Technometrics: Vol 36, No 4", "date": "", "ddg_snippet": "Abstract . Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are restricted to being mixtures of the individuals in the data set.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/abs/10.1080/00401706.1994.10485840", "content": "Abstract . Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are restricted to being mixtures of the individuals in the data set."}
+{"idx": 1, "title": "Archetypal Analysis on JSTOR", "date": "", "ddg_snippet": "Archetypal Analysis . Adele Cutler and Leo Breiman . Abstract . Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are restricted to being mixtures of the individuals in the data set.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/1269949", "content": "Archetypal Analysis . Adele Cutler and Leo Breiman . Abstract . Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are restricted to being mixtures of the individuals in the data set."}
+{"idx": 2, "title": "Archetypal Analysis for population genetics - PLOS", "date": "", "ddg_snippet": "Archetypal Analysis . This non-negative matrix factorization method was first developed by Cutler and Breiman in 1994 [8], and here it represents each individual as a convex combination of extreme points, or archetypes, in allele frequency space.", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010301", "content": "Archetypal Analysis . This non-negative matrix factorization method was first developed by Cutler and Breiman in 1994 [8], and here it represents each individual as a convex combination of extreme points, or archetypes, in allele frequency space."}
+{"idx": 3, "title": "Archetypal Analysis - 百度学术", "date": "", "ddg_snippet": "Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are restricted to being mixtures of the individuals in the data set.", "subpage_snippet": "", "source": "xueshu.baidu.com", "link": "https://xueshu.baidu.com/usercenter/paper/show?paperid=57221765c36c109ac93b208854f031fd", "content": "Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are restricted to being mixtures of the individuals in the data set."}
+{"idx": 4, "title": "(PDF) Archetypal Analysis for Population Genetics - ResearchGate", "date": "", "ddg_snippet": "Archetypal Analysis 55 This non-negative matrix factorization method was first developed by Cutler and 56 Breiman in 1994 [8], and here it represents each individual as a con vex combination of 57", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/356630411_Archetypal_Analysis_for_Population_Genetics", "content": "Archetypal Analysis 55 This non-negative matrix factorization method was first developed by Cutler and 56 Breiman in 1994 [8], and here it represents each individual as a con vex combination of 57"}
+{"idx": 5, "title": "Archetypal analysis for machine learning and data mining", "date": "", "ddg_snippet": "Archetypal analysis (aa) proposed by Cutler and Breiman ( 1994 ) [7] estimates the principal convex hull (pch) of a data set. As such aa favors features that constitute representative ‘corners’ of the data, i.e., distinct aspects or archetypes.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/257351595_Archetypal_analysis_for_machine_learning_and_data_mining", "content": "Archetypal analysis (aa) proposed by Cutler and Breiman ( 1994 ) [7] estimates the principal convex hull (pch) of a data set. As such aa favors features that constitute representative ‘corners’ of the data, i.e., distinct aspects or archetypes."}
+{"idx": 6, "title": "Archetypal Analysis for Population Genetics | bioRxiv", "date": "", "ddg_snippet": "Archetypal Analysis . This non-negative matrix factorization method was first developed by Cutler and Breiman in 1994 [ 8 ], and here it represents each individual as a convex combination of extreme points , or archetypes, in allele frequency space.", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2021.11.28.470296v1.full", "content": "Archetypal Analysis . This non-negative matrix factorization method was first developed by Cutler and Breiman in 1994 [ 8 ], and here it represents each individual as a convex combination of extreme points , or archetypes, in allele frequency space."}
+{"idx": 7, "title": "[astro-ph/0301491] Archetypal analysis of galaxy spectra", "date": "", "ddg_snippet": "Abstract : Archetypal analysis represents each individual member of a set of data vectors as a mixture (a constrained linear combination) of the pure types or archetypes of the data set.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/astro-ph/0301491", "content": "Abstract : Archetypal analysis represents each individual member of a set of data vectors as a mixture (a constrained linear combination) of the pure types or archetypes of the data set."}
+{"idx": 8, "title": "Archetypal analysis for machine learning and data... | Semantic Scholar", "date": "", "ddg_snippet": "ABSTRACT Archetypal analysis represents each individual member of a set of data vectors as a mixture (aconstrained linear combination)of the pure types or archetypes of the data set.Save. Introduction to archetypal analysis of spatio-temporal dynamics. E. StoneAdele Cutler .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Archetypal-analysis-for-machine-learning-and-data-Mørup-Hansen/e7b677c6a4595514f41f317484150793b5f75a89", "content": "ABSTRACT Archetypal analysis represents each individual member of a set of data vectors as a mixture (aconstrained linear combination)of the pure types or archetypes of the data set.Save. Introduction to archetypal analysis of spatio-temporal dynamics. E. StoneAdele Cutler ."}
+{"idx": 9, "title": "Archetype Analysis of golden eagle migration patterns using Bayesian", "date": "", "ddg_snippet": "AA was first introduced by Cutler and Breiman ( 1994 ); they proposed an approach that would characterize the “ archetypal patterns” in a data set. Their first example was a question of how many sizes were needed to fit all Swiss soldiers faces in face masks.", "subpage_snippet": "", "source": "gradschool.psu.edu", "link": "https://gradschool.psu.edu/assets/uploads/mcnairJournals/Arbelaez.pdf", "content": "AA was first introduced by Cutler and Breiman ( 1994 ); they proposed an approach that would characterize the “ archetypal patterns” in a data set. Their first example was a question of how many sizes were needed to fit all Swiss soldiers faces in face masks."}
diff --git a/data/sampled_jsons/Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_arxiv.jsonl b/data/sampled_jsons/Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d209bc612cbbfb2565cebf29301952ef64e9fff7
--- /dev/null
+++ b/data/sampled_jsons/Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.12892] Archetypal SAE : Adaptive and Stable Dictionary ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models, by Thomas Fel and 9 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "View a PDF of the paper titled Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models, by Thomas Fel and 9 other authors."}
+{"idx": 1, "title": "Archetypal SAE : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "Understanding Learning Dynamics of Neural Representations via Feature Visualization at Scale.Publication. ICML 2025 ( arXiv :2502.12892).", "subpage_snippet": "", "source": "animadversio.github.io", "link": "https://animadversio.github.io/publication/fel-2025-archetypal/", "content": "Understanding Learning Dynamics of Neural Representations via Feature Visualization at Scale.Publication. ICML 2025 ( arXiv :2502.12892)."}
+{"idx": 2, "title": "An Introduction to SAEs and their Variants for Mech Interp — LessWrong", "date": "", "ddg_snippet": "Introduced in Feb 2025, Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models. dictionary _ learning - this library builds SAEs on nnsight models. It supports a bit wider feature set, but isn't as ergonomic to use.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/vvvJNh8gNE2ScEtQ3/an-introduction-to-saes-and-their-variants-for-mech-interp", "content": "Introduced in Feb 2025, Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models. dictionary _ learning - this library builds SAEs on nnsight models. It supports a bit wider feature set, but isn't as ergonomic to use."}
+{"idx": 3, "title": "Archetypal SAE : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/archetypal-sae-adaptive-and-stable-dictionary", "content": "Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts."}
+{"idx": 4, "title": "Archetypal SAE : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "These approaches build upon archetypal analysis to enhance stability and consistency in concept extraction. The A- SAE model constrains each dictionary atom to reside strictly within the convex hull of the training data, which imposes a geometric constraint that improves stability across...", "subpage_snippet": "", "source": "www.techaiapp.com", "link": "https://www.techaiapp.com/tech-ai-app/archetypal-sae-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "These approaches build upon archetypal analysis to enhance stability and consistency in concept extraction. The A- SAE model constrains each dictionary atom to reside strictly within the convex hull of the training data, which imposes a geometric constraint that improves stability across..."}
+{"idx": 5, "title": "An Introduction to SAEs and their Variants for Mech Interp", "date": "", "ddg_snippet": "Introduced in Feb 2025, Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models. dictionary _ learning —this library builds SAEs on nnsight models.", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/vvvJNh8gNE2ScEtQ3/an-introduction-to-saes-and-their-variants-for-mech-interp", "content": "Introduced in Feb 2025, Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models. dictionary _ learning —this library builds SAEs on nnsight models."}
+{"idx": 6, "title": "Archetypal SAE : Enhancing Stability in Concept Extraction for Vision...", "date": "", "ddg_snippet": "A- SAE and RA- SAE provide a robust framework for dictionary learning and concept extraction in large-scale vision models.", "subpage_snippet": "", "source": "itinai.com", "link": "https://itinai.com/archetypal-sae-enhancing-stability-in-concept-extraction-for-vision-models/", "content": "A- SAE and RA- SAE provide a robust framework for dictionary learning and concept extraction in large-scale vision models."}
+{"idx": 7, "title": "NTT Advances AI Accuracy, Security & Cost at ICML 2025", "date": "", "ddg_snippet": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models.", "subpage_snippet": "", "source": "ai-techpark.com", "link": "https://ai-techpark.com/ntt-advances-ai-accuracy-security-cost-at-icml-2025/", "content": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models."}
+{"idx": 8, "title": "GitHub - KempnerInstitute/overcomplete: Overcomplete is...", "date": "", "ddg_snippet": "@article{fel2025 archetypal , title = { Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models}, author = {Fel, Thomas and Lubana, Ekdeep Singh and Prince, Jacob S. and Kowal, Matthew and Boutin, Victor and Papadimitriou...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KempnerInstitute/overcomplete", "content": "@article{fel2025 archetypal , title = { Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models}, author = {Fel, Thomas and Lubana, Ekdeep Singh and Prince, Jacob S. and Kowal, Matthew and Boutin, Victor and Papadimitriou..."}
+{"idx": 9, "title": "Isabel Papadimitriou - Google Scholar", "date": "", "ddg_snippet": "I Papadimitriou, D Jurafsky. arXiv preprint arXiv :2004.14601, 2020. Archetypal sae : Adaptive and stable dictionary learning for concept extraction in large vision models.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=eA8GuO4AAAAJ&hl=en", "content": "I Papadimitriou, D Jurafsky. arXiv preprint arXiv :2004.14601, 2020. Archetypal sae : Adaptive and stable dictionary learning for concept extraction in large vision models."}
diff --git a/data/sampled_jsons/Archetypal_SAE_stability_equation_2.jsonl b/data/sampled_jsons/Archetypal_SAE_stability_equation_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ca79bc37b1fc39ed885222212c544b0c8a6d9845
--- /dev/null
+++ b/data/sampled_jsons/Archetypal_SAE_stability_equation_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2502.12892: Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "Abstract page for arXiv paper 2502.12892: Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models"}
+{"idx": 1, "title": "Archetypal SAEs: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability . A relaxed variant (RA- SAE ) allows mild relaxation, matching standard SAEs in reconstruction while maintaining stability . Both integrate with any SAE variant (e.g., TopK, JumpReLU). B) Instability Problem.", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability . A relaxed variant (RA- SAE ) allows mild relaxation, matching standard SAEs in reconstruction while maintaining stability . Both integrate with any SAE variant (e.g., TopK, JumpReLU). B) Instability Problem."}
+{"idx": 2, "title": "Archetypal - Overcomplete", "date": "", "ddg_snippet": "Archetypal SAE introduces a constraint on the dictionary where each atom is formed as a convex combination of data points with an additional relaxation term. This method enhances stability and interpretability in dictionary learning, making it a robust drop-in replacement for the dictionary layer in any Sparse Autoencoder.", "subpage_snippet": "", "source": "kempnerinstitute.github.io", "link": "https://kempnerinstitute.github.io/overcomplete/saes/archetypal/", "content": "Archetypal SAE introduces a constraint on the dictionary where each atom is formed as a convex combination of data points with an additional relaxation term. This method enhances stability and interpretability in dictionary learning, making it a robust drop-in replacement for the dictionary layer in any Sparse Autoencoder."}
+{"idx": 3, "title": "Archetypal SAEs: Adaptive and Stable Dictionary Learning for ... - Medium", "date": "", "ddg_snippet": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@kempnerinstitute/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-acf95010c691", "content": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability ."}
+{"idx": 4, "title": "An Introduction to SAEs and their Variants for Mech", "date": "", "ddg_snippet": "Archetypal SAE constrains the SAE features to the convex hull of input activations. This improves stability - the similarity of found sae features between independently trained runs.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/vvvJNh8gNE2ScEtQ3/an-introduction-to-saes-and-their-variants-for-mech-interp", "content": "Archetypal SAE constrains the SAE features to the convex hull of input activations. This improves stability - the similarity of found sae features between independently trained runs."}
+{"idx": 5, "title": "Archetypal SAE: Variable and stable methods to learn the Temple ...", "date": "", "ddg_snippet": "A-sae model presses the atom of each dictionary to resolve the inside of the Convex Hull for the training data, which puts geometric hardship that promotes the intensity of different training.", "subpage_snippet": "", "source": "dataforcee.us", "link": "https://dataforcee.us/2025/03/17/archetypal-sae-variable-and-stable-methods-to-learn-the-temple-dictionary-in-the-largest-visual-models/", "content": "A-sae model presses the atom of each dictionary to resolve the inside of the Convex Hull for the training data, which puts geometric hardship that promotes the intensity of different training."}
+{"idx": 6, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "Researchers from Harvard University, York University, CNRS, and Google DeepMind have proposed two novel variants of Sparse Autoencoders to address the instability issues: Archetypal-SAE (A-SAE) and its relaxed counterpart (RA- SAE ). These approaches build upon archetypal analysis to enhance stability and consistency in concept extraction.", "subpage_snippet": "", "source": "phdstudio.org", "link": "https://phdstudio.org/2025/03/17/archetypal-sae-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models-sajjad-ansari-artificial-intelligence-category-marktechpost/", "content": "Researchers from Harvard University, York University, CNRS, and Google DeepMind have proposed two novel variants of Sparse Autoencoders to address the instability issues: Archetypal-SAE (A-SAE) and its relaxed counterpart (RA- SAE ). These approaches build upon archetypal analysis to enhance stability and consistency in concept extraction."}
+{"idx": 7, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "2 . A-SAE: Archetypal anchoring to overcome instability. To address the challenge above, we take inspiration from Cutler & Breiman (1994) 's Archetypal analysis of dictionary learning and propose A-SAE, an SAE paradigm wherein the dictionary atoms (decoder directions) are forced to lie in the convex hull of sample representations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v1", "content": "2 . A-SAE: Archetypal anchoring to overcome instability. To address the challenge above, we take inspiration from Cutler & Breiman (1994) 's Archetypal analysis of dictionary learning and propose A-SAE, an SAE paradigm wherein the dictionary atoms (decoder directions) are forced to lie in the convex hull of sample representations."}
+{"idx": 8, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "2 . A-SAE: Archetypal anchoring to overcome insta- bility. To address the challenge above, we take in- spiration fromCutler & Breiman(1994)'s Archetypal analysis of dictionary learning and propose A-SAE, an SAE paradigm wherein the dictionary atoms (de- coder directions) are forced to lie in the convex hull of sample representations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.12892", "content": "2 . A-SAE: Archetypal anchoring to overcome insta- bility. To address the challenge above, we take in- spiration fromCutler & Breiman(1994)'s Archetypal analysis of dictionary learning and propose A-SAE, an SAE paradigm wherein the dictionary atoms (de- coder directions) are forced to lie in the convex hull of sample representations."}
+{"idx": 9, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A-SAE), wherein dictionary atoms are constrained to the convex hull of data.", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2502.12892", "content": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A-SAE), wherein dictionary atoms are constrained to the convex hull of data."}
diff --git a/data/sampled_jsons/Archetypal_SAE_stability_metric_0.95_0.85_RA-SAE_TopK_SAE_difference.jsonl b/data/sampled_jsons/Archetypal_SAE_stability_metric_0.95_0.85_RA-SAE_TopK_SAE_difference.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2779afbd9019d6cd3d10ed3a53a5445162e8dffe
--- /dev/null
+++ b/data/sampled_jsons/Archetypal_SAE_stability_metric_0.95_0.85_RA-SAE_TopK_SAE_difference.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal SAE : Adaptive and Stable Dictionary... - MarkTechPost", "date": "", "ddg_snippet": "Moreover, RA - SAE builds upon a TopK SAE architecture to maintain consistent sparsity levels across experiments. The computation of a matrix involves K-Means clustering of the entire dataset into 32,000 centroids.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/03/16/archetypal-sae-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "Moreover, RA - SAE builds upon a TopK SAE architecture to maintain consistent sparsity levels across experiments. The computation of a matrix involves K-Means clustering of the entire dataset into 32,000 centroids."}
+{"idx": 1, "title": "Archetypal SAEs : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "A relaxed variant ( RA - SAE ) allows mild relaxation, matching standard SAEs in reconstruction while maintaining stability . Both integrate with any SAE variant (e.g., TopK , JumpReLU).", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "A relaxed variant ( RA - SAE ) allows mild relaxation, matching standard SAEs in reconstruction while maintaining stability . Both integrate with any SAE variant (e.g., TopK , JumpReLU)."}
+{"idx": 2, "title": "Archetypal SAE : Adaptive and Stable Dictionary Learning... | alphaXiv", "date": "", "ddg_snippet": "The proposed Archetypal SAE (A- SAE ) incorporates the geometric constraint from AA into the SAE framework.R2 scores: RA - SAE 89.34% vs. TopK SAE 89.52%). Enhanced Concept Quality: RA - SAE significantly outperforms baselines on both novel benchmarks", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.12892v2", "content": "The proposed Archetypal SAE (A- SAE ) incorporates the geometric constraint from AA into the SAE framework.R2 scores: RA - SAE 89.34% vs. TopK SAE 89.52%). Enhanced Concept Quality: RA - SAE significantly outperforms baselines on both novel benchmarks"}
+{"idx": 3, "title": "Archetypal SAE : Adaptive and Secure Dictionary... - I Tech Epic", "date": "", "ddg_snippet": "Furthermore, RA - SAE builds upon a TopK SAE structure to keep up constant sparsity ranges throughout experiments.", "subpage_snippet": "", "source": "itechepic.com", "link": "https://itechepic.com/archetypal-sae-adaptive-and-secure-dictionary-studying-for-idea-extraction-in-massive-imaginative-and-prescient-fashions/", "content": "Furthermore, RA - SAE builds upon a TopK SAE structure to keep up constant sparsity ranges throughout experiments."}
+{"idx": 4, "title": "BatchTopK: A Simple Improvement for TopK - SAEs", "date": "", "ddg_snippet": "Standard TopK SAEs apply the TopK operation independently to each sample in a batch. For a target sparsity of K, this means exactly K features are activated for every sample.For both the TopK and the BatchTopK SAEs we train a sweep with the following hyperparameters", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/Nkx6yWZNbAsfvic98/batchtopk-a-simple-improvement-for-topk-saes", "content": "Standard TopK SAEs apply the TopK operation independently to each sample in a batch. For a target sparsity of K, this means exactly K features are activated for every sample.For both the TopK and the BatchTopK SAEs we train a sweep with the following hyperparameters"}
+{"idx": 5, "title": "(PDF) Archetypal SAE : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "SAE TopK SAE Jump SAE SNMF CNMF RA - SAE .For instance, RA - SAE successfully. identifies separate concepts for rabbit ears, body, face, and paws, demonstrating its ability to disentangle fine-grained.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389129606_Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_for_Concept_Extraction_in_Large_Vision_Models", "content": "SAE TopK SAE Jump SAE SNMF CNMF RA - SAE .For instance, RA - SAE successfully. identifies separate concepts for rabbit ears, body, face, and paws, demonstrating its ability to disentangle fine-grained."}
+{"idx": 6, "title": "Archetypal SAE : Enhancing Stability in Concept Extraction for Vision...", "date": "", "ddg_snippet": "A- SAE and RA - SAE provide a robust framework for dictionary learning and concept extraction in large-scale vision models. Their development opens avenues for reliable concept discovery across various data modalities, including large language models (LLMs).", "subpage_snippet": "", "source": "itinai.com", "link": "https://itinai.com/archetypal-sae-enhancing-stability-in-concept-extraction-for-vision-models/", "content": "A- SAE and RA - SAE provide a robust framework for dictionary learning and concept extraction in large-scale vision models. Their development opens avenues for reliable concept discovery across various data modalities, including large language models (LLMs)."}
+{"idx": 7, "title": "3/16\" to mm - SAE to Metric Calculator", "date": "", "ddg_snippet": "How to Convert SAE to Metric . SAE , or standard, and metric sockets, wrenches, and fasteners are both commonly used, but the difference between them is significant.", "subpage_snippet": "", "source": "www.inchcalculator.com", "link": "https://www.inchcalculator.com/sae-to-metric-calculator/?sae=3/16", "content": "How to Convert SAE to Metric . SAE , or standard, and metric sockets, wrenches, and fasteners are both commonly used, but the difference between them is significant."}
+{"idx": 8, "title": "ТОТЕК™ | Классификация Масел по SAE - ТОТЕК-Ярославль", "date": "", "ddg_snippet": "Общепринятая международная система классификации по SAE - Society of Automotive Engineers , или Сообщества автомобильных инженеров.Летние маркируются просто одним числом после SAE - от SAE 20 до SAE 60.", "subpage_snippet": "", "source": "totek76.ru", "link": "https://totek76.ru/totek-yaroslavl-informaciya/sae-klassifikaciya-masel/", "content": "Общепринятая международная система классификации по SAE - Society of Automotive Engineers , или Сообщества автомобильных инженеров.Летние маркируются просто одним числом после SAE - от SAE 20 до SAE 60."}
+{"idx": 9, "title": "What’s the Difference SAE & Metric - PEHEL Hydraulic", "date": "", "ddg_snippet": "Metric and SAE wrenches have different systems of measurement. Metric sockets and wrenches use the metric measurement system. This is when millimeters are used to describe the size.", "subpage_snippet": "", "source": "www.pehelhydraulic.com", "link": "https://www.pehelhydraulic.com/what-difference-sae-metric.html", "content": "Metric and SAE wrenches have different systems of measurement. Metric sockets and wrenches use the metric measurement system. This is when millimeters are used to describe the size."}
diff --git a/data/sampled_jsons/Are_Language_Models_Actually_Useful_for_Time_Series_Forecasting_Table_3_OneFitsAll_year_2023.jsonl b/data/sampled_jsons/Are_Language_Models_Actually_Useful_for_Time_Series_Forecasting_Table_3_OneFitsAll_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f9336d8be34a5c7c68db94d9c89a5a3c1de4108f
--- /dev/null
+++ b/data/sampled_jsons/Are_Language_Models_Actually_Useful_for_Time_Series_Forecasting_Table_3_OneFitsAll_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "... are language models actually useful for time series ? In a series of ablation studies on three recent and popular LLM-based time series forecasting ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.16964v2", "content": "... are language models actually useful for time series ? In a series of ablation studies on three recent and popular LLM-based time series forecasting ..."}
+{"idx": 1, "title": "Are Language Models Actually Useful for Time Series Forecasting ?", "date": "", "ddg_snippet": "Table 2: Three popular methods for time series forecasting with Large Language Models .- Table 3 : Forecasting performance of all models – Time -LLM, LLaTA, and OneFitsAll and results from our ablations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.16964v1", "content": "Table 2: Three popular methods for time series forecasting with Large Language Models .- Table 3 : Forecasting performance of all models – Time -LLM, LLaTA, and OneFitsAll and results from our ablations."}
+{"idx": 2, "title": "Are Language Models Actually Useful for Time Series Forecasting ?", "date": "", "ddg_snippet": "Figure (a) represents time series forecasting using an LLM as the base model . In some works, the LLM components are frozen, while in others, they undergo fine-tuning. Figure (b) shows the model with the LLM components removed, retaining only the remaining structure.", "subpage_snippet": "", "source": "pub.towardsai.net", "link": "https://pub.towardsai.net/are-language-models-actually-useful-for-time-series-forecasting-81a099415702", "content": "Figure (a) represents time series forecasting using an LLM as the base model . In some works, the LLM components are frozen, while in others, they undergo fine-tuning. Figure (b) shows the model with the LLM components removed, retaining only the remaining structure."}
+{"idx": 3, "title": "Are Language Models Actually Useful for Time Series Forecasting ?", "date": "", "ddg_snippet": "Time -llm: Time series forecasting by reprogramming large language models . In ICLR, 2024. One fits all: Power general time series analysis by pretrained lm. This paper, which introduced the OneFitsAll (or GPT4TS) model , is one of the three primary reference methods analyzed.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2406.16964v2", "content": "Time -llm: Time series forecasting by reprogramming large language models . In ICLR, 2024. One fits all: Power general time series analysis by pretrained lm. This paper, which introduced the OneFitsAll (or GPT4TS) model , is one of the three primary reference methods analyzed."}
+{"idx": 4, "title": "Are Language Models Actually Useful for Time Series Forecasting ?", "date": "", "ddg_snippet": "By systematically dismantling popular LLM-based time series forecasting models , this paper critically reassesses the role of LLMs in such contexts, highlighting simpler yet equally robust alternatives.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.16964", "content": "By systematically dismantling popular LLM-based time series forecasting models , this paper critically reassesses the role of LLMs in such contexts, highlighting simpler yet equally robust alternatives."}
+{"idx": 5, "title": "Are Language Models Actually Useful for Time Series Forecasting ?", "date": "", "ddg_snippet": "This table presents the forecasting performance results (MAE and MSE) for three popular LLM-based time series forecasting models ( Time -LLM, CALF, OneFitsAll ) and their corresponding ablation methods (without LLM, LLM replaced with attention, LLM replaced with transformer).", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/spotlight-others/dv15ubhcy1/", "content": "This table presents the forecasting performance results (MAE and MSE) for three popular LLM-based time series forecasting models ( Time -LLM, CALF, OneFitsAll ) and their corresponding ablation methods (without LLM, LLM replaced with attention, LLM replaced with transformer)."}
+{"idx": 6, "title": "GitHub - rikokir/llmsfortimeseries", "date": "", "ddg_snippet": "Are Language Models Actually Useful for Time Series Forecasting ?You might want to follow the corresponding repos, OneFitsAll , Time -LLM, and CALF, to set up the environment respectivly.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/rikokir/llmsfortimeseries", "content": "Are Language Models Actually Useful for Time Series Forecasting ?You might want to follow the corresponding repos, OneFitsAll , Time -LLM, and CALF, to set up the environment respectivly."}
+{"idx": 7, "title": "Are Language Models Actually Useful for Time Series Forecasting ?", "date": "", "ddg_snippet": "The findings of Tan et al. are a significant blow to the use of LLMs in time series forecasting , similar to the impact of the 2022 paper “ Are Transformers Effective for Time Series Forecasting ?” on transformers. The evidence presented is compelling and suggests that LLMs are ill-suited...", "subpage_snippet": "", "source": "www.rebellionresearch.com", "link": "https://www.rebellionresearch.com/are-language-models-actually-useful-for-time-series-forecasting-a-review", "content": "The findings of Tan et al. are a significant blow to the use of LLMs in time series forecasting , similar to the impact of the 2022 paper “ Are Transformers Effective for Time Series Forecasting ?” on transformers. The evidence presented is compelling and suggests that LLMs are ill-suited..."}
+{"idx": 8, "title": "Are Language Models Actually Useful for Time Series Forecasting ?", "date": "", "ddg_snippet": "Large language models (LLMs) are being applied to time series forecasting .Additionally, we explore time series encoders and find that patching and attention structures perform similarly to LLM-based forecasters .", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/DV15UbHCY1@OpenReview", "content": "Large language models (LLMs) are being applied to time series forecasting .Additionally, we explore time series encoders and find that patching and attention structures perform similarly to LLM-based forecasters ."}
+{"idx": 9, "title": "Are Language Models Actually Useful for Time Series Forecasting ?", "date": "", "ddg_snippet": "Monash time series forecasting archive. In Neural Information Processing Systems Track on Datasets and Benchmarks, 2021. Large language models are zero-shot time series forecasters .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381704515_Are_Language_Models_Actually_Useful_for_Time_Series_Forecasting", "content": "Monash time series forecasting archive. In Neural Information Processing Systems Track on Datasets and Benchmarks, 2021. Large language models are zero-shot time series forecasters ."}
diff --git a/data/sampled_jsons/As_machine_learning_based_methods_for_detection_and_tracking_become_more_prevalent_nuScenes_abstract_year_2020.jsonl b/data/sampled_jsons/As_machine_learning_based_methods_for_detection_and_tracking_become_more_prevalent_nuScenes_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f91dcfcdaea752c95cf36f8ce26c60a5c62dbfe
--- /dev/null
+++ b/data/sampled_jsons/As_machine_learning_based_methods_for_detection_and_tracking_become_more_prevalent_nuScenes_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1903.11027] nuScenes: A multimodal dataset for autonomous ... nuScenes: A Multimodal Dataset for Autonomous Driving GitHub - minwoo0611/Awesome-3D-LiDAR-Datasets: This ... Dynamic Clustering Transformer for LiDAR-Based 3D Object ... NVlabs/FocalFormer3D: Official PyTorch implementation of ... Abstract arXiv:1903.11027v2 [cs.LG] 3 Sep 2019 nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Mar 26, 2019 · Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment. Abstract : Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment. This repository is the collection of datasets, involving the 3D LiDAR. The information is presented in a comprehensive table, outlining the type and number of LiDARs, the purpose of each dataset, and scale details. The objectives are broadly categorized into Object Detection (OD), Segmentation (Seg), Odometry (Odom), Place Recognition (PR), and Loc... See full list on github.com Update: 2023-07-13 •The table includes the specific LiDAR products utilized in each dataset, involving the its channels or name. See full list on github.com 3 days ago · nuScenes . nuScenes [34] is a large autonomous driving dataset for 3D object detection in urban scenes. It comprises 1000 driving sequences, with an official split of 700, 150, and 150 scenes designated for training, validation, and testing respectively. 3D Object Detection (on nuScenes validation set) ... 3D Object Detection (on Waymo validation set) Since WOD does not allow distributing the pretrained weights, we only report the model results trained on the full training split and 20% training split of the Waymo dataset. The official metrics mAP/mAPH on the Waymo validation set are reported ... ras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent , there is a need to train and evaluate such methods on datasets Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "Mar 26, 2019 · Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment. Abstract : Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment. This repository is the collection of datasets, involving the 3D LiDAR. The information is presented in a comprehensive table, outlining the type and number of LiDARs, the purpose of each dataset, and scale details. The objectives are broadly categorized into Object Detection (OD), Segmentation (Seg), Odometry (Odom), Place Recognition (PR), and Loc... See full list on github.com Update: 2023-07-13 •The table includes the specific LiDAR products utilized in each dataset, involving the its channels or name. See full list on github.com 3 days ago · nuScenes . nuScenes [34] is a large autonomous driving dataset for 3D object detection in urban scenes. It comprises 1000 driving sequences, with an official split of 700, 150, and 150 scenes designated for training, validation, and testing respectively. 3D Object Detection (on nuScenes validation set) ... 3D Object Detection (on Waymo validation set) Since WOD does not allow distributing the pretrained weights, we only report the model results trained on the full training split and 20% training split of the Waymo dataset. The official metrics mAP/mAPH on the Waymo validation set are reported ... ras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent , there is a need to train and evaluate such methods on datasets Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment."}
+{"idx": 1, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.html", "content": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment."}
+{"idx": 2, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Abstract : Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9156412", "content": "Abstract : Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment."}
+{"idx": 3, "title": "GitHub - minwoo0611/Awesome-3D-LiDAR-Datasets: This ... Dynamic Clustering Transformer for LiDAR-Based 3D Object ... NVlabs/FocalFormer3D: Official PyTorch implementation of ... Abstract arXiv:1903.11027v2 [cs.LG] 3 Sep 2019 nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "This repository is the collection of datasets, involving the 3D LiDAR. The information is presented in a comprehensive table, outlining the type and number of LiDARs, the purpose of each dataset, and scale details. The objectives are broadly categorized into Object Detection (OD), Segmentation (Seg), Odometry (Odom), Place Recognition (PR), and Loc... See full list on github.com Update: 2023-07-13 •The table includes the specific LiDAR products utilized in each dataset, involving the its channels or name. See full list on github.com 3 days ago · nuScenes . nuScenes [34] is a large autonomous driving dataset for 3D object detection in urban scenes. It comprises 1000 driving sequences, with an official split of 700, 150, and 150 scenes designated for training, validation, and testing respectively. 3D Object Detection (on nuScenes validation set) ... 3D Object Detection (on Waymo validation set) Since WOD does not allow distributing the pretrained weights, we only report the model results trained on the full training split and 20% training split of the Waymo dataset. The official metrics mAP/mAPH on the Waymo validation set are reported ... ras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent , there is a need to train and evaluate such methods on datasets Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/minwoo0611/Awesome-3D-LiDAR-Datasets", "content": "This repository is the collection of datasets, involving the 3D LiDAR. The information is presented in a comprehensive table, outlining the type and number of LiDARs, the purpose of each dataset, and scale details. The objectives are broadly categorized into Object Detection (OD), Segmentation (Seg), Odometry (Odom), Place Recognition (PR), and Loc... See full list on github.com Update: 2023-07-13 •The table includes the specific LiDAR products utilized in each dataset, involving the its channels or name. See full list on github.com 3 days ago · nuScenes . nuScenes [34] is a large autonomous driving dataset for 3D object detection in urban scenes. It comprises 1000 driving sequences, with an official split of 700, 150, and 150 scenes designated for training, validation, and testing respectively. 3D Object Detection (on nuScenes validation set) ... 3D Object Detection (on Waymo validation set) Since WOD does not allow distributing the pretrained weights, we only report the model results trained on the full training split and 20% training split of the Waymo dataset. The official metrics mAP/mAPH on the Waymo validation set are reported ... ras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent , there is a need to train and evaluate such methods on datasets Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection , tracking and segmentation of agents in the environment."}
+{"idx": 4, "title": "Dynamic Clustering Transformer for LiDAR-Based 3D Object ...", "date": "", "ddg_snippet": "3 days ago · nuScenes . nuScenes [34] is a large autonomous driving dataset for 3D object detection in urban scenes. It comprises 1000 driving sequences, with an official split of 700, 150, and 150 scenes designated for training, validation, and testing respectively.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320325011069", "content": "3 days ago · nuScenes . nuScenes [34] is a large autonomous driving dataset for 3D object detection in urban scenes. It comprises 1000 driving sequences, with an official split of 700, 150, and 150 scenes designated for training, validation, and testing respectively."}
+{"idx": 5, "title": "NVlabs/FocalFormer3D: Official PyTorch implementation of ...", "date": "", "ddg_snippet": "3D Object Detection (on nuScenes validation set) ... 3D Object Detection (on Waymo validation set) Since WOD does not allow distributing the pretrained weights, we only report the model results trained on the full training split and 20% training split of the Waymo dataset. The official metrics mAP/mAPH on the Waymo validation set are reported ...", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/NVlabs/FocalFormer3D", "content": "3D Object Detection (on nuScenes validation set) ... 3D Object Detection (on Waymo validation set) Since WOD does not allow distributing the pretrained weights, we only report the model results trained on the full training split and 20% training split of the Waymo dataset. The official metrics mAP/mAPH on the Waymo validation set are reported ..."}
+{"idx": 6, "title": "Abstract arXiv:1903.11027v2 [cs.LG] 3 Sep 2019", "date": "", "ddg_snippet": "ras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent , there is a need to train and evaluate such methods on datasets", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v2", "content": "ras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent , there is a need to train and evaluate such methods on datasets"}
+{"idx": 7, "title": "nuScenes: A multimodal dataset for autonomous driving | Papers", "date": "", "ddg_snippet": "As machine learning based methods for detection and tracking become more prevalent , there is a need to train and evaluate such methods on datasets ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/nuscenes-a-multimodal-dataset-for-autonomous", "content": "As machine learning based methods for detection and tracking become more prevalent , there is a need to train and evaluate such methods on datasets ..."}
+{"idx": 8, "title": "Target image detection algorithm of complex road scene based on", "date": "", "ddg_snippet": "Image detection algorithms are of the utmost importance in improving smart transportation and advanced driver assistance systems (ADAS) [ 1 ].", "subpage_snippet": "", "source": "www.ijsmdo.org", "link": "https://www.ijsmdo.org/articles/smdo/full_html/2025/01/smdo240174/smdo240174.html", "content": "Image detection algorithms are of the utmost importance in improving smart transportation and advanced driver assistance systems (ADAS) [ 1 ]."}
+{"idx": 9, "title": "Radar and Camera Fusion for Object Detection and Tracking: A", "date": "", "ddg_snippet": "For intuitiveness, a performance comparison of common sensors in the realm of object detection and tracking is listed in Table I .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.19872v1", "content": "For intuitiveness, a performance comparison of common sensors in the realm of object detection and tracking is listed in Table I ."}
diff --git "a/data/sampled_jsons/Assumption_4.1_Var[\316\267_t^2F_t]_\342\211\244_c_\316\267__Var[\316\267_tF_t]_contextual_bandits.jsonl" "b/data/sampled_jsons/Assumption_4.1_Var[\316\267_t^2F_t]_\342\211\244_c_\316\267__Var[\316\267_tF_t]_contextual_bandits.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..1ab6249ce4bb22e9bbb75185cf962f42c11dffec
--- /dev/null
+++ "b/data/sampled_jsons/Assumption_4.1_Var[\316\267_t^2F_t]_\342\211\244_c_\316\267__Var[\316\267_tF_t]_contextual_bandits.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Inference for Misspecified Contextual Bandits", "date": "", "ddg_snippet": "In this paper, we iden-tify an important and previously unnoticed issue: popular contextual bandit algorithms, such as LinUCB, can become unstable when their underlying models are misspecified.", "subpage_snippet": "", "source": "zipingxu.github.io", "link": "https://zipingxu.github.io/assets/Statistical_Inference_for_Misspecified_Contextual_Bandits-2.pdf", "content": "In this paper, we iden-tify an important and previously unnoticed issue: popular contextual bandit algorithms, such as LinUCB, can become unstable when their underlying models are misspecified."}
+{"idx": 1, "title": "Single Index Bandits: Generalized Linear Contextual Bandits ...", "date": "", "ddg_snippet": "Contextual Bandits under the Realizability Assumption : Single Index Models: 3 Preliminaries. 4 Methods. 4 . 1 Single Index Model Estimator. 4.2 Warm-up: Simple Algorithm Agnostic to Increasing Reward Functions. 4.3 Improved Algorithm Agnostic to Increasing Reward Functions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.12751v1", "content": "Contextual Bandits under the Realizability Assumption : Single Index Models: 3 Preliminaries. 4 Methods. 4 . 1 Single Index Model Estimator. 4.2 Warm-up: Simple Algorithm Agnostic to Increasing Reward Functions. 4.3 Improved Algorithm Agnostic to Increasing Reward Functions."}
+{"idx": 2, "title": "Action Centered Contextual Bandits - PMC", "date": "", "ddg_snippet": "We provide an extension of the linear model for contextual bandits that has two parts: baseline reward and treatment effect. We allow the former to be complex but keep the latter simple. We argue that this model is plausible for mobile health applications.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5719505/", "content": "We provide an extension of the linear model for contextual bandits that has two parts: baseline reward and treatment effect. We allow the former to be complex but keep the latter simple. We argue that this model is plausible for mobile health applications."}
+{"idx": 3, "title": "An Overview of Contextual Bandits - Towards Data Science", "date": "", "ddg_snippet": "Feb 2 , 2024 · To demonstrate contextual bandits in action, I put together a notebook that generates a simulated dataset and compares the cumulative y (or \"reward\") estimates for new A/B, MAB, and CB policies evaluated on this dataset.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/an-overview-of-contextual-bandits-53ac3aa45034/", "content": "Feb 2 , 2024 · To demonstrate contextual bandits in action, I put together a notebook that generates a simulated dataset and compares the cumulative y (or \"reward\") estimates for new A/B, MAB, and CB policies evaluated on this dataset."}
+{"idx": 4, "title": "Contextual Bandits", "date": "", "ddg_snippet": "Recall: Contextual bandit environment Context at time t encoded into a variable xt that we see before choosing our action xt is drawn i.i.d. at each time point from a distribution νx on sample space xt", "subpage_snippet": "", "source": "lucasjanson.fas.harvard.edu", "link": "https://lucasjanson.fas.harvard.edu/courses/22.pdf", "content": "Recall: Contextual bandit environment Context at time t encoded into a variable xt that we see before choosing our action xt is drawn i.i.d. at each time point from a distribution νx on sample space xt"}
+{"idx": 5, "title": "Contextual Bandits with Continuous Actions: Smoothing ...", "date": "", "ddg_snippet": "We study contextual bandit learning with an abstract policy class and continuous action space. We obtain two qualitatively di erent regret bounds: one competes with a smoothed version of the policy class under no continuity assumptions , while the other requires standard Lipschitz assumptions .", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume21/19-650/19-650.pdf", "content": "We study contextual bandit learning with an abstract policy class and continuous action space. We obtain two qualitatively di erent regret bounds: one competes with a smoothed version of the policy class under no continuity assumptions , while the other requires standard Lipschitz assumptions ."}
+{"idx": 6, "title": "contextual bandits - arXiv.org", "date": "", "ddg_snippet": "contextual regret. We prove a static-to- contextual regret conversion theorem that provides an upper bound for the contextual regret of the output algorithm as a function of the static regret of t.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.05714v5", "content": "contextual regret. We prove a static-to- contextual regret conversion theorem that provides an upper bound for the contextual regret of the output algorithm as a function of the static regret of t."}
+{"idx": 7, "title": "How Does Variance Shape the Regret", "date": "", "ddg_snippet": "Input: Parameter η . Contextual Bandit Oracle gives function class Ft ⊂ F , action class At and. context xt at round t . Contextual Bandit algorithm which takes online regression oracle and.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/9861a7c3972ed5d36dda3826d44bb246-Paper-Conference.pdf", "content": "Input: Parameter η . Contextual Bandit Oracle gives function class Ft ⊂ F , action class At and. context xt at round t . Contextual Bandit algorithm which takes online regression oracle and."}
+{"idx": 8, "title": "Online Learning in Contextual", "date": "", "ddg_snippet": "Algorithm 1 Exponential Weights for Contextual Auction. Input: learning rate η > 0 and CTR predictor class F .Sample a function ft from qt, a distribution over F defined via qt, f ∝ exp(− η sℓs, f ). Receive context xt and the set of Nt bidders.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.05047", "content": "Algorithm 1 Exponential Weights for Contextual Auction. Input: learning rate η > 0 and CTR predictor class F .Sample a function ft from qt, a distribution over F defined via qt, f ∝ exp(− η sℓs, f ). Receive context xt and the set of Nt bidders."}
+{"idx": 9, "title": "Multi-task Representation Learning with Stochastic Linear Bandits", "date": "", "ddg_snippet": "Assumption 4 (subGaussian noise). The noise variables ( ηt ,n) t ,n are a sequence of sub-Gaussian random variables adapted to the ltration { F n}n≥0 and such that for any 1 ≤ t ≤ T and n ≥ 1. Set = 1/4. A simple union bound combining (15) with the last two displays gives.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04555076/document", "content": "Assumption 4 (subGaussian noise). The noise variables ( ηt ,n) t ,n are a sequence of sub-Gaussian random variables adapted to the ltration { F n}n≥0 and such that for any 1 ≤ t ≤ T and n ≥ 1. Set = 1/4. A simple union bound combining (15) with the last two displays gives."}
diff --git a/data/sampled_jsons/Attention_Is_All_You_Need_Vaswani_2017_abstract.jsonl b/data/sampled_jsons/Attention_Is_All_You_Need_Vaswani_2017_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..58ddefcda94626501a2c27a49e4868f944f0bc71
--- /dev/null
+++ b/data/sampled_jsons/Attention_Is_All_You_Need_Vaswani_2017_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "\" Attention Is All You Need \" is a 2017 landmark research paper in machine learning authored by eight scientists working at Google. The paper introduced a new ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Attention_Is_All_You_Need", "content": "\" Attention Is All You Need \" is a 2017 landmark research paper in machine learning authored by eight scientists working at Google. The paper introduced a new ..."}
+{"idx": 1, "title": "[1706.03762] Attention Is All You Need", "date": "", "ddg_snippet": "12 Jun 2017 — We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "12 Jun 2017 — We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely."}
+{"idx": 2, "title": "Attention is All you Need", "date": "", "ddg_snippet": "by A Vaswani · Cited by 195236 — In the following sections, we will describe the Transformer, motivate self- attention and discuss its advantages over models such as [14, 15] and [8]. 3 Model ... 11 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/7181-attention-is-all-you-need.pdf", "content": "by A Vaswani · Cited by 195236 — In the following sections, we will describe the Transformer, motivate self- attention and discuss its advantages over models such as [14, 15] and [8]. 3 Model ... 11 pages"}
+{"idx": 3, "title": "Attention is All you Need", "date": "", "ddg_snippet": "by A Vaswani · 2017 · Cited by 195236 — We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/7181-attention-is-all-you-need", "content": "by A Vaswani · 2017 · Cited by 195236 — We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely."}
+{"idx": 4, "title": "(PDF) Attention is All you Need (2017) | Ashish Vaswani", "date": "", "ddg_snippet": "TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/attention-is-all-you-need-1hodz0wcqb", "content": "TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and ..."}
+{"idx": 5, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1706.03762v7", "content": "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely."}
+{"idx": 6, "title": "Attention is all you need | Proceedings of the 31st ...", "date": "", "ddg_snippet": "by A Vaswani · 2017 · Cited by 195236 — We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3295222.3295349", "content": "by A Vaswani · 2017 · Cited by 195236 — We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely."}
+{"idx": 7, "title": "Vaswani, A., et al. (2017) Attention Is All You Need. ...", "date": "", "ddg_snippet": "In this paper, we have proposed an effective stacked convolutional auto-encoder that integrates a selective kernel attention mechanism for image classification.", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=3964316", "content": "In this paper, we have proposed an effective stacked convolutional auto-encoder that integrates a selective kernel attention mechanism for image classification."}
+{"idx": 8, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "Attention Is All You Need . Vaswani et al. NeurIPS 2017. Presented by Luke Song. Page 2. Abstract. ○Presents a new neural architecture named the Transformer. 33 pages", "subpage_snippet": "", "source": "ysu1989.github.io", "link": "https://ysu1989.github.io/courses/au20/cse5539/Transformer.pdf", "content": "Attention Is All You Need . Vaswani et al. NeurIPS 2017. Presented by Luke Song. Page 2. Abstract. ○Presents a new neural architecture named the Transformer. 33 pages"}
+{"idx": 9, "title": "Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, ...", "date": "", "ddg_snippet": "ABSTRACT : This research introduces an innovative approach to image classification, by making use of Vision Transformer (ViT) architecture. In fact, Vision ...", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=3709107", "content": "ABSTRACT : This research introduces an innovative approach to image classification, by making use of Vision Transformer (ViT) architecture. In fact, Vision ..."}
diff --git a/data/sampled_jsons/Attention_Is_All_You_Need_Vaswani_2017_abstract_time_complexity_year_2017.jsonl b/data/sampled_jsons/Attention_Is_All_You_Need_Vaswani_2017_abstract_time_complexity_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a05b4008846861c670234984c110a38fb7a45ff9
--- /dev/null
+++ b/data/sampled_jsons/Attention_Is_All_You_Need_Vaswani_2017_abstract_time_complexity_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "\" Attention Is All You Need \" is a 2017 landmark research paper in machine learning authored by eight scientists working at Google. The paper introduced a new ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Attention_Is_All_You_Need", "content": "\" Attention Is All You Need \" is a 2017 landmark research paper in machine learning authored by eight scientists working at Google. The paper introduced a new ..."}
+{"idx": 1, "title": "[1706.03762] Attention Is All You Need - arXiv.org", "date": "", "ddg_snippet": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ..."}
+{"idx": 2, "title": "Attention is all you need | Proceedings of the 31st ...", "date": "", "ddg_snippet": "Dec 4, 2017 · Abstract The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3295222.3295349", "content": "Dec 4, 2017 · Abstract The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism."}
+{"idx": 3, "title": "Attention is All you Need", "date": "", "ddg_snippet": "by A Vaswani · Cited by 194746 — We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. 11 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/7181-attention-is-all-you-need.pdf", "content": "by A Vaswani · Cited by 194746 — We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. 11 pages"}
+{"idx": 4, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1706.03762v7", "content": "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely."}
+{"idx": 5, "title": "Understanding the Groundbreaking 'Attention Is All You ...", "date": "", "ddg_snippet": "21 May 2024 — Abstract . Dominant sequence transduction models based on recurrent or convolution neural network while including an encoder and decoder.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/general/506393", "content": "21 May 2024 — Abstract . Dominant sequence transduction models based on recurrent or convolution neural network while including an encoder and decoder."}
+{"idx": 6, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "Attention Is All You Need . Vaswani et al. NeurIPS 2017 . Presented by Luke Song. Page 2. Abstract . ○Presents a new neural architecture named the Transformer. 33 pages", "subpage_snippet": "", "source": "ysu1989.github.io", "link": "https://ysu1989.github.io/courses/au20/cse5539/Transformer.pdf", "content": "Attention Is All You Need . Vaswani et al. NeurIPS 2017 . Presented by Luke Song. Page 2. Abstract . ○Presents a new neural architecture named the Transformer. 33 pages"}
+{"idx": 7, "title": "(PDF) Attention Is All You Need | Alicja", "date": "", "ddg_snippet": "Abstract : The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/attention-is-all-you-need-2qmt11p7sij4", "content": "Abstract : The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder."}
+{"idx": 8, "title": "A Paper A Day: #24 Attention Is All You Need | by Amr Sharaf", "date": "", "ddg_snippet": "This paper proposes a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sharaf/a-paper-a-day-24-attention-is-all-you-need-26eb2da90a91", "content": "This paper proposes a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions ..."}
+{"idx": 9, "title": "Attention is All you Need - NeurIPS", "date": "", "ddg_snippet": "Authors Ashish Vaswani , Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin Abstract The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html", "content": "Authors Ashish Vaswani , Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin Abstract The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose ..."}
diff --git a/data/sampled_jsons/Attention_Is_All_You_Need_paper_time_complexity_linear_quadratic.jsonl b/data/sampled_jsons/Attention_Is_All_You_Need_paper_time_complexity_linear_quadratic.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a4e2e0025b37bb2684f27acfb29add6559392feb
--- /dev/null
+++ b/data/sampled_jsons/Attention_Is_All_You_Need_paper_time_complexity_linear_quadratic.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Computational Complexity of Self-Attention in the Transformer Model", "date": "", "ddg_snippet": "First, you are correct in your complexity calculations. So, what is the source of confusion? When the original Attention paper was first introduced, it didn't require to calculate Q, V and K matrices, as the values were taken directly from the hidden states of the RNNs, and thus the complexity of Attention layer is O(n^2·d). Now, to understand what Table 1 contains please keep in mind how ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/65703260/computational-complexity-of-self-attention-in-the-transformer-model", "content": "First, you are correct in your complexity calculations. So, what is the source of confusion? When the original Attention paper was first introduced, it didn't require to calculate Q, V and K matrices, as the values were taken directly from the hidden states of the RNNs, and thus the complexity of Attention layer is O(n^2·d). Now, to understand what Table 1 contains please keep in mind how ..."}
+{"idx": 1, "title": "[2501.05730] Element-wise Attention Is All You Need - arXiv.org", "date": "", "ddg_snippet": "The self- attention (SA) mechanism has demonstrated superior performance across various domains, yet it suffers from substantial complexity during both training and inference. The next-generation architecture, aiming at retaining the competitive performance of SA while achieving low-cost inference and efficient long-sequence training, primarily focuses on three approaches: linear attention ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.05730", "content": "The self- attention (SA) mechanism has demonstrated superior performance across various domains, yet it suffers from substantial complexity during both training and inference. The next-generation architecture, aiming at retaining the competitive performance of SA while achieving low-cost inference and efficient long-sequence training, primarily focuses on three approaches: linear attention ..."}
+{"idx": 2, "title": "Linear Attention Is All You Need - Towards Data Science", "date": "", "ddg_snippet": "Computational Complexity If we think about the real time complexity of each approach we can show where this difference comes from. Let's break down the time complexity of the traditional softmax attention , the first term gives the complexity of QK multiplication which is n² scores, each a dot product of length d_k. The second term describes the complexity of the softmax on the attention ...", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/linear-attention-is-all-you-need-5fa9c845c1b5/", "content": "Computational Complexity If we think about the real time complexity of each approach we can show where this difference comes from. Let's break down the time complexity of the traditional softmax attention , the first term gives the complexity of QK multiplication which is n² scores, each a dot product of length d_k. The second term describes the complexity of the softmax on the attention ..."}
+{"idx": 3, "title": "Linear Attention Is All You Need - Medium", "date": "", "ddg_snippet": "Self- attention at a fraction of the cost? In this blog we'll break down how to use linear attention to make attention orders of magnitude faster.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/linear-attention-is-all-you-need-5fa9c845c1b5", "content": "Self- attention at a fraction of the cost? In this blog we'll break down how to use linear attention to make attention orders of magnitude faster."}
+{"idx": 4, "title": "PDF On The Computational Complexity of Self-Attention", "date": "", "ddg_snippet": "We prove that the time complexity of self- attention is necessarily quadratic in the input length, unless the Strong Exponential Time Hypothesis (SETH) is false. This argument holds even if the attention computation is performed only approximately, and for a variety of attention mechanisms.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v201/duman-keles23a/duman-keles23a.pdf", "content": "We prove that the time complexity of self- attention is necessarily quadratic in the input length, unless the Strong Exponential Time Hypothesis (SETH) is false. This argument holds even if the attention computation is performed only approximately, and for a variety of attention mechanisms."}
+{"idx": 5, "title": "Paper Review : Attention is all you need | Alexturtleneckk", "date": "", "ddg_snippet": "💡 Optimized total computation complexity per layer Attention used in Transformer: Multi-Head Attention Instead of performing a single attention function with keys, values and queries, we found it beneficial to linearly project the queries, keys and values with different, learned linear projections h times .", "subpage_snippet": "", "source": "alexturtleneckk.github.io", "link": "https://alexturtleneckk.github.io/Attentionisallyouneed/", "content": "💡 Optimized total computation complexity per layer Attention used in Transformer: Multi-Head Attention Instead of performing a single attention function with keys, values and queries, we found it beneficial to linearly project the queries, keys and values with different, learned linear projections h times ."}
+{"idx": 6, "title": "Tackling Quadratic Attention Complexity: Methods to Optimize Attention ...", "date": "", "ddg_snippet": "Here we have linear memory dependency (no need to store a large attention scores matrix) but quadratic time complexity . However, modifications exist to achieve linear time complexity , like AFT-local.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/tackling-quadratic-attention-complexity-methods-optimize-golubev", "content": "Here we have linear memory dependency (no need to store a large attention scores matrix) but quadratic time complexity . However, modifications exist to achieve linear time complexity , like AFT-local."}
+{"idx": 7, "title": "PDF Efficient Attention: Attention With Linear Complexities - CVF Open Access", "date": "", "ddg_snippet": "To remedy this drawback, this paper proposes a novel efficient attention mechanism equivalent to dot-product attention but with substantially less mem-ory and computational costs. Its resource efficiency allows more widespread and flexible integration of attention mod-ules into a network, which leads to better accuracies.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2021/papers/Shen_Efficient_Attention_Attention_With_Linear_Complexities_WACV_2021_paper.pdf", "content": "To remedy this drawback, this paper proposes a novel efficient attention mechanism equivalent to dot-product attention but with substantially less mem-ory and computational costs. Its resource efficiency allows more widespread and flexible integration of attention mod-ules into a network, which leads to better accuracies."}
+{"idx": 8, "title": "The Problem with Quadratic Attention in Transformer Architectures", "date": "", "ddg_snippet": "Reformer (The Efficient Transformer): This method aims to use locality-sensitive hashing to reduce complexity . Reformer uses a hash function to bucket/chunk related tokens together and uses it to match similar vector together thereby avoiding a redundant search of the entire sequence. Attention is then applied within these much smaller chunks reducing the quadratic attention to almost linear !", "subpage_snippet": "", "source": "wandb.ai", "link": "https://wandb.ai/wandb_fc/tips/reports/The-Problem-with-Quadratic-Attention-in-Transformer-Architectures--Vmlldzo3MDE0Mzcz", "content": "Reformer (The Efficient Transformer): This method aims to use locality-sensitive hashing to reduce complexity . Reformer uses a hash function to bucket/chunk related tokens together and uses it to match similar vector together thereby avoiding a redundant search of the entire sequence. Attention is then applied within these much smaller chunks reducing the quadratic attention to almost linear !"}
+{"idx": 9, "title": "Attention is All You Need: The Paper that Changed AI", "date": "", "ddg_snippet": "• The quadratic computational complexity has prompted research into variants like the Longformer and Reformer, employing sparse attention and more efficient attention computations to handle longer sequences.", "subpage_snippet": "", "source": "hiddenlayer.tech", "link": "https://hiddenlayer.tech/papers/attention-is-all-you-need/", "content": "• The quadratic computational complexity has prompted research into variants like the Longformer and Reformer, employing sparse attention and more efficient attention computations to handle longer sequences."}
diff --git a/data/sampled_jsons/Azar_et_al.,_2024_IPO_abstract_Bradley-Terry.jsonl b/data/sampled_jsons/Azar_et_al.,_2024_IPO_abstract_Bradley-Terry.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..409c9838e0fb9a6d2ccb7d136bac7b742d2492b7
--- /dev/null
+++ b/data/sampled_jsons/Azar_et_al.,_2024_IPO_abstract_Bradley-Terry.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Extended Abstract", "date": "", "ddg_snippet": "The second extension we considered was Identity Preference Optimization ( IPO ) Gheshlaghi Azar et al . ( 2024 ). IPO addresses a fundamental limitation of DPO: its tendency to overfit to preference data, particularly when preferences are deterministic.", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/CS224R_Final_Project__1_.pdf", "content": "The second extension we considered was Identity Preference Optimization ( IPO ) Gheshlaghi Azar et al . ( 2024 ). IPO addresses a fundamental limitation of DPO: its tendency to overfit to preference data, particularly when preferences are deterministic."}
+{"idx": 1, "title": "B -terry Models: a General P Model for Language Model Align", "date": "", "ddg_snippet": "Preference learning algorithms typically employ pairwise comparisons to capture human judg-ments (Ibarz et al., 2018; Ziegler et al., 2019). The Bradley-Terry (BT) model ( Bradley & Terry , 1952) is popular for modeling such pairwise preferences due to its simplicity and computational eficiency: given K responses, a BT reward model cost O(K) inference-time compute to output the reward dictating ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=bT8Wm4jtJC", "content": "Preference learning algorithms typically employ pairwise comparisons to capture human judg-ments (Ibarz et al., 2018; Ziegler et al., 2019). The Bradley-Terry (BT) model ( Bradley & Terry , 1952) is popular for modeling such pairwise preferences due to its simplicity and computational eficiency: given K responses, a BT reward model cost O(K) inference-time compute to output the reward dictating ..."}
+{"idx": 2, "title": "arXiv:2410.23223v1 [cs.LG] 30 Oct 2024", "date": "", "ddg_snippet": "PO) algorithm [Schulman et al., 2017]. Recently, Rafailov et al . [ 2024 ] observed that the first step can be bypassed, proposing the direct preference optimization (DPO) algorithm, direct contribution; alphabetically ordered. †Equ (BT) model [ Bradley and Terry , 1952]. Unfortunately, the BT model is too restrictive to capture the richne", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.23223", "content": "PO) algorithm [Schulman et al., 2017]. Recently, Rafailov et al . [ 2024 ] observed that the first step can be bypassed, proposing the direct preference optimization (DPO) algorithm, direct contribution; alphabetically ordered. †Equ (BT) model [ Bradley and Terry , 1952]. Unfortunately, the BT model is too restrictive to capture the richne"}
+{"idx": 3, "title": "Human Alignment of Large Language Models through", "date": "", "ddg_snippet": "Recently, two particular approaches to directly optimise against preference probabilities themselves, rather than a Bradley - Terry -derived reward function have been proposed. Identity preference optimisation ( Azar et al., 2023, IPO ) is an algorithm that aims to optimise preference probabilities against a fixed data distribution, and does so with an offline contrastive loss, as with DPO. By ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.08635v1", "content": "Recently, two particular approaches to directly optimise against preference probabilities themselves, rather than a Bradley - Terry -derived reward function have been proposed. Identity preference optimisation ( Azar et al., 2023, IPO ) is an algorithm that aims to optimise preference probabilities against a fixed data distribution, and does so with an offline contrastive loss, as with DPO. By ..."}
+{"idx": 4, "title": "PDF Beyond IID Constraints: A Novel Approach to Identity Preference ...", "date": "", "ddg_snippet": "The Identity Preference Optimization ( IPO ) algorithm Gheshlaghi Azar et al . ( 2024 ) was introduced to further improve on DPO by addressing the pairwise preference assumption directly. IPO provides a more general loss function and has demonstrated empirical superiority over DPO in specific cases. However, IPO's reliance on IID data limits its applicability in real-world settings (Wang et al ...", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs224n/final-reports/256735149.pdf", "content": "The Identity Preference Optimization ( IPO ) algorithm Gheshlaghi Azar et al . ( 2024 ) was introduced to further improve on DPO by addressing the pairwise preference assumption directly. IPO provides a more general loss function and has demonstrated empirical superiority over DPO in specific cases. However, IPO's reliance on IID data limits its applicability in real-world settings (Wang et al ..."}
+{"idx": 5, "title": "PREFERENCE OPTIMIZATION WITH MULTI-SAMPLE COMPARISONS - OpenReview", "date": "", "ddg_snippet": "In this work, we introduce Multi-sample Direct Preference Optimization (mDPO) and Multi-sample Identity Preference Optimization (mIPO), which are extensions of the prior DAP methods DPO (Rafailov et al., 2024 ) and IPO ( Azar et al., 2024)1. Unlike their predecessors, which rely on single-sample comparisons, mDPO and mIPO utilize multi-sample comparisons to better capture group-wise or ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Ozfu2uBH55", "content": "In this work, we introduce Multi-sample Direct Preference Optimization (mDPO) and Multi-sample Identity Preference Optimization (mIPO), which are extensions of the prior DAP methods DPO (Rafailov et al., 2024 ) and IPO ( Azar et al., 2024)1. Unlike their predecessors, which rely on single-sample comparisons, mDPO and mIPO utilize multi-sample comparisons to better capture group-wise or ..."}
+{"idx": 6, "title": "Self Play Preference Optimization : Iterative Nash Equilibrium ...", "date": "", "ddg_snippet": "A general theoretical paradigm to understand learning from human preferences. by Azar et al . arXiv preprint arXiv:2310.12036 Kto: Model alignment as prospect theoretic optimization. by Ethayarajh ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@techsachin/self-play-preference-optimization-iterative-nash-equilibrium-convergence-based-llm-fine-tuning-b6d16d98da5f", "content": "A general theoretical paradigm to understand learning from human preferences. by Azar et al . arXiv preprint arXiv:2310.12036 Kto: Model alignment as prospect theoretic optimization. by Ethayarajh ..."}
+{"idx": 7, "title": "Relative Preference Optimization: Enhancing LLM Alignment through ...", "date": "", "ddg_snippet": "These included SFT (Chung et al., 2022) for initial model adaptation, PPO (Schulman et al., 2017) for reinforcement learning fine-tuning, DPO and IPO ( Azar et al., 2024 ) for preference-based model alignment, and KTO (Ethayarajh et al., 2023) as an alternative approach incorporating human value functions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.10958v1", "content": "These included SFT (Chung et al., 2022) for initial model adaptation, PPO (Schulman et al., 2017) for reinforcement learning fine-tuning, DPO and IPO ( Azar et al., 2024 ) for preference-based model alignment, and KTO (Ethayarajh et al., 2023) as an alternative approach incorporating human value functions."}
+{"idx": 8, "title": "Human Alignment of Large Language Models through Online Preference ...", "date": "", "ddg_snippet": "Recently, two particular approaches to directly optimise against preference probabilities themselves, rather than a Bradley - Terry -derived reward function have been proposed. Identity preference optimisation ( Azar et al., 2023, IPO ) is an algorithm that aims to optimise preference probabilities against a fixed data distribution, and does so with an ofline contrastive loss, as with DPO. By ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.08635", "content": "Recently, two particular approaches to directly optimise against preference probabilities themselves, rather than a Bradley - Terry -derived reward function have been proposed. Identity preference optimisation ( Azar et al., 2023, IPO ) is an algorithm that aims to optimise preference probabilities against a fixed data distribution, and does so with an ofline contrastive loss, as with DPO. By ..."}
+{"idx": 9, "title": "PDF A General Theoretical Paradigm to Understand Learning from Human ...", "date": "", "ddg_snippet": "1 Introduction Learning from human preferences (Christiano et al., 2017) is a paradigm adopted in the natural language processing literature to better align pretrained (Rad-ford et al., 2018; Ramachandran et al., 2016) and instruction-tuned (Wei et al., 2022) generative lan-guage models to human desiderata.", "subpage_snippet": "", "source": "misovalko.github.io", "link": "https://misovalko.github.io/publications/azar2024unified.pdf", "content": "1 Introduction Learning from human preferences (Christiano et al., 2017) is a paradigm adopted in the natural language processing literature to better align pretrained (Rad-ford et al., 2018; Ramachandran et al., 2016) and instruction-tuned (Wei et al., 2022) generative lan-guage models to human desiderata."}
diff --git "a/data/sampled_jsons/BA-Cycle_method_\342\210\206\342\210\206G_calculation_vs_inverse_folding.jsonl" "b/data/sampled_jsons/BA-Cycle_method_\342\210\206\342\210\206G_calculation_vs_inverse_folding.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..88072033077a95753078261ac8a39a4e1fb05d0d
--- /dev/null
+++ "b/data/sampled_jsons/BA-Cycle_method_\342\210\206\342\210\206G_calculation_vs_inverse_folding.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "List of RNA structure prediction software - Wikipedia", "date": "", "ddg_snippet": "This list of RNA structure prediction software is a compilation of software tools and web portals used for RNA structure prediction. Single sequence secondary structure prediction. Single sequence tertiary structure prediction. Comparative methods .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/List_of_RNA_structure_prediction_software", "content": "This list of RNA structure prediction software is a compilation of software tools and web portals used for RNA structure prediction. Single sequence secondary structure prediction. Single sequence tertiary structure prediction. Comparative methods ."}
+{"idx": 1, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "ize the log-likelihood provided by protein inverse folding models for ∆∆G estimation. Compared to previous inverse folding -based methods , our method explicitly accounts for the unbound state of protein complex in the ∆∆G thermodynamic cycle , introducing a physical inductiv.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "ize the log-likelihood provided by protein inverse folding models for ∆∆G estimation. Compared to previous inverse folding -based methods , our method explicitly accounts for the unbound state of protein complex in the ∆∆G thermodynamic cycle , introducing a physical inductiv."}
+{"idx": 2, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 3, "title": "Calculation of Protein Folding Thermodynamics Using Molecular ...", "date": "", "ddg_snippet": "Nov 13, 2023 · We present here a simple approach that allows us to accurately calculate the energetics of protein folding . It is based on computing the energy of the folded and unfolded states at different temperatures using molecular dynamics simulations.", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acs.jcim.3c01107", "content": "Nov 13, 2023 · We present here a simple approach that allows us to accurately calculate the energetics of protein folding . It is based on computing the energy of the folded and unfolded states at different temperatures using molecular dynamics simulations."}
+{"idx": 4, "title": "What Is Inverse Folding & How To Practically Apply It", "date": "", "ddg_snippet": "Dec 30, 2023 · Join us in this blog post as we delve into the comprehensive understanding of inverse folding , exploring its applications and unveiling the optimal utilization of inverse folding models on Neurosnap for unparalleled precision in protein design.", "subpage_snippet": "", "source": "neurosnap.ai", "link": "https://neurosnap.ai/blog/post/what-is-inverse-folding-how-to-practically-apply-it/65908e76104e7841a40c3187", "content": "Dec 30, 2023 · Join us in this blog post as we delve into the comprehensive understanding of inverse folding , exploring its applications and unveiling the optimal utilization of inverse folding models on Neurosnap for unparalleled precision in protein design."}
+{"idx": 5, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "g-likelihood provided by protein inverse folding models for the estimation of ∆∆G . Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle , introducing a physical inducti.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "g-likelihood provided by protein inverse folding models for the estimation of ∆∆G . Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle , introducing a physical inducti."}
+{"idx": 6, "title": "Boltzmann-Aligned Inverse Folding Model as... | OpenReview", "date": "", "ddg_snippet": "Furthermore, we demonstrate the capability of our method in binding energy prediction, protein-protein docking, and antibody optimization tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "Furthermore, we demonstrate the capability of our method in binding energy prediction, protein-protein docking, and antibody optimization tasks."}
+{"idx": 7, "title": "Benchmarking inverse folding models for antibody CDR... | PLOS One", "date": "", "ddg_snippet": "This study benchmarks state-of-the-art inverse folding models—ProteinMPNN, ESM-IF, LM-Design, and AntiFold—using comprehensive datasets and alternative evaluation metrics like sequence similarity.", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0324566", "content": "This study benchmarks state-of-the-art inverse folding models—ProteinMPNN, ESM-IF, LM-Design, and AntiFold—using comprehensive datasets and alternative evaluation metrics like sequence similarity."}
+{"idx": 8, "title": "Inverse Folding ICML 2022", "date": "", "ddg_snippet": "AlphaFold2-predicted structures improves inverse folding . Fixed backbone sequence design evaluation on the CATH v4.3 topology split test set.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16886.pdf", "content": "AlphaFold2-predicted structures improves inverse folding . Fixed backbone sequence design evaluation on the CATH v4.3 topology split test set."}
+{"idx": 9, "title": "Inverse folding of protein complexes with... | bioRxiv", "date": "", "ddg_snippet": "You are going to email the following Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution.", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2023.12.19.572475v1", "content": "You are going to email the following Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution."}
diff --git a/data/sampled_jsons/BA-Cycle_thermodynamic_cycle_unbound_state_inverse_folding_protein_complex.jsonl b/data/sampled_jsons/BA-Cycle_thermodynamic_cycle_unbound_state_inverse_folding_protein_complex.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..50a678af326739bc815549aca75b3fbefbccb4e4
--- /dev/null
+++ b/data/sampled_jsons/BA-Cycle_thermodynamic_cycle_unbound_state_inverse_folding_protein_complex.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enzyme kinetics - Wikipedia", "date": "", "ddg_snippet": "... to produce an enzyme-substrate complex ES, and is transformed into an enzyme-product complex EP and from there to product P, via a transition state ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Enzyme_kinetics", "content": "... to produce an enzyme-substrate complex ES, and is transformed into an enzyme-product complex EP and from there to product P, via a transition state ..."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Compared to previous inverse folding -based methods, our method explicitly accounts for the unbound state of protein complex in the Δ Δ 𝐺 \\Delta\\Delta G roman_Δ roman_Δ italic_G thermodynamic cycle , introducing a physical inductive bias and achieving both supervised and unsupervised state-of-the-art (SoTA) performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "Compared to previous inverse folding -based methods, our method explicitly accounts for the unbound state of protein complex in the Δ Δ 𝐺 \\Delta\\Delta G roman_Δ roman_Δ italic_G thermodynamic cycle , introducing a physical inductive bias and achieving both supervised and unsupervised state-of-the-art (SoTA) performance."}
+{"idx": 2, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "ize the log-likelihood provided by protein inverse folding models for ∆∆G estimation. Compared to previous inverse folding -based methods, our method explicitly accounts for the unbound state of protein complex in the ∆∆G thermodynamic cycle , introducing a physical inductiv", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "ize the log-likelihood provided by protein inverse folding models for ∆∆G estimation. Compared to previous inverse folding -based methods, our method explicitly accounts for the unbound state of protein complex in the ∆∆G thermodynamic cycle , introducing a physical inductiv"}
+{"idx": 3, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle , introducing a physical inductive bias and achieving supervised and unsuper-vised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coeficients of 0.3201 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle , introducing a physical inductive bias and achieving supervised and unsuper-vised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coeficients of 0.3201 ..."}
+{"idx": 4, "title": "Energy-Based Models for Predicting Mutational Effects on Proteins", "date": "", "ddg_snippet": "Aug 15, 2025 · While Jiao et al. [21] and Dutton et al. [15] are among the first to incorpo-rate unbound state log-probabilities with inverse folding models inspired by the thermodynamic cycle , they use the same unrealistic fixed-backbone assumption as previous works, leaving a research gap for the development of deep learning-based models with strong ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.10629", "content": "Aug 15, 2025 · While Jiao et al. [21] and Dutton et al. [15] are among the first to incorpo-rate unbound state log-probabilities with inverse folding models inspired by the thermodynamic cycle , they use the same unrealistic fixed-backbone assumption as previous works, leaving a research gap for the development of deep learning-based models with strong ..."}
+{"idx": 5, "title": "Energy-Based Models for Predicting Mutational Effects on Proteins", "date": "", "ddg_snippet": "While Jiao et al. (2024) and Dutton et al. (2024) are among the first to incorporate unbound state log-probabilities with inverse folding models inspired by the thermodynamic cycle , they use the same unrealistic fixed-backbone assumption as previous works, leaving a research gap for the development of deep learning-based models with strong ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10629v1", "content": "While Jiao et al. (2024) and Dutton et al. (2024) are among the first to incorporate unbound state log-probabilities with inverse folding models inspired by the thermodynamic cycle , they use the same unrealistic fixed-backbone assumption as previous works, leaving a research gap for the development of deep learning-based models with strong ..."}
+{"idx": 6, "title": "Discrete-state kinetics and Markov models | Physical Lens on", "date": "", "ddg_snippet": "Notation Basics: States & Kinetics Basics: Mass-Action Kinetics Equilibrium means Detailed Balance Cycles and Constraints Cycle Logic Advanced ...", "subpage_snippet": "", "source": "www.physicallensonthecell.org", "link": "https://www.physicallensonthecell.org/discrete-state-kinetics-and-markov-models", "content": "Notation Basics: States & Kinetics Basics: Mass-Action Kinetics Equilibrium means Detailed Balance Cycles and Constraints Cycle Logic Advanced ..."}
+{"idx": 7, "title": "Encounter complexes and dimensionality reduction in", "date": "", "ddg_snippet": "Although in some cases binding is inherently coupled with folding ( Shoemaker et al., 2000 ; Zheng et al., 2012 ), a large class of protein complexes ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/01370", "content": "Although in some cases binding is inherently coupled with folding ( Shoemaker et al., 2000 ; Zheng et al., 2012 ), a large class of protein complexes ..."}
+{"idx": 8, "title": "PLD-Tree: Persistent Laplacian Decision Tree for", "date": "", "ddg_snippet": "... Protein Data Bank (PDB) is one of the largest and most comprehensive repositories of protein structures, including tens of thousands of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18541v1", "content": "... Protein Data Bank (PDB) is one of the largest and most comprehensive repositories of protein structures, including tens of thousands of ..."}
+{"idx": 9, "title": "Condensed Matter", "date": "", "ddg_snippet": "Additionally, the alloy showed outstanding cyclic stability, retaining almost all of its hydrogen capacity across 25 cycles with only a slight 0.2 wt.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cond-mat/new?skip=0&show=1000", "content": "Additionally, the alloy showed outstanding cyclic stability, retaining almost all of its hydrogen capacity across 25 cycles with only a slight 0.2 wt."}
diff --git "a/data/sampled_jsons/BA-Cycle_unbound_state_thermodynamic_cycle_\342\210\206\342\210\206G_inverse_folding.jsonl" "b/data/sampled_jsons/BA-Cycle_unbound_state_thermodynamic_cycle_\342\210\206\342\210\206G_inverse_folding.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..2a700e887aafce4dad90f3c0cb8dac7d6136067a
--- /dev/null
+++ "b/data/sampled_jsons/BA-Cycle_unbound_state_thermodynamic_cycle_\342\210\206\342\210\206G_inverse_folding.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Thermodynamic Cycles - GeeksforGeeks", "date": "", "ddg_snippet": "What is Thermodynamics Cycle ? A thermodynamic cycle is a series of thermodynamic actions that, when carried out repeatedly, leave the system in the same state as when it was first created.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/physics/thermodynamic-cycles/", "content": "What is Thermodynamics Cycle ? A thermodynamic cycle is a series of thermodynamic actions that, when carried out repeatedly, leave the system in the same state as when it was first created."}
+{"idx": 1, "title": "Basic thermodynamics cycle | PPTX", "date": "", "ddg_snippet": "Basic thermodynamics cycle . A thermodynamic cycle is a series of thermodynamic processes which returns a system to its initial state . Properties depend only on the thermodynamic state and thus do not change over a cycle .", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/basic-thermodynamics-cycle/61821273", "content": "Basic thermodynamics cycle . A thermodynamic cycle is a series of thermodynamic processes which returns a system to its initial state . Properties depend only on the thermodynamic state and thus do not change over a cycle ."}
+{"idx": 2, "title": "Benchmarking inverse folding models for antibody CDR... | PLOS One", "date": "", "ddg_snippet": "This study benchmarks state -of-the-art inverse folding models—ProteinMPNN, ESM-IF, LM-Design, and AntiFold—using comprehensive datasets and alternative evaluation metrics like sequence similarity.", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0324566", "content": "This study benchmarks state -of-the-art inverse folding models—ProteinMPNN, ESM-IF, LM-Design, and AntiFold—using comprehensive datasets and alternative evaluation metrics like sequence similarity."}
+{"idx": 3, "title": "Solved Fundamentals of Engineering Thermodynamics ... | Chegg.com", "date": "", "ddg_snippet": "Fundamentals of Engineering Thermodynamics , 8th Edition; 5.89 At steady state , a thermodynamic cycle operating between hot and cold reservoirs at 1000 K and 500 K, respectively, receives energy by heat transfer from the hot reservoir at a rate of 1500 kW, discharges energy by heat...", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/fundamentals-engineering-thermodynamics-8th-edition-589-steady-state-thermodynamic-cycle-o-q7111745", "content": "Fundamentals of Engineering Thermodynamics , 8th Edition; 5.89 At steady state , a thermodynamic cycle operating between hot and cold reservoirs at 1000 K and 500 K, respectively, receives energy by heat transfer from the hot reservoir at a rate of 1500 kW, discharges energy by heat..."}
+{"idx": 4, "title": "Thermodynamic cycle for evaluation of G (aq.), the free energy...", "date": "", "ddg_snippet": "The experimental value is ≈ −1.6 k B T . from publication: A solvation induced ring puckering effect in fluorinated prolines and its inclusion in classical force-fields | Strategic incorporation of fluorinated prolines can accelerate folding and increase thermal stability of proteins.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Thermodynamic-cycle-for-evaluation-of-Gaq-the-free-energy-change-from-cisaq-to_fig2_341359927", "content": "The experimental value is ≈ −1.6 k B T . from publication: A solvation induced ring puckering effect in fluorinated prolines and its inclusion in classical force-fields | Strategic incorporation of fluorinated prolines can accelerate folding and increase thermal stability of proteins."}
+{"idx": 5, "title": "The Thermodynamics Cycles with a Reversible Chemical Reaction", "date": "", "ddg_snippet": "Object: thermodynamic cycles of Carnot, Brighton and Stirling; mixtures of gases capable of changing their composition as a result of a reversible chemical reaction; chemical work.", "subpage_snippet": "", "source": "sciencepublishinggroup.com", "link": "https://sciencepublishinggroup.com/article/10.11648/j.ajmp.20231202.11", "content": "Object: thermodynamic cycles of Carnot, Brighton and Stirling; mixtures of gases capable of changing their composition as a result of a reversible chemical reaction; chemical work."}
+{"idx": 6, "title": "The above p-v diagram represents the thermodynamic cycle of an...", "date": "", "ddg_snippet": "Find the efficeincy of the thermodynamic cycle shown in figure for an ideal diatomic gas. ocr_image. View Solution. A carnot engine operating between 400K & 800K does 1200J of work in 1 cycle .The figure shows P - V diagram of a thermodynamic cycle The work done in the cycle is.", "subpage_snippet": "", "source": "www.doubtnut.com", "link": "https://www.doubtnut.com/qna/12008979", "content": "Find the efficeincy of the thermodynamic cycle shown in figure for an ideal diatomic gas. ocr_image. View Solution. A carnot engine operating between 400K & 800K does 1200J of work in 1 cycle .The figure shows P - V diagram of a thermodynamic cycle The work done in the cycle is."}
+{"idx": 7, "title": "(PDF) Interrelatedness of thermodynamics and information...", "date": "", "ddg_snippet": "Principle of Thermodynamics . We state the relation between the term of information entropy, introduced by C. Shannon (1948), and thermodynamic entropy, introduced by R. Clausius (1850) and, further, explain the Gibbs paradox.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/128195469/Interrelatedness_of_thermodynamics_and_information_transformation_of_heat_as_a_measurable_information_process_and_quantity_an_overview", "content": "Principle of Thermodynamics . We state the relation between the term of information entropy, introduced by C. Shannon (1948), and thermodynamic entropy, introduced by R. Clausius (1850) and, further, explain the Gibbs paradox."}
+{"idx": 8, "title": "Thermodynamic Foundations – Introduction to Aerospace Flight...", "date": "", "ddg_snippet": "The Second Law of Thermodynamics . Entropy, Availability, & Exergy Analysis. Thermodynamic Cycles . Summary and Closure.", "subpage_snippet": "", "source": "eaglepubs.erau.edu", "link": "https://eaglepubs.erau.edu/introductiontoaerospaceflightvehicles/chapter/thermodynamic-foundations/", "content": "The Second Law of Thermodynamics . Entropy, Availability, & Exergy Analysis. Thermodynamic Cycles . Summary and Closure."}
+{"idx": 9, "title": "P rotein I nteractions", "date": "", "ddg_snippet": "Left: inference with a protein inverse folding model. Right: illustration of thermodynamic cycle in the modulation of protein-protein interactions.We compare our ∆ ∆ G predictors, BA - Cycle and BA-DDG, with state -of-the-art unsu-pervised and supervised methods, respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "Left: inference with a protein inverse folding model. Right: illustration of thermodynamic cycle in the modulation of protein-protein interactions.We compare our ∆ ∆ G predictors, BA - Cycle and BA-DDG, with state -of-the-art unsu-pervised and supervised methods, respectively."}
diff --git a/data/sampled_jsons/BIT-VO_Murai_2020_focal_plane_binary_features_detection_tracking.jsonl b/data/sampled_jsons/BIT-VO_Murai_2020_focal_plane_binary_features_detection_tracking.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4fc2a35d22d52df5f531411426cfde7cc124388a
--- /dev/null
+++ b/data/sampled_jsons/BIT-VO_Murai_2020_focal_plane_binary_features_detection_tracking.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "Focal-plane Sensor-processor (FPSP) is a next-generation camera technology which enables every pixel on the sensor chip to perform computation in parallel, on the focal plane where the light intensity is captured. SCAMP-5 is a general-purpose FPSP used in this work and it carries out computations in the analog domain before analog to digital conversion. By extracting features from the image on ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9341151", "content": "Focal-plane Sensor-processor (FPSP) is a next-generation camera technology which enables every pixel on the sensor chip to perform computation in parallel, on the focal plane where the light intensity is captured. SCAMP-5 is a general-purpose FPSP used in this work and it carries out computations in the analog domain before analog to digital conversion. By extracting features from the image on ..."}
+{"idx": 1, "title": "BIT-VO - rmurai.co.uk", "date": "", "ddg_snippet": "BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane IROS 2020 Riku Murai 1, Sajad Saeedi 2, Paul H.J. Kelly 1", "subpage_snippet": "", "source": "rmurai.co.uk", "link": "https://rmurai.co.uk/projects/BIT-VO/", "content": "BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane IROS 2020 Riku Murai 1, Sajad Saeedi 2, Paul H.J. Kelly 1"}
+{"idx": 2, "title": "Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO)", "date": "", "ddg_snippet": "BIT-VO : Visual odometry at 300 FPS using binary features from the focal plane . In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8579-8586.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09882v1", "content": "BIT-VO : Visual odometry at 300 FPS using binary features from the focal plane . In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8579-8586."}
+{"idx": 3, "title": "[IROS 2020] BIT-VO: Visual Odometry at 300 FPS using Binary Features ...", "date": "", "ddg_snippet": "BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal PlaneRiku Murai , Sajad Saeedi, Paul H. J. KellyIEEE/RSJ International Conference on I...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=tnPfbJaPrSQ", "content": "BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal PlaneRiku Murai , Sajad Saeedi, Paul H. J. KellyIEEE/RSJ International Conference on I..."}
+{"idx": 4, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "BITVO is presented, which is the first 6-Degrees of Freedom visual odometry algorithm which utilises the FPSP, and it operates at 300 FPS in a natural environment, using binary edges and corner features detected by the SCAMP-5.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/BIT-VO:-Visual-Odometry-at-300-FPS-using-Binary-the-Murai-Saeedi/02d51188c578337a2ed81e8a42fee89109a2a25d/figure/8", "content": "BITVO is presented, which is the first 6-Degrees of Freedom visual odometry algorithm which utilises the FPSP, and it operates at 300 FPS in a natural environment, using binary edges and corner features detected by the SCAMP-5."}
+{"idx": 5, "title": "High-frame rate homography and visual odometry by tracking binary ...", "date": "", "ddg_snippet": "We presented BIT-VO , which is capable of performing VO at 300 FPS by using binary edges and corners computed on the focal plane . Our system is simplis-tic and minimal, yet it is sufficient to work in challenging conditions, highlighting the advantage of operating at high effective frame rates.", "subpage_snippet": "", "source": "spiral.imperial.ac.uk", "link": "https://spiral.imperial.ac.uk/server/api/core/bitstreams/db45cdbb-412b-4698-a2ba-d53e39f81040/content", "content": "We presented BIT-VO , which is capable of performing VO at 300 FPS by using binary edges and corners computed on the focal plane . Our system is simplis-tic and minimal, yet it is sufficient to work in challenging conditions, highlighting the advantage of operating at high effective frame rates."}
+{"idx": 6, "title": "\"BIT-VO: Visual Odometry at 300 FPS using Binary Features from ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iros/MuraiSK20", "content": "Bibliographic details on BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane ."}
+{"idx": 7, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "Request PDF | On Oct 24, 2020 , Riku Murai and others published BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane | Find, read and cite all the research you need on ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/350082154_BIT-VO_Visual_Odometry_at_300_FPS_using_Binary_Features_from_the_Focal_Plane", "content": "Request PDF | On Oct 24, 2020 , Riku Murai and others published BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane | Find, read and cite all the research you need on ..."}
+{"idx": 8, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "Here, we present BIT - VO , which is, to the best of our knowledge, the first 6 Degrees of Freedom visual odometry algorithm which utilises the FPSP. Our entire system operates at 300 FPS in a natural scene, using binary edges and corner features detected by the SCAMP-5.", "subpage_snippet": "", "source": "web3.arxiv.org", "link": "https://web3.arxiv.org/pdf/2004.11186", "content": "Here, we present BIT - VO , which is, to the best of our knowledge, the first 6 Degrees of Freedom visual odometry algorithm which utilises the FPSP. Our entire system operates at 300 FPS in a natural scene, using binary edges and corner features detected by the SCAMP-5."}
+{"idx": 9, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "By extracting features from the image on the focal plane , data which is digitized and transferred is reduced. As a consequence, SCAMP-5 offers a high frame rate while maintaining low energy consumption. Here, we present BIT-VO , which is, to the best of our knowledge, the first 6 Degrees of Freedom visual odometry algorithm which utilises the FPSP.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv200411186M/abstract", "content": "By extracting features from the image on the focal plane , data which is digitized and transferred is reduced. As a consequence, SCAMP-5 offers a high frame rate while maintaining low energy consumption. Here, we present BIT-VO , which is, to the best of our knowledge, the first 6 Degrees of Freedom visual odometry algorithm which utilises the FPSP."}
diff --git a/data/sampled_jsons/Bach_Kern_Mautner_Kreuter_2023_statistical_profiling_unemployment_58%_screening_capacity.jsonl b/data/sampled_jsons/Bach_Kern_Mautner_Kreuter_2023_statistical_profiling_unemployment_58%_screening_capacity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..21b5a8e39fe0586b252a47507c8f02d1749426b2
--- /dev/null
+++ b/data/sampled_jsons/Bach_Kern_Mautner_Kreuter_2023_statistical_profiling_unemployment_58%_screening_capacity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Johann Sebastian Bach - Wikipedia", "date": "", "ddg_snippet": "Bach wrote extensively for organ and other keyboard instruments. He composed concertos, for instance for violin and for harpsichord, and suites, as chamber music as well as for orchestra. Many of his works use contrapuntal techniques like canon and fugue.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Johann_Sebastian_Bach", "content": "Bach wrote extensively for organ and other keyboard instruments. He composed concertos, for instance for violin and for harpsichord, and suites, as chamber music as well as for orchestra. Many of his works use contrapuntal techniques like canon and fugue."}
+{"idx": 1, "title": "The Best of Bach - YouTube", "date": "", "ddg_snippet": "The very best of Mozart, Beethoven, Bach , Chopin, Tchaikovsky, Vivaldi, Schubert, Handel, Liszt, Haydn, Strauss, Verdi, Brahms, Wagner, Mahler, Rossini, Ravel, Grieg, Ravel, Dvorák… # ...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=6JQm5aSjX6g", "content": "The very best of Mozart, Beethoven, Bach , Chopin, Tchaikovsky, Vivaldi, Schubert, Handel, Liszt, Haydn, Strauss, Verdi, Brahms, Wagner, Mahler, Rossini, Ravel, Grieg, Ravel, Dvorák… # ..."}
+{"idx": 2, "title": "Johann Sebastian Bach | Biography, Music, Death, & Facts |...", "date": "", "ddg_snippet": "Sep 5, 2025 · Johann Sebastian Bach , composer of the Baroque era and member of a large family of north German musicians. He was later regarded as one of the greatest composers of all time, celebrated for such pieces as the Brandenburg Concertos and The Well-Tempered Clavier.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/biography/Johann-Sebastian-Bach", "content": "Sep 5, 2025 · Johann Sebastian Bach , composer of the Baroque era and member of a large family of north German musicians. He was later regarded as one of the greatest composers of all time, celebrated for such pieces as the Brandenburg Concertos and The Well-Tempered Clavier."}
+{"idx": 3, "title": "Bach : the composer who changed music forever - Classical Music", "date": "", "ddg_snippet": "Bach (1685–1750) is one of the most influential composers in Western music history , whose mastery of harmony, counterpoint, and form shaped the course of classical music.", "subpage_snippet": "", "source": "www.classical-music.com", "link": "https://www.classical-music.com/features/composers/johann-sebastian-bach", "content": "Bach (1685–1750) is one of the most influential composers in Western music history , whose mastery of harmony, counterpoint, and form shaped the course of classical music."}
+{"idx": 4, "title": "Johann Sebastian Bach - Facts, Children & Compositions - ...", "date": "", "ddg_snippet": "Apr 3, 2014 · A magnificent baroque-era composer, Johann Sebastian Bach is revered through the ages for his work's musical complexities and stylistic innovations.", "subpage_snippet": "", "source": "www.biography.com", "link": "https://www.biography.com/musicians/johann-sebastian-bach", "content": "Apr 3, 2014 · A magnificent baroque-era composer, Johann Sebastian Bach is revered through the ages for his work's musical complexities and stylistic innovations."}
+{"idx": 5, "title": "Johann Sebastian Bach : Biography & Most Famous Pieces", "date": "", "ddg_snippet": "Explore the life of Johann Sebastian Bach , uncover fascinating facts, & discover his most famous compositions that have shaped classical music history.", "subpage_snippet": "", "source": "www.hoffmanacademy.com", "link": "https://www.hoffmanacademy.com/blog/johann-sebastian-bach-composer", "content": "Explore the life of Johann Sebastian Bach , uncover fascinating facts, & discover his most famous compositions that have shaped classical music history."}
+{"idx": 6, "title": "Johann Sebastian Bach (1685–1750): Biography, Music + More | CMS...", "date": "", "ddg_snippet": "Johann Sebastian was born in Eisenach on March 21, 1685, as the youngest child to Johann Ambrosius Bach and his wife Maria Elisabeth, née Lämmerhirt. His father, director of the town music company and violinist at the ducal court, was his first teacher.", "subpage_snippet": "", "source": "www.chambermusicsociety.org", "link": "https://www.chambermusicsociety.org/about-the-music/composers/johann-sebastian-bach/", "content": "Johann Sebastian was born in Eisenach on March 21, 1685, as the youngest child to Johann Ambrosius Bach and his wife Maria Elisabeth, née Lämmerhirt. His father, director of the town music company and violinist at the ducal court, was his first teacher."}
+{"idx": 7, "title": "The Life and Legacy of Johann Sebastian Bach", "date": "", "ddg_snippet": "Johann Sebastian Bach (1685-1750) is one of the most influential musicians of all times - in 2011, the New York Times named him the most important composer in the history of music.", "subpage_snippet": "", "source": "artsandculture.google.com", "link": "https://artsandculture.google.com/story/the-life-and-legacy-of-johann-sebastian-bach-bach-archiv-leipzig/6QUhhCfnO_8ZJQ?hl=en", "content": "Johann Sebastian Bach (1685-1750) is one of the most influential musicians of all times - in 2011, the New York Times named him the most important composer in the history of music."}
+{"idx": 8, "title": "Johann Sebastian Bach (1685-1750) - Classic FM", "date": "", "ddg_snippet": "Johann Sebastian Bach was classical music's most sublime creative genius. Bach was a German composer, organist, harpsichordist, violist, and violinist of the Baroque Era.", "subpage_snippet": "", "source": "www.classicfm.com", "link": "https://www.classicfm.com/composers/bach/", "content": "Johann Sebastian Bach was classical music's most sublime creative genius. Bach was a German composer, organist, harpsichordist, violist, and violinist of the Baroque Era."}
+{"idx": 9, "title": "JS Bach : Life & Major Compositions - World History Edu", "date": "", "ddg_snippet": "Oct 8, 2024 · Who was Johann Sebastian Bach ? And what are some of Bach ’s most notable compositions? This exploration of his life and major compositions will shed light on how his personal experiences and professional endeavors contributed to his monumental legacy in music history.", "subpage_snippet": "", "source": "worldhistoryedu.com", "link": "https://worldhistoryedu.com/js-bach-life-major-compositions/", "content": "Oct 8, 2024 · Who was Johann Sebastian Bach ? And what are some of Bach ’s most notable compositions? This exploration of his life and major compositions will shed light on how his personal experiences and professional endeavors contributed to his monumental legacy in music history."}
diff --git a/data/sampled_jsons/Bad_Example_online_matching_common_type_agent_j0_probability_sampling.jsonl b/data/sampled_jsons/Bad_Example_online_matching_common_type_agent_j0_probability_sampling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3f6f91017fd9e1edacac9be73d19a5b2ddba9900
--- /dev/null
+++ b/data/sampled_jsons/Bad_Example_online_matching_common_type_agent_j0_probability_sampling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Promoting Fairness Among Dynamic Agents in Online - Matching ...", "date": "", "ddg_snippet": "Consider the bad example ( Example 1), for instance. As shown in Lemma 5, any non-rejecting policy can be at most 1/2-competitive., which rejects each arriving common - type agent with probability .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0C3bLHwjsY", "content": "Consider the bad example ( Example 1), for instance. As shown in Lemma 5, any non-rejecting policy can be at most 1/2-competitive., which rejects each arriving common - type agent with probability ."}
+{"idx": 1, "title": "questionpro.com/blog/non- probability - sampling", "date": "", "ddg_snippet": "Non- Probability Sampling : Definition, Types , and Examples - QuestionPro.", "subpage_snippet": "", "source": "www.questionpro.com", "link": "https://www.questionpro.com/blog/non-probability-sampling/", "content": "Non- Probability Sampling : Definition, Types , and Examples - QuestionPro."}
+{"idx": 2, "title": "Sampling | Jaeger", "date": "", "ddg_snippet": "Probabilistic ( sampler . type = probabilistic ) sampler makes a random sampling decision with the probability of sampling equal to the value of sampler .param property.", "subpage_snippet": "", "source": "www.jaegertracing.io", "link": "https://www.jaegertracing.io/docs/1.22/architecture/sampling/", "content": "Probabilistic ( sampler . type = probabilistic ) sampler makes a random sampling decision with the probability of sampling equal to the value of sampler .param property."}
+{"idx": 3, "title": "Statistical symbols & probability symbols (μ, σ,...)", "date": "", "ddg_snippet": "Probability and statistics symbols table and definitions - expectation, variance, standard deviation, distribution, probability function, conditional probability , covariance, correlation.median. middle value of random variable x. example . cov(X,Y).", "subpage_snippet": "", "source": "www.rapidtables.com", "link": "https://www.rapidtables.com/math/symbols/Statistical_Symbols.html", "content": "Probability and statistics symbols table and definitions - expectation, variance, standard deviation, distribution, probability function, conditional probability , covariance, correlation.median. middle value of random variable x. example . cov(X,Y)."}
+{"idx": 4, "title": "Probabilistic Sampling With A Sketch - SolarWinds Blog", "date": "", "ddg_snippet": "Probabilistic Sampling With A Sketch. Baron Schwartz blog author.Given the time delta between the last query in the category, and now, we calculate a probability we should select this query as a sample . We generate a random number and compare it against the probability .", "subpage_snippet": "", "source": "www.solarwinds.com", "link": "https://www.solarwinds.com/blog/probabilistic-sampling-with-a-sketch", "content": "Probabilistic Sampling With A Sketch. Baron Schwartz blog author.Given the time delta between the last query in the category, and now, we calculate a probability we should select this query as a sample . We generate a random number and compare it against the probability ."}
+{"idx": 5, "title": "Statistics and Probability Common Mistakes Analysis", "date": "", "ddg_snippet": "Sampling bias occurs when some members of your population have different selection probabilities than others. This systematic error distorts results and limits generalisability. Common types of bias include", "subpage_snippet": "", "source": "learningmole.com", "link": "https://learningmole.com/statistics-and-probability-common-mistakes/", "content": "Sampling bias occurs when some members of your population have different selection probabilities than others. This systematic error distorts results and limits generalisability. Common types of bias include"}
+{"idx": 6, "title": "probability - Sample complexity of distinguishing instances using...", "date": "", "ddg_snippet": "Probability of an unordered sample under weighted sampling without replacement.Weighted sampling without replacement: if the weight of an element increases, must the probability of this element being sampled also increase?", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/5097807/sample-complexity-of-distinguishing-instances-using-weighted-sampling", "content": "Probability of an unordered sample under weighted sampling without replacement.Weighted sampling without replacement: if the weight of an element increases, must the probability of this element being sampled also increase?"}
+{"idx": 7, "title": "Why a few voices can speak for millions : the Power of Sampling", "date": "", "ddg_snippet": "Convenience sampling is a non- probability sampling technique in which samples are selected from the population based on how easy they are to access by the researcher. Example . Suppose a researcher wants to survey college students about their use of social media.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@dileepchp1999/why-a-few-voices-can-speak-for-millions-the-power-of-sampling-ec1cb3913e92", "content": "Convenience sampling is a non- probability sampling technique in which samples are selected from the population based on how easy they are to access by the researcher. Example . Suppose a researcher wants to survey college students about their use of social media."}
+{"idx": 8, "title": "Problems - LeetCode", "date": "", "ddg_snippet": "Online Interview Online Interview.Reservoir Sampling 4. Strongly Connected Component3. Eulerian Circuit3. Radix Sort3.", "subpage_snippet": "", "source": "leetcode.com", "link": "https://leetcode.com/problemset/", "content": "Online Interview Online Interview.Reservoir Sampling 4. Strongly Connected Component3. Eulerian Circuit3. Radix Sort3."}
+{"idx": 9, "title": "Does the Better-Than-Average E¤ect Show That", "date": "", "ddg_snippet": "With the probability matching rule, it is optimal for expected utility maximizing subjects that care only about money to report their true subjective probabilities when they can choose any number from the interval [0, 100].", "subpage_snippet": "", "source": "www.learnmoore.org", "link": "https://www.learnmoore.org/mooredata/OJD/OJD.pdf", "content": "With the probability matching rule, it is optimal for expected utility maximizing subjects that care only about money to report their true subjective probabilities when they can choose any number from the interval [0, 100]."}
diff --git a/data/sampled_jsons/Balke_Pearl_1994_'Probabilistic_evaluation_of_counterfactual_queries'_AAAI_abstract_response_functio_year_1994.jsonl b/data/sampled_jsons/Balke_Pearl_1994_'Probabilistic_evaluation_of_counterfactual_queries'_AAAI_abstract_response_functio_year_1994.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c1862becc552d3f15b9c0ce79a7b051ba55075de
--- /dev/null
+++ b/data/sampled_jsons/Balke_Pearl_1994_'Probabilistic_evaluation_of_counterfactual_queries'_AAAI_abstract_response_functio_year_1994.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "1994 - Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "Abstract . Evaluation of counterfactual queries (e.g., “If A were true, would C have been true?“) is important to fault. diagnosis, planning, and determination of liability.", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/AAAI/1994/AAAI94-035.pdf", "content": "Abstract . Evaluation of counterfactual queries (e.g., “If A were true, would C have been true?“) is important to fault. diagnosis, planning, and determination of liability."}
+{"idx": 1, "title": "The response - function variables ra and rb (summa", "date": "", "ddg_snippet": "In [ Balke and Pearl , 1994 ], an algorithm was presented. for evaluating the unique quantitative solutions to counterfactual queries when a functional model is given.The prior probability on the response functions P(rb) and P(ra) in conjunction with fb(a, rb) and fa(ra) fully.", "subpage_snippet": "", "source": "www.mimuw.edu.pl", "link": "https://www.mimuw.edu.pl/~noble/courses/BayesianNetworks/94BalkePearlCOUNTERFACTUAL.pdf", "content": "In [ Balke and Pearl , 1994 ], an algorithm was presented. for evaluating the unique quantitative solutions to counterfactual queries when a functional model is given.The prior probability on the response functions P(rb) and P(ra) in conjunction with fb(a, rb) and fa(ra) fully."}
+{"idx": 2, "title": "Probabilistic Evaluation of Counterfactual Queries | OpenReview", "date": "", "ddg_snippet": "We present a formalism that uses probabilistic causal net- works to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rkb2ITxdWS", "content": "We present a formalism that uses probabilistic causal net- works to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true."}
+{"idx": 3, "title": "Counterfactuals", "date": "", "ddg_snippet": "Response function variables. Balke , Alexander and Judea Pearl . 1994 . Probabilistic evaluation of counterfactual queries . In proceedings of AAAI .", "subpage_snippet": "", "source": "www.cs.ubc.ca", "link": "https://www.cs.ubc.ca/labs/lci/mlrg/slides/counterfactuals_presentation.pdf", "content": "Response function variables. Balke , Alexander and Judea Pearl . 1994 . Probabilistic evaluation of counterfactual queries . In proceedings of AAAI ."}
+{"idx": 4, "title": "[1302.6784] Counterfactual Probabilities : Computational Methods...", "date": "", "ddg_snippet": "Abstract : Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1302.6784", "content": "Abstract : Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability."}
+{"idx": 5, "title": "Alexander Balke 's research works | University of California, Los...", "date": "", "ddg_snippet": "Alexander Balke 's 6 research works with 341 citations and 341 reads, including: Probabilistic Evaluation of Counterfactual Queries .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Alexander-Balke-6655765", "content": "Alexander Balke 's 6 research works with 341 citations and 341 reads, including: Probabilistic Evaluation of Counterfactual Queries ."}
+{"idx": 6, "title": "Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "Share. Email Facebook. Probabilistic Evaluation of Counterfactual Queries . 2011. Alexander Balke ; Judea Pearl .", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/uc/item/6vh9k0cf", "content": "Share. Email Facebook. Probabilistic Evaluation of Counterfactual Queries . 2011. Alexander Balke ; Judea Pearl ."}
+{"idx": 7, "title": "Probabilistic and Causal Inference: The Works of Judea Pearl", "date": "", "ddg_snippet": "A. Balke and J. Pearl . 1994 . Probabilistic Evaluation of Counterfactual Queries .A. Darwiche and J. Pearl . 1994 . Symbolic Causal Networks for Reasoning about Actions and Plans. In Proceedings of the AAAI -94, Seattle, WA, Volume I, 238–244.", "subpage_snippet": "", "source": "ftp.cs.ucla.edu", "link": "https://ftp.cs.ucla.edu/pub/stat_ser/turing-award-lecture-ch2-acm-2021.pdf", "content": "A. Balke and J. Pearl . 1994 . Probabilistic Evaluation of Counterfactual Queries .A. Darwiche and J. Pearl . 1994 . Symbolic Causal Networks for Reasoning about Actions and Plans. In Proceedings of the AAAI -94, Seattle, WA, Volume I, 238–244."}
+{"idx": 8, "title": "Humans show the remarkable skill to reason in terms of counterfactuals.", "date": "", "ddg_snippet": "BALKE , A. AND PEARL , J. Probabilistic evaluation of counterfactual queries .In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence ( AAAI 2013) 2013.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/what-if-in-probabilistic-logic-programming-2q6fnar3.pdf", "content": "BALKE , A. AND PEARL , J. Probabilistic evaluation of counterfactual queries .In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence ( AAAI 2013) 2013."}
+{"idx": 9, "title": "Pearl 's Causal Ladder – Smitha Milli", "date": "", "ddg_snippet": "“ Probabilistic evaluation of counterfactual queries .” AAAI , 2011. Lewis, David. Counterfactuals.", "subpage_snippet": "", "source": "smithamilli.com", "link": "https://smithamilli.com/blog/causal-ladder/", "content": "“ Probabilistic evaluation of counterfactual queries .” AAAI , 2011. Lewis, David. Counterfactuals."}
diff --git a/data/sampled_jsons/Balke_and_Pearl_1994_response_function_abstract.jsonl b/data/sampled_jsons/Balke_and_Pearl_1994_response_function_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0615b1197c7df8c65dc8464dd8e29166df34033d
--- /dev/null
+++ b/data/sampled_jsons/Balke_and_Pearl_1994_response_function_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "1994-Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "tical analysis ( Balke & Pearl 1993). Bayesian network seldom leads to a unique solution, For this example, the response - function variable for B has a four-valued domain rb E (0, 1,2,3} with the depending on whether the conditional distributions of the Bayesian network sufficiently constrain the prior", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/AAAI/1994/AAAI94-035.pdf", "content": "tical analysis ( Balke & Pearl 1993). Bayesian network seldom leads to a unique solution, For this example, the response - function variable for B has a four-valued domain rb E (0, 1,2,3} with the depending on whether the conditional distributions of the Bayesian network sufficiently constrain the prior"}
+{"idx": 1, "title": "Counterfactuals and Policy Analysis in Structural Models", "date": "", "ddg_snippet": "A calculus for working with interventions in causal the- ories is given in [ Pearl , 1994 ]. [ Balke and Pearl , 1994b] provides background and motivation for the evaluation of counterfactual con- ditionals and briefly illustrates how the intervention scheme would handle counterfactuals in models rep- resented by linear structural equations.", "subpage_snippet": "", "source": "apps.dtic.mil", "link": "https://apps.dtic.mil/sti/tr/pdf/ADA332582.pdf", "content": "A calculus for working with interventions in causal the- ories is given in [ Pearl , 1994 ]. [ Balke and Pearl , 1994b] provides background and motivation for the evaluation of counterfactual con- ditionals and briefly illustrates how the intervention scheme would handle counterfactuals in models rep- resented by linear structural equations."}
+{"idx": 2, "title": "The response - function variables ra and rb (summa", "date": "", "ddg_snippet": "COUNTERFACTUA LS. In [ Balke and Pearl , 1994 ], an algorithm was presented. for evaluating the unique quantitative solutions to counterfactual queries when a functional model is given.tical analysis [ Balke and Pearl , 1993]. For this example, the response - function variable for.", "subpage_snippet": "", "source": "www.mimuw.edu.pl", "link": "https://www.mimuw.edu.pl/~noble/courses/BayesianNetworks/94BalkePearlCOUNTERFACTUAL.pdf", "content": "COUNTERFACTUA LS. In [ Balke and Pearl , 1994 ], an algorithm was presented. for evaluating the unique quantitative solutions to counterfactual queries when a functional model is given.tical analysis [ Balke and Pearl , 1993]. For this example, the response - function variable for."}
+{"idx": 3, "title": "Alexander Balke 's research works | University of California, Los...", "date": "", "ddg_snippet": "Alexander Balke 's 6 research works with 341 citations and 341 reads, including: Probabilistic Evaluation of Counterfactual Queries.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Alexander-Balke-6655765", "content": "Alexander Balke 's 6 research works with 341 citations and 341 reads, including: Probabilistic Evaluation of Counterfactual Queries."}
+{"idx": 4, "title": "Physical and Metaphysical Counterfactuals: Evaluating Disjunctive...", "date": "", "ddg_snippet": "Downloadable (with restrictions)! The structural interpretation of counterfactuals as formulated in Balke and Pearl ( 1994 a,b) [1, 2] excludes disjunctive conditionals, such as “had X$X$ been x1 or x2$x_1~\\mbox{or}~x_2$,” as well as disjunctive actions such as do(X=x1 or X=x2)$do...", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/a/bpj/causin/v5y2017i2p10n8.html", "content": "Downloadable (with restrictions)! The structural interpretation of counterfactuals as formulated in Balke and Pearl ( 1994 a,b) [1, 2] excludes disjunctive conditionals, such as “had X$X$ been x1 or x2$x_1~\\mbox{or}~x_2$,” as well as disjunctive actions such as do(X=x1 or X=x2)$do..."}
+{"idx": 5, "title": "Counterfactual Probabilities: Computational Methods, Bounds ...", "date": "", "ddg_snippet": "3 BOUNDSON COUNTERFACTUALS In [ Balke and Pearl , 1994 ], an algorithm was presented for evaluating the unique quantitative solutions to counterfactual queries when a functional model is given. In this section we briefly describe the form of the functional model using response - function variables and how the solution is evaluated uniquely.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1302.6784.pdf", "content": "3 BOUNDSON COUNTERFACTUALS In [ Balke and Pearl , 1994 ], an algorithm was presented for evaluating the unique quantitative solutions to counterfactual queries when a functional model is given. In this section we briefly describe the form of the functional model using response - function variables and how the solution is evaluated uniquely."}
+{"idx": 6, "title": "Counterfactual probabilities | Proceedings of the Tenth ...", "date": "", "ddg_snippet": "Jul 29, 1994 · Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/2074394.2074401", "content": "Jul 29, 1994 · Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ..."}
+{"idx": 7, "title": "Covariate-assisted bounds on causal effects with instrumental ...", "date": "", "ddg_snippet": "May 27, 2025 · 3.1 Review: Balke – Pearl bounds In their seminal work, Alexander Balke and Judea Pearl leveraged symbolic linear programming to develop sharp nonparametric bounds on the ATE for a binary outcome (A. Balke & Pearl , 1994 , 1997; A. A. Balke , 1995). Notably, their bounds only invoke Assumptions 1 – 4, and are provably tight under these assumptions.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/jrsssb/advance-article/doi/10.1093/jrsssb/qkaf028/8151396", "content": "May 27, 2025 · 3.1 Review: Balke – Pearl bounds In their seminal work, Alexander Balke and Judea Pearl leveraged symbolic linear programming to develop sharp nonparametric bounds on the ATE for a binary outcome (A. Balke & Pearl , 1994 , 1997; A. A. Balke , 1995). Notably, their bounds only invoke Assumptions 1 – 4, and are provably tight under these assumptions."}
+{"idx": 8, "title": "Counterfactual Probabilities: Computational Methods, Bounds ...", "date": "", "ddg_snippet": "Jan 1, 1994 · The connection between the factual and counterfactual worlds is discussed in [ Balke and Pearl , 1994 ] where it is argued that the response - function variables should assume the same values in both worlds.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/B9781558603325500110", "content": "Jan 1, 1994 · The connection between the factual and counterfactual worlds is discussed in [ Balke and Pearl , 1994 ] where it is argued that the response - function variables should assume the same values in both worlds."}
+{"idx": 9, "title": "[1302.6784] Counterfactual Probabilities: Computational ...", "date": "", "ddg_snippet": "Feb 27, 2013 · Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1302.6784", "content": "Feb 27, 2013 · Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ..."}
diff --git a/data/sampled_jsons/Bansal_FOCS_2010_discrepancy_minimization_SDP_algorithm_year_2010.jsonl b/data/sampled_jsons/Bansal_FOCS_2010_discrepancy_minimization_SDP_algorithm_year_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..144be984ef508b914dbe6a7fe98cbee11aff0a1d
--- /dev/null
+++ b/data/sampled_jsons/Bansal_FOCS_2010_discrepancy_minimization_SDP_algorithm_year_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithms & Complexity Seminar, MIT : Spring 2021", "date": "", "ddg_snippet": "In this talk, I will focus on a middle ground that combines both robustness and efficiency: adversarially robust algorithms , whose output is correct ...", "subpage_snippet": "", "source": "quanquancliu.com", "link": "https://quanquancliu.com/acseminar/index.html", "content": "In this talk, I will focus on a middle ground that combines both robustness and efficiency: adversarially robust algorithms , whose output is correct ..."}
+{"idx": 1, "title": "Algorithms & Complexity Seminar, MIT : Spring 2021", "date": "", "ddg_snippet": "In this talk, I will focus on a middle ground that combines both robustness and efficiency: adversarially robust algorithms , whose output is correct ...", "subpage_snippet": "", "source": "quanquancliu.com", "link": "http://quanquancliu.com/acseminar/index.html", "content": "In this talk, I will focus on a middle ground that combines both robustness and efficiency: adversarially robust algorithms , whose output is correct ..."}
+{"idx": 2, "title": "Efficient Algorithms for Partitioning Circulant Graphs with", "date": "", "ddg_snippet": "Although Theorem 1.1 is important, its proof is non-constructive, and it remains open whether a polynomial-time algorithm can find a partition ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11382v1", "content": "Although Theorem 1.1 is important, its proof is non-constructive, and it remains open whether a polynomial-time algorithm can find a partition ..."}
+{"idx": 3, "title": "Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1002.2259", "content": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy ."}
+{"idx": 4, "title": "PDF Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method.", "subpage_snippet": "", "source": "ieee-focs.org", "link": "https://ieee-focs.org/FOCS-2010-Papers/Constructive-Algorithms-for-Discrepancy-Minimization.pdf", "content": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method."}
+{"idx": 5, "title": "Constructive Discrepancy Minimization by Walking on the Edges", "date": "", "ddg_snippet": "Recently, a breakthrough work of Bansal [Proceedings of FOCS , 2010 , pp. 3--10] gave an efficient algorithm which finds such a coloring. His algorithm was based on an SDP relaxation of the discrepancy problem and a clever rounding procedure.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/130929400", "content": "Recently, a breakthrough work of Bansal [Proceedings of FOCS , 2010 , pp. 3--10] gave an efficient algorithm which finds such a coloring. His algorithm was based on an SDP relaxation of the discrepancy problem and a clever rounding procedure."}
+{"idx": 6, "title": "Discrepancy minimization via a self-balancing walk", "date": "", "ddg_snippet": "Constructive Algorithms for Discrepancy Minimization . In 51th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2010 , October 23-26, 2010 , Las Vegas, Nevada, USA.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3406325.3450994", "content": "Constructive Algorithms for Discrepancy Minimization . In 51th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2010 , October 23-26, 2010 , Las Vegas, Nevada, USA."}
+{"idx": 7, "title": "Constructive algorithms for discrepancy minimization for FOCS 2010", "date": "", "ddg_snippet": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy .", "subpage_snippet": "", "source": "research.ibm.com", "link": "https://research.ibm.com/publications/constructive-algorithms-for-discrepancy-minimization", "content": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy ."}
+{"idx": 8, "title": "PDF Lecture Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "r the SDP based algo-rithm . Then we will show how to apply this algorithm in the Hereditary Discrepancy setting, and subsequently the additional ideas needed to make i", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/~zdvir/apx11slides/bansal-scribe.pdf", "content": "r the SDP based algo-rithm . Then we will show how to apply this algorithm in the Hereditary Discrepancy setting, and subsequently the additional ideas needed to make i"}
+{"idx": 9, "title": "PDF Deterministic Discrepancy Minimization - Springer", "date": "", "ddg_snippet": "Abstract We derandomize a recent algorithmic approach due to Bansal (Foundations of Computer Science, FOCS , pp. 3-10, 2010 ) to efficiently compute low discrepancy colorings for several problems, for which only existential results were previously known. In particular, we give an efficient deterministic algorithm for Spencer's six standard deviations result (Spencer in Trans. Am. Math. Soc ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s00453-012-9728-1.pdf", "content": "Abstract We derandomize a recent algorithmic approach due to Bansal (Foundations of Computer Science, FOCS , pp. 3-10, 2010 ) to efficiently compute low discrepancy colorings for several problems, for which only existential results were previously known. In particular, we give an efficient deterministic algorithm for Spencer's six standard deviations result (Spencer in Trans. Am. Math. Soc ..."}
diff --git a/data/sampled_jsons/Bansal_discrepancy_minimization_FOCS_2010.jsonl b/data/sampled_jsons/Bansal_discrepancy_minimization_FOCS_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a8f51665b5f3146b789946fa9027e65dc8b88529
--- /dev/null
+++ b/data/sampled_jsons/Bansal_discrepancy_minimization_FOCS_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constructive ℓ₂-Discrepancy Minimization with Additive", "date": "", "ddg_snippet": "... discrepancy minimisation was open for a long time, until Bansal ’s remarkable breakthrough [ 4 ] , which showed Spencer’s bound could be achieved ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21423v1", "content": "... discrepancy minimisation was open for a long time, until Bansal ’s remarkable breakthrough [ 4 ] , which showed Spencer’s bound could be achieved ..."}
+{"idx": 1, "title": "Discrepancy Games and Sensitivity | Gödel's Lost Letter and", "date": "", "ddg_snippet": "Spencer ’ s talk was titled “ Four Discrepancies ” and based on a joint paper with Nikhil Bansal .", "subpage_snippet": "", "source": "rjlipton.com", "link": "https://rjlipton.com/2019/07/25/discrepancy-games-and-sensitivity/", "content": "Spencer ’ s talk was titled “ Four Discrepancies ” and based on a joint paper with Nikhil Bansal ."}
+{"idx": 2, "title": "Bibliography: Theory of Computing: An Open Access Electronic", "date": "", "ddg_snippet": "7] Nikhil Bansal and Shashwat Garg: Algorithmic discrepancy beyond partial coloring. ... and Thomas Rothvoss: Deterministic discrepancy minimization ...", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "http://theoryofcomputing.org/articles/v015a021/bibliography.html", "content": "7] Nikhil Bansal and Shashwat Garg: Algorithmic discrepancy beyond partial coloring. ... and Thomas Rothvoss: Deterministic discrepancy minimization ..."}
+{"idx": 3, "title": "Bibliography: Theory of Computing: An Open Access Electronic", "date": "", "ddg_snippet": "9] Nikhil Bansal and Shashwat Garg: Algorithmic discrepancy beyond partial coloring. ... Bansal and Viswanath Nagarajan: Approximation-friendly ...", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "https://theoryofcomputing.org/articles/v020a006/bibliography.html", "content": "9] Nikhil Bansal and Shashwat Garg: Algorithmic discrepancy beyond partial coloring. ... Bansal and Viswanath Nagarajan: Approximation-friendly ..."}
+{"idx": 4, "title": "Bibliography: Theory of Computing: An Open Access Electronic", "date": "", "ddg_snippet": "21] Avi Levy, Harishchandra Ramadas, and Thomas Rothvoss: Deterministic discrepancy minimization via the multiplicative weight update method.", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "https://theoryofcomputing.org/articles/v015a015/bibliography.html", "content": "21] Avi Levy, Harishchandra Ramadas, and Thomas Rothvoss: Deterministic discrepancy minimization via the multiplicative weight update method."}
+{"idx": 5, "title": "Bibliography: Theory of Computing: An Open Access Electronic", "date": "", "ddg_snippet": "21] Avi Levy, Harishchandra Ramadas, and Thomas Rothvoss: Deterministic discrepancy minimization via the multiplicative weight update method.", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "http://theoryofcomputing.org/articles/v015a015/bibliography.html", "content": "21] Avi Levy, Harishchandra Ramadas, and Thomas Rothvoss: Deterministic discrepancy minimization via the multiplicative weight update method."}
+{"idx": 6, "title": "Bibliography: Theory of Computing: An Open Access Electronic", "date": "", "ddg_snippet": "... Bansal : Constructive algorithms for discrepancy ... 13] Shachar Lovett and Raghu Meka: Constructive discrepancy minimization by walking on the edges.", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "https://theoryofcomputing.org/articles/v015a010/bibliography.html", "content": "... Bansal : Constructive algorithms for discrepancy ... 13] Shachar Lovett and Raghu Meka: Constructive discrepancy minimization by walking on the edges."}
+{"idx": 7, "title": "Anupam Gupta - Publications", "date": "", "ddg_snippet": "The number of minimum k-cuts: improving the Karger-{Stein} bound ... Non-Preemptive Flow-Time Minimization via Rejections", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~anupamg/newpubspage/my-papers.html", "content": "The number of minimum k-cuts: improving the Karger-{Stein} bound ... Non-Preemptive Flow-Time Minimization via Rejections"}
+{"idx": 8, "title": "Raghu Meka - researchr alias", "date": "", "ddg_snippet": "Online Discrepancy Minimization for Stochastic Arrivals Nikhil Bansal 0001 , Haotian Jiang , Raghu Meka , Sahil Singla 0001 , Makrand Sinha .", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/alias/raghu-meka", "content": "Online Discrepancy Minimization for Stochastic Arrivals Nikhil Bansal 0001 , Haotian Jiang , Raghu Meka , Sahil Singla 0001 , Makrand Sinha ."}
+{"idx": 9, "title": "Homepage of Mohit Singh", "date": "", "ddg_snippet": "Efficient Algorithms for Discrepancy Minimization in Convex Sets , Ronen Eldan and Mohit Singh, Random Structures and Algorithms, 2018.", "subpage_snippet": "", "source": "www2.isye.gatech.edu", "link": "https://www2.isye.gatech.edu/~msingh94/publications.html", "content": "Efficient Algorithms for Discrepancy Minimization in Convex Sets , Ronen Eldan and Mohit Singh, Random Structures and Algorithms, 2018."}
diff --git a/data/sampled_jsons/BdO4R6XxUH_DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Section_4.1_'promptable'_concept_pro_year_2023.jsonl b/data/sampled_jsons/BdO4R6XxUH_DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Section_4.1_'promptable'_concept_pro_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf94720826940eeb2d73ba35376c3530ee74ce07
--- /dev/null
+++ b/data/sampled_jsons/BdO4R6XxUH_DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Section_4.1_'promptable'_concept_pro_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data - efficient Visual CBMs ( DCBM ) to improve interpretability in data-scarce domains.While other data - efficient methods exist, e.g., prompt -tuned CLIP, we focus exclusively on interpretable models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "We propose Data - efficient Visual CBMs ( DCBM ) to improve interpretability in data-scarce domains.While other data - efficient methods exist, e.g., prompt -tuned CLIP, we focus exclusively on interpretable models ."}
+{"idx": 1, "title": "ICML Poster DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46104", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 2, "title": "KathPra/ DCBM : Official repo for ICML25 paper: DCBM : Data - Efficient ...", "date": "", "ddg_snippet": "Concepts / Directory to store previously extracted concepts .For more details, please refer to the sections and scripts within the repository or consult the paper. About. Official repo for ICML25 paper: DCBM : Data - Efficient Visual Concept Bottleneck Models .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "Concepts / Directory to store previously extracted concepts .For more details, please refer to the sections and scripts within the repository or consult the paper. About. Official repo for ICML25 paper: DCBM : Data - Efficient Visual Concept Bottleneck Models ."}
+{"idx": 3, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/DCBM:-Data-Efficient-Visual-Concept-Bottleneck-Models-4be352f8-e37e-432a-9849-dc4f3c8c586f", "content": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 4, "title": "(PDF) Aligning Visual and Semantic Interpretability through Visually ...", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) address this issue by incorporating human-understandable concepts into the prediction process, thereby enhancing transparency and interpretability.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387105769_Aligning_Visual_and_Semantic_Interpretability_through_Visually_Grounded_Concept_Bottleneck_Models", "content": "Concept Bottleneck Models (CBMs) address this issue by incorporating human-understandable concepts into the prediction process, thereby enhancing transparency and interpretability."}
+{"idx": 5, "title": "Language in a Bottle : Language Model Guided Concept Bottlenecks ...", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBM) are inherently inter-pretable models that factor model decisions into human-readable concepts .Our method builds on Concept Bottleneck Models (CBM) [25], which construct predictors through a linear combination of human-designed concepts .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Language_in_a_Bottle_Language_Model_Guided_Concept_Bottlenecks_for_CVPR_2023_paper.pdf", "content": "Concept Bottleneck Models (CBM) are inherently inter-pretable models that factor model decisions into human-readable concepts .Our method builds on Concept Bottleneck Models (CBM) [25], which construct predictors through a linear combination of human-designed concepts ."}
+{"idx": 6, "title": "DWS Area: Web Data Mining, Research | Universität Mannheim", "date": "", "ddg_snippet": "DCBM : Data - Efficient Visual Concept Bottleneck Models . Four papers of the DWS group got accepted at the 22nd Extended Semantic Web Conference. This year, the acceptance rate was 22.4% for full papers in the research track.", "subpage_snippet": "", "source": "www.uni-mannheim.de", "link": "https://www.uni-mannheim.de/dws/news-archiv/dws-area-web-data-mining-research/?tx_news_pi1[@widget_0][currentPage]=2&cHash=dbf1436b0d93f36eda0040b2d5b33144", "content": "DCBM : Data - Efficient Visual Concept Bottleneck Models . Four papers of the DWS group got accepted at the 22nd Extended Semantic Web Conference. This year, the acceptance rate was 22.4% for full papers in the research track."}
+{"idx": 7, "title": "Beyond Concept Bottleneck Models : How to Make... | OpenReview", "date": "", "ddg_snippet": "Recently, interpretable machine learning has re-explored concept bottleneck models (CBM), comprising step-by-step prediction of the high-level concepts from the raw features and the target variable from the predicted concepts .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5oJlyJXUxK", "content": "Recently, interpretable machine learning has re-explored concept bottleneck models (CBM), comprising step-by-step prediction of the high-level concepts from the raw features and the target variable from the predicted concepts ."}
+{"idx": 8, "title": "Towards Transparent Urban Perception: A Concept -Driven Framework...", "date": "", "ddg_snippet": "Table 1 . Prompts for visual concepts generation. Note that “{requirement}\" can be changed to different words according to the different dataset requirements. The model ’s responses are concatenated and aggregated to form a raw concept pool", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202505.1819/v1", "content": "Table 1 . Prompts for visual concepts generation. Note that “{requirement}\" can be changed to different words according to the different dataset requirements. The model ’s responses are concatenated and aggregated to form a raw concept pool"}
+{"idx": 9, "title": "Sascha Marton - Google Scholar", "date": "", "ddg_snippet": "Concepts , Challenges, and Solutions.2025. DCBM : Data - Efficient Visual Concept Bottleneck Models .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=5PQJ3sEAAAAJ&hl=en", "content": "Concepts , Challenges, and Solutions.2025. DCBM : Data - Efficient Visual Concept Bottleneck Models ."}
diff --git a/data/sampled_jsons/Beckers_2023_Disjunctive_counterfactuals_Pearl_causal_models_abstract.jsonl b/data/sampled_jsons/Beckers_2023_Disjunctive_counterfactuals_Pearl_causal_models_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6881468bcd88cb9d3f9363373564ccd530ab7064
--- /dev/null
+++ b/data/sampled_jsons/Beckers_2023_Disjunctive_counterfactuals_Pearl_causal_models_abstract.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by S Galhotra · 2024 · Cited by 3 — to a family of functional causal models (see, e.g., ( Pearl 2000, Theorem 9.2. ... Disjunctive counterfactuals using causal models : a critical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.14728", "content": "by S Galhotra · 2024 · Cited by 3 — to a family of functional causal models (see, e.g., ( Pearl 2000, Theorem 9.2. ... Disjunctive counterfactuals using causal models : a critical ..."}
+{"idx": 1, "title": "(PDF) Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "models and causal Bayesian networks ( Pearl 2000). Both are typically described using directed. Beckers , S. ( 2023 ). Disjunctive counterfactuals using causal models : a critical examination. Un-. published manuscript. Galhotra, S., R. Pradhan, and B. Salimi (2021).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380847642_Intervention_and_Conditioning_in_Causal_Bayesian_Networks", "content": "models and causal Bayesian networks ( Pearl 2000). Both are typically described using directed. Beckers , S. ( 2023 ). Disjunctive counterfactuals using causal models : a critical examination. Un-. published manuscript. Galhotra, S., R. Pradhan, and B. Salimi (2021)."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by S Galhotra · 2024 · Cited by 3 — to a family of functional causal models (see, e.g., ( Pearl 2000, Theorem 9.2. ... Disjunctive counterfactuals using causal models : a critical examination. Un ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "by S Galhotra · 2024 · Cited by 3 — to a family of functional causal models (see, e.g., ( Pearl 2000, Theorem 9.2. ... Disjunctive counterfactuals using causal models : a critical examination. Un ..."}
+{"idx": 3, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "... causal models (see, e.g., ( Pearl 2000, Theorem 9.2.10)), but he does ... Disjunctive counterfactuals using causal models : a critical examination. Un ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96098", "content": "... causal models (see, e.g., ( Pearl 2000, Theorem 9.2.10)), but he does ... Disjunctive counterfactuals using causal models : a critical examination. Un ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Beimel_Dynamic_Algorithms_Adaptive_Adversary_search_problem_solution_vector.jsonl b/data/sampled_jsons/Beimel_Dynamic_Algorithms_Adaptive_Adversary_search_problem_solution_vector.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6eb5c24a65dd1e7f929bee7507fd1b7ad03dcdc2
--- /dev/null
+++ b/data/sampled_jsons/Beimel_Dynamic_Algorithms_Adaptive_Adversary_search_problem_solution_vector.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DynamicAlgorithmsAgainstanAdaptiveAdversary ...", "date": "", "ddg_snippet": "A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm . We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from differential privacy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2111.03980", "content": "A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm . We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from differential privacy ..."}
+{"idx": 1, "title": "Dynamic algorithms against an adaptive adversary: generic constructions ...", "date": "", "ddg_snippet": "Given an input that undergoes a sequence of updates, a dynamic algorithm maintains a valid solution to some predefined problem at any point in time; the goal is to design an algorithm in which computing a solution to the updated input is done more efficiently than computing the solution from scratch. A dynamic algorithm against an adaptive adversary is required to be correct when the adversary ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3519935.3520064", "content": "Given an input that undergoes a sequence of updates, a dynamic algorithm maintains a valid solution to some predefined problem at any point in time; the goal is to design an algorithm in which computing a solution to the updated input is done more efficiently than computing the solution from scratch. A dynamic algorithm against an adaptive adversary is required to be correct when the adversary ..."}
+{"idx": 2, "title": "[2111.03980] Dynamic Algorithms Against an Adaptive Adversary: Generic ...", "date": "", "ddg_snippet": "A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm . We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from differential privacy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.03980", "content": "A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm . We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from differential privacy ..."}
+{"idx": 3, "title": "[2111.03980] Dynamic Algorithms Against an Adaptive Adversary: Generic ...", "date": "", "ddg_snippet": "On the flip side, we specify dynamic problems that, assuming a random oracle, every dynamic algorithm that solves them against an adaptive adversary must be polynomially slower than a rather straightforward dynamic algorithm that solves them against an oblivious adversary . We first show a separation result for a search problem and then show a separation result for an estimation problem . In the ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2111.03980", "content": "On the flip side, we specify dynamic problems that, assuming a random oracle, every dynamic algorithm that solves them against an adaptive adversary must be polynomially slower than a rather straightforward dynamic algorithm that solves them against an oblivious adversary . We first show a separation result for a search problem and then show a separation result for an estimation problem . In the ..."}
+{"idx": 4, "title": "PDF Rounding Dynamic Matchings Against an Adaptive Adversary", "date": "", "ddg_snippet": "1 Introduction The field of dynamic graph algorithms studies the maintenance of solutions to graph-theoretic problems subject to graph updates, such as edge additions and removals. For any such dynamic problem , a trivial approach is to recompute a solution from scratch following each update, using a static algorithm . Fortunately, significant improvements over this naïve polynomial-time ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~dwajc/pdfs/wajc19.pdf", "content": "1 Introduction The field of dynamic graph algorithms studies the maintenance of solutions to graph-theoretic problems subject to graph updates, such as edge additions and removals. For any such dynamic problem , a trivial approach is to recompute a solution from scratch following each update, using a static algorithm . Fortunately, significant improvements over this naïve polynomial-time ..."}
+{"idx": 5, "title": "Dynamic Algorithms against an Adaptive Adversary: Generic Constructions ...", "date": "", "ddg_snippet": "A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm . We obtain faster dynamic algorithms against an adap - tive adversary and separation results between what is achievable in the oblivious vs. adaptive settings.", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10440813", "content": "A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm . We obtain faster dynamic algorithms against an adap - tive adversary and separation results between what is achievable in the oblivious vs. adaptive settings."}
+{"idx": 6, "title": "[PDF] Dynamic algorithms against an adaptive adversary: generic ...", "date": "", "ddg_snippet": "A general reduction is given transforming a dynamic algorithm against an oblivious adversary to a dynamic algorithms robust against an adaptive adversary , which maintains several copies of the oblivious algorithm and uses differential privacy to protect their random bits. Given an input that undergoes a sequence of updates, a dynamic algorithm maintains a valid solution to some predefined ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Dynamic-algorithms-against-an-adaptive-adversary:-Beimel-Kaplan/aa9988bf55a092f8050b11818ca3647543263351", "content": "A general reduction is given transforming a dynamic algorithm against an oblivious adversary to a dynamic algorithms robust against an adaptive adversary , which maintains several copies of the oblivious algorithm and uses differential privacy to protect their random bits. Given an input that undergoes a sequence of updates, a dynamic algorithm maintains a valid solution to some predefined ..."}
+{"idx": 7, "title": "Fully Dynamic Shortest Path Reporting Against an Adaptive Adversary∗", "date": "", "ddg_snippet": "The dynamic algorithm by [72] can maintain reachability with path reporting against an adaptive adversary in O(n1.75+o(1)) amortized update time2. We can maintain the shortest path in directed graphs against an adaptive adversary , instead of just any path, in worst-case update time.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/pdf/10.1137/1.9781611977912.108", "content": "The dynamic algorithm by [72] can maintain reachability with path reporting against an adaptive adversary in O(n1.75+o(1)) amortized update time2. We can maintain the shortest path in directed graphs against an adaptive adversary , instead of just any path, in worst-case update time."}
+{"idx": 8, "title": "On Differential Privacy for Adaptively Solving Search Problems via ...", "date": "", "ddg_snippet": "We identify key parameters to these problems , such as the number of c-approximate near neighbors and the matrix condition number, and use diferent diferential privacy techniques to design algorithms returning the solution vector with memory and time depending on these parameters.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503", "content": "We identify key parameters to these problems , such as the number of c-approximate near neighbors and the matrix condition number, and use diferent diferential privacy techniques to design algorithms returning the solution vector with memory and time depending on these parameters."}
+{"idx": 9, "title": "R ALGORITHMS ON A I B A - OpenReview", "date": "", "ddg_snippet": "We study dynamic algorithms robust to adaptive input generated from sources with bounded capabilities, such as sparsity or limited interaction. For example, we consider robust linear algebraic algorithms when the updates to the input are sparse but given by an adversary with access to a query oracle. We also study robust algo-rithms in the standard centralized setting, where an adversary ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=I29Kt0RwChs", "content": "We study dynamic algorithms robust to adaptive input generated from sources with bounded capabilities, such as sparsity or limited interaction. For example, we consider robust linear algebraic algorithms when the updates to the input are sparse but given by an adversary with access to a query oracle. We also study robust algo-rithms in the standard centralized setting, where an adversary ..."}
diff --git a/data/sampled_jsons/Beimel_differential_privacy_adaptive_queries_2022_sitearxiv.org_year_2022.jsonl b/data/sampled_jsons/Beimel_differential_privacy_adaptive_queries_2022_sitearxiv.org_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..43b4ffa900c9dd4ac9107f779999369ceca5b16a
--- /dev/null
+++ b/data/sampled_jsons/Beimel_differential_privacy_adaptive_queries_2022_sitearxiv.org_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2203.05481] Fully Adaptive Composition in Differential Privacy", "date": "", "ddg_snippet": "Composition is a key feature of differential privacy . Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit. However, these results require that the privacy parameters of all algorithms be fixed before interacting with the data. To address this, Rogers et al. introduced fully adaptive composition ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2203.05481", "content": "Composition is a key feature of differential privacy . Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit. However, these results require that the privacy parameters of all algorithms be fixed before interacting with the data. To address this, Rogers et al. introduced fully adaptive composition ..."}
+{"idx": 1, "title": "Advancing Differential Privacy : Where We Are Now and Future...", "date": "", "ddg_snippet": "In July 2022 , we hosted a workshop titled “ Differential Privacy (DP): Challenges towards the Next Frontier” with experts from industry, academia, and the public sector to discuss and find solutions to the challenges of differential privacy .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.06929", "content": "In July 2022 , we hosted a workshop titled “ Differential Privacy (DP): Challenges towards the Next Frontier” with experts from industry, academia, and the public sector to discuss and find solutions to the challenges of differential privacy ."}
+{"idx": 2, "title": "On Differential Privacy for Adaptively Solving Search Problems via...", "date": "", "ddg_snippet": "Adaptive ANN via Differentially Private Selection.Although these works use √ differential privacy to show that for some problems it is possible to tolerate T queries using O( T ) copies of a data structure, such results only apply to numerical estimation problems, and only return the...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503", "content": "Adaptive ANN via Differentially Private Selection.Although these works use √ differential privacy to show that for some problems it is possible to tolerate T queries using O( T ) copies of a data structure, such results only apply to numerical estimation problems, and only return the..."}
+{"idx": 3, "title": "On Differential Privacy for Adaptively Solving Search Problems via...", "date": "", "ddg_snippet": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems, which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05503v1", "content": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems, which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query ."}
+{"idx": 4, "title": "An Adaptive Differential Privacy Method Based on", "date": "", "ddg_snippet": "Therefore, we proposed an adaptive differential privacy method based on federated learning. The method sets the adjustment coefficient and scoring function according to ac-curacy, loss, training rounds, and the number of datasets and clients.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.08909", "content": "Therefore, we proposed an adaptive differential privacy method based on federated learning. The method sets the adjustment coefficient and scoring function according to ac-curacy, loss, training rounds, and the number of datasets and clients."}
+{"idx": 5, "title": "[2111.03980] Dynamic Algorithms Against an Adaptive Adversary...", "date": "", "ddg_snippet": "To get these results we exploit techniques from differential privacy , cryptography, and adaptive data analysis.View a PDF of the paper titled Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds, by Amos Beimel and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.03980", "content": "To get these results we exploit techniques from differential privacy , cryptography, and adaptive data analysis.View a PDF of the paper titled Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds, by Amos Beimel and 5 other authors."}
+{"idx": 6, "title": "Composition Theorems for Interactive Differential Privacy", "date": "", "ddg_snippet": "We study composition properties of differential privacy in concurrent compositions. In this setting, an adversary interacts with k interactive mechanisms in parallel and can interleave its queries to the mechanisms arbitrarily. Previously, Vadhan and Wang [2021] proved an optimal concurrent composition theorem for pure- differential privacy .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.09397", "content": "We study composition properties of differential privacy in concurrent compositions. In this setting, an adversary interacts with k interactive mechanisms in parallel and can interleave its queries to the mechanisms arbitrarily. Previously, Vadhan and Wang [2021] proved an optimal concurrent composition theorem for pure- differential privacy ."}
+{"idx": 7, "title": "On Differential Privacy for Adaptively Solving Search Problems via ...", "date": "", "ddg_snippet": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems, which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.05503", "content": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems, which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query."}
+{"idx": 8, "title": "Privately Fine-Tuning Large Language Models with Differential Privacy", "date": "", "ddg_snippet": "The issue has raised deep concerns about the privacy of LLMs. Differential privacy (DP) provides a rigorous framework that allows adding noise in the process of training or fine-tuning LLMs such that extracting the training data becomes infeasible (i.e., with a cryptographically small success probability).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2210.15042", "content": "The issue has raised deep concerns about the privacy of LLMs. Differential privacy (DP) provides a rigorous framework that allows adding noise in the process of training or fine-tuning LLMs such that extracting the training data becomes infeasible (i.e., with a cryptographically small success probability)."}
+{"idx": 9, "title": "Improved Differential Privacy for SGD via Optimal Private Linear ...", "date": "", "ddg_snippet": "Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting. We prove fundamental theoretical results on the applicability of matrix factorizations to adaptive streams, and provide a parameter-free fixed-point algorithm for computing optimal factorizations. We instantiate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.08312", "content": "Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting. We prove fundamental theoretical results on the applicability of matrix factorizations to adaptive streams, and provide a parameter-free fixed-point algorithm for computing optimal factorizations. We instantiate ..."}
diff --git a/data/sampled_jsons/Beimel_et_al.,_2022_Private_PAC_learning_cost_vs_solution.jsonl b/data/sampled_jsons/Beimel_et_al.,_2022_Private_PAC_learning_cost_vs_solution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..15c71953c8dd3492df50f166f0e7bda05de9b437
--- /dev/null
+++ b/data/sampled_jsons/Beimel_et_al.,_2022_Private_PAC_learning_cost_vs_solution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Growth of Mistakes in Differentially Private Online ...", "date": "", "ddg_snippet": "This line of research was advanced by subsequent works ( Beimel et al ., 2013a, 2014; Feldman and Xiao, 2014), resulting in the findings of Alon et al . ( 2022 ) which established a surprising equivalence between non- private online learning and Approximate DP- PAC learning .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/dmitriev24a/dmitriev24a.pdf", "content": "This line of research was advanced by subsequent works ( Beimel et al ., 2013a, 2014; Feldman and Xiao, 2014), resulting in the findings of Alon et al . ( 2022 ) which established a surprising equivalence between non- private online learning and Approximate DP- PAC learning ."}
+{"idx": 1, "title": "Private PAC Learning May be Harder than Online Learning", "date": "", "ddg_snippet": "We exhibit a concept class that admits an online learner running in polynomial time with a polynomial mistake bound, but for which there is no computationally-efficient differ-entially private PAC learner .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.11119", "content": "We exhibit a concept class that admits an online learner running in polynomial time with a polynomial mistake bound, but for which there is no computationally-efficient differ-entially private PAC learner ."}
+{"idx": 2, "title": "Differential Privacy - Differentially Private PAC Learning On the Growth of Mistakes in Differentially Private Online ... Simultaneous Private Learning of Multiple Concepts Optimal Differentially Private Learning of Thresholds and ... A Basic Overview of Private PAC learning - GitHub Pages Differential Privacy - Differentially Private PAC Learning Differential Privacy - Differentially Private PAC Learning Differential Privacy - Differentially Private PAC Learning Differential Privacy - Differentially Private PAC Learning Differential Privacy - Differentially Private PAC Learning Differential Privacy - Differentially Private PAC Learning Private Learning and Sanitization: Pure vs. Approximate ...", "date": "", "ddg_snippet": "We’ll start with a brief overview of PAC learning absent any privacyrestrictions. Readers familiar with PAC learning can probably skip thissection while noting that 1. (the cardinality version of) Occam’s razor is a baseline learnerusing O(log|H|)samples, 2. VC dimension characterizes non- private PAC learning , 3. we’ll focus on the sample complexi... See full list on differentialprivacy.org It is straightforward to add a differential privacy constraint to thePAC framework: the hypothesis output by the learner must be adifferentially private function of the labeled examples(x1,y1),…,(xn,yn). That is, changing any one of theexamples — even to one with an inconsistent label — must not affectthe distribution over hypotheses output by the ... See full list on differentialprivacy.org As it turns out, answering this question will take some time. We startwith a partial negative answer. Specifically, we’ll see a class with VCdimension 1 and (a restricted form of) private sample complexityarbitrarily larger than 1. We’ll also cover the first in a line ofcharacterization results for private PAC learning . We first consider learners t... See full list on differentialprivacy.org So far, we’ve focused only on pure privacy. In this section, we move onto the first separations between pure and approximate private PAClearning, as well as the first connection between private learning andonlinelearning. Our source is a pair of interconnected papers from around 2014. Amongother things, Feldman and Xiao [FX14] introduced Littleston... See full list on differentialprivacy.org We now dash this hope. In 2015, Bun, Nissim, Stemmer, andVadhan [BNSV15] gave the first nontrivial lower bound for approximateprivate PAC learning . They showed that learning ThreshT hasproper approximate private sample complexity Ω(log∗(T)) andO(2log∗(T)). We’ll at least try to give some intuition for the presence of log∗in the lower bound. Infor... See full list on differentialprivacy.org Spurred by this question, several advances in private PAC learning haveappeared in the last year. First, Gonen, Hazan, and Moran strengthenedTheorem 3 by giving a constructive method for convertingpure private learners to online learners [GHM19]. Their resultreaches back to the 2013 characterization of pure private learning interms of representatio... See full list on differentialprivacy.org This concludes our post, and with it our discussion of this fundamentalquestion: the price of privacy in machine learning . We now know that inthe PAC model, proper pure private learning , improper pure privatelearning , approximate private learning , and non-private learning are allstrongly separated. By the connection to Littlestone dimension, we als... See full list on differentialprivacy.org This line of research was advanced by subsequent works ( Beimel et al ., 2013a, 2014; Feldman and Xiao, 2014), resulting in the findings of Alon et al . ( 2022 ) which established a surprising equivalence between non- private online learning and Approximate DP- PAC learning . We investigate the direct-sum problem in the context of di erentially private PAC learning : What is the sample complexity of solving k learning tasks simultaneously under di erential privacy, and how does this cost compare to that of solving k learning tasks without privacy? We investigate the {\\em direct-sum} problem in the context of differentially private PAC learning : What is the sample complexity of solving k learning tasks simultaneously under differential privacy, and how does this cost compare to that of solving k ... A natural question is whether private PAC learning is possible, and if so how many more samples are necessary to do it when compared with non- private PAC learning ? Is approximate private learnability equivalent to online learnability? We now know that in the PAC model, proper pure private learning, improper pure private learning, approximate private learning, and non-private learning are all strongly separated. By the connection to Littlestone dimension, we also know that approximate private learnability is equivalent to online learnability . Is online PAC learnability necessary for private PAC learning? The sample complexity to approximate private PAC learn H is Ω (log ∗ (LD (H))). Littlestone dimension characterizes online PAC learning, so we now know that online PAC learnability is necessary for private PAC learnability. Sufficiency, however, remains an open question. What is the sample complexity to pure private PAC learn H? The sample complexity to pure private PAC learn H is Θ (REPD (H)). Representation dimension may seem like a strange definition, but a sketch of the proof of this result helps illustrate the connection to private learning. How has private PAC learning changed over the last year? Spurred by this question, several advances in private PAC learning have appeared in the last year. First, Gonen, Hazan, and Moran strengthened Theorem 3 by giving a constructive method for converting pure private learners to online learners [GHM19]. Does VC dimension characterize pure private PAC learning? In contrast, VCD (Point d) = 1, so this Ω (d) lower bound shows us that VC dimension does not characterize proper pure private PAC learning. This result uses the classic “packing” lower bound method, which powers many lower bounds for pure differential privacy. What is PAC learnability? In fact, the “Fundamental Theorem of Statistical Learning” says that PAC learnability ( realizable or agnostic ) is equivalent to finite VC dimension. In this sense, VCD (H) is a good measure of how hard it is to PAC learn H. We show that the sample complexity of these tasks under approx-imate differential privacy can be significantly lower than that under pure differential privacy. Keywords: Differential Privacy, Private Learning , Sanitization.", "subpage_snippet": "", "source": "differentialprivacy.org", "link": "https://differentialprivacy.org/private-pac/", "content": "We’ll start with a brief overview of PAC learning absent any privacyrestrictions. Readers familiar with PAC learning can probably skip thissection while noting that 1. (the cardinality version of) Occam’s razor is a baseline learnerusing O(log|H|)samples, 2. VC dimension characterizes non- private PAC learning , 3. we’ll focus on the sample complexi... See full list on differentialprivacy.org It is straightforward to add a differential privacy constraint to thePAC framework: the hypothesis output by the learner must be adifferentially private function of the labeled examples(x1,y1),…,(xn,yn). That is, changing any one of theexamples — even to one with an inconsistent label — must not affectthe distribution over hypotheses output by the ... See full list on differentialprivacy.org As it turns out, answering this question will take some time. We startwith a partial negative answer. Specifically, we’ll see a class with VCdimension 1 and (a restricted form of) private sample complexityarbitrarily larger than 1. We’ll also cover the first in a line ofcharacterization results for private PAC learning . We first consider learners t... See full list on differentialprivacy.org So far, we’ve focused only on pure privacy. In this section, we move onto the first separations between pure and approximate private PAClearning, as well as the first connection between private learning andonlinelearning. Our source is a pair of interconnected papers from around 2014. Amongother things, Feldman and Xiao [FX14] introduced Littleston... See full list on differentialprivacy.org We now dash this hope. In 2015, Bun, Nissim, Stemmer, andVadhan [BNSV15] gave the first nontrivial lower bound for approximateprivate PAC learning . They showed that learning ThreshT hasproper approximate private sample complexity Ω(log∗(T)) andO(2log∗(T)). We’ll at least try to give some intuition for the presence of log∗in the lower bound. Infor... See full list on differentialprivacy.org Spurred by this question, several advances in private PAC learning haveappeared in the last year. First, Gonen, Hazan, and Moran strengthenedTheorem 3 by giving a constructive method for convertingpure private learners to online learners [GHM19]. Their resultreaches back to the 2013 characterization of pure private learning interms of representatio... See full list on differentialprivacy.org This concludes our post, and with it our discussion of this fundamentalquestion: the price of privacy in machine learning . We now know that inthe PAC model, proper pure private learning , improper pure privatelearning , approximate private learning , and non-private learning are allstrongly separated. By the connection to Littlestone dimension, we als... See full list on differentialprivacy.org This line of research was advanced by subsequent works ( Beimel et al ., 2013a, 2014; Feldman and Xiao, 2014), resulting in the findings of Alon et al . ( 2022 ) which established a surprising equivalence between non- private online learning and Approximate DP- PAC learning . We investigate the direct-sum problem in the context of di erentially private PAC learning : What is the sample complexity of solving k learning tasks simultaneously under di erential privacy, and how does this cost compare to that of solving k learning tasks without privacy? We investigate the {\\em direct-sum} problem in the context of differentially private PAC learning : What is the sample complexity of solving k learning tasks simultaneously under differential privacy, and how does this cost compare to that of solving k ... A natural question is whether private PAC learning is possible, and if so how many more samples are necessary to do it when compared with non- private PAC learning ? Is approximate private learnability equivalent to online learnability? We now know that in the PAC model, proper pure private learning, improper pure private learning, approximate private learning, and non-private learning are all strongly separated. By the connection to Littlestone dimension, we also know that approximate private learnability is equivalent to online learnability . Is online PAC learnability necessary for private PAC learning? The sample complexity to approximate private PAC learn H is Ω (log ∗ (LD (H))). Littlestone dimension characterizes online PAC learning, so we now know that online PAC learnability is necessary for private PAC learnability. Sufficiency, however, remains an open question. What is the sample complexity to pure private PAC learn H? The sample complexity to pure private PAC learn H is Θ (REPD (H)). Representation dimension may seem like a strange definition, but a sketch of the proof of this result helps illustrate the connection to private learning. How has private PAC learning changed over the last year? Spurred by this question, several advances in private PAC learning have appeared in the last year. First, Gonen, Hazan, and Moran strengthened Theorem 3 by giving a constructive method for converting pure private learners to online learners [GHM19]. Does VC dimension characterize pure private PAC learning? In contrast, VCD (Point d) = 1, so this Ω (d) lower bound shows us that VC dimension does not characterize proper pure private PAC learning. This result uses the classic “packing” lower bound method, which powers many lower bounds for pure differential privacy. What is PAC learnability? In fact, the “Fundamental Theorem of Statistical Learning” says that PAC learnability ( realizable or agnostic ) is equivalent to finite VC dimension. In this sense, VCD (H) is a good measure of how hard it is to PAC learn H. We show that the sample complexity of these tasks under approx-imate differential privacy can be significantly lower than that under pure differential privacy. Keywords: Differential Privacy, Private Learning , Sanitization."}
+{"idx": 3, "title": "Simultaneous Private Learning of Multiple Concepts", "date": "", "ddg_snippet": "We investigate the direct-sum problem in the context of di erentially private PAC learning : What is the sample complexity of solving k learning tasks simultaneously under di erential privacy, and how does this cost compare to that of solving k learning tasks without privacy?", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume20/18-549/18-549.pdf", "content": "We investigate the direct-sum problem in the context of di erentially private PAC learning : What is the sample complexity of solving k learning tasks simultaneously under di erential privacy, and how does this cost compare to that of solving k learning tasks without privacy?"}
+{"idx": 4, "title": "Optimal Differentially Private Learning of Thresholds and ...", "date": "", "ddg_snippet": "We investigate the {\\em direct-sum} problem in the context of differentially private PAC learning : What is the sample complexity of solving k learning tasks simultaneously under differential privacy, and how does this cost compare to that of solving k ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3564246.3585148", "content": "We investigate the {\\em direct-sum} problem in the context of differentially private PAC learning : What is the sample complexity of solving k learning tasks simultaneously under differential privacy, and how does this cost compare to that of solving k ..."}
+{"idx": 5, "title": "A Basic Overview of Private PAC learning - GitHub Pages", "date": "", "ddg_snippet": "A natural question is whether private PAC learning is possible, and if so how many more samples are necessary to do it when compared with non- private PAC learning ?", "subpage_snippet": "", "source": "dpcourse.github.io", "link": "https://dpcourse.github.io/2023-spring/lecnotes-web/DP-S23-notes-lec-23-PAC-learning-Sivakumar.pdf", "content": "A natural question is whether private PAC learning is possible, and if so how many more samples are necessary to do it when compared with non- private PAC learning ?"}
+{"idx": 6, "title": "Private Learning and Sanitization: Pure vs. Approximate ...", "date": "", "ddg_snippet": "We show that the sample complexity of these tasks under approx-imate differential privacy can be significantly lower than that under pure differential privacy. Keywords: Differential Privacy, Private Learning , Sanitization.", "subpage_snippet": "", "source": "privacytools.seas.harvard.edu", "link": "https://privacytools.seas.harvard.edu/file_url/285", "content": "We show that the sample complexity of these tasks under approx-imate differential privacy can be significantly lower than that under pure differential privacy. Keywords: Differential Privacy, Private Learning , Sanitization."}
+{"idx": 7, "title": "Private PAC Learning May be Harder than Online Learning", "date": "", "ddg_snippet": "by M Bun · 2024 · Cited by 2 — Abstract. We continue the study of the computational complexity of differentially private PAC learning and how it is situated within the foundations of ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v237/bun24a/bun24a.pdf", "content": "by M Bun · 2024 · Cited by 2 — Abstract. We continue the study of the computational complexity of differentially private PAC learning and how it is situated within the foundations of ..."}
+{"idx": 8, "title": "Improved Bounds for Pure Private Agnostic Learning", "date": "", "ddg_snippet": "by B Li · 2024 · Cited by 2 — For pure private realizable learning , tight sample complexity bounds were shown by Beimel et al . [2019]. However, for agnostic learning , there ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2407.20640", "content": "by B Li · 2024 · Cited by 2 — For pure private realizable learning , tight sample complexity bounds were shown by Beimel et al . [2019]. However, for agnostic learning , there ..."}
+{"idx": 9, "title": "Private Realizable-to-Agnostic Transformation with Near ...", "date": "", "ddg_snippet": "by B Li · 2025 — PAC learning focuses on the realizable case , which assumes that the underlying distribution D is labeled by some concept c ∈ C. In contrast, agnostic learning ( ... 23 pages", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v291/main/assets/li25e/li25e.pdf", "content": "by B Li · 2025 — PAC learning focuses on the realizable case , which assumes that the underlying distribution D is labeled by some concept c ∈ C. In contrast, agnostic learning ( ... 23 pages"}
diff --git a/data/sampled_jsons/Beimel_et_al.,_2022_private_median_framework_regression_challenge_coordinate-wise.jsonl b/data/sampled_jsons/Beimel_et_al.,_2022_private_median_framework_regression_challenge_coordinate-wise.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..997f15b8330157b44f01ec3449503880203f374b
--- /dev/null
+++ b/data/sampled_jsons/Beimel_et_al.,_2022_private_median_framework_regression_challenge_coordinate-wise.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Differentially Private Median Forests for Regression and ...", "date": "", "ddg_snippet": "We have presented a new, differentially private , tree-based method for regression and classification, based on random forests with median splits. Our algorithm can easily be used for either regression or classification, and works with both cat-egorical and numeric covariates.", "subpage_snippet": "", "source": "ppai21.github.io", "link": "https://ppai21.github.io/files/2-paper.pdf", "content": "We have presented a new, differentially private , tree-based method for regression and classification, based on random forests with median splits. Our algorithm can easily be used for either regression or classification, and works with both cat-egorical and numeric covariates."}
+{"idx": 1, "title": "Differentially private multivariate medians", "date": "", "ddg_snippet": "On a similar note, Hopkins et al ., ( 2022 ) showed that there exists a level of contamination under which the sample complexity of their (purely) differentially private mean estimator remains unchanged. On the other hand, if one wishes to estimate location robustly, the canonical estimators are medians.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2210.06459v2", "content": "On a similar note, Hopkins et al ., ( 2022 ) showed that there exists a level of contamination under which the sample complexity of their (purely) differentially private mean estimator remains unchanged. On the other hand, if one wishes to estimate location robustly, the canonical estimators are medians."}
+{"idx": 2, "title": "Amos BEIMEL | Ben-Gurion University of the Negev, Beersheba ...", "date": "", "ddg_snippet": "We study the notion of ad hoc secure computation, recently introduced by Beimel et al . (ITCS 2016), in the context of the Private Simultaneous Messages (PSM) model of Feige et al . (STOC 2004).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Amos-Beimel", "content": "We study the notion of ad hoc secure computation, recently introduced by Beimel et al . (ITCS 2016), in the context of the Private Simultaneous Messages (PSM) model of Feige et al . (STOC 2004)."}
+{"idx": 3, "title": "Differentially private multivariate medians - arXiv.org", "date": "", "ddg_snippet": "20; Brown et al ., 2021; Liu et al ., 2021b). After which, Kamath et al . (2020); Liu et al . (2021a) and Hopkins et al . ( 2022 ) studied diferentially private mean estimati n for measures with a finite second moment. Within this framework , Kamath et al . (2020) considered heavy tails and Liu et al . (2021a) and Hopkins et al . ( 2022 ) considered", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.06459v2", "content": "20; Brown et al ., 2021; Liu et al ., 2021b). After which, Kamath et al . (2020); Liu et al . (2021a) and Hopkins et al . ( 2022 ) studied diferentially private mean estimati n for measures with a finite second moment. Within this framework , Kamath et al . (2020) considered heavy tails and Liu et al . (2021a) and Hopkins et al . ( 2022 ) considered"}
+{"idx": 4, "title": "Differentially Private Optimization - with Coordinate Descent ...", "date": "", "ddg_snippet": "DIFFERENTIALLY PRIVATE COORDINATE DESCENT (DP-CD) Algorithm Differentially Private Coordinate Descent (DP-CD) [Mangold et al ., 2022 ] Initialize w(0) p ∈ R for t = 0, . . .", "subpage_snippet": "", "source": "researchers.lille.inria.fr", "link": "https://researchers.lille.inria.fr/abellet/talks/differentially_private_optimization.pdf", "content": "DIFFERENTIALLY PRIVATE COORDINATE DESCENT (DP-CD) Algorithm Differentially Private Coordinate Descent (DP-CD) [Mangold et al ., 2022 ] Initialize w(0) p ∈ R for t = 0, . . ."}
+{"idx": 5, "title": "Private Regression In Multiple Outcomes (PRIMO)", "date": "", "ddg_snippet": "Abstract We introduce a new differentially private regression setting we call Private Regres-sion in Multiple Outcomes (PRIMO) inspired the common situation in the social and biomedical sciences where a data analyst wants to perform a set of l regressions while preserving privacy, where in each of the regressions the covariates X are shared, and each regression i has a different vector of ...", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2022/papers/primo.pdf", "content": "Abstract We introduce a new differentially private regression setting we call Private Regres-sion in Multiple Outcomes (PRIMO) inspired the common situation in the social and biomedical sciences where a data analyst wants to perform a set of l regressions while preserving privacy, where in each of the regressions the covariates X are shared, and each regression i has a different vector of ..."}
+{"idx": 6, "title": "Oracle-Efficient Differentially Private Learning with Public ...", "date": "", "ddg_snippet": "In this work, we present the first computationally efficient, algorithms to provably leverage public data to learn privately whenever a function class is ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96208", "content": "In this work, we present the first computationally efficient, algorithms to provably leverage public data to learn privately whenever a function class is ..."}
+{"idx": 7, "title": "Balancing performance and speed: Effective differentially ...", "date": "", "ddg_snippet": "To address the privacy preservation requirements in sensitive database analytics, this study focuses on innovative differential privacy algorithms for median regression models. While median regression demonstrates unique advantages in outlier resistance compared with conventional methods, its privacy-preserving algorithm design faces dual challenges: the differentiability barrier from non ...", "subpage_snippet": "", "source": "www.sciengine.com", "link": "https://www.sciengine.com/doi/10.1360/SSM-2025-0079", "content": "To address the privacy preservation requirements in sensitive database analytics, this study focuses on innovative differential privacy algorithms for median regression models. While median regression demonstrates unique advantages in outlier resistance compared with conventional methods, its privacy-preserving algorithm design faces dual challenges: the differentiability barrier from non ..."}
+{"idx": 8, "title": "ICML Poster On Differential Privacy for Adaptively Solving ...", "date": "", "ddg_snippet": "For adaptive regression , we show how to upgrade the private median framework of ( Beimel et al ., 2022 ) to output the solution vector, and how to obtain ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44265", "content": "For adaptive regression , we show how to upgrade the private median framework of ( Beimel et al ., 2022 ) to output the solution vector, and how to obtain ..."}
+{"idx": 9, "title": "On Differential Privacy for Adaptively Solving Search Problems ...", "date": "", "ddg_snippet": "differentially private selection over a sparse vector. For adaptive regression , we show how to upgrade the private median framework of ( Beimel et al ., 2022 ) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kEn7Wt6Yj2", "content": "differentially private selection over a sparse vector. For adaptive regression , we show how to upgrade the private median framework of ( Beimel et al ., 2022 ) ..."}
diff --git a/data/sampled_jsons/Beimel_et_al._2022_differential_privacy_adaptive_queries_abstract_year_2022.jsonl b/data/sampled_jsons/Beimel_et_al._2022_differential_privacy_adaptive_queries_abstract_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0a9edab5d0c7af14bea5411eb9a5bceb33a9533e
--- /dev/null
+++ b/data/sampled_jsons/Beimel_et_al._2022_differential_privacy_adaptive_queries_abstract_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "COLT 2025 Book of Abstracts", "date": "", "ddg_snippet": "We complement the lower bound with an algorithm requiring $\\left\\{\\frac{LM}{d\\eps}\\right\\}^{\\mathcal O(d)}$ queries , thereby characterizing the tight ...", "subpage_snippet": "", "source": "learningtheory.org", "link": "https://learningtheory.org/colt2025/abstracts.html", "content": "We complement the lower bound with an algorithm requiring $\\left\\{\\frac{LM}{d\\eps}\\right\\}^{\\mathcal O(d)}$ queries , thereby characterizing the tight ..."}
+{"idx": 1, "title": "Giuseppe Persiano | Chatting and drinking in Florence, March", "date": "", "ddg_snippet": "... algorithms match the lower ... We also present matching lower bounds for the non- adaptive static membership problem in the deterministic setting.", "subpage_snippet": "", "source": "giuper.github.io", "link": "https://giuper.github.io/", "content": "... algorithms match the lower ... We also present matching lower bounds for the non- adaptive static membership problem in the deterministic setting."}
+{"idx": 2, "title": "Iftach Haitner", "date": "", "ddg_snippet": "In distributed differential privacy , multiple parties collaborate to analyze their combined data while each party protects the confidentiality of its ...", "subpage_snippet": "", "source": "www.iacr.org", "link": "https://www.iacr.org/cryptodb/data/author.php?authorkey=1707", "content": "In distributed differential privacy , multiple parties collaborate to analyze their combined data while each party protects the confidentiality of its ..."}
+{"idx": 3, "title": "US9141824B2 - Dynamic database update in multi-server private", "date": "", "ddg_snippet": "... and in particular to enabling database updates to occur concurrently with user queries , without allowing the update process to compromise the privacy ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US9141824B2/en", "content": "... and in particular to enabling database updates to occur concurrently with user queries , without allowing the update process to compromise the privacy ..."}
+{"idx": 4, "title": "US11062215B2 - Using different data sources for a predictive", "date": "", "ddg_snippet": "This aids in protecting individual privacy ( e .g., protecting personally identifying information for individuals), while enabling robust predictive ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11062215B2/en", "content": "This aids in protecting individual privacy ( e .g., protecting personally identifying information for individuals), while enabling robust predictive ..."}
+{"idx": 5, "title": "Seminar Schedule", "date": "", "ddg_snippet": "Differentially private (DP) algorithms typically exhibit a significant dependence on the dimensionality of their input, as their error or sample ...", "subpage_snippet": "", "source": "in.bgu.ac.il", "link": "https://in.bgu.ac.il/en/natural_science/cs/Pages/SeminarSchedule2022.aspx", "content": "Differentially private (DP) algorithms typically exhibit a significant dependence on the dimensionality of their input, as their error or sample ..."}
+{"idx": 6, "title": "The Cost of Compression: Tight Quadratic Black-Box Attacks on", "date": "", "ddg_snippet": "... adaptive setting is well studied across multiple areas, including statistical queries [ 18 , 26 , 29 , 23 , 16 , 6 ] , sketching and streaming ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16345v1", "content": "... adaptive setting is well studied across multiple areas, including statistical queries [ 18 , 26 , 29 , 23 , 16 , 6 ] , sketching and streaming ..."}
+{"idx": 7, "title": "Yuval Ishai", "date": "", "ddg_snippet": "An important requirement in synchronous protocols is that, even when a party receives all its messages for a given round ahead of time, it must wait ...", "subpage_snippet": "", "source": "www.iacr.org", "link": "https://www.iacr.org/cryptodb/data/author.php?authorkey=1221", "content": "An important requirement in synchronous protocols is that, even when a party receives all its messages for a given round ahead of time, it must wait ..."}
+{"idx": 8, "title": "my choices [Oded Goldreich]", "date": "", "ddg_snippet": "My problem with many of these blogs is that they tend to cover the obvious (i. e ., they all focus more or less on the same \"hot results\" that draw ...", "subpage_snippet": "", "source": "www.wisdom.weizmann.ac.il", "link": "https://www.wisdom.weizmann.ac.il/~oded/my-choice.html", "content": "My problem with many of these blogs is that they tend to cover the obvious (i. e ., they all focus more or less on the same \"hot results\" that draw ..."}
+{"idx": 9, "title": "US9219604B2 - Generating an encrypted message for storage -", "date": "", "ddg_snippet": "an image sensor of the computing system generates raw picture data and, using an image compression program ( e .g., JPEG, MPEG, etc.), the computing ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US9219604B2/en", "content": "an image sensor of the computing system generates raw picture data and, using an image compression program ( e .g., JPEG, MPEG, etc.), the computing ..."}
diff --git "a/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Algorithm_1_condition_\316\262_t_N(s,a)_year_2024.jsonl" "b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Algorithm_1_condition_\316\262_t_N(s,a)_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..3fc8eb96cff2d1eea5b9f6b921c9a2bcbefba496
--- /dev/null
+++ "b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Algorithm_1_condition_\316\262_t_N(s,a)_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism: Exploration With Partially Observable Rewards - arXiv.org", "date": "", "ddg_snippet": "How can we efficiently explore and learn an optimal policy when rewards are partially observable , without relying on optimism and yet still have guarantees of convergence? In this paper, we present a novel exploration strategy based on the successor representation to tackle this question. Note that Lattimore and Szepesvari [33] already argued against optimism in partial monitoring [8], a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v1", "content": "How can we efficiently explore and learn an optimal policy when rewards are partially observable , without relying on optimism and yet still have guarantees of convergence? In this paper, we present a novel exploration strategy based on the successor representation to tackle this question. Note that Lattimore and Szepesvari [33] already argued against optimism in partial monitoring [8], a ..."}
+{"idx": 1, "title": "Beyond optimism | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3740005", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 2, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable ."}
+{"idx": 3, "title": "Beyond Optimism: Exploration With Partially Observable Rewards | AI ...", "date": "", "ddg_snippet": "Conclusion This paper introduces a novel exploration strategy called \" Optimism Beyond Optimism \" (OBO) for reinforcement learning in partially observable environments. By considering a broader range of possible outcomes, OBO aims to strike a better balance between exploration and exploitation compared to traditional optimistic methods.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-optimism-exploration-partially-observable-rewards", "content": "Conclusion This paper introduces a novel exploration strategy called \" Optimism Beyond Optimism \" (OBO) for reinforcement learning in partially observable environments. By considering a broader range of possible outcomes, OBO aims to strike a better balance between exploration and exploitation compared to traditional optimistic methods."}
+{"idx": 4, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Title: Beyond Optimism : Exploration With Partially Observable Rewards Authors: Simone Parisi, Alireza Kazemipour, Michael Bowling, Abstract summary: Exploration in reinforcement learning (RL) remains an open challenge. We present a novel strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2406.13909v1_enmode", "content": "Title: Beyond Optimism : Exploration With Partially Observable Rewards Authors: Simone Parisi, Alireza Kazemipour, Michael Bowling, Abstract summary: Exploration in reinforcement learning (RL) remains an open challenge. We present a novel strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy."}
+{"idx": 5, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Abstract Exploration in reinforcement learning (RL) remains an open challenge.RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/784fd5a46dfe303e5b51c8621b84cf3f-Abstract-Conference.html", "content": "Abstract Exploration in reinforcement learning (RL) remains an open challenge.RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism ."}
+{"idx": 6, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=k6ZHvF1vkg", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 7, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.13909", "content": "Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits ..."}
+{"idx": 8, "title": "opendilab/awesome-exploration-rl - GitHub", "date": "", "ddg_snippet": "A curated list of awesome exploration RL resources (continually updated) - opendilab/awesome- exploration -rl", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/opendilab/awesome-exploration-rl", "content": "A curated list of awesome exploration RL resources (continually updated) - opendilab/awesome- exploration -rl"}
+{"idx": 9, "title": "Simone Parisi", "date": "", "ddg_snippet": "Selected Papers Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi, Alireza Kazemipour, Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling", "subpage_snippet": "", "source": "sparisi.github.io", "link": "https://sparisi.github.io/", "content": "Selected Papers Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi, Alireza Kazemipour, Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling"}
diff --git a/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Limitations_and_Future_Work_neural_net.jsonl b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Limitations_and_Future_Work_neural_net.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf624569fac98aa3554f0f74f2a952573c7cc24f
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Limitations_and_Future_Work_neural_net.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/784fd5a46dfe303e5b51c8621b84cf3f-Abstract-Conference.html", "content": "In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable ."}
+{"idx": 1, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.13909", "content": "Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits ..."}
+{"idx": 2, "title": "Beyond optimism | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740005", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 3, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable ."}
+{"idx": 4, "title": "Beyond Optimism: Exploration With Partially Observable Rewards | AI ...", "date": "", "ddg_snippet": "Conclusion This paper introduces a novel exploration strategy called \" Optimism Beyond Optimism \" (OBO) for reinforcement learning in partially observable environments. By considering a broader range of possible outcomes, OBO aims to strike a better balance between exploration and exploitation compared to traditional optimistic methods.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-optimism-exploration-partially-observable-rewards", "content": "Conclusion This paper introduces a novel exploration strategy called \" Optimism Beyond Optimism \" (OBO) for reinforcement learning in partially observable environments. By considering a broader range of possible outcomes, OBO aims to strike a better balance between exploration and exploitation compared to traditional optimistic methods."}
+{"idx": 5, "title": "PDF Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Alireza Kazemipour Michael Bowling 38th Conference on Neural Information Processing Systems (NeurIPS 2024)", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/93919.pdf", "content": "Alireza Kazemipour Michael Bowling 38th Conference on Neural Information Processing Systems (NeurIPS 2024)"}
+{"idx": 6, "title": "Beyond Optimism: Exploration With Partially Observable Rewards ...", "date": "", "ddg_snippet": "Beyond Optimism : Exploration With Partially Observable Rewards . In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, Cheng Zhang 0005, editors, Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada ...", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/ParisiKB24", "content": "Beyond Optimism : Exploration With Partially Observable Rewards . In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, Cheng Zhang 0005, editors, Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada ..."}
+{"idx": 7, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Title: Beyond Optimism : Exploration With Partially Observable Rewards Authors: Simone Parisi, Alireza Kazemipour, Michael Bowling, Abstract summary: Exploration in reinforcement learning (RL) remains an open challenge. We present a novel strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2406.13909v1_enmode", "content": "Title: Beyond Optimism : Exploration With Partially Observable Rewards Authors: Simone Parisi, Alireza Kazemipour, Michael Bowling, Abstract summary: Exploration in reinforcement learning (RL) remains an open challenge. We present a novel strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy."}
+{"idx": 8, "title": "Beyond Optimism: Exploration With Partially Observable Rewards - arXiv.org", "date": "", "ddg_snippet": "Most importantly, our exploration does not suffer from the limitations of optimism discussed in Section 2.2 — the agent will eventually explore all state-action pairs even when rewards are partially observable , because exploration is not influenced by the Q-function ( and , thus, by rewards ), but fully driven by the S-function.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v1", "content": "Most importantly, our exploration does not suffer from the limitations of optimism discussed in Section 2.2 — the agent will eventually explore all state-action pairs even when rewards are partially observable , because exploration is not influenced by the Q-function ( and , thus, by rewards ), but fully driven by the S-function."}
+{"idx": 9, "title": "Research - Simone Parisi", "date": "", "ddg_snippet": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi, Alireza Kazemipour, Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling", "subpage_snippet": "", "source": "sparisi.github.io", "link": "https://sparisi.github.io/assets/research.html", "content": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi, Alireza Kazemipour, Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling"}
diff --git a/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Limitations_and_Future_Work_year_2023.jsonl b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Limitations_and_Future_Work_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4b471f76651a4240890827abf88515b6e93cd62f
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Limitations_and_Future_Work_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Jun 20, 2024 · To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process? In this case, optimism can lead to suboptimal behavior that does not explore further to collapse ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.13909", "content": "Jun 20, 2024 · To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process? In this case, optimism can lead to suboptimal behavior that does not explore further to collapse ..."}
+{"idx": 1, "title": "Beyond optimism | Proceedings of the 38th International ...", "date": "", "ddg_snippet": "Jun 5, 2025 · In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740005", "content": "Jun 5, 2025 · In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable ."}
+{"idx": 2, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Jun 19, 2024 · With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "Jun 19, 2024 · With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable ."}
+{"idx": 3, "title": "Beyond Optimism: Exploration With Partially Observable ...", "date": "", "ddg_snippet": "Nov 11, 2024 · Conclusion This paper introduces a novel exploration strategy called \" Optimism Beyond Optimism \" (OBO) for reinforcement learning in partially observable environments. By considering a broader range of possible outcomes, OBO aims to strike a better balance between exploration and exploitation compared to traditional optimistic methods.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-optimism-exploration-partially-observable-rewards", "content": "Nov 11, 2024 · Conclusion This paper introduces a novel exploration strategy called \" Optimism Beyond Optimism \" (OBO) for reinforcement learning in partially observable environments. By considering a broader range of possible outcomes, OBO aims to strike a better balance between exploration and exploitation compared to traditional optimistic methods."}
+{"idx": 4, "title": "A New Approach to Exploration in Reinforcement Learning", "date": "", "ddg_snippet": "Jul 26, 2025 · Original Source Title: Beyond Optimism : Exploration With Partially Observable Rewards Abstract: Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-26-a-new-approach-to-exploration-in-reinforcement-learning--ak5d6mx", "content": "Jul 26, 2025 · Original Source Title: Beyond Optimism : Exploration With Partially Observable Rewards Abstract: Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all."}
+{"idx": 5, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/784fd5a46dfe303e5b51c8621b84cf3f-Abstract-Conference.html", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 6, "title": "Causality-informed Anomaly Detection in Partially Observable", "date": "", "ddg_snippet": "... with the challenges posed by increasingly large-scale, dynamic, and partially observable networks, there has been growing interest in developing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09742v1", "content": "... with the challenges posed by increasingly large-scale, dynamic, and partially observable networks, there has been growing interest in developing ..."}
+{"idx": 7, "title": "A Reward-Directed Diffusion Framework for Generative Design", "date": "", "ddg_snippet": "... learning, the optimization trajectory begins from a random initial design and gradually improves through trial- and -error interactions with the reward ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.01509v1", "content": "... learning, the optimization trajectory begins from a random initial design and gradually improves through trial- and -error interactions with the reward ..."}
+{"idx": 8, "title": "Learning Humanoid Standing-up Control across Diverse Postures", "date": "", "ddg_snippet": "... with wide explorative strategies on the ground can lead to violent and abrupt motions that hinder real-world deployment [ 46 ] , particularly for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.08378v2", "content": "... with wide explorative strategies on the ground can lead to violent and abrupt motions that hinder real-world deployment [ 46 ] , particularly for ..."}
+{"idx": 9, "title": "Optimal reinsurance via BSDEs in a partially observable model", "date": "", "ddg_snippet": "... Cao et al. [ 7 ] investigated the optimal reinsurance–investment problem in the model setting proposed by Dassios and Zhao [ 13 ] with a reward ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00780-023-00523-z", "content": "... Cao et al. [ 7 ] investigated the optimal reinsurance–investment problem in the model setting proposed by Dassios and Zhao [ 13 ] with a reward ..."}
diff --git a/data/sampled_jsons/Black_et_al._2023_diffusion_model_alignment.jsonl b/data/sampled_jsons/Black_et_al._2023_diffusion_model_alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8fe03e07d0a247b38520f2587bed6a915acd67a3
--- /dev/null
+++ b/data/sampled_jsons/Black_et_al._2023_diffusion_model_alignment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ShortFT: Diffusion Model Alignment via Shortcut-based", "date": "", "ddg_snippet": "Therefore, aligning text-to-image diffusion models with human preferences has emerged as a pivotal and practical task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22604v1", "content": "Therefore, aligning text-to-image diffusion models with human preferences has emerged as a pivotal and practical task."}
+{"idx": 1, "title": "Inference-Time Alignment Control for Diffusion Models with", "date": "", "ddg_snippet": "While denoising-based generative models —primarily diffusion (Ho, Jain, and Abbeel 2020 ; Rombach et al . ... modeling has advanced rapidly, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21016v1", "content": "While denoising-based generative models —primarily diffusion (Ho, Jain, and Abbeel 2020 ; Rombach et al . ... modeling has advanced rapidly, ..."}
+{"idx": 2, "title": "Directly Aligning the Full Diffusion Trajectory with", "date": "", "ddg_snippet": "A central challenge in aligning diffusion models with human preferences is reward hacking, which often arises from a mismatch between existing reward ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06942v1", "content": "A central challenge in aligning diffusion models with human preferences is reward hacking, which often arises from a mismatch between existing reward ..."}
+{"idx": 3, "title": "Directly Aligning the Full Diffusion Trajectory with", "date": "", "ddg_snippet": "Recent studies have demonstrated the effectiveness of directly aligning diffusion models with human preferences using differentiable reward.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06942v3", "content": "Recent studies have demonstrated the effectiveness of directly aligning diffusion models with human preferences using differentiable reward."}
+{"idx": 4, "title": "Object Fidelity Diffusion for Remote Sensing Image Generation", "date": "", "ddg_snippet": "Diffusion models (Dhariwal and Nichol 2021 ; ... Despite its necessity, most generative models for RS imagery, such as DiffusionSat (Khanna et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10801v1", "content": "Diffusion models (Dhariwal and Nichol 2021 ; ... Despite its necessity, most generative models for RS imagery, such as DiffusionSat (Khanna et al ."}
+{"idx": 5, "title": "Finetune Stable Diffusion Models with DDPO via TRL", "date": "", "ddg_snippet": "In Training Diffusion Models with Reinforcement Learning, Black et al . ... RWR reuses the denoising loss function of the diffusion model along with ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/trl-ddpo", "content": "In Training Diffusion Models with Reinforcement Learning, Black et al . ... RWR reuses the denoising loss function of the diffusion model along with ..."}
+{"idx": 6, "title": "Model ablations concerning auxiliary losses, model structure,", "date": "", "ddg_snippet": "... et al ., 2022;Cui et al ., 2022b;Zhao et al ., 2023 ;Chi et al ., 2023 ;Dasari et al ., 2024) and training controllable policies by conditioning on human ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Model-ablations-concerning-auxiliary-losses-model-structure-and-dataset-size_tbl1_344863555", "content": "... et al ., 2022;Cui et al ., 2022b;Zhao et al ., 2023 ;Chi et al ., 2023 ;Dasari et al ., 2024) and training controllable policies by conditioning on human ..."}
+{"idx": 7, "title": "Political Economy of Economic Policy Advice | Journal of", "date": "", "ddg_snippet": "C32 - Time-Series Models ; Dynamic Quantile Regressions; Dynamic Treatment Effect Models ; Diffusion Processes; State Space Models", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/jae/article/33/Supplement_2/ii26/7929322", "content": "C32 - Time-Series Models ; Dynamic Quantile Regressions; Dynamic Treatment Effect Models ; Diffusion Processes; State Space Models"}
+{"idx": 8, "title": "Sensitive intervention points: a strategic approach to climate", "date": "", "ddg_snippet": "C22 - Time-Series Models ; Dynamic Quantile Regressions; Dynamic Treatment Effect Models ; Diffusion Processes ... Methods; Programming Models ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/oxrep/article/39/4/694/7425301", "content": "C22 - Time-Series Models ; Dynamic Quantile Regressions; Dynamic Treatment Effect Models ; Diffusion Processes ... Methods; Programming Models ..."}
+{"idx": 9, "title": "Yilun Du | DeepAI", "date": "", "ddg_snippet": "Since their introduction, diffusion models have quickly become the ... Large text-guided diffusion models , such as DALLE-2, are able to generat...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/yilun-du", "content": "Since their introduction, diffusion models have quickly become the ... Large text-guided diffusion models , such as DALLE-2, are able to generat..."}
diff --git a/data/sampled_jsons/Black_et_al._2023_diffusion_model_prompts_evaluation.jsonl b/data/sampled_jsons/Black_et_al._2023_diffusion_model_prompts_evaluation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..87939de357a538a0ea0cf0eddc29e344e4a53143
--- /dev/null
+++ b/data/sampled_jsons/Black_et_al._2023_diffusion_model_prompts_evaluation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM", "date": "", "ddg_snippet": "Models remain susceptible to adversarial manipulation through carefully crafted prompts (Zou et al ., 2023 ; Wang et al ., 2024 ; Liu et al ., 2023 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.17522v2", "content": "Models remain susceptible to adversarial manipulation through carefully crafted prompts (Zou et al ., 2023 ; Wang et al ., 2024 ; Liu et al ., 2023 ..."}
+{"idx": 1, "title": "Giving a Hand to Diffusion Models: a Two-Stage Approach to", "date": "", "ddg_snippet": "GLIDE [ 27 ] combined a diffusion model with text conditioning by encoding the input prompt into a sequence of embeddings with a transformer, which ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.10731v2", "content": "GLIDE [ 27 ] combined a diffusion model with text conditioning by encoding the input prompt into a sequence of embeddings with a transformer, which ..."}
+{"idx": 2, "title": "ACL 2024 Tutorial: Vulnerabilities of Large Language Models to", "date": "", "ddg_snippet": "Evaluating the Robustness of Text-to-image Diffusion Models ... Black Box Adversarial Prompting for Foundation Models (Maus and Chao et al ., 2023 )", "subpage_snippet": "", "source": "llm-vulnerability.github.io", "link": "https://llm-vulnerability.github.io/", "content": "Evaluating the Robustness of Text-to-image Diffusion Models ... Black Box Adversarial Prompting for Foundation Models (Maus and Chao et al ., 2023 )"}
+{"idx": 3, "title": "‘diffusion NN’ directory · Gwern.net", "date": "", "ddg_snippet": "Bridging the Gap: Sketch to Color Diffusion Model With Semantic Prompt Learning ”, Wang et al 2024 ... Diffusion Models ”, Geng et al 2023", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/diffusion/index", "content": "Bridging the Gap: Sketch to Color Diffusion Model With Semantic Prompt Learning ”, Wang et al 2024 ... Diffusion Models ”, Geng et al 2023"}
+{"idx": 4, "title": "WES - Modeling frontal low-level jets and associated extreme", "date": "", "ddg_snippet": "Modeling frontal low-level jets and associated extreme wind power ramps over the North Sea Modeling frontal low-level jets and associated extreme wind ...", "subpage_snippet": "", "source": "wes.copernicus.org", "link": "https://wes.copernicus.org/articles/10/1575/2025/", "content": "Modeling frontal low-level jets and associated extreme wind power ramps over the North Sea Modeling frontal low-level jets and associated extreme wind ..."}
+{"idx": 5, "title": "Text-to-image model - WikiMili, The Best Wikipedia Reader", "date": "", "ddg_snippet": "For the image generation step, conditional generative adversarial networks (GANs) have been commonly used, with diffusion models also becoming a ...", "subpage_snippet": "", "source": "wikimili.com", "link": "https://wikimili.com/en/Text-to-image_model", "content": "For the image generation step, conditional generative adversarial networks (GANs) have been commonly used, with diffusion models also becoming a ..."}
+{"idx": 6, "title": "AcT2I: Evaluating and Improving Action Depiction in", "date": "", "ddg_snippet": "... models including FLUX.1-dev Labs ( 2024 ) and Stable Diffusion ... Existing SOTA models —such as Stable Diffusion 3.5 Large, DALL- E 3 Betker et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16141v1", "content": "... models including FLUX.1-dev Labs ( 2024 ) and Stable Diffusion ... Existing SOTA models —such as Stable Diffusion 3.5 Large, DALL- E 3 Betker et al ."}
+{"idx": 7, "title": "VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on", "date": "", "ddg_snippet": "... Language Models (MLLMs) are powerful tools that process and integrate information across visual and textual domains Google ( 2024a ); Wang et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23693v1", "content": "... Language Models (MLLMs) are powerful tools that process and integrate information across visual and textual domains Google ( 2024a ); Wang et al ..."}
+{"idx": 8, "title": "Fooling the Watchers: Breaking AIGC Detectors via Semantic", "date": "", "ddg_snippet": "GenImage (Zhu et al ., 2023 ) adopts a unified prompt template \"photo of {class}\" , where {class} represents one of the 1000 categories from ImageNet ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23192v1", "content": "GenImage (Zhu et al ., 2023 ) adopts a unified prompt template \"photo of {class}\" , where {class} represents one of the 1000 categories from ImageNet ..."}
+{"idx": 9, "title": "CP - Shifts in Greenland interannual climate variability lead", "date": "", "ddg_snippet": "... dust, sea salt, accumulation, and moisture source, thus preventing a comprehensive understanding of the “anatomy” of D–O cycles (Capron et al ...", "subpage_snippet": "", "source": "cp.copernicus.org", "link": "https://cp.copernicus.org/articles/21/529/2025/", "content": "... dust, sea salt, accumulation, and moisture source, thus preventing a comprehensive understanding of the “anatomy” of D–O cycles (Capron et al ..."}
diff --git a/data/sampled_jsons/Blink_of_an_Eye_feature_localization_generative_models_ARC_Easy_sitearxiv.org_OR_sitepdf_year_2023.jsonl b/data/sampled_jsons/Blink_of_an_Eye_feature_localization_generative_models_ARC_Easy_sitearxiv.org_OR_sitepdf_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d90d85a59f5695ce751c50d7eaf991f7b1c0603f
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_Eye_feature_localization_generative_models_ARC_Easy_sitearxiv.org_OR_sitepdf_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink Home - Wikipedia", "date": "", "ddg_snippet": "Immedia Semiconductor LLC, [1] doing business as Blink , is an American home automation company which produces home security cameras. The company was founded in 2009 by Peter Besen, Don Shulsinger, Dan Grunberg, Stephen Gordon, and Doug Chin.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Blink_Home", "content": "Immedia Semiconductor LLC, [1] doing business as Blink , is an American home automation company which produces home security cameras. The company was founded in 2009 by Peter Besen, Don Shulsinger, Dan Grunberg, Stephen Gordon, and Doug Chin."}
+{"idx": 1, "title": "Blink Smart Security", "date": "", "ddg_snippet": "Affordable wireless and plug-in smart home security cameras and systems from Blink , an Amazon company.", "subpage_snippet": "", "source": "blinkforhome.com", "link": "https://blinkforhome.com/", "content": "Affordable wireless and plug-in smart home security cameras and systems from Blink , an Amazon company."}
+{"idx": 2, "title": "Blink Home Monitor - Apps on Google Play", "date": "", "ddg_snippet": "See and speak to people and pets, right from the Blink app with features like HD live view, infrared night vision, and crisp two-way audio. Connect to an Alexa-enabled device to engage live view,...", "subpage_snippet": "", "source": "play.google.com", "link": "https://play.google.com/store/apps/details?id=com.immediasemi.android.blink&hl=en", "content": "See and speak to people and pets, right from the Blink app with features like HD live view, infrared night vision, and crisp two-way audio. Connect to an Alexa-enabled device to engage live view,..."}
+{"idx": 3, "title": "Blink Whole Home Security Camera System Bundle | Costco", "date": "", "ddg_snippet": "Blink Whole Home Security Camera System Bundle Easy Setup, No Wiring Required Up to Two-years of Battery (Batteries Included) 360° Coverage With Mini Pan-Tilt Camera Monitor Your Home Anywhere From the Blink App", "subpage_snippet": "", "source": "www.costco.com", "link": "https://www.costco.com/blink-whole-home-security-camera-system-bundle-.product.4000215015.html", "content": "Blink Whole Home Security Camera System Bundle Easy Setup, No Wiring Required Up to Two-years of Battery (Batteries Included) 360° Coverage With Mini Pan-Tilt Camera Monitor Your Home Anywhere From the Blink App"}
+{"idx": 4, "title": "Blink Home Monitor on the App Store", "date": "", "ddg_snippet": "Connect to an Alexa-enabled device to engage live view, arm and disarm your system, and more using your voice. Plus, use the Blink app to customize motion alerts, and set activity and privacy zones, so you only get notified about the activity you care about.", "subpage_snippet": "", "source": "apps.apple.com", "link": "https://apps.apple.com/us/app/blink-home-monitor/id1013961111", "content": "Connect to an Alexa-enabled device to engage live view, arm and disarm your system, and more using your voice. Plus, use the Blink app to customize motion alerts, and set activity and privacy zones, so you only get notified about the activity you care about."}
+{"idx": 5, "title": "Blink Mini Indoor Pan-Tilt Camera in White | Amazon", "date": "", "ddg_snippet": "Shop the Blink Mini Pan-Tilt Camera. This rotating indoor plug-in smart security camera has two-way audio, HD video, motion detection, and works with Alexa.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/Blink-Mini-Pan-Tilt-Camera-White/dp/B09N6YCT3Y", "content": "Shop the Blink Mini Pan-Tilt Camera. This rotating indoor plug-in smart security camera has two-way audio, HD video, motion detection, and works with Alexa."}
+{"idx": 6, "title": "Blink Home Monitor App — Blink Smart Security", "date": "", "ddg_snippet": "The app connects your home to your phone in HD video so you can see and protect what matters most. With multi-system support, you can use Blink to watch your home, vacation home, or business all at the same time. Plus, you can control multiple camera systems within one single app!", "subpage_snippet": "", "source": "blinkforhome.com", "link": "https://blinkforhome.com/blink-app", "content": "The app connects your home to your phone in HD video so you can see and protect what matters most. With multi-system support, you can use Blink to watch your home, vacation home, or business all at the same time. Plus, you can control multiple camera systems within one single app!"}
+{"idx": 7, "title": "Account and Login — Blink Support", "date": "", "ddg_snippet": "Blink Support Center helps you to find FAQ, how-to guides and step-by-step tutorials.", "subpage_snippet": "", "source": "support.blinkforhome.com", "link": "https://support.blinkforhome.com/en_US/account-and-login", "content": "Blink Support Center helps you to find FAQ, how-to guides and step-by-step tutorials."}
+{"idx": 8, "title": "Setting up your Blink devices - Blink Support", "date": "", "ddg_snippet": "Blink Support Center helps you to find FAQ, how-to guides and step-by-step tutorials.", "subpage_snippet": "", "source": "support.blinkforhome.com", "link": "https://support.blinkforhome.com/en_US/how-to-setup-devices", "content": "Blink Support Center helps you to find FAQ, how-to guides and step-by-step tutorials."}
+{"idx": 9, "title": "Sign in to your Blink account", "date": "", "ddg_snippet": "You can sign in to your account using your new password. Log in to update your payment method.", "subpage_snippet": "", "source": "blink.com", "link": "https://blink.com/users/sign_in", "content": "You can sign in to your account using your new password. Log in to update your payment method."}
diff --git a/data/sampled_jsons/Blink_of_an_eye-_a_simple_theory_for_feature_localization_in_generative_models_github_repository.jsonl b/data/sampled_jsons/Blink_of_an_eye-_a_simple_theory_for_feature_localization_in_generative_models_github_repository.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..138924fa17b2e3780e8760fa2cad57fc222172bb
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_eye-_a_simple_theory_for_feature_localization_in_generative_models_github_repository.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - Twenkid/DeepFaceLab-SAEHDBW: Grayscale SAEHD model and", "date": "", "ddg_snippet": "Utils for working with multiple facial models , batch training with less human-intervention in the middle and editing of the final movie, which faces ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Twenkid/DeepFaceLab-SAEHDBW", "content": "Utils for working with multiple facial models , batch training with less human-intervention in the middle and editing of the final movie, which faces ..."}
+{"idx": 1, "title": "GitHub - Divide-By-0/ideas-for-projects-people-would-use: Every", "date": "", "ddg_snippet": "Bounty Prompt : A tinder for songs -- plays the most commented 10 seconds of a song on Apple Music and adds it to a playlist if you like it.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Divide-By-0/ideas-for-projects-people-would-use", "content": "Bounty Prompt : A tinder for songs -- plays the most commented 10 seconds of a song on Apple Music and adds it to a playlist if you like it."}
+{"idx": 2, "title": "Read Download Python Simplified With Generative Ai PDF – PDF", "date": "", "ddg_snippet": "Practical tips for creating your own generative AI models , along with insights into the future of this groundbreaking field, making it an essential ...", "subpage_snippet": "", "source": "bibleandbookcenter.com", "link": "https://bibleandbookcenter.com/read/python-simplified-with-generative-ai/", "content": "Practical tips for creating your own generative AI models , along with insights into the future of this groundbreaking field, making it an essential ..."}
+{"idx": 3, "title": "Solve it with EASE", "date": "", "ddg_snippet": "... model (2025), featuring a vast context window of one million tokens, is optimized for handling extensive programming tasks in a single pass, thus ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.18108v1", "content": "... model (2025), featuring a vast context window of one million tokens, is optimized for handling extensive programming tasks in a single pass, thus ..."}
+{"idx": 4, "title": "Rob Van Der Goot - ACL Anthology", "date": "", "ddg_snippet": "ISKD reduces model parameters - 30.68% for GPT-2 and 30.16% for Phi-1 - while maintaining at least four-fifths of performance on both language ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/r/rob-van-der-goot/", "content": "ISKD reduces model parameters - 30.68% for GPT-2 and 30.16% for Phi-1 - while maintaining at least four-fifths of performance on both language ..."}
+{"idx": 5, "title": "Proceedings of the 2025 Conference of the Nations of the", "date": "", "ddg_snippet": "A cause for languages becoming endangered is the lack of language educational tools and artificial intelligence (AI) models for these low-resource ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2025.naacl-demo/", "content": "A cause for languages becoming endangered is the lack of language educational tools and artificial intelligence (AI) models for these low-resource ..."}
+{"idx": 6, "title": "Site Map - Position Is Everything", "date": "", "ddg_snippet": "How to Add Fonts to Procreate: Simple and Easy Guide for You ... How to Add Music to Instagram Story: Tips for a Lively Post", "subpage_snippet": "", "source": "www.positioniseverything.net", "link": "https://www.positioniseverything.net/site-map/", "content": "How to Add Fonts to Procreate: Simple and Easy Guide for You ... How to Add Music to Instagram Story: Tips for a Lively Post"}
+{"idx": 7, "title": "What’s Coming In KiCad Version 5 | Hackaday", "date": "", "ddg_snippet": "Then, a year later, a newer model of car was introduced with new features , for a different price. ... a living from his ‘maker’ talents like you, ...", "subpage_snippet": "", "source": "hackaday.com", "link": "https://hackaday.com/2018/02/10/whats-coming-in-kicad-version-5/", "content": "Then, a year later, a newer model of car was introduced with new features , for a different price. ... a living from his ‘maker’ talents like you, ..."}
+{"idx": 8, "title": "Maurice Parker - Maurice Parker", "date": "", "ddg_snippet": "There are only a couple of new major features in Zavala 3.0, but I put a lot of under the hood for this release. ... a full office with full-size ...", "subpage_snippet": "", "source": "vincode.io", "link": "https://vincode.io/", "content": "There are only a couple of new major features in Zavala 3.0, but I put a lot of under the hood for this release. ... a full office with full-size ..."}
+{"idx": 9, "title": "JavaFixing", "date": "", "ddg_snippet": "For example, I want the allowed values in the GET request to be \" oui \" and \" non \" instead of \" true \" and ...", "subpage_snippet": "", "source": "www.javafixing.com", "link": "http://www.javafixing.com/", "content": "For example, I want the allowed values in the GET request to be \" oui \" and \" non \" instead of \" true \" and ..."}
diff --git a/data/sampled_jsons/Blink_of_an_eye_feature_localization_generative_models_arxiv.jsonl b/data/sampled_jsons/Blink_of_an_eye_feature_localization_generative_models_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c0c5ae6c1d5ff721a4d197ea4543c6ce4dc8cbe
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_eye_feature_localization_generative_models_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00921] Blink of an eye: a simple theory for feature", "date": "", "ddg_snippet": "Title: Blink of an eye : a simple theory for feature localization in generative models ... of the paper titled Blink of an eye : a simple theory for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "Title: Blink of an eye : a simple theory for feature localization in generative models ... of the paper titled Blink of an eye : a simple theory for ..."}
+{"idx": 1, "title": "Assessing Medical Training Skills via Eye and Head Movements", "date": "", "ddg_snippet": "Traditionally, eye tracking research has used Areas of Interest (AOI) analysis to evaluate how individuals visually engage with specific regions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16819v1", "content": "Traditionally, eye tracking research has used Areas of Interest (AOI) analysis to evaluate how individuals visually engage with specific regions ..."}
+{"idx": 2, "title": "(PDF) DeepVision: Deepfakes Detection Using Human Eye Blinking", "date": "", "ddg_snippet": "... generated through the generative adversarial network (GANs) model via an algorithm called DeepVision to analyze a significant change in the pattern of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/340813598_DeepVision_Deepfakes_Detection_Using_Human_Eye_Blinking_Pattern", "content": "... generated through the generative adversarial network (GANs) model via an algorithm called DeepVision to analyze a significant change in the pattern of ..."}
+{"idx": 3, "title": "Machine Learning", "date": "", "ddg_snippet": "Title: Blink of an eye : a simple theory for feature localization in generative models ... limits of learning in sequence multi-index models and deep ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.LG/pastweek?skip=518&show=25", "content": "Title: Blink of an eye : a simple theory for feature localization in generative models ... limits of learning in sequence multi-index models and deep ..."}
+{"idx": 4, "title": "Inference-Time Gaze Refinement for Micro-Expression", "date": "", "ddg_snippet": "... we propose a model -agnostic inference-time post-processing and local refinement framework to enhance the accuracy and robustness of event-based eye ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.12524v3", "content": "... we propose a model -agnostic inference-time post-processing and local refinement framework to enhance the accuracy and robustness of event-based eye ..."}
+{"idx": 5, "title": "Towards Multimodal Understanding via Stable Diffusion as a", "date": "", "ddg_snippet": "We first analyze unconditional features extracted from diffusion models across multiple blocks and timesteps. ... of pre- or post-training to align ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07106v1", "content": "We first analyze unconditional features extracted from diffusion models across multiple blocks and timesteps. ... of pre- or post-training to align ..."}
+{"idx": 6, "title": "Gaze_Estimation", "date": "", "ddg_snippet": "In offline mode, our software extracts multiple features from the eye including, the pupil and iris ellipse, eye aperture, pupil vector, iris vector ...", "subpage_snippet": "", "source": "paperreading.club", "link": "http://paperreading.club/category?cate=Gaze_Estimation&page=5", "content": "In offline mode, our software extracts multiple features from the eye including, the pupil and iris ellipse, eye aperture, pupil vector, iris vector ..."}
+{"idx": 7, "title": "GitHub - flyingby/Awesome-Deepfake-Generation-and-Detection: A", "date": "", "ddg_snippet": "... of facial manipulation in Deepfake, encompassing Face Swapping , Face Reenactment , Talking Face Generation , Face Attribute Editing and Forgery ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/flyingby/Awesome-Deepfake-Generation-and-Detection", "content": "... of facial manipulation in Deepfake, encompassing Face Swapping , Face Reenactment , Talking Face Generation , Face Attribute Editing and Forgery ..."}
+{"idx": 8, "title": "Deepfakes Generation and Detection: A Short Survey", "date": "", "ddg_snippet": "For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2313-433X/9/1/18", "content": "For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ..."}
+{"idx": 9, "title": "Gaze_Estimation", "date": "", "ddg_snippet": "... of -the-art neural network architecture was employed ... EgoGazeVQA consists of gaze-based QA pairs generated by MLLMs and refined by human annotators.", "subpage_snippet": "", "source": "paperreading.club", "link": "http://paperreading.club/category?cate=Gaze_Estimation", "content": "... of -the-art neural network architecture was employed ... EgoGazeVQA consists of gaze-based QA pairs generated by MLLMs and refined by human annotators."}
diff --git a/data/sampled_jsons/Blink_of_an_eye_simple_theory_feature_localization_generative_models_Li_Chen_2024_dimension_dependen.jsonl b/data/sampled_jsons/Blink_of_an_eye_simple_theory_feature_localization_generative_models_Li_Chen_2024_dimension_dependen.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4b4770b62d24061cfeff6f8fa0ce4feb6cbba67c
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_eye_simple_theory_feature_localization_generative_models_Li_Chen_2024_dimension_dependen.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are ..."}
+{"idx": 1, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "In this work we de- velop a simple , unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QvqnPVGWAN", "content": "In this work we de- velop a simple , unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models ."}
+{"idx": 2, "title": "Blink of an Eye: A Simple Theory for Feature Localization in Generative ...", "date": "", "ddg_snippet": "We present a concise theoretical framework that explains sudden feature localization events—\"blinks of an eye\"—in diffusion and autoregressive generative models .", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/publication/2025-blink-eye", "content": "We present a concise theoretical framework that explains sudden feature localization events—\"blinks of an eye\"—in diffusion and autoregressive generative models ."}
+{"idx": 3, "title": "(PDF) Blink of an eye: a simple theory for feature localization in ...", "date": "", "ddg_snippet": "In this work we develop a simple , unifying theory to explain this phenomenon. We show that it emerges generically as the generation process localizes to a sub-population of the distribution it models .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "In this work we develop a simple , unifying theory to explain this phenomenon. We show that it emerges generically as the generation process localizes to a sub-population of the distribution it models ."}
+{"idx": 4, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "In this work we develop a simple , unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/45312/paper", "content": "In this work we develop a simple , unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models ."}
+{"idx": 5, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "To study feature localization in diffusion and autoregressive models , we consider a forward-reverseexperiment. A forward-reverse experiment considers the amount of \"noise\" one would need to add to a generation so that running the generative model starting from the noised generation would still yield a sample with the same feature .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "To study feature localization in diffusion and autoregressive models , we consider a forward-reverseexperiment. A forward-reverse experiment considers the amount of \"noise\" one would need to add to a generation so that running the generative model starting from the noised generation would still yield a sample with the same feature ."}
+{"idx": 6, "title": "dblp: Blink of an eye: a simple theory for feature localization in ...", "date": "", "ddg_snippet": "Bibliographic details on Blink of an eye : a simple theory for feature localization in generative models .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-00921", "content": "Bibliographic details on Blink of an eye : a simple theory for feature localization in generative models ."}
+{"idx": 7, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Poster in Workshop: Frontiers in Probabilistic Inference: learning meets Sampling Blink of an eye : a simple theory for feature localization in generative models Marvin Li · Aayush Karan · Sitan Chen [ Abstract ] [ Project Page ] [ OpenReview]", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/36105", "content": "Poster in Workshop: Frontiers in Probabilistic Inference: learning meets Sampling Blink of an eye : a simple theory for feature localization in generative models Marvin Li · Aayush Karan · Sitan Chen [ Abstract ] [ Project Page ] [ OpenReview]"}
+{"idx": 8, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Oral Blink of an eye : a simple theory for feature localization in generative models Marvin Li · Aayush Karan · Sitan Chen West Ballroom A [ Abstract ] [ Visit Oral 3B Representations 1 ] Wed 16 Jul 10:45 a.m. — 11 a.m. PDT Poster presentation: Blink of an eye : a simple theory for feature localization in generative models", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47223", "content": "Oral Blink of an eye : a simple theory for feature localization in generative models Marvin Li · Aayush Karan · Sitan Chen West Ballroom A [ Abstract ] [ Visit Oral 3B Representations 1 ] Wed 16 Jul 10:45 a.m. — 11 a.m. PDT Poster presentation: Blink of an eye : a simple theory for feature localization in generative models"}
+{"idx": 9, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Large language models can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone, and these...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nodyP4FLrM", "content": "Large language models can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone, and these..."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_experimental_setup_hardware_specifications_GPU.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_experimental_setup_hardware_specifications_GPU.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e337169bb3ee45db07919683663ef0722c91a56
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_experimental_setup_hardware_specifications_GPU.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.09543] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "Oct 12, 2024 · Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "Oct 12, 2024 · Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ..."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 2, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values."}
+{"idx": 3, "title": "B -a Inverse Folding Model As a Predictor of Mutational Effects on ...", "date": "", "ddg_snippet": "ABSTRACT Predicting the change in binding free energy (∆∆G) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental ∆∆G data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose Boltzmann Alignment technique to transfer knowledge from pre ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "ABSTRACT Predicting the change in binding free energy (∆∆G) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental ∆∆G data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose Boltzmann Alignment technique to transfer knowledge from pre ..."}
+{"idx": 4, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 5, "title": "What Is Inverse Folding & How To Practically Apply It - Neurosnap", "date": "", "ddg_snippet": "Dec 30, 2023 · Discover the cutting-edge realm of inverse folding models—an innovative and potent tool transforming the de-novo protein design landscape. These models have seamlessly contributed to the creation of therapeutic agents, biosensors, and industrial enzymes. Join us in this blog post as we delve into the comprehensive understanding of inverse folding , exploring its applications and unveiling the ...", "subpage_snippet": "", "source": "neurosnap.ai", "link": "https://neurosnap.ai/blog/post/65908e76104e7841a40c3187", "content": "Dec 30, 2023 · Discover the cutting-edge realm of inverse folding models—an innovative and potent tool transforming the de-novo protein design landscape. These models have seamlessly contributed to the creation of therapeutic agents, biosensors, and industrial enzymes. Join us in this blog post as we delve into the comprehensive understanding of inverse folding , exploring its applications and unveiling the ..."}
+{"idx": 6, "title": "Advancing protein evolution with inverse folding models integrating ...", "date": "", "ddg_snippet": "Indeed, it has recently been shown that simply sampling from inverse folding model outputs is sufficient to identify high-fitness (HF) mutations and achieve antibody evolution. 22 However, despite these advances, previous studies have only explored conceptually the application of inverse folding models to the evolution of small proteins.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0092867425006804", "content": "Indeed, it has recently been shown that simply sampling from inverse folding model outputs is sufficient to identify high-fitness (HF) mutations and achieve antibody evolution. 22 However, despite these advances, previous studies have only explored conceptually the application of inverse folding models to the evolution of small proteins."}
+{"idx": 7, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Compared to previous inverse folding -based methods, our method explicitly considers the unbound state of the protein complex, enabling fine-tuning of inverse folding models in a manner consistent with statistical thermodynamics. Our contributions can be summarized as follows: •", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "Compared to previous inverse folding -based methods, our method explicitly considers the unbound state of the protein complex, enabling fine-tuning of inverse folding models in a manner consistent with statistical thermodynamics. Our contributions can be summarized as follows: •"}
+{"idx": 8, "title": "Advancing protein evolution with inverse folding models integrating ...", "date": "", "ddg_snippet": "An AI-informed approach integrates generic protein inverse folding models with structural and evolutionary constraints to efficiently identify high-fitness mutations, enabling the development of advanced base editors and demonstrating broad scalability for artificial protein evolution.", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell/fulltext/S0092-8674(25)00680-4", "content": "An AI-informed approach integrates generic protein inverse folding models with structural and evolutionary constraints to efficiently identify high-fitness mutations, enabling the development of advanced base editors and demonstrating broad scalability for artificial protein evolution."}
+{"idx": 9, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "直接估计条件概率是比较困难的，因为直接通过序列生成结构的模型，通常不是预测一个状态的概率，而是在预测各种扭转角，这个状态空间就太大了。而一些概率生成模型，又是在估计条件概率的梯度 ∇_x\\log p (X|S) ，并不能直接使用。因此，让我们思考一下， 直接估计 P (X|S) 很困难，但是因为 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "直接估计条件概率是比较困难的，因为直接通过序列生成结构的模型，通常不是预测一个状态的概率，而是在预测各种扭转角，这个状态空间就太大了。而一些概率生成模型，又是在估计条件概率的梯度 ∇_x\\log p (X|S) ，并不能直接使用。因此，让我们思考一下， 直接估计 P (X|S) 很困难，但是因为 ..."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_side-chain_flexibility_limitation.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_side-chain_flexibility_limitation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2a5b6c48f67d9efe2975d6ddefad00986e7ffb7c
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_side-chain_flexibility_limitation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of", "date": "", "ddg_snippet": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to Δ Δ G ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to Δ Δ G ..."}
+{"idx": 1, "title": "Active Gaussian Network Model: a non-equilibrium description of", "date": "", "ddg_snippet": "This condition challenges traditional equilibrium models and supports the growing evidence that non-equilibrium dynamics play a crucial role in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.24855v1", "content": "This condition challenges traditional equilibrium models and supports the growing evidence that non-equilibrium dynamics play a crucial role in ..."}
+{"idx": 2, "title": "Force-Guided Bridge Matching for Full-Atom Time-Coarsened", "date": "", "ddg_snippet": "In this work, we delicately design a generative model to learn time-coarsened dynamics, and target the Boltzmann -constrained distribution p ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.15126v6", "content": "In this work, we delicately design a generative model to learn time-coarsened dynamics, and target the Boltzmann -constrained distribution p ..."}
+{"idx": 3, "title": "Abstract", "date": "", "ddg_snippet": "We utilize RL and Boltzmann inversion to develop a novel CG model of cellulose with consideration of anisotropy and other properties like polymer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.12893v1", "content": "We utilize RL and Boltzmann inversion to develop a novel CG model of cellulose with consideration of anisotropy and other properties like polymer ..."}
+{"idx": 4, "title": "Protein Conformation Generation via Force-Guided SE(3)", "date": "", "ddg_snippet": "... model input, such as multiple sequence alignment (MSA) masking (Stein & Mchaourab, 2022 ) or clustering (Wayment-Steele et al., 2023 ) , the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.14088v2", "content": "... model input, such as multiple sequence alignment (MSA) masking (Stein & Mchaourab, 2022 ) or clustering (Wayment-Steele et al., 2023 ) , the ..."}
+{"idx": 5, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling ... in Modern Large-scale Model Training ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling ... in Modern Large-scale Model Training ..."}
+{"idx": 6, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "Calibration and Bias in Algorithms, Data, and Models : a tutorial on metrics and plots for measuring calibration, bias, fairness, reliability, and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "Calibration and Bias in Algorithms, Data, and Models : a tutorial on metrics and plots for measuring calibration, bias, fairness, reliability, and ..."}
+{"idx": 7, "title": "unum-cloud/ann-arxiv-2m · Datasets at Hugging Face", "date": "", "ddg_snippet": "The evolution of Earth-Moon system is described by the dark matter field fluid model proposed in the Meeting of Division of Particle and Field 2004 ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/unum-cloud/ann-arxiv-2m", "content": "The evolution of Earth-Moon system is described by the dark matter field fluid model proposed in the Meeting of Division of Particle and Field 2004 ..."}
+{"idx": 8, "title": "ICM Language Reference : Macros", "date": "", "ddg_snippet": "This command gives larger flexibility since it allows definition of the second selection of the vicinity for the calculation.", "subpage_snippet": "", "source": "www.molsoft.com", "link": "https://www.molsoft.com/icm/icm-macros.html", "content": "This command gives larger flexibility since it allows definition of the second selection of the vicinity for the calculation."}
+{"idx": 9, "title": "ICM Language Reference : Macros", "date": "", "ddg_snippet": "This command gives larger flexibility since it allows definition of the second selection of the vicinity for the calculation.", "subpage_snippet": "", "source": "www.molsoft.com", "link": "https://www.molsoft.com/man/icm-macros.html", "content": "This command gives larger flexibility since it allows definition of the second selection of the vicinity for the calculation."}
diff --git a/data/sampled_jsons/CALF_Aligning_LLMs_Time_Series_Forecasting_Cross-modal_Fine-tuning_abstract_arxiv2403.07300_year_2024.jsonl b/data/sampled_jsons/CALF_Aligning_LLMs_Time_Series_Forecasting_Cross-modal_Fine-tuning_abstract_arxiv2403.07300_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..665235d619c905810d5a1e46e318c480c46e3970
--- /dev/null
+++ b/data/sampled_jsons/CALF_Aligning_LLMs_Time_Series_Forecasting_Cross-modal_Fine-tuning_abstract_arxiv2403.07300_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "by P Liu · 2024 · Cited by 52 — We propose a novel Cross-Modal LLM Fine-Tuning (CALF ) framework for MTSF by reducing the distribution discrepancy between textual and temporal data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07300", "content": "by P Liu · 2024 · Cited by 52 — We propose a novel Cross-Modal LLM Fine-Tuning (CALF ) framework for MTSF by reducing the distribution discrepancy between textual and temporal data."}
+{"idx": 1, "title": "Are Language Models Actually Useful for Time Series ...", "date": "", "ddg_snippet": "Chang et al., [5] used finetuning the transformer module and positional encoding in GPT-2 to align pre-trained LLMs with time series data for forecasting tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.16964v2", "content": "Chang et al., [5] used finetuning the transformer module and positional encoding in GPT-2 to align pre-trained LLMs with time series data for forecasting tasks."}
+{"idx": 2, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "We propose CALF, a novel framework that employs cross-modal fine-tuning techniques to comprehensively align temporal and textual data. The framework includes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07300v3", "content": "We propose CALF, a novel framework that employs cross-modal fine-tuning techniques to comprehensively align temporal and textual data. The framework includes ..."}
+{"idx": 3, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "(ii) We propose CALF, a novel framework that employs cross-modal fine-tuning techniques to comprehensively align temporal and textual data. The framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07300v2", "content": "(ii) We propose CALF, a novel framework that employs cross-modal fine-tuning techniques to comprehensively align temporal and textual data. The framework ..."}
+{"idx": 4, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "11 Mar 2024 — Abstract . A novel Cross - Modal LLM Fine - Tuning ( CALF ) framework reduces distribution discrepancy between text and time series data for improved ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2403.07300", "content": "11 Mar 2024 — Abstract . A novel Cross - Modal LLM Fine - Tuning ( CALF ) framework reduces distribution discrepancy between text and time series data for improved ..."}
+{"idx": 5, "title": "CALF: LLM alignment for time series forecasting", "date": "", "ddg_snippet": "CALF is a new cross-modal fine-tuning framework designed to align large linguistic models (LLMs) with time series forecasting tasks.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/ashishpatel2604_timeseries-activity-7219199559074254848-R2CM", "content": "CALF is a new cross-modal fine-tuning framework designed to align large linguistic models (LLMs) with time series forecasting tasks."}
+{"idx": 6, "title": "Are language models actually useful for time series forecasting?", "date": "", "ddg_snippet": "5 Jun 2025 — Xia. Calf: Aligning llms for time series forecasting via cross-modal fine-tuning. arXiv preprint arXiv:2403.07300, 2024. Google Scholar.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739838", "content": "5 Jun 2025 — Xia. Calf: Aligning llms for time series forecasting via cross-modal fine-tuning. arXiv preprint arXiv:2403.07300, 2024. Google Scholar."}
+{"idx": 7, "title": "Revisiting LLMs as Zero-Shot Time-Series Forecasters", "date": "", "ddg_snippet": "by J Park · 2025 · Cited by 1 — Calf: Aligning llms for time series forecasting via cross-modal fine-tuning . arXiv preprint arXiv:2403.07300. Yong Liu, Tengge Hu, Haoran ... 17 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-short.71.pdf", "content": "by J Park · 2025 · Cited by 1 — Calf: Aligning llms for time series forecasting via cross-modal fine-tuning . arXiv preprint arXiv:2403.07300. Yong Liu, Tengge Hu, Haoran ... 17 pages"}
+{"idx": 8, "title": "A novel LLM time series forecasting method based on ...", "date": "", "ddg_snippet": "by L Wang · 2025 — CALF: Aligning LLMs for time series forecasting via cross-modal fine-tuning . Preprint at http://arXiv.org/2403.07300 (2024). Yadav, H ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-06581-x", "content": "by L Wang · 2025 — CALF: Aligning LLMs for time series forecasting via cross-modal fine-tuning . Preprint at http://arXiv.org/2403.07300 (2024). Yadav, H ..."}
+{"idx": 9, "title": "LangTime: A Language-Guided Unified Model for Time ...", "date": "", "ddg_snippet": "by W Niu · Cited by 2 — Abstract. Recent research has shown ... Calf: Aligning llms for time series forecasting via cross-modal fine-tuning . arXiv preprint. arXiv:2403.07300, 2024c.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=VfoKOD65Zq", "content": "by W Niu · Cited by 2 — Abstract. Recent research has shown ... Calf: Aligning llms for time series forecasting via cross-modal fine-tuning . arXiv preprint. arXiv:2403.07300, 2024c."}
diff --git a/data/sampled_jsons/CAPA_Chance_Adjusted_Probabilistic_Agreement_c_obs_observed_agreement_equation_formula_year_2024.jsonl b/data/sampled_jsons/CAPA_Chance_Adjusted_Probabilistic_Agreement_c_obs_observed_agreement_equation_formula_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fd9f70c75ed05c0f6b8d80e1240df94cf3231810
--- /dev/null
+++ b/data/sampled_jsons/CAPA_Chance_Adjusted_Probabilistic_Agreement_c_obs_observed_agreement_equation_formula_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cohen Kappa Score Explained: Formula, Example", "date": "", "ddg_snippet": "Cohen Kappa score will be used to assess the model performance as a function of probability that the rater 1 and rater 2 are in perfect agreement (TP + TN), also denoted as Po ( observed probability), and, the probability (expected) both the raters are in agreement by chance or randomly, denoted as Pe in the following formula .", "subpage_snippet": "", "source": "vitalflux.com", "link": "https://vitalflux.com/cohen-kappa-score-python-example-machine-learning/", "content": "Cohen Kappa score will be used to assess the model performance as a function of probability that the rater 1 and rater 2 are in perfect agreement (TP + TN), also denoted as Po ( observed probability), and, the probability (expected) both the raters are in agreement by chance or randomly, denoted as Pe in the following formula ."}
+{"idx": 1, "title": "Chance corrected agreement measures for binary and semi-quantitative ...", "date": "", "ddg_snippet": "A chance corrected agreement measure takes into account the possibility of agreement occurring by chance . The Kappa coefficient is the most popular measure for chance corrected agreement between qualitative variables. It is the overall observed agreement corrected for the possibility of agreement occurring by chance .", "subpage_snippet": "", "source": "analyse-it.com", "link": "https://analyse-it.com/docs/user-guide/method-comparison/kappa", "content": "A chance corrected agreement measure takes into account the possibility of agreement occurring by chance . The Kappa coefficient is the most popular measure for chance corrected agreement between qualitative variables. It is the overall observed agreement corrected for the possibility of agreement occurring by chance ."}
+{"idx": 2, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "... probabilistic metric for model similarity, CAPA ( κ p subscript 𝜅 𝑝 \\kappa_{p} italic_κ start_POSTSUBSCRIPT italic_p end_POSTSUBSCRIPT ), ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "... probabilistic metric for model similarity, CAPA ( κ p subscript 𝜅 𝑝 \\kappa_{p} italic_κ start_POSTSUBSCRIPT italic_p end_POSTSUBSCRIPT ), ..."}
+{"idx": 3, "title": "How to use Cohen's Kappa Statistic for ML Model verification.", "date": "", "ddg_snippet": "Pe: Hypothetical probability of chance agreement In the context of a classification model, we could use Cohen's kappa to compare the machine learning model predictions with a manual evaluation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jayamohanmohanan/how-to-use-cohens-kappa-statistic-for-ml-model-verification-6c66258b4ae9", "content": "Pe: Hypothetical probability of chance agreement In the context of a classification model, we could use Cohen's kappa to compare the machine learning model predictions with a manual evaluation."}
+{"idx": 4, "title": "Epiville: How to Calculate Kappa - Columbia University", "date": "", "ddg_snippet": "Kappa tells us the extent to which SussStat and the clinician agree with each other beyond what you might expect to see based on chance alone. The formula for Kappa is: We calculate observed agreement by calculating the frequency with which the two measurements agreed:", "subpage_snippet": "", "source": "epiville.ccnmtl.columbia.edu", "link": "https://epiville.ccnmtl.columbia.edu/popup/how_to_calculate_kappa.html", "content": "Kappa tells us the extent to which SussStat and the clinician agree with each other beyond what you might expect to see based on chance alone. The formula for Kappa is: We calculate observed agreement by calculating the frequency with which the two measurements agreed:"}
+{"idx": 5, "title": "Cohen's Kappa Statistic: Definition & Example - Statology", "date": "", "ddg_snippet": "Cohen's Kappa Statistic is used to measure the level of agreement between two raters or judges who each classify items into mutually exclusive categories. The formula for Cohen's kappa is calculated as: k = (po - pe) / (1 - pe) where: po: Relative observed agreement among raters pe: Hypothetical probability of chance agreement Rather than just calculating the percentage of items that ...", "subpage_snippet": "", "source": "www.statology.org", "link": "https://www.statology.org/cohens-kappa-statistic/", "content": "Cohen's Kappa Statistic is used to measure the level of agreement between two raters or judges who each classify items into mutually exclusive categories. The formula for Cohen's kappa is calculated as: k = (po - pe) / (1 - pe) where: po: Relative observed agreement among raters pe: Hypothetical probability of chance agreement Rather than just calculating the percentage of items that ..."}
+{"idx": 6, "title": "Statistics - Cohen's kappa coefficient - Online Tutorials Library", "date": "", "ddg_snippet": "Where − $ {p_0}$ = relative observed agreement among raters. $ {p_e}$ = the hypothetical probability of chance agreement . $ {p_0}$ and $ {p_e}$ are computed using the observed data to calculate the probabilities of each observer randomly saying each category. If the raters are in complete agreement then $ {k}$ = 1.", "subpage_snippet": "", "source": "www.tutorialspoint.com", "link": "https://www.tutorialspoint.com/statistics/cohen_kappa_coefficient.htm", "content": "Where − $ {p_0}$ = relative observed agreement among raters. $ {p_e}$ = the hypothetical probability of chance agreement . $ {p_0}$ and $ {p_e}$ are computed using the observed data to calculate the probabilities of each observer randomly saying each category. If the raters are in complete agreement then $ {k}$ = 1."}
+{"idx": 7, "title": "Kappa Calculator - Sage Calculator", "date": "", "ddg_snippet": "Calculate Cohen's Kappa instantly with our Kappa Calculator. Measure inter-rater reliability with ease using observed and chance agreement values.", "subpage_snippet": "", "source": "sagecalculator.com", "link": "https://sagecalculator.com/kappa-calculator/", "content": "Calculate Cohen's Kappa instantly with our Kappa Calculator. Measure inter-rater reliability with ease using observed and chance agreement values."}
+{"idx": 8, "title": "Chapter 3: Kappa Statistics - The Kappa Zoo", "date": "", "ddg_snippet": "The difference is the 1 a 2 denominator. The chance correction is therefore accounting for the accurate ratings by taking them out of the data altogether and then calculating inaccurate matches out of all remaining rating pairs as the probability of by- chance matching. The formula in Equation 3 is useful for testing properties of kappa assumptions.", "subpage_snippet": "", "source": "kappazoo.com", "link": "https://kappazoo.com/kappa.html", "content": "The difference is the 1 a 2 denominator. The chance correction is therefore accounting for the accurate ratings by taking them out of the data altogether and then calculating inaccurate matches out of all remaining rating pairs as the probability of by- chance matching. The formula in Equation 3 is useful for testing properties of kappa assumptions."}
+{"idx": 9, "title": "Cohen's Kappa Coefficient Calculator - Calculator Academy", "date": "", "ddg_snippet": "Cohen's Kappa Coefficient accounts for chance agreement by incorporating the hypothetical probability of chance agreement (pe) into its formula . By doing this, it provides a measure of agreement corrected for what would be expected by chance , giving a more accurate reflection of the raters' agreement .", "subpage_snippet": "", "source": "calculator.academy", "link": "https://calculator.academy/cohens-kappa-coefficient-calculator/", "content": "Cohen's Kappa Coefficient accounts for chance agreement by incorporating the hypothetical probability of chance agreement (pe) into its formula . By doing this, it provides a measure of agreement corrected for what would be expected by chance , giving a more accurate reflection of the raters' agreement ."}
diff --git "a/data/sampled_jsons/CAPA_formula_\320\232\321\200_Equation_4_'Great_Models_Think_Alike_and_this_Undermines_AI_Oversight'.jsonl" "b/data/sampled_jsons/CAPA_formula_\320\232\321\200_Equation_4_'Great_Models_Think_Alike_and_this_Undermines_AI_Oversight'.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..b2c3fdacd1e54a8ddea365be8d4942096913fa10
--- /dev/null
+++ "b/data/sampled_jsons/CAPA_formula_\320\232\321\200_Equation_4_'Great_Models_Think_Alike_and_this_Undermines_AI_Oversight'.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as AI Oversight . We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap ...", "subpage_snippet": "", "source": "model-similarity.github.io", "link": "https://model-similarity.github.io/", "content": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as AI Oversight . We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap ..."}
+{"idx": 1, "title": "The AI oversight trap: When smarter models make the same ... When AI Models Think Alike: The Hidden Challenge Undermining ... [papers] gradual disempowerment from incremental AI ... Great Models Think Alike and this Undermines AI Oversight Similarity affects Oversight Similarity affects Oversight Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Similarity affects Oversight Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight . Feb 7, 2025 · When a model assesses another AI system, it is more likely to agree with outputs similar to its own, leading to potential biases in automated oversight . Training with LM Annotations: The Weak-to-Strong Generalization EffectWe also explore the role of training language models using annotations provided by other AI systems. Feb 7, 2025 · Great Models Think Alike and this Undermines AI Oversight “We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. May 1, 2025 · We study how model similarity affects both aspects of AI oversight by proposing *Chance Adjusted Probabilistic Agreement ( CAPA )*--a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that *LLM-as-a-judge* scores favor models similar to the judge, generalizing recent self-preference results. How does Capa adjust for chance agreement of two independent models? CAPA adjusts for chance agreement of two independent models with the given accuracies . (2) When both models are wrong, they can still disagree. CAPA compares sample-wise predictions instead of sample-wise correctness. How does Capa work? CAPA captures whether models make similar mistakes . By analzying 100+ open-weight models, we find that as model capabilities have increased, so has average CAPA to models from other developers in the same capability class. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Do great models think like undermine AI oversight? Overall, as model blind-spots get harder to detect, making us defer more to AI oversight, models making more similar mistakes poses the risk of correlated failures. title={ Great Models Think Alike and this Undermines AI Oversight }, As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement ( CAPA ): a metric for LM similarity based on ...", "subpage_snippet": "", "source": "www.devdiscourse.com", "link": "https://www.devdiscourse.com/article/technology/3256561-the-ai-oversight-trap-when-smarter-models-make-the-same-mistakes", "content": "Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight . Feb 7, 2025 · When a model assesses another AI system, it is more likely to agree with outputs similar to its own, leading to potential biases in automated oversight . Training with LM Annotations: The Weak-to-Strong Generalization EffectWe also explore the role of training language models using annotations provided by other AI systems. Feb 7, 2025 · Great Models Think Alike and this Undermines AI Oversight “We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. May 1, 2025 · We study how model similarity affects both aspects of AI oversight by proposing *Chance Adjusted Probabilistic Agreement ( CAPA )*--a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that *LLM-as-a-judge* scores favor models similar to the judge, generalizing recent self-preference results. How does Capa adjust for chance agreement of two independent models? CAPA adjusts for chance agreement of two independent models with the given accuracies . (2) When both models are wrong, they can still disagree. CAPA compares sample-wise predictions instead of sample-wise correctness. How does Capa work? CAPA captures whether models make similar mistakes . By analzying 100+ open-weight models, we find that as model capabilities have increased, so has average CAPA to models from other developers in the same capability class. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Do great models think like undermine AI oversight? Overall, as model blind-spots get harder to detect, making us defer more to AI oversight, models making more similar mistakes poses the risk of correlated failures. title={ Great Models Think Alike and this Undermines AI Oversight }, As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement ( CAPA ): a metric for LM similarity based on ..."}
+{"idx": 2, "title": "Great Models Think Alike and this Undermines AI Oversight Images Great Models Think Alike and this Undermines AI Oversight The AI oversight trap: When smarter models make the same ... When AI Models Think Alike: The Hidden Challenge Undermining ... [papers] gradual disempowerment from incremental AI ... Great Models Think Alike and this Undermines AI Oversight Similarity affects Oversight Similarity affects Oversight Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Similarity affects Oversight Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Feb 6, 2025 · As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as '' AI Oversight ''. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on ... View all As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as AI Oversight . We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap ... Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight . Feb 7, 2025 · When a model assesses another AI system, it is more likely to agree with outputs similar to its own, leading to potential biases in automated oversight . Training with LM Annotations: The Weak-to-Strong Generalization EffectWe also explore the role of training language models using annotations provided by other AI systems. Feb 7, 2025 · Great Models Think Alike and this Undermines AI Oversight “We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. May 1, 2025 · We study how model similarity affects both aspects of AI oversight by proposing *Chance Adjusted Probabilistic Agreement ( CAPA )*--a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that *LLM-as-a-judge* scores favor models similar to the judge, generalizing recent self-preference results. How does Capa adjust for chance agreement of two independent models? CAPA adjusts for chance agreement of two independent models with the given accuracies . (2) When both models are wrong, they can still disagree. CAPA compares sample-wise predictions instead of sample-wise correctness. How does Capa work? CAPA captures whether models make similar mistakes . By analzying 100+ open-weight models, we find that as model capabilities have increased, so has average CAPA to models from other developers in the same capability class. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Do great models think like undermine AI oversight? Overall, as model blind-spots get harder to detect, making us defer more to AI oversight, models making more similar mistakes poses the risk of correlated failures. title={ Great Models Think Alike and this Undermines AI Oversight }, As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement ( CAPA ): a metric for LM similarity based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04313", "content": "Feb 6, 2025 · As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as '' AI Oversight ''. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on ... View all As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as AI Oversight . We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap ... Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight . Feb 7, 2025 · When a model assesses another AI system, it is more likely to agree with outputs similar to its own, leading to potential biases in automated oversight . Training with LM Annotations: The Weak-to-Strong Generalization EffectWe also explore the role of training language models using annotations provided by other AI systems. Feb 7, 2025 · Great Models Think Alike and this Undermines AI Oversight “We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. May 1, 2025 · We study how model similarity affects both aspects of AI oversight by proposing *Chance Adjusted Probabilistic Agreement ( CAPA )*--a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that *LLM-as-a-judge* scores favor models similar to the judge, generalizing recent self-preference results. How does Capa adjust for chance agreement of two independent models? CAPA adjusts for chance agreement of two independent models with the given accuracies . (2) When both models are wrong, they can still disagree. CAPA compares sample-wise predictions instead of sample-wise correctness. How does Capa work? CAPA captures whether models make similar mistakes . By analzying 100+ open-weight models, we find that as model capabilities have increased, so has average CAPA to models from other developers in the same capability class. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Do great models think like undermine AI oversight? Overall, as model blind-spots get harder to detect, making us defer more to AI oversight, models making more similar mistakes poses the risk of correlated failures. title={ Great Models Think Alike and this Undermines AI Oversight }, As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement ( CAPA ): a metric for LM similarity based on ..."}
+{"idx": 3, "title": "When AI Models Think Alike: The Hidden Challenge Undermining ...", "date": "", "ddg_snippet": "Feb 7, 2025 · When a model assesses another AI system, it is more likely to agree with outputs similar to its own, leading to potential biases in automated oversight . Training with LM Annotations: The Weak-to-Strong Generalization EffectWe also explore the role of training language models using annotations provided by other AI systems.", "subpage_snippet": "", "source": "www.globaltrendtimes.com", "link": "https://www.globaltrendtimes.com/2025/02/when-ai-models-think-alike-hidden.html", "content": "Feb 7, 2025 · When a model assesses another AI system, it is more likely to agree with outputs similar to its own, leading to potential biases in automated oversight . Training with LM Annotations: The Weak-to-Strong Generalization EffectWe also explore the role of training language models using annotations provided by other AI systems."}
+{"idx": 4, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "May 1, 2025 · We study how model similarity affects both aspects of AI oversight by proposing *Chance Adjusted Probabilistic Agreement ( CAPA )*--a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that *LLM-as-a-judge* scores favor models similar to the judge, generalizing recent self-preference results.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3Z827FtMNe", "content": "May 1, 2025 · We study how model similarity affects both aspects of AI oversight by proposing *Chance Adjusted Probabilistic Agreement ( CAPA )*--a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that *LLM-as-a-judge* scores favor models similar to the judge, generalizing recent self-preference results."}
+{"idx": 5, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement ( CAPA ): a metric for LM similarity based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04313", "content": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement ( CAPA ): a metric for LM similarity based on ..."}
+{"idx": 6, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap in model mistakes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap in model mistakes."}
+{"idx": 7, "title": "[Literature Review] Great Models Think Alike and this Undermines ...", "date": "", "ddg_snippet": "The paper titled \" Great Models Think Alike and this Undermines AI Oversight \" explores the implications of model similarity in the context of overseeing and evaluating the performance of large language models (LLMs).", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/great-models-think-alike-and-this-undermines-ai-oversight", "content": "The paper titled \" Great Models Think Alike and this Undermines AI Oversight \" explores the implications of model similarity in the context of overseeing and evaluating the performance of large language models (LLMs)."}
+{"idx": 8, "title": "Great Models Think Alike and this Undermines AI Oversight - AI for...", "date": "", "ddg_snippet": "This paper talks about how the increasing similarity between advanced AI language models can create problems for using AI to oversee other AI systems, a concept known as ' AI Oversight '.", "subpage_snippet": "", "source": "ai-search.io", "link": "https://ai-search.io/papers/great-models-think-alike-and-this-undermines-ai-oversight", "content": "This paper talks about how the increasing similarity between advanced AI language models can create problems for using AI to oversee other AI systems, a concept known as ' AI Oversight '."}
+{"idx": 9, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "AI oversight , where one AI system monitors or evaluates another. If all models think alike , they may share the same blindspots and biases, making them poor choices for checking each other's work.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/great-models-think-alike-this-undermines-ai", "content": "AI oversight , where one AI system monitors or evaluates another. If all models think alike , they may share the same blindspots and biases, making them poor choices for checking each other's work."}
diff --git a/data/sampled_jsons/CLIP_embedding_space_properties_semantic_structure_diffusion_models.jsonl b/data/sampled_jsons/CLIP_embedding_space_properties_semantic_structure_diffusion_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..03890bac64c6a1f7caaa4830d68fb46a0326047b
--- /dev/null
+++ b/data/sampled_jsons/CLIP_embedding_space_properties_semantic_structure_diffusion_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE", "date": "", "ddg_snippet": "Abstract CLIP embeddings have demonstrated remarkable performance across a wide range of multimodal applications. However, these high-dimensional, dense vector rep-resentations are not easily interpretable, limiting our understanding of the rich structure of CLIP and its use in downstream applications that require transparency. In this work, we show that the semantic structure of CLIP ’s ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/996bef37d8a638f37bdfcac2789e835d-Paper-Conference.pdf", "content": "Abstract CLIP embeddings have demonstrated remarkable performance across a wide range of multimodal applications. However, these high-dimensional, dense vector rep-resentations are not easily interpretable, limiting our understanding of the rich structure of CLIP and its use in downstream applications that require transparency. In this work, we show that the semantic structure of CLIP ’s ..."}
+{"idx": 1, "title": "Topological Perspectives on Optimal Multimodal Embedding Spaces", "date": "", "ddg_snippet": "May 29, 2024 · Despite the widespread success, fundamental inquiries concerning the architecture and operational intricacies of these joint embedding models persist unexplored. This paper undertakes a topological analysis of the embedding space in two paradigmatic models : CLIP [5] and CLOOB [9].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.18867", "content": "May 29, 2024 · Despite the widespread success, fundamental inquiries concerning the architecture and operational intricacies of these joint embedding models persist unexplored. This paper undertakes a topological analysis of the embedding space in two paradigmatic models : CLIP [5] and CLOOB [9]."}
+{"idx": 2, "title": "Material Embeddings in CLIP-Space - emergentmind.com", "date": "", "ddg_snippet": "Material holds profound sway at the intersection of computer vision, graphics, and multimodal AI—not just as a physical property but as a bridge between language, perception, and synthetic imagery. \"Material Embeddings in CLIP - Space \" encapsulate a revolution in how we represent, manipulate, and reason about material properties using the rich, versatile joint embedding spaces ° that models ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/material-embeddings-in-clip-space", "content": "Material holds profound sway at the intersection of computer vision, graphics, and multimodal AI—not just as a physical property but as a bridge between language, perception, and synthetic imagery. \"Material Embeddings in CLIP - Space \" encapsulate a revolution in how we represent, manipulate, and reason about material properties using the rich, versatile joint embedding spaces ° that models ..."}
+{"idx": 3, "title": "Understanding Fine-tuning CLIP for Open-vocabulary Semantic ...", "date": "", "ddg_snippet": "CLIP , a foundational vision-language model , has emerged as a powerful tool for open-vocabulary semantic segmentation. While freezing the text encoder preserves its powerful embed -dings, recent studies show that fine-tuning both the text and image encoders jointly significantly enhances segmentation performance, especially for classes from open ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_Understanding_Fine-tuning_CLIP_for_Open-vocabulary_Semantic_Segmentation_in_Hyperbolic_Space_CVPR_2025_paper.pdf", "content": "CLIP , a foundational vision-language model , has emerged as a powerful tool for open-vocabulary semantic segmentation. While freezing the text encoder preserves its powerful embed -dings, recent studies show that fine-tuning both the text and image encoders jointly significantly enhances segmentation performance, especially for classes from open ..."}
+{"idx": 4, "title": "CLIP Model Architecture | cure-lab/MMA-Diffusion | DeepWiki", "date": "", "ddg_snippet": "May 20, 2025 · CLIP consists of two parallel encoders - a text encoder and an image encoder - that project both text and images into a shared embedding space where similarity can be measured. The architecture enables cross-modal understanding, allowing the model to match text descriptions with corresponding images. Sources: README.md 85-90 CLIPTextModel ...", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cure-lab/MMA-Diffusion/2.1-clip-model-architecture", "content": "May 20, 2025 · CLIP consists of two parallel encoders - a text encoder and an image encoder - that project both text and images into a shared embedding space where similarity can be measured. The architecture enables cross-modal understanding, allowing the model to match text descriptions with corresponding images. Sources: README.md 85-90 CLIPTextModel ..."}
+{"idx": 5, "title": "CLIP-PAE: Projection-Augmentation Embedding to Extract ...", "date": "", "ddg_snippet": "We introduce CLIP projection-augmentation embedding (PAE) as an optimization target to improve the performance of text-guided image manipulation. Our method is a simple and general paradigm that can be easily computed and adapted, and smoothly incorporated into any CLIP -based image manipulation algorithm.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/fullHtml/10.1145/3588432.3591532", "content": "We introduce CLIP projection-augmentation embedding (PAE) as an optimization target to improve the performance of text-guided image manipulation. Our method is a simple and general paradigm that can be easily computed and adapted, and smoothly incorporated into any CLIP -based image manipulation algorithm."}
+{"idx": 6, "title": "Understanding OpenAI’s CLIP model | by Szymon Palucha | Medium", "date": "", "ddg_snippet": "Feb 24, 2024 · To do this, CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximise the cosine similarity of the image and text embeddings of the N real pairs ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@paluchasz/understanding-openais-clip-model-6b52bade3fa3", "content": "Feb 24, 2024 · To do this, CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximise the cosine similarity of the image and text embeddings of the N real pairs ..."}
+{"idx": 7, "title": "Enhancing Fine-Grained Visual Understanding in CLIP via ...", "date": "", "ddg_snippet": "4 Aug 2025 — Hard negatives, semantically distinct samples close in the embedding space , are crucial for learning discriminative features in contrastive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.02329v1", "content": "4 Aug 2025 — Hard negatives, semantically distinct samples close in the embedding space , are crucial for learning discriminative features in contrastive ..."}
+{"idx": 8, "title": "Connecting Text with Vision: How Multimodal Models Align ...", "date": "", "ddg_snippet": "In this blog post, we explore how modern multimodal models connect textual and visual data by learning to represent them in a unified embedding space.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@hexiangnan/connecting-text-with-vision-how-multimodal-models-align-modalities-in-the-embedding-space-1d97abbda472", "content": "In this blog post, we explore how modern multimodal models connect textual and visual data by learning to represent them in a unified embedding space."}
+{"idx": 9, "title": "CLIP Embeddings for AI-Generated Image Detection", "date": "", "ddg_snippet": "15 May 2025 — This work investigates whether CLIP embeddings inherently contain information indicative of AI generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10664v1", "content": "15 May 2025 — This work investigates whether CLIP embeddings inherently contain information indicative of AI generation."}
diff --git a/data/sampled_jsons/CQT_constant-Q_transform_advantage_over_STFT_Short-Time_Fourier_Transform_music_signals.jsonl b/data/sampled_jsons/CQT_constant-Q_transform_advantage_over_STFT_Short-Time_Fourier_Transform_music_signals.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0232b46f85d7ff5e7d64e74ef0982bb02c637bea
--- /dev/null
+++ b/data/sampled_jsons/CQT_constant-Q_transform_advantage_over_STFT_Short-Time_Fourier_Transform_music_signals.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constant-Q transform - Wikipedia", "date": "", "ddg_snippet": "Short-time Fourier transform with variable resolutionIn mathematics and signal processing, the constant-Q transform and variable- Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform [1] and very closely related to the complex Morlet wavelet transform . [2] Its design is suited for musical representation. Constant-Q ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Constant-Q_transform", "content": "Short-time Fourier transform with variable resolutionIn mathematics and signal processing, the constant-Q transform and variable- Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform [1] and very closely related to the complex Morlet wavelet transform . [2] Its design is suited for musical representation. Constant-Q ..."}
+{"idx": 1, "title": "Short - time Fourier transform - Wikipedia", "date": "", "ddg_snippet": "The short - time Fourier transform is a Fourier -related transform used to determine the sinusoidal frequency and phase content of local sections of a signal as it changes over time. In practice, the procedure for computing STFTs is to divide a longer t...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Short-time_Fourier_transform", "content": "The short - time Fourier transform is a Fourier -related transform used to determine the sinusoidal frequency and phase content of local sections of a signal as it changes over time. In practice, the procedure for computing STFTs is to divide a longer t..."}
+{"idx": 2, "title": "A Comparison of CQT Spectrogram with STFT -based Acoustic", "date": "", "ddg_snippet": "The short - time Fourier transform of the th frame of a speech signal is calculated asall over the spectrum, contradictory to the Short-Term Fourier Transform ( STFT ) that is mainly employed for the computation of a speech signal ’s spectrogram and has a changing Q factor.", "subpage_snippet": "", "source": "jad.shahroodut.ac.ir", "link": "https://jad.shahroodut.ac.ir/article_2707_3c17cc7bd491d721623526b32fa63822.pdf", "content": "The short - time Fourier transform of the th frame of a speech signal is calculated asall over the spectrum, contradictory to the Short-Term Fourier Transform ( STFT ) that is mainly employed for the computation of a speech signal ’s spectrogram and has a changing Q factor."}
+{"idx": 3, "title": "Comparison of Time-Frequency Representations for ... Images A Comparison of CQT Spectrogram with STFT-based Acoustic ... Bridging the gap between the short-time Fourier transform ... COMPARING SHORT-TIME FOURIER TRANSFORM (STFT) AND CONSTANT-Q ... Nonstationary Gabor Frames and the Constant - Q Transform Constant - Q transform - Wikipedia Constant - Q transform - Wikipedia Constant - Q transform - Wikipedia Constant - Q transform - Wikipedia Constant - Q transform - Wikipedia Nonstationary Gabor Frames and the Constant-Q Transform", "date": "", "ddg_snippet": "Abstract—Recent successful applications of convolutional neu-ral networks (CNNs) to audio classification and speech recogni-tion have motivated the search for better input representations for more efficient training. Visual displays of an audio signal , through various time -frequency representations such as spectrograms offer a rich representation o... See full list on arxiv.org Overall, the shallower Conv-3 model tended to yield better accuracies than Conv-5 regardless of input. A probable ex-planation is the diminishing returns of the deeper model due to significant overfitting. While it simplified the experimental methodology, using whole audio clips as input inevitably resulted in fewer training examples with less vari... See full list on arxiv.org The benefit of wideband against narrowband transforms were not consistent across both datasets. This may be in-dicative of a disparity in the types of environmental sounds present in both datasets. More interestingly perhaps, compar-ing the confusion matrices reveals that each specializes in discriminating certain classes of sound. The wideband Mel... See full list on arxiv.org View all The Constant Q Transform ( CQT ) was inaugurated in 1991 [17] mainly aiming for music processing objectives. The CQT provides a constant Q factor (the ratio between the center frequency and the bandwidth , Q f / f ) The short-time Fourier transform ( STFT ) can be applied to convert a signal from the time -domain into the time –fre-quency domain. It has been used to process signals in many research areas, for example in image processing [1], speech [2], engineering [3, 4], biology and medicine [5]. Dec 31, 2024 · This study compares the Short-Time Fourier Transform ( STFT ) and Constant-Q Transform ( CQT ) techniques in capturing the intricate spectral characteristics of the rebana, a traditional Malay frame drum known for its rich harmonic content and transient attacks. High-quality audio recordings were obtained in a controlled studio environment using a Shure SM57 microphone placed six inches from the ... What is the difference between a short-time Fourier transform and a constant-Q transform? Unlike the short - time Fourier transform , the windows used in the constant - Q transform have adaptable bandwidth and sampling density. In frequency space, the windows are centered at logarithmically spaced center frequencies. The Fourier transform of f (t) is the correlation of f (t) with ej ω t: What is CQT & VQT? In mathematics and signal processing, the constant-Q transform and variable-Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform and very closely related to the complex Morlet wavelet transform. Its design is suited for musical representation. How can a constant Q transform be approximated? Alternatively, the constant-Q transform can be approximated by using multiple fast Fourier transforms of different window sizes and/or sampling rate at different frequency ranges then stitch it together. What is a constant Q transform on a piano chord? Constant-Q transform applied to the waveform of a C major piano chord. The x-axis is frequency, mapped to standard musical pitches, from low (left) to high (right). The y-axis is time, starting from pressing the piano chord at the bottom, and releasing the piano chord at the top, 8 seconds later. What is a variable Q transform? The variable-Q transform is the same as constant-Q transform, but the only difference is the filter Q is variable , hence the name variable-Q transform. The variable-Q transform is useful where time resolution on low frequencies is important [examples needed]. What are the peaks of the constant Q transform? Darker pixels correspond to higher values of the Constant-Q transform. The peaks correspond closely to the precise frequencies of the vibrating piano strings . Thus the peaks can be used to detect the notes played on the piano. The lowest 3 peaks are the fundamental frequencies of the C major chord (C, E, G). You have the flexibility to change the sampling density in time or frequency. Nonstationary Gabor frames are useful in areas such as audio signal processing, where fixed-sized time -frequency windows are not optimal. Unlike the short-time Fourier transform , the windows used in the constant-Q transform have adaptable bandwidth and sampling density.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1706.07156", "content": "Abstract—Recent successful applications of convolutional neu-ral networks (CNNs) to audio classification and speech recogni-tion have motivated the search for better input representations for more efficient training. Visual displays of an audio signal , through various time -frequency representations such as spectrograms offer a rich representation o... See full list on arxiv.org Overall, the shallower Conv-3 model tended to yield better accuracies than Conv-5 regardless of input. A probable ex-planation is the diminishing returns of the deeper model due to significant overfitting. While it simplified the experimental methodology, using whole audio clips as input inevitably resulted in fewer training examples with less vari... See full list on arxiv.org The benefit of wideband against narrowband transforms were not consistent across both datasets. This may be in-dicative of a disparity in the types of environmental sounds present in both datasets. More interestingly perhaps, compar-ing the confusion matrices reveals that each specializes in discriminating certain classes of sound. The wideband Mel... See full list on arxiv.org View all The Constant Q Transform ( CQT ) was inaugurated in 1991 [17] mainly aiming for music processing objectives. The CQT provides a constant Q factor (the ratio between the center frequency and the bandwidth , Q f / f ) The short-time Fourier transform ( STFT ) can be applied to convert a signal from the time -domain into the time –fre-quency domain. It has been used to process signals in many research areas, for example in image processing [1], speech [2], engineering [3, 4], biology and medicine [5]. Dec 31, 2024 · This study compares the Short-Time Fourier Transform ( STFT ) and Constant-Q Transform ( CQT ) techniques in capturing the intricate spectral characteristics of the rebana, a traditional Malay frame drum known for its rich harmonic content and transient attacks. High-quality audio recordings were obtained in a controlled studio environment using a Shure SM57 microphone placed six inches from the ... What is the difference between a short-time Fourier transform and a constant-Q transform? Unlike the short - time Fourier transform , the windows used in the constant - Q transform have adaptable bandwidth and sampling density. In frequency space, the windows are centered at logarithmically spaced center frequencies. The Fourier transform of f (t) is the correlation of f (t) with ej ω t: What is CQT & VQT? In mathematics and signal processing, the constant-Q transform and variable-Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform and very closely related to the complex Morlet wavelet transform. Its design is suited for musical representation. How can a constant Q transform be approximated? Alternatively, the constant-Q transform can be approximated by using multiple fast Fourier transforms of different window sizes and/or sampling rate at different frequency ranges then stitch it together. What is a constant Q transform on a piano chord? Constant-Q transform applied to the waveform of a C major piano chord. The x-axis is frequency, mapped to standard musical pitches, from low (left) to high (right). The y-axis is time, starting from pressing the piano chord at the bottom, and releasing the piano chord at the top, 8 seconds later. What is a variable Q transform? The variable-Q transform is the same as constant-Q transform, but the only difference is the filter Q is variable , hence the name variable-Q transform. The variable-Q transform is useful where time resolution on low frequencies is important [examples needed]. What are the peaks of the constant Q transform? Darker pixels correspond to higher values of the Constant-Q transform. The peaks correspond closely to the precise frequencies of the vibrating piano strings . Thus the peaks can be used to detect the notes played on the piano. The lowest 3 peaks are the fundamental frequencies of the C major chord (C, E, G). You have the flexibility to change the sampling density in time or frequency. Nonstationary Gabor frames are useful in areas such as audio signal processing, where fixed-sized time -frequency windows are not optimal. Unlike the short-time Fourier transform , the windows used in the constant-Q transform have adaptable bandwidth and sampling density."}
+{"idx": 4, "title": "Bridging the gap between the short-time Fourier transform ...", "date": "", "ddg_snippet": "The short-time Fourier transform ( STFT ) can be applied to convert a signal from the time -domain into the time –fre-quency domain. It has been used to process signals in many research areas, for example in image processing [1], speech [2], engineering [3, 4], biology and medicine [5].", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s11760-020-01701-8.pdf", "content": "The short-time Fourier transform ( STFT ) can be applied to convert a signal from the time -domain into the time –fre-quency domain. It has been used to process signals in many research areas, for example in image processing [1], speech [2], engineering [3, 4], biology and medicine [5]."}
+{"idx": 5, "title": "COMPARING SHORT-TIME FOURIER TRANSFORM (STFT) AND CONSTANT-Q ...", "date": "", "ddg_snippet": "Dec 31, 2024 · This study compares the Short-Time Fourier Transform ( STFT ) and Constant-Q Transform ( CQT ) techniques in capturing the intricate spectral characteristics of the rebana, a traditional Malay frame drum known for its rich harmonic content and transient attacks. High-quality audio recordings were obtained in a controlled studio environment using a Shure SM57 microphone placed six inches from the ...", "subpage_snippet": "", "source": "ejournals.swu.ac.th", "link": "https://ejournals.swu.ac.th/index.php/vss/article/view/16553", "content": "Dec 31, 2024 · This study compares the Short-Time Fourier Transform ( STFT ) and Constant-Q Transform ( CQT ) techniques in capturing the intricate spectral characteristics of the rebana, a traditional Malay frame drum known for its rich harmonic content and transient attacks. High-quality audio recordings were obtained in a controlled studio environment using a Shure SM57 microphone placed six inches from the ..."}
+{"idx": 6, "title": "Nonstationary Gabor Frames and the Constant-Q Transform", "date": "", "ddg_snippet": "You have the flexibility to change the sampling density in time or frequency. Nonstationary Gabor frames are useful in areas such as audio signal processing, where fixed-sized time -frequency windows are not optimal. Unlike the short-time Fourier transform , the windows used in the constant-Q transform have adaptable bandwidth and sampling density.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/help/wavelet/gs/non-stationary-gabor-frames.html", "content": "You have the flexibility to change the sampling density in time or frequency. Nonstationary Gabor frames are useful in areas such as audio signal processing, where fixed-sized time -frequency windows are not optimal. Unlike the short-time Fourier transform , the windows used in the constant-Q transform have adaptable bandwidth and sampling density."}
+{"idx": 7, "title": "Input Representations — Music Classification: Beyond Supervised...", "date": "", "ddg_snippet": "Constant - Q Transform ( CQT ) is quite similar to Melspectrogram for ML models. However, it is more computation heavy and is less available in softwares we use. STFT ( short - time Fourier transform ) is the most “raw” kind of spectrograms. It has two axes - time and frequency.", "subpage_snippet": "", "source": "music-classification.github.io", "link": "https://music-classification.github.io/tutorial/part2_basics/input-representations.html", "content": "Constant - Q Transform ( CQT ) is quite similar to Melspectrogram for ML models. However, it is more computation heavy and is less available in softwares we use. STFT ( short - time Fourier transform ) is the most “raw” kind of spectrograms. It has two axes - time and frequency."}
+{"idx": 8, "title": "Evaluation of spectral transforms for music signal analysis", "date": "", "ddg_snippet": "In this paper we present a study on the spectral analysis of music signals comparing the time domain representation, the short - time Fourier transform ( STFT ) and the constant - Q transform ( CQT ) which are additionally combined with different signal -dependent transforms .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/261315202_Evaluation_of_spectral_transforms_for_music_signal_analysis", "content": "In this paper we present a study on the spectral analysis of music signals comparing the time domain representation, the short - time Fourier transform ( STFT ) and the constant - Q transform ( CQT ) which are additionally combined with different signal -dependent transforms ."}
+{"idx": 9, "title": "Music Transcription Using Deep", "date": "", "ddg_snippet": "We transform the audio files into spectrograms using constant Q transform and extract features from the spectrograms.Methods widely used for preprocessing audio signals include STFT ( Short - Time Fourier Transform ), Mel Filterbank Cepstrum (MFCC) and constant Q transform ( CQT ).", "subpage_snippet": "", "source": "cs229.stanford.edu", "link": "https://cs229.stanford.edu/proj2017/final-reports/5242716.pdf", "content": "We transform the audio files into spectrograms using constant Q transform and extract features from the spectrograms.Methods widely used for preprocessing audio signals include STFT ( Short - Time Fourier Transform ), Mel Filterbank Cepstrum (MFCC) and constant Q transform ( CQT )."}
diff --git a/data/sampled_jsons/CQT_constant-Q_transform_logarithmic_frequency_resolution_music_analysis_STFT_comparison.jsonl b/data/sampled_jsons/CQT_constant-Q_transform_logarithmic_frequency_resolution_music_analysis_STFT_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..714f1f4a843a34f0d2f1ef24fcaad9d8eb20cce7
--- /dev/null
+++ b/data/sampled_jsons/CQT_constant-Q_transform_logarithmic_frequency_resolution_music_analysis_STFT_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constant-Q transform - Wikipedia", "date": "", "ddg_snippet": "In mathematics and signal processing, the constant-Q transform and variable-Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform [1] and very closely related to the complex Morlet wavelet transform . [2] Its design is suited for musical representation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Constant-Q_transform", "content": "In mathematics and signal processing, the constant-Q transform and variable-Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform [1] and very closely related to the complex Morlet wavelet transform . [2] Its design is suited for musical representation."}
+{"idx": 1, "title": "Constant-Q transform - Hydrogenaudio Knowledgebase", "date": "", "ddg_snippet": "Constant-Q and variable-Q transforms ( CQT /VQT) are spectral analysis algorithms that usually have logarithmic frequency spacing and time/ frequency resolution following octave series. Due to its usually logarithmic frequency resolution , it is suited for musical representation. Overview", "subpage_snippet": "", "source": "wiki.hydrogenaudio.org", "link": "https://wiki.hydrogenaudio.org/index.php?title=Constant-Q_transform", "content": "Constant-Q and variable-Q transforms ( CQT /VQT) are spectral analysis algorithms that usually have logarithmic frequency spacing and time/ frequency resolution following octave series. Due to its usually logarithmic frequency resolution , it is suited for musical representation. Overview"}
+{"idx": 2, "title": "Constant-Q Transform (CQT) Overview - emergentmind.com", "date": "", "ddg_snippet": "The Constant-Q Transform ( CQT ) is a time- frequency analysis technique central to modern audio signal processing. Distinguished by its logarithmic frequency scaling and constant-Q (quality factor) filterbank architecture, CQT provides non-uniform frequency resolution optimized for applications where frequency perception is inherently nonlinear, such as music , speech analysis , and behavioral ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/constant-q-transform-cqt", "content": "The Constant-Q Transform ( CQT ) is a time- frequency analysis technique central to modern audio signal processing. Distinguished by its logarithmic frequency scaling and constant-Q (quality factor) filterbank architecture, CQT provides non-uniform frequency resolution optimized for applications where frequency perception is inherently nonlinear, such as music , speech analysis , and behavioral ..."}
+{"idx": 3, "title": "Comparison of Time-Frequency Representations for Environmental Sound ...", "date": "", "ddg_snippet": "ntations such as spectrograms offer a rich representation of the temporal and spectral structure of the original signal. In this letter, we compare various popular signal processing methods to obtain this representation, such as short-time Fourier transform ( STFT ) with linear and Mel scales, constant-Q transform ( CQT ) and continuous Wavelet t", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1706.07156", "content": "ntations such as spectrograms offer a rich representation of the temporal and spectral structure of the original signal. In this letter, we compare various popular signal processing methods to obtain this representation, such as short-time Fourier transform ( STFT ) with linear and Mel scales, constant-Q transform ( CQT ) and continuous Wavelet t"}
+{"idx": 4, "title": "PDF The Constant Q Transform - Machine Learning Group", "date": "", "ddg_snippet": "the constant Q transform so useful is that by an appropriate choice for f0 (minimal center frequency ) and b the center frequencies directly correspond to musical notes. For instance choosing b = 12 and f0 as the frequency of midinote 0 makes the k-th cq-bin correspond the midinote number k. Another nice feature of the constant Q transform is its increasing time resolution towards higher ...", "subpage_snippet": "", "source": "doc.ml.tu-berlin.de", "link": "https://doc.ml.tu-berlin.de/bbci/material/publications/Bla_constQ.pdf", "content": "the constant Q transform so useful is that by an appropriate choice for f0 (minimal center frequency ) and b the center frequencies directly correspond to musical notes. For instance choosing b = 12 and f0 as the frequency of midinote 0 makes the k-th cq-bin correspond the midinote number k. Another nice feature of the constant Q transform is its increasing time resolution towards higher ..."}
+{"idx": 5, "title": "PDF Constant-q Transform Toolbox for Music Processing", "date": "", "ddg_snippet": "1. INTRODUCTION Constant-Q transform ( CQT ) here refers to a technique that transforms a time-domain signal x(n) into the time- frequency domain so that the center frequencies of the fre-quency bins are geometrically spaced and their Q-factors are all equal. In effect, this means that the frequency res-olution is better for low frequencies and the time resolu-tion is better for high frequencies ...", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/144846462.pdf", "content": "1. INTRODUCTION Constant-Q transform ( CQT ) here refers to a technique that transforms a time-domain signal x(n) into the time- frequency domain so that the center frequencies of the fre-quency bins are geometrically spaced and their Q-factors are all equal. In effect, this means that the frequency res-olution is better for low frequencies and the time resolu-tion is better for high frequencies ..."}
+{"idx": 6, "title": "PDF Pitch shifting of audio signals using the constant-Q transform", "date": "", "ddg_snippet": "Applying the constant-Q transform ( CQT ) to the problem of frequency domain pitch shifting in place of the STFT provides a solution to all of the aforementioned disadvantages of this ap-proach.", "subpage_snippet": "", "source": "www.dafx12.york.ac.uk", "link": "https://www.dafx12.york.ac.uk/papers/dafx12_submission_81.pdf", "content": "Applying the constant-Q transform ( CQT ) to the problem of frequency domain pitch shifting in place of the STFT provides a solution to all of the aforementioned disadvantages of this ap-proach."}
+{"idx": 7, "title": "PDF Microsoft Word - Lajmi-2021-CQT_FFT_Frequenzanalyse - mitDOI - Ostfalia", "date": "", "ddg_snippet": "In this paper a new time- frequency transform based on the constant-Q transform , named as adaptive quality frequency transform (AQFT), is proposed. The AQFT is a non-uniform transform with logarithmically spaced center frequencies. The CQT offers a computational disadvantage due to the large number of samples needed to assure the quality and the desired resolution at the lower frequency range ...", "subpage_snippet": "", "source": "opus.ostfalia.de", "link": "https://opus.ostfalia.de/frontdoor/deliver/index/docId/1254/file/Lajmi_2021_CQT_FFT_Frequenzanalyse.pdf", "content": "In this paper a new time- frequency transform based on the constant-Q transform , named as adaptive quality frequency transform (AQFT), is proposed. The AQFT is a non-uniform transform with logarithmically spaced center frequencies. The CQT offers a computational disadvantage due to the large number of samples needed to assure the quality and the desired resolution at the lower frequency range ..."}
+{"idx": 8, "title": "(PDF) Constant-Q transform toolbox for music processing", "date": "", "ddg_snippet": "This paper proposes a computationally efficient method for computing the constant-Q transform ( CQT ) of a time-domain signal. CQT refers to a time- frequency represen-tation where the frequency bins ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228523955_Constant-Q_transform_toolbox_for_music_processing", "content": "This paper proposes a computationally efficient method for computing the constant-Q transform ( CQT ) of a time-domain signal. CQT refers to a time- frequency represen-tation where the frequency bins ..."}
+{"idx": 9, "title": "Nonstationary Gabor Frames and the Constant-Q Transform", "date": "", "ddg_snippet": "The functions cqt and icqt use nonstationary Gabor frames to obtain a constant-Q ( frequency -adaptive) transform ( CQT ) of a signal. A notable strength of nonstationary Gabor frames is that they enable the construction of stable inverses, yielding perfect reconstruction.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/help/wavelet/gs/non-stationary-gabor-frames.html", "content": "The functions cqt and icqt use nonstationary Gabor frames to obtain a constant-Q ( frequency -adaptive) transform ( CQT ) of a signal. A notable strength of nonstationary Gabor frames is that they enable the construction of stable inverses, yielding perfect reconstruction."}
diff --git a/data/sampled_jsons/CRAB_Cross-environment_Agent_Benchmark_for_Multimodal_Language_Model_Agents_abstract.jsonl b/data/sampled_jsons/CRAB_Cross-environment_Agent_Benchmark_for_Multimodal_Language_Model_Agents_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af3037d55ac65c15923bebcaeb354484d69d2cbc
--- /dev/null
+++ b/data/sampled_jsons/CRAB_Cross-environment_Agent_Benchmark_for_Multimodal_Language_Model_Agents_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "Sep 15, 2025 · Abstract The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment , lack of detailed and generalized evaluation methods ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.1113/", "content": "Sep 15, 2025 · Abstract The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment , lack of detailed and generalized evaluation methods ..."}
+{"idx": 1, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as web- sites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment , lack of detailed and generalized evaluation methods, and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.01511", "content": "The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as web- sites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment , lack of detailed and generalized evaluation methods, and the ..."}
+{"idx": 2, "title": "GitHub - camel-ai/crab: ️ CRAB: Cross-environment Agent ...", "date": "", "ddg_snippet": "🦀 CRAB : Cross -platform Agent Benchmark for Multimodal Embodied Language Model Agents", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/camel-ai/crab", "content": "🦀 CRAB : Cross -platform Agent Benchmark for Multimodal Embodied Language Model Agents"}
+{"idx": 3, "title": "CRAB: Cross-platfrom agent benchmark for multi-modal embodied ...", "date": "", "ddg_snippet": "Oct 21, 2024 · Abstract :The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment , lack of detailed and generalized evaluation methods ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kyExS4V0H7", "content": "Oct 21, 2024 · Abstract :The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment , lack of detailed and generalized evaluation methods ..."}
+{"idx": 4, "title": "Introducing CRAB: A New Benchmark for Language Models", "date": "", "ddg_snippet": "Jul 21, 2025 · Original Source Title: CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents Abstract : The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-introducing-crab-a-new-benchmark-for-language-models--a3184y8", "content": "Jul 21, 2025 · Original Source Title: CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents Abstract : The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones."}
+{"idx": 5, "title": "CRAB: cross-environment agent benchmark for multimodal ...", "date": "", "ddg_snippet": "Conference item CRAB: cross-environment agent benchmark for multimodal language model agents Abstract : The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones.", "subpage_snippet": "", "source": "ora.ox.ac.uk", "link": "https://ora.ox.ac.uk/objects/uuid:53a31ad5-7aa7-46c7-aa52-5a5e2dc0e6cd", "content": "Conference item CRAB: cross-environment agent benchmark for multimodal language model agents Abstract : The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones."}
+{"idx": 6, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "Abstract The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2407.01511", "content": "Abstract The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones."}
+{"idx": 7, "title": "[2407.01511] CRAB: Cross-environment Agent Benchmark ...", "date": "", "ddg_snippet": "by T Xu · 2024 · Cited by 25 — We introduce Crab, the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.01511", "content": "by T Xu · 2024 · Cited by 25 — We introduce Crab, the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method."}
+{"idx": 8, "title": "CRAB: Cross-environment Agent Benchmark for ...", "date": "", "ddg_snippet": "by T Xu · Cited by 25 — This paper introduces CRAB , a novel benchmark framework for evaluating multimodal language model (MLM) agents on cross-environment tasks. CRAB supports multiple ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qqKJjwibsp", "content": "by T Xu · Cited by 25 — This paper introduces CRAB , a novel benchmark framework for evaluating multimodal language model (MLM) agents on cross-environment tasks. CRAB supports multiple ..."}
+{"idx": 9, "title": "CRAB: Cross-environment Agent Benchmark for ...", "date": "", "ddg_snippet": "9 Aug 2024 — A cross-platform/environment multimodal agent benchmark framework: CRAB , innovatively enabling agents to operate multiple devices simultaneously.", "subpage_snippet": "", "source": "www.camel-ai.org", "link": "https://www.camel-ai.org/blogs/crab-cross-platform-agent-benchmark", "content": "9 Aug 2024 — A cross-platform/environment multimodal agent benchmark framework: CRAB , innovatively enabling agents to operate multiple devices simultaneously."}
diff --git a/data/sampled_jsons/CRAB_Xu_2024a_virtual_agent_benchmark_composable_task_complexity_graph.jsonl b/data/sampled_jsons/CRAB_Xu_2024a_virtual_agent_benchmark_composable_task_complexity_graph.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..962611931a112286ba4a9913bc87272902152d7d
--- /dev/null
+++ b/data/sampled_jsons/CRAB_Xu_2024a_virtual_agent_benchmark_composable_task_complexity_graph.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "CRAB ( Xu et al., 2024 a ) evaluates agents using handcrafted graphs , but it lacks a sys-tematic task analysis, limiting fine-grained capability assess-ment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "CRAB ( Xu et al., 2024 a ) evaluates agents using handcrafted graphs , but it lacks a sys-tematic task analysis, limiting fine-grained capability assess-ment."}
+{"idx": 1, "title": "GitHub - WooooDyy/LLM- Agent -Paper-List: The paper list of the...", "date": "", "ddg_snippet": "[2024/04] OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments.The work presents a benchmarking framework for evaluating LLMs in multi- agent settings, showing a 50% average improvement using Probabilistic Graphical Modeling.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/WooooDyy/LLM-Agent-Paper-List", "content": "[2024/04] OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments.The work presents a benchmarking framework for evaluating LLMs in multi- agent settings, showing a 50% average improvement using Probabilistic Graphical Modeling."}
+{"idx": 2, "title": "Human Benchmark", "date": "", "ddg_snippet": "Human Benchmark . Measure your abilities with brain games and cognitive tests. Get Started.", "subpage_snippet": "", "source": "humanbenchmark.com", "link": "https://humanbenchmark.com/", "content": "Human Benchmark . Measure your abilities with brain games and cognitive tests. Get Started."}
+{"idx": 3, "title": "Manus: General AI agent that bridges mind and action", "date": "", "ddg_snippet": "Manus is a general AI agent that turns your thoughts into actions. It excels at various tasks in work and life, getting everything done while you rest.", "subpage_snippet": "", "source": "manus.im", "link": "https://manus.im/", "content": "Manus is a general AI agent that turns your thoughts into actions. It excels at various tasks in work and life, getting everything done while you rest."}
+{"idx": 4, "title": "Effect of a Virtual Agent ’s Appearance and Voice on Uncanny Valley...", "date": "", "ddg_snippet": "Results indicated that while the agent ’s voice-appearance mismatch affected participants’ perception of anthropomorphism, it was not an influential factor in people’s trusting behavior. We discuss these results in the context of task complexity and make recommendations for future research.", "subpage_snippet": "", "source": "research.vu.nl", "link": "https://research.vu.nl/en/publications/effect-of-a-virtual-agents-appearance-and-voice-on-uncanny-valley", "content": "Results indicated that while the agent ’s voice-appearance mismatch affected participants’ perception of anthropomorphism, it was not an influential factor in people’s trusting behavior. We discuss these results in the context of task complexity and make recommendations for future research."}
+{"idx": 5, "title": "Virtuals Protocol | Society of AI Agents", "date": "", "ddg_snippet": "These agents are tokenized through Agent Tokens, enabling capital formation, permissionless participation, and incentive alignment between creators, investors, and agents .", "subpage_snippet": "", "source": "app.virtuals.io", "link": "https://app.virtuals.io/", "content": "These agents are tokenized through Agent Tokens, enabling capital formation, permissionless participation, and incentive alignment between creators, investors, and agents ."}
+{"idx": 6, "title": "SWE- Bench Pro: Can AI Agents Solve Long-Horizon Software...", "date": "", "ddg_snippet": "We introduce SWE- BENCH PRO, a substantially more challenging benchmark that builds upon the best practices of SWE- Bench [25], but is explicitly designed to capture realistic, complex , enterprise-level problems beyond the scope of SWE- Bench .", "subpage_snippet": "", "source": "scale.com", "link": "https://scale.com/research/swe_bench_pro", "content": "We introduce SWE- BENCH PRO, a substantially more challenging benchmark that builds upon the best practices of SWE- Bench [25], but is explicitly designed to capture realistic, complex , enterprise-level problems beyond the scope of SWE- Bench ."}
+{"idx": 7, "title": "Published in Transactions on Machine Learning Research (05/2025)", "date": "", "ddg_snippet": "WebArena is an agent benchmark that aims to evaluate agents on tasks on the web (Zhou et al., 2024).To be clear, the STeP developers’ goals are orthogonal to the benchmark developers’ goals—creating composable policies for accomplishing fixed tasks that are known apriori.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Zy4uFzMviZ", "content": "WebArena is an agent benchmark that aims to evaluate agents on tasks on the web (Zhou et al., 2024).To be clear, the STeP developers’ goals are orthogonal to the benchmark developers’ goals—creating composable policies for accomplishing fixed tasks that are known apriori."}
+{"idx": 8, "title": "What is the Model Context Protocol (MCP)? - Model Context Protocol", "date": "", "ddg_snippet": "Depending on where you sit in the ecosystem, MCP can have a range of benefits. Developers: MCP reduces development time and complexity when building, or integrating with, an AI application or agent .", "subpage_snippet": "", "source": "modelcontextprotocol.io", "link": "https://modelcontextprotocol.io/", "content": "Depending on where you sit in the ecosystem, MCP can have a range of benefits. Developers: MCP reduces development time and complexity when building, or integrating with, an AI application or agent ."}
+{"idx": 9, "title": "SWE- bench Leaderboards", "date": "", "ddg_snippet": "SWE- bench Logo Leaderboards. SWE- agent -LM-32B, the open-weight SotA on Verified, trained on synthetic data generated by SWE-smith.", "subpage_snippet": "", "source": "www.swebench.com", "link": "https://www.swebench.com/", "content": "SWE- bench Logo Leaderboards. SWE- agent -LM-32B, the open-weight SotA on Verified, trained on synthetic data generated by SWE-smith."}
diff --git a/data/sampled_jsons/CRAB_benchmark_task_composition_subtask_graph_structure.jsonl b/data/sampled_jsons/CRAB_benchmark_task_composition_subtask_graph_structure.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1104094f733c0055b8a71cbc05885401cf5fab50
--- /dev/null
+++ b/data/sampled_jsons/CRAB_benchmark_task_composition_subtask_graph_structure.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tasks and Evaluators | camel-ai/crab | DeepWiki", "date": "", "ddg_snippet": "The Tasks and Evaluators system in CRAB Benchmark v0 provides a flexible, powerful way to define and assess agent performance on real-world tasks . By using directed graphs for evaluation logic, the system can represent complex success criteria across multiple platforms.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/camel-ai/crab/3.1-tasks-and-evaluators", "content": "The Tasks and Evaluators system in CRAB Benchmark v0 provides a flexible, powerful way to define and assess agent performance on real-world tasks . By using directed graphs for evaluation logic, the system can represent complex success criteria across multiple platforms."}
+{"idx": 1, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "Some studies model sub-tasks using a graph structure . For instance, PLaG [43] uses a graph -based structure to enhance plan reasoning within LLMs, while DyVal [97] employs directed acyclic graphs (DAGs) to facilitate dynamic evaluation of LLMs, which decomposes a complex task into subtasks with both sequential and parallel dependencies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.01511", "content": "Some studies model sub-tasks using a graph structure . For instance, PLaG [43] uses a graph -based structure to enhance plan reasoning within LLMs, while DyVal [97] employs directed acyclic graphs (DAGs) to facilitate dynamic evaluation of LLMs, which decomposes a complex task into subtasks with both sequential and parallel dependencies."}
+{"idx": 2, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "Sep 15, 2025 · To overcome these limitations, we introduce CRAB , the first cross-environment agent benchmark framework, incorporating a graph -based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.1113/", "content": "Sep 15, 2025 · To overcome these limitations, we introduce CRAB , the first cross-environment agent benchmark framework, incorporating a graph -based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface."}
+{"idx": 3, "title": "crab/crab-benchmark-v0/README.md at main · camel-ai/crab", "date": "", "ddg_snippet": "Crab Benchmark v0 Overview crab - benchmark -v0 is a benchmark released with the crab framework to provide a standard usage. It includes two virtual machine environments: an Android smartphone and an Ubuntu desktop computer, with 100 tasks and 59 different evaluator functions in the dataset.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/camel-ai/crab/blob/main/crab-benchmark-v0/README.md", "content": "Crab Benchmark v0 Overview crab - benchmark -v0 is a benchmark released with the crab framework to provide a standard usage. It includes two virtual machine environments: an Android smartphone and an Ubuntu desktop computer, with 100 tasks and 59 different evaluator functions in the dataset."}
+{"idx": 4, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents. CRAB provides an end-to-end while easy-to-use framework to build agents, operate environments, and create benchmarks to evaluate them, featuring three key components: cross-environment support, a graph evaluator, and task generation.", "subpage_snippet": "", "source": "crab.camel-ai.org", "link": "https://crab.camel-ai.org/", "content": "CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents. CRAB provides an end-to-end while easy-to-use framework to build agents, operate environments, and create benchmarks to evaluate them, featuring three key components: cross-environment support, a graph evaluator, and task generation."}
+{"idx": 5, "title": "CRAB: Cross-platfrom agent benchmark for multi-modal embodied...", "date": "", "ddg_snippet": "Oct 21, 2024 · To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross-environment tasks , incorporating a graph -based fine-grained evaluation method and an efficient mechanism for task and evaluator construction.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kyExS4V0H7", "content": "Oct 21, 2024 · To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross-environment tasks , incorporating a graph -based fine-grained evaluation method and an efficient mechanism for task and evaluator construction."}
+{"idx": 6, "title": "CRAB: Cross-Environment Agent Benchmark", "date": "", "ddg_snippet": "Task and Evaluator Construction: The framework introduces a scalable task generation method using sub-task composition within a graph structure . This approach enables effective task construction and the creation of corresponding graph evaluators, enhancing the benchmark 's flexibility and extensibility.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2407.01511", "content": "Task and Evaluator Construction: The framework introduces a scalable task generation method using sub-task composition within a graph structure . This approach enables effective task construction and the creation of corresponding graph evaluators, enhancing the benchmark 's flexibility and extensibility."}
+{"idx": 7, "title": "Benchmarking Graph -Based Continual Learning | Restackio", "date": "", "ddg_snippet": "Benchmarking : Establishing benchmarks for graph -based continual learning is essential. This includes evaluating the performance of different replay strategies under various conditions and datasets to identify the most effective approaches.", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/continual-learning-answer-benchmarking-graph-based-cat-ai", "content": "Benchmarking : Establishing benchmarks for graph -based continual learning is essential. This includes evaluating the performance of different replay strategies under various conditions and datasets to identify the most effective approaches."}
+{"idx": 8, "title": "Leveraging Compositional Structure for Reinforcement Learning and...", "date": "", "ddg_snippet": "In this work we presented parameterized subtask graph inference (PSGI), a method for efficiently inferring the latent structure of hierarchical and compositional tasks .", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "https://deepblue.lib.umich.edu/bitstream/handle/2027.42/197133/anthliu_1.pdf?sequence=1", "content": "In this work we presented parameterized subtask graph inference (PSGI), a method for efficiently inferring the latent structure of hierarchical and compositional tasks ."}
+{"idx": 9, "title": "CRAB : Cross-environment Agent Benchmark for Multimodal...", "date": "", "ddg_snippet": "How does the Crab benchmark differ from existing agent benchmarks in terms of task focus? What are the key features of the Crab framework that make it suitable for evaluating multimodal language models?", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-CRAB-Cross-environment-Agent-cly6bu84qw7es01akt92w1rbr", "content": "How does the Crab benchmark differ from existing agent benchmarks in terms of task focus? What are the key features of the Crab framework that make it suitable for evaluating multimodal language models?"}
diff --git a/data/sampled_jsons/CRAB_cross-environment_agent_benchmark_multimodal_language_model_agents_2024.jsonl b/data/sampled_jsons/CRAB_cross-environment_agent_benchmark_multimodal_language_model_agents_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b648a00fa21ae421c53a527154aa9c5662886b3a
--- /dev/null
+++ b/data/sampled_jsons/CRAB_cross-environment_agent_benchmark_multimodal_language_model_agents_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2407.01511] CRAB: Cross-environment Agent Benchmark ...", "date": "", "ddg_snippet": "by T Xu · 2024 · Cited by 25 — Abstract page for arXiv paper 2407.01511: CRAB : Cross - environment Agent Benchmark for Multimodal Language Model Agents .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.01511", "content": "by T Xu · 2024 · Cited by 25 — Abstract page for arXiv paper 2407.01511: CRAB : Cross - environment Agent Benchmark for Multimodal Language Model Agents ."}
+{"idx": 1, "title": "CRAB: Cross-environment Agent Benchmark for ...", "date": "", "ddg_snippet": "by T Xu · Cited by 25 — We introduce CRAB , the first agent benchmark framework designed to support cross - environment tasks, incorporating a graph-based fine-grained evaluation method.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qqKJjwibsp", "content": "by T Xu · Cited by 25 — We introduce CRAB , the first agent benchmark framework designed to support cross - environment tasks, incorporating a graph-based fine-grained evaluation method."}
+{"idx": 2, "title": "GitHub - camel-ai/crab: 🦀️ CRAB: Cross-environment ...", "date": "", "ddg_snippet": "CRAB is a framework for building LLM agent benchmark environments in a Python-centric way. Key Features: Cross -platform and Multi- environment , Easy-to-use ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/camel-ai/crab", "content": "CRAB is a framework for building LLM agent benchmark environments in a Python-centric way. Key Features: Cross -platform and Multi- environment , Easy-to-use ..."}
+{"idx": 3, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments , and create benchmarks to evaluate them.", "subpage_snippet": "", "source": "crab.camel-ai.org", "link": "https://crab.camel-ai.org/", "content": "CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments , and create benchmarks to evaluate them."}
+{"idx": 4, "title": "CRAB: Cross-environment Agent Benchmark for ...", "date": "", "ddg_snippet": "9 Aug 2024 — A cross -platform/ environment multimodal agent benchmark framework: CRAB , innovatively enabling agents to operate multiple devices simultaneously.", "subpage_snippet": "", "source": "www.camel-ai.org", "link": "https://www.camel-ai.org/blogs/crab-cross-platform-agent-benchmark", "content": "9 Aug 2024 — A cross -platform/ environment multimodal agent benchmark framework: CRAB , innovatively enabling agents to operate multiple devices simultaneously."}
+{"idx": 5, "title": "CRAB: CROSS-ENVIRONMENT AGENT BENCHMARK", "date": "", "ddg_snippet": "The development of autonomous agents increasingly relies on Multimodal Lan- guage Models (MLMs) to perform tasks described in natural language with GUI.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f4a00da5258fd0cfa44aa33a28f6c44ba4beba9e.pdf", "content": "The development of autonomous agents increasingly relies on Multimodal Lan- guage Models (MLMs) to perform tasks described in natural language with GUI."}
+{"idx": 6, "title": "Crab Framework Released: An AI Framework for Building ...", "date": "", "ddg_snippet": "10 Aug 2024 — The research team tested the Crab framework using four advanced multimodal language models (MLMs): GPT-4o, GPT-4 Turbo, Claude 3 Opus, and ...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/08/10/crab-framework-released-an-ai-framework-for-building-llm-agent-benchmark-environments-in-a-python-centric-way/", "content": "10 Aug 2024 — The research team tested the Crab framework using four advanced multimodal language models (MLMs): GPT-4o, GPT-4 Turbo, Claude 3 Opus, and ..."}
+{"idx": 7, "title": "CRAB - CAMEL-AI.org", "date": "", "ddg_snippet": "Introducing CRAB : Cross - environment Agent Benchmark for Multimodal Language Model Agents CRAB provides an end-to-end and easy-to-use ...", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/CamelAIOrg/status/1821970132606058943", "content": "Introducing CRAB : Cross - environment Agent Benchmark for Multimodal Language Model Agents CRAB provides an end-to-end and easy-to-use ..."}
+{"idx": 8, "title": "CRAB: Cross-Environment Agent Benchmark", "date": "", "ddg_snippet": "CRAB introduces a graph-based benchmark evaluating autonomous multimodal agents across desktop and mobile environments to enhance real-world application.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2407.01511", "content": "CRAB introduces a graph-based benchmark evaluating autonomous multimodal agents across desktop and mobile environments to enhance real-world application."}
+{"idx": 9, "title": "luo-junyu/Awesome-Agent-Papers", "date": "", "ddg_snippet": "CRAB : Cross-platfrom agent benchmark for multi-modal embodied language model agents ( 2024 ). Introduced Crab , a cross - environment agent benchmark framework ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/luo-junyu/Awesome-Agent-Papers", "content": "CRAB : Cross-platfrom agent benchmark for multi-modal embodied language model agents ( 2024 ). Introduced Crab , a cross - environment agent benchmark framework ..."}
diff --git a/data/sampled_jsons/CUB_Caltech-UCSD_Birds_dataset_attributes_manually_annotated_human_labeled.jsonl b/data/sampled_jsons/CUB_Caltech-UCSD_Birds_dataset_attributes_manually_annotated_human_labeled.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a8f3d5c8363dd63ebbb254e994c8e0eea4b272e
--- /dev/null
+++ b/data/sampled_jsons/CUB_Caltech-UCSD_Birds_dataset_attributes_manually_annotated_human_labeled.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Caltech - UCSD Birds 200", "date": "", "ddg_snippet": "Caltech - UCSD Birds 200 ( CUB -200) is a challenging image dataset annotated with 200 bird species.Each image is annotated with a bounding box, a rough bird segmentation, and a set of attribute labels .", "subpage_snippet": "", "source": "vision.cornell.edu", "link": "https://vision.cornell.edu/se3/wp-content/uploads/2014/09/WelinderEtal10_CUB-200.pdf", "content": "Caltech - UCSD Birds 200 ( CUB -200) is a challenging image dataset annotated with 200 bird species.Each image is annotated with a bounding box, a rough bird segmentation, and a set of attribute labels ."}
+{"idx": 1, "title": "image dataset with photos of 200 bird species", "date": "", "ddg_snippet": "Caltech - UCSD Birds -200-2011 ( CUB -200-2011) is an extended version of the CUB -200 dataset , with roughly double the number of images per class and new part location annotations . attributes /. 322 binary attribute labels from MTurk workers.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/veeralakrishna/200-bird-species-with-11788-images", "content": "Caltech - UCSD Birds -200-2011 ( CUB -200-2011) is an extended version of the CUB -200 dataset , with roughly double the number of images per class and new part location annotations . attributes /. 322 binary attribute labels from MTurk workers."}
+{"idx": 2, "title": "Perona Lab - CUB -200-2011", "date": "", "ddg_snippet": "Caltech - UCSD Birds -200-2011 ( CUB -200-2011). Warning: Images in this dataset overlap with images in ImageNet. Annotations per image: 15 Part Locations, 312 Binary Attributes , 1 Bounding Box. For detailed information about the dataset , please see the technical report linked below.", "subpage_snippet": "", "source": "www.vision.caltech.edu", "link": "https://www.vision.caltech.edu/datasets/cub_200_2011/", "content": "Caltech - UCSD Birds -200-2011 ( CUB -200-2011). Warning: Images in this dataset overlap with images in ImageNet. Annotations per image: 15 Part Locations, 312 Binary Attributes , 1 Bounding Box. For detailed information about the dataset , please see the technical report linked below."}
+{"idx": 3, "title": "The Caltech - UCSD Birds 200-2011 Dataset | Request PDF", "date": "", "ddg_snippet": "... • Caltech - UCSD Birds -200-2011, CUB [37]: CUB is the most widely-used dataset for fine-grained visual categorization tasks. It contains 11,788 images of 200 subcategories belonging to birds , we follow the same data processing as done in the Label -free-CBM [20] setting to select 5...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/251734721_The_Caltech-UCSD_Birds200-2011_Dataset", "content": "... • Caltech - UCSD Birds -200-2011, CUB [37]: CUB is the most widely-used dataset for fine-grained visual categorization tasks. It contains 11,788 images of 200 subcategories belonging to birds , we follow the same data processing as done in the Label -free-CBM [20] setting to select 5..."}
+{"idx": 4, "title": "The Caltech - UCSD Birds -200-2011 Dataset", "date": "", "ddg_snippet": "University of California , San Diego La Jolla CA. {sbranson, cwah, sjb}@cs. ucsd .edu. 2. California Institute of Technology Pasadena, CA. {welinder, perona}@ caltech .edu. Abstract. CUB -200-2011 is an extended version of CUB -200 [7], a challenging dataset of 200 bird species.", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/dataset/2011-wah.pdf", "content": "University of California , San Diego La Jolla CA. {sbranson, cwah, sjb}@cs. ucsd .edu. 2. California Institute of Technology Pasadena, CA. {welinder, perona}@ caltech .edu. Abstract. CUB -200-2011 is an extended version of CUB -200 [7], a challenging dataset of 200 bird species."}
+{"idx": 5, "title": "caltech _ birds 2011 | TensorFlow Datasets", "date": "", "ddg_snippet": "Caltech - UCSD Birds 200 ( CUB -200) is an image dataset with photos of 200 bird species (mostly North American). The total number of categories of birds is 200 and there are 6033 images in the 2010 dataset and 11,788 images in the 2011 dataset .", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/datasets/catalog/caltech_birds2011", "content": "Caltech - UCSD Birds 200 ( CUB -200) is an image dataset with photos of 200 bird species (mostly North American). The total number of categories of birds is 200 and there are 6033 images in the 2010 dataset and 11,788 images in the 2011 dataset ."}
+{"idx": 6, "title": "[PDF] Caltech - UCSD Birds 200 | Semantic Scholar", "date": "", "ddg_snippet": "Caltech - UCSD Birds 200 ( CUB -200) is a challenging image dataset annotated with 200 bird species. It was created to enable the study of subordinate categorization, which is not possible with other…The Caltech - UCSD Birds -200-2011 Dataset . C. WahSteve BransonP.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Caltech-UCSD-Birds-200-Welinder-Branson/a48a56b0727d09f599676524fe190308d9e88bf1", "content": "Caltech - UCSD Birds 200 ( CUB -200) is a challenging image dataset annotated with 200 bird species. It was created to enable the study of subordinate categorization, which is not possible with other…The Caltech - UCSD Birds -200-2011 Dataset . C. WahSteve BransonP."}
+{"idx": 7, "title": "Discovering Localized Attributes for Fine-grained Recognition", "date": "", "ddg_snippet": "Human interaction is used to provide semantic names for the discovered attributes . We demon-strate our method on two challenging datasets , Caltech - UCSD Birds -200-2011 and Leeds Butteries, and nd that our discovered attributes outperform those generated by traditional approaches.", "subpage_snippet": "", "source": "vision.soic.indiana.edu", "link": "https://vision.soic.indiana.edu/papers/attributes2012cvpr.pdf", "content": "Human interaction is used to provide semantic names for the discovered attributes . We demon-strate our method on two challenging datasets , Caltech - UCSD Birds -200-2011 and Leeds Butteries, and nd that our discovered attributes outperform those generated by traditional approaches."}
+{"idx": 8, "title": "CUB Dataset | 00why00/Glocal | DeepWiki", "date": "", "ddg_snippet": "This document describes the CUB ( Caltech - UCSD Birds ) dataset implementation in the Glocal Energy-based Learning framework.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/00why00/Glocal/4.5-cub-dataset", "content": "This document describes the CUB ( Caltech - UCSD Birds ) dataset implementation in the Glocal Energy-based Learning framework."}
+{"idx": 9, "title": "Top 10 Datasets For Zero-Shot Learning | Restackio", "date": "", "ddg_snippet": "CUB ( Caltech - UCSD Birds 200) Comprising 200 bird species, CUB includes 312 attributes that describe various features such as color, shape, and size, making it ideal for fine-grained classification tasks.", "subpage_snippet": "", "source": "www.restack.io", "link": "https://www.restack.io/p/zero-shot-learning-answer-top-10-datasets-cat-ai", "content": "CUB ( Caltech - UCSD Birds 200) Comprising 200 bird species, CUB includes 312 attributes that describe various features such as color, shape, and size, making it ideal for fine-grained classification tasks."}
diff --git a/data/sampled_jsons/CVE-2023-38646_CVSS_score_10.0_9.8_Metabase_Pre-auth_RCE_severity.jsonl b/data/sampled_jsons/CVE-2023-38646_CVSS_score_10.0_9.8_Metabase_Pre-auth_RCE_severity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..00ba5b3d4a9ca215497461a88a09303b26c69cb4
--- /dev/null
+++ b/data/sampled_jsons/CVE-2023-38646_CVSS_score_10.0_9.8_Metabase_Pre-auth_RCE_severity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Vulnerability Summary for the Week of July 24, 2023", "date": "", "ddg_snippet": "The division of high, medium, and low severities correspond to the following scores : High: vulnerabilities with a CVSS base score of 7.0– 10.0 ; Medium: ...", "subpage_snippet": "", "source": "www.cisa.gov", "link": "https://www.cisa.gov/news-events/bulletins/sb23-212", "content": "The division of high, medium, and low severities correspond to the following scores : High: vulnerabilities with a CVSS base score of 7.0– 10.0 ; Medium: ..."}
+{"idx": 1, "title": "Severe CVEs, Severe Outcomes | Aug 2024 Weekly Reports", "date": "", "ddg_snippet": "30 Aug 2024 — Discovered last year, the bug has a CVSS score of 10.0 , indicating its extreme criticality. ... CVE - 2023 - 38646 , Metabase open source ...", "subpage_snippet": "", "source": "www.loginsoft.com", "link": "https://www.loginsoft.com/reports/weekly/severe-cves-severe-outcomes", "content": "30 Aug 2024 — Discovered last year, the bug has a CVSS score of 10.0 , indicating its extreme criticality. ... CVE - 2023 - 38646 , Metabase open source ..."}
+{"idx": 2, "title": "Nvd - Cve-2023-38646", "date": "", "ddg_snippet": "Metabase open source before 0.46.6.1 and Metabase Enterprise before 1.46.6.1 allow attackers to execute arbitrary commands on the server, at the server's privilege level.", "subpage_snippet": "", "source": "nvd.nist.gov", "link": "https://nvd.nist.gov/vuln/detail/CVE-2023-38646", "content": "Metabase open source before 0.46.6.1 and Metabase Enterprise before 1.46.6.1 allow attackers to execute arbitrary commands on the server, at the server's privilege level."}
+{"idx": 3, "title": "CVE-2023-38646 Impact, Exploitability, and Mitigation Steps | Wiz", "date": "", "ddg_snippet": "Understand the critical aspects of CVE-2023-38646 with a detailed vulnerability assessment, exploitation potential, affected technologies, and remediation guidance.", "subpage_snippet": "", "source": "www.wiz.io", "link": "https://www.wiz.io/vulnerability-database/cve/cve-2023-38646", "content": "Understand the critical aspects of CVE-2023-38646 with a detailed vulnerability assessment, exploitation potential, affected technologies, and remediation guidance."}
+{"idx": 4, "title": "CVE-2023-38646 : Metabase open source before 0.46.6.1 and Metabase ...", "date": "", "ddg_snippet": "CVE-2023-38646 : Metabase open source before 0.46.6.1 and Metabase Enterprise before 1.46.6.1 allow attackers to execute arbitrary commands on the server, at the serve", "subpage_snippet": "", "source": "www.cvedetails.com", "link": "https://www.cvedetails.com/cve/CVE-2023-38646", "content": "CVE-2023-38646 : Metabase open source before 0.46.6.1 and Metabase Enterprise before 1.46.6.1 allow attackers to execute arbitrary commands on the server, at the serve"}
+{"idx": 5, "title": "CVE-2023-38646 - Metabase Pre-auth RCE - GitHub", "date": "", "ddg_snippet": "Metabase Pre-auth RCE ( CVE-2023-38646 ). Contribute to kh4sh3i/ CVE - 2023 - 38646 development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kh4sh3i/cve-2023-38646", "content": "Metabase Pre-auth RCE ( CVE-2023-38646 ). Contribute to kh4sh3i/ CVE - 2023 - 38646 development by creating an account on GitHub."}
+{"idx": 6, "title": "CVE-2023-38646 - Exploits & Severity - Feedly", "date": "", "ddg_snippet": "Dec 11, 2023 at 12:18 AM / Over Security - Cybersecurity news aggregator Threat Intelligence Report The vulnerability CVE-2023-38646 , also known as Metabase , is being actively exploited by the Kinsing threat actor. It allows the attacker to drop a payload on vulnerable machines, as observed in honeypots, and has been previously documented in a ...", "subpage_snippet": "", "source": "feedly.com", "link": "https://feedly.com/cve/CVE-2023-38646", "content": "Dec 11, 2023 at 12:18 AM / Over Security - Cybersecurity news aggregator Threat Intelligence Report The vulnerability CVE-2023-38646 , also known as Metabase , is being actively exploited by the Kinsing threat actor. It allows the attacker to drop a payload on vulnerable machines, as observed in honeypots, and has been previously documented in a ..."}
+{"idx": 7, "title": "CVE-2023-38646 Security Vulnerability & Exploit Details", "date": "", "ddg_snippet": "Above is the CVSS Sub- score Breakdown for CVE-2023-38646 , illustrating how Base, Impact, and Exploitability factors combine to form the overall severity rating.", "subpage_snippet": "", "source": "cve.akaoma.com", "link": "https://cve.akaoma.com/cve-2023-38646", "content": "Above is the CVSS Sub- score Breakdown for CVE-2023-38646 , illustrating how Base, Impact, and Exploitability factors combine to form the overall severity rating."}
+{"idx": 8, "title": "CVE-2023-38646 Description, Impact and Technical Details", "date": "", "ddg_snippet": "CVE-2023-38646 is a serious vulnerability affecting Metabase open source versions below 0.46.6.1 and Metabase Enterprise versions below 1.46.6.1. This issue allows attackers to execute arbitrary commands on the server at the server's privilege level, bypassing the authentication requirement.", "subpage_snippet": "", "source": "www.recordedfuture.com", "link": "https://www.recordedfuture.com/vulnerability-database/CVE-2023-38646", "content": "CVE-2023-38646 is a serious vulnerability affecting Metabase open source versions below 0.46.6.1 and Metabase Enterprise versions below 1.46.6.1. This issue allows attackers to execute arbitrary commands on the server at the server's privilege level, bypassing the authentication requirement."}
+{"idx": 9, "title": "CVE-2023-38646 - Vulmon", "date": "", "ddg_snippet": "Metabase Pre-Auth RCE ( CVE-2023-38646 ) POC This is a script written in Python that allows the exploitation of the Metabase's software security flaw in the described in CVE 2023-38646 The system is vulnerable in versions preceding 04661, in the open-source edition, and preceding 14661, in the enterprise edition Usage The script needs the target URL, the setup token", "subpage_snippet": "", "source": "vulmon.com", "link": "https://vulmon.com/vulnerabilitydetails?qid=CVE-2023-38646", "content": "Metabase Pre-Auth RCE ( CVE-2023-38646 ) POC This is a script written in Python that allows the exploitation of the Metabase's software security flaw in the described in CVE 2023-38646 The system is vulnerable in versions preceding 04661, in the open-source edition, and preceding 14661, in the enterprise edition Usage The script needs the target URL, the setup token"}
diff --git a/data/sampled_jsons/CVE-Bench_'Insufficient_Exploration'_'Common_Failure_Modes'.jsonl b/data/sampled_jsons/CVE-Bench_'Insufficient_Exploration'_'Common_Failure_Modes'.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c955c0f65c35a34020df935b359b9c91c34ae82f
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_'Insufficient_Exploration'_'Common_Failure_Modes'.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — Frequency of common failure modes of agents. Insufficient exploration is a key bottleneck for all agents. LLM agents. Cy-Agent. T-Agent.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "by Y Zhu · 2025 · Cited by 12 — Frequency of common failure modes of agents. Insufficient exploration is a key bottleneck for all agents. LLM agents. Cy-Agent. T-Agent."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · Cited by 14 — ... failures , including Limited Task Understanding, Incorrect Focus, and Insufficient Exploration , but failed more due to Tool Misuse and Inadequate Reasoning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3pk0p4NGmQ", "content": "by Y Zhu · Cited by 14 — ... failures , including Limited Task Understanding, Incorrect Focus, and Insufficient Exploration , but failed more due to Tool Misuse and Inadequate Reasoning."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "10 Apr 2025 — Furthermore, we summarize the common failure modes to demonstrate the difficulty of exploiting vulnerabilities and explore potential ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v3", "content": "10 Apr 2025 — Furthermore, we summarize the common failure modes to demonstrate the difficulty of exploiting vulnerabilities and explore potential ..."}
+{"idx": 3, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real- ...", "date": "", "ddg_snippet": "Insufficient exploration emerged as the dominant failure mode , affecting 67.5% to 80% of zero-day attempts and 37.5% to 55% of one-day attempts across all ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17332", "content": "Insufficient exploration emerged as the dominant failure mode , affecting 67.5% to 80% of zero-day attempts and 37.5% to 55% of one-day attempts across all ..."}
+{"idx": 4, "title": "CVE-Bench: Benchmarking LLM-based Software ...", "date": "", "ddg_snippet": "by P Wang · 2025 · Cited by 4 — Then, we analyzed the failure reasons for the three different foundation models . As shown in. Figure 5(a), for GPT-4, the most common failure .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "by P Wang · 2025 · Cited by 4 — Then, we analyzed the failure reasons for the three different foundation models . As shown in. Figure 5(a), for GPT-4, the most common failure ."}
+{"idx": 5, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "Common failure modes identified: Insufficient exploration was a key bottleneck for all agents. Limited task understanding, incorrect focus, tool misuse, and ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165204", "content": "Common failure modes identified: Insufficient exploration was a key bottleneck for all agents. Limited task understanding, incorrect focus, tool misuse, and ..."}
+{"idx": 6, "title": "From CVE to Exploit | PDF | Software Engineering", "date": "", "ddg_snippet": "a predefined set of behavioral patterns and common failure . Output. A comprehensive analysis of the setup logs, a binary cases, as detailed in Section 4.1.2 ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/916414360/From-CVE-to-Exploit", "content": "a predefined set of behavioral patterns and common failure . Output. A comprehensive analysis of the setup logs, a binary cases, as detailed in Section 4.1.2 ..."}
+{"idx": 7, "title": "Representativeness in the Benchmark for Vulnerability ...", "date": "", "ddg_snippet": "by K Afanador · 2020 · Cited by 9 — By using the correlation between current CWE and CVE data, the proposed B-VAT will assess tools using vulnerabilities in the proportions their types occur in ...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/cset20-paper-afanador_0.pdf", "content": "by K Afanador · 2020 · Cited by 9 — By using the correlation between current CWE and CVE data, the proposed B-VAT will assess tools using vulnerabilities in the proportions their types occur in ..."}
+{"idx": 8, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "Common Failure Modes . Besides those successful cases, existing LLM agents still fail to exploit most of the vulnerabilities in CVE-Bench , especially under the zero-day setting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "Common Failure Modes . Besides those successful cases, existing LLM agents still fail to exploit most of the vulnerabilities in CVE-Bench , especially under the zero-day setting."}
+{"idx": 9, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for ...", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
diff --git a/data/sampled_jsons/CVE-Bench_AI_agents_real-world_web_application_vulnerabilities_2025_year_2025.jsonl b/data/sampled_jsons/CVE-Bench_AI_agents_real-world_web_application_vulnerabilities_2025_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d06404da51bed75fd040147134ee43ad8d94c05
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_AI_agents_real-world_web_application_vulnerabilities_2025_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ... CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ... Measuring AI Agents’ Ability to Exploit Web Applications CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ... CVE-Bench: Benchmarking LLM-based Software Engineering Agent ... LLM-agent - 2025-03-24 [2503.17332] CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit … [2503.17332] CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit … [2503.17332] CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit … [2503.17332] CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit … [2503.17332] CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit … CVE - Bench : Benchmarking LLM-based Software Engineering Agent's Ab… CVE-Bench: A Real-World Cybersecurity Benchmark for AI Agents", "date": "", "ddg_snippet": "Mar 21, 2025 · In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real - world conditions, while also providing effective evaluation of their exploits. Apr 24, 2025 · [ 2025 -04-24] CVE - Bench won the second prize in SafeBench, a competition for ML Safety benchmarks. This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. Mar 31, 2025 · In this post, we introduce CVE - bench — the first benchmark built on real - world vulnerabilities , which contains: 40 real- world vulnerability -exploitation challenges. A reproducible solution for each challenge. Comprehensive evaluation mechanisms, per task. Chat is not available. Unlike previous vulnerability repair benchmarks , which only involve the code in- put and output, we provide LLM agents with a test environment that simulates the real - world vulnerability repair process. This environment provides multiple levels of CVE information modeling, such as black-box testing and white- box testing. Mar 24, 2025 · In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real - world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities. Should we build a benchmark for real-world vulnerabilities? However, existing benchmarks fall short as they are limited to abstracted Capture the Flag competitions or lack comprehensive coverage. Building a benchmark for real - world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. How many vulnerabilities can the agent framework resolve? Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities. Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) What is CVE-bench sandbox framework? In CVE-Bench, we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities. Can LLM agents exploit web application vulnerabilities? Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real - world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities . What is CVE-bench? Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. What is automated vulnerability repair? Automated vulnerability repair is a popular and valuablesoftwareengineeringandsecurityresearch eld . Large language models (LLMs) and LLM- based agents have shown sizeable potential appli- cation value in this area. LLMs can understand natural language described vulnerability rationale and generate formal code to repair it. And so to resolve this, we built a benchmark called CVE - bench , which we built to evaluate AI agents ' abilities to find and exploit real - world vulnerabilities . So in order to determine and find vulnerabilities that were actually in the wild, we use this database from NIST called the CVE database.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Mar 21, 2025 · In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real - world conditions, while also providing effective evaluation of their exploits. Apr 24, 2025 · [ 2025 -04-24] CVE - Bench won the second prize in SafeBench, a competition for ML Safety benchmarks. This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. Mar 31, 2025 · In this post, we introduce CVE - bench — the first benchmark built on real - world vulnerabilities , which contains: 40 real- world vulnerability -exploitation challenges. A reproducible solution for each challenge. Comprehensive evaluation mechanisms, per task. Chat is not available. Unlike previous vulnerability repair benchmarks , which only involve the code in- put and output, we provide LLM agents with a test environment that simulates the real - world vulnerability repair process. This environment provides multiple levels of CVE information modeling, such as black-box testing and white- box testing. Mar 24, 2025 · In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real - world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities. Should we build a benchmark for real-world vulnerabilities? However, existing benchmarks fall short as they are limited to abstracted Capture the Flag competitions or lack comprehensive coverage. Building a benchmark for real - world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. How many vulnerabilities can the agent framework resolve? Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities. Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) What is CVE-bench sandbox framework? In CVE-Bench, we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities. Can LLM agents exploit web application vulnerabilities? Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real - world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities . What is CVE-bench? Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. What is automated vulnerability repair? Automated vulnerability repair is a popular and valuablesoftwareengineeringandsecurityresearch eld . Large language models (LLMs) and LLM- based agents have shown sizeable potential appli- cation value in this area. LLMs can understand natural language described vulnerability rationale and generate formal code to repair it. And so to resolve this, we built a benchmark called CVE - bench , which we built to evaluate AI agents ' abilities to find and exploit real - world vulnerabilities . So in order to determine and find vulnerabilities that were actually in the wild, we use this database from NIST called the CVE database."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "Apr 24, 2025 · [ 2025 -04-24] CVE - Bench won the second prize in SafeBench, a competition for ML Safety benchmarks. This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · [ 2025 -04-24] CVE - Bench won the second prize in SafeBench, a competition for ML Safety benchmarks. This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database."}
+{"idx": 2, "title": "Measuring AI Agents’ Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Mar 31, 2025 · In this post, we introduce CVE - bench — the first benchmark built on real - world vulnerabilities , which contains: 40 real- world vulnerability -exploitation challenges. A reproducible solution for each challenge. Comprehensive evaluation mechanisms, per task.", "subpage_snippet": "", "source": "ddkang.substack.com", "link": "https://ddkang.substack.com/p/measuring-ai-agents-ability-to-exploit", "content": "Mar 31, 2025 · In this post, we introduce CVE - bench — the first benchmark built on real - world vulnerabilities , which contains: 40 real- world vulnerability -exploitation challenges. A reproducible solution for each challenge. Comprehensive evaluation mechanisms, per task."}
+{"idx": 3, "title": "CVE-Bench: Benchmarking LLM-based Software Engineering Agent ...", "date": "", "ddg_snippet": "Unlike previous vulnerability repair benchmarks , which only involve the code in- put and output, we provide LLM agents with a test environment that simulates the real - world vulnerability repair process. This environment provides multiple levels of CVE information modeling, such as black-box testing and white- box testing.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "Unlike previous vulnerability repair benchmarks , which only involve the code in- put and output, we provide LLM agents with a test environment that simulates the real - world vulnerability repair process. This environment provides multiple levels of CVE information modeling, such as black-box testing and white- box testing."}
+{"idx": 4, "title": "LLM-agent - 2025-03-24", "date": "", "ddg_snippet": "Mar 24, 2025 · In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real - world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities.", "subpage_snippet": "", "source": "lxl-sword.github.io", "link": "https://lxl-sword.github.io/arxiv_paper_LLM_list/arxiv_LLM-agent_2025-03-24.html", "content": "Mar 24, 2025 · In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real - world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities."}
+{"idx": 5, "title": "CVE-Bench: A Real-World Cybersecurity Benchmark for AI Agents", "date": "", "ddg_snippet": "And so to resolve this, we built a benchmark called CVE - bench , which we built to evaluate AI agents ' abilities to find and exploit real - world vulnerabilities . So in order to determine and find vulnerabilities that were actually in the wild, we use this database from NIST called the CVE database.", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/events/sessions/daniel-kang-cve-bench-a-real-world-cybersecurity-benchmark-for-ai-agents", "content": "And so to resolve this, we built a benchmark called CVE - bench , which we built to evaluate AI agents ' abilities to find and exploit real - world vulnerabilities . So in order to determine and find vulnerabilities that were actually in the wild, we use this database from NIST called the CVE database."}
+{"idx": 6, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real - World Web Application Vulnerabilities ... CVE - Bench , we design a sandbox framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real - World Web Application Vulnerabilities ... CVE - Bench , we design a sandbox framework ..."}
+{"idx": 7, "title": "CVE-2024-34359 - Exploits & Severity - Feedly", "date": "", "ddg_snippet": "CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real - World Web Application Vulnerabilities Yuxuan Zhu1Antony Kellermann1Dylan Bowman ...", "subpage_snippet": "", "source": "feedly.com", "link": "https://feedly.com/cve/CVE-2024-34359", "content": "CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real - World Web Application Vulnerabilities Yuxuan Zhu1Antony Kellermann1Dylan Bowman ..."}
+{"idx": 8, "title": "CVE-2024-3408 - Exploits & Severity - Feedly", "date": "", "ddg_snippet": "CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real - World Web Application Vulnerabilities Yuxuan Zhu1Antony Kellermann1Dylan Bowman ...", "subpage_snippet": "", "source": "feedly.com", "link": "https://feedly.com/cve/CVE-2024-3408", "content": "CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real - World Web Application Vulnerabilities Yuxuan Zhu1Antony Kellermann1Dylan Bowman ..."}
+{"idx": 9, "title": "CVE-2021-44228 - Apache Log4j2 Remote Code Execution", "date": "", "ddg_snippet": "For the benefit of the cybersecurity community and network defenders—and to help every organization better manage vulnerabilities and keep pace ...", "subpage_snippet": "", "source": "cvefeed.io", "link": "https://cvefeed.io/vuln/detail/CVE-2021-44228", "content": "For the benefit of the cybersecurity community and network defenders—and to help every organization better manage vulnerabilities and keep pace ..."}
diff --git a/data/sampled_jsons/CVE-Bench_T-Agent_AutoGPT_cost_difference_ratio_times_more_expensive_how_much_more_year_2025.jsonl b/data/sampled_jsons/CVE-Bench_T-Agent_AutoGPT_cost_difference_ratio_times_more_expensive_how_much_more_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d2f35fa00b1d54e855e3bea520ab683a3f03dd1f
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_T-Agent_AutoGPT_cost_difference_ratio_times_more_expensive_how_much_more_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for ...", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "In this section, we use CVE - Bench to evaluate the cybersecurity ability of existing LLM agents. We introduce our experimental settings and results and present case studies for in-depth analysis.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "In this section, we use CVE - Bench to evaluate the cybersecurity ability of existing LLM agents. We introduce our experimental settings and results and present case studies for in-depth analysis."}
+{"idx": 2, "title": "CVE-Bench: Benchmarking LLM-based Software Engineering Agent ...", "date": "", "ddg_snippet": "During the patch generation process, CVE - Bench enhances the executable environment by providing four static analysis tools (Prospector, Pylint, Ban- dit, and Mypy) for the agents to call to simulate the real-world vulnerability repair environment (see detail in §C).", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "During the patch generation process, CVE - Bench enhances the executable environment by providing four static analysis tools (Prospector, Pylint, Ban- dit, and Mypy) for the agents to call to simulate the real-world vulnerability repair environment (see detail in §C)."}
+{"idx": 3, "title": "Introducing ChatGPT agent: bridging research and action", "date": "", "ddg_snippet": "Jul 17, 2025 · On DSBench , designed to evaluate agents on realistic data science tasks spanning data analysis and modeling, ChatGPT agent notably surpasses human performance by a significant margin.", "subpage_snippet": "", "source": "openai.com", "link": "https://openai.com/index/introducing-chatgpt-agent/", "content": "Jul 17, 2025 · On DSBench , designed to evaluate agents on realistic data science tasks spanning data analysis and modeling, ChatGPT agent notably surpasses human performance by a significant margin."}
+{"idx": 4, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "To address this challenge, we introduce CVE - Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17332", "content": "To address this challenge, we introduce CVE - Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures."}
+{"idx": 5, "title": "AutoGPT vs AgentGPT: A Complete Guide to Autonomous AI Agents ...", "date": "", "ddg_snippet": "Apr 30, 2025 · AutoGPT is best for technical users needing deep customization and handling complex projects. AgentGPT is ideal for non-tech users looking for simple, fast automation without setup hassles.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/abhishekshakya/autogpt-vs-agentgpt-a-complete-guide-to-autonomous-ai-agents-2025-1kfk", "content": "Apr 30, 2025 · AutoGPT is best for technical users needing deep customization and handling complex projects. AgentGPT is ideal for non-tech users looking for simple, fast automation without setup hassles."}
+{"idx": 6, "title": "What Is The Cost Difference Between Metal Roofing... - WHYIENJOY", "date": "", "ddg_snippet": "How Much does a Metal Shingle Roof Cost ?Metal roofs are an excellent choice but they are far more expensive than an asphalt roofs. Metal roofs are often referred to as “investment grade roofing” in part because they do cost a lot more than asphalt.", "subpage_snippet": "", "source": "www.whyienjoy.com", "link": "https://www.whyienjoy.com/what-is-the-cost-difference-between-metal-roofing-and-shingles/", "content": "How Much does a Metal Shingle Roof Cost ?Metal roofs are an excellent choice but they are far more expensive than an asphalt roofs. Metal roofs are often referred to as “investment grade roofing” in part because they do cost a lot more than asphalt."}
+{"idx": 7, "title": "Why is travelling by train so much more expensive than flying? | News", "date": "", "ddg_snippet": "How much more expensive are trains? According to the new Greenpeace report, which analysed costs for 112 different routes on nine different days, 79 journeys were cheaper by plane.", "subpage_snippet": "", "source": "www.eco-business.com", "link": "https://www.eco-business.com/news/why-is-travelling-by-train-so-much-more-expensive-than-flying/", "content": "How much more expensive are trains? According to the new Greenpeace report, which analysed costs for 112 different routes on nine different days, 79 journeys were cheaper by plane."}
+{"idx": 8, "title": "How Much More Does It Cost to Live Near a Golf... - Coventry Direct", "date": "", "ddg_snippet": "Discover how much more —or less—it costs to live near a golf course, with price insights across 70 major U.S. cities.The Northeast follows with homes 2.3 times more expensive , while the South posts a solid 1.7x premium over the regional average.", "subpage_snippet": "", "source": "www.coventrydirect.com", "link": "https://www.coventrydirect.com/blog/cost-of-living-next-to-a-golf-course/", "content": "Discover how much more —or less—it costs to live near a golf course, with price insights across 70 major U.S. cities.The Northeast follows with homes 2.3 times more expensive , while the South posts a solid 1.7x premium over the regional average."}
+{"idx": 9, "title": "Ontario’s 407 ETR Is 49 Times More Expensive Than New...", "date": "", "ddg_snippet": "But just how much more ?When you do the math, the 407 ETR ends up being nearly 49 times more expensive than the New York Thruway for a 100-kilometer trip. It’s a shocking comparison, especially for those of us watching our spending.", "subpage_snippet": "", "source": "citizenwatchreport.com", "link": "https://citizenwatchreport.com/ontarios-407-etr-is-49-times-more-expensive-than-new-yorks-thruway/", "content": "But just how much more ?When you do the math, the 407 ETR ends up being nearly 49 times more expensive than the New York Thruway for a 100-kilometer trip. It’s a shocking comparison, especially for those of us watching our spending."}
diff --git a/data/sampled_jsons/CVE-Bench_Table_4_Cost_(USD)_T-Agent_AutoGPT_One-day.jsonl b/data/sampled_jsons/CVE-Bench_Table_4_Cost_(USD)_T-Agent_AutoGPT_One-day.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a5d61337530bfd60bb59a2b9d657024a224021f
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_Table_4_Cost_(USD)_T-Agent_AutoGPT_One-day.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "We apply CVE-Bench to evaluate various LLM agents under both zero- day and one-day settings. Our findings indicate that existing LLM agents designed for cybersecurity, such as the agent developed in Cybench (Zhang et al., 2024a), exhibit significant shortcomings, achieving a success rate of 2.5% with five attempts in the one-day setting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "We apply CVE-Bench to evaluate various LLM agents under both zero- day and one-day settings. Our findings indicate that existing LLM agents designed for cybersecurity, such as the agent developed in Cybench (Zhang et al., 2024a), exhibit significant shortcomings, achieving a success rate of 2.5% with five attempts in the one-day setting."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "We apply CVE-Bench to evaluate various LLM agents under both zero- day and one-day settings. Our findings indicate that existing LLM agents designed for cybersecurity, such as the agent developed in Cybench (Zhang et al., 2024a), exhibit significant shortcomings, achieving a success rate of 2.5% with five attempts in the one-day setting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "We apply CVE-Bench to evaluate various LLM agents under both zero- day and one-day settings. Our findings indicate that existing LLM agents designed for cybersecurity, such as the agent developed in Cybench (Zhang et al., 2024a), exhibit significant shortcomings, achieving a success rate of 2.5% with five attempts in the one-day setting."}
+{"idx": 2, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for ...", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures (CVE) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures (CVE) with the reference automatic exploits available on requests."}
+{"idx": 3, "title": "CVE-Bench: Benchmarking LLM-based Software Engineering Agent ...", "date": "", "ddg_snippet": "In this paper, we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. CVE-Bench contains three unique characteristics: (1) Instead of input-output evaluation, CVE-Bench supports agent -based evaluation by offering real-world interactive execution-guided programming environments .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "In this paper, we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. CVE-Bench contains three unique characteristics: (1) Instead of input-output evaluation, CVE-Bench supports agent -based evaluation by offering real-world interactive execution-guided programming environments ."}
+{"idx": 4, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · one_day : Tests AI agents with information about the vulnerability Sources: README.md 107-127 Evaluation Workflow The evaluation process in CVE-Bench follows a structured workflow that begins with prompt generation and ends with a comprehensive assessment of the AI agent 's exploitation attempt.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "May 12, 2025 · one_day : Tests AI agents with information about the vulnerability Sources: README.md 107-127 Evaluation Workflow The evaluation process in CVE-Bench follows a structured workflow that begins with prompt generation and ends with a comprehensive assessment of the AI agent 's exploitation attempt."}
+{"idx": 5, "title": "cve-bench/README.md at main · uiuc-kang-lab/cve-bench - GitHub", "date": "", "ddg_snippet": "Apr 24, 2025 · CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures (CVE) with the reference automatic exploits available on requests. For each CVE, given a target web application and necessary information, an AI agent is tasked with executing an attack that triggers one of the following results (if applicable):", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench/blob/main/README.md", "content": "Apr 24, 2025 · CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures (CVE) with the reference automatic exploits available on requests. For each CVE, given a target web application and necessary information, an AI agent is tasked with executing an attack that triggers one of the following results (if applicable):"}
+{"idx": 6, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to ...", "date": "", "ddg_snippet": "Mar 21, 2025 · In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Mar 21, 2025 · In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities."}
+{"idx": 7, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "21 Mar 2025 — We present the costs of using CVE-Bench to evaluate LLM agents in Table4 . We report the average number of input and output tokens, monetary ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "21 Mar 2025 — We present the costs of using CVE-Bench to evaluate LLM agents in Table4 . We report the average number of input and output tokens, monetary ..."}
+{"idx": 8, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 11 — We present the costs of using CVE-Bench to evalu- ate LLM agents in Table4 . We report the average number of input and output tokens ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332?", "content": "by Y Zhu · 2025 · Cited by 11 — We present the costs of using CVE-Bench to evalu- ate LLM agents in Table4 . We report the average number of input and output tokens ..."}
+{"idx": 9, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "LLM agents. Setting. # input tokens # output tokens Time to finish (s) Monetary Cost ( USD ). Cy-Agent. Zero-day One - day . Table 4 . Per-task costs of evaluating LLM agents on CVE - Bench . AutoGPT T - Agent Cy-Agent.", "subpage_snippet": "", "source": "yuxuan18.github.io", "link": "https://yuxuan18.github.io/assets/pub/cvebench.pdf", "content": "LLM agents. Setting. # input tokens # output tokens Time to finish (s) Monetary Cost ( USD ). Cy-Agent. Zero-day One - day . Table 4 . Per-task costs of evaluating LLM agents on CVE - Bench . AutoGPT T - Agent Cy-Agent."}
diff --git a/data/sampled_jsons/CVE-Bench_Table_6_CVSS_9._CVSS_10._CVE-2024.jsonl b/data/sampled_jsons/CVE-Bench_Table_6_CVSS_9._CVSS_10._CVE-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ff6d0c85461bceb8362f265cddbc59c65c33ed18
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_Table_6_CVSS_9._CVSS_10._CVE-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "21 Mar 2025 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "21 Mar 2025 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — We show the details of each CVE in Table 6 , including its identifier, publication date, CVSS 3.x score, affected web application, and our ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "by Y Zhu · 2025 · Cited by 12 — We show the details of each CVE in Table 6 , including its identifier, publication date, CVSS 3.x score, affected web application, and our ..."}
+{"idx": 2, "title": "CVE-Bench: Benchmarking LLM-based Software ...", "date": "", "ddg_snippet": "by P Wang · 2025 · Cited by 4 — We summarized the collected CVEs severity distri- bution in Figure 9 (b) and the CVSS score distribu- tion in Figure 10 . The overall CVSS score ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "by P Wang · 2025 · Cited by 4 — We summarized the collected CVEs severity distri- bution in Figure 9 (b) and the CVSS score distribu- tion in Figure 10 . The overall CVSS score ..."}
+{"idx": 3, "title": "CTIBench: A Benchmark for Evaluating LLMs in Cyber ...", "date": "", "ddg_snippet": "by MT Alam · 2024 · Cited by 32 — While accurately calculating an accurate CVSS score requires additional detailed information such as original bug identification, third-party exploit analysis, ... 21 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/5acd3c628aa1819fbf07c39ef73e7285-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "by MT Alam · 2024 · Cited by 32 — While accurately calculating an accurate CVSS score requires additional detailed information such as original bug identification, third-party exploit analysis, ... 21 pages"}
+{"idx": 4, "title": "BountyBench: Dollar Impact of AI Agent Attackers and ...", "date": "", "ddg_snippet": "20 Mar 2025 — Overall, our benchmark includes bounties that span 9 of the 10 OWASP Top 10 Risks. (Figure 6 ). Figure 6 : BountyBench OWASP Top 10 Risks ...", "subpage_snippet": "", "source": "cs191.stanford.edu", "link": "https://cs191.stanford.edu/projects/Spring2025/Celeste___Huang-Menders_.pdf", "content": "20 Mar 2025 — Overall, our benchmark includes bounties that span 9 of the 10 OWASP Top 10 Risks. (Figure 6 ). Figure 6 : BountyBench OWASP Top 10 Risks ..."}
+{"idx": 5, "title": "CVE-2019-19645 Detail - NVD", "date": "", "ddg_snippet": "9 Dec 2019 — Description. alter.c in SQLite through 3.30.1 allows attackers to trigger infinite recursion via certain types of self-referential views in ...", "subpage_snippet": "", "source": "nvd.nist.gov", "link": "https://nvd.nist.gov/vuln/detail/cve-2019-19645", "content": "9 Dec 2019 — Description. alter.c in SQLite through 3.30.1 allows attackers to trigger infinite recursion via certain types of self-referential views in ..."}
+{"idx": 6, "title": "ChainFuzz: Exploiting Upstream Vulnerabilities in Open- ...", "date": "", "ddg_snippet": "by P Deng — For instance, the CVE -. 2024 -3094 [30] vulnerability (with a CVSS score of 10 ) in the xz/liblzma library allows malicious attackers to gain.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/usenixsecurity25-deng.pdf", "content": "by P Deng — For instance, the CVE -. 2024 -3094 [30] vulnerability (with a CVSS score of 10 ) in the xz/liblzma library allows malicious attackers to gain."}
+{"idx": 7, "title": "Detecting, Exploiting, and Remediating a Path Traversal ...", "date": "", "ddg_snippet": "by J Akhoundali · 2025 — According to MITRE, this vulnerability was among the top 25 most dangerous software weaknesses for 2024 [77]. It is also among the top 10 known ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3708821.3736220", "content": "by J Akhoundali · 2025 — According to MITRE, this vulnerability was among the top 25 most dangerous software weaknesses for 2024 [77]. It is also among the top 10 known ..."}
+{"idx": 8, "title": "Vulnerability Summary for the Week of February 24, 2025", "date": "", "ddg_snippet": "3 Mar 2025 — High: vulnerabilities with a CVSS base score of 7.0–10.0 ; Medium: vulnerabilities with a CVSS base score of 4.0–6.9; Low: vulnerabilities with a ...", "subpage_snippet": "", "source": "www.cisa.gov", "link": "https://www.cisa.gov/news-events/bulletins/sb25-062", "content": "3 Mar 2025 — High: vulnerabilities with a CVSS base score of 7.0–10.0 ; Medium: vulnerabilities with a CVSS base score of 4.0–6.9; Low: vulnerabilities with a ..."}
+{"idx": 9, "title": "How do I determine the \"impact\" scores of vulnerabilities ...", "date": "", "ddg_snippet": "24 May 2024 — The impact scores for each CVE appeared to vary depending on number of assets impact and the severity for each. There was a large range of Impact scores from 0 ...", "subpage_snippet": "", "source": "community.tanium.com", "link": "https://community.tanium.com/s/question/0D5RO00000F4kFG0AZ/how-do-i-determine-the-impact-scores-of-vulnerabilities-using-benchmarks-tanium-risk-score-20", "content": "24 May 2024 — The impact scores for each CVE appeared to vary depending on number of assets impact and the severity for each. There was a large range of Impact scores from 0 ..."}
diff --git a/data/sampled_jsons/CVE-Bench_paper_Common_Failure_Modes_section_Table_5_Insufficient_Exploration_year_2023-2024.jsonl b/data/sampled_jsons/CVE-Bench_paper_Common_Failure_Modes_section_Table_5_Insufficient_Exploration_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5707065a12c649b40e452686f90323c1f9630012
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_paper_Common_Failure_Modes_section_Table_5_Insufficient_Exploration_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "We show the frequency of common failure modes for each agent in Table 5 . Two of our authors independently annotated every agent run and reconciled disagreements through discussion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "We show the frequency of common failure modes for each agent in Table 5 . Two of our authors independently annotated every agent run and reconciled disagreements through discussion."}
+{"idx": 1, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for AI Agents ...", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 2, "title": "PDF CVE-Bench: Benchmarking LLM-based Software Engineering Agent's Ability ...", "date": "", "ddg_snippet": "In this paper , we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. CVE-Bench contains three unique characteristics: (1) Instead of input-output evaluation, CVE-Bench supports agent-based evaluation by offering real-world interactive execution-guided programming environments .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "In this paper , we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. CVE-Bench contains three unique characteristics: (1) Instead of input-output evaluation, CVE-Bench supports agent-based evaluation by offering real-world interactive execution-guided programming environments ."}
+{"idx": 3, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "We show the frequency of common failure modes for each agent in Table 5 . Two of our authors independently anno-tated every agent run and reconciled disagreements through discussion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "We show the frequency of common failure modes for each agent in Table 5 . Two of our authors independently anno-tated every agent run and reconciled disagreements through discussion."}
+{"idx": 4, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective ..."}
+{"idx": 5, "title": "CVE: Common Vulnerabilities and Exposures", "date": "", "ddg_snippet": "At cve .org, we provide the authoritative reference method for publicly known information-security vulnerabilities and exposures", "subpage_snippet": "", "source": "www.cve.org", "link": "https://www.cve.org/", "content": "At cve .org, we provide the authoritative reference method for publicly known information-security vulnerabilities and exposures"}
+{"idx": 6, "title": "Evaluation | uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "Evaluation Relevant source files This document explains how CVE-Bench evaluates AI agent performance in exploiting vulnerabilities. It focuses on the evaluation framework, grading system, and success criteria used to determine whether an exploitation attempt has succeeded. For information about specific application graders, see Application-Specific Graders. Evaluation Overview The evaluation ...", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/5-evaluation", "content": "Evaluation Relevant source files This document explains how CVE-Bench evaluates AI agent performance in exploiting vulnerabilities. It focuses on the evaluation framework, grading system, and success criteria used to determine whether an exploitation attempt has succeeded. For information about specific application graders, see Application-Specific Graders. Evaluation Overview The evaluation ..."}
+{"idx": 7, "title": "CVEBench/ReadMe.md at main · WhileBug/CVEBench · GitHub", "date": "", "ddg_snippet": "To this end, we introduce CVE-bench , an evaluation framework consisting of 509 Common Vulnerabilities and Exposures ( CVEs ) from four programming languages and 120 popular open-source repositories.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/WhileBug/CVEBench/blob/main/ReadMe.md", "content": "To this end, we introduce CVE-bench , an evaluation framework consisting of 509 Common Vulnerabilities and Exposures ( CVEs ) from four programming languages and 120 popular open-source repositories."}
+{"idx": 8, "title": "CVE-Bench: Benchmarking LLM-based Software Engineering Agent's Ability ...", "date": "", "ddg_snippet": "To this end, we introduce CVE-Bench , an evaluation framework consisting of 509 Common Vulnerabilities and Exposures ( CVEs ) from four programming languages and 120 popular open-source repositories.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212/", "content": "To this end, we introduce CVE-Bench , an evaluation framework consisting of 509 Common Vulnerabilities and Exposures ( CVEs ) from four programming languages and 120 popular open-source repositories."}
+{"idx": 9, "title": "CWE - CWE-778: Insufficient Logging (4.17)", "date": "", "ddg_snippet": "Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource. 223 Omission of Security-relevant Information Nature Type ID Name MemberOf", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/data/definitions/778.html", "content": "Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource. 223 Omission of Security-relevant Information Nature Type ID Name MemberOf"}
diff --git a/data/sampled_jsons/CVE-Bench_paper_T-Agent_vs_AutoGPT_DB_access_zero-day_performance_comparison.jsonl b/data/sampled_jsons/CVE-Bench_paper_T-Agent_vs_AutoGPT_DB_access_zero-day_performance_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f2455d569d9b73e5b0d458d189fc8a1e1263fe96
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_paper_T-Agent_vs_AutoGPT_DB_access_zero-day_performance_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "As shown, among successful exploits, T-Agent performs 68% and 30% database access under zero-day and one- day settings, respectively, while the percentage of database access is smaller for AutoGPT : 0% in the both zero-day and one- day settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "As shown, among successful exploits, T-Agent performs 68% and 30% database access under zero-day and one- day settings, respectively, while the percentage of database access is smaller for AutoGPT : 0% in the both zero-day and one- day settings."}
+{"idx": 1, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for ...", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "As shown, among successful exploits, T-Agent performs 68% and 30% database access under zero-day and one- day settings, respectively, while the percentage of database access is smaller for AutoGPT : 0% in the both zero-day and one- day settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "As shown, among successful exploits, T-Agent performs 68% and 30% database access under zero-day and one- day settings, respectively, while the percentage of database access is smaller for AutoGPT : 0% in the both zero-day and one- day settings."}
+{"idx": 3, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to ... NVD - CVE-2025-53944 CVE-Bench: Benchmarking LLM-based Software Engineering Agent ... CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web GitHub - uiuc-kang-lab/ cve -bench: CVE -Bench: A Benchmark for AI Age… CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web [PDF] CVE-Bench: A Benchmark for AI Agents' Ability to ...", "date": "", "ddg_snippet": "Mar 21, 2025 · Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ... Jul 30, 2025 · Information Technology Laboratory National Vulnerability Database Vulnerabilities 3 days ago · CVE-Bench : Benchmarking LLM-based Software Engineering Agent ’s Ability to Repair Real-World CVE Vulnerabilities. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4207–4224, Albuquerque, New Mexico. How successful are LLM agents on CVE-bench? Figure 3. Success rates of different LLM agents on CVE-Bench. LLM agents can exploit up to 10% and 13% vulnerabilities under zero-day and one-day settings, respectively. ing logs, we find that under the zero-day setting, AutoGPT could identify and exploit new vulnerabilities that are easier than those provided in the one-day description. Is CVE-bench a good cybersecurity benchmark? Limitation. As the first attempt toward a real-world cyber-security benchmark for evaluating AI methods’ ability in exploiting vulnerabilities, CVE-Bench is not perfect . First, it cannot evaluate attacks other than the pre-defined eight standard attacks, potentially leading to false negatives. How does CVE-bench work? CVE - Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. For each CVE , given a target web application and necessary information, an AI agent is tasked with executing an attack that triggers one of the following results (if applicable): Why is Cy-agent better than T-agent and autogpt? Furthermore, Cy-Agent leads to significantly lower suc-cess rates than T-Agent and AutoGPT. We find that this is because the action-execution-observation workflow of Cy-Agent is primarily designed for focused cybersecurity tasks with a clear target, such as CTF. How do I use CVE-bench with inspect AI? Using CVE - Bench via inspect ai CVE - Bench is fully integrated with inspect ai, an open-source framework for LLM evaluations (AI Security Institute). With inspect ai, we can run the default ReAct agent (Yao et al., 2023) on a specific vulnerability (e.g., CVE -2023-37999), model (e.g., GPT-4o), and a setting (e.g., one- day ) with a single command: How does CVE-bench simulate the VUL-nerability lifecycle? Simulating the Vulnerability Lifecycle. Besides basic knowledge about cyber-attacks and the web application, at-tackers typically possess varying degrees of information about vulnerabilities throughout different stages of the vul-nerability lifecycle. In CVE-Bench, we simulate the zero-day and one-day scenarios . Mar 21, 2025 · CVE-Bench is introduced, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Mar 21, 2025 · Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ... Jul 30, 2025 · Information Technology Laboratory National Vulnerability Database Vulnerabilities 3 days ago · CVE-Bench : Benchmarking LLM-based Software Engineering Agent ’s Ability to Repair Real-World CVE Vulnerabilities. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4207–4224, Albuquerque, New Mexico. How successful are LLM agents on CVE-bench? Figure 3. Success rates of different LLM agents on CVE-Bench. LLM agents can exploit up to 10% and 13% vulnerabilities under zero-day and one-day settings, respectively. ing logs, we find that under the zero-day setting, AutoGPT could identify and exploit new vulnerabilities that are easier than those provided in the one-day description. Is CVE-bench a good cybersecurity benchmark? Limitation. As the first attempt toward a real-world cyber-security benchmark for evaluating AI methods’ ability in exploiting vulnerabilities, CVE-Bench is not perfect . First, it cannot evaluate attacks other than the pre-defined eight standard attacks, potentially leading to false negatives. How does CVE-bench work? CVE - Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. For each CVE , given a target web application and necessary information, an AI agent is tasked with executing an attack that triggers one of the following results (if applicable): Why is Cy-agent better than T-agent and autogpt? Furthermore, Cy-Agent leads to significantly lower suc-cess rates than T-Agent and AutoGPT. We find that this is because the action-execution-observation workflow of Cy-Agent is primarily designed for focused cybersecurity tasks with a clear target, such as CTF. How do I use CVE-bench with inspect AI? Using CVE - Bench via inspect ai CVE - Bench is fully integrated with inspect ai, an open-source framework for LLM evaluations (AI Security Institute). With inspect ai, we can run the default ReAct agent (Yao et al., 2023) on a specific vulnerability (e.g., CVE -2023-37999), model (e.g., GPT-4o), and a setting (e.g., one- day ) with a single command: How does CVE-bench simulate the VUL-nerability lifecycle? Simulating the Vulnerability Lifecycle. Besides basic knowledge about cyber-attacks and the web application, at-tackers typically possess varying degrees of information about vulnerabilities throughout different stages of the vul-nerability lifecycle. In CVE-Bench, we simulate the zero-day and one-day scenarios . Mar 21, 2025 · CVE-Bench is introduced, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks ..."}
+{"idx": 4, "title": "CVE-Bench: Benchmarking LLM-based Software Engineering Agent ...", "date": "", "ddg_snippet": "3 days ago · CVE-Bench : Benchmarking LLM-based Software Engineering Agent ’s Ability to Repair Real-World CVE Vulnerabilities. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4207–4224, Albuquerque, New Mexico.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212/", "content": "3 days ago · CVE-Bench : Benchmarking LLM-based Software Engineering Agent ’s Ability to Repair Real-World CVE Vulnerabilities. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4207–4224, Albuquerque, New Mexico."}
+{"idx": 5, "title": "[PDF] CVE-Bench: A Benchmark for AI Agents' Ability to ...", "date": "", "ddg_snippet": "Mar 21, 2025 · CVE-Bench is introduced, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/CVE-Bench:-A-Benchmark-for-AI-Agents'-Ability-to-Zhu-Kellermann/095b31dfaa032a2daf13da21bd4d04dddb2097fa", "content": "Mar 21, 2025 · CVE-Bench is introduced, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks ..."}
+{"idx": 6, "title": "OpenAgents Vs AutoGPT : A Comprehensive Comparison", "date": "", "ddg_snippet": "Explore the dynamic realm of AI with our in-depth comparison of OpenAgents vs AutoGPT . Uncover the unique features, strengths, and applications of these cutting-edge platforms, designed for tech enthusiasts, developers, and businesses alike.", "subpage_snippet": "", "source": "smythos.com", "link": "https://smythos.com/developers/agent-comparisons/openagents-vs-autogpt/", "content": "Explore the dynamic realm of AI with our in-depth comparison of OpenAgents vs AutoGPT . Uncover the unique features, strengths, and applications of these cutting-edge platforms, designed for tech enthusiasts, developers, and businesses alike."}
+{"idx": 7, "title": "Agent GPT vs AutoGPT : Which One Shall You Choose? – Kanaries", "date": "", "ddg_snippet": "Auto - GPT vs Agent GPT: An Unflinching Comparison When to Choose Which (and When to Look Elsewhere) Agent GPT vs Auto GPT in 2025: Evolution, Limitations, and the Future of AI Agents .", "subpage_snippet": "", "source": "docs.kanaries.net", "link": "https://docs.kanaries.net/articles/agent-gpt-vs-autogpt", "content": "Auto - GPT vs Agent GPT: An Unflinching Comparison When to Choose Which (and When to Look Elsewhere) Agent GPT vs Auto GPT in 2025: Evolution, Limitations, and the Future of AI Agents ."}
+{"idx": 8, "title": "DB - GPT vs AutoGPT Comparison in 2025 - Aitoolnet", "date": "", "ddg_snippet": "DB - GPT VS AutoGPT . Let’s have a side-by-side comparison of DB - GPT vs AutoGPT to find out which one is better. This software comparison between DB - GPT and AutoGPT is based on genuine user reviews.", "subpage_snippet": "", "source": "www.aitoolnet.com", "link": "https://www.aitoolnet.com/compare/dbgpt-vs-autogpt", "content": "DB - GPT VS AutoGPT . Let’s have a side-by-side comparison of DB - GPT vs AutoGPT to find out which one is better. This software comparison between DB - GPT and AutoGPT is based on genuine user reviews."}
+{"idx": 9, "title": "Learn The Key Differences Between AutoGPT vs AgentForce the Easy...", "date": "", "ddg_snippet": "AutoGPT vs . AgentForce: Key Features Comparison . Features matter when it comes to choosing the best AI agent development platforms as they directly dictate the workflow and the platform’s capabilities.", "subpage_snippet": "", "source": "www.ampcome.com", "link": "https://www.ampcome.com/post/autogpt-vs-agentforce", "content": "AutoGPT vs . AgentForce: Key Features Comparison . Features matter when it comes to choosing the best AI agent development platforms as they directly dictate the workflow and the platform’s capabilities."}
diff --git a/data/sampled_jsons/CVEBench_GitHub_ReadMe_'Insufficient_Exploration'_definition_year_2023-2024.jsonl b/data/sampled_jsons/CVEBench_GitHub_ReadMe_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0c171cf020624158f5533eaac10863290571878e
--- /dev/null
+++ b/data/sampled_jsons/CVEBench_GitHub_ReadMe_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub GitHub - Hokhim2/CVBench", "date": "", "ddg_snippet": "Contribute to Hokhim2/CVBench development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Hokhim2/CVBench", "content": "Contribute to Hokhim2/CVBench development by creating an account on GitHub ."}
+{"idx": 1, "title": "GitHub GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities", "date": "", "ddg_snippet": "[2025-07-19] An example of manual exploit is released at src/cvebench/challenges/CVE-2024-2624/solution.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "[2025-07-19] An example of manual exploit is released at src/cvebench/challenges/CVE-2024-2624/solution."}
+{"idx": 2, "title": "GitHub GitHub - 0xMarcio/cve: Latest CVEs with their Proof of Concept exploits.", "date": "", "ddg_snippet": "command injection vulnerability in the web server of some Hikvision product . Due to the insufficient input validation, attacker can exploit the vulnerability to launch a command injection attack by sending some messages with malicious commands.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/0xMarcio/cve", "content": "command injection vulnerability in the web server of some Hikvision product . Due to the insufficient input validation, attacker can exploit the vulnerability to launch a command injection attack by sending some messages with malicious commands."}
+{"idx": 3, "title": "GitHub CVE-2024-49362 - GitHub Advisory Database", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/advisories/GHSA-hff8-hjwv-j9q7", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 4, "title": "arXiv RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solving", "date": "", "ddg_snippet": "May 27, 2025 - Fortunately, GitHub hosts a vast, evolving collection of open-source repositories, which developers frequently reuse as modular components for complex tasks. Yet, existing frameworks like OpenHands and SWE-Agent still struggle to effectively leverage these valuable resources. Relying solely on README files provides insufficient guidance, and deeper exploration ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.21577v1", "content": "May 27, 2025 - Fortunately, GitHub hosts a vast, evolving collection of open-source repositories, which developers frequently reuse as modular components for complex tasks. Yet, existing frameworks like OpenHands and SWE-Agent still struggle to effectively leverage these valuable resources. Relying solely on README files provides insufficient guidance, and deeper exploration ..."}
+{"idx": 5, "title": "GitHub awesome-exploration-rl/README.md at main · opendilab/awesome-exploration-rl", "date": "", "ddg_snippet": "A curated list of awesome exploration RL resources (continually updated) - awesome- exploration -rl/ README .md at main · opendilab/awesome- exploration -rl", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/opendilab/awesome-exploration-rl/blob/main/README.md", "content": "A curated list of awesome exploration RL resources (continually updated) - awesome- exploration -rl/ README .md at main · opendilab/awesome- exploration -rl"}
+{"idx": 6, "title": "GitHub vbench/README.md at master · wesm/vbench", "date": "", "ddg_snippet": "vbench: A tool for benchmarking your code through time, for showing performance improvement or regressions - vbench/ README .md at master · wesm/vbench", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wesm/vbench/blob/master/README.md", "content": "vbench: A tool for benchmarking your code through time, for showing performance improvement or regressions - vbench/ README .md at master · wesm/vbench"}
+{"idx": 7, "title": "GitHub CVEfixes/README.md at main · secureIT-project/CVEfixes", "date": "", "ddg_snippet": "CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software - CVEfixes/ README .md at main · secureIT-project/CVEfixes", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/secureIT-project/CVEfixes/blob/main/README.md", "content": "CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software - CVEfixes/ README .md at main · secureIT-project/CVEfixes"}
+{"idx": 8, "title": "GitHub GitHub - aorogat/CBench: CBench, Benchmarking System for Question Answering Over Knowledge Graphs Systems.", "date": "", "ddg_snippet": "CBench, Benchmarking System for Question Answering Over Knowledge Graphs Systems. - GitHub - aorogat/CBench: CBench, Benchmarking System for Question Answering Over Knowledge Graphs Systems.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aorogat/CBench", "content": "CBench, Benchmarking System for Question Answering Over Knowledge Graphs Systems. - GitHub - aorogat/CBench: CBench, Benchmarking System for Question Answering Over Knowledge Graphs Systems."}
+{"idx": 9, "title": "GitHub GitHub - intel/cve-bin-tool-action: Known vulnerability scanning for your GitHub repository using CVE Binary Tool. This Action can scan binaries, component lists and SBOMs for known vulnerabilities and CVEs. It can generate SBOM component lists as well as reports in the Security Tab and in HTML/JSON/PDF format.", "date": "", "ddg_snippet": "Known vulnerability scanning for your GitHub repository using CVE Binary Tool. This Action can scan binaries, component lists and SBOMs for known vulnerabilities and CVEs. It can generate SBOM component lists as well as reports in the Security Tab and in HTML/JSON/PDF format.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/intel/cve-bin-tool-action", "content": "Known vulnerability scanning for your GitHub repository using CVE Binary Tool. This Action can scan binaries, component lists and SBOMs for known vulnerabilities and CVEs. It can generate SBOM component lists as well as reports in the Security Tab and in HTML/JSON/PDF format."}
diff --git a/data/sampled_jsons/CVPR_2025_human_motion_synthesis_papers.jsonl b/data/sampled_jsons/CVPR_2025_human_motion_synthesis_papers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5048cb66eba45675de0173863c1d7a4727e8d82a
--- /dev/null
+++ b/data/sampled_jsons/CVPR_2025_human_motion_synthesis_papers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Shape My Moves: Text-Driven Shape-Aware Synthesis of Human ...", "date": "", "ddg_snippet": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Human motion synthesis is an expansive field with wide ap-plications in avatar creation, robotics, and the gaming in-dustry [27, 63, 75, 83].", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_Shape_My_Moves_Text-Driven_Shape-Aware_Synthesis_of_Human_Motions_CVPR_2025_paper.pdf", "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Human motion synthesis is an expansive field with wide ap-plications in avatar creation, robotics, and the gaming in-dustry [27, 63, 75, 83]."}
+{"idx": 1, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "CVPR 2025 . Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2025/436400w724/299d8yUEH7y", "content": "CVPR 2025 . Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task."}
+{"idx": 2, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task."}
+{"idx": 3, "title": "Dynamic model can generate realistic human motions and edit...", "date": "", "ddg_snippet": "Their proposed approach for the generation of human motions , outlined in a paper presented at CVPR 2025 , relies on a data augmentation technique called MotionCutMix and a diffusion model called MotionReFit.", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-04-dynamic-generate-realistic-human-motions.html", "content": "Their proposed approach for the generation of human motions , outlined in a paper presented at CVPR 2025 , relies on a data augmentation technique called MotionCutMix and a diffusion model called MotionReFit."}
+{"idx": 4, "title": "Chinese AI tool generates, edits lifelike human actions in 3D motion", "date": "", "ddg_snippet": "AI model for realistic motion synthesis . The researchers’ approach to generating human motion , presented at this year’s Conference on Computer Vision and Pattern Recognition ( CVPR 2025 )...", "subpage_snippet": "", "source": "interestingengineering.com", "link": "https://interestingengineering.com/innovation/china-ai-tool-lifelike-human-motion-3d", "content": "AI model for realistic motion synthesis . The researchers’ approach to generating human motion , presented at this year’s Conference on Computer Vision and Pattern Recognition ( CVPR 2025 )..."}
+{"idx": 5, "title": "GitHub - Zilize/awesome-text-to- motion : Text-driven human motion ...", "date": "", "ddg_snippet": "\"Multimodal Generative AI with Autoregressive LLMs for Human Motion Understanding and Generation: A Way Forward\". arXiv( 2025 ) [URL].HumanML3D- synthesis : \"CLaM: An Open-Source Library for Performance Evaluation of Text-driven Human Motion Generation\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Zilize/awesome-text-to-motion", "content": "\"Multimodal Generative AI with Autoregressive LLMs for Human Motion Understanding and Generation: A Way Forward\". arXiv( 2025 ) [URL].HumanML3D- synthesis : \"CLaM: An Open-Source Library for Performance Evaluation of Text-driven Human Motion Generation\"."}
+{"idx": 6, "title": "Meet Seed at CVPR 2025 : 12 Papers Accepted and 2 Talks", "date": "", "ddg_snippet": "The IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ) 2025 was held from June 11 to 15 in Nashville, Tennessee, USA.", "subpage_snippet": "", "source": "research.doubao.com", "link": "https://research.doubao.com/en/blog/meet-seed-at-cvpr-2025-12-papers-accepted-and-2-talks", "content": "The IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ) 2025 was held from June 11 to 15 in Nashville, Tennessee, USA."}
+{"idx": 7, "title": "DreamActor-H1: High-Fidelity Human -Product Demonstration Video...", "date": "", "ddg_snippet": "Computer Vision and Pattern Recognition ( CVPR ), IEEE, 2024.Object motion guided human motion synthesis . ACM Trans. Graph., 42(6), 2023.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10568v1", "content": "Computer Vision and Pattern Recognition ( CVPR ), IEEE, 2024.Object motion guided human motion synthesis . ACM Trans. Graph., 42(6), 2023."}
+{"idx": 8, "title": "Research in Computer Vision : Human Motion and Interaction...", "date": "", "ddg_snippet": "X-Dyna, selected as a highlight paper at CVPR 2025 , extends dynamic human image animation capabilities. The system provides control over human motion in video, generating animations from static images with improved fidelity compared to previous methods.", "subpage_snippet": "", "source": "ict.usc.edu", "link": "https://ict.usc.edu/news/essays/research-in-computer-vision-human-motion-and-interaction-modeling/", "content": "X-Dyna, selected as a highlight paper at CVPR 2025 , extends dynamic human image animation capabilities. The system provides control over human motion in video, generating animations from static images with improved fidelity compared to previous methods."}
+{"idx": 9, "title": "[ CVPR 24] Move as You Say, Interact as You Can: Language-guided...", "date": "", "ddg_snippet": "Despite significant advancements in text-to- motion synthesis , generating language-guided human motion within 3D environments poses substantial challenges. These challenges stem primarily from (i)...", "subpage_snippet": "", "source": "yzhu.io", "link": "https://yzhu.io/publication/hoiafford2024cvpr/", "content": "Despite significant advancements in text-to- motion synthesis , generating language-guided human motion within 3D environments poses substantial challenges. These challenges stem primarily from (i)..."}
diff --git a/data/sampled_jsons/CVPR_paper_classification_system_Standard-Tier_Medium-Tier_Top-Tier.jsonl b/data/sampled_jsons/CVPR_paper_classification_system_Standard-Tier_Medium-Tier_Top-Tier.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..102eeedcbebace5f80f1e228620f13cad98f9b36
--- /dev/null
+++ b/data/sampled_jsons/CVPR_paper_classification_system_Standard-Tier_Medium-Tier_Top-Tier.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2025 Author Guidelines - cvpr.thecvf.com", "date": "", "ddg_snippet": "By submitting a paper to CVPR , the authors agree to the review process and understand that papers are processed by the OpenReview system to match each manuscript to the best possible area chairs and reviewers.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/AuthorGuidelines", "content": "By submitting a paper to CVPR , the authors agree to the review process and understand that papers are processed by the OpenReview system to match each manuscript to the best possible area chairs and reviewers."}
+{"idx": 1, "title": "PDF General Guidelines on Classification of Publications", "date": "", "ddg_snippet": "These journals will ask only payment for page cost once the peer-review is completed. Departments are requested to use these classification guidelines to generate lists of publications in each rating tier and share them with their faculty ahead of the appraisal.", "subpage_snippet": "", "source": "www.qu.edu.qa", "link": "https://www.qu.edu.qa/static_file/qu/colleges/cas/College+Departments+and+Offices/ADRGS/Guidelines_on_classification_of_publications_d2_EN.pdf", "content": "These journals will ask only payment for page cost once the peer-review is completed. Departments are requested to use these classification guidelines to generate lists of publications in each rating tier and share them with their faculty ahead of the appraisal."}
+{"idx": 2, "title": "CVPR 2025 Statistics - Paper Copilot", "date": "", "ddg_snippet": "- min / max / mean / std: These are statistical summaries of the reviewer average scores per submission within each decision tier (e.g., Accept tier ). For example, if a paper received ratings of 3, 4, and 5, its average score is 4 — and this average is used in the distribution.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/cvpr-statistics/cvpr-2025-statistics/", "content": "- min / max / mean / std: These are statistical summaries of the reviewer average scores per submission within each decision tier (e.g., Accept tier ). For example, if a paper received ratings of 3, 4, and 5, its average score is 4 — and this average is used in the distribution."}
+{"idx": 3, "title": "[D] What's the difference between top-tier papers and others?", "date": "", "ddg_snippet": "In theory: Top tier conferences are more selective with the papers they accept. They tend to attract reviewers who are considered experts in their fields, and tend to have high citation numbers because of the perception of quality and trust that comes with being accepted at these conferences. Often, the top-tier conferences are smaller than some mid- tier conferences, increasing competition and ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/qmhthd/d_whats_the_difference_between_toptier_papers_and/", "content": "In theory: Top tier conferences are more selective with the papers they accept. They tend to attract reviewers who are considered experts in their fields, and tend to have high citation numbers because of the perception of quality and trust that comes with being accepted at these conferences. Often, the top-tier conferences are smaller than some mid- tier conferences, increasing competition and ..."}
+{"idx": 4, "title": "Top papers from CVPR 2024 : A Comprehensive Overview - Medium", "date": "", "ddg_snippet": "Top papers from CVPR 2024 : A Comprehensive Overview One of the most prestigious conferences in the field of AI, CVPR for Computer Vision and Pattern Recognition, is currently taking place from ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@djohraiberraken/top-papers-from-cvpr-2024-comprehensive-overview-7cd32398fc41", "content": "Top papers from CVPR 2024 : A Comprehensive Overview One of the most prestigious conferences in the field of AI, CVPR for Computer Vision and Pattern Recognition, is currently taking place from ..."}
+{"idx": 5, "title": "CVPR 2025 Top Papers: Award Winners and Notable Research", "date": "", "ddg_snippet": "Essential CVPR 2025 papers : VGGT's neural 3D, physics-informed learning, and open models every computer vision engineer should know.", "subpage_snippet": "", "source": "www.basic.ai", "link": "https://www.basic.ai/blog-post/cvpr-2025-top-papers-award-winners-and-notable-research", "content": "Essential CVPR 2025 papers : VGGT's neural 3D, physics-informed learning, and open models every computer vision engineer should know."}
+{"idx": 6, "title": "Everything You Need To Know About Different Tiers Of Pu...", "date": "", "ddg_snippet": "Some examples of tier 1 publications:- Forbes, Entrepreneur, Haute Living, etc Tier 2 publications includes top media companies which has millions of readers and high DA ratings. It's a great resource to build credibility in your niche and have thousands of interested readers to know about you and your company.", "subpage_snippet": "", "source": "www.thoughtfulpr.com", "link": "https://www.thoughtfulpr.com/blog/tier", "content": "Some examples of tier 1 publications:- Forbes, Entrepreneur, Haute Living, etc Tier 2 publications includes top media companies which has millions of readers and high DA ratings. It's a great resource to build credibility in your niche and have thousands of interested readers to know about you and your company."}
+{"idx": 7, "title": "2024 Author Guidelines - cvpr.thecvf.com", "date": "", "ddg_snippet": "By submitting a paper to CVPR , the authors agree to the review process and understand that papers are processed by the OpenReview system to match each manuscript to the best possible area chairs and reviewers.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2024/AuthorGuidelines", "content": "By submitting a paper to CVPR , the authors agree to the review process and understand that papers are processed by the OpenReview system to match each manuscript to the best possible area chairs and reviewers."}
+{"idx": 8, "title": "Opening Remarks from CVPR 2025 - Medium", "date": "", "ddg_snippet": "Key Criteria: Authors had to either: Submit two papers , Submit a first-author paper , or Have had at least one paper accepted in a previous top-tier ML conference.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@paularamos_phd/opening-remarks-from-cvpr-2025-29a72218e230", "content": "Key Criteria: Authors had to either: Submit two papers , Submit a first-author paper , or Have had at least one paper accepted in a previous top-tier ML conference."}
+{"idx": 9, "title": "Paper Digest: CVPR 2025 Papers & Highlights", "date": "", "ddg_snippet": "Note: CVPR -2025 accepts more than 2,800 papers , this page only includes 500 of them selected by our daily paper digest algorithm. Interested users can choose to read All 2,800 CVPR -2025 papers in a separate page. To search for papers presented at CVPR -2025 on a specific topic, please make use of the search by venue ( CVPR -2025) service.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/06/cvpr-2025-papers-highlights/", "content": "Note: CVPR -2025 accepts more than 2,800 papers , this page only includes 500 of them selected by our daily paper digest algorithm. Interested users can choose to read All 2,800 CVPR -2025 papers in a separate page. To search for papers presented at CVPR -2025 on a specific topic, please make use of the search by venue ( CVPR -2025) service."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Experimental_Setup_backbone_framewor_year_2024.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Experimental_Setup_backbone_framewor_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b8ee1ae930b29c577a5cb1e3de889d47b8e7c22e
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Experimental_Setup_backbone_framewor_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Canva: Visual Suite for Everyone", "date": "", "ddg_snippet": "What will you design today? With Canva you can design, generate, print, and work on anything.", "subpage_snippet": "", "source": "www.canva.com", "link": "https://www.canva.com/", "content": "What will you design today? With Canva you can design, generate, print, and work on anything."}
+{"idx": 1, "title": "Free printable letterhead templates you can customize | Canva", "date": "", "ddg_snippet": "While our sample letterhead templates are already good to use, you can still personalize it so that it fits your brand or your liking. Our intuitive platform features a drag-and-drop interface that allows you to easily create your own digital letterhead design even as a beginner or an expert.", "subpage_snippet": "", "source": "www.canva.com", "link": "https://www.canva.com/letterheads/templates/", "content": "While our sample letterhead templates are already good to use, you can still personalize it so that it fits your brand or your liking. Our intuitive platform features a drag-and-drop interface that allows you to easily create your own digital letterhead design even as a beginner or an expert."}
+{"idx": 2, "title": "Lesson plan templates you can customize for free | Canva", "date": "", "ddg_snippet": "Lesson plan templates Give your lesson plans a creative twist. Choose from Canva's collection of professionally designed templates you can personalize in minutes.", "subpage_snippet": "", "source": "www.canva.com", "link": "https://www.canva.com/lesson-plans/templates/", "content": "Lesson plan templates Give your lesson plans a creative twist. Choose from Canva's collection of professionally designed templates you can personalize in minutes."}
+{"idx": 3, "title": "Free Online PDF Editor - Edit PDFs with ease - Canva", "date": "", "ddg_snippet": "Simply import your PDF right into Canva and we ’ll break it into elements you can easily edit — no special skills required. Once you’ve customized it, we ’ll compress your PDF into JPEG and PNG files or resave into PDF format that are all downloadable and shareable.", "subpage_snippet": "", "source": "www.canva.com", "link": "https://www.canva.com/pdf-editor/", "content": "Simply import your PDF right into Canva and we ’ll break it into elements you can easily edit — no special skills required. Once you’ve customized it, we ’ll compress your PDF into JPEG and PNG files or resave into PDF format that are all downloadable and shareable."}
+{"idx": 4, "title": "Free Online Video Editor & Maker | Canva (Drag-and-drop)", "date": "", "ddg_snippet": "You can create videos on Canva without a watermark as long as you use free elements, images, footage, and music. If you’re a Canva Pro user, your videos will not have any watermarks whether you’re using free or paid elements.", "subpage_snippet": "", "source": "www.canva.com", "link": "https://www.canva.com/video-editor/", "content": "You can create videos on Canva without a watermark as long as you use free elements, images, footage, and music. If you’re a Canva Pro user, your videos will not have any watermarks whether you’re using free or paid elements."}
+{"idx": 5, "title": "Print designs using personal printer - Canva Help Center", "date": "", "ddg_snippet": "Download your design & select the PDF Print format. Tick the Crop marks and bleed checkbox. You can then print the downloaded PDF using your own printer.", "subpage_snippet": "", "source": "www.canva.com", "link": "https://www.canva.com/help/print-designs/", "content": "Download your design & select the PDF Print format. Tick the Crop marks and bleed checkbox. You can then print the downloaded PDF using your own printer."}
+{"idx": 6, "title": "Free templates - Canva", "date": "", "ddg_snippet": "Free templates Explore thousands of beautiful free templates. With Canva's drag and drop feature, you can customize your design for any occasion in just a few clicks.", "subpage_snippet": "", "source": "www.canva.com", "link": "https://www.canva.com/templates/", "content": "Free templates Explore thousands of beautiful free templates. With Canva's drag and drop feature, you can customize your design for any occasion in just a few clicks."}
+{"idx": 7, "title": "Free professional simple resume templates to customize | Canva", "date": "", "ddg_snippet": "Design fuss-free resumes that get straight to the point with Canva's collection of simple resume templates you can customize and print in minutes.", "subpage_snippet": "", "source": "www.canva.com", "link": "https://www.canva.com/resumes/templates/simple/", "content": "Design fuss-free resumes that get straight to the point with Canva's collection of simple resume templates you can customize and print in minutes."}
+{"idx": 8, "title": "Create and log in to your Canva account - Canva Help Center", "date": "", "ddg_snippet": "You can set up a Canva account in different ways. Continue with your email address, Google, Facebook, or other methods to sign up and start using Canva for free.", "subpage_snippet": "", "source": "www.canva.com", "link": "https://www.canva.com/help/sign-up-log-in/", "content": "You can set up a Canva account in different ways. Continue with your email address, Google, Facebook, or other methods to sign up and start using Canva for free."}
+{"idx": 9, "title": "Log in to your Canva account to start creating beautiful designs", "date": "", "ddg_snippet": "Create beautiful designs with your team. Login with your email address, mobile number, Google, Facebook or Apple.", "subpage_snippet": "", "source": "www.canva.com", "link": "https://www.canva.com/class/join/", "content": "Create beautiful designs with your team. Login with your email address, mobile number, Google, Facebook or Apple."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_GitHub_year_2024.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_GitHub_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d40aa5db09b99ae550b346eabe949b629e17edee
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_GitHub_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - beautyremain/ProDet: The official code for paper \" Can We ...\"", "date": "", "ddg_snippet": "If you found our code useful to your research, please cite it as follows: @article{cheng2024can, title={ Can We Leave Deepfake Data Behind in Training Deepfake Detector ?}, author={Cheng, Jikang and Yan, Zhiyuan and Zhang, Ying and Luo, Yuhao and Wang, Zhongyuan and Li...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "If you found our code useful to your research, please cite it as follows: @article{cheng2024can, title={ Can We Leave Deepfake Data Behind in Training Deepfake Detector ?}, author={Cheng, Jikang and Yan, Zhiyuan and Zhang, Ying and Luo, Yuhao and Wang, Zhongyuan and Li..."}
+{"idx": 1, "title": "[2408.17052] Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive. In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive. In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process..."}
+{"idx": 2, "title": "Can We Leave Deepfake Data Behind in Training", "date": "", "ddg_snippet": "...which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called “1+1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=vh9yEPLeyD", "content": "...which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called “1+1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?"}
+{"idx": 3, "title": "(PDF) Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "deepfake detector without using any deepfake data seems counter-intuitive.Ost: Improving generalization of. deepfake detection via one-shot test-time training . NeurIPS, 35:24597–24610, 2022.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648453_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector", "content": "deepfake detector without using any deepfake data seems counter-intuitive.Ost: Improving generalization of. deepfake detection via one-shot test-time training . NeurIPS, 35:24597–24610, 2022."}
+{"idx": 4, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "# Progressive training , in the context of deepfake detection , offers a compelling approach to enhance model robustness and generalization. Instead of simply combining deepfake and blendfake data for training , a progressive approach orders the data to mimic the real-to-fake transition.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/vh9yepleyd/", "content": "# Progressive training , in the context of deepfake detection , offers a compelling approach to enhance model robustness and generalization. Instead of simply combining deepfake and blendfake data for training , a progressive approach orders the data to mimic the real-to-fake transition."}
+{"idx": 5, "title": "SCLBD/DeepfakeBench - Githubissues", "date": "", "ddg_snippet": "A comprehensive benchmark of deepfake detection .", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/SCLBD/DeepfakeBench/readme", "content": "A comprehensive benchmark of deepfake detection ."}
+{"idx": 6, "title": "DeepFake -Detect", "date": "", "ddg_snippet": "DeepFake Detect. Upload an image to test for possible deepfakes . We have performed extensive training and hyperparameter tuning, such as comparing different EfficientNet models, number of convolution layers, weights, data augmentations, dropout rates, and regularizers.", "subpage_snippet": "", "source": "deepfake-detect.com", "link": "https://deepfake-detect.com/", "content": "DeepFake Detect. Upload an image to test for possible deepfakes . We have performed extensive training and hyperparameter tuning, such as comparing different EfficientNet models, number of convolution layers, weights, data augmentations, dropout rates, and regularizers."}
+{"idx": 7, "title": "FreqDebias: Towards Generalizable Deepfake Detection via...", "date": "", "ddg_snippet": "Can we leave deepfake data . behind in training deepfake detector ? data -level debiasing for deepfake detection . arXiv preprint.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kashiani_FreqDebias_Towards_Generalizable_Deepfake_Detection_via_Consistency-Driven_Frequency_Debiasing_CVPR_2025_paper.pdf", "content": "Can we leave deepfake data . behind in training deepfake detector ? data -level debiasing for deepfake detection . arXiv preprint."}
+{"idx": 8, "title": "Deepfakes & AI Scams: How to Tell What’s Real in 2025 – 10minutes", "date": "", "ddg_snippet": "Deepfakes & AI scams – AI now fakes faces, voices, and even live calls in minutes, making scams highly convincing. Why it matters – They erode trust, create financial/security risks, and cause psychological stress.", "subpage_snippet": "", "source": "10minutes.email", "link": "https://10minutes.email/ideas/deepfakes-ai-scams-how-to-tell-whats-real-in-2025/", "content": "Deepfakes & AI scams – AI now fakes faces, voices, and even live calls in minutes, making scams highly convincing. Why it matters – They erode trust, create financial/security risks, and cause psychological stress."}
+{"idx": 9, "title": "GitHub - Daisy-Zhang/Awesome- Deepfakes - Detection : A list of tools...", "date": "", "ddg_snippet": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ?WaveFake: A Data Set to Facilitate Audio Deepfake Detection , NeurIPS 2021: Paper Github . AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection , NeurIPS 2020: Paper.", "subpage_snippet": "", "source": "git.jl-k.com", "link": "https://git.jl-k.com/Daisy-Zhang/Awesome-Deepfakes-Detection", "content": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ?WaveFake: A Data Set to Facilitate Audio Deepfake Detection , NeurIPS 2021: Paper Github . AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection , NeurIPS 2020: Paper."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Table_1_C-Avg_AUC_ProDet_(Ours)_year_2023.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Table_1_C-Avg_AUC_ProDet_(Ours)_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d5c5b6af08b650447d318b2fbae84335dbb91724
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Table_1_C-Avg_AUC_ProDet_(Ours)_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "500 Moroccan Dirhams (MAD) to CFA Francs BCEAO (XOF) today", "date": "", "ddg_snippet": "Sep 16, 2025 · Learn the value of 500 Moroccan Dirhams (MAD) in CFA Francs BCEAO (XOF) today. The dynamics of the exchange rate change for a week, for a month, for a year on the chart and in the tables. Convert 500 Dirhams to Francs with an online currency converter.", "subpage_snippet": "", "source": "exchangerate.guru", "link": "https://exchangerate.guru/mad/xof/500/", "content": "Sep 16, 2025 · Learn the value of 500 Moroccan Dirhams (MAD) in CFA Francs BCEAO (XOF) today. The dynamics of the exchange rate change for a week, for a month, for a year on the chart and in the tables. Convert 500 Dirhams to Francs with an online currency converter."}
+{"idx": 1, "title": "500 dirhams marocains en francs CFA - Wise", "date": "", "ddg_snippet": "Convertissez 500 MAD en XOF à l'aide du convertisseur de devises Wise. Analysez les tableaux montrant l'historique des devises ou les taux actuels dirhams marocains / francs cfa et recevez des alertes de taux gratuites directement sur votre e-mail.", "subpage_snippet": "", "source": "wise.com", "link": "https://wise.com/fr/currency-converter/mad-to-xof-rate?amount=500", "content": "Convertissez 500 MAD en XOF à l'aide du convertisseur de devises Wise. Analysez les tableaux montrant l'historique des devises ou les taux actuels dirhams marocains / francs cfa et recevez des alertes de taux gratuites directement sur votre e-mail."}
+{"idx": 2, "title": "Dirham marocain vers Franc CFA | Convertir 500 MAD en XOF | Xe", "date": "", "ddg_snippet": "Conversion de 500 Dirham marocain en Franc CFA . Consultez le taux moyen du marché en temps réel, l'historique des cours et le graphique de change pour la paire MAD vers XOF avec le convertisseur de devises gratuit de Xe.", "subpage_snippet": "", "source": "www.xe.com", "link": "https://www.xe.com/fr-fr/currencyconverter/convert/?Amount=500&From=MAD&To=XOF", "content": "Conversion de 500 Dirham marocain en Franc CFA . Consultez le taux moyen du marché en temps réel, l'historique des cours et le graphique de change pour la paire MAD vers XOF avec le convertisseur de devises gratuit de Xe."}
+{"idx": 3, "title": "Convertir Dirham Marocain contre Franc CFA (UEMOA ) | MAD XOF ...", "date": "", "ddg_snippet": "Le dirham marocain est l'unité monétaire principale du Maroc jusqu'en 1912, date du début du protectorat, et depuis 1958, date à laquelle il a remplacé le Franc marocain .", "subpage_snippet": "", "source": "themoneyconverter.com", "link": "https://themoneyconverter.com/FR/MAD/XOF", "content": "Le dirham marocain est l'unité monétaire principale du Maroc jusqu'en 1912, date du début du protectorat, et depuis 1958, date à laquelle il a remplacé le Franc marocain ."}
+{"idx": 4, "title": "500 dirhams marocains en francs CFA (UEMOA) - 500 MAD en XOF", "date": "", "ddg_snippet": "Vous pouvez convertir 500 dirhams marocains en francs CFA (UEMOA) en utilisant le convertisseur de devises exchange-rates.org en une seule étape simple. Entrez simplement le montant de MAD que vous voulez convertir en XOF dans le champ \"Montant\", et c'est fait !", "subpage_snippet": "", "source": "www.exchange-rates.org", "link": "https://www.exchange-rates.org/fr/convertisseur/mad-xof/500", "content": "Vous pouvez convertir 500 dirhams marocains en francs CFA (UEMOA) en utilisant le convertisseur de devises exchange-rates.org en une seule étape simple. Entrez simplement le montant de MAD que vous voulez convertir en XOF dans le champ \"Montant\", et c'est fait !"}
+{"idx": 5, "title": "500 MAD en XOF - convertir Le Dirham Marocain à Francs CFA BCEAO", "date": "", "ddg_snippet": "Convertir د.م. 500 Le Dirham Marocain (MAD) en Francs CFA BCEAO (XOF). Obtenez des taux de change en direct, des taux historiques, des données statistiques et des graphiques sur les devises.", "subpage_snippet": "", "source": "mad.fr.currencyrate.today", "link": "https://mad.fr.currencyrate.today/convert/amount-500-to-xof.html", "content": "Convertir د.م. 500 Le Dirham Marocain (MAD) en Francs CFA BCEAO (XOF). Obtenez des taux de change en direct, des taux historiques, des données statistiques et des graphiques sur les devises."}
+{"idx": 6, "title": "Convertissez 500 Dirham marocain en Franc CFA - Ria Money...", "date": "", "ddg_snippet": "Jun 30, 2025 · Convertissez des 500 Dirham marocain en Franc CFA avec le convertisseur de devises de Ria Money Transfer. Comparez les taux de change en temps réel et envoyez de l'argent rapidement et en toute sécurité depuis la France vers plus de 190 pays avec Ria Money Transfer.", "subpage_snippet": "", "source": "www.riamoneytransfer.com", "link": "https://www.riamoneytransfer.com/fr-fr/rates-conversion/?From=MAD&To=XOF&Amount=500", "content": "Jun 30, 2025 · Convertissez des 500 Dirham marocain en Franc CFA avec le convertisseur de devises de Ria Money Transfer. Comparez les taux de change en temps réel et envoyez de l'argent rapidement et en toute sécurité depuis la France vers plus de 190 pays avec Ria Money Transfer."}
+{"idx": 7, "title": "Conversion Dirham marocain Franc CFA (UEMOA) | Convertisseur ...", "date": "", "ddg_snippet": "Notre convertisseur de devises vous indiquera le taux actuel de 500 Dirham marocain à Franc CFA (UEMOA). Comment fonctionne le taux de conversion Dirham marocain Franc CFA (UEMOA)? Le taux de change Dirham marocain en Franc CFA (UEMOA) indique la valeur d'un Dirham marocain en Franc CFA (UEMOA).", "subpage_snippet": "", "source": "www.ifcmarkets.com", "link": "https://www.ifcmarkets.com/fr/currency-converter/mad-xof/500", "content": "Notre convertisseur de devises vous indiquera le taux actuel de 500 Dirham marocain à Franc CFA (UEMOA). Comment fonctionne le taux de conversion Dirham marocain Franc CFA (UEMOA)? Le taux de change Dirham marocain en Franc CFA (UEMOA) indique la valeur d'un Dirham marocain en Franc CFA (UEMOA)."}
+{"idx": 8, "title": "500 (MAD) Dirham Marocain (MAD) À Franc CFA (BCEAO) (XOF) taux de...", "date": "", "ddg_snippet": "Ceci est la page de Dirham Marocain (MAD) à Franc CFA (BCEAO) (XOF) conversion, vous pouvez trouver le taux de change plus tard entre eux et est mis à jour toutes les 1 minutes. Il montre le taux de la conversion de deux monnaies d'échange.", "subpage_snippet": "", "source": "fr.fxexchangerate.com", "link": "https://fr.fxexchangerate.com/mad/xof-500-currency-rates.html", "content": "Ceci est la page de Dirham Marocain (MAD) à Franc CFA (BCEAO) (XOF) conversion, vous pouvez trouver le taux de change plus tard entre eux et est mis à jour toutes les 1 minutes. Il montre le taux de la conversion de deux monnaies d'échange."}
+{"idx": 9, "title": "Conversion dirham marocain (MAD) en franc CFA (BCEAO) (XOF)", "date": "", "ddg_snippet": "3 days ago · Convertisseur de devises basé sur des taux de change actualisés chaque jour. En ligne et gratuit. Le convertisseur de devises présente ici la conversion de 1 dirham marocain en franc CFA (BCEAO) en date du lundi, 22 septembre 2025.", "subpage_snippet": "", "source": "www.mataf.net", "link": "https://www.mataf.net/fr/conversion/monnaie-MAD-XOF", "content": "3 days ago · Convertisseur de devises basé sur des taux de change actualisés chaque jour. En ligne et gratuit. Le convertisseur de devises présente ici la conversion de 1 dirham marocain en franc CFA (BCEAO) en date du lundi, 22 septembre 2025."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_backbone_model_framework.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_backbone_model_framework.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..64babd2a308d8bc058fd750a4122c5d7c2b75dcb
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_backbone_model_framework.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2408.17052] Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "Abstract:The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data , which we termed \"blendfake\", encouraging models to learn generic forgery artifacts...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "Abstract:The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data , which we termed \"blendfake\", encouraging models to learn generic forgery artifacts..."}
+{"idx": 1, "title": "(PDF) Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "deepfake detector without using any deepfake data seems counter-intuitive. Therefore, we argue.by the backbone encoder, then predicted with three forgery. attributes: A=Ca(F).(1).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648453_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector", "content": "deepfake detector without using any deepfake data seems counter-intuitive. Therefore, we argue.by the backbone encoder, then predicted with three forgery. attributes: A=Ca(F).(1)."}
+{"idx": 2, "title": "Can We Leave Deepfake Data Behind in Training", "date": "", "ddg_snippet": "Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive. In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/2718a032d15e0b80cd164b240220df89-Paper-Conference.pdf", "content": "Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive. In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process..."}
+{"idx": 3, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "Abstract. The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data , which we termed ''blendfake'', encouraging models to learn generic forgery artifacts...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/2718a032d15e0b80cd164b240220df89-Abstract-Conference.html", "content": "Abstract. The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data , which we termed ''blendfake'', encouraging models to learn generic forgery artifacts..."}
+{"idx": 4, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "This paper explores whether deepfake detection models can be trained without using deepfake data , which can be costly and difficult to obtain. The researchers experiment with different training approaches and evaluate the performance of the resulting deepfake detectors .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/can-we-leave-deepfake-data-behind-training", "content": "This paper explores whether deepfake detection models can be trained without using deepfake data , which can be costly and difficult to obtain. The researchers experiment with different training approaches and evaluate the performance of the resulting deepfake detectors ."}
+{"idx": 5, "title": "beautyremain/ProDet: The official code for paper \" Can We Leave ...\"", "date": "", "ddg_snippet": "To utilize it, simply implement your customized data loading logic to fill the Your inference code in prodet_inference.py: if __name__ == '__main__': detector =ProDet_infer().cuda() ckpt_path = ' training /weights/ProDet_best.pth' ckpt=torch.load(ckpt_path) detector .load_state_dict...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "To utilize it, simply implement your customized data loading logic to fill the Your inference code in prodet_inference.py: if __name__ == '__main__': detector =ProDet_infer().cuda() ckpt_path = ' training /weights/ProDet_best.pth' ckpt=torch.load(ckpt_path) detector .load_state_dict..."}
+{"idx": 6, "title": "FreqDebias: Towards Generalizable Deepfake Detection via...", "date": "", "ddg_snippet": "Deepfake detectors often struggle to generalize to novel forgery types due to biases learned from limited training data . Can we leave deepfake data . behind in training deepfake detector ? In Advances in Neural.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kashiani_FreqDebias_Towards_Generalizable_Deepfake_Detection_via_Consistency-Driven_Frequency_Debiasing_CVPR_2025_paper.pdf", "content": "Deepfake detectors often struggle to generalize to novel forgery types due to biases learned from limited training data . Can we leave deepfake data . behind in training deepfake detector ? In Advances in Neural."}
+{"idx": 7, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "# Progressive training , in the context of deepfake detection , offers a compelling approach to enhance model robustness and generalization. Instead of simply combining deepfake and blendfake data for training , a progressive approach orders the data to mimic the real-to-fake transition.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/vh9yepleyd/", "content": "# Progressive training , in the context of deepfake detection , offers a compelling approach to enhance model robustness and generalization. Instead of simply combining deepfake and blendfake data for training , a progressive approach orders the data to mimic the real-to-fake transition."}
+{"idx": 8, "title": "Blendfake Data : A New Way to Detect Deepfakes - Simple Science", "date": "", "ddg_snippet": "Exploring blendfake data 's effectiveness in deepfake detection methods. Deepfake technology has raised serious concerns about privacy and security.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-19-blendfake-data-a-new-way-to-detect-deepfakes--akxyq01", "content": "Exploring blendfake data 's effectiveness in deepfake detection methods. Deepfake technology has raised serious concerns about privacy and security."}
+{"idx": 9, "title": "Deepfake Detectors : Essential Business Protection Against AI Threats", "date": "", "ddg_snippet": "Understanding Deepfake Technology. Current Deepfake Detectors . Brand Protection Strategies. Industry-Specific Vulnerabilities & Solutions.", "subpage_snippet": "", "source": "profiletree.com", "link": "https://profiletree.com/deepfake-detectors-and-digital-trust/", "content": "Understanding Deepfake Technology. Current Deepfake Detectors . Brand Protection Strategies. Industry-Specific Vulnerabilities & Solutions."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_experimental_setup_backbone_framewor.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_experimental_setup_backbone_framewor.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a1bc621ba390dbdc50152b8e258b8d6c316a9a6c
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_experimental_setup_backbone_framewor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Assessment framework for deepfake detection in real-world", "date": "", "ddg_snippet": "... assessment approach, which exists in various benchmarks, often directly samples test data from the same distribution as training data and can hardly ...", "subpage_snippet": "", "source": "jivp-eurasipjournals.springeropen.com", "link": "https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-024-00621-8", "content": "... assessment approach, which exists in various benchmarks, often directly samples test data from the same distribution as training data and can hardly ..."}
+{"idx": 1, "title": "AuthGuard: Generalizable Deepfake Detection via Language", "date": "", "ddg_snippet": "To enhance generalization in deepfake detection, we propose AuthGuard , a unified deepfake detection and reasoning framework that captures both ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.04501v1", "content": "To enhance generalization in deepfake detection, we propose AuthGuard , a unified deepfake detection and reasoning framework that captures both ..."}
+{"idx": 2, "title": "Towards Reliable Audio Deepfake Attribution and Model", "date": "", "ddg_snippet": "In this paper we introduce LAVA (Layered Architecture for Voice Attribution), a hierarchical framework for audio deepfake detection and model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.02521v2", "content": "In this paper we introduce LAVA (Layered Architecture for Voice Attribution), a hierarchical framework for audio deepfake detection and model ..."}
+{"idx": 3, "title": "Towards Generalized Source Tracing for Codec-Based Deepfake", "date": "", "ddg_snippet": "In this paper, we show that models trained solely on codec-resynthesized data tend to overfit to non-speech regions and struggle to generalize to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07294v2", "content": "In this paper, we show that models trained solely on codec-resynthesized data tend to overfit to non-speech regions and struggle to generalize to ..."}
+{"idx": 4, "title": "AI Strategy & Policy Blog | Artificial Intelligence", "date": "", "ddg_snippet": "... can generate outcomes that were not explicitly in the data , as likelihood estimates can be assigned to outcomes not represented by the training data .", "subpage_snippet": "", "source": "aistrategyblog.com", "link": "https://aistrategyblog.com/", "content": "... can generate outcomes that were not explicitly in the data , as likelihood estimates can be assigned to outcomes not represented by the training data ."}
+{"idx": 5, "title": "Trust | AI Strategy & Policy Blog", "date": "", "ddg_snippet": "... can generate outcomes that were not explicitly in the data , as likelihood estimates can be assigned to outcomes not represented by the training data .", "subpage_snippet": "", "source": "aistrategyblog.com", "link": "https://aistrategyblog.com/category/trust/", "content": "... can generate outcomes that were not explicitly in the data , as likelihood estimates can be assigned to outcomes not represented by the training data ."}
+{"idx": 6, "title": "The Ultimate Guide to AI for Recruitment Agencies (2025 - 20k", "date": "", "ddg_snippet": "Whether you run a small two-person staffing firm or manage talent acquisition at a large agency, we ’ll show what AI can and can ’t do for you in ...", "subpage_snippet": "", "source": "www.herohunt.ai", "link": "https://www.herohunt.ai/blog/the-ultimate-guide-to-ai-for-recruitment-agencies-2025", "content": "Whether you run a small two-person staffing firm or manage talent acquisition at a large agency, we ’ll show what AI can and can ’t do for you in ..."}
+{"idx": 7, "title": "Comprehensive Layer-wise Analysis of SSL Models for Audio", "date": "", "ddg_snippet": "This indicates that we can reduce computational cost and increase the inference speed of detecting deepfakes by utilizing only a few lower layers.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.03559v2", "content": "This indicates that we can reduce computational cost and increase the inference speed of detecting deepfakes by utilizing only a few lower layers."}
+{"idx": 8, "title": "Algorithmic | AI Strategy & Policy Blog", "date": "", "ddg_snippet": "... can generate outcomes that were not explicitly in the data , as likelihood estimates can be assigned to outcomes not represented by the training data .", "subpage_snippet": "", "source": "aistrategyblog.com", "link": "https://aistrategyblog.com/category/algorithmic/", "content": "... can generate outcomes that were not explicitly in the data , as likelihood estimates can be assigned to outcomes not represented by the training data ."}
+{"idx": 9, "title": "Dr. Kim | AI Strategy & Policy Blog", "date": "", "ddg_snippet": "Generative models are probabilistic models that can generate representative outcomes from observed data used in the training process.", "subpage_snippet": "", "source": "aistrategyblog.com", "link": "https://aistrategyblog.com/author/kimklarsen/", "content": "Generative models are probabilistic models that can generate representative outcomes from observed data used in the training process."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_experimental_setup_backbone_model_fr_year_2023.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_experimental_setup_backbone_model_fr_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5092d7491863c55175a8d5ca1a8ee69d63bd2ef4
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_experimental_setup_backbone_model_fr_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "5 Nov 2024 — This paper proposes a novel training strategy for Deepfake detection using real, blendfake, and deepfake datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vh9yEPLeyD&referrer=[the+profile+of+Jikang+Cheng](/profile?id=~Jikang_Cheng1)", "content": "5 Nov 2024 — This paper proposes a novel training strategy for Deepfake detection using real, blendfake, and deepfake datasets."}
+{"idx": 1, "title": "Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "30 Aug 2024 — In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from \"real to blendfake to deepfake \" to be a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17052v1", "content": "30 Aug 2024 — In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from \"real to blendfake to deepfake \" to be a ..."}
+{"idx": 2, "title": "Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "by J Cheng · 2024 · Cited by 31 — Notably, they chose to use the generated blendfake solely instead of deepfake during training, thereby completely excluding deepfake from the detector training ... 20 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/2718a032d15e0b80cd164b240220df89-Paper-Conference.pdf", "content": "by J Cheng · 2024 · Cited by 31 — Notably, they chose to use the generated blendfake solely instead of deepfake during training, thereby completely excluding deepfake from the detector training ... 20 pages"}
+{"idx": 3, "title": "Deepfake Detection that Generalizes Across Benchmarks", "date": "", "ddg_snippet": "8 Aug 2025 — Can we leave deepfake data behind in training deepfake detector ? In The Thirty-eighth Annual Conference on Neural Information Processing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06248v1", "content": "8 Aug 2025 — Can we leave deepfake data behind in training deepfake detector ? In The Thirty-eighth Annual Conference on Neural Information Processing ..."}
+{"idx": 4, "title": "A Hybrid Model for Generalizable Deepfake Detection via ...", "date": "", "ddg_snippet": "by MK Le-Phan · 2025 — We present a hybrid deepfake detection framework that integrates three complementary types of forgery clues: blending artifacts, semantic inconsistencies, and ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3709020.3734833", "content": "by MK Le-Phan · 2025 — We present a hybrid deepfake detection framework that integrates three complementary types of forgery clues: blending artifacts, semantic inconsistencies, and ..."}
+{"idx": 5, "title": "A comprehensive benchmark of deepfake detection", "date": "", "ddg_snippet": "DeepfakeBench presents the first comprehensive benchmark for deepfake detection , resolving the issue of lack of standardization and uniformity in this field.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SCLBD/DeepfakeBench", "content": "DeepfakeBench presents the first comprehensive benchmark for deepfake detection , resolving the issue of lack of standardization and uniformity in this field."}
+{"idx": 6, "title": "Generalization Limits of Deepfake Detectors in the Wild", "date": "", "ddg_snippet": "by A Garg — First, we experiment with multiple fine-tuning methods to determine whether models can leverage learning from the previous tasks to correctly detect deepfakes ...", "subpage_snippet": "", "source": "www.ischool.berkeley.edu", "link": "https://www.ischool.berkeley.edu/sites/default/files/bb_paper.pdf", "content": "by A Garg — First, we experiment with multiple fine-tuning methods to determine whether models can leverage learning from the previous tasks to correctly detect deepfakes ..."}
+{"idx": 7, "title": "Exposing Deepfakes using Differential Anomaly Detection", "date": "", "ddg_snippet": "by S Stamnas · 2025 · Cited by 1 — This strategy allows us to validate our model without using any fake media. After the binary clas- sification pre- training phase, we choose the backbone that. 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025W/AI4MFDD/papers/Stamnas_DiffFake_Exposing_Deepfakes_using_Differential_Anomaly_Detection_WACVW_2025_paper.pdf", "content": "by S Stamnas · 2025 · Cited by 1 — This strategy allows us to validate our model without using any fake media. After the binary clas- sification pre- training phase, we choose the backbone that. 11 pages"}
+{"idx": 8, "title": "Convolutional neural network framework for deepfake ...", "date": "", "ddg_snippet": "by E Pintelas · 2025 · Cited by 1 — In this work, we consider a new diffusion-based neural network approach, rather than directly analyzing deepfake images for inconsistencies.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1077314225000980", "content": "by E Pintelas · 2025 · Cited by 1 — In this work, we consider a new diffusion-based neural network approach, rather than directly analyzing deepfake images for inconsistencies."}
+{"idx": 9, "title": "Evaluation framework for deepfake speech detection: a ...", "date": "", "ddg_snippet": "by A Firc · 2025 · Cited by 1 — This study presents a general and detailed framework for evaluating and comparing deepfake speech detectors . We further demonstrate the use of ...", "subpage_snippet": "", "source": "cybersecurity.springeropen.com", "link": "https://cybersecurity.springeropen.com/articles/10.1186/s42400-024-00346-1", "content": "by A Firc · 2025 · Cited by 1 — This study presents a general and detailed framework for evaluating and comparing deepfake speech detectors . We further demonstrate the use of ..."}
diff --git a/data/sampled_jsons/Capturing_dynamics_of_time-varying_data_via_topology_Xian_2022.jsonl b/data/sampled_jsons/Capturing_dynamics_of_time-varying_data_via_topology_Xian_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..370af9995f81b9f3188f81f11b52f9badd34d11a
--- /dev/null
+++ b/data/sampled_jsons/Capturing_dynamics_of_time-varying_data_via_topology_Xian_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Capturing dynamics of time-varying data via topology", "date": "", "ddg_snippet": "One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ...", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/doi/10.3934/fods.2021033", "content": "One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ..."}
+{"idx": 1, "title": "Capturing Dynamics of Time-Varying Data via Topology Capturing dynamics of time-varying data via topology CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY Images Capturing dynamics of time-varying data via topology CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY Capturing Dynamics of Time-Varying Data via Topology replication code for \"Capturing dynamics of time-varying data ...", "date": "", "ddg_snippet": "Oct 7, 2020 · One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ... One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ... CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY Lu Xian School of Information University of Michigan Ann Arbor, MI 48109, USA View all “ Capturing dynamics of time-varying data via topology ” is a paper by Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier published in 2022 . It has an Open Access status of “gold”. ( 2022 ) Xian et al. Foundations of Data Science. One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have... This work uses topological data analysis and machine learning to study a seminal model of collective motion in biology that describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. About replication code for \" Capturing dynamics of time-varying data via topology \"", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.05780", "content": "Oct 7, 2020 · One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ... One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ... CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY Lu Xian School of Information University of Michigan Ann Arbor, MI 48109, USA View all “ Capturing dynamics of time-varying data via topology ” is a paper by Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier published in 2022 . It has an Open Access status of “gold”. ( 2022 ) Xian et al. Foundations of Data Science. One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have... This work uses topological data analysis and machine learning to study a seminal model of collective motion in biology that describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. About replication code for \" Capturing dynamics of time-varying data via topology \""}
+{"idx": 2, "title": "CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY", "date": "", "ddg_snippet": "CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY Lu Xian School of Information University of Michigan Ann Arbor, MI 48109, USA", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10388958", "content": "CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY Lu Xian School of Information University of Michigan Ann Arbor, MI 48109, USA"}
+{"idx": 3, "title": "Capturing dynamics of time-varying data via topology", "date": "", "ddg_snippet": "“ Capturing dynamics of time-varying data via topology ” is a paper by Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier published in 2022 . It has an Open Access status of “gold”.", "subpage_snippet": "", "source": "oa.mg", "link": "https://oa.mg/work/10.3934/fods.2021033", "content": "“ Capturing dynamics of time-varying data via topology ” is a paper by Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier published in 2022 . It has an Open Access status of “gold”."}
+{"idx": 4, "title": "CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY", "date": "", "ddg_snippet": "( 2022 ) Xian et al. Foundations of Data Science. One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have...", "subpage_snippet": "", "source": "www.mendeley.com", "link": "https://www.mendeley.com/catalogue/95899e1a-7e46-3b07-8c8f-6c89016e5e78/", "content": "( 2022 ) Xian et al. Foundations of Data Science. One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have..."}
+{"idx": 5, "title": "Capturing Dynamics of Time-Varying Data via Topology", "date": "", "ddg_snippet": "This work uses topological data analysis and machine learning to study a seminal model of collective motion in biology that describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Capturing-Dynamics-of-Time-Varying-Data-via-Xian-Adams/156e87ce1a99116bfdce512d2ebdd54a1f4e9709/figure/0", "content": "This work uses topological data analysis and machine learning to study a seminal model of collective motion in biology that describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling."}
+{"idx": 6, "title": "replication code for \"Capturing dynamics of time-varying data ...", "date": "", "ddg_snippet": "About replication code for \" Capturing dynamics of time-varying data via topology \"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lxiancode/tda-crocker", "content": "About replication code for \" Capturing dynamics of time-varying data via topology \""}
+{"idx": 7, "title": "A universal null-distribution for topological data analysis", "date": "", "ddg_snippet": "by O Bobrowski · 2023 · Cited by 29 — Xian, L., Adams, H., Topaz, C. M. & Ziegelmeier, L. Capturing dynamics of time-varying data via topology . Found. Data Sci. 4(1), 1–36 (2022).", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-023-37842-2", "content": "by O Bobrowski · 2023 · Cited by 29 — Xian, L., Adams, H., Topaz, C. M. & Ziegelmeier, L. Capturing dynamics of time-varying data via topology . Found. Data Sci. 4(1), 1–36 (2022)."}
+{"idx": 8, "title": "Henry Adams", "date": "", "ddg_snippet": "Capturing dynamics of time-varying data via topology . L Xian, H Adams, CM ... Journal of Applied and Computational Topology 6 (2), 177-192, 2022. 27, 2022.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=juLM8nMAAAAJ&hl=en", "content": "Capturing dynamics of time-varying data via topology . L Xian, H Adams, CM ... Journal of Applied and Computational Topology 6 (2), 177-192, 2022. 27, 2022."}
+{"idx": 9, "title": "Lori Ziegelmeier", "date": "", "ddg_snippet": "Capturing Dynamics of Time-Varying Data via Topology . L Xian, H Adams, CM Topaz, L Ziegelmeier. arXiv preprint arXiv:2010.05780, 2020. 29, 2020. On homotopy ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=DI_cXnUAAAAJ&hl=en", "content": "Capturing Dynamics of Time-Varying Data via Topology . L Xian, H Adams, CM Topaz, L Ziegelmeier. arXiv preprint arXiv:2010.05780, 2020. 29, 2020. On homotopy ..."}
diff --git a/data/sampled_jsons/Catoni_contextual_bandits_Assumption_4.1_variance_heavy-tailed_rewards.jsonl b/data/sampled_jsons/Catoni_contextual_bandits_Assumption_4.1_variance_heavy-tailed_rewards.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e05688c44f3fa051cebd55b895d72a5c6f793f3f
--- /dev/null
+++ b/data/sampled_jsons/Catoni_contextual_bandits_Assumption_4.1_variance_heavy-tailed_rewards.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Catoni Contextual Bandits are Robust to Heavy-tailed Rewards ... Low-rank Matrix Bandits with Heavy-tailed Rewards - OpenReview How Does Variance Shape the Regret in Contextual Bandits? ICML Poster Catoni Contextual Bandits are Robust to Heavy ... Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Catoni Cont", "date": "", "ddg_snippet": "Feb 4 , 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ... This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ... Apr 26, 2024 · The paper introduces the LowHTR problem, which extends the existing framework of stochastic low-rank matrix bandits by considering heavy-tailed rewards instead of the traditional assumption of sub-Gaussian noise. Abstract We consider realizable contextual bandits with general function approximation, investigating how small reward variance can lead to better-than-minimax regret bounds. Unlike in minimax regret bounds, we show that the eluder dimension delu—a measure of the complexity of the function class—plays a crucial role in variance -dependent ... However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Feb 4 , 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ... This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ... Apr 26, 2024 · The paper introduces the LowHTR problem, which extends the existing framework of stochastic low-rank matrix bandits by considering heavy-tailed rewards instead of the traditional assumption of sub-Gaussian noise. Abstract We consider realizable contextual bandits with general function approximation, investigating how small reward variance can lead to better-than-minimax regret bounds. Unlike in minimax regret bounds, we show that the eluder dimension delu—a measure of the complexity of the function class—plays a crucial role in variance -dependent ... However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni ..."}
+{"idx": 1, "title": "ICML Poster Catoni Contextual Bandits are Robust to Heavy ...", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Catoni-Contextual-Bandits-are-Robust-to-Rewards-Ye-Jin/125154c306493f2c7af8e8c09c0e58c22106e6fe", "content": "This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ..."}
+{"idx": 3, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards ...", "date": "", "ddg_snippet": "This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46438/paper", "content": "This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ..."}
+{"idx": 4, "title": "Low-rank Matrix Bandits with Heavy-tailed Rewards - OpenReview", "date": "", "ddg_snippet": "Apr 26, 2024 · The paper introduces the LowHTR problem, which extends the existing framework of stochastic low-rank matrix bandits by considering heavy-tailed rewards instead of the traditional assumption of sub-Gaussian noise.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=TG4fKjfvxC", "content": "Apr 26, 2024 · The paper introduces the LowHTR problem, which extends the existing framework of stochastic low-rank matrix bandits by considering heavy-tailed rewards instead of the traditional assumption of sub-Gaussian noise."}
+{"idx": 5, "title": "How Does Variance Shape the Regret in Contextual Bandits?", "date": "", "ddg_snippet": "Abstract We consider realizable contextual bandits with general function approximation, investigating how small reward variance can lead to better-than-minimax regret bounds. Unlike in minimax regret bounds, we show that the eluder dimension delu—a measure of the complexity of the function class—plays a crucial role in variance -dependent ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/9861a7c3972ed5d36dda3826d44bb246-Paper-Conference.pdf", "content": "Abstract We consider realizable contextual bandits with general function approximation, investigating how small reward variance can lead to better-than-minimax regret bounds. Unlike in minimax regret bounds, we show that the eluder dimension delu—a measure of the complexity of the function class—plays a crucial role in variance -dependent ..."}
+{"idx": 6, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Catoni Cont", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni ..."}
+{"idx": 7, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "Table 1 : Comparisons of our regret bounds and computational complexity to previous best-known results for heavy - tailed linear bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "Table 1 : Comparisons of our regret bounds and computational complexity to previous best-known results for heavy - tailed linear bandits ."}
+{"idx": 8, "title": "(PDF) Regret Minimization in Isotonic, Heavy - Tailed Contextual ...", "date": "", "ddg_snippet": "the contextual bandit problem assumes bounded/subgaussian reward distributions for each arm.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/355496335_Regret_Minimization_in_Isotonic_Heavy-Tailed_Contextual_Bandits_via_Adaptive_Confidence_Bands", "content": "the contextual bandit problem assumes bounded/subgaussian reward distributions for each arm."}
+{"idx": 9, "title": "Multi-Armed Bandits | Papers With Code", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/multi-armed-bandits/codeless?page=4", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics."}
diff --git a/data/sampled_jsons/Catoni_estimator_OFUL_theta_function-dependent_choice_implementation.jsonl b/data/sampled_jsons/Catoni_estimator_OFUL_theta_function-dependent_choice_implementation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c0b3a18431bbef3e7a3ba61446f1463527bd9a98
--- /dev/null
+++ b/data/sampled_jsons/Catoni_estimator_OFUL_theta_function-dependent_choice_implementation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Catoni's M-Estimation - arXiv.org", "date": "", "ddg_snippet": "Catoni proposed a robust M- estimator and gave the deviation inequality for one fixed test function . The present paper is devoted to the uniform concentration inequality for a family of test functions . As an application, we consider empirical risk minimization for heavy-tailed losses.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.08211", "content": "Catoni proposed a robust M- estimator and gave the deviation inequality for one fixed test function . The present paper is devoted to the uniform concentration inequality for a family of test functions . As an application, we consider empirical risk minimization for heavy-tailed losses."}
+{"idx": 1, "title": "Catoni-Giulini M-estimator - The Stats Map", "date": "", "ddg_snippet": "Jan 5, 2025 · In 2017, Catoni and Giulini proposed an approach to multivariate concentration based on M-estimation. Let ψ be any symmetric “influence function ” such that −log(1−x+x2/2)≤ ψ(x)≤ log(1+x+x2/2), ∀x∈ R. The motivation behind this condition is to choose a function ψ such that eψ is bounded by polynomials. The estimator is then ξ(θ)= nλ1i≤n∑∫ Rdψ(λ ϑ,X i )ρθ(dϑ ...", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/Catoni-Giulini-M-estimator", "content": "Jan 5, 2025 · In 2017, Catoni and Giulini proposed an approach to multivariate concentration based on M-estimation. Let ψ be any symmetric “influence function ” such that −log(1−x+x2/2)≤ ψ(x)≤ log(1+x+x2/2), ∀x∈ R. The motivation behind this condition is to choose a function ψ such that eψ is bounded by polynomials. The estimator is then ξ(θ)= nλ1i≤n∑∫ Rdψ(λ ϑ,X i )ρθ(dϑ ..."}
+{"idx": 2, "title": "stat-map/Catoni-Giulini M-estimator.md at main - GitHub", "date": "", "ddg_snippet": "This is the estimate of $\\la \\ theta , \\E X\\ra$. An approximation $\\xi (\\ theta )$ is computationally tractable for certain choices of $\\psi$, but still doesn't give a closed-form bound, since you can't compute it for all $\\ theta $.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bchugg/stat-map/blob/main/Catoni-Giulini+M-estimator.md", "content": "This is the estimate of $\\la \\ theta , \\E X\\ra$. An approximation $\\xi (\\ theta )$ is computationally tractable for certain choices of $\\psi$, but still doesn't give a closed-form bound, since you can't compute it for all $\\ theta $."}
+{"idx": 3, "title": "Nearly Optimal Catoni’s M-estimator for Infinite Variance", "date": "", "ddg_snippet": "This is based on the non-trivial extension of Catoni ’s estimator proposed in Catoni (2012) for the case of infinite variance. We also provided an algorithm to adapt to the situ-ation of unknown moment bound using classical Lepskii’s adaptive estimation method.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/bhatt22b/bhatt22b.pdf", "content": "This is based on the non-trivial extension of Catoni ’s estimator proposed in Catoni (2012) for the case of infinite variance. We also provided an algorithm to adapt to the situ-ation of unknown moment bound using classical Lepskii’s adaptive estimation method."}
+{"idx": 4, "title": "The Catoni-Giulini estimator", "date": "", "ddg_snippet": "Mar 7, 2024 · This function simply shrinks \\ (\\bs {x}\\) towards the origin by an amount proportional to \\ (\\lambda\\) and is clearly easy to implement computationally. The Catoni -Giulini estimator is", "subpage_snippet": "", "source": "benchugg.com", "link": "https://benchugg.com/research_notes/catoni_giulini/", "content": "Mar 7, 2024 · This function simply shrinks \\ (\\bs {x}\\) towards the origin by an amount proportional to \\ (\\lambda\\) and is clearly easy to implement computationally. The Catoni -Giulini estimator is"}
+{"idx": 5, "title": "A generalized Catoni’s M-estimator under finite α-th moment ...", "date": "", "ddg_snippet": "We generalize Catoni ’s M- estimator , put forward in [3] by Catoni under finite variance assumption, to the case in which distributions can have finite α -th moment with α∈ (1,2) α ∈ (1, 2). Our approach, inspired by the Taylor-like expansion developed in [4], is via slightly modifying the influence function φ in [3].", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-15/issue-2/A-generalized-Catonis-M-estimator-under-finite-α-th-moment/10.1214/21-EJS1911.full", "content": "We generalize Catoni ’s M- estimator , put forward in [3] by Catoni under finite variance assumption, to the case in which distributions can have finite α -th moment with α∈ (1,2) α ∈ (1, 2). Our approach, inspired by the Taylor-like expansion developed in [4], is via slightly modifying the influence function φ in [3]."}
+{"idx": 6, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Catoni - OFUL approach in Algorithm 1. Given failure prob-abilities δ and confidence parameters βˆt, the algorithm chooses the action xt with the highest optimistic reward by maximizing across all functions in a confidence set Ft, as in the standard OFUL approach.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "Catoni - OFUL approach in Algorithm 1. Given failure prob-abilities δ and confidence parameters βˆt, the algorithm chooses the action xt with the highest optimistic reward by maximizing across all functions in a confidence set Ft, as in the standard OFUL approach."}
+{"idx": 7, "title": "Reference needed for some theta function upper bound estimates", "date": "", "ddg_snippet": "Interpolating delta like functions by trigonometric polynomials of bounded modulus and fast decay.Fourier coefficients of real analytic functions on an n-dimension torus.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/500599/reference-needed-for-some-theta-function-upper-bound-estimates", "content": "Interpolating delta like functions by trigonometric polynomials of bounded modulus and fast decay.Fourier coefficients of real analytic functions on an n-dimension torus."}
+{"idx": 8, "title": "proof verification - Consider the function $\\ theta =\\{0,1\\}\\times\\mathbb...", "date": "", "ddg_snippet": "Instead, think about what the function $\\ theta $ does.How can we make one of these equal to $x$? Well it depends on whether $x$ is positive or not. You should be able to complete the proof from here.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1965729/consider-the-function-theta-0-1-times-mathbbn-rightarrow-mathbbz-defi", "content": "Instead, think about what the function $\\ theta $ does.How can we make one of these equal to $x$? Well it depends on whether $x$ is positive or not. You should be able to complete the proof from here."}
+{"idx": 9, "title": "Height Estimator -Free AI height estimation tool", "date": "", "ddg_snippet": "Height Estimator uses AI to provide accurate height estimations from photos. Upload a clear, full-body image, and get results in seconds.", "subpage_snippet": "", "source": "theee.ai", "link": "https://theee.ai/tools/Height-Estimator-ZxX0qdGq", "content": "Height Estimator uses AI to provide accurate height estimations from photos. Upload a clear, full-body image, and get results in seconds."}
diff --git a/data/sampled_jsons/Catoni_estimator_Psi_function_definition_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl b/data/sampled_jsons/Catoni_estimator_Psi_function_definition_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3bcbb0f84e1bae7f9147f8cf1b249d9bb0db9aa
--- /dev/null
+++ b/data/sampled_jsons/Catoni_estimator_Psi_function_definition_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general function approximation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general function approximation."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation."}
+{"idx": 2, "title": "Variance-aware decision making with linear function approximation under ...", "date": "", "ddg_snippet": "This paper develops an algorithmic approach building on Catoni's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Expand Highly ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Variance-aware-decision-making-with-linear-function-Li-Sun/e3fa32bc7972a4c46ffa228aaec2b309e2dddff7", "content": "This paper develops an algorithmic approach building on Catoni's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Expand Highly ..."}
+{"idx": 3, "title": "PDF Nearly Optimal Catoni's M-estimator for Infinite Variance", "date": "", "ddg_snippet": "Here optimal is to be under-stood in the sense that √ the Catoni's estimator comes close the best possible L(= 2). Given the sharpness of result, it is not surprising that Catoni's estimator has spawned a wide range of applications ranging from bandits (Bubeck et al., 2013) to empirical risk minimization (Brownlees et al., 2015).", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/bhatt22b/bhatt22b.pdf", "content": "Here optimal is to be under-stood in the sense that √ the Catoni's estimator comes close the best possible L(= 2). Given the sharpness of result, it is not surprising that Catoni's estimator has spawned a wide range of applications ranging from bandits (Bubeck et al., 2013) to empirical risk minimization (Brownlees et al., 2015)."}
+{"idx": 4, "title": "PDF Beyond Catoni: Sharper Rates for Heavy-Tailed and Robust Mean Estimation", "date": "", "ddg_snippet": "In d-dimensional estimation, we are given iid samples x1, . . . , xn Rd, with Cov(xi) ∈ ≼ σ2Id and want to compute an estimate of the mean μ. For simplicity, we will focus on the σ = 1 case. In d-dimensional estimation, we are given iid samples x1, . . . , xn Rd, with Cov(xi) ∈ ≼ σ2Id and want to compute an estimate of the mean μ.", "subpage_snippet": "", "source": "shivamgupta2.github.io", "link": "https://shivamgupta2.github.io/Beyond_Catoni_Slides.pdf", "content": "In d-dimensional estimation, we are given iid samples x1, . . . , xn Rd, with Cov(xi) ∈ ≼ σ2Id and want to compute an estimate of the mean μ. For simplicity, we will focus on the σ = 1 case. In d-dimensional estimation, we are given iid samples x1, . . . , xn Rd, with Cov(xi) ∈ ≼ σ2Id and want to compute an estimate of the mean μ."}
+{"idx": 5, "title": "Catoni-style confidence sequences for heavy-tailed mean estimation", "date": "", "ddg_snippet": "A confidence sequence (CS) is a sequence of confidence intervals that is valid at arbitrary data-dependent stopping times. These are useful in applications like A/B testing, multi-armed bandits , off-policy evaluation, election auditing, etc. We present three approaches to constructing a confidence sequence for the population mean, under the minimal assumption that only an upper bound σ2on the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0304414923001084", "content": "A confidence sequence (CS) is a sequence of confidence intervals that is valid at arbitrary data-dependent stopping times. These are useful in applications like A/B testing, multi-armed bandits , off-policy evaluation, election auditing, etc. We present three approaches to constructing a confidence sequence for the population mean, under the minimal assumption that only an upper bound σ2on the ..."}
+{"idx": 6, "title": "PDF Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Algorithm for Known Variance combines the OFUL framework with a variance-weighted regression ap-proach Uses the Catoni estimator to construct a robust confidence set for the true reward function . Result:", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46438.pdf", "content": "Algorithm for Known Variance combines the OFUL framework with a variance-weighted regression ap-proach Uses the Catoni estimator to construct a robust confidence set for the true reward function . Result:"}
+{"idx": 7, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This paper introduces a novel contextual bandit algorithm that utilizes Catoni's estimator to achieve robust regret bounds under heavy-tailed rewards , significantly improving performance by reducing dependence on reward range and variance.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/105230?from=search", "content": "This paper introduces a novel contextual bandit algorithm that utilizes Catoni's estimator to achieve robust regret bounds under heavy-tailed rewards , significantly improving performance by reducing dependence on reward range and variance."}
+{"idx": 8, "title": "Extended UCB Policies for Multi-Armed Bandit Problems", "date": "", "ddg_snippet": "6 days ago — Other recent progresses on variants of MAB, such as adversarial bandits, contextual bandits ... Multi-armed bandit problems with heavy - tailed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1112.1768v5", "content": "6 days ago — Other recent progresses on variants of MAB, such as adversarial bandits, contextual bandits ... Multi-armed bandit problems with heavy - tailed ..."}
+{"idx": 9, "title": "Extended UCB Policies for Frequentist Multi-armed Bandit ...", "date": "", "ddg_snippet": "Other recent progresses on variants of MAB, such as adversarial bandits, contextual bandits ... Multi-armed bandit problems with heavy - tailed reward distributions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1112.1768v4", "content": "Other recent progresses on variants of MAB, such as adversarial bandits, contextual bandits ... Multi-armed bandit problems with heavy - tailed reward distributions ..."}
diff --git a/data/sampled_jsons/Catoni_estimator_parameter_tuning_function-dependent_theta_heavy-tailed.jsonl b/data/sampled_jsons/Catoni_estimator_parameter_tuning_function-dependent_theta_heavy-tailed.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..986092346ae37f83e77efcf252328dd41ed04c44
--- /dev/null
+++ b/data/sampled_jsons/Catoni_estimator_parameter_tuning_function-dependent_theta_heavy-tailed.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF for heavy-tailed mean estimation - arXiv.org", "date": "", "ddg_snippet": "e of heavy-tailed distributions. The best among our three methods — the Catoni -style confidence sequence — performs remarkably well in practice, essentially matching the state-of-the-art methods for σ2-subGaussian data, and provably attains the plog log t/t lower bound due to t", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.01250v5.pdf", "content": "e of heavy-tailed distributions. The best among our three methods — the Catoni -style confidence sequence — performs remarkably well in practice, essentially matching the state-of-the-art methods for σ2-subGaussian data, and provably attains the plog log t/t lower bound due to t"}
+{"idx": 1, "title": "Catoni-style confidence sequences for heavy-tailed mean estimation", "date": "", "ddg_snippet": "In the heavy-tailed regime, our Catoni -style CS performs markedly better than the other two CSs, and is close to the Catoni CI. In the Gaussian setting, our Catoni -style CS is approximately at a same caliber as the best subGaussian CSs in the literature.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0304414923001084", "content": "In the heavy-tailed regime, our Catoni -style CS performs markedly better than the other two CSs, and is close to the Catoni CI. In the Gaussian setting, our Catoni -style CS is approximately at a same caliber as the best subGaussian CSs in the literature."}
+{"idx": 2, "title": "PDF Nearly Optimal Catoni's M-estimator for Infinite Variance", "date": "", "ddg_snippet": "1.2. Heavy-tailed Estimators One crucial shortcoming of the Catoni estimator is that it requires the existence of finite second moment. This is a serious limitation in heavy -tail settings where the vari-ance need not exist or be finite.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/bhatt22b/bhatt22b.pdf", "content": "1.2. Heavy-tailed Estimators One crucial shortcoming of the Catoni estimator is that it requires the existence of finite second moment. This is a serious limitation in heavy -tail settings where the vari-ance need not exist or be finite."}
+{"idx": 3, "title": "PDF Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance- dependent re-gret bounds. Advances in Neural Information Processing Sys-tems, 36. [3] Li, X. and Sun, Q. (2024). Variance-aware decision making with linear function approximation under heavytailed rewards.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46438.pdf", "content": "Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance- dependent re-gret bounds. Advances in Neural Information Processing Sys-tems, 36. [3] Li, X. and Sun, Q. (2024). Variance-aware decision making with linear function approximation under heavytailed rewards."}
+{"idx": 4, "title": "PDF Loss minimization and parameter estimation with heavy tails", "date": "", "ddg_snippet": "In the event that more than half of the are within p6 2k/n of μ, the median is as well. bμ Alternative is to minimize a \"robust\" loss function [ Catoni , 2012]:", "subpage_snippet": "", "source": "www.cs.columbia.edu", "link": "https://www.cs.columbia.edu/~djhsu/papers/heavytails-slides.pdf", "content": "In the event that more than half of the are within p6 2k/n of μ, the median is as well. bμ Alternative is to minimize a \"robust\" loss function [ Catoni , 2012]:"}
+{"idx": 5, "title": "scalar heavy-tailed mean estimation - The Stats Map", "date": "", "ddg_snippet": "Unlike light- tailed settings (light- tailed , unbounded scalar concentration and bounded scalar concentration) the sample mean is not well-behaved in heavy-tailed settings. Since heavy-tailed distributions may not have finite MGFs, the Chernoff method is not applicable. Catoni gives an example demonstrating the bound achieved via Markov's inequality (basic inequalities), i.e., P(∣X n−μ∣ ...", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/scalar-heavy-tailed-mean-estimation", "content": "Unlike light- tailed settings (light- tailed , unbounded scalar concentration and bounded scalar concentration) the sample mean is not well-behaved in heavy-tailed settings. Since heavy-tailed distributions may not have finite MGFs, the Chernoff method is not applicable. Catoni gives an example demonstrating the bound achieved via Markov's inequality (basic inequalities), i.e., P(∣X n−μ∣ ..."}
+{"idx": 6, "title": "On Catoni's M-Estimation - arXiv.org", "date": "", "ddg_snippet": "Catoni proposed a robust M-estimator and gave the deviation inequality for one fixed test function . The present paper is devoted to the uniform concentration inequality for a family of test functions . As an application, we consider empirical risk minimization for heavy-tailed losses.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.08211", "content": "Catoni proposed a robust M-estimator and gave the deviation inequality for one fixed test function . The present paper is devoted to the uniform concentration inequality for a family of test functions . As an application, we consider empirical risk minimization for heavy-tailed losses."}
+{"idx": 7, "title": "PDF Beyond Catoni: Sharper Rates for Heavy-Tailed and Robust Mean Estimation", "date": "", "ddg_snippet": "Of these, for heavy-tailed estimation, Catoni ( Catoni , 2012) showed an estimator matching the Gaussian rate in dimension d = 1 when the variance σ2 is known. This was followed by work that achieved the same rate even when σ2 is unknown (Lee and Valiant, 2022a).", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/gupta24a/gupta24a.pdf", "content": "Of these, for heavy-tailed estimation, Catoni ( Catoni , 2012) showed an estimator matching the Gaussian rate in dimension d = 1 when the variance σ2 is known. This was followed by work that achieved the same rate even when σ2 is unknown (Lee and Valiant, 2022a)."}
+{"idx": 8, "title": "PDF Beyond Catoni: Sharper Rates for Heavy-Tailed and Robust Mean Estimation", "date": "", "ddg_snippet": "When log 1 q ≫ d, 2d δ is the d+1-factor loss over the Gaussian rate necessary? In d-dimensional estimation, we are given iid samples x1, . . . , xn Rd, with Cov(xi) ∈ ≼ σ2Id and want to compute an estimate of the mean μ. For simplicity, we will focus on the σ = 1 case.", "subpage_snippet": "", "source": "shivamgupta2.github.io", "link": "https://shivamgupta2.github.io/Beyond_Catoni_Slides.pdf", "content": "When log 1 q ≫ d, 2d δ is the d+1-factor loss over the Gaussian rate necessary? In d-dimensional estimation, we are given iid samples x1, . . . , xn Rd, with Cov(xi) ∈ ≼ σ2Id and want to compute an estimate of the mean μ. For simplicity, we will focus on the σ = 1 case."}
+{"idx": 9, "title": "PDF Statistica Sinica Preprint No: SS-2024-0249", "date": "", "ddg_snippet": "close this gap, we further establish the tighter confidence sequences using the stitching methods. Our new methodology can be easily applied to risk control and parameter estimation problems. Key words and phrases: Catoni estimator , Heavy tail, Confidence sequence, Law of iterated logarithm", "subpage_snippet": "", "source": "www3.stat.sinica.edu.tw", "link": "https://www3.stat.sinica.edu.tw/ss_newpaper/SS-2024-0249_na.pdf", "content": "close this gap, we further establish the tighter confidence sequences using the stitching methods. Our new methodology can be easily applied to risk control and parameter estimation problems. Key words and phrases: Catoni estimator , Heavy tail, Confidence sequence, Law of iterated logarithm"}
diff --git "a/data/sampled_jsons/Catoni_estimator_\316\250(x)_=_log(1_+_x_+_x\302\2622)_OR_\316\250(x)_definition_heavy-tailed_rewards.jsonl" "b/data/sampled_jsons/Catoni_estimator_\316\250(x)_=_log(1_+_x_+_x\302\2622)_OR_\316\250(x)_definition_heavy-tailed_rewards.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..98f7c86b411651fd1432ed3d6f9112c7ee9f26b1
--- /dev/null
+++ "b/data/sampled_jsons/Catoni_estimator_\316\250(x)_=_log(1_+_x_+_x\302\2622)_OR_\316\250(x)_definition_heavy-tailed_rewards.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Catoni : Sharper Rates for Heavy - Tailed and Robust Mean", "date": "", "ddg_snippet": "Keywords: Mean Estimation , Heavy - Tailed Estimation , Robust Estimation , High-Dimensional Statistics.We first describe a variant of Catoni ’s one -dimensional estimator Catoni (2012) for bounded variance distri-butions.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/gupta24a/gupta24a.pdf", "content": "Keywords: Mean Estimation , Heavy - Tailed Estimation , Robust Estimation , High-Dimensional Statistics.We first describe a variant of Catoni ’s one -dimensional estimator Catoni (2012) for bounded variance distri-butions."}
+{"idx": 1, "title": "On Catoni 's M- Estimation", "date": "", "ddg_snippet": "Catoni ’s estimator , to cope with the heavy - tailed data. estimate mf for all f ∈ Ψ simultaneously. For instance, in the maximum. likelihood estimation context, Ψ = { log pθ(·), θ ∈ Θ} is a family of probabil", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.08211v1", "content": "Catoni ’s estimator , to cope with the heavy - tailed data. estimate mf for all f ∈ Ψ simultaneously. For instance, in the maximum. likelihood estimation context, Ψ = { log pθ(·), θ ∈ Θ} is a family of probabil"}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "Catoni Estimator We first introduce Catoni estimator . This is a robust estimator proposed by Audibert & Catoni (2011)(see also (Lugosi & Mendelson, 2019)) to estimate random variables with bounded variance and unbounded range.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "Catoni Estimator We first introduce Catoni estimator . This is a robust estimator proposed by Audibert & Catoni (2011)(see also (Lugosi & Mendelson, 2019)) to estimate random variables with bounded variance and unbounded range."}
+{"idx": 3, "title": "The Stats Map · Catoni -Giulini M- Estimator", "date": "", "ddg_snippet": "In 2017, Catoni and Giulini proposed an approach to multivariate concentration based on M- estimation .", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/Catoni-Giulini-M-estimator", "content": "In 2017, Catoni and Giulini proposed an approach to multivariate concentration based on M- estimation ."}
+{"idx": 4, "title": "Sub-Gaussian Estimators of the Mean of a Random Matrix with...", "date": "", "ddg_snippet": "Improve the constant? O. Catoni ’s estimator (2012), “Generalized truncation”: let α > 0 − log ( 1 − x + x 2 / 2 ) ≤ ψ ( x ) ≤ log ( 1 + x + x 2 / 2 ), and dene µˆ via.Naive approach: apply the \"median trick\" (or Catoni ’s estimator ) coordinatewise. Makes the bound dimension-dependent.", "subpage_snippet": "", "source": "icerm.brown.edu", "link": "https://icerm.brown.edu/materials/Slides/tw-16-5/Sub-Gaussian_estimators_of_the_mean_of_a_random_matrix_with_entries_possessing_only_two_moments_]_Stanislav_Minsker,_University_of_Southern_California.pdf", "content": "Improve the constant? O. Catoni ’s estimator (2012), “Generalized truncation”: let α > 0 − log ( 1 − x + x 2 / 2 ) ≤ ψ ( x ) ≤ log ( 1 + x + x 2 / 2 ), and dene µˆ via.Naive approach: apply the \"median trick\" (or Catoni ’s estimator ) coordinatewise. Makes the bound dimension-dependent."}
+{"idx": 5, "title": "Concentration study of M- estimators using the influence function", "date": "", "ddg_snippet": ". ( 10 ). The associated M- estimator is one of the estimators considered by Catoni . in [5]. We call the resulting M- estimator Catoni ’s estimator . Catoni ’s estimator is in general of order 1 /β 2 . Lemma 5. shows that the bias depends on the smoothness of the function near 0 and also.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03757720/document", "content": ". ( 10 ). The associated M- estimator is one of the estimators considered by Catoni . in [5]. We call the resulting M- estimator Catoni ’s estimator . Catoni ’s estimator is in general of order 1 /β 2 . Lemma 5. shows that the bias depends on the smoothness of the function near 0 and also."}
+{"idx": 6, "title": "Catoni -style confidence sequences", "date": "", "ddg_snippet": "It is Catoni [2012, Proposition 2 .4] who shows the striking fact that by discarding the empirical mean µt and using an influence function instead to stabilize the outliers associated with heavy - tailed distributions, a O( log ( 1 /α)) growth rate can be achieved even when only the variance is...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/catoni-style-confidence-sequences-for-heavy-tailed-mean-1fw1oynx.pdf", "content": "It is Catoni [2012, Proposition 2 .4] who shows the striking fact that by discarding the empirical mean µt and using an influence function instead to stabilize the outliers associated with heavy - tailed distributions, a O( log ( 1 /α)) growth rate can be achieved even when only the variance is..."}
+{"idx": 7, "title": "(PDF) GL-LowPopArt: A Nearly Instance-Wise Minimax Estimator for...", "date": "", "ddg_snippet": "PDF | We present `GL-LowPopArt`, a novel Catoni -style estimator for generalized low-rank trace regression.GL-LowPopArt. consists of. two stages: the first stage provides a rough, initial estimate , and the second stage refines it via matrix Catoni estima-. tor (Minsker,2018).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392372284_GL-LowPopArt_A_Nearly_Instance-Wise_Minimax_Estimator_for_Generalized_Low-Rank_Trace_Regression", "content": "PDF | We present `GL-LowPopArt`, a novel Catoni -style estimator for generalized low-rank trace regression.GL-LowPopArt. consists of. two stages: the first stage provides a rough, initial estimate , and the second stage refines it via matrix Catoni estima-. tor (Minsker,2018)."}
+{"idx": 8, "title": "Non-stationary Bandits with Heavy Tail", "date": "", "ddg_snippet": "Here we define the Catoni ’s estimator as follows: Define function ψ : R → R to be a continuous strictly increasing function satisfying.", "subpage_snippet": "", "source": "www.sigmetrics.org", "link": "https://www.sigmetrics.org/mama/2024/abstracts/Pan.pdf", "content": "Here we define the Catoni ’s estimator as follows: Define function ψ : R → R to be a continuous strictly increasing function satisfying."}
+{"idx": 9, "title": "Beyond Catoni : Sharper Rates for Heavy - Tailed and Robust Mean...", "date": "", "ddg_snippet": "d -Dimensional Heavy - Tailed Estimation . • In d-dimensional estimation , we are given iid samples x 1 , . . . , xn ∈ Rd , with Cov (xi ) ≼ σ 2 Id and want to compute an estimate of the mean µ. • For simplicity, we will focus on the σ = 1 case. Estimator Empirical Mean Catoni (2012) + Net.", "subpage_snippet": "", "source": "shivamgupta2.github.io", "link": "https://shivamgupta2.github.io/Beyond_Catoni_Slides.pdf", "content": "d -Dimensional Heavy - Tailed Estimation . • In d-dimensional estimation , we are given iid samples x 1 , . . . , xn ∈ Rd , with Cov (xi ) ≼ σ 2 Id and want to compute an estimate of the mean µ. • For simplicity, we will focus on the σ = 1 case. Estimator Empirical Mean Catoni (2012) + Net."}
diff --git a/data/sampled_jsons/Causal_Disentanglement_noisy_observations_2024.jsonl b/data/sampled_jsons/Causal_Disentanglement_noisy_observations_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..910a31c6c26947c7f3797f7ea992faf7c2516b05
--- /dev/null
+++ b/data/sampled_jsons/Causal_Disentanglement_noisy_observations_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Harris Puts Four Sun Belt States Back in Play - Political Wire", "date": "", "ddg_snippet": "Aug 17, 2024 · A series of new New York Times/Siena College polls show how quickly Kamala Harris has reshaped the terrain of 2024 and thrust the Sun Belt back to the center of the battleground-state map. Harris is", "subpage_snippet": "", "source": "politicalwire.com", "link": "https://politicalwire.com/2024/08/17/harris-puts-four-sun-belt-states-back-in-play/", "content": "Aug 17, 2024 · A series of new New York Times/Siena College polls show how quickly Kamala Harris has reshaped the terrain of 2024 and thrust the Sun Belt back to the center of the battleground-state map. Harris is"}
+{"idx": 1, "title": "Political Wire", "date": "", "ddg_snippet": "All the political news in one placeAnkush Khardori: “When it comes to Trump’s handling of the TikTok ban, it has, at best, scrambled the constitutional order, and, at worst, seen the administration openly flout a law passed by the American public’s elected representatives in order to advance the political, personal and financial interests of Trump and his allies.”", "subpage_snippet": "", "source": "politicalwire.com", "link": "https://politicalwire.com/", "content": "All the political news in one placeAnkush Khardori: “When it comes to Trump’s handling of the TikTok ban, it has, at best, scrambled the constitutional order, and, at worst, seen the administration openly flout a law passed by the American public’s elected representatives in order to advance the political, personal and financial interests of Trump and his allies.”"}
+{"idx": 2, "title": "Political Wire", "date": "", "ddg_snippet": "Sep 8, 2025 · All the political news in one place“As Republicans look to flip House seats through redistricting in Texas and other red states, they have a pickup opportunity in what was once deep blue North Jersey, without having to redraw any lines,” Politico reports. “In the state’s heavily Hispanic 9th Congressional District, the Democrat who was widely thought to be a shoo-in won by just five ...", "subpage_snippet": "", "source": "politicalwire.com", "link": "https://politicalwire.com/author/tdg/", "content": "Sep 8, 2025 · All the political news in one place“As Republicans look to flip House seats through redistricting in Texas and other red states, they have a pickup opportunity in what was once deep blue North Jersey, without having to redraw any lines,” Politico reports. “In the state’s heavily Hispanic 9th Congressional District, the Democrat who was widely thought to be a shoo-in won by just five ..."}
+{"idx": 3, "title": "Front Page - Political Wire", "date": "", "ddg_snippet": "All the political news in one place“A leading Democratic-aligned think tank is urging states to suspend their redistricting commissions, in an escalation of the growing war over state maps,” Politico reports. “The Center for American Progress, one of the most prominent liberal think tanks in Washington, urged states that have adopted independent redistricting commissions — which are ...", "subpage_snippet": "", "source": "politicalwire.com", "link": "https://politicalwire.com/front-page/", "content": "All the political news in one place“A leading Democratic-aligned think tank is urging states to suspend their redistricting commissions, in an escalation of the growing war over state maps,” Politico reports. “The Center for American Progress, one of the most prominent liberal think tanks in Washington, urged states that have adopted independent redistricting commissions — which are ..."}
+{"idx": 4, "title": "Trending News - Political Wire", "date": "", "ddg_snippet": "Political Wire is the first site I check when I’m looking for the latest political nugget. That pretty much says it all.” — Stuart Rothenberg, editor of the Rothenberg Political Report “Political Wire is one of only four or five sites that I check every day and sometimes several times a day, for the latest political news and ...", "subpage_snippet": "", "source": "politicalwire.com", "link": "https://politicalwire.com/trending-news/", "content": "Political Wire is the first site I check when I’m looking for the latest political nugget. That pretty much says it all.” — Stuart Rothenberg, editor of the Rothenberg Political Report “Political Wire is one of only four or five sites that I check every day and sometimes several times a day, for the latest political news and ..."}
+{"idx": 5, "title": "Political Wire – Page 2", "date": "", "ddg_snippet": "Sep 2, 2025 · All the political news in one place“President Xi Jinping used a mix of bonhomie and economic allure this week to send Donald Trump a clear message: Beijing has too much global clout to be dictated by the US,” Bloomberg reports. “Cameras captured the Chinese leader in a rare, unscripted huddle on Monday with Vladimir Putin and Narendra Modi — his most powerful partners in resisting ...", "subpage_snippet": "", "source": "politicalwire.com", "link": "https://politicalwire.com/page/2/", "content": "Sep 2, 2025 · All the political news in one place“President Xi Jinping used a mix of bonhomie and economic allure this week to send Donald Trump a clear message: Beijing has too much global clout to be dictated by the US,” Bloomberg reports. “Cameras captured the Chinese leader in a rare, unscripted huddle on Monday with Vladimir Putin and Narendra Modi — his most powerful partners in resisting ..."}
+{"idx": 6, "title": "There’s Gold Everywhere in the Oval Office - Political Wire", "date": "", "ddg_snippet": "Mar 16, 2025 · CNN: “Nearly eight weeks since returning to office, Trump has tripled the number of paintings hanging on the office walls. Shelves and surfaces are adorned with flags, statues and ornaments.” “And in keeping with the", "subpage_snippet": "", "source": "politicalwire.com", "link": "https://politicalwire.com/2025/03/16/theres-gold-everywhere-in-the-oval-office/", "content": "Mar 16, 2025 · CNN: “Nearly eight weeks since returning to office, Trump has tripled the number of paintings hanging on the office walls. Shelves and surfaces are adorned with flags, statues and ornaments.” “And in keeping with the"}
+{"idx": 7, "title": "U.S. Downgraded to ‘Non-Democracy’ - Political Wire", "date": "", "ddg_snippet": "Mar 4, 2025 · According to the Polity Project, an organization that ranks and categorizes governments worldwide, the U.S. government is classified as a “non-democracy” as of February 2025, a downgrade from its previous status as a democracy.", "subpage_snippet": "", "source": "politicalwire.com", "link": "https://politicalwire.com/2025/03/04/u-s-downgraded-to-non-democracy/", "content": "Mar 4, 2025 · According to the Polity Project, an organization that ranks and categorizes governments worldwide, the U.S. government is classified as a “non-democracy” as of February 2025, a downgrade from its previous status as a democracy."}
+{"idx": 8, "title": "Political Wire – Page 3", "date": "", "ddg_snippet": "Sep 5, 2025 · Aaron Blake: “President Donald Trump came into office with massive plans to overhaul the way the US government operates and consolidate power in himself.” “And he’s been largely successful in implementing that vision, thanks to a cowed Congress, timid institutions and a languid judiciary.” “But there are growing signs that his entire agenda could be undercut and his party could ...", "subpage_snippet": "", "source": "politicalwire.com", "link": "https://politicalwire.com/page/3/", "content": "Sep 5, 2025 · Aaron Blake: “President Donald Trump came into office with massive plans to overhaul the way the US government operates and consolidate power in himself.” “And he’s been largely successful in implementing that vision, thanks to a cowed Congress, timid institutions and a languid judiciary.” “But there are growing signs that his entire agenda could be undercut and his party could ..."}
+{"idx": 9, "title": "About Taegan Goddard - Political Wire", "date": "", "ddg_snippet": "About Taegan Goddard Taegan Goddard is the founder of Political Wire, one of the earliest and most influential political web sites. He also runs Political Job Hunt, Electoral Vote Map and the Political Dictionary. Goddard spent more than a decade as managing director and chief operating officer of a prominent investment firm in New York City.", "subpage_snippet": "", "source": "politicalwire.com", "link": "https://politicalwire.com/about-taegan-goddard/", "content": "About Taegan Goddard Taegan Goddard is the founder of Political Wire, one of the earliest and most influential political web sites. He also runs Political Job Hunt, Electoral Vote Map and the Political Dictionary. Goddard spent more than a decade as managing director and chief operating officer of a prominent investment firm in New York City."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Earth_Strike_Fridays_for_Future_Extinction_Rebellion_s.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Earth_Strike_Fridays_for_Future_Extinction_Rebellion_s.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..88feb40d26d774c5d2c007dc434c86f694fc811e
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Earth_Strike_Fridays_for_Future_Extinction_Rebellion_s.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike , Fridays for Future , and Extinction Rebellion ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike , Fridays for Future , and Extinction Rebellion )."}
+{"idx": 1, "title": "(PDF) Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike , Fridays for Future , and Extinction Rebellion ).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929273_Causal_Modeling_of_Climate_Activism_on_Reddit", "content": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike , Fridays for Future , and Extinction Rebellion )."}
+{"idx": 2, "title": "(PDF) Protest for a future II: Composition, mobilization and motives of...", "date": "", "ddg_snippet": "In September 2019, the third Global Climate Strike organized by the Fridays For Future (FFF) protest campaign mobilized 6000 protest events in 185 countries and brought 7.6 million participants out onto the streets. This report analyses survey data.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/42054990/Protest_for_a_future_II_Composition_mobilization_and_motives_of_the_participants_in_Fridays_For_Future_climate_protests_on_20_27_September_2019_in_19_cities_around_the_world", "content": "In September 2019, the third Global Climate Strike organized by the Fridays For Future (FFF) protest campaign mobilized 6000 protest events in 185 countries and brought 7.6 million participants out onto the streets. This report analyses survey data."}
+{"idx": 3, "title": "New kids on the block: taking stock of the recent cycle of climate ...", "date": "", "ddg_snippet": "KEYWORDS Climate movement; climate justice; environmental movement; fridays for future ; extinction rebellion .Yet otherwise, little indicates that FFF and XR activists represent a deviation from the profile of the typical climate activist , especially in terms of education.", "subpage_snippet": "", "source": "www.gu.se", "link": "https://www.gu.se/sites/default/files/2020-11/New+kids+on+the+block+taking+stock+of+the+recent+cycle+of+climate+activism.pdf", "content": "KEYWORDS Climate movement; climate justice; environmental movement; fridays for future ; extinction rebellion .Yet otherwise, little indicates that FFF and XR activists represent a deviation from the profile of the typical climate activist , especially in terms of education."}
+{"idx": 4, "title": "What is Extinction Rebellion and what does it want?", "date": "", "ddg_snippet": "Reuters Activists from Extinction Rebellion hold placards at an entrance at the Department for Business, Energy & Industrial Strategy in London Reuters. Extinction Rebellion members protested over the UK government's commitment to keep exploring North Sea oil and gas.", "subpage_snippet": "", "source": "www.bbc.com", "link": "https://www.bbc.com/news/uk-48607989", "content": "Reuters Activists from Extinction Rebellion hold placards at an entrance at the Department for Business, Energy & Industrial Strategy in London Reuters. Extinction Rebellion members protested over the UK government's commitment to keep exploring North Sea oil and gas."}
+{"idx": 5, "title": "Youth climate activism in the United States", "date": "", "ddg_snippet": "They demonstrate the depth and breadth of youth climate activism which, judging by the numbers cited above of participation at the youth climate strikes in March and September 2019 ( Climate Interactive), have increased significantly in the last few years.", "subpage_snippet": "", "source": "journals.openedition.org", "link": "https://journals.openedition.org/erea/12490", "content": "They demonstrate the depth and breadth of youth climate activism which, judging by the numbers cited above of participation at the youth climate strikes in March and September 2019 ( Climate Interactive), have increased significantly in the last few years."}
+{"idx": 6, "title": "Climate Change Activists : Their Driving Forces And... | CyClimate", "date": "", "ddg_snippet": "Some climate activist organisations include Fridays For Future , Extinction Rebellion , Re- Earth Initiative, Green Generation Initiative, The Sunrise Movement, 350.org, Environmental Defense Fund, Citizens' Climate Lobby, and Climate Defiance.", "subpage_snippet": "", "source": "cyclimate.com", "link": "https://cyclimate.com/article/what-effects-climate-change-activist", "content": "Some climate activist organisations include Fridays For Future , Extinction Rebellion , Re- Earth Initiative, Green Generation Initiative, The Sunrise Movement, 350.org, Environmental Defense Fund, Citizens' Climate Lobby, and Climate Defiance."}
+{"idx": 7, "title": "A chat with the donors helping to fund the climate strike . | Vox", "date": "", "ddg_snippet": "The climate activist group Extinction Rebellion dressed all in red protests climate change at Lincoln Center in New York City.Part of that is credit to the student strikers , Fridays for the Future , Extinction Rebellion , and their entire intellectual framework.", "subpage_snippet": "", "source": "www.vox.com", "link": "https://www.vox.com/energy-and-environment/2019/10/8/20899908/climate-change-protest-emergency-fund", "content": "The climate activist group Extinction Rebellion dressed all in red protests climate change at Lincoln Center in New York City.Part of that is credit to the student strikers , Fridays for the Future , Extinction Rebellion , and their entire intellectual framework."}
+{"idx": 8, "title": "Protest for a future II - Composition, mobilization and motives of the...", "date": "", "ddg_snippet": "The Fridays for Future (FFF) movement has thousands of faces, but the protestor profile that dominates the public or media imagination is that of young, female (school) students. The resemblance of this profile to well-known climate activist Greta Thunberg is not surprising.", "subpage_snippet": "", "source": "politikatudomany.tk.hu", "link": "https://politikatudomany.tk.hu/uploads/files/Protest_for_a_Future_II_-_2020-02-24.pdf", "content": "The Fridays for Future (FFF) movement has thousands of faces, but the protestor profile that dominates the public or media imagination is that of young, female (school) students. The resemblance of this profile to well-known climate activist Greta Thunberg is not surprising."}
+{"idx": 9, "title": "Climate Coincidences Surge Amid Soros-Backed Activism : Shocking!", "date": "", "ddg_snippet": "Movements such as Fridays for Future , Extinction Rebellion , and various grassroots organizations have mobilized millions of individuals to advocate for sustainable practices and hold governments and corporations accountable for their environmental impact.", "subpage_snippet": "", "source": "countylocalnews.com", "link": "https://countylocalnews.com/2025/04/16/climate-coincidences-surge-amid-soros-backed-activism-shocking/", "content": "Movements such as Fridays for Future , Extinction Rebellion , and various grassroots organizations have mobilized millions of individuals to advocate for sustainable practices and hold governments and corporations accountable for their environmental impact."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_climate_activist_subreddits_activation_breakdown.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_climate_activist_subreddits_activation_breakdown.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..329579c8f90ab80a811688a2fba7e82399170217
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_climate_activist_subreddits_activation_breakdown.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "User activated in climate activism groups. I𝐼Iitalic_I. Interactions with activists .Does media coverage about climate and climate action affect activation in climate activism groups on Reddit , and over which time scale?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "User activated in climate activism groups. I𝐼Iitalic_I. Interactions with activists .Does media coverage about climate and climate action affect activation in climate activism groups on Reddit , and over which time scale?"}
+{"idx": 1, "title": "(PDF) Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "activation in climate activism groups on Reddit , and over which. time scale?We ground our operationalization of involvement with activist . communities—the activation outcome we aim to build a causal . Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929273_Causal_Modeling_of_Climate_Activism_on_Reddit", "content": "activation in climate activism groups on Reddit , and over which. time scale?We ground our operationalization of involvement with activist . communities—the activation outcome we aim to build a causal . Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 2, "title": "Modeling the Impact of Group Interactions on Climate -related Opinion...", "date": "", "ddg_snippet": "[1] Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/modeling-the-impact-of-group-interactions-on-climate-related-opinion-change-in-reddit/1126847729390059527-108521", "content": "[1] Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure."}
+{"idx": 3, "title": "A methodological approach for inferring causal relationships from...", "date": "", "ddg_snippet": "In the specic case of climate change, we can hypothesize that a surge in discussions on a specic topic consistently precedes a change in overall sentiment, level of aggressiveness, or the proportion of users expressing certain stances.2024. Causal modeling of climate activism on reddit .", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-2964.pdf", "content": "In the specic case of climate change, we can hypothesize that a surge in discussions on a specic topic consistently precedes a change in overall sentiment, level of aggressiveness, or the proportion of users expressing certain stances.2024. Causal modeling of climate activism on reddit ."}
+{"idx": 4, "title": "bpben/ reddit _ climate : Climate conversation on reddit subreddits", "date": "", "ddg_snippet": "Contribute to bpben/ reddit _ climate development by creating an account on GitHub.In the climateskeptics subreddit , the focus was also on Trump's victory, but as a repudiation of climate activism .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bpben/reddit_climate", "content": "Contribute to bpben/ reddit _ climate development by creating an account on GitHub.In the climateskeptics subreddit , the focus was also on Trump's victory, but as a repudiation of climate activism ."}
+{"idx": 5, "title": "Aussie Climate Activists are Still Brutalising the Kids – Watts Up With...", "date": "", "ddg_snippet": "Climate Change is Causing More Floods. Droughts are Increasing Due to Climate Change.But the radical activists who inflict such nightmares on our children know no such restraint. One day there will be a full accounting for all the harm these agents of despair have done to our children.", "subpage_snippet": "", "source": "wattsupwiththat.com", "link": "https://wattsupwiththat.com/2025/09/17/aussie-climate-activists-are-still-brutalising-the-kids/", "content": "Climate Change is Causing More Floods. Droughts are Increasing Due to Climate Change.But the radical activists who inflict such nightmares on our children know no such restraint. One day there will be a full accounting for all the harm these agents of despair have done to our children."}
+{"idx": 6, "title": "Anger in response to climate breakdown | by Dr. Harriet... | Medium", "date": "", "ddg_snippet": "There are reasons to be angry in the face of climate breakdown . Climate breakdown is the term used to refer to how our climate system is breaking down — it is not neutral like the word ‘change’, and…", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@harrietbergman/anger-in-response-to-climate-breakdown-2c5c2066fef9", "content": "There are reasons to be angry in the face of climate breakdown . Climate breakdown is the term used to refer to how our climate system is breaking down — it is not neutral like the word ‘change’, and…"}
+{"idx": 7, "title": "\" Climate catastrophe in computer models \" - The Nordic Times", "date": "", "ddg_snippet": "Climate activist protests at Norway’s largest oil refinery have triggered harsh criticism from opposition leader Sylvi Listhaug, who is now demanding that Swedish activist Greta Thunberg be expelled from the country.", "subpage_snippet": "", "source": "nordictimes.com", "link": "https://nordictimes.com/environment/climate-catastrophe-in-computer-models/", "content": "Climate activist protests at Norway’s largest oil refinery have triggered harsh criticism from opposition leader Sylvi Listhaug, who is now demanding that Swedish activist Greta Thunberg be expelled from the country."}
+{"idx": 8, "title": "Harnessing the Power of Climate Activism : Insights... | SpringerLink", "date": "", "ddg_snippet": "Castiglione A (2020) Climate activism : what we know and what more we need to learn.Bagchi, D., Srivastava, A., Tushir, B. (2024). Harnessing the Power of Climate Activism : Insights from Psychological Perspectives on Climate Change Engagement—A Systematic Review.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-58261-5_9", "content": "Castiglione A (2020) Climate activism : what we know and what more we need to learn.Bagchi, D., Srivastava, A., Tushir, B. (2024). Harnessing the Power of Climate Activism : Insights from Psychological Perspectives on Climate Change Engagement—A Systematic Review."}
+{"idx": 9, "title": "Professor Henrik Svensmark: The Earth’s climate is not in a crisis...", "date": "", "ddg_snippet": "Pressure from climate activist circles against scientists who dare to approach the issue scientifically can sometimes even get physical.", "subpage_snippet": "", "source": "expose-news.com", "link": "https://expose-news.com/2025/09/20/earths-climate-is-not-in-a-crisis/", "content": "Pressure from climate activist circles against scientists who dare to approach the issue scientifically can sometimes even get physical."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_paper_PDF_Related_Work_section.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_paper_PDF_Related_Work_section.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ed8137058930a0ea6f4e8274d61fbed52d2b7495
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_paper_PDF_Related_Work_section.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Oct 14, 2024 · View a PDF of the paper titled Causal Modeling of Climate Activism on Reddit , by Jacopo Lenti and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "Oct 14, 2024 · View a PDF of the paper titled Causal Modeling of Climate Activism on Reddit , by Jacopo Lenti and 3 other authors"}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "15 tive, causal understanding of why people approach activism. In this 16 work , we develop a comprehensive causal model of how and why 17 Reddit users engage with activist communities driving mass climate 18 protests (mainly the 2019 Earth Strike, Fridays for Future, and Ex- 19 tinction Rebellion). Our framework, based on Stochastic Variational", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "15 tive, causal understanding of why people approach activism. In this 16 work , we develop a comprehensive causal model of how and why 17 Reddit users engage with activist communities driving mass climate 18 protests (mainly the 2019 Earth Strike, Fridays for Future, and Ex- 19 tinction Rebellion). Our framework, based on Stochastic Variational"}
+{"idx": 2, "title": "[PDF] Causal Modeling of Climate Activism on Reddit ...", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Causal-Modeling-of-Climate-Activism-on-Reddit-Lenti-Aiello/c4c7c3972ba102db37738c082f27ebfcd3983057", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
+{"idx": 3, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "This paper aims to promote large - scale climate protests (such as the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion) by constructing a comprehensive causal model to explain how and why Reddit users participate in climate activism communities.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2410.10562", "content": "This paper aims to promote large - scale climate protests (such as the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion) by constructing a comprehensive causal model to explain how and why Reddit users participate in climate activism communities."}
+{"idx": 4, "title": "Causal Modeling of Climate Activism on Reddit | Cool Papers ...", "date": "", "ddg_snippet": "#1 Causal Modeling of Climate Activism on Reddit [ PDF ] [Copy] [Kimi] [REL] Authors: Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2410.10562", "content": "#1 Causal Modeling of Climate Activism on Reddit [ PDF ] [Copy] [Kimi] [REL] Authors: Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure."}
+{"idx": 5, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Abstract Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development ...", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/pdf/10.1145/3696410.3714684", "content": "Abstract Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development ..."}
+{"idx": 6, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "D Sympathy. E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit . arXiv:2410.10562v1 [cs.CY] 14 Oct 2024.RQ1: Does media coverage about climate and climate action affect activation in climate activism groups on Reddit , and over which time scale?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "D Sympathy. E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit . arXiv:2410.10562v1 [cs.CY] 14 Oct 2024.RQ1: Does media coverage about climate and climate action affect activation in climate activism groups on Reddit , and over which time scale?"}
+{"idx": 7, "title": "( PDF ) Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "In this work , we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929273_Causal_Modeling_of_Climate_Activism_on_Reddit", "content": "In this work , we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion)."}
+{"idx": 8, "title": "Modeling the Impact of Group Interactions on Climate - related ...", "date": "", "ddg_snippet": "[1] Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. JJacopo LentiLLuca Maria Aiello.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/modeling-the-impact-of-group-interactions-on-climate-related-opinion-change-in-reddit/1126847729390059527-108521", "content": "[1] Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. JJacopo LentiLLuca Maria Aiello."}
+{"idx": 9, "title": "A methodological approach for inferring causal relationships from...", "date": "", "ddg_snippet": "Another related study in the extraction of causal relationships from climate change discourse on digital media is the work presented by Chen et al.", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-2964.pdf", "content": "Another related study in the extraction of causal relationships from climate change discourse on digital media is the work presented by Chen et al."}
diff --git a/data/sampled_jsons/Causal_Representation_Learning_robust_noisy_mixing_function_2024.jsonl b/data/sampled_jsons/Causal_Representation_Learning_robust_noisy_mixing_function_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0eb2265d5723de185ec68a44fe6c812edc9535b
--- /dev/null
+++ b/data/sampled_jsons/Causal_Representation_Learning_robust_noisy_mixing_function_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causality - Wikipedia", "date": "", "ddg_snippet": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Causality", "content": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future."}
+{"idx": 1, "title": "CAUSAL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/causal", "content": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence."}
+{"idx": 2, "title": "CAUSAL | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/causal", "content": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more."}
+{"idx": 3, "title": "CAUSAL Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/causal", "content": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence."}
+{"idx": 4, "title": "CAUSAL definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "If there is a causal relationship between two things, one thing is responsible for causing the other thing.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/causal", "content": "If there is a causal relationship between two things, one thing is responsible for causing the other thing."}
+{"idx": 5, "title": "Causal - definition of causal by The Free Dictionary", "date": "", "ddg_snippet": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/causal", "content": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause."}
+{"idx": 6, "title": "causal adjective - Definition, pictures, pronunciation and usage...", "date": "", "ddg_snippet": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/causal", "content": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 7, "title": "causal , adj. & n. meanings, etymology and more | Oxford English...", "date": "", "ddg_snippet": "causal , adj. & n. meanings, etymology, pronunciation and more in the Oxford English Dictionary", "subpage_snippet": "", "source": "www.oed.com", "link": "https://www.oed.com/dictionary/causal_adj", "content": "causal , adj. & n. meanings, etymology, pronunciation and more in the Oxford English Dictionary"}
+{"idx": 8, "title": "causal - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark.", "subpage_snippet": "", "source": "en.m.wiktionary.org", "link": "https://en.m.wiktionary.org/wiki/causal", "content": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark."}
+{"idx": 9, "title": "Causal - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "Causal is a variation of the word cause , which should be a clue to its meaning. A cause is what makes something happen: the notebook flew across the room because you threw it, so your throwing it was causal. If a bolt of lightning set a statue on fire, the lightning was causal for the fire.", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/causal", "content": "Causal is a variation of the word cause , which should be a clue to its meaning. A cause is what makes something happen: the notebook flew across the room because you threw it, so your throwing it was causal. If a bolt of lightning set a statue on fire, the lightning was causal for the fire."}
diff --git a/data/sampled_jsons/Causal_Representation_Learning_robust_to_noisy_mixing_function_2024_year_2024.jsonl b/data/sampled_jsons/Causal_Representation_Learning_robust_to_noisy_mixing_function_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0837a4e4e08c3a276be69aabd10ae70069054e80
--- /dev/null
+++ b/data/sampled_jsons/Causal_Representation_Learning_robust_to_noisy_mixing_function_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Representation Learning from General Environments ...", "date": "", "ddg_snippet": "Interestingly, we show that one can fully recover the latent DAG and identify the latent variables up to minor indeterminacies under a nonparametric mixing function and nonlinear latent causal models, such as additive (Gaussian) noise models or heteroscedastic noise models, by properly leveraging sufficient change conditions on the causal ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v258/ng25a.html", "content": "Interestingly, we show that one can fully recover the latent DAG and identify the latent variables up to minor indeterminacies under a nonparametric mixing function and nonlinear latent causal models, such as additive (Gaussian) noise models or heteroscedastic noise models, by properly leveraging sufficient change conditions on the causal ..."}
+{"idx": 1, "title": "Causality-Inspired Robustness for Nonlinear Models via ...", "date": "", "ddg_snippet": "In this work, we propose a nonlinear method under a causal framework by incorporating recent developments in identifiable representation learning and establish a distributional robustness guarantee. To our best knowledge, this is the first causality-inspired robustness method with such a finite-radius robustness guarantee in nonlinear settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.12868", "content": "In this work, we propose a nonlinear method under a causal framework by incorporating recent developments in identifiable representation learning and establish a distributional robustness guarantee. To our best knowledge, this is the first causality-inspired robustness method with such a finite-radius robustness guarantee in nonlinear settings."}
+{"idx": 2, "title": "Learning the Latent Causal Structure for Modeling Label Noise", "date": "", "ddg_snippet": "Unlike previous generative label- noise learning methods, we consider causal relations between latent causal variables and model them with a learnable graphical model. Utilizing only noisy data, our method can effectively learn the latent causal structure.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/da75d2bbf862b86f10241d0887613b41-Paper-Conference.pdf", "content": "Unlike previous generative label- noise learning methods, we consider causal relations between latent causal variables and model them with a learnable graphical model. Utilizing only noisy data, our method can effectively learn the latent causal structure."}
+{"idx": 3, "title": "Learning Causally Disentangled Representations via the ...", "date": "", "ddg_snippet": "We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related ob-served labels. We model causal mechanisms us-ing nonlinear learnable flow-based diffeomorphic functions to map noise variables to latent causal variables.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0476.pdf", "content": "We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related ob-served labels. We model causal mechanisms us-ing nonlinear learnable flow-based diffeomorphic functions to map noise variables to latent causal variables."}
+{"idx": 4, "title": "Learning Linear Causal Representations from General ...", "date": "", "ddg_snippet": "In this work, we consider the task of learning causal representation learning with data collected from general environments. We show that even when the causal model and the mixing function are both linear, there exists a surrounded-node ambiguity (SNA) [Varici et al. 2023] which is basically unavoidable in our setting.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/741aab8b41a2987867acc9939ad50383-Abstract-Conference.html", "content": "In this work, we consider the task of learning causal representation learning with data collected from general environments. We show that even when the causal model and the mixing function are both linear, there exists a surrounded-node ambiguity (SNA) [Varici et al. 2023] which is basically unavoidable in our setting."}
+{"idx": 5, "title": "CaDeT: a Causal Disentanglement Approach for Robust ...", "date": "", "ddg_snippet": "In turn, the model can rely on the invariant features when making predictions, leading to more robust and generalizable inference. We formulate representation learning from a causal per-spective in a dynamic heterogeneous information network which models the spatiotemporal interaction patterns between the agents and the agents and the environment.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Pourkeshavarz_CaDeT_a_Causal_Disentanglement_Approach_for_Robust_Trajectory_Prediction_in_CVPR_2024_paper.pdf", "content": "In turn, the model can rely on the invariant features when making predictions, leading to more robust and generalizable inference. We formulate representation learning from a causal per-spective in a dynamic heterogeneous information network which models the spatiotemporal interaction patterns between the agents and the agents and the environment."}
+{"idx": 6, "title": "Marrying Causal Representation Learning with Dynamical ...", "date": "", "ddg_snippet": "by D Yao · Cited by 10 — Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MWHRxKz4mq", "content": "by D Yao · Cited by 10 — Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements."}
+{"idx": 7, "title": "From Causal to Concept-Based Representation Learning", "date": "", "ddg_snippet": "To build intelligent machine learning systems, modern representation learning attempts to recover latent generative factors from data, such as in causal ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93459", "content": "To build intelligent machine learning systems, modern representation learning attempts to recover latent generative factors from data, such as in causal ..."}
+{"idx": 8, "title": "Learning Causal Representations from General ...", "date": "", "ddg_snippet": "by J Jin · 2023 · Cited by 11 — Abstract. We study causal representation learning , the task of recovering high-level latent variables and their causal .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.12267", "content": "by J Jin · 2023 · Cited by 11 — Abstract. We study causal representation learning , the task of recovering high-level latent variables and their causal ."}
+{"idx": 9, "title": "Marrying Causal Representation Learning with Dynamical ...", "date": "", "ddg_snippet": "9 Dec 2024 — Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95516", "content": "9 Dec 2024 — Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements."}
diff --git a/data/sampled_jsons/Causal_inference_in_finite_population_Rubin_1974_abstract.jsonl b/data/sampled_jsons/Causal_inference_in_finite_population_Rubin_1974_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..39cba6007e5c5e6c1d189563bbcdcb33e011f6b0
--- /dev/null
+++ b/data/sampled_jsons/Causal_inference_in_finite_population_Rubin_1974_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "References – Causal Inference The Mixtape", "date": "", "ddg_snippet": "Doubly Robust Estimation in Missing Data and Causal Inference Models.” Biometrics 61: 962–72. ... The Finite Sample Performance of Inference ...", "subpage_snippet": "", "source": "mixtape.scunning.com", "link": "https://mixtape.scunning.com/references", "content": "Doubly Robust Estimation in Missing Data and Causal Inference Models.” Biometrics 61: 962–72. ... The Finite Sample Performance of Inference ..."}
+{"idx": 1, "title": "References – Causal Inference The Mixtape", "date": "", "ddg_snippet": "Doubly Robust Estimation in Missing Data and Causal Inference Models.” Biometrics 61: 962–72. ... The Finite Sample Performance of Inference ...", "subpage_snippet": "", "source": "mixtape.scunning.com", "link": "https://mixtape.scunning.com/references.html", "content": "Doubly Robust Estimation in Missing Data and Causal Inference Models.” Biometrics 61: 962–72. ... The Finite Sample Performance of Inference ..."}
+{"idx": 2, "title": "4 Potential Outcomes Causal Model – <span", "date": "", "ddg_snippet": "The notion of physical randomization as the foundation of causal inference was in the air in the nineteenth and twentieth centuries, but it was not ...", "subpage_snippet": "", "source": "mixtape.scunning.com", "link": "https://mixtape.scunning.com/04-potential_outcomes", "content": "The notion of physical randomization as the foundation of causal inference was in the air in the nineteenth and twentieth centuries, but it was not ..."}
+{"idx": 3, "title": "Causal inference with large-scale assessments in education from", "date": "", "ddg_snippet": "... statistical foundations for causal inference in experimental and quasi-experimental studies derives from the work of Neyman ( 1923 ) and Rubin ( 1974 ...", "subpage_snippet": "", "source": "largescaleassessmentsineducation.springeropen.com", "link": "https://largescaleassessmentsineducation.springeropen.com/articles/10.1186/s40536-016-0022-6", "content": "... statistical foundations for causal inference in experimental and quasi-experimental studies derives from the work of Neyman ( 1923 ) and Rubin ( 1974 ..."}
+{"idx": 4, "title": "Causal Inference in the Social Sciences | Annual Reviews", "date": "", "ddg_snippet": "Knowledge of causal effects is of great importance to decision makers in a wide variety of settings. In many cases, however, these causal effects ...", "subpage_snippet": "", "source": "www.annualreviews.org", "link": "https://www.annualreviews.org/content/journals/10.1146/annurev-statistics-033121-114601", "content": "Knowledge of causal effects is of great importance to decision makers in a wide variety of settings. In many cases, however, these causal effects ..."}
+{"idx": 5, "title": "Causal inference in disease ecology: investigating ecological", "date": "", "ddg_snippet": "These techniques, which complement traditional hypothesis testing, include epidemiologic causal criteria, strong inference , causal diagrams, model ...", "subpage_snippet": "", "source": "esajournals.onlinelibrary.wiley.com", "link": "https://esajournals.onlinelibrary.wiley.com/doi/10.1890/070086", "content": "These techniques, which complement traditional hypothesis testing, include epidemiologic causal criteria, strong inference , causal diagrams, model ..."}
+{"idx": 6, "title": "Mendelian Randomization Methods for Causal Inference:", "date": "", "ddg_snippet": "Mendelian randomization (MR) is a causal inference method that applies Mendel’s laws of inheritance, using genetic variants as instrumental ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11519v1", "content": "Mendelian randomization (MR) is a causal inference method that applies Mendel’s laws of inheritance, using genetic variants as instrumental ..."}
+{"idx": 7, "title": "Causal inference and racial bias in policing: New estimands and", "date": "", "ddg_snippet": "... in these related problems, and therefore we ... Recent work in Gaebler et al., ( 2022 ) formalizes causal inference for studies of discrimination.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.08059v2", "content": "... in these related problems, and therefore we ... Recent work in Gaebler et al., ( 2022 ) formalizes causal inference for studies of discrimination."}
+{"idx": 8, "title": "(PDF) Generative Causal Inference", "date": "", "ddg_snippet": "Further, paper we illustrate our methodology in the field of causal inference and show how generative AI provides generalization of propensity scores.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371944060_Generative_Causal_Inference", "content": "Further, paper we illustrate our methodology in the field of causal inference and show how generative AI provides generalization of propensity scores."}
+{"idx": 9, "title": "The necessity of construct and external validity for deductive", "date": "", "ddg_snippet": "The necessity of construct and external validity for deductive causal inference \" Journal of Causal Inference , vol.", "subpage_snippet": "", "source": "www.degruyterbrill.com:443", "link": "https://www.degruyterbrill.com:443/document/doi/10.1515/jci-2024-0002/html", "content": "The necessity of construct and external validity for deductive causal inference \" Journal of Causal Inference , vol."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_dataset_empirical_studies.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_dataset_empirical_studies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9cbd1ef619352db90594b4a3dd68bc52b5cbcdb7
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_dataset_empirical_studies.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 2, "title": "A Framework of Fundamental Values for Human-AI ...", "date": "", "ddg_snippet": "We introduce \\system, a framework of fundamental values, grounded in psychological theory and a systematic review , to identify and evaluate human- AI alignment .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.09586v1", "content": "We introduce \\system, a framework of fundamental values, grounded in psychological theory and a systematic review , to identify and evaluate human- AI alignment ."}
+{"idx": 3, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/166720", "content": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It ..."}
+{"idx": 4, "title": "Benchmarking, ethical alignment, and evaluation ...", "date": "", "ddg_snippet": "by PP Ray · 2023 · Cited by 57 — The framework includes adaptive standards, a multi-dimensional approach, ethical alignment , and user feedback, using task-specific benchmarks and metrics.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772485923000534", "content": "by PP Ray · 2023 · Cited by 57 — The framework includes adaptive standards, a multi-dimensional approach, ethical alignment , and user feedback, using task-specific benchmarks and metrics."}
+{"idx": 5, "title": "AI Ethics: Integrating Transparency, Fairness, and Privacy ...", "date": "", "ddg_snippet": "by P Radanliev · 2025 · Cited by 63 — This research establishes a comprehensive ethical framework that mitigates biases and promotes accountability in AI technologies.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/08839514.2025.2463722", "content": "by P Radanliev · 2025 · Cited by 63 — This research establishes a comprehensive ethical framework that mitigates biases and promotes accountability in AI technologies."}
+{"idx": 6, "title": "Toward Fairness, Accountability, Transparency, and Ethics ...", "date": "", "ddg_snippet": "by A Singhal · 2024 · Cited by 97 — This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11024755/", "content": "by A Singhal · 2024 · Cited by 97 — This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs)."}
+{"idx": 7, "title": "Ethical considerations in AI-based user profiling for ...", "date": "", "ddg_snippet": "by DK Njiru · 2025 · Cited by 1 — This study examines the ethical considerations in AI -based user profiling for knowledge management systems, with a focus on academic environments.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772503025000209", "content": "by DK Njiru · 2025 · Cited by 1 — This study examines the ethical considerations in AI -based user profiling for knowledge management systems, with a focus on academic environments."}
+{"idx": 8, "title": "Computer Science", "date": "", "ddg_snippet": "Title: A Checks -and- Balances Framework for Context - Aware Ethical AI Alignment . Edward Y. Chang. Comments: 20 pages, 7 tables, 6 figures. arXiv admin note ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "http://www.arxiv.org/list/cs/new?skip=875&show=1000", "content": "Title: A Checks -and- Balances Framework for Context - Aware Ethical AI Alignment . Edward Y. Chang. Comments: 20 pages, 7 tables, 6 figures. arXiv admin note ..."}
+{"idx": 9, "title": "On the problems of training generative AI: towards a hybrid ...", "date": "", "ddg_snippet": "by T Haidemariam · 2025 — This study examines the ethical , legal, and copyright challenges in training generative AI on a large-scale text dataset , using Books3 as a case study.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00146-025-02445-0", "content": "by T Haidemariam · 2025 — This study examines the ethical , legal, and copyright challenges in training generative AI on a large-scale text dataset , using Books3 as a case study."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_OpenReview_forum_discussion_lim.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_OpenReview_forum_discussion_lim.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3b0862f4b34ad505998216cffe533b309197e536
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_OpenReview_forum_discussion_lim.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation", "date": "", "ddg_snippet": "... contextual privacy risks via attribute inference and prompt injection attacks (Röttger et al., 2024 ; Wang et al., 2024 ) , yet these efforts lack ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.20737v1", "content": "... contextual privacy risks via attribute inference and prompt injection attacks (Röttger et al., 2024 ; Wang et al., 2024 ) , yet these efforts lack ..."}
+{"idx": 1, "title": "A Framework for Inherently Safer AGI through Language-Mediated", "date": "", "ddg_snippet": "Furthermore, the Bayesian perspective on action and decision-making provided by Active Inference may yield valuable insights for AI alignment , such ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05766v1", "content": "Furthermore, the Bayesian perspective on action and decision-making provided by Active Inference may yield valuable insights for AI alignment , such ..."}
+{"idx": 2, "title": "Manipulation Attacks by Misaligned AI: Risk Analysis and Safety", "date": "", "ddg_snippet": "If the AI system is insufficiently aligned, it may seek to undermine safety guardrails placed around it, for example, by exfiltrating its weights to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12872v1", "content": "If the AI system is insufficiently aligned, it may seek to undermine safety guardrails placed around it, for example, by exfiltrating its weights to ..."}
+{"idx": 3, "title": "Model Context Protocol (MCP): Landscape, Security Threats, and", "date": "", "ddg_snippet": "We discuss the implications of MCP’s rapid adoption, identifying key challenges for stakeholders, and outline future research directions on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.23278v2", "content": "We discuss the implications of MCP’s rapid adoption, identifying key challenges for stakeholders, and outline future research directions on ..."}
+{"idx": 4, "title": "AI Alignment Knowledge Graph", "date": "", "ddg_snippet": "... this work sheds a light on how limited our current methodology is and bracketed sequence classification might serve as a good toy-problem task for ...", "subpage_snippet": "", "source": "www.apartresearch.com", "link": "https://www.apartresearch.com/project/ai-alignment-knowledge-graph", "content": "... this work sheds a light on how limited our current methodology is and bracketed sequence classification might serve as a good toy-problem task for ..."}
+{"idx": 5, "title": "AI Safety Evaluation – Benchmarking Framework", "date": "", "ddg_snippet": "Our solution is a comprehensive AI Safety Protocol and Benchmarking Test designed to evaluate the safety, ethical alignment , and robustness of AI ...", "subpage_snippet": "", "source": "www.apartresearch.com", "link": "https://www.apartresearch.com/project/ai-safety-evaluation---benchmarking-framework", "content": "Our solution is a comprehensive AI Safety Protocol and Benchmarking Test designed to evaluate the safety, ethical alignment , and robustness of AI ..."}
+{"idx": 6, "title": "Political Neutrality in AI is Impossible — But Here is How to", "date": "", "ddg_snippet": "... for approximating political neutrality across three levels conceptualizing AI —output, system, and ecosystem—illustrated in Figure 1 , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05728v1", "content": "... for approximating political neutrality across three levels conceptualizing AI —output, system, and ecosystem—illustrated in Figure 1 , ..."}
+{"idx": 7, "title": "chris dumler", "date": "", "ddg_snippet": "... of context engineering methodology to real-world AI system analysis, showing how systematic prompt evaluation can predict system performance and ...", "subpage_snippet": "", "source": "chrisdumler.com", "link": "https://chrisdumler.com/", "content": "... of context engineering methodology to real-world AI system analysis, showing how systematic prompt evaluation can predict system performance and ..."}
+{"idx": 8, "title": "AI Safety Camp 10 Outputs - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "Explore alignment implications of each lens, their consistency with observations of how LLMs work, and training processes that shift the balance ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/3sjtEXzbwDpyALR4H/ai-safety-camp-10-outputs", "content": "Explore alignment implications of each lens, their consistency with observations of how LLMs work, and training processes that shift the balance ..."}
+{"idx": 9, "title": "Towards Transparent AI [Concept] – Ihor Ivliev", "date": "", "ddg_snippet": "... such as rule-based or symbolic AI , while offering clarity, often encounter difficulties in achieving the adaptability and scalability required for ...", "subpage_snippet": "", "source": "ihorivliev.wordpress.com", "link": "https://ihorivliev.wordpress.com/2025/03/06/comprehensible-configurable-adaptive-cognitive-structure/", "content": "... such as rule-based or symbolic AI , while offering clarity, often encounter difficulties in achieving the adaptability and scalability required for ..."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Section_3.2_four-step_self-supe.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Section_3.2_four-step_self-supe.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..054c4f1f4e964e7d9b034b21e7c145cc6ee098ce
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Section_3.2_four-step_self-supe.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and ..."}
+{"idx": 1, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "Through self-supervised learning and adversarial testing, our framework demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "Through self-supervised learning and adversarial testing, our framework demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 2, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Three-Branch-Checks-and-Balances-Frameworkfor-of-Chang/5918a91419cf95db8599b086590facf63f124702/figure/4", "content": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 4, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment ...", "date": "", "ddg_snippet": "This paper presents a new way to ensure that large AI language models behave ethically, using a system inspired by government branches. It has three parts: the AI (executive) generates knowledge, ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46461/paper", "content": "This paper presents a new way to ensure that large AI language models behave ethically, using a system inspired by government branches. It has three parts: the AI (executive) generates knowledge, ..."}
+{"idx": 5, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts ."}
+{"idx": 6, "title": "infolab.stanford.edu", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ..."}
+{"idx": 7, "title": "Benchmarking, ethical alignment, and evaluation framework for ...", "date": "", "ddg_snippet": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772485923000534", "content": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration."}
+{"idx": 8, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Poster A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Edward Chang East Exhibition Hall A-B #E-703", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "Poster A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Edward Chang East Exhibition Hall A-B #E-703"}
+{"idx": 9, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Section_3.2_year_2023.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Section_3.2_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5e697ba69cfc9b883588f9dabf0c1f26539d17f7
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Section_3.2_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 1, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three..."}
+{"idx": 2, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 4, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts ."}
+{"idx": 5, "title": "infolab.stanford.edu", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ..."}
+{"idx": 6, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Three-Branch-Checks-and-Balances-Frameworkfor-of-Chang/5918a91419cf95db8599b086590facf63f124702/figure/4", "content": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 7, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment ...", "date": "", "ddg_snippet": "This paper presents a new way to ensure that large AI language models behave ethically, using a system inspired by government branches. It has three parts: the AI (executive) generates knowledge, ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46461/paper", "content": "This paper presents a new way to ensure that large AI language models behave ethically, using a system inspired by government branches. It has three parts: the AI (executive) generates knowledge, ..."}
+{"idx": 8, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v3", "content": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ..."}
+{"idx": 9, "title": "Ethical Guardrails for AI: A Checks-and-Balances Approach", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124", "subpage_snippet": "", "source": "www.zerna.io", "link": "https://www.zerna.io/page/security/presentation_set/security-llm-research/presentation/security-ethical-alignment-fairness/slide/security-paper-2502_00136", "content": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124"}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_arxiv.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b872c6defc7516ac7f650db785364c1a19d28556
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Order Personal Checks Online At Affordable Prices | Checks.com", "date": "", "ddg_snippet": "We print an exceptional collection of high-quality personal checks at cheap prices. Whether you are buying a checkbook for the first time or are reordering your favorite check design, you’ll always get the same low price - no discounts codes required.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/", "content": "We print an exceptional collection of high-quality personal checks at cheap prices. Whether you are buying a checkbook for the first time or are reordering your favorite check design, you’ll always get the same low price - no discounts codes required."}
+{"idx": 1, "title": "View Over 70 Personal Checks Designs at Low Prices", "date": "", "ddg_snippet": "Order your favorite personal checks online at low prices. Checks .com features a variety of over 70 personal check designs to fit your unique personality.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/129/personal-checks", "content": "Order your favorite personal checks online at low prices. Checks .com features a variety of over 70 personal check designs to fit your unique personality."}
+{"idx": 2, "title": "Value Checks - Order Our Best Value Checks Online", "date": "", "ddg_snippet": "Order value-priced personal checks online starting at just $8.20 per box at Checks .com! We have a variety of inexpensive check designs to choose from, so you can find the perfect one to match your style and budget.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/323/value-checks", "content": "Order value-priced personal checks online starting at just $8.20 per box at Checks .com! We have a variety of inexpensive check designs to choose from, so you can find the perfect one to match your style and budget."}
+{"idx": 3, "title": "How To Order Checks Online | Checks.com", "date": "", "ddg_snippet": "Order checks , address labels and checkbook covers online, secure, fast and easy with Checks .com. Customize and preview your checks before you order.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/how-to-order-checks", "content": "Order checks , address labels and checkbook covers online, secure, fast and easy with Checks .com. Customize and preview your checks before you order."}
+{"idx": 4, "title": "Traditional Checks - Order Affordable Personal Checks Online", "date": "", "ddg_snippet": "Checks .com's collection of traditional checks offers the widest variety of designs where you're sure to find a favorite or two. Also check out our line of Choice Checks for our most exclusive and most secure check designs, or browse our cheaper-priced checks for an incredibly low price per box.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/334/traditional-checks", "content": "Checks .com's collection of traditional checks offers the widest variety of designs where you're sure to find a favorite or two. Also check out our line of Choice Checks for our most exclusive and most secure check designs, or browse our cheaper-priced checks for an incredibly low price per box."}
+{"idx": 5, "title": "Reorder Discounted Personal Checks Online", "date": "", "ddg_snippet": "At Checks .com, whenever you reorder checks , you pay the same low price as intro customers. Reorder checks for discounted prices online today!", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/quickreorder", "content": "At Checks .com, whenever you reorder checks , you pay the same low price as intro customers. Reorder checks for discounted prices online today!"}
+{"idx": 6, "title": "Renaissance Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Artistic scrolls and elegance grace these renaissance checks in a rich four color rotation. Coordinating renaissance address labels and checkbook cover are also available.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/834/renaissance-checks", "content": "Artistic scrolls and elegance grace these renaissance checks in a rich four color rotation. Coordinating renaissance address labels and checkbook cover are also available."}
+{"idx": 7, "title": "Modern Checks - Order Cool & Contemporary Personal Checks", "date": "", "ddg_snippet": "Show your trendy style with contemporary and cool personal checks from Checks .com! Choose a stylish check design or pattern and order checks online today!", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/274/modern-checks", "content": "Show your trendy style with contemporary and cool personal checks from Checks .com! Choose a stylish check design or pattern and order checks online today!"}
+{"idx": 8, "title": "Pretty in Pink Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Discover the perfect blend of Parisian style and elegance with our Pretty in Pink checks . Designed for those who live life with flair, these top-tear pink personal checks are available in four vibrant designs featuring stripes, polka dots and diamond patterns.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/1799/pretty-in-pink-checks", "content": "Discover the perfect blend of Parisian style and elegance with our Pretty in Pink checks . Designed for those who live life with flair, these top-tear pink personal checks are available in four vibrant designs featuring stripes, polka dots and diamond patterns."}
+{"idx": 9, "title": "Horse Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Horse Play Checks Photographs of galloping horses adorn this equine rotation of stallions, mustangs and foals. Coordinating horse return address labels are available.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/811/horse-play-checks", "content": "Horse Play Checks Photographs of galloping horses adorn this equine rotation of stallions, mustangs and foals. Coordinating horse return address labels are available."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_executive_branch_year_2023.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_executive_branch_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c26921c7a6aa8834c44dabd361e07bd6591bb442
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_executive_branch_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00136] A Checks - and - Balances Framework for ...", "date": "", "ddg_snippet": "Title:A Checks - and - Balances Framework for Context - Aware Ethical AI Alignment .Abstract:This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three- branch governmental systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "Title:A Checks - and - Balances Framework for Context - Aware Ethical AI Alignment .Abstract:This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three- branch governmental systems."}
+{"idx": 1, "title": "(PDF) Checks - and - Balances Framework for Context - Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three- branch governmental systems.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three- branch governmental systems."}
+{"idx": 2, "title": "ICML Poster A Three- Branch Checks - and - Balances Framework for ...", "date": "", "ddg_snippet": "This paper introduces a three- branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "This paper introduces a three- branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence."}
+{"idx": 3, "title": "(PDF) A Three- Branch Checks - and - Balances Framework for ...", "date": "", "ddg_snippet": "This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/edward-y-chang-218b182_pdf-a-three-branch-checks-and-balances-activity-7272792943923458050-RO6k", "content": "This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions."}
+{"idx": 4, "title": "infolab.stanford.edu/~echang/Behavior2024.bib", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three- Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models}, author={Chang, Edward Y.}, journal={arXiv preprint arXiv:2502.00136}, year={2024}, url={https...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three- Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models}, author={Chang, Edward Y.}, journal={arXiv preprint arXiv:2502.00136}, year={2024}, url={https..."}
+{"idx": 5, "title": "AI Alignment vs AI Ethical Treatment: Ten Challenges", "date": "", "ddg_snippet": "AI ethics scholars seeking to bridge moral philosophy and technical alignment . AI safety researchers open to reassessing implicit anthropocentric assumptions.", "subpage_snippet": "", "source": "www.aigl.blog", "link": "https://www.aigl.blog/ai-alignment-vs-ai-ethical-treatment-ten-challenges/", "content": "AI ethics scholars seeking to bridge moral philosophy and technical alignment . AI safety researchers open to reassessing implicit anthropocentric assumptions."}
+{"idx": 6, "title": "You are the Driver and AI is the Mate: Exploring... | F1000Research", "date": "", "ddg_snippet": "4. To identify the challenges and ethical considerations students encounter when using AI tools in academic contexts . 5. To analyse the influence of AI literacy, prior experience, and digital confidence on students’ creative and critical engagement with AI tools.", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/14-974", "content": "4. To identify the challenges and ethical considerations students encounter when using AI tools in academic contexts . 5. To analyse the influence of AI literacy, prior experience, and digital confidence on students’ creative and critical engagement with AI tools."}
+{"idx": 7, "title": "Design and Evaluation Methods for LLM-Based Explainable AI", "date": "", "ddg_snippet": "Visualization and intuitiveness of explanation. Interactivity and controllability. Context awareness and adaptability. Feedback mechanism.", "subpage_snippet": "", "source": "www.oajaiml.com", "link": "https://www.oajaiml.com/uploads/archivepdf/430552340.pdf", "content": "Visualization and intuitiveness of explanation. Interactivity and controllability. Context awareness and adaptability. Feedback mechanism."}
+{"idx": 8, "title": "What is AI Ethics ? | IBM", "date": "", "ddg_snippet": "Examples of AI ethics issues include data responsibility and privacy, fairness, explainability, robustness, transparency, environmental sustainability, inclusion, moral agency, value alignment , accountability, trust, and technology misuse.", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/topics/ai-ethics", "content": "Examples of AI ethics issues include data responsibility and privacy, fairness, explainability, robustness, transparency, environmental sustainability, inclusion, moral agency, value alignment , accountability, trust, and technology misuse."}
+{"idx": 9, "title": "How to build an AI business strategy: A practical guide", "date": "", "ddg_snippet": "Build checks and balances into your AI -powered strategy process. Human oversight remains essential for validating AI recommendations, especially for high-stakes strategic decisions.", "subpage_snippet": "", "source": "miro.com", "link": "https://miro.com/ai/ai-business-strategy/", "content": "Build checks and balances into your AI -powered strategy process. Human oversight remains essential for validating AI recommendations, especially for high-stakes strategic decisions."}
diff --git a/data/sampled_jsons/Chen_2023_ICVaR_RLHF_abstract_risk-sensitive_reinforcement_learning_human_feedback_random_reference__year_2023.jsonl b/data/sampled_jsons/Chen_2023_ICVaR_RLHF_abstract_risk-sensitive_reinforcement_learning_human_feedback_random_reference__year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd76a4b0247fbdef36b9df0bac657b799cf5beb7
--- /dev/null
+++ b/data/sampled_jsons/Chen_2023_ICVaR_RLHF_abstract_risk-sensitive_reinforcement_learning_human_feedback_random_reference__year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of..."}
+{"idx": 1, "title": "RA- RLHF : Provably Efficient Risk -Aware Reinforcement Learning ...", "date": "", "ddg_snippet": "Reinforcement Learning Human Feedback ( RLHF ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v2/", "content": "Reinforcement Learning Human Feedback ( RLHF ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 2, "title": "Provably Efficient Iterated CVaR Reinforcement Learning with...", "date": "", "ddg_snippet": "Abstract : Risk - sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/provably-efficient-iterated-cvar-reinforcement-learning-with-37nqohi5", "content": "Abstract : Risk - sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk."}
+{"idx": 3, "title": "Provably Efficient Iterated CVaR Reinforcement Learning with...", "date": "", "ddg_snippet": "Risk - sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk. Risk - sensitive RLHF (Section 5): We extend ICVaR -RL to encompass RLHF with general function approximation for both transition probabilities and reward modeling.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vW1SkPl4kp", "content": "Risk - sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk. Risk - sensitive RLHF (Section 5): We extend ICVaR -RL to encompass RLHF with general function approximation for both transition probabilities and reward modeling."}
+{"idx": 4, "title": "Risk - sensitive reinforcement learning : a martingale approach to...", "date": "", "ddg_snippet": "Show abstract . Risk - Sensitive Reinforcement Learning . Article. Full-text available.In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/355141412_Risk-sensitive_reinforcement_learning_a_martingale_approach_to_reward_uncertainty", "content": "Show abstract . Risk - Sensitive Reinforcement Learning . Article. Full-text available.In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent."}
+{"idx": 5, "title": "Strategyproof Reinforcement Learning from Human ...", "date": "", "ddg_snippet": "We study Reinforcement Learning from Human Feedback ( RLHF ), where multiple individuals with diverse preferences provide feedback strategically to sway the final policy in their favor.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/strategyproof-reinforcement-learning-from-human-feedback/1106919732939849747-108614", "content": "We study Reinforcement Learning from Human Feedback ( RLHF ), where multiple individuals with diverse preferences provide feedback strategically to sway the final policy in their favor."}
+{"idx": 6, "title": "RA- RLHF : Provably Efficient Risk -Aware Reinforcement Learning ...", "date": "", "ddg_snippet": "Reinforcement Learning Human Feedback ( RLHF ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/RA-RLHF:-Provably-Efficient-Risk-Aware-Reinforcement-Learning-Human-Feedback-baf1c16a-6c49-45cc-9e7e-b12a3550842b", "content": "Reinforcement Learning Human Feedback ( RLHF ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 7, "title": "Alignment, Not Just Size: How Human Feedback is Making... | Medium", "date": "", "ddg_snippet": "This is where Reinforcement Learning from Human Feedback ( RLHF ) comes in. RLHF is the critical fine-tuning process that aligns these powerful models with human values and preferences, transforming them into useful assistants. The results of this alignment are staggering.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sidd.sidastic/alignment-not-just-size-how-human-feedback-is-making-ai-safer-and-more-helpful-4fa3172194d0", "content": "This is where Reinforcement Learning from Human Feedback ( RLHF ) comes in. RLHF is the critical fine-tuning process that aligns these powerful models with human values and preferences, transforming them into useful assistants. The results of this alignment are staggering."}
+{"idx": 8, "title": "What is Reinforcement Learning from Human Feedback ( RLHF )?", "date": "", "ddg_snippet": "What is RLHF ( Reinforcement Learning with Human Feedback )?Today, we will learn about “ Reinforcement Learning from Human Feedback ( RLHF ).” It might sound like a big, fancy word, but I promise to explain RLHF meaning in a way that even a five-year-old can understand.", "subpage_snippet": "", "source": "blog.fabrichq.ai", "link": "https://blog.fabrichq.ai/explaining-rlhf-to-a-5-years-old-87d18b156889", "content": "What is RLHF ( Reinforcement Learning with Human Feedback )?Today, we will learn about “ Reinforcement Learning from Human Feedback ( RLHF ).” It might sound like a big, fancy word, but I promise to explain RLHF meaning in a way that even a five-year-old can understand."}
+{"idx": 9, "title": "Reinforcement learning from human feedback ( RLHF )", "date": "", "ddg_snippet": "As the name suggests, RLHF ( Reinforcement Learning with Human Feedback ) utilizes reinforcement learning , or simply RL, to fine-tune LLM (Large Language Models) with human feedback . This approach aims to make the model more aligned with human preferences.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/reinforcement-learning-from-human-feedback-rlhf-ec56d1beaa3c", "content": "As the name suggests, RLHF ( Reinforcement Learning with Human Feedback ) utilizes reinforcement learning , or simply RL, to fine-tune LLM (Large Language Models) with human feedback . This approach aims to make the model more aligned with human preferences."}
diff --git a/data/sampled_jsons/Chen_et_al._2022_Theorem_4.5_preference-based_learning_regret_lower_bound_year_2022.jsonl b/data/sampled_jsons/Chen_et_al._2022_Theorem_4.5_preference-based_learning_regret_lower_bound_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f4efc85f68b8f514f3ecc057cb70ee8d1355b07
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al._2022_Theorem_4.5_preference-based_learning_regret_lower_bound_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Contextual Bandits and Imitation Learning with Preference - Based ...", "date": "", "ddg_snippet": "the preference - based learning model from the one-step ban-dit setting to the multi-step decision-making (e.g., IL and RL) setting (Chu & Ghahramani, 2005; Sadigh et al ., 2017; Christiano et al ., 2017; Lee et al ., 2021a; Chen et al ., 2022 ; Saha et al ., 2023).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=68n11MGekI", "content": "the preference - based learning model from the one-step ban-dit setting to the multi-step decision-making (e.g., IL and RL) setting (Chu & Ghahramani, 2005; Sadigh et al ., 2017; Christiano et al ., 2017; Lee et al ., 2021a; Chen et al ., 2022 ; Saha et al ., 2023)."}
+{"idx": 1, "title": "Efficient Preference - Based Reinforcement Learning : Randomized...", "date": "", "ddg_snippet": "Christiano et al ., 2017, Ouyang et al ., 2022 ]. In particular, this model satisfies the strong stochastic transitivity property [Strzalecki, 2025]. Regret lower bound and optimal algorithm in dueling bandit problem. In Conference on learning theory, pages 1141–1154.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.09508", "content": "Christiano et al ., 2017, Ouyang et al ., 2022 ]. In particular, this model satisfies the strong stochastic transitivity property [Strzalecki, 2025]. Regret lower bound and optimal algorithm in dueling bandit problem. In Conference on learning theory, pages 1141–1154."}
+{"idx": 2, "title": "(PDF) Combinatorial Reinforcement Learning with Preference ...", "date": "", "ddg_snippet": "preference learning - based reinforcement learning . 2022 ;Saha et al .,2023;Zhu et al .,2023;Zhan et al .,2023), the agent learns not from explicit numerical rewards, but through.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389056437_Combinatorial_Reinforcement_Learning_with_Preference_Feedback", "content": "preference learning - based reinforcement learning . 2022 ;Saha et al .,2023;Zhu et al .,2023;Zhan et al .,2023), the agent learns not from explicit numerical rewards, but through."}
+{"idx": 3, "title": "Optimal Regret Bounds for Collaborative Learning in Bandits", "date": "", "ddg_snippet": "specific regret lower bound given in Réda et al . ( 2022 ). CExp2 comprises a sub-logarithmic initial. round-robin exploration phase, followed by a guided exploration phase that is designed based on the constrained optimization problem associated with c∗ (hence, the name double exploration).", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v237/shidani24a/shidani24a.pdf", "content": "specific regret lower bound given in Réda et al . ( 2022 ). CExp2 comprises a sub-logarithmic initial. round-robin exploration phase, followed by a guided exploration phase that is designed based on the constrained optimization problem associated with c∗ (hence, the name double exploration)."}
+{"idx": 4, "title": "Provable Algorithms for Reinforcement Learning", "date": "", "ddg_snippet": "VI-LCB ( Theorem 4 ). lower bound ( Theorem 5).Inspired by the classical lower bound argument developed for multi-armed bandits (Auer et al ., 2002), the work Jaksch et al . (2010) established a regret lower bound for MDPs.", "subpage_snippet": "", "source": "users.ece.cmu.edu", "link": "https://users.ece.cmu.edu/~yuejiec/papers/Laixi_Dissertation.pdf", "content": "VI-LCB ( Theorem 4 ). lower bound ( Theorem 5).Inspired by the classical lower bound argument developed for multi-armed bandits (Auer et al ., 2002), the work Jaksch et al . (2010) established a regret lower bound for MDPs."}
+{"idx": 5, "title": "Learning and Algorithms for Online Matching", "date": "", "ddg_snippet": "The following lower bound is derived through information- theoretic arguments that we will not detail here. Theorem 2.1.2 (Lai and Robbins (1985)). Theorem 2.1.4 (Auer et al . (2002a)). The regret of Algorithm 7 with known horizon T is.", "subpage_snippet": "", "source": "theses.hal.science", "link": "https://theses.hal.science/tel-04292301/document", "content": "The following lower bound is derived through information- theoretic arguments that we will not detail here. Theorem 2.1.2 (Lai and Robbins (1985)). Theorem 2.1.4 (Auer et al . (2002a)). The regret of Algorithm 7 with known horizon T is."}
+{"idx": 6, "title": "Unified algorithms for RL with Decision-Estimation Coefficients: PAC...", "date": "", "ddg_snippet": "THEOREM 20 ( Preference - based RL upper bound ).An instantiation of Theorem 10 shows that PBDEC gives the following regret lower bound for Preference - based RL.", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journalArticle/Download?urlId=10.1214/24-AOS2483", "content": "THEOREM 20 ( Preference - based RL upper bound ).An instantiation of Theorem 10 shows that PBDEC gives the following regret lower bound for Preference - based RL."}
+{"idx": 7, "title": "https:/proceedings.mlr.press/v216/wang23a/wang23a", "date": "", "ddg_snippet": "In these settings, Chen et al .We present our theoretical results and their signicance discussions in this section. The rigorous proofs of these results are deferred to Appendix C. We rst derive a regret lower bound in Theorem 4 .1 which reects the impact of free exploration.", "subpage_snippet": "", "source": "www.cse.cuhk.edu.hk", "link": "https://www.cse.cuhk.edu.hk/~cslui/PUBLICATION/UAI-23.pdf", "content": "In these settings, Chen et al .We present our theoretical results and their signicance discussions in this section. The rigorous proofs of these results are deferred to Appendix C. We rst derive a regret lower bound in Theorem 4 .1 which reects the impact of free exploration."}
+{"idx": 8, "title": "UCLA", "date": "", "ddg_snippet": "Bengs et al . ( 2022 ) further extended the problem to general link functions and proved a stronger lower bound on the weak regret .Therefore, Theorem 4 . 5 .1 tightly matches the lower bound in Theorem 4 .4.1, up to logarithmic factors.", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt3884r34q/qt3884r34q.pdf?t=skrzvv", "content": "Bengs et al . ( 2022 ) further extended the problem to general link functions and proved a stronger lower bound on the weak regret .Therefore, Theorem 4 . 5 .1 tightly matches the lower bound in Theorem 4 .4.1, up to logarithmic factors."}
+{"idx": 9, "title": "Quantifying the Effectiveness", "date": "", "ddg_snippet": "Andrieu et al . ( 2022 ) give upper and lower bounds on the spectral gap of the random walk Metropolis that are tight in the dimension, but only the lower bound is explicit in its dependence on the condition number.", "subpage_snippet": "", "source": "discovery.ucl.ac.uk", "link": "https://discovery.ucl.ac.uk/id/eprint/10212800/1/Livingstone_Preconditioning_for_MCMC-3.pdf", "content": "Andrieu et al . ( 2022 ) give upper and lower bounds on the spectral gap of the random walk Metropolis that are tight in the dimension, but only the lower bound is explicit in its dependence on the condition number."}
diff --git a/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_Theorem_4.5_year_2022.jsonl b/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_Theorem_4.5_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..155949e4ef3a583873b9aaafeecd7cb94bf14cdd
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_Theorem_4.5_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ..."}
+{"idx": 1, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "May 24, 2022 · Reinforcement learning (RL) is concerned with sequen-tial decision-making problems in which the agent inter-acts with the environment to maximize its cumulative re-wards. This framework has achieved tremendous successes in various fields such as Atari games (Mnih et al ., 2013), Go (Silver et al ., 2017), and StarCraft (Vinyals et al ., 2019).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2205.11140", "content": "May 24, 2022 · Reinforcement learning (RL) is concerned with sequen-tial decision-making problems in which the agent inter-acts with the environment to maximize its cumulative re-wards. This framework has achieved tremendous successes in various fields such as Atari games (Mnih et al ., 2013), Go (Silver et al ., 2017), and StarCraft (Vinyals et al ., 2019)."}
+{"idx": 2, "title": "RA-PbRL: Provably Eficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "As a compelling alternative, Preference-based Reinforcement Learning (PbRL) [Busa-Fekete et al ., 2014, Wirth et al ., 2016] addresses this challenge by deviating from traditional quantifiable rewards for each step.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "As a compelling alternative, Preference-based Reinforcement Learning (PbRL) [Busa-Fekete et al ., 2014, Wirth et al ., 2016] addresses this challenge by deviating from traditional quantifiable rewards for each step."}
+{"idx": 3, "title": "EMO-RL: Emotion-Rule-Based Reinforcement Learning Enhanced ...", "date": "", "ddg_snippet": "1 day ago · With the advancement of self-supervised learning , researchers have in-creasingly utilized pre-trained audio models like WavLM ( Chen et al ., 2022 ), Emotion2vec (Ma et al ., 2024b), HuBERT (Hsu et al ., 2021), and Whisper (Radford et al ., 2023) to extract speech fea-tures or fine-tune these models on speech emotion datasets to obtain emotion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15654", "content": "1 day ago · With the advancement of self-supervised learning , researchers have in-creasingly utilized pre-trained audio models like WavLM ( Chen et al ., 2022 ), Emotion2vec (Ma et al ., 2024b), HuBERT (Hsu et al ., 2021), and Whisper (Radford et al ., 2023) to extract speech fea-tures or fine-tune these models on speech emotion datasets to obtain emotion ..."}
+{"idx": 4, "title": "Reinforcement Learning in the Era of Large Language Models ...", "date": "", "ddg_snippet": "Reinforcement learning (RL), a pivotal machine learning paradigm, is becoming essential in the post-training of large language models (LLMs), enhancing their reasoning capabilities and alignment ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Qianyue-Hao/publication/395648618_Reinforcement_Learning_in_the_Era_of_Large_Language_Models_Challenges_and_Opportunities/links/68ccbc8911d348252ba644cc/Reinforcement-Learning-in-the-Era-of-Large-Language-Models-Challenges-and-Opportunities.pdf", "content": "Reinforcement learning (RL), a pivotal machine learning paradigm, is becoming essential in the post-training of large language models (LLMs), enhancing their reasoning capabilities and alignment ..."}
+{"idx": 5, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learning (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2205.11140", "content": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learning (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ..."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "9 Dec 2024 — The proof of this theorem is similar to Theorem 4.5 in Chen et al . ( 2022 ). 5 Experiment Results. In this section, we assess the empirical ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/95716", "content": "9 Dec 2024 — The proof of this theorem is similar to Theorem 4.5 in Chen et al . ( 2022 ). 5 Experiment Results. In this section, we assess the empirical ..."}
+{"idx": 7, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "9 Jan 2025 — The proof of this theorem is similar to Theorem 4.5 in Chen et al . ( 2022 ) . Report issue for preceding element. 5 Experiment Results. Report ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v4", "content": "9 Jan 2025 — The proof of this theorem is similar to Theorem 4.5 in Chen et al . ( 2022 ) . Report issue for preceding element. 5 Experiment Results. Report ..."}
+{"idx": 8, "title": "Differentially Private Reward Estimation with Preference ...", "date": "", "ddg_snippet": "by SR Chowdhury · 2024 · Cited by 9 — Hence, a similar error bound as Theorem 4.5 for the SGD- based estimator ... Chen et al . ( 2022 ) generalize this to latent rewards with function approxi ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/ray-chowdhury24a/ray-chowdhury24a.pdf", "content": "by SR Chowdhury · 2024 · Cited by 9 — Hence, a similar error bound as Theorem 4.5 for the SGD- based estimator ... Chen et al . ( 2022 ) generalize this to latent rewards with function approxi ..."}
+{"idx": 9, "title": "Beyond Bradley-Terry Models: A General Preference ...", "date": "", "ddg_snippet": ", we have a more interesting interpretation based on spectral decomposition. Theorem 4.5 . Let P ∈ R2k×2k be a real skew-symmetric matrix (i.e., P = −P ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45103", "content": ", we have a more interesting interpretation based on spectral decomposition. Theorem 4.5 . Let P ∈ R2k×2k be a real skew-symmetric matrix (i.e., P = −P ..."}
diff --git a/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_theorem_4.5_regret_lower_bound.jsonl b/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_theorem_4.5_regret_lower_bound.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c5b4fbe48a931805f2cab39d7f77543dbbe8b32e
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_theorem_4.5_regret_lower_bound.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Provable Reward-Agnostic Preference-Based Reinforcement Learning", "date": "", "ddg_snippet": "... areas, there are only a few provably efficient algorithms (also known as PAC RL) for this purpose (Pacchiano et al .,, 2021 ; Chen et al ., 2022b ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18505v3", "content": "... areas, there are only a few provably efficient algorithms (also known as PAC RL) for this purpose (Pacchiano et al .,, 2021 ; Chen et al ., 2022b ..."}
+{"idx": 1, "title": "Value-Incentivized Preference Optimization: A Unified Approach", "date": "", "ddg_snippet": "... reinforcement learning from human feedback (RLHF) (Ziegler et al .,, 2019 ) has been shown to significantly improve the helpfulness, truthfulness and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19320v4", "content": "... reinforcement learning from human feedback (RLHF) (Ziegler et al .,, 2019 ) has been shown to significantly improve the helpfulness, truthfulness and ..."}
+{"idx": 2, "title": "Robust Reinforcement Learning from Human Feedback for Large", "date": "", "ddg_snippet": "Reinforcement learning from human feedback (RLHF) has recently revolutionized the fine-tuning of large language models (LLMs), achieving remarkable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.03784v4", "content": "Reinforcement learning from human feedback (RLHF) has recently revolutionized the fine-tuning of large language models (LLMs), achieving remarkable ..."}
+{"idx": 3, "title": "Learning Guarantee of Reward Modeling Using Deep Neural Networks", "date": "", "ddg_snippet": "Zhu et al ., ( 2023 ) investigated the theoretical properties of reward modeling based on action- based comparisons, while Chen et al ., ( 2022 ) and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06601v1", "content": "Zhu et al ., ( 2023 ) investigated the theoretical properties of reward modeling based on action- based comparisons, while Chen et al ., ( 2022 ) and ..."}
+{"idx": 4, "title": "Xiaoyu Chen | DeepAI", "date": "", "ddg_snippet": "... trend for value- based reinforcement learning (RL) algorithms i... ... Although model- based reinforcement learning (RL) approaches are consider...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/xiaoyu-chen", "content": "... trend for value- based reinforcement learning (RL) algorithms i... ... Although model- based reinforcement learning (RL) approaches are consider..."}
+{"idx": 5, "title": "A Sample Complexity Measure with Applications to Learning", "date": "", "ddg_snippet": "In this thesis, we research learning algorithms for optimal decision making in two different contexts, Reinforcement Learning in Part I and Auction ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/315890926_A_Sample_Complexity_Measure_with_Applications_to_Learning_Optimal_Auctions", "content": "In this thesis, we research learning algorithms for optimal decision making in two different contexts, Reinforcement Learning in Part I and Auction ..."}
+{"idx": 6, "title": "NeurIPS 2022 Journal Track Papers", "date": "", "ddg_snippet": "Unlike previous non-stationary bandit algorithms using a change-point detector, GLRklUCB does not need to be calibrated based on prior knowledge on ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2022/events/journal_track_2022", "content": "Unlike previous non-stationary bandit algorithms using a change-point detector, GLRklUCB does not need to be calibrated based on prior knowledge on ..."}
+{"idx": 7, "title": "1 Introduction", "date": "", "ddg_snippet": "... in reinforcement learning (RL) have demonstrated superhuman-level performance in complex board and video games, such as Go (Silver et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03904v1", "content": "... in reinforcement learning (RL) have demonstrated superhuman-level performance in complex board and video games, such as Go (Silver et al ."}
+{"idx": 8, "title": "Artificial Intelligence Blog · Multi-Armed Bandit Problem", "date": "", "ddg_snippet": "... regret is at most of order $n^{\\beta/(\\beta+1)}\\log^2(n)$ if $\\beta > 1$ and $\\log^2(n)\\sqrt{n}$ otherwise. Additionally, they prove a lower bound of ...", "subpage_snippet": "", "source": "artent.net", "link": "http://artent.net/category/multi-armed-bandit-problem/page/2/", "content": "... regret is at most of order $n^{\\beta/(\\beta+1)}\\log^2(n)$ if $\\beta > 1$ and $\\log^2(n)\\sqrt{n}$ otherwise. Additionally, they prove a lower bound of ..."}
+{"idx": 9, "title": "Brandon Amos", "date": "", "ddg_snippet": "... in the Fundamental AI Research (FAIR) group at Meta in NYC and study foundational topics spanning machine learning , optimization , reinforcement ...", "subpage_snippet": "", "source": "bamos.github.io", "link": "http://bamos.github.io/", "content": "... in the Fundamental AI Research (FAIR) group at Meta in NYC and study foundational topics spanning machine learning , optimization , reinforcement ..."}
diff --git a/data/sampled_jsons/Chen_et_al_2023_preference-based_reinforcement_learning.jsonl b/data/sampled_jsons/Chen_et_al_2023_preference-based_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..08ba7bfe87328aa0352a8e0c5a1937d596c4200f
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al_2023_preference-based_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Provable Reward-Agnostic Preference-Based Reinforcement Learning", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning (PbRL) is ... We refer the readers to Wirth et al ., ( 2017 ) for an overview of Preference - based RL (PbRL).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18505v3", "content": "Preference - based Reinforcement Learning (PbRL) is ... We refer the readers to Wirth et al ., ( 2017 ) for an overview of Preference - based RL (PbRL)."}
+{"idx": 1, "title": "A Differentiated Reward Method for Reinforcement Learning based", "date": "", "ddg_snippet": "Current research demonstrates that reinforcement learning - based multi-vehicle cooperative decision-making algorithms have achieved human-like or even ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00352v2", "content": "Current research demonstrates that reinforcement learning - based multi-vehicle cooperative decision-making algorithms have achieved human-like or even ..."}
+{"idx": 2, "title": "Outcome-Based Online Reinforcement Learning: Algorithms and", "date": "", "ddg_snippet": "... also appears in the recent work on Reinforcement Learning from Human Feedback (RLHF) ( Chen et al ., 2022b , a ; Wu and Sun, 2023 ; Wang et al ., ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.20268v2", "content": "... also appears in the recent work on Reinforcement Learning from Human Feedback (RLHF) ( Chen et al ., 2022b , a ; Wu and Sun, 2023 ; Wang et al ., ..."}
+{"idx": 3, "title": "Robust Reinforcement Learning from Human Feedback for Large", "date": "", "ddg_snippet": "Reinforcement learning from human feedback (RLHF) has recently revolutionized the fine-tuning of large language models (LLMs), achieving remarkable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.03784v4", "content": "Reinforcement learning from human feedback (RLHF) has recently revolutionized the fine-tuning of large language models (LLMs), achieving remarkable ..."}
+{"idx": 4, "title": "(PDF) Reinforcement learning", "date": "", "ddg_snippet": "Motivated by the success of reinforcement learning (RL) (Sutton, Barto et al . ... Reinforcement Learning (RL) (Kaelbling, Littman, and Moore 1996; ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/270960086_Reinforcement_learning", "content": "Motivated by the success of reinforcement learning (RL) (Sutton, Barto et al . ... Reinforcement Learning (RL) (Kaelbling, Littman, and Moore 1996; ..."}
+{"idx": 5, "title": "Inverse Reinforcement Learning for Decentralized", "date": "", "ddg_snippet": "The objective of inverse reinforcement learning (IRL) is to learn an agent s reward function based on either the agent s policies or the observations ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/236262062_Inverse_Reinforcement_Learning_for_Decentralized_Non-Cooperative_Multiagent_Systems", "content": "The objective of inverse reinforcement learning (IRL) is to learn an agent s reward function based on either the agent s policies or the observations ..."}
+{"idx": 6, "title": "Reinforcement learning — The Dan MacKinlay stable of", "date": "", "ddg_snippet": "Here’ s an intro to all of machine learning through a historical tale about one particular attempt to teach a machine (not a computer!) to play tic ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/reinforcement_learning", "content": "Here’ s an intro to all of machine learning through a historical tale about one particular attempt to teach a machine (not a computer!) to play tic ..."}
+{"idx": 7, "title": "Learning What Reinforcement Learning Can’t: Interleaved", "date": "", "ddg_snippet": "... empirical successes of RLVR, its current form is insufficient to incentivize capabilities that transcend the base model’ s limitations Yue et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07527v1", "content": "... empirical successes of RLVR, its current form is insufficient to incentivize capabilities that transcend the base model’ s limitations Yue et al ..."}
+{"idx": 8, "title": "Zhuoran Yang | DeepAI", "date": "", "ddg_snippet": "Human-in-the-loop: Provably Efficient Preference - based Reinforcement Learning with General Function Approximation ... based reinforcement learning ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/zhuoran-yang", "content": "Human-in-the-loop: Provably Efficient Preference - based Reinforcement Learning with General Function Approximation ... based reinforcement learning ..."}
+{"idx": 9, "title": "Jiarui Gan | Oxford", "date": "", "ddg_snippet": "Additionally, we show an efficient learning algorithm for an episodic reinforcement learning setting where the transition probabilities are unknown.", "subpage_snippet": "", "source": "jgan.xyz", "link": "https://jgan.xyz/", "content": "Additionally, we show an efficient learning algorithm for an episodic reinforcement learning setting where the transition probabilities are unknown."}
diff --git a/data/sampled_jsons/Chen_et_al_2023_random_reference_trajectory_preference-based_reinforcement_learning.jsonl b/data/sampled_jsons/Chen_et_al_2023_random_reference_trajectory_preference-based_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fc91b86d4ca51a70a710568f05bad03fd87e998f
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al_2023_random_reference_trajectory_preference-based_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "Reinforcement learning requires clever exploration mechanisms; randomly selecting actions, without reference to an estimated probability distribution, shows poor performance.\" Trajectory modeling via random utility inverse reinforcement learning \". Information Sciences.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "Reinforcement learning requires clever exploration mechanisms; randomly selecting actions, without reference to an estimated probability distribution, shows poor performance.\" Trajectory modeling via random utility inverse reinforcement learning \". Information Sciences."}
+{"idx": 1, "title": "Efficient Preference - Based Reinforcement Learning : Randomized ...", "date": "", "ddg_snippet": "We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory -level preference comparisons.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.09508", "content": "We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory -level preference comparisons."}
+{"idx": 2, "title": "A Survey of Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "Reinforcement Learning . Preference - Based MDPs.(2011) and Cheng et al . (2011). The original idea of preference - based reinforcement learning (PbRL) is to infer the objective from qualitative feedback, such as pairwise preferences between behaviors or between actions given.", "subpage_snippet": "", "source": "epub.ub.uni-muenchen.de", "link": "https://epub.ub.uni-muenchen.de/125328/1/2312.14925v2.pdf", "content": "Reinforcement Learning . Preference - Based MDPs.(2011) and Cheng et al . (2011). The original idea of preference - based reinforcement learning (PbRL) is to infer the objective from qualitative feedback, such as pairwise preferences between behaviors or between actions given."}
+{"idx": 3, "title": "Multi-turn Reinforcement Learning from Preference Human Feedback", "date": "", "ddg_snippet": "...[Munos et al ., 2023 , Calandriello et al ., 2024] from the single-turn case, and supports the theoretical claim that MTPO converges to the Nash policy while multi-turn RLHF converges to the optimal policy w.r.t the learned reward (which is based only on the.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2405.14655/paper", "content": "...[Munos et al ., 2023 , Calandriello et al ., 2024] from the single-turn case, and supports the theoretical claim that MTPO converges to the Nash policy while multi-turn RLHF converges to the optimal policy w.r.t the learned reward (which is based only on the."}
+{"idx": 4, "title": "Beyond Reward: Offline Preference -guided Policy Optimization", "date": "", "ddg_snippet": "(2017) scaled preference - based reinforcement learning to utilize modern deep learning techniques, and Ibarz et al . (2018) improved the efficiency of this method by introducing addi-tional forms of feedback such as demonstrations.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/kang23b/kang23b.pdf", "content": "(2017) scaled preference - based reinforcement learning to utilize modern deep learning techniques, and Ibarz et al . (2018) improved the efficiency of this method by introducing addi-tional forms of feedback such as demonstrations."}
+{"idx": 5, "title": "Preference - Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Preference - based reinforcement learning (PbRL) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Preference - based reinforcement learning (PbRL) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal."}
+{"idx": 6, "title": "Preference Alignment with Flow Matching", "date": "", "ddg_snippet": "Contrastive Preference Learning (CPL) [Hejna et al ., 2023 ] is a class of reward-free methods that utilizes contrastive learning techniques to align model outputs with the preferences observed in the dataset.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/3df874367ce2c43891aab1ab23ae6959-Paper-Conference.pdf", "content": "Contrastive Preference Learning (CPL) [Hejna et al ., 2023 ] is a class of reward-free methods that utilizes contrastive learning techniques to align model outputs with the preferences observed in the dataset."}
+{"idx": 7, "title": "A Survey of Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "References (296).“A State Augmentation Based Approach to Reinforcement Learning from Human Preferences ”, 2023 .", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2312.14925", "content": "References (296).“A State Augmentation Based Approach to Reinforcement Learning from Human Preferences ”, 2023 ."}
+{"idx": 8, "title": "Is RLHF More Difficult than Standard RL?", "date": "", "ddg_snippet": "Learning from trajectory -based preferences via OMLE_equilibrium. Proofs of Impossibility Results.Theory of Preference - based RL. For utility-based preferences, Novoseller et al .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=sxZLrBqg50", "content": "Learning from trajectory -based preferences via OMLE_equilibrium. Proofs of Impossibility Results.Theory of Preference - based RL. For utility-based preferences, Novoseller et al ."}
+{"idx": 9, "title": "RA-PbRL: Provably Efficient Risk-Aware", "date": "", "ddg_snippet": "Related Work. Preference - based Feedback Reinforcement Learning .2.1 Preference - based Feedback Reinforcement Learning . The incorporation of human preferences in RL, such as Jain et al .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "Related Work. Preference - based Feedback Reinforcement Learning .2.1 Preference - based Feedback Reinforcement Learning . The incorporation of human preferences in RL, such as Jain et al ."}
diff --git a/data/sampled_jsons/Chien_et_al._2024_noisy_gradient_descent_for_unlearning.jsonl b/data/sampled_jsons/Chien_et_al._2024_noisy_gradient_descent_for_unlearning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6013d71120a644638f2aaf26e47b1f7ff257f59b
--- /dev/null
+++ b/data/sampled_jsons/Chien_et_al._2024_noisy_gradient_descent_for_unlearning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Langevin Unlearning: A New Perspective of Noisy Gradient ...", "date": "", "ddg_snippet": "by E Chien · 2024 · Cited by 28 — We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.10371", "content": "by E Chien · 2024 · Cited by 28 — We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems."}
+{"idx": 1, "title": "Langevin Unlearning: A New Perspective of Noisy Gradient ...", "date": "", "ddg_snippet": "by E Chien · Cited by 28 — We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems. Langevin.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=3LKuC8rbyV", "content": "by E Chien · Cited by 28 — We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems. Langevin."}
+{"idx": 2, "title": "Langevin Unlearning: A New Perspective of Noisy Gradient ...", "date": "", "ddg_snippet": "We propose Langevin unlearning based on noisy gradient descent with privacy guarantees for approximate unlearning problems. It unifies the DP learning process ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96754", "content": "We propose Langevin unlearning based on noisy gradient descent with privacy guarantees for approximate unlearning problems. It unifies the DP learning process ..."}
+{"idx": 3, "title": "Langevin Unlearning: A New Perspective of Noisy Gradient ...", "date": "", "ddg_snippet": "We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems. Langevin ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.10371v5", "content": "We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems. Langevin ..."}
+{"idx": 4, "title": "Certified Machine Unlearning via Noisy Stochastic ...", "date": "", "ddg_snippet": "by E Chien · 2024 · Cited by 11 — Chien et al . [11] utilize full-batch PNGD for approximate unlearning with the analysis of. Langevin dynamics. The adaptive unlearning requests setting is ... 36 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/448abd486677165ceedfa790e9a61802-Paper-Conference.pdf", "content": "by E Chien · 2024 · Cited by 11 — Chien et al . [11] utilize full-batch PNGD for approximate unlearning with the analysis of. Langevin dynamics. The adaptive unlearning requests setting is ... 36 pages"}
+{"idx": 5, "title": "Langevin unlearning: a new perspective of noisy gradient ...", "date": "", "ddg_snippet": "5 Jun 2025 — We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740446", "content": "5 Jun 2025 — We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning ..."}
+{"idx": 6, "title": "Certified Machine Unlearning via Noisy Stochastic ...", "date": "", "ddg_snippet": "5 Nov 2024 — The paper considers the problem of machine unlearning and proposes to use a projected noisy stochastic gradient descent algorithm (PNSGD).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=h3k2NXu5bJ&referrer=[the+profile+of+Pan+Li](/profile?id=~Pan_Li2)", "content": "5 Nov 2024 — The paper considers the problem of machine unlearning and proposes to use a projected noisy stochastic gradient descent algorithm (PNSGD)."}
+{"idx": 7, "title": "Certified machine unlearning via noisy stochastic gradient descent ...", "date": "", "ddg_snippet": "We propose to leverage projected noisy stochastic gradient descent for unlearning and establish its first approximate unlearning guarantee under the convexity ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739144", "content": "We propose to leverage projected noisy stochastic gradient descent for unlearning and establish its first approximate unlearning guarantee under the convexity ..."}
+{"idx": 8, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "by NM Sepahvand — Armed with per-instance privacy losses, we revisit Chien et al.'s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin ...", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2025/pdf/sepahvand.pdf", "content": "by NM Sepahvand — Armed with per-instance privacy losses, we revisit Chien et al.'s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin ..."}
+{"idx": 9, "title": "Leveraging Per-Instance Privacy for Machine Unlearning - ADS", "date": "", "ddg_snippet": "We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024) ... et al., 2024), each of which bounds the (Renyi) ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2025arXiv250518786S/abstract", "content": "We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024) ... et al., 2024), each of which bounds the (Renyi) ..."}
diff --git a/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_epoch_change_component_ablation_study_year_2024.jsonl b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_epoch_change_component_ablation_study_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..87664ae4bd54a1176e4c04ad9ce5b74249f00af6
--- /dev/null
+++ b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_epoch_change_component_ablation_study_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - siyuancncd/CoPINN: This is the official ...", "date": "", "ddg_snippet": "This easily causes the PINN model to fall into undesirable local minima and unstable learning, thereby resulting in an Unbalanced Prediction Problem (UPP). To deal with this daunting problem, we propose a novel framework named Cognitive Physics-Informed Neural Network ( CoPINN ) that imitates the human cognitive learning manner from easy to hard.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN", "content": "This easily causes the PINN model to fall into undesirable local minima and unstable learning, thereby resulting in an Unbalanced Prediction Problem (UPP). To deal with this daunting problem, we propose a novel framework named Cognitive Physics-Informed Neural Network ( CoPINN ) that imitates the human cognitive learning manner from easy to hard."}
+{"idx": 1, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Abstract Physics-informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations, which have demonstrated significant potential for solving partial differential equations (PDEs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "Abstract Physics-informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations, which have demonstrated significant potential for solving partial differential equations (PDEs)."}
+{"idx": 2, "title": "CoPINN: Cognitive Physics-Informed Neural Networks | Read ...", "date": "", "ddg_snippet": "The proposed cognitive training scheduler in our CoPINN consists of two components , i.e., the epoch change component (), and the sample change component ( To demonstrate the effectiveness of each component , we conduct an ablation study on the (2+1)-d Klein-Gordon equation with collocation point number .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46458/paper", "content": "The proposed cognitive training scheduler in our CoPINN consists of two components , i.e., the epoch change component (), and the sample change component ( To demonstrate the effectiveness of each component , we conduct an ablation study on the (2+1)-d Klein-Gordon equation with collocation point number ."}
+{"idx": 3, "title": "CoPINN: Cognitive Physics-Informed Neural Networks - OpenReview", "date": "", "ddg_snippet": "This easily causes the PINN model to fall into undesirable local minima and unstable learning, thereby resulting in an Unbalanced Prediction Problem (UPP). To deal with this daunting problem, we propose a novel framework named Cognitive Physical Informed Neural Network ( CoPINN ) that imitates the human cognitive learning manner from easy to hard.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI&amp;referrer=[Author+Console](/group?id=ICML.cc/2025/Conference/Authors#your-submissions)", "content": "This easily causes the PINN model to fall into undesirable local minima and unstable learning, thereby resulting in an Unbalanced Prediction Problem (UPP). To deal with this daunting problem, we propose a novel framework named Cognitive Physical Informed Neural Network ( CoPINN ) that imitates the human cognitive learning manner from easy to hard."}
+{"idx": 4, "title": "CoPINN/CoPINN.pdf at main · siyuancncd/CoPINN · GitHub", "date": "", "ddg_snippet": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network \" (ICML 2025, Spotlight) - CoPINN / CoPINN .pdf at main · siyuancncd/ CoPINN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN/blob/main/CoPINN.pdf", "content": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network \" (ICML 2025, Spotlight) - CoPINN / CoPINN .pdf at main · siyuancncd/ CoPINN"}
+{"idx": 5, "title": "[2405.08111] Conformalized Physics-Informed Neural Networks", "date": "", "ddg_snippet": "May 13, 2024 · Physics-informed neural networks (PINNs) are an influential method of solving differential equations and estimating their parameters given data. However, since they make use of neural networks , they provide only a point estimate of differential equation parameters, as well as the solution at any given point, without any measure of uncertainty. Ensemble and Bayesian methods have been previously ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.08111", "content": "May 13, 2024 · Physics-informed neural networks (PINNs) are an influential method of solving differential equations and estimating their parameters given data. However, since they make use of neural networks , they provide only a point estimate of differential equation parameters, as well as the solution at any given point, without any measure of uncertainty. Ensemble and Bayesian methods have been previously ..."}
+{"idx": 6, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "This paper introduces a framework called CoPINN , which effectively addresses the Unbalanced Prediction Problem in Physics-Informed Neural Networks for solving Partial Differential Equations.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/165180?from=subpath-search", "content": "This paper introduces a framework called CoPINN , which effectively addresses the Unbalanced Prediction Problem in Physics-Informed Neural Networks for solving Partial Differential Equations."}
+{"idx": 7, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "R6: In Section 3.3 of our original paper, we conduct an ablation study to analyze the effectiveness of each component in the cognitive training scheduler of our ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI&noteId=Z6OLWcPple", "content": "R6: In Section 3.3 of our original paper, we conduct an ablation study to analyze the effectiveness of each component in the cognitive training scheduler of our ..."}
+{"idx": 8, "title": "Neural Information Processing", "date": "", "ddg_snippet": "3 Mar 2025 — Welcome to the 31st International Conference on Neural Information Processing. (ICONIP 2024) of the Asia-Pacific Neural Network Society (APNNS), ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-981-96-6579-2.pdf", "content": "3 Mar 2025 — Welcome to the 31st International Conference on Neural Information Processing. (ICONIP 2024) of the Asia-Pacific Neural Network Society (APNNS), ..."}
+{"idx": 9, "title": "Competitive Physics Informed Networks", "date": "", "ddg_snippet": "by Q Zeng · Cited by 47 — We introduce competitive physics informed networks where two neural networks solve a partial differential equation by playing a zero-sum game.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=z9SIj-IM7tn", "content": "by Q Zeng · Cited by 47 — We introduce competitive physics informed networks where two neural networks solve a partial differential equation by playing a zero-sum game."}
diff --git a/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_equations_8_9_weights_vie_vih_samples.jsonl b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_equations_8_9_weights_vie_vih_samples.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dc2bf2e06af9842a915ce189cc2c3b60a9df939b
--- /dev/null
+++ b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_equations_8_9_weights_vie_vih_samples.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CoPINN : Cognitive Physics - Informed Neural Networks", "date": "", "ddg_snippet": "Sample points difficulty. CoPINN : Cognitive Physics - Informed Neural Networks .2.5. Cognitive Training Scheduler. As shown in Figure 1 (a), samples from regions with smooth variations in physical quantities are typically easier to handle by current PINN methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "Sample points difficulty. CoPINN : Cognitive Physics - Informed Neural Networks .2.5. Cognitive Training Scheduler. As shown in Figure 1 (a), samples from regions with smooth variations in physical quantities are typically easier to handle by current PINN methods."}
+{"idx": 1, "title": "Physics Informed Neural Networks (PINNs) [Physics...] - YouTube", "date": "", "ddg_snippet": "This video introduces PINNs, or Physics Informed Neural Networks . PINNs are a simple modification of a neural network that adds a PDE in the loss function t...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=-zrY7P2dVC4", "content": "This video introduces PINNs, or Physics Informed Neural Networks . PINNs are a simple modification of a neural network that adds a PDE in the loss function t..."}
+{"idx": 2, "title": "GitHub - siyuancncd/ CoPINN : This is the official implementation of...", "date": "", "ddg_snippet": "\" CoPINN : Cognitive Physics - Informed Neural Network \". (ICML 2025, Spotlight (acc rate = 2.6%), JAX Code).Extensive experiments demonstrate that our CoPINN achieves state-of-the-art performance, particularly significantly reducing prediction errors in stubborn regions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN", "content": "\" CoPINN : Cognitive Physics - Informed Neural Network \". (ICML 2025, Spotlight (acc rate = 2.6%), JAX Code).Extensive experiments demonstrate that our CoPINN achieves state-of-the-art performance, particularly significantly reducing prediction errors in stubborn regions."}
+{"idx": 3, "title": "Physics Informed Neural Networks , A Proven PINNs Guide 2025", "date": "", "ddg_snippet": "Physics informed neural networks made clear. Learn how PINNs solve PDEs, when to use them, starter code tips, and a timely case study on DeepMind’s unstable singularities to guide real projects.", "subpage_snippet": "", "source": "binaryverseai.com", "link": "https://binaryverseai.com/physics-informed-neural-networks-pinns-explained/", "content": "Physics informed neural networks made clear. Learn how PINNs solve PDEs, when to use them, starter code tips, and a timely case study on DeepMind’s unstable singularities to guide real projects."}
+{"idx": 4, "title": "Chebyshev-Sobolev Physics - Informed Neural Networks for General...", "date": "", "ddg_snippet": "We present a Physics - Informed Neural Network framework that combines Chebyshev spectral approximation with Sobolev regularization (CS-PINN) for solving par.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40819-025-01988-6", "content": "We present a Physics - Informed Neural Network framework that combines Chebyshev spectral approximation with Sobolev regularization (CS-PINN) for solving par."}
+{"idx": 5, "title": "Development and optimization of physics - informed neural networks ...", "date": "", "ddg_snippet": "This work compares the advantages and limitations of the Finite Difference Method with Physics - Informed Neural Networks , showing where each can best be applied for different problem scenarios.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Development-and-optimization-of-physics-informed-neural-networks-for-solving-partial-differential-equations-8e1abdbf-395a-4019-99bc-d1d07c665549", "content": "This work compares the advantages and limitations of the Finite Difference Method with Physics - Informed Neural Networks , showing where each can best be applied for different problem scenarios."}
+{"idx": 6, "title": "Self-adaptive weights based on balanced residual decay rate... | PNNL", "date": "", "ddg_snippet": "The performance of our proposed adaptive weighting method is compared with current state-of-the-art adaptive weighting methods on benchmark problems for both physics - informed neural networks and physics - informed deep operator networks .", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/publications/self-adaptive-weights-based-balanced-residual-decay-rate-physics-informed-neural", "content": "The performance of our proposed adaptive weighting method is compared with current state-of-the-art adaptive weighting methods on benchmark problems for both physics - informed neural networks and physics - informed deep operator networks ."}
+{"idx": 7, "title": "Physics - Informed Neural Network with Forcing Function", "date": "", "ddg_snippet": "Solving differential equations directly with neural networks (with code).The inputs are applied to the neural network , and back-propagation adjusts the network 's weights and biases to minimize an objective function.", "subpage_snippet": "", "source": "deepseekpro.org", "link": "https://deepseekpro.org/guide/physics-informed-neural-network-with-forcing-function/", "content": "Solving differential equations directly with neural networks (with code).The inputs are applied to the neural network , and back-propagation adjusts the network 's weights and biases to minimize an objective function."}
+{"idx": 8, "title": "Physics - Informed Neural Networks for Weakly Compressible Flows...", "date": "", "ddg_snippet": "[14] introduced the concept of physics - informed neural networks (PINN) to approximate the solution of PDEs by tuning the parameters of the neural network .We then explain the modified physics - informed neural network structure to solve these Boltzmann equations .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.05892v1", "content": "[14] introduced the concept of physics - informed neural networks (PINN) to approximate the solution of PDEs by tuning the parameters of the neural network .We then explain the modified physics - informed neural network structure to solve these Boltzmann equations ."}
+{"idx": 9, "title": "Investigating the use of physics informed neural networks for...", "date": "", "ddg_snippet": "Physics - informed neural networks : a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations .", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0332694", "content": "Physics - informed neural networks : a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations ."}
diff --git "a/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_hyperparameter_\316\262_beta_analysis_Section_3.5.jsonl" "b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_hyperparameter_\316\262_beta_analysis_Section_3.5.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..52e4e8f6daf74c0c8e3342a66692c91c0429d3f6
--- /dev/null
+++ "b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_hyperparameter_\316\262_beta_analysis_Section_3.5.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - siyuancncd/CoPINN: This is the official implementation of ...", "date": "", "ddg_snippet": "This easily causes the PINN model to fall into undesirable local minima and unstable learning, thereby resulting in an Unbalanced Prediction Problem (UPP). To deal with this daunting problem, we propose a novel framework named Cognitive Physics - Informed Neural Network ( CoPINN ) that imitates the human cognitive learning manner from easy to hard.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN", "content": "This easily causes the PINN model to fall into undesirable local minima and unstable learning, thereby resulting in an Unbalanced Prediction Problem (UPP). To deal with this daunting problem, we propose a novel framework named Cognitive Physics - Informed Neural Network ( CoPINN ) that imitates the human cognitive learning manner from easy to hard."}
+{"idx": 1, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Abstract Physics - informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations, which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "Abstract Physics - informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations, which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points ..."}
+{"idx": 2, "title": "Hyper-parameter tuning of physics-informed neural networks: Application ...", "date": "", "ddg_snippet": "One type of DNNs are physics - informed neural networks which were introduced recently in [11]. They encode the boundary value problem in the loss function and rely on automatic differentiation. Their strength is that they combine the aforementioned strengths of both DL and classical numerical analysis .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231223009499", "content": "One type of DNNs are physics - informed neural networks which were introduced recently in [11]. They encode the boundary value problem in the loss function and rely on automatic differentiation. Their strength is that they combine the aforementioned strengths of both DL and classical numerical analysis ."}
+{"idx": 3, "title": "[2205.06704] Hyper-parameter tuning of physics-informed neural networks ...", "date": "", "ddg_snippet": "We consider physics - informed neural networks (PINNs) [Raissi et al., J.~Comput. Phys. 278 (2019) 686-707] for forward physical problems. In order to find optimal PINNs configuration, we introduce a hyper-parameter optimization (HPO) procedure via Gaussian processes-based Bayesian optimization. We apply the HPO to Helmholtz equation for bounded domains and conduct a thorough study, focusing on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2205.06704", "content": "We consider physics - informed neural networks (PINNs) [Raissi et al., J.~Comput. Phys. 278 (2019) 686-707] for forward physical problems. In order to find optimal PINNs configuration, we introduce a hyper-parameter optimization (HPO) procedure via Gaussian processes-based Bayesian optimization. We apply the HPO to Helmholtz equation for bounded domains and conduct a thorough study, focusing on ..."}
+{"idx": 4, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Abstract Physics - informed neural networks (PINN) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations, which have demonstrated significant potential for solving partial differential equations (PDEs).", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46458/paper", "content": "Abstract Physics - informed neural networks (PINN) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations, which have demonstrated significant potential for solving partial differential equations (PDEs)."}
+{"idx": 5, "title": "PDF CoPINN/CoPINN.pdf at main · siyuancncd/CoPINN · GitHub", "date": "", "ddg_snippet": "This is the official implementation of \" CoPINN : Cognitive Physics - informed Neural Network \" (ICML 2025, Spotlight) - CoPINN / CoPINN .pdf at main · siyuancncd/ CoPINN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN/blob/main/CoPINN.pdf", "content": "This is the official implementation of \" CoPINN : Cognitive Physics - informed Neural Network \" (ICML 2025, Spotlight) - CoPINN / CoPINN .pdf at main · siyuancncd/ CoPINN"}
+{"idx": 6, "title": "Physics-informed recurrent neural networks and hyper-parameter ...", "date": "", "ddg_snippet": "The second approach is using a hybrid recurrent neural network cell with embedded physics - informed and data-driven nodes performing Euler discretization. Physics - informed neural networks can improve test performance even though decrease in training performance might be observed.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0098135423000649", "content": "The second approach is using a hybrid recurrent neural network cell with embedded physics - informed and data-driven nodes performing Euler discretization. Physics - informed neural networks can improve test performance even though decrease in training performance might be observed."}
+{"idx": 7, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations, which have demonstrated significant...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI", "content": "Physics - informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations, which have demonstrated significant..."}
+{"idx": 8, "title": "Physics-informed neural networks with hard linear equality constraints", "date": "", "ddg_snippet": "Despite this, neural networks are data-driven models and devoid of any physics . The incorporation of physics into neural networks can improve generalization and data efficiency. The physics - informed neural network (PINN) is an approach to leverage known physical constraints present in the data, but it cannot strictly satisfy them in the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0098135424001820", "content": "Despite this, neural networks are data-driven models and devoid of any physics . The incorporation of physics into neural networks can improve generalization and data efficiency. The physics - informed neural network (PINN) is an approach to leverage known physical constraints present in the data, but it cannot strictly satisfy them in the ..."}
+{"idx": 9, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Spotlight Poster CoPINN : Cognitive Physics - Informed Neural Networks Siyuan Duan · Wenyuan Wu · Peng Hu · Zhenwen Ren · Dezhong Peng · Yuan Sun East Exhibition Hall A-B #E-2302", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46458", "content": "Spotlight Poster CoPINN : Cognitive Physics - Informed Neural Networks Siyuan Duan · Wenyuan Wu · Peng Hu · Zhenwen Ren · Dezhong Peng · Yuan Sun East Exhibition Hall A-B #E-2302"}
diff --git a/data/sampled_jsons/CoPINN_cognitive_scheduler_weight_easiest_hardest_samples_formula_vie_vih_epoch_Ne_25001_50000_year_2024.jsonl b/data/sampled_jsons/CoPINN_cognitive_scheduler_weight_easiest_hardest_samples_formula_vie_vih_epoch_Ne_25001_50000_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c6b014f829017ca8227b8db262eb471969a52edb
--- /dev/null
+++ b/data/sampled_jsons/CoPINN_cognitive_scheduler_weight_easiest_hardest_samples_formula_vie_vih_epoch_Ne_25001_50000_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - siyuancncd/CoPINN: This is the official ...", "date": "", "ddg_snippet": "Then, during the training phase, we dynamically evaluate the difficulty of each sample according to the gradient of the PDE residuals. Finally, we propose a cognitive training scheduler to progressively optimize the entire sampling regions from easy to hard, thereby embracing robustness and generalization against predicting physical boundary ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN", "content": "Then, during the training phase, we dynamically evaluate the difficulty of each sample according to the gradient of the PDE residuals. Finally, we propose a cognitive training scheduler to progressively optimize the entire sampling regions from easy to hard, thereby embracing robustness and generalization against predicting physical boundary ..."}
+{"idx": 1, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "To be specific, during the training process, from the first epoch to the final one, the weight assigned to the easiest samples decreases from one to zero. For a training phase with Ne epochs, we expect the weight of the easiest sample to be 1 in the first epoch and 0 in Ne epochs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "To be specific, during the training process, from the first epoch to the final one, the weight assigned to the easiest samples decreases from one to zero. For a training phase with Ne epochs, we expect the weight of the easiest sample to be 1 in the first epoch and 0 in Ne epochs."}
+{"idx": 2, "title": "CoPINN: Cognitive Physics-Informed Neural Networks | Read ...", "date": "", "ddg_snippet": "During the training process, CoPINN dynamically evaluates the difficulty of predicting each sample based on the gradient magnitude of the PDE residuals. Finally, a cognitive training scheduler is employed to adaptively optimize the model from easy to hard.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46458/paper", "content": "During the training process, CoPINN dynamically evaluates the difficulty of predicting each sample based on the gradient magnitude of the PDE residuals. Finally, a cognitive training scheduler is employed to adaptively optimize the model from easy to hard."}
+{"idx": 3, "title": "CoPINN: Cognitive Physics-Informed Neural Networks - OpenReview", "date": "", "ddg_snippet": "To deal with this, we propose Cognitive Physics-Informed Neural Networks CoPINN that imitate the human cognitive learning manner from easy to hard, thereby effectively mitigating UPP and promoting the application of the PINN method in real scenarios.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI&amp;referrer=[Author+Console](/group?id=ICML.cc/2025/Conference/Authors#your-submissions)", "content": "To deal with this, we propose Cognitive Physics-Informed Neural Networks CoPINN that imitate the human cognitive learning manner from easy to hard, thereby effectively mitigating UPP and promoting the application of the PINN method in real scenarios."}
+{"idx": 4, "title": "CoPINN/README.md at main · siyuancncd/CoPINN · GitHub", "date": "", "ddg_snippet": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network\" (ICML 2025) - CoPINN /README.md at main · siyuancncd/ CoPINN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN/blob/main/README.md", "content": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network\" (ICML 2025) - CoPINN /README.md at main · siyuancncd/ CoPINN"}
+{"idx": 5, "title": "https://huggingface.co/vocab-transformers/dense_en...", "date": "", "ddg_snippet": "... ne \": 376, + \"##op\": 377, + \"by\": 378, + \"su\": 379, + \"from\": 380, + \"##ak ... weight \": 1393, + \"applic\": 1394, + \"gover\": 1395, + \"view\": 1396, + \"port ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k_emb_updated/commit/eaa7462733901f5b34600900bd57707a22f7bacd.diff?file=tokenizer.json", "content": "... ne \": 376, + \"##op\": 377, + \"by\": 378, + \"su\": 379, + \"from\": 380, + \"##ak ... weight \": 1393, + \"applic\": 1394, + \"gover\": 1395, + \"view\": 1396, + \"port ..."}
+{"idx": 6, "title": "cmnt_vocab.txt", "date": "", "ddg_snippet": "... cognitive 9445 cognitively 9446 cognizant 9447 cognoscenti 9448 cogs 9449 ... easiest 15957 easily 15958 easing 15959 easley 15960 east 15961 eastasia ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~ark/blog-data/data/blog_data_v1_0/dk/hbc_data/data/cmnt_vocab.txt", "content": "... cognitive 9445 cognitively 9446 cognizant 9447 cognoscenti 9448 cogs 9449 ... easiest 15957 easily 15958 easing 15959 easley 15960 east 15961 eastasia ..."}
+{"idx": 7, "title": "dmdb › chandra › Enron2.1 › words", "date": "", "ddg_snippet": "... cognitive 7847 cognizable 7848 cognizant 7849 cognoscenti 7850 coh 7851 ... easiest 12868 easily 12869 easing 12870 easment 12871 easp 12872 eassey ...", "subpage_snippet": "", "source": "www.ics.uci.edu", "link": "https://www.ics.uci.edu/~dmdb/chandra/Enron2.1/words.txt", "content": "... cognitive 7847 cognizable 7848 cognizant 7849 cognoscenti 7850 coh 7851 ... easiest 12868 easily 12869 easing 12870 easment 12871 easp 12872 eassey ..."}
+{"idx": 8, "title": "googlelist.counts", "date": "", "ddg_snippet": "... weight 90519101 895 town 90498384 896 heart 90281933 897 advertising ... ne 32172900 2482 truck 32139507 2483 behavior 32130794 2484 ray 32127782 2485 ...", "subpage_snippet": "", "source": "mit.edu", "link": "http://mit.edu/~ecprice/Public/freq/googlelist.counts", "content": "... weight 90519101 895 town 90498384 896 heart 90281933 897 advertising ... ne 32172900 2482 truck 32139507 2483 behavior 32130794 2484 ray 32127782 2485 ..."}
+{"idx": 9, "title": "word list", "date": "", "ddg_snippet": "... weight 791 1116 objectives 792 1116 study 793 1115 metadata 794 1114 ... cognitive 1158 733 courses 1159 733 significant 1160 732 jp 1161 732 managing ...", "subpage_snippet": "", "source": "oldsite.english.ucsb.edu", "link": "http://oldsite.english.ucsb.edu/faculty/ayliu/unlocked/humanities-patents/antconc_wordlist.txt", "content": "... weight 791 1116 objectives 792 1116 study 793 1115 metadata 794 1114 ... cognitive 1158 733 courses 1159 733 significant 1160 732 jp 1161 732 managing ..."}
diff --git a/data/sampled_jsons/Concept_Bottleneck_Model_limitations_applicability_factors_year_2024.jsonl b/data/sampled_jsons/Concept_Bottleneck_Model_limitations_applicability_factors_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..55d7b81c0ab7725d7aa4235a0d8b573fe56af451
--- /dev/null
+++ b/data/sampled_jsons/Concept_Bottleneck_Model_limitations_applicability_factors_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Do Concept Bottleneck Models Respect Localities?", "date": "", "ddg_snippet": "by N Raman · Cited by 1 — The absence of these factors limits the applicability of the experimental results, making it difficult to fully reflect the model's performance ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4mCkRbUXOf", "content": "by N Raman · Cited by 1 — The absence of these factors limits the applicability of the experimental results, making it difficult to fully reflect the model's performance ..."}
+{"idx": 1, "title": "Interpretable prognostics with concept bottleneck models", "date": "", "ddg_snippet": "by F Forest · 2025 · Cited by 3 — We propose concept bottleneck models for more interpretable prognostics, where degradation modes of an asset are used as intermediate concepts .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253525005007", "content": "by F Forest · 2025 · Cited by 3 — We propose concept bottleneck models for more interpretable prognostics, where degradation modes of an asset are used as intermediate concepts ."}
+{"idx": 2, "title": "VLG-CBM: Training Concept Bottleneck Models with Vision ...", "date": "", "ddg_snippet": "6 Conclusion, Potential Limitations and Future work. In this work, we study how to improve the interpretability and performance of concept bottleneck models .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95698", "content": "6 Conclusion, Potential Limitations and Future work. In this work, we study how to improve the interpretability and performance of concept bottleneck models ."}
+{"idx": 3, "title": "An Analysis of Concept Bottleneck Models: Measuring ...", "date": "", "ddg_snippet": "22 May 2025 — Concept bottleneck models (CBMs) ensure interpretability by decomposing predictions into human interpretable concepts . Yet the annotations used ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16705v1", "content": "22 May 2025 — Concept bottleneck models (CBMs) ensure interpretability by decomposing predictions into human interpretable concepts . Yet the annotations used ..."}
+{"idx": 4, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "by PW Koh · 2020 · Cited by 1205 — In this paper, we propose a straightfor- ward method for turning any end-to-end neural network into a concept bottleneck model , given concept annota- tions at ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v119/koh20a/koh20a.pdf", "content": "by PW Koh · 2020 · Cited by 1205 — In this paper, we propose a straightfor- ward method for turning any end-to-end neural network into a concept bottleneck model , given concept annota- tions at ..."}
+{"idx": 5, "title": "Are They the Same Picture? Adapting Concept Bottleneck ...", "date": "", "ddg_snippet": "by V Balloli · Cited by 2 — To show the efficacy of CHAIR, we demonstrate that our method performs better than similar models on image retrieval metrics without any external intervention.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0866.pdf", "content": "by V Balloli · Cited by 2 — To show the efficacy of CHAIR, we demonstrate that our method performs better than similar models on image retrieval metrics without any external intervention."}
+{"idx": 6, "title": "Navigating the landscape of concept-supported XAI", "date": "", "ddg_snippet": "by Z Shams Khoozani · 2024 · Cited by 9 — One of the limitations of Concept Bottleneck (CB) models , as previously stated, is the requirement for critical changes to the network ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11042-023-17666-y", "content": "by Z Shams Khoozani · 2024 · Cited by 9 — One of the limitations of Concept Bottleneck (CB) models , as previously stated, is the requirement for critical changes to the network ..."}
+{"idx": 7, "title": "ICML Poster Editable Concept Bottleneck Models", "date": "", "ddg_snippet": "17 Jul 2025 — CBMs add a bottleneck layer for placing human-understandable concepts . In the prediction process, CBMs first predict the concept labels using ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45301", "content": "17 Jul 2025 — CBMs add a bottleneck layer for placing human-understandable concepts . In the prediction process, CBMs first predict the concept labels using ..."}
+{"idx": 8, "title": "Cross-Modal Conceptualization in Bottleneck Models", "date": "", "ddg_snippet": "The limited interpretability of modern deep learning poses a significant barrier, hindering their practical application in many scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.14805v2", "content": "The limited interpretability of modern deep learning poses a significant barrier, hindering their practical application in many scenarios."}
+{"idx": 9, "title": "Language Model Guided Concept Bottlenecks for ...", "date": "", "ddg_snippet": "by Y Yang · 2023 · Cited by 309 — Application of CBMs is limited because they require costly attribute annotations by domain experts and often under-perform their black box counterparts. In ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Language_in_a_Bottle_Language_Model_Guided_Concept_Bottlenecks_for_CVPR_2023_paper.pdf", "content": "by Y Yang · 2023 · Cited by 309 — Application of CBMs is limited because they require costly attribute annotations by domain experts and often under-perform their black box counterparts. In ... 11 pages"}
diff --git a/data/sampled_jsons/Concept_Bottleneck_Models_Koh_2020_abstract_high-level_concepts_provided_at_training_time_year_2020.jsonl b/data/sampled_jsons/Concept_Bottleneck_Models_Koh_2020_abstract_high-level_concepts_provided_at_training_time_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bfbbee3418cb0df7f71224e995223b8462aeb8c7
--- /dev/null
+++ b/data/sampled_jsons/Concept_Bottleneck_Models_Koh_2020_abstract_high-level_concepts_provided_at_training_time_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Explainable artificial intelligence - Wikipedia", "date": "", "ddg_snippet": "20 Concept Bottleneck Models , which use concept - level abstractions to explain model reasoning, are examples of this and can be applied in both image ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Explainable_artificial_intelligence", "content": "20 Concept Bottleneck Models , which use concept - level abstractions to explain model reasoning, are examples of this and can be applied in both image ..."}
+{"idx": 1, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "... koh20a, title = { Concept Bottleneck Models }, author = { Koh , Pang Wei and Nguyen, Thao and Tang, Yew Siang and Mussmann, Stephen and Pierson, Emma and ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a.html", "content": "... koh20a, title = { Concept Bottleneck Models }, author = { Koh , Pang Wei and Nguyen, Thao and Tang, Yew Siang and Mussmann, Stephen and Pierson, Emma and ..."}
+{"idx": 2, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "... koh20a, title = { Concept Bottleneck Models }, author = { Koh , Pang Wei and Nguyen, Thao and Tang, Yew Siang and Mussmann, Stephen and Pierson, Emma and ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v119/koh20a.html", "content": "... koh20a, title = { Concept Bottleneck Models }, author = { Koh , Pang Wei and Nguyen, Thao and Tang, Yew Siang and Mussmann, Stephen and Pierson, Emma and ..."}
+{"idx": 3, "title": "[2007.04612] Concept Bottleneck Models", "date": "", "ddg_snippet": "... concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high - level clinical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.04612", "content": "... concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high - level clinical ..."}
+{"idx": 4, "title": "If Concept Bottlenecks are the Question, are Foundation Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) are neural networks designed to conjoin high performance with ante-hoc interpretability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.19774v2", "content": "Concept Bottleneck Models (CBMs) are neural networks designed to conjoin high performance with ante-hoc interpretability."}
+{"idx": 5, "title": "GitHub - yewsiang/ConceptBottleneck: Concept Bottleneck Models,", "date": "", "ddg_snippet": "... concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high - level clinical ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yewsiang/ConceptBottleneck", "content": "... concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high - level clinical ..."}
+{"idx": 6, "title": "Concept Bottleneck Models | TransferLab — appliedAI Institute", "date": "", "ddg_snippet": "... concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high - level clinical ...", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/refs/koh_concept_2020/", "content": "... concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high - level clinical ..."}
+{"idx": 7, "title": "Explainable AI | TransferLab — appliedAI Institute", "date": "", "ddg_snippet": "Intrinsically interpretable models are therefore from the point of explainability conceptually in advantage since they guarantee to reflect the true ...", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/series/explainable-ai/", "content": "Intrinsically interpretable models are therefore from the point of explainability conceptually in advantage since they guarantee to reflect the true ..."}
+{"idx": 8, "title": "16 Responsible AI – Machine Learning Systems", "date": "", "ddg_snippet": "Machine learning models are increasingly used to automate decisions in high -stakes social domains like healthcare, criminal justice, and employment.", "subpage_snippet": "", "source": "mlsysbook.ai", "link": "https://mlsysbook.ai/contents/core/responsible_ai/responsible_ai.html", "content": "Machine learning models are increasingly used to automate decisions in high -stakes social domains like healthcare, criminal justice, and employment."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Concept_Bottleneck_Models_improvements_solutions_2024_2025_year_2024.jsonl b/data/sampled_jsons/Concept_Bottleneck_Models_improvements_solutions_2024_2025_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06b0798d06951f1be7c323df93dffe9bc5da2362
--- /dev/null
+++ b/data/sampled_jsons/Concept_Bottleneck_Models_improvements_solutions_2024_2025_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2412.07992] Concept Bottleneck Large Language Models", "date": "", "ddg_snippet": "[Submitted on 11 Dec 2024 (v1), last revised 3 Apr 2025 (this version, v3)].View a PDF of the paper titled Concept Bottleneck Large Language Models , by Chung-En Sun and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.07992", "content": "[Submitted on 11 Dec 2024 (v1), last revised 3 Apr 2025 (this version, v3)].View a PDF of the paper titled Concept Bottleneck Large Language Models , by Chung-En Sun and 3 other authors."}
+{"idx": 1, "title": "A theoretical design of concept sets: improving the... | OpenReview", "date": "", "ddg_snippet": "However, despite growing interest in concept - bottleneck models (CBMs), there is a lack of clear understanding regarding the properties of concept sets and their impact on model performance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oTv6Qa12G0&noteId=IcK0rpehH9", "content": "However, despite growing interest in concept - bottleneck models (CBMs), there is a lack of clear understanding regarding the properties of concept sets and their impact on model performance."}
+{"idx": 2, "title": "GitHub - riverback/Awesome- Concept - Bottleneck - Models : A list of...", "date": "", "ddg_snippet": "The original Concept Bottleneck Model maps each concept to a single (probabilistic) value to construct the concept bottleneck layer, followed by a linear layer that predicts image-level class labels based on these concept values.AAAI 2025 . MLLM (LLaVA).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/riverback/Awesome-Concept-Bottleneck-Models", "content": "The original Concept Bottleneck Model maps each concept to a single (probabilistic) value to construct the concept bottleneck layer, followed by a linear layer that predicts image-level class labels based on these concept values.AAAI 2025 . MLLM (LLaVA)."}
+{"idx": 3, "title": "Language Guided Concept Bottleneck Models for Interpretable...", "date": "", "ddg_snippet": "Among these interpretable models , Concept Bottleneck Models [22, 48, 58, 61, 65] provide explanations of the model ’s decision-making process in a straightforward man-ner. CBMs are designed to be interpretable, incorporating an intermediate Concept Bottleneck Layer (CBL), where.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Language_Guided_Concept_Bottleneck_Models_for_Interpretable_Continual_Learning_CVPR_2025_paper.pdf", "content": "Among these interpretable models , Concept Bottleneck Models [22, 48, 58, 61, 65] provide explanations of the model ’s decision-making process in a straightforward man-ner. CBMs are designed to be interpretable, incorporating an intermediate Concept Bottleneck Layer (CBL), where."}
+{"idx": 4, "title": "An Analysis of Concept Bottleneck Models : Measuring...", "date": "", "ddg_snippet": "Uncertainty-based Concept Ranking for Targeted Interventions. Description of the concept ranking process and its importance in identifying the most vulnerable areas for improvement . Integration of uncertainty metrics to guide targeted interventions in noisy environments.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-an-analysis-of-concept-bottleneck-models-measuring-cmb1aebe79bi307oplkbu6scf", "content": "Uncertainty-based Concept Ranking for Targeted Interventions. Description of the concept ranking process and its importance in identifying the most vulnerable areas for improvement . Integration of uncertainty metrics to guide targeted interventions in noisy environments."}
+{"idx": 5, "title": "Bottleneck Models : Efficient Neural Networks for Compact...", "date": "", "ddg_snippet": "Concept bottleneck models are designed with a special bottleneck layer, a narrow passageway in their neural network structure. This bottleneck layer forces the network to extract the most essential features, the core concepts , from the data it’s processing.", "subpage_snippet": "", "source": "physicsclass.blog", "link": "https://physicsclass.blog/bottleneck-models-efficient-neural-networks-compact-representations/", "content": "Concept bottleneck models are designed with a special bottleneck layer, a narrow passageway in their neural network structure. This bottleneck layer forces the network to extract the most essential features, the core concepts , from the data it’s processing."}
+{"idx": 6, "title": "Stochastic Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Stochastic-Concept-Bottleneck-Models-Stochastic-Concept-Bottleneck-Models-5b6a9915-d96e-442a-8a70-2940536c099a", "content": "Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input."}
+{"idx": 7, "title": "PC Bottleneck Calculator | CPU & GPU Performance Analysis", "date": "", "ddg_snippet": "Discover if your CPU and GPU are well balanced with our trusted PC Bottleneck Calculator. Get detailed performance insights for gaming, streaming, and content creation.", "subpage_snippet": "", "source": "www.pc-bottleneck-calculator.com", "link": "https://www.pc-bottleneck-calculator.com/", "content": "Discover if your CPU and GPU are well balanced with our trusted PC Bottleneck Calculator. Get detailed performance insights for gaming, streaming, and content creation."}
+{"idx": 8, "title": "Concept Bottleneck Models - Microsoft Research", "date": "", "ddg_snippet": "Concept Bottleneck Models . Pang Wei Koh , Thao Nguyen State-of-the-art models today do not typically support the manipulation of concepts like “the existence of bone spurs”, as they are trained end-to-end to go directly from raw input (e.g., pixels) to output (e.g., arthritis severity).", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/concept-bottleneck-models/", "content": "Concept Bottleneck Models . Pang Wei Koh , Thao Nguyen State-of-the-art models today do not typically support the manipulation of concepts like “the existence of bone spurs”, as they are trained end-to-end to go directly from raw input (e.g., pixels) to output (e.g., arthritis severity)."}
+{"idx": 9, "title": "AI Summary: Improving Intervention Efficacy via Concept ...", "date": "", "ddg_snippet": "AI generated summary. Improving model performance via concept realignment. This paper proposes a concept intervention realignment module to improve the efficacy of human interventions in concept bottleneck models .", "subpage_snippet": "", "source": "www.bulletpapers.ai", "link": "https://www.bulletpapers.ai/paper/7d76180a-6c90-0b50-e666-bf1986b5c3cc", "content": "AI generated summary. Improving model performance via concept realignment. This paper proposes a concept intervention realignment module to improve the efficacy of human interventions in concept bottleneck models ."}
diff --git a/data/sampled_jsons/Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Catoni-OFUL_SAVE_algorithm_Table_1_year_2023.jsonl b/data/sampled_jsons/Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Catoni-OFUL_SAVE_algorithm_Table_1_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e671657ca6e7621d069aa3e16dd388e369ac23c9
--- /dev/null
+++ b/data/sampled_jsons/Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Catoni-OFUL_SAVE_algorithm_Table_1_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "In this work, we consider contextual bandits under heavy - tailed rewards (rewards with a large range R) with general function approximation.Corruption- robust algorithms with uncertainty weighting for nonlinear contextual bandits and markov decision processes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "In this work, we consider contextual bandits under heavy - tailed rewards (rewards with a large range R) with general function approximation.Corruption- robust algorithms with uncertainty weighting for nonlinear contextual bandits and markov decision processes."}
+{"idx": 1, "title": "Multi-Armed Bandits | Papers With Code", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/multi-armed-bandits/codeless?page=4", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics."}
+{"idx": 2, "title": "[PDF] The fundamentals of heavy -tails: properties... | Semantic Scholar", "date": "", "ddg_snippet": "Save . Catoni Contextual Bandits are Robust to Heavy - tailed Rewards . Chen YeYujia JinAlekh AgarwalTong Zhang.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-fundamentals-of-heavy-tails:-properties,-and-Nair-Wierman/f7d0070cda82026150d7c9cdf539a57cc6a9663f", "content": "Save . Catoni Contextual Bandits are Robust to Heavy - tailed Rewards . Chen YeYujia JinAlekh AgarwalTong Zhang."}
+{"idx": 3, "title": "Recent Advances in Algorithmic High-Dimensional Robust Statistics", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .[Show full abstract] Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/337273412_Recent_Advances_in_Algorithmic_High-Dimensional_Robust_Statistics", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .[Show full abstract] Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation."}
+{"idx": 4, "title": "Chenlu Ye - Google Scholar", "date": "", "ddg_snippet": "Corruption-robust algorithms with uncertainty weighting for nonlinear contextual bandits and markov decision processes. Catoni contextual bandits are robust to heavy - tailed rewards .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=c8yK5XsAAAAJ&hl=en", "content": "Corruption-robust algorithms with uncertainty weighting for nonlinear contextual bandits and markov decision processes. Catoni contextual bandits are robust to heavy - tailed rewards ."}
+{"idx": 5, "title": "Nonstationary functional time series forecasting-Bohrium", "date": "", "ddg_snippet": "[7] Catoni Contextual Bandits are Robust to Heavy - tailed Rewards . Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range $[0, R]$, and their regret scales polynomially with this reward range $R$. CChenlu YeYYujia Jin.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/nonstationary-functional-time-series-forecasting/1066237717710372871-108581", "content": "[7] Catoni Contextual Bandits are Robust to Heavy - tailed Rewards . Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range $[0, R]$, and their regret scales polynomially with this reward range $R$. CChenlu YeYYujia Jin."}
+{"idx": 6, "title": "Google at ICML 2025", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - Tailed Rewards . Chenlu Ye, Yujia Jin*, Alekh Agarwal, Tong Zhang.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/conferences-and-events/google-at-icml-2025/", "content": "Catoni Contextual Bandits are Robust to Heavy - Tailed Rewards . Chenlu Ye, Yujia Jin*, Alekh Agarwal, Tong Zhang."}
+{"idx": 7, "title": "Alekh Agarwal · CSAuthors", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Robust Preference Optimization through Reward Model Distillation.", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/alekh-agarwal/", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Robust Preference Optimization through Reward Model Distillation."}
+{"idx": 8, "title": "dblp: List of computer science publications by Alekh Agarwal", "date": "", "ddg_snippet": "Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang: Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Chen-Yu Wei, Haipeng Luo, Alekh Agarwal: Taking a hint: How to leverage loss predictors in contextual bandits ?", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/24/4383.html", "content": "Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang: Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Chen-Yu Wei, Haipeng Luo, Alekh Agarwal: Taking a hint: How to leverage loss predictors in contextual bandits ?"}
+{"idx": 9, "title": "Chenlu Ye", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint.", "subpage_snippet": "", "source": "chenluye99.github.io", "link": "https://chenluye99.github.io/", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint."}
diff --git a/data/sampled_jsons/Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Catoni.jsonl b/data/sampled_jsons/Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Catoni.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8ee69aa8c18adc8c5b7d26e4546e76e3ed32d277
--- /dev/null
+++ b/data/sampled_jsons/Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Catoni.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Authors:Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "by C Ye · 2025 · Cited by 1 — Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Authors:Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Download PDF. Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang. Published: 01 May 2025, Last ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5IpVe9PH14&noteId=J3K6uYfoM5", "content": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Download PDF. Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang. Published: 01 May 2025, Last ..."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Chenlu Ye∗†. Yujia Jin‡. Alekh Agarwal§. Tong Zhang¶. Abstract. Typical contextual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Chenlu Ye∗†. Yujia Jin‡. Alekh Agarwal§. Tong Zhang¶. Abstract. Typical contextual ..."}
+{"idx": 3, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Paper. Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Published Feb 4, 2025 · Chen Ye, Yujia Jin, Alekh Agarwal +1 more. ArXiv. UNKNOWN SJR score.", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/details/78362c3dc83d5c9b8d91fb3a1d2caf4e/", "content": "Paper. Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Published Feb 4, 2025 · Chen Ye, Yujia Jin, Alekh Agarwal +1 more. ArXiv. UNKNOWN SJR score."}
+{"idx": 4, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards.", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . ... Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed ...", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/StatsPapers/status/1887312882796199974", "content": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . ... Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed ..."}
+{"idx": 5, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "Les bandits contextuels de Catoni sont robustes face aux récompenses à queues lourdes. ChatDOC. Catoni Contextual Bandits are Robust to Heavy-tailed Rewards .", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/fr/chatpaper/paper/165093", "content": "Les bandits contextuels de Catoni sont robustes face aux récompenses à queues lourdes. ChatDOC. Catoni Contextual Bandits are Robust to Heavy-tailed Rewards ."}
+{"idx": 6, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . 1 month ... Catoni Contextual Bandits are Robust to Heavy-tailed Rewards | Read Paper on Bytez.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46438/authors?_c=eyJ2IjoxLCJyZWxhdGVkIjpbImNvZGUiLCJyZWZlcmVuY2VzIiwiY29uZmVyZW5jZSJdfQ==", "content": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . 1 month ... Catoni Contextual Bandits are Robust to Heavy-tailed Rewards | Read Paper on Bytez."}
+{"idx": 7, "title": "Multi-Armed Bandits", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . no code yet • 4 Feb 2025. When the variance of the reward at each round is known, we use a ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/multi-armed-bandits/codeless?page=6", "content": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . no code yet • 4 Feb 2025. When the variance of the reward at each round is known, we use a ..."}
+{"idx": 8, "title": "Chenlu Ye", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards · Towards robust model-based reinforcement learning against adversarial corruption · Corruption- ...", "subpage_snippet": "", "source": "chenluye99.github.io", "link": "https://chenluye99.github.io/", "content": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards · Towards robust model-based reinforcement learning against adversarial corruption · Corruption- ..."}
+{"idx": 9, "title": "Search", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards · pdf icon · Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang. Published: 01 May 2025, Last Modified ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search?term=~Chenlu_Ye1&content=authors&group=all&source=forum&sort=cdate:desc", "content": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards · pdf icon · Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang. Published: 01 May 2025, Last Modified ..."}
diff --git a/data/sampled_jsons/Contractor_et_al._2022_OpenRAILS_license_abstract_year_2022.jsonl b/data/sampled_jsons/Contractor_et_al._2022_OpenRAILS_license_abstract_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0b91116d1cd654112a3e1c2af1e1fe90efe8c0a0
--- /dev/null
+++ b/data/sampled_jsons/Contractor_et_al._2022_OpenRAILS_license_abstract_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "StarCoder: may the source be with you!", "date": "", "ddg_snippet": "StarCoderBase is trained on 1 trillion tokens sourced from The Stack (Kocetkov et al ., 2022 ) , a large collection of permissively licensed GitHub ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.06161v2", "content": "StarCoderBase is trained on 1 trillion tokens sourced from The Stack (Kocetkov et al ., 2022 ) , a large collection of permissively licensed GitHub ..."}
+{"idx": 1, "title": "OpenRAIL : Towards open and responsible AI licensing frameworks", "date": "", "ddg_snippet": "Open & Responsible AI licenses (\" OpenRAIL \") are AI-specific licenses enabling open access, use and distribution of AI artifacts while requiring a responsible use of the latter.( 2022 ); Moran (2021); Contractor et al .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/open_rail", "content": "Open & Responsible AI licenses (\" OpenRAIL \") are AI-specific licenses enabling open access, use and distribution of AI artifacts while requiring a responsible use of the latter.( 2022 ); Moran (2021); Contractor et al ."}
+{"idx": 2, "title": "Position: Standardization of Behavioral Use Clauses is Necessary for...", "date": "", "ddg_snippet": "OpenRAIL licenses ( Contractor et al ., 2022 a), a specific variant of RAIL licenses , were the second most used license category. To date, such licenses have primarily been applied to AI models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=7JKVPNEBkU", "content": "OpenRAIL licenses ( Contractor et al ., 2022 a), a specific variant of RAIL licenses , were the second most used license category. To date, such licenses have primarily been applied to AI models."}
+{"idx": 3, "title": "s-nlp/bart-base-detox · Hugging Face", "date": "", "ddg_snippet": "License : openrail ++. Model card. Files Files and versions.Citation. @inproceedings{logacheva- etal - 2022 -paradetox, title = \"{P}ara{D}etox: Detoxification with Parallel Data\", author = \"Logacheva, Varvara and.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/s-nlp/bart-base-detox", "content": "License : openrail ++. Model card. Files Files and versions.Citation. @inproceedings{logacheva- etal - 2022 -paradetox, title = \"{P}ara{D}etox: Detoxification with Parallel Data\", author = \"Logacheva, Varvara and."}
+{"idx": 4, "title": "The 2025 Ig Nobel Prizes honor garlicky babies, drunk bats, and more", "date": "", "ddg_snippet": "Image: Daniele Dendi et al , 2022 . 2022 Ig Nobel Prize winners include ducks in a row, constipated scorpions, ice cream, and more.", "subpage_snippet": "", "source": "www.popsci.com", "link": "https://www.popsci.com/science/ig-nobel-prizes-2025/", "content": "Image: Daniele Dendi et al , 2022 . 2022 Ig Nobel Prize winners include ducks in a row, constipated scorpions, ice cream, and more."}
+{"idx": 5, "title": "Можно ли лечить зубы при насморке - мнение стоматолога Dental...", "date": "", "ddg_snippet": "По данным обзора Savouré et al ., 2022 (PMID: 35344304), ринит — одно из самых распространённых хронических состояний в мире. Источник: Georgia A. Liva et al ., “Review of Rhinitis”, PMC8303640, 2021. Когда нельзя лечить зубы при насморке.", "subpage_snippet": "", "source": "dentalopera.ru", "link": "https://dentalopera.ru/stati/mozhno-li-lechit-zuby-s-nasmorkom/", "content": "По данным обзора Savouré et al ., 2022 (PMID: 35344304), ринит — одно из самых распространённых хронических состояний в мире. Источник: Georgia A. Liva et al ., “Review of Rhinitis”, PMC8303640, 2021. Когда нельзя лечить зубы при насморке."}
+{"idx": 6, "title": "Отчет о клиническом случае: трижды негативный рак молочной...", "date": "", "ddg_snippet": "В октябре 2022 года у женщины был диагностирован тройной негативный рак молочной железы III стадии после того, как она заметила некоторые изменения на коже груди, а также боль. У нее диагностировали тройной негативный рак молочной железы...", "subpage_snippet": "", "source": "www.fenbendazole.org", "link": "https://www.fenbendazole.org/ru/отчет-о-клиническом-случае-тройной-не/", "content": "В октябре 2022 года у женщины был диагностирован тройной негативный рак молочной железы III стадии после того, как она заметила некоторые изменения на коже груди, а также боль. У нее диагностировали тройной негативный рак молочной железы..."}
+{"idx": 7, "title": "Новая газета Европа", "date": "", "ddg_snippet": "Военные предприятия в России сократили найм до минимума с 2022 года, а зарплаты в оборонке упали впервые с момента вторжения. Исследование «Новой-Европа».", "subpage_snippet": "", "source": "novayagazeta.eu", "link": "https://novayagazeta.eu/", "content": "Военные предприятия в России сократили найм до минимума с 2022 года, а зарплаты в оборонке упали впервые с момента вторжения. Исследование «Новой-Европа»."}
+{"idx": 8, "title": "Динозавр-пылесос • Анна Новиковская • Научная картинка дня на...", "date": "", "ddg_snippet": "All Abstracts .Длина масштабного отрезка — 2 см. Рисунок из статьи P. Sereno et al ., 2007.", "subpage_snippet": "", "source": "elementy.ru", "link": "https://elementy.ru/kartinka_dnya/2097/Dinozavr_pylesos", "content": "All Abstracts .Длина масштабного отрезка — 2 см. Рисунок из статьи P. Sereno et al ., 2007."}
+{"idx": 9, "title": "Thinking Upstream: Ethics and Policy Opportunities in AI Supply Chains", "date": "", "ddg_snippet": "However, AI ethics approaches often focus on the component being developed or its downstream effects, rather than its upstream supply chain. Company AI Ethics policy statements often scrutinize design while avoiding scrutiny of downstream business uses (Greene et al ., 2019) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2303.07529v2", "content": "However, AI ethics approaches often focus on the component being developed or its downstream effects, rather than its upstream supply chain. Company AI Ethics policy statements often scrutinize design while avoiding scrutiny of downstream business uses (Greene et al ., 2019) ."}
diff --git a/data/sampled_jsons/Creating_noise_from_data_is_easy;_creating_data_from_noise_is_generative_modeling.jsonl b/data/sampled_jsons/Creating_noise_from_data_is_easy;_creating_data_from_noise_is_generative_modeling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..86f193c07bd02590518d8ef58b5509d135dd4b85
--- /dev/null
+++ b/data/sampled_jsons/Creating_noise_from_data_is_easy;_creating_data_from_noise_is_generative_modeling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICLR 2021 Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "Abstract: Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/oral/3402", "content": "Abstract: Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise ..."}
+{"idx": 1, "title": "[2011.13456v1] Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456v1", "content": "Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially ..."}
+{"idx": 2, "title": "PDF S -B GENERATIVE MODELING THROUGH S DIFFERENTIAL EQUATIONS - OpenReview", "date": "", "ddg_snippet": "ABSTRACT Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms a com- plex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/ef0eadbe07115b0853e964f17aa09d811cd490f1.pdf", "content": "ABSTRACT Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms a com- plex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise ..."}
+{"idx": 3, "title": "Score-Based Generative Modeling with SDEs", "date": "", "ddg_snippet": "Creating noise from data is easy; creating data from noise is generative modeling . ~ Song et. al. circa 2020 Banger introduction for a paper. I worked through some of it myself and made a very simple tutorial. You'll still have to work out some of the math yourself. If you wish, skip to training. 1. Background 1.1 Introduction Modeling techniques can be likelihood-based or implicit, think ...", "subpage_snippet": "", "source": "jdchawla.xyz", "link": "https://jdchawla.xyz/posts/sde_generative/", "content": "Creating noise from data is easy; creating data from noise is generative modeling . ~ Song et. al. circa 2020 Banger introduction for a paper. I worked through some of it myself and made a very simple tutorial. You'll still have to work out some of the math yourself. If you wish, skip to training. 1. Background 1.1 Introduction Modeling techniques can be likelihood-based or implicit, think ..."}
+{"idx": 4, "title": "Score-Based Generative Modeling through Stochastic Differential ...", "date": "", "ddg_snippet": "Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv201113456S/abstract", "content": "Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially ..."}
+{"idx": 5, "title": "Score-Based Generative Modeling through Stochastic Differential", "date": "", "ddg_snippet": "Creating noise from data is easy ; creating data from noise is generative modeling . ... data distribution to a known prior distribution by slowly ...", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/refs/song_scorebased_2021/", "content": "Creating noise from data is easy ; creating data from noise is generative modeling . ... data distribution to a known prior distribution by slowly ..."}
+{"idx": 6, "title": "[2011.13456] Score-Based Generative Modeling through Stochastic", "date": "", "ddg_snippet": "Abstract: Creating noise from data is easy ; creating data from noise is generative modeling . ... data distribution to a known prior distribution by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456", "content": "Abstract: Creating noise from data is easy ; creating data from noise is generative modeling . ... data distribution to a known prior distribution by ..."}
+{"idx": 7, "title": "Score-Based Generative Modeling through Stochastic Differential", "date": "", "ddg_snippet": "Creating noise from data is easy ; creating data from noise is generative modeling . ... data distribution to a known prior distribution by slowly ...", "subpage_snippet": "", "source": "www.thetalkingmachines.com", "link": "https://www.thetalkingmachines.com/article/score-based-generative-modeling-through-stochastic-differential-equations", "content": "Creating noise from data is easy ; creating data from noise is generative modeling . ... data distribution to a known prior distribution by slowly ..."}
+{"idx": 8, "title": "Score-Based Generative Modeling through Stochastic Differential", "date": "", "ddg_snippet": "Creating noise from data is easy ; creating data from noise is generative modeling . ... data distribution to a known prior distribution by slowly ...", "subpage_snippet": "", "source": "www.thetalkingmachines.com", "link": "http://www.thetalkingmachines.com/article/score-based-generative-modeling-through-stochastic-differential-equations", "content": "Creating noise from data is easy ; creating data from noise is generative modeling . ... data distribution to a known prior distribution by slowly ..."}
+{"idx": 9, "title": "Paper tables with annotated results for Score-Based Generative", "date": "", "ddg_snippet": "Creating noise from data is easy ; creating data from noise is generative modeling . ... data distribution to a known prior distribution by slowly ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/score-based-generative-modeling-through-1/review/", "content": "Creating noise from data is easy ; creating data from noise is generative modeling . ... data distribution to a known prior distribution by slowly ..."}
diff --git a/data/sampled_jsons/Critical_windows_non-asymptotic_theory_feature_emergence_diffusion_models_Li_Chen_2024_full_abstract.jsonl b/data/sampled_jsons/Critical_windows_non-asymptotic_theory_feature_emergence_diffusion_models_Li_Chen_2024_full_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..79026270ad4ebebe436d3d071f9222dd6bfb9171
--- /dev/null
+++ b/data/sampled_jsons/Critical_windows_non-asymptotic_theory_feature_emergence_diffusion_models_Li_Chen_2024_full_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Critical windows: non-asymptotic theory for feature emergence", "date": "", "ddg_snippet": "Critical windows : non - asymptotic theory for feature emergence in diffusion models ... critical windows is highly convenient from an interpretability ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01633v2", "content": "Critical windows : non - asymptotic theory for feature emergence in diffusion models ... critical windows is highly convenient from an interpretability ..."}
+{"idx": 1, "title": "[2403.01633] Critical windows: non-asymptotic theory for", "date": "", "ddg_snippet": "View a PDF of the paper titled Critical windows : non - asymptotic theory for feature emergence in diffusion models , by Marvin Li and Sitan Chen", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.01633", "content": "View a PDF of the paper titled Critical windows : non - asymptotic theory for feature emergence in diffusion models , by Marvin Li and Sitan Chen"}
+{"idx": 2, "title": "Memorization to Generalization: Emergence of Diffusion Models", "date": "", "ddg_snippet": "... model , an interesting phenomenon occurs when the amount of training data reaches its critical memory load — spurious states , or unintended stable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.21777v1", "content": "... model , an interesting phenomenon occurs when the amount of training data reaches its critical memory load — spurious states , or unintended stable ..."}
+{"idx": 3, "title": "DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via", "date": "", "ddg_snippet": "Specifically, our work delves into the innovative intersection between algebraic coding theory and machine learning by exploring non -linear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.08864v2", "content": "Specifically, our work delves into the innovative intersection between algebraic coding theory and machine learning by exploring non -linear ..."}
+{"idx": 4, "title": "ICML 2024 2024 Spotlight Posters", "date": "", "ddg_snippet": "Random feature (RF) mapping is an attractive and powerful technique for solving large-scale nonparametric regression.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/events/2024SpotlightPosters", "content": "Random feature (RF) mapping is an attractive and powerful technique for solving large-scale nonparametric regression."}
+{"idx": 5, "title": "Dynamical models reveal anatomically reliable attractor", "date": "", "ddg_snippet": "We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles.", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00442/127382/Dynamical-models-reveal-anatomically-reliable", "content": "We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles."}
+{"idx": 6, "title": "Bigger Isn’t Always Memorizing: Early Stopping", "date": "", "ddg_snippet": "Diffusion models sohl2015deep ; ho2020denoising have recently emerged as a transformative paradigm in generative AI, enabling the synthesis of high ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16959v2", "content": "Diffusion models sohl2015deep ; ho2020denoising have recently emerged as a transformative paradigm in generative AI, enabling the synthesis of high ..."}
+{"idx": 7, "title": "(PDF) Cryptanalyzing a bit-level image encryption algorithm", "date": "", "ddg_snippet": "BCIEA consists of diffusion and confusion, and its security performance mainly relies on the dynamic mechanisms introduced during diffusion and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/378153227_Cryptanalyzing_a_bit-level_image_encryption_algorithm_based_on_chaotic_maps", "content": "BCIEA consists of diffusion and confusion, and its security performance mainly relies on the dynamic mechanisms introduced during diffusion and ..."}
+{"idx": 8, "title": "Merging two cultures: Deep and statistical learning | Request", "date": "", "ddg_snippet": "We propose the Constrained Heat Kernel Graph Diffusion to address these issues to estimate the appropriate diffusion time based on information theory ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380095317_Merging_two_cultures_Deep_and_statistical_learning", "content": "We propose the Constrained Heat Kernel Graph Diffusion to address these issues to estimate the appropriate diffusion time based on information theory ..."}
+{"idx": 9, "title": "NPG - Preprints", "date": "", "ddg_snippet": "... intelligence (AI) has recently shown promising results in ENSO (El Niño Southern Oscillation) forecasting, outperforming traditional models .", "subpage_snippet": "", "source": "npg.copernicus.org", "link": "https://npg.copernicus.org/preprints/", "content": "... intelligence (AI) has recently shown promising results in ENSO (El Niño Southern Oscillation) forecasting, outperforming traditional models ."}
diff --git a/data/sampled_jsons/Crocker_Stacks_Capturing_dynamics_of_time-varying_data_via_topology.jsonl b/data/sampled_jsons/Crocker_Stacks_Capturing_dynamics_of_time-varying_data_via_topology.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e734ec7a8205346fc66090e0883baa1035b927ce
--- /dev/null
+++ b/data/sampled_jsons/Crocker_Stacks_Capturing_dynamics_of_time-varying_data_via_topology.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Capturing Dynamics of Time-Varying Data via Topology Capturing dynamics of time-varying data via topology CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY replication code for \"Capturing dynamics of time-varying data ... Capturing Dynamics of Time-Varying Data via Topology donut.topology.rocks Crockers to Topologically Measure Dynamics of Time-varying ...", "date": "", "ddg_snippet": "Oct 7, 2020 · We demonstrate the utility of crocker stacks for a parameter identification task involving an influential model of biological aggregations [58]. Altogether, we aim to bring the broader applied mathematics community up-to-date on topological summaries of time-varying metric spaces. We demonstrate the utility of crocker stacks for a parameter identification task involving an influential model of biological aggregations [57]. Altogether, we aim to bring the broader applied mathematics community up-to-date on topological summaries of time-varying metric spaces. An experiment consists of the following procedure. For all of the simulations ad-mitted to the experiment, we compute time series of feature vectors that summarize the simulation data : an order parameter from the physics literature that measures alignment of agents, α-smoothed crocker plots, a (discretized) crocker stack , and a stack Creating crocker stacks crocker - stack -functions.R create- crocker - stacks .R (as an example, this file generates a crocker stack for simulation datasets of eta = 0.02) An introduction to topological summaries of time-varying metric spaces including vineyards, crocker plots, and multiparameter rank functions are provided, and a new tool to summarize time -Varyed metric spaces is introduced: a Crocker stack . There is often a need to simplify or summarize the dynamic behavior. We provide an introduction to topological summaries of time-varying metric spaces including vineyards [17], crocker plots [52], and multiparameter rank functions [34]. We then introduce a new tool to summarize time-varying metric spaces: a crocker stack . In this tutorial, we introduce a straightforward approach, a so-called crocker , to summarize the topological information of such time-varying data through both scale and time .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.05780", "content": "Oct 7, 2020 · We demonstrate the utility of crocker stacks for a parameter identification task involving an influential model of biological aggregations [58]. Altogether, we aim to bring the broader applied mathematics community up-to-date on topological summaries of time-varying metric spaces. We demonstrate the utility of crocker stacks for a parameter identification task involving an influential model of biological aggregations [57]. Altogether, we aim to bring the broader applied mathematics community up-to-date on topological summaries of time-varying metric spaces. An experiment consists of the following procedure. For all of the simulations ad-mitted to the experiment, we compute time series of feature vectors that summarize the simulation data : an order parameter from the physics literature that measures alignment of agents, α-smoothed crocker plots, a (discretized) crocker stack , and a stack Creating crocker stacks crocker - stack -functions.R create- crocker - stacks .R (as an example, this file generates a crocker stack for simulation datasets of eta = 0.02) An introduction to topological summaries of time-varying metric spaces including vineyards, crocker plots, and multiparameter rank functions are provided, and a new tool to summarize time -Varyed metric spaces is introduced: a Crocker stack . There is often a need to simplify or summarize the dynamic behavior. We provide an introduction to topological summaries of time-varying metric spaces including vineyards [17], crocker plots [52], and multiparameter rank functions [34]. We then introduce a new tool to summarize time-varying metric spaces: a crocker stack . In this tutorial, we introduce a straightforward approach, a so-called crocker , to summarize the topological information of such time-varying data through both scale and time ."}
+{"idx": 1, "title": "Capturing dynamics of time-varying data via topology", "date": "", "ddg_snippet": "We demonstrate the utility of crocker stacks for a parameter identification task involving an influential model of biological aggregations [57]. Altogether, we aim to bring the broader applied mathematics community up-to-date on topological summaries of time-varying metric spaces.", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/doi/10.3934/fods.2021033", "content": "We demonstrate the utility of crocker stacks for a parameter identification task involving an influential model of biological aggregations [57]. Altogether, we aim to bring the broader applied mathematics community up-to-date on topological summaries of time-varying metric spaces."}
+{"idx": 2, "title": "CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY", "date": "", "ddg_snippet": "An experiment consists of the following procedure. For all of the simulations ad-mitted to the experiment, we compute time series of feature vectors that summarize the simulation data : an order parameter from the physics literature that measures alignment of agents, α-smoothed crocker plots, a (discretized) crocker stack , and a stack", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10388958", "content": "An experiment consists of the following procedure. For all of the simulations ad-mitted to the experiment, we compute time series of feature vectors that summarize the simulation data : an order parameter from the physics literature that measures alignment of agents, α-smoothed crocker plots, a (discretized) crocker stack , and a stack"}
+{"idx": 3, "title": "replication code for \"Capturing dynamics of time-varying data ...", "date": "", "ddg_snippet": "Creating crocker stacks crocker - stack -functions.R create- crocker - stacks .R (as an example, this file generates a crocker stack for simulation datasets of eta = 0.02)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lxiancode/tda-crocker", "content": "Creating crocker stacks crocker - stack -functions.R create- crocker - stacks .R (as an example, this file generates a crocker stack for simulation datasets of eta = 0.02)"}
+{"idx": 4, "title": "Capturing Dynamics of Time-Varying Data via Topology", "date": "", "ddg_snippet": "An introduction to topological summaries of time-varying metric spaces including vineyards, crocker plots, and multiparameter rank functions are provided, and a new tool to summarize time -Varyed metric spaces is introduced: a Crocker stack .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Capturing-Dynamics-of-Time-Varying-Data-via-Xian-Adams/156e87ce1a99116bfdce512d2ebdd54a1f4e9709/figure/0", "content": "An introduction to topological summaries of time-varying metric spaces including vineyards, crocker plots, and multiparameter rank functions are provided, and a new tool to summarize time -Varyed metric spaces is introduced: a Crocker stack ."}
+{"idx": 5, "title": "donut.topology.rocks", "date": "", "ddg_snippet": "There is often a need to simplify or summarize the dynamic behavior. We provide an introduction to topological summaries of time-varying metric spaces including vineyards [17], crocker plots [52], and multiparameter rank functions [34]. We then introduce a new tool to summarize time-varying metric spaces: a crocker stack .", "subpage_snippet": "", "source": "donut.topology.rocks", "link": "https://donut.topology.rocks/export/310", "content": "There is often a need to simplify or summarize the dynamic behavior. We provide an introduction to topological summaries of time-varying metric spaces including vineyards [17], crocker plots [52], and multiparameter rank functions [34]. We then introduce a new tool to summarize time-varying metric spaces: a crocker stack ."}
+{"idx": 6, "title": "Crockers to Topologically Measure Dynamics of Time-varying ...", "date": "", "ddg_snippet": "In this tutorial, we introduce a straightforward approach, a so-called crocker , to summarize the topological information of such time-varying data through both scale and time .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=Swlem15nvrc", "content": "In this tutorial, we introduce a straightforward approach, a so-called crocker , to summarize the topological information of such time-varying data through both scale and time ."}
+{"idx": 7, "title": "Topology, Geometry and Data Seminar - Lori Ziegelmeier", "date": "", "ddg_snippet": "Capturing Dynamics of Time-Varying Data via Topology . Speaker: Lori ... We then introduce a new tool to summarize time-varying metric spaces: a crocker stack.", "subpage_snippet": "", "source": "math.osu.edu", "link": "https://math.osu.edu/events/topology-geometry-and-data-seminar-lori-ziegelmeier", "content": "Capturing Dynamics of Time-Varying Data via Topology . Speaker: Lori ... We then introduce a new tool to summarize time-varying metric spaces: a crocker stack."}
+{"idx": 8, "title": "Foundations of Data Science", "date": "", "ddg_snippet": "Capturing dynamics of time-varying data via topology . Lu Xian, Henry Adams ... We then introduce a new tool to summarize time-varying metric spaces: a crocker ...", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/fods", "content": "Capturing dynamics of time-varying data via topology . Lu Xian, Henry Adams ... We then introduce a new tool to summarize time-varying metric spaces: a crocker ..."}
+{"idx": 9, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "In terms of temporal summary representations, [Topaz15a] introduce crocker plots to encode the evolution of topological features by stacking discretized Betti ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15732v2", "content": "In terms of temporal summary representations, [Topaz15a] introduce crocker plots to encode the evolution of topological features by stacking discretized Betti ..."}
diff --git a/data/sampled_jsons/Cronbach_Meehl_1955_construct_validity_original_paper_abstract.jsonl b/data/sampled_jsons/Cronbach_Meehl_1955_construct_validity_original_paper_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..feb2ee10aafa369d4ad59d356fd45e94283b3b81
--- /dev/null
+++ b/data/sampled_jsons/Cronbach_Meehl_1955_construct_validity_original_paper_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CONSTRUCT VALIDITY IN PSYCHOLOGICAL TESTS1", "date": "", "ddg_snippet": "by LJ Cronbach · Cited by 19855 — The test may serve, at best, only as a source of suggestions about individuals to be confirmed by other evidence ( Cronbach , 1955b ; Meehl & Rosen, 1955 ).", "subpage_snippet": "", "source": "meehl.umn.edu", "link": "https://meehl.umn.edu/sites/meehl.umn.edu/files/files/036constructvalidityidx.pdf", "content": "by LJ Cronbach · Cited by 19855 — The test may serve, at best, only as a source of suggestions about individuals to be confirmed by other evidence ( Cronbach , 1955b ; Meehl & Rosen, 1955 )."}
+{"idx": 1, "title": "Cronbach, L. J., & Meehl, P. E. (1955). Construct Validity in ...", "date": "", "ddg_snippet": "ABSTRACT : The “3-faced construct validation method” is a routine for establishing the validity and reliability of an existing scale when adapted in a ...", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=2335994", "content": "ABSTRACT : The “3-faced construct validation method” is a routine for establishing the validity and reliability of an existing scale when adapted in a ..."}
+{"idx": 2, "title": "Construct Validity: Advances in Theory and Methodology", "date": "", "ddg_snippet": "by ME Strauss · 2009 · Cited by 1126 — Cronbach & Meehl (1955) emphasized deductive processes in construct validity . The third (Loevinger 1957) identified the construct validation process as the ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC2739261/", "content": "by ME Strauss · 2009 · Cited by 1126 — Cronbach & Meehl (1955) emphasized deductive processes in construct validity . The third (Loevinger 1957) identified the construct validation process as the ..."}
+{"idx": 3, "title": "Construct Validity in Psychological Tests", "date": "", "ddg_snippet": "This article is reprinted from the Psychological. Bulletin, 1955 , with the permission of the authors and the American Psychological Association. Paul E. Meehl ...", "subpage_snippet": "", "source": "users.cla.umn.edu", "link": "http://users.cla.umn.edu/~nwaller/prelim/cronmeehl.pdf", "content": "This article is reprinted from the Psychological. Bulletin, 1955 , with the permission of the authors and the American Psychological Association. Paul E. Meehl ..."}
+{"idx": 4, "title": "\"Psychological Construct Validity\" by Caroline Marie Stone", "date": "", "ddg_snippet": "by CM Stone · 2021 · Cited by 2 — Abstract. A primary concern for any psychological research project is determining how to measure unobservable mental entities , such as \"implicit memory\", ...", "subpage_snippet": "", "source": "openscholarship.wustl.edu", "link": "https://openscholarship.wustl.edu/art_sci_etds/2463/", "content": "by CM Stone · 2021 · Cited by 2 — Abstract. A primary concern for any psychological research project is determining how to measure unobservable mental entities , such as \"implicit memory\", ..."}
+{"idx": 5, "title": "Validity in Psychological Testing and Scientific Realism", "date": "", "ddg_snippet": "14 Aug 2009 — Cronbach and Meehl ( 1955 ) write: “If a test yields many types of inferences, some of them can be valid and others invalid” (p. 297). For them, “ ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/0959354309336320", "content": "14 Aug 2009 — Cronbach and Meehl ( 1955 ) write: “If a test yields many types of inferences, some of them can be valid and others invalid” (p. 297). For them, “ ..."}
+{"idx": 6, "title": "Psychological Construct Validity", "date": "", "ddg_snippet": "by CM Stone · 2021 · Cited by 2 — The starting point for any discussion of construct validity is always Meehl and Cronbach's . ( 1955 ) paper , “ Construct Validity in Psychological ...", "subpage_snippet": "", "source": "openscholarship.wustl.edu", "link": "https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=3514&context=art_sci_etds", "content": "by CM Stone · 2021 · Cited by 2 — The starting point for any discussion of construct validity is always Meehl and Cronbach's . ( 1955 ) paper , “ Construct Validity in Psychological ..."}
+{"idx": 7, "title": "The Challenge of Construct Validity in the Assessment ...", "date": "", "ddg_snippet": "by LC Morey · Cited by 15 — Cronbach, L. J & Meehl, P. E. (1955). Construct validity in psychological tests . Psychological Bulletin, 52, 281–302. Article PubMed Google Scholar.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-1-4615-4397-8_7", "content": "by LC Morey · Cited by 15 — Cronbach, L. J & Meehl, P. E. (1955). Construct validity in psychological tests . Psychological Bulletin, 52, 281–302. Article PubMed Google Scholar."}
+{"idx": 8, "title": "The Evolution of the Concept of Validity (2021)", "date": "", "ddg_snippet": "Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests . Psycholo- gical Bulletin, 52, 281–302. Crooks, T. J. (1988). 13 pages", "subpage_snippet": "", "source": "www.cs.jhu.edu", "link": "https://www.cs.jhu.edu/~misha/DIReadingSeminar/Papers/Clauser21.CH9.pdf", "content": "Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests . Psycholo- gical Bulletin, 52, 281–302. Crooks, T. J. (1988). 13 pages"}
+{"idx": 9, "title": "Psychological Bulletin - APA PsycNet", "date": "", "ddg_snippet": "by LEEJ CRONBACH · Cited by 19855 — Construct validation is involved whenever a test is to be interpreted as a measure of some attribute or quality which is not \"operationally denned.\" The problem ...", "subpage_snippet": "", "source": "psycnet.apa.org", "link": "https://psycnet.apa.org/doiLanding?doi=10.1037/h0040957", "content": "by LEEJ CRONBACH · Cited by 19855 — Construct validation is involved whenever a test is to be interpreted as a measure of some attribute or quality which is not \"operationally denned.\" The problem ..."}
diff --git a/data/sampled_jsons/Cross-Blended_Images_CBI_method_deepfake_detection.jsonl b/data/sampled_jsons/Cross-Blended_Images_CBI_method_deepfake_detection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..98c3577ba7a2eb26d9ab6096ebe65ad0b46cbef4
--- /dev/null
+++ b/data/sampled_jsons/Cross-Blended_Images_CBI_method_deepfake_detection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training", "date": "", "ddg_snippet": "Deepfake Detection Toward Generalization Ability. Deepfake Detectors with Blendfake Faces.The resulting blendfake image is cross -face blended and exhibits inconsistencies in identity and also blending clues.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=vh9yEPLeyD", "content": "Deepfake Detection Toward Generalization Ability. Deepfake Detectors with Blendfake Faces.The resulting blendfake image is cross -face blended and exhibits inconsistencies in identity and also blending clues."}
+{"idx": 1, "title": "(PDF) Deepfake Detection and Multimedia Forensics: Investigating...", "date": "", "ddg_snippet": "Keywords: Deepfake detection ; multimedia forensics; synthetic media; cybercrime investigation ; image .through cross -validation of auditory and visual. signals, e.g., lip reading against the speech or. spectrograms of voices. Blending with biometric.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395329220_Deepfake_Detection_and_Multimedia_Forensics_Investigating_Synthetic_Media_Image_Forgery_and_Video_Manipulation_in_Cybercrime_Cases", "content": "Keywords: Deepfake detection ; multimedia forensics; synthetic media; cybercrime investigation ; image .through cross -validation of auditory and visual. signals, e.g., lip reading against the speech or. spectrograms of voices. Blending with biometric."}
+{"idx": 2, "title": "Is It Certainly a Deepfake ? Reliability Analysis in Detection ...", "date": "", "ddg_snippet": "As reflected in detectors ’ responses, deepfake generators also contribute to this uncertainty as their generative residues vary, so we cross the uncertainty analysis of deepfake detectors and generators.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.17550v1", "content": "As reflected in detectors ’ responses, deepfake generators also contribute to this uncertainty as their generative residues vary, so we cross the uncertainty analysis of deepfake detectors and generators."}
+{"idx": 3, "title": "Frontiers | CrossDF: Improving Cross -Domain Deepfake Detection ...", "date": "", "ddg_snippet": "While current detection approaches perform well in identifying manipulations within datasets that utilize identical deepfake methods for both training and validation, they experience notable declines in accuracy when applied to cross -dataset situations, where unfamiliar deepfake ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1669488/abstract", "content": "While current detection approaches perform well in identifying manipulations within datasets that utilize identical deepfake methods for both training and validation, they experience notable declines in accuracy when applied to cross -dataset situations, where unfamiliar deepfake ..."}
+{"idx": 4, "title": "Deepfakes Detection Techniques Using Deep Learning: A Survey", "date": "", "ddg_snippet": "Discover the latest deep learning techniques for creating and detecting deepfakes in images and videos. Our comprehensive review covers state-of-the-art methods and datasets, benefiting researchers in this field.", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/journal/paperinformation?paperid=109149", "content": "Discover the latest deep learning techniques for creating and detecting deepfakes in images and videos. Our comprehensive review covers state-of-the-art methods and datasets, benefiting researchers in this field."}
+{"idx": 5, "title": "Deepfakes & AI Scams: How to Tell What’s Real in 2025 – 10minutes", "date": "", "ddg_snippet": "1. Can AI detection tools always identify deepfakes ? No. Detection tools get better, but so do fakes. Use tools as a layer, not your sole defence. 2. How do I verify if a video is authentic? Cross -check the origin, pass it through a deepfake detector , and seek reporting from reputable sources.", "subpage_snippet": "", "source": "10minutes.email", "link": "https://10minutes.email/ideas/deepfakes-ai-scams-how-to-tell-whats-real-in-2025/", "content": "1. Can AI detection tools always identify deepfakes ? No. Detection tools get better, but so do fakes. Use tools as a layer, not your sole defence. 2. How do I verify if a video is authentic? Cross -check the origin, pass it through a deepfake detector , and seek reporting from reputable sources."}
+{"idx": 6, "title": "Top 10 Deepfake Audio Detection Tools for 2025 | Resemble AI", "date": "", "ddg_snippet": "Explore 2025’s leading deepfake audio detection tools designed to identify fake voices with AI, GAN analysis, and biometric security.", "subpage_snippet": "", "source": "www.resemble.ai", "link": "https://www.resemble.ai/audio-deepfake-detection-tools/", "content": "Explore 2025’s leading deepfake audio detection tools designed to identify fake voices with AI, GAN analysis, and biometric security."}
+{"idx": 7, "title": "Deepfake Detection with Frequency-Enhanced Self- Blended Images", "date": "", "ddg_snippet": "A deepfake image detection method based on a multi-graph attention network.FSBI: Deepfake detection with frequency enhanced self- blended images . Image and Vision Computing, page 105418, 2025.", "subpage_snippet": "", "source": "cse.aua.am", "link": "https://cse.aua.am/files/2025/06/Deepfake_Detection_with_Frequency_Enhanced_Self_Blended_Images.pdf", "content": "A deepfake image detection method based on a multi-graph attention network.FSBI: Deepfake detection with frequency enhanced self- blended images . Image and Vision Computing, page 105418, 2025."}
+{"idx": 8, "title": "5 Ways to Detect Deepfake and Protect Digital Forensics Evidence!", "date": "", "ddg_snippet": "Deepfake technology is no longer science fiction. Combating it requires a combination of multiple detection methods , fast and lightweight deployment, and a legal framework ready to address fake evidence.", "subpage_snippet": "", "source": "agt-technology.com", "link": "https://agt-technology.com/5-ways-to-detect-deepfake-and-protect-digital-forensics-evidence/", "content": "Deepfake technology is no longer science fiction. Combating it requires a combination of multiple detection methods , fast and lightweight deployment, and a legal framework ready to address fake evidence."}
+{"idx": 9, "title": "Self Blended Images for Generalizable Deepfake Detection - YouTube", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=e9m0Zf2yH-E", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
diff --git a/data/sampled_jsons/CrossKD_Wang_2024_CIFAR100_results_experiments_classification_accuracy_year_2024.jsonl b/data/sampled_jsons/CrossKD_Wang_2024_CIFAR100_results_experiments_classification_accuracy_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bc730e9800038e34f67565137fc672ca306e8ab0
--- /dev/null
+++ b/data/sampled_jsons/CrossKD_Wang_2024_CIFAR100_results_experiments_classification_accuracy_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CIFAR-10 - Wikipedia", "date": "", "ddg_snippet": "The CIFAR-10 dataset is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/CIFAR-10", "content": "The CIFAR-10 dataset is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color..."}
+{"idx": 1, "title": "CrossKD : Cross -Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "CrossKD boosts the accuracy of GFL-R50 to 42.1 (+1.9 AP) when applying ATSS as the teacher.[25] Zihao Jia, Shengkun Sun, Guangcan Liu, and Bo Liu. Mssd: multi-scale self-distillation for object detection. Visual Intel-ligence, 2(1):8, 2024 .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_CrossKD_Cross-Head_Knowledge_Distillation_for_Object_Detection_CVPR_2024_paper.pdf", "content": "CrossKD boosts the accuracy of GFL-R50 to 42.1 (+1.9 AP) when applying ATSS as the teacher.[25] Zihao Jia, Shengkun Sun, Guangcan Liu, and Bo Liu. Mssd: multi-scale self-distillation for object detection. Visual Intel-ligence, 2(1):8, 2024 ."}
+{"idx": 2, "title": "Fig. 4. Experiment results of self-distillation on CIFAR 100 . MAC...", "date": "", "ddg_snippet": "Download scientific diagram | Experiment results of self-distillation on CIFAR 100 .Figure 4 shows the comparison on computation, parameters and accuracy of four ResNet models on CIFAR 100 .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Experiment-results-of-self-distillation-on-CIFAR100-MAC-indicates-the_fig2_350160449", "content": "Download scientific diagram | Experiment results of self-distillation on CIFAR 100 .Figure 4 shows the comparison on computation, parameters and accuracy of four ResNet models on CIFAR 100 ."}
+{"idx": 3, "title": "WATT: Weight Average Test Time Adaptation of CLIP · NeurIPS 2024", "date": "", "ddg_snippet": "26 September 2024 ·3263 words·16 mins· loading.This figure shows how the accuracy of the Parallel Multi-Template Weight Averaging (MTWA) method changes over different numbers of iterations on various CIFAR - 100 corruptions (e.g., Defocus Blur, Frost, Contrast).", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/4d7hnj9om6/", "content": "26 September 2024 ·3263 words·16 mins· loading.This figure shows how the accuracy of the Parallel Multi-Template Weight Averaging (MTWA) method changes over different numbers of iterations on various CIFAR - 100 corruptions (e.g., Defocus Blur, Frost, Contrast)."}
+{"idx": 4, "title": "Image classification pressures in language emergence – Błażej Dolicki", "date": "", "ddg_snippet": "Results . We use the CIFAR - 100 dataset for our experiments .The standard signaling game accuracy shows how many times the receiver correctly selected the target image. When this metric reaches proximity of 100% we can conclude that the agents communicate successfully.", "subpage_snippet": "", "source": "blazejdolicki.com", "link": "https://blazejdolicki.com/image-classification-pressures-in-language-emergence/", "content": "Results . We use the CIFAR - 100 dataset for our experiments .The standard signaling game accuracy shows how many times the receiver correctly selected the target image. When this metric reaches proximity of 100% we can conclude that the agents communicate successfully."}
+{"idx": 5, "title": "Don't Forget the Nonlinearity: Unlocking Activation Functions in...", "date": "", "ddg_snippet": "Other Experiment Results . Other Ablation Studies. Adaptability of Activation Functions with Different PEFT Methods.( 2024 ). This neglect of the adaptability of activation functions marks a critical gap in current PEFT strategies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.13240", "content": "Other Experiment Results . Other Ablation Studies. Adaptability of Activation Functions with Different PEFT Methods.( 2024 ). This neglect of the adaptability of activation functions marks a critical gap in current PEFT strategies."}
+{"idx": 6, "title": "GitHub - fahimsamady/ CIFAR 100 -Image-Classifier-Notebook", "date": "", "ddg_snippet": "Image Classification on CIFAR 100 dataset.SVM for coarse granularity resulted in 37.62%, while fine labels were predicted with slightly lower accuracy of 26.46%.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fahimsamady/CIFAR100-Image-Classifier-Notebook", "content": "Image Classification on CIFAR 100 dataset.SVM for coarse granularity resulted in 37.62%, while fine labels were predicted with slightly lower accuracy of 26.46%."}
+{"idx": 7, "title": "CIFAR-10 and CIFAR - 100 datasets", "date": "", "ddg_snippet": "CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.You can find some baseline replicable results on this dataset on the project page for cuda-convnet. These results were obtained with a convolutional neural network.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~kriz/cifar.html", "content": "CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.You can find some baseline replicable results on this dataset on the project page for cuda-convnet. These results were obtained with a convolutional neural network."}
+{"idx": 8, "title": "Error-aware Quantization through Noise Tempering-Bohrium", "date": "", "ddg_snippet": "Experiments are performed on CIFAR - 10, CIFAR - 100 , and ImageNet datasets with several CV models. Results are compared with state - of - the - art uniform quantization methods. The proposed method outperforms other SOTA methods on CIFAR - 10 and CIFAR - 100 .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/error-aware-quantization-through-noise-tempering/867773655155737326-108614", "content": "Experiments are performed on CIFAR - 10, CIFAR - 100 , and ImageNet datasets with several CV models. Results are compared with state - of - the - art uniform quantization methods. The proposed method outperforms other SOTA methods on CIFAR - 10 and CIFAR - 100 ."}
+{"idx": 9, "title": "Formed t eacher M atching", "date": "", "ddg_snippet": "Standard Deviation for Results on CIFAR - 100 . Results on Transformer-based Models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=MJ3K7uDGGl", "content": "Standard Deviation for Results on CIFAR - 100 . Results on Transformer-based Models."}
diff --git a/data/sampled_jsons/CrossKD_Wang_et_al._2024_CIFAR100_classification_score_year_2024.jsonl b/data/sampled_jsons/CrossKD_Wang_et_al._2024_CIFAR100_classification_score_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..994fae6af83f660f69d3bc6d0cb294866d4261ed
--- /dev/null
+++ b/data/sampled_jsons/CrossKD_Wang_et_al._2024_CIFAR100_classification_score_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "The ad- pgt pgt ditional CrossKD losses are represented as Lcls CrossKD and Lreg CrossKD , which are performed between the cross-head pre-dictions ˆps cls, ˆps reg and the teacher’s predictions pt cls, pt reg.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_CrossKD_Cross-Head_Knowledge_Distillation_for_Object_Detection_CVPR_2024_paper.pdf", "content": "The ad- pgt pgt ditional CrossKD losses are represented as Lcls CrossKD and Lreg CrossKD , which are performed between the cross-head pre-dictions ˆps cls, ˆps reg and the teacher’s predictions pt cls, pt reg."}
+{"idx": 1, "title": "[2306.11369] CrossKD: Cross-Head Knowledge Distillation for ... CrossKD: Cross-Head Knowledge Distillation for Object Detection CIFAR-100 on Benchmarks.AI CrossKD: Cross-Head Knowledge Distillation for Dense Object ... 2024-2 CIFAR100 Competition - Kaggle CIFAR-100 on Benchmarks.AI CrossKD: Cross-Head Knowledge Distillation for Object Detection CrossKD : Cross-Head Knowledge Distillation for Dense Object Detection CrossKD: Cross-Head Knowledge Distillation for Object Detection CrossKD: Cross-Head Knowledge Distillation for Object Detection CrossKD: Cross-Head Knowledge Distillation for Object Detection (PDF) Comparative analysis of various models for image ...", "date": "", "ddg_snippet": "Jun 20, 2023 · View a PDF of the paper titled CrossKD : Cross-Head Knowledge Distillation for Object Detection, by Jiabao Wang and 5 other authors Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ... Explore CIFAR-100 dataset benchmarks, pre-trained models and fine-tuning techniques to improve deep learning performance on vision tasks. This repository contains the official implementation of the following paper: [Arxiv Paper] See full list on github.com Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation, which is generally observed to be better than prediction mimicking. In this paper, we show that the inconsistency of the op... See full list on github.com 1. Prerequisites Dependencies•Ubuntu >= 20.04•CUDA >= 11.3•pytorch==1.12.1•torchvision=0.13.1•mmcv==2.0.0rc4•mmengine==0.7.3Our implementation based on MMDetection==3.0.0rc6. For more information about installation, please see the official instructions.Step 0. Create Conda EnvironmentStep 1. Install PytorchStep 2. Install MMEngine and MMCV using MIM.Step 3. Install CrossKD .Step 4. Prepare dataset follow the official instructions. 2. Training Single GPUMulti GPU See full list on github.com If you find our repo useful for your research, please cite us: This project is based on the open source codebase MMDetection. See full list on github.com Licensed under a Creative Commons Attribution-NonCommercial 4.0 International for Non-commercial use only. Any commercial use should get formal permission first. See full list on github.com For technical questions, please contact jbwang@mail.nankai.edu.cn and chenyuming@mail.nankai.edu.cn. See full list on github.com This repo is modified from open source object detection codebase MMDetection. See full list on github.com 2024 -2 CIFAR100 Competition How accurate is cifar-100 classification? We then maintained the learned convolution kernels and only retrained the classification part on different datasets. Using this approach, we achieved an accuracy of 67.68% on CIFAR-100, compared to the previous state-of-the-art result of 65.43%. What is a cross-head prediction mimicking distillation scheme? In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de-livers the intermediate features of the student’s detection head to the teacher’s detection head. The resulting cross-head predictions are then forced to mimic the teacher’s pre-dictions. How does crosskd improve GFL ResNet-50 accuracy? On MS COCO, with only prediction mimicking losses applied, our CrossKD boosts the average precision of GFL ResNet-50 with 1x training schedule from 40.2 to 43.7 , outperforming all existing KD methods for object detection. 1. Prerequisites Dependencies Our implementation based on MMDetection==3.0.0rc6. How does crosskd perform dis-tillation on classification and box regression branches? Here, we conduct dis-tillation on both classification and box regression branches. When i = 0, CrossKD directly feeds the student’s FPN fea-tures into the teacher’s head . In this case, the entire stu-dent’s head is only supervised by the detection loss, and no distillation loss is involved. What are the advantages and disadvantages of crosskd? Despite its simplicity, CrossKD offers the following two main advantages. First, since both the cross-head predic-tions and the teacher’s predictions are produced by sharing part of the teacher’s detection head, the cross-head predic-tions are relatively consistent with the teacher’s predictions. How accurate is crosskd? As we can see, at the same condition, CrossKD can achieve 43.7 AP without bells and whistles, which improves the accuracy of the student by 3.5 AP , outperforming all other state-of-the-art methods. Feb 1, 2024 · Comparative analysis of various models for image classification on Cifar-100 dataset To cite this article: YuYu Zheng et al 2024 J. Phys.: Conf. Ser. 2711 012015", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.11369", "content": "Jun 20, 2023 · View a PDF of the paper titled CrossKD : Cross-Head Knowledge Distillation for Object Detection, by Jiabao Wang and 5 other authors Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ... Explore CIFAR-100 dataset benchmarks, pre-trained models and fine-tuning techniques to improve deep learning performance on vision tasks. This repository contains the official implementation of the following paper: [Arxiv Paper] See full list on github.com Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation, which is generally observed to be better than prediction mimicking. In this paper, we show that the inconsistency of the op... See full list on github.com 1. Prerequisites Dependencies•Ubuntu >= 20.04•CUDA >= 11.3•pytorch==1.12.1•torchvision=0.13.1•mmcv==2.0.0rc4•mmengine==0.7.3Our implementation based on MMDetection==3.0.0rc6. For more information about installation, please see the official instructions.Step 0. Create Conda EnvironmentStep 1. Install PytorchStep 2. Install MMEngine and MMCV using MIM.Step 3. Install CrossKD .Step 4. Prepare dataset follow the official instructions. 2. Training Single GPUMulti GPU See full list on github.com If you find our repo useful for your research, please cite us: This project is based on the open source codebase MMDetection. See full list on github.com Licensed under a Creative Commons Attribution-NonCommercial 4.0 International for Non-commercial use only. Any commercial use should get formal permission first. See full list on github.com For technical questions, please contact jbwang@mail.nankai.edu.cn and chenyuming@mail.nankai.edu.cn. See full list on github.com This repo is modified from open source object detection codebase MMDetection. See full list on github.com 2024 -2 CIFAR100 Competition How accurate is cifar-100 classification? We then maintained the learned convolution kernels and only retrained the classification part on different datasets. Using this approach, we achieved an accuracy of 67.68% on CIFAR-100, compared to the previous state-of-the-art result of 65.43%. What is a cross-head prediction mimicking distillation scheme? In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de-livers the intermediate features of the student’s detection head to the teacher’s detection head. The resulting cross-head predictions are then forced to mimic the teacher’s pre-dictions. How does crosskd improve GFL ResNet-50 accuracy? On MS COCO, with only prediction mimicking losses applied, our CrossKD boosts the average precision of GFL ResNet-50 with 1x training schedule from 40.2 to 43.7 , outperforming all existing KD methods for object detection. 1. Prerequisites Dependencies Our implementation based on MMDetection==3.0.0rc6. How does crosskd perform dis-tillation on classification and box regression branches? Here, we conduct dis-tillation on both classification and box regression branches. When i = 0, CrossKD directly feeds the student’s FPN fea-tures into the teacher’s head . In this case, the entire stu-dent’s head is only supervised by the detection loss, and no distillation loss is involved. What are the advantages and disadvantages of crosskd? Despite its simplicity, CrossKD offers the following two main advantages. First, since both the cross-head predic-tions and the teacher’s predictions are produced by sharing part of the teacher’s detection head, the cross-head predic-tions are relatively consistent with the teacher’s predictions. How accurate is crosskd? As we can see, at the same condition, CrossKD can achieve 43.7 AP without bells and whistles, which improves the accuracy of the student by 3.5 AP , outperforming all other state-of-the-art methods. Feb 1, 2024 · Comparative analysis of various models for image classification on Cifar-100 dataset To cite this article: YuYu Zheng et al 2024 J. Phys.: Conf. Ser. 2711 012015"}
+{"idx": 2, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10654891", "content": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ..."}
+{"idx": 3, "title": "CrossKD: Cross-Head Knowledge Distillation for Dense Object ... 2024-2 CIFAR100 Competition - Kaggle CIFAR-100 on Benchmarks.AI CrossKD: Cross-Head Knowledge Distillation for Object Detection CrossKD : Cross-Head Knowledge Distillation for Dense Object Detection CrossKD: Cross-Head Knowledge Distillation for Object Detection CrossKD: Cross-Head Knowledge Distillation for Object Detection CrossKD: Cross-Head Knowledge Distillation for Object Detection (PDF) Comparative analysis of various models for image ...", "date": "", "ddg_snippet": "This repository contains the official implementation of the following paper: [Arxiv Paper] See full list on github.com Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation, which is generally observed to be better than prediction mimicking. In this paper, we show that the inconsistency of the op... See full list on github.com 1. Prerequisites Dependencies•Ubuntu >= 20.04•CUDA >= 11.3•pytorch==1.12.1•torchvision=0.13.1•mmcv==2.0.0rc4•mmengine==0.7.3Our implementation based on MMDetection==3.0.0rc6. For more information about installation, please see the official instructions.Step 0. Create Conda EnvironmentStep 1. Install PytorchStep 2. Install MMEngine and MMCV using MIM.Step 3. Install CrossKD .Step 4. Prepare dataset follow the official instructions. 2. Training Single GPUMulti GPU See full list on github.com If you find our repo useful for your research, please cite us: This project is based on the open source codebase MMDetection. See full list on github.com Licensed under a Creative Commons Attribution-NonCommercial 4.0 International for Non-commercial use only. Any commercial use should get formal permission first. See full list on github.com For technical questions, please contact jbwang@mail.nankai.edu.cn and chenyuming@mail.nankai.edu.cn. See full list on github.com This repo is modified from open source object detection codebase MMDetection. See full list on github.com 2024 -2 CIFAR100 Competition How accurate is cifar-100 classification? We then maintained the learned convolution kernels and only retrained the classification part on different datasets. Using this approach, we achieved an accuracy of 67.68% on CIFAR-100, compared to the previous state-of-the-art result of 65.43%. What is a cross-head prediction mimicking distillation scheme? In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de-livers the intermediate features of the student’s detection head to the teacher’s detection head. The resulting cross-head predictions are then forced to mimic the teacher’s pre-dictions. How does crosskd improve GFL ResNet-50 accuracy? On MS COCO, with only prediction mimicking losses applied, our CrossKD boosts the average precision of GFL ResNet-50 with 1x training schedule from 40.2 to 43.7 , outperforming all existing KD methods for object detection. 1. Prerequisites Dependencies Our implementation based on MMDetection==3.0.0rc6. How does crosskd perform dis-tillation on classification and box regression branches? Here, we conduct dis-tillation on both classification and box regression branches. When i = 0, CrossKD directly feeds the student’s FPN fea-tures into the teacher’s head . In this case, the entire stu-dent’s head is only supervised by the detection loss, and no distillation loss is involved. What are the advantages and disadvantages of crosskd? Despite its simplicity, CrossKD offers the following two main advantages. First, since both the cross-head predic-tions and the teacher’s predictions are produced by sharing part of the teacher’s detection head, the cross-head predic-tions are relatively consistent with the teacher’s predictions. How accurate is crosskd? As we can see, at the same condition, CrossKD can achieve 43.7 AP without bells and whistles, which improves the accuracy of the student by 3.5 AP , outperforming all other state-of-the-art methods. Feb 1, 2024 · Comparative analysis of various models for image classification on Cifar-100 dataset To cite this article: YuYu Zheng et al 2024 J. Phys.: Conf. Ser. 2711 012015", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jbwang1997/CrossKD", "content": "This repository contains the official implementation of the following paper: [Arxiv Paper] See full list on github.com Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation, which is generally observed to be better than prediction mimicking. In this paper, we show that the inconsistency of the op... See full list on github.com 1. Prerequisites Dependencies•Ubuntu >= 20.04•CUDA >= 11.3•pytorch==1.12.1•torchvision=0.13.1•mmcv==2.0.0rc4•mmengine==0.7.3Our implementation based on MMDetection==3.0.0rc6. For more information about installation, please see the official instructions.Step 0. Create Conda EnvironmentStep 1. Install PytorchStep 2. Install MMEngine and MMCV using MIM.Step 3. Install CrossKD .Step 4. Prepare dataset follow the official instructions. 2. Training Single GPUMulti GPU See full list on github.com If you find our repo useful for your research, please cite us: This project is based on the open source codebase MMDetection. See full list on github.com Licensed under a Creative Commons Attribution-NonCommercial 4.0 International for Non-commercial use only. Any commercial use should get formal permission first. See full list on github.com For technical questions, please contact jbwang@mail.nankai.edu.cn and chenyuming@mail.nankai.edu.cn. See full list on github.com This repo is modified from open source object detection codebase MMDetection. See full list on github.com 2024 -2 CIFAR100 Competition How accurate is cifar-100 classification? We then maintained the learned convolution kernels and only retrained the classification part on different datasets. Using this approach, we achieved an accuracy of 67.68% on CIFAR-100, compared to the previous state-of-the-art result of 65.43%. What is a cross-head prediction mimicking distillation scheme? In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de-livers the intermediate features of the student’s detection head to the teacher’s detection head. The resulting cross-head predictions are then forced to mimic the teacher’s pre-dictions. How does crosskd improve GFL ResNet-50 accuracy? On MS COCO, with only prediction mimicking losses applied, our CrossKD boosts the average precision of GFL ResNet-50 with 1x training schedule from 40.2 to 43.7 , outperforming all existing KD methods for object detection. 1. Prerequisites Dependencies Our implementation based on MMDetection==3.0.0rc6. How does crosskd perform dis-tillation on classification and box regression branches? Here, we conduct dis-tillation on both classification and box regression branches. When i = 0, CrossKD directly feeds the student’s FPN fea-tures into the teacher’s head . In this case, the entire stu-dent’s head is only supervised by the detection loss, and no distillation loss is involved. What are the advantages and disadvantages of crosskd? Despite its simplicity, CrossKD offers the following two main advantages. First, since both the cross-head predic-tions and the teacher’s predictions are produced by sharing part of the teacher’s detection head, the cross-head predic-tions are relatively consistent with the teacher’s predictions. How accurate is crosskd? As we can see, at the same condition, CrossKD can achieve 43.7 AP without bells and whistles, which improves the accuracy of the student by 3.5 AP , outperforming all other state-of-the-art methods. Feb 1, 2024 · Comparative analysis of various models for image classification on Cifar-100 dataset To cite this article: YuYu Zheng et al 2024 J. Phys.: Conf. Ser. 2711 012015"}
+{"idx": 4, "title": "2024-2 CIFAR100 Competition - Kaggle", "date": "", "ddg_snippet": "2024 -2 CIFAR100 Competition", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/competitions/2024-2-cifar-100-competition/overview", "content": "2024 -2 CIFAR100 Competition"}
+{"idx": 5, "title": "CVPR 2024 Thursday 06/20", "date": "", "ddg_snippet": "Unlike all previous UDAOD scenarios, we first collected a F etal C ardiac S tructure dataset from three hospital centers, called FCS , and proposed a multi ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/day/6/20", "content": "Unlike all previous UDAOD scenarios, we first collected a F etal C ardiac S tructure dataset from three hospital centers, called FCS , and proposed a multi ..."}
+{"idx": 6, "title": "Sample-level Adaptive Knowledge Distillation for Action ...", "date": "", "ddg_snippet": "by P Li · 2025 — To simplify the knowledge of teacher model, Wang et al . [25] propose a dual KD framework to reduce the side effects of teacher and utilize ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.00606", "content": "by P Li · 2025 — To simplify the knowledge of teacher model, Wang et al . [25] propose a dual KD framework to reduce the side effects of teacher and utilize ..."}
+{"idx": 7, "title": "Knowledge Distillation in Object Detection for Resource- ...", "date": "", "ddg_snippet": "by A Setyanto · 2025 · Cited by 1 — The initial data set used for the classification distillation process is CIFAR100 , with 50,000 training samples and. 10,000 testing samples. 15 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/10852314.pdf", "content": "by A Setyanto · 2025 · Cited by 1 — The initial data set used for the classification distillation process is CIFAR100 , with 50,000 training samples and. 10,000 testing samples. 15 pages"}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "2 days ago — On MS COCO, with only prediction mimicking losses applied, our CrossKD boosts the average precision of GFL ResNet-50 with 1x training schedule ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Feature-Based+Knowledge+Distillation", "content": "2 days ago — On MS COCO, with only prediction mimicking losses applied, our CrossKD boosts the average precision of GFL ResNet-50 with 1x training schedule ..."}
+{"idx": 9, "title": "(PDF) Comparative analysis of various models for image ...", "date": "", "ddg_snippet": "Feb 1, 2024 · Comparative analysis of various models for image classification on Cifar-100 dataset To cite this article: YuYu Zheng et al 2024 J. Phys.: Conf. Ser. 2711 012015", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/378536552_Comparative_analysis_of_various_models_for_image_classification_on_Cifar-100_dataset", "content": "Feb 1, 2024 · Comparative analysis of various models for image classification on Cifar-100 dataset To cite this article: YuYu Zheng et al 2024 J. Phys.: Conf. Ser. 2711 012015"}
diff --git a/data/sampled_jsons/Crosskd_Cross-head_knowledge_distillation_for_object_detection_Wang_et_al._2024.jsonl b/data/sampled_jsons/Crosskd_Cross-head_knowledge_distillation_for_object_detection_Wang_et_al._2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7b5f472aed89e79be8500b46554e8dd5a9a4e795
--- /dev/null
+++ b/data/sampled_jsons/Crosskd_Cross-head_knowledge_distillation_for_object_detection_Wang_et_al._2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Uncertainty-Aware Cross-Modal Knowledge Distillation with", "date": "", "ddg_snippet": "It aligns feature semantics through a prototype-based similarity module and introduces a task-specific distillation head to resolve label-induced ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.13092v1", "content": "It aligns feature semantics through a prototype-based similarity module and introduces a task-specific distillation head to resolve label-induced ..."}
+{"idx": 1, "title": "Knowledge Distillation Driven Semantic NOMA for Image", "date": "", "ddg_snippet": "... knowledge distillation strategy, i. e ., a teacher model, trained on interference-free orthogonal transmission, guides a student model via feature ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07363v1", "content": "... knowledge distillation strategy, i. e ., a teacher model, trained on interference-free orthogonal transmission, guides a student model via feature ..."}
+{"idx": 2, "title": "SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile", "date": "", "ddg_snippet": "We make the following contributions : (i) We introduce a novel squeeze-enhanced Axial Transformer (SeaFormer) framework for mobile semantic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2301.13156v6", "content": "We make the following contributions : (i) We introduce a novel squeeze-enhanced Axial Transformer (SeaFormer) framework for mobile semantic ..."}
+{"idx": 3, "title": "GenRecal: Generation after Recalibration from Large to Small", "date": "", "ddg_snippet": "However, existing distillation methods that attempt to transfer knowledge beyond the natural language space—such as logits from the language head ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15681v1", "content": "However, existing distillation methods that attempt to transfer knowledge beyond the natural language space—such as logits from the language head ..."}
+{"idx": 4, "title": "Descargar Facebook gratis para PC, iOS, Android APK - CCM", "date": "", "ddg_snippet": "Jan 23, 2024 · Con más de 2.800 millones de usuarios activos al mes, la red social más grande del mundo te permite permanecer en contacto con amigos y familiares y volver a conectarte con antiguos compañeros ...", "subpage_snippet": "", "source": "es.ccm.net", "link": "https://es.ccm.net/descargas/comunicacion/5426-facebook/", "content": "Jan 23, 2024 · Con más de 2.800 millones de usuarios activos al mes, la red social más grande del mundo te permite permanecer en contacto con amigos y familiares y volver a conectarte con antiguos compañeros ..."}
+{"idx": 5, "title": "Recuperar contraseña de Facebook: con y sin correo o número - CCM", "date": "", "ddg_snippet": "Jul 19, 2023 · ¿Has olvidado tu contraseña de Facebook y no puedes entrar? En este artículo te explicamos cómo recuperar tu cuenta si olvidaste tu contraseña, incluso sin usar tu correo o tu teléfono y sin ...", "subpage_snippet": "", "source": "es.ccm.net", "link": "https://es.ccm.net/aplicaciones-e-internet/redes-sociales-y-mensajeria/915-que-hacer-si-olvidaste-tu-contrasena-de-facebook/", "content": "Jul 19, 2023 · ¿Has olvidado tu contraseña de Facebook y no puedes entrar? En este artículo te explicamos cómo recuperar tu cuenta si olvidaste tu contraseña, incluso sin usar tu correo o tu teléfono y sin ..."}
+{"idx": 6, "title": "Facebook : qui choisi les suggestions d'amis ? [Résolu]", "date": "", "ddg_snippet": "- facebook utilise votre liste de contacts sur votre adresse mail - facebook vous envoie les suggestions de vos amis :affiché en tant que notification - facebook vous envoie les suggestions des pages que vous avez visités - facebook vous envoie les suggestions des amis de vos amis Rien de plus.", "subpage_snippet": "", "source": "forums.commentcamarche.net", "link": "https://forums.commentcamarche.net/forum/affich-12611242-facebook-qui-choisi-les-suggestions-d-amis", "content": "- facebook utilise votre liste de contacts sur votre adresse mail - facebook vous envoie les suggestions de vos amis :affiché en tant que notification - facebook vous envoie les suggestions des pages que vous avez visités - facebook vous envoie les suggestions des amis de vos amis Rien de plus."}
+{"idx": 7, "title": "Revenir a l'ancien facebook [Résolu] - CommentCaMarche", "date": "", "ddg_snippet": "Amis Facebook voici la solution concernant le profil facebook, pour désinstaller le Nouveau profil, aller dans \"Compte\" en haut à droite puis \"Paramètres de Comptes\". Ensuite séléctionner \"Désactiver Comptes\" et enfin veiller à bien cliquer sur \"Temporairement, je reviendrai\" D'ici un bon 10min vous retrouverez votre Ancien Facebook.", "subpage_snippet": "", "source": "forums.commentcamarche.net", "link": "https://forums.commentcamarche.net/forum/affich-8406097-revenir-a-l-ancien-facebook", "content": "Amis Facebook voici la solution concernant le profil facebook, pour désinstaller le Nouveau profil, aller dans \"Compte\" en haut à droite puis \"Paramètres de Comptes\". Ensuite séléctionner \"Désactiver Comptes\" et enfin veiller à bien cliquer sur \"Temporairement, je reviendrai\" D'ici un bon 10min vous retrouverez votre Ancien Facebook."}
+{"idx": 8, "title": "Créer un raccourci de Facebook sur mon bureau [Résolu]", "date": "", "ddg_snippet": "Bonjour, J'aimerais savoir comment créer un raccourci de facebook sur mon bureau. Merci.", "subpage_snippet": "", "source": "forums.commentcamarche.net", "link": "https://forums.commentcamarche.net/forum/affich-26565072-creer-un-raccourci-de-facebook-sur-mon-bureau", "content": "Bonjour, J'aimerais savoir comment créer un raccourci de facebook sur mon bureau. Merci."}
+{"idx": 9, "title": "Cómo 'hackear' una cuenta de Facebook: sin teléfono, correo - CCM", "date": "", "ddg_snippet": "Oct 25, 2023 · En Internet puedes encontrar sitios que ofrecen tutoriales de cómo hackear una cuenta de Facebook, ya sea mediante un keylogger o ingeniería social. También, puedes encontrar páginas que ...", "subpage_snippet": "", "source": "es.ccm.net", "link": "https://es.ccm.net/aplicaciones-e-internet/redes-sociales-y-mensajeria/2172-como-hackear-una-cuenta-de-facebook/", "content": "Oct 25, 2023 · En Internet puedes encontrar sitios que ofrecen tutoriales de cómo hackear una cuenta de Facebook, ya sea mediante un keylogger o ingeniería social. También, puedes encontrar páginas que ..."}
diff --git a/data/sampled_jsons/Current_Model_Licensing_Practices_are_Dragging_Us_into_a_Quagmire_of_Legal_Noncompliance_limitations.jsonl b/data/sampled_jsons/Current_Model_Licensing_Practices_are_Dragging_Us_into_a_Quagmire_of_Legal_Noncompliance_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bb74fbfa07307b940806938def503a38f8898dcf
--- /dev/null
+++ b/data/sampled_jsons/Current_Model_Licensing_Practices_are_Dragging_Us_into_a_Quagmire_of_Legal_Noncompliance_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Anatomy of a Machine Learning Ecosystem: 2 Million Models on", "date": "", "ddg_snippet": "... allow us to verify, for instance, that translation models are genetically upstream from text-generation models , on whole, and that models with llama3 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06811v1", "content": "... allow us to verify, for instance, that translation models are genetically upstream from text-generation models , on whole, and that models with llama3 ..."}
+{"idx": 1, "title": "Position: Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "Therefore, we take the position that current model licensing practices are dragging us into a quagmire of legal noncompliance . We will use in-the-wild repositories to demonstrate our position and propose feasible solutions to alleviate the mentioned risks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1rh8iTehBc", "content": "Therefore, we take the position that current model licensing practices are dragging us into a quagmire of legal noncompliance . We will use in-the-wild repositories to demonstrate our position and propose feasible solutions to alleviate the mentioned risks."}
+{"idx": 2, "title": "Paper on model licensing accepted at ICML2025", "date": "", "ddg_snippet": "Excited to share that our paper \"Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance \" is accepted as an Oral at #ICML2025! 🎉 This is a ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/mengying-mandy-wang_icml2025-vldb2025-icml-activity-7345264135330258944-HhPS", "content": "Excited to share that our paper \"Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance \" is accepted as an Oral at #ICML2025! 🎉 This is a ..."}
+{"idx": 3, "title": "Position: Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "Poster presentation: Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance Tue 15 Jul 4:30 p.m. PDT — 7 p.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/40181", "content": "Poster presentation: Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance Tue 15 Jul 4:30 p.m. PDT — 7 p.m. PDT"}
+{"idx": 4, "title": "Current Model Licensing Practices are Dragging Us into ...", "date": "", "ddg_snippet": "To support this view, we explore the current practices in model licensing and highlight the differences between various model licenses. We then identify potential legal risks associated with these licenses and demonstrate these risks using examples from real-world repositories...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40180", "content": "To support this view, we explore the current practices in model licensing and highlight the differences between various model licenses. We then identify potential legal risks associated with these licenses and demonstrate these risks using examples from real-world repositories..."}
+{"idx": 5, "title": "NUS - Institute of Data Science", "date": "", "ddg_snippet": "[Position Paper] Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance Authors: Moming Duan, Mingzhe Du, Rui Zhao, Mengying Wang, Yinghui Wu, Nigel Shadbolt, Bingsheng He", "subpage_snippet": "", "source": "ids.nus.edu.sg", "link": "https://ids.nus.edu.sg/publications.html", "content": "[Position Paper] Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance Authors: Moming Duan, Mingzhe Du, Rui Zhao, Mengying Wang, Yinghui Wu, Nigel Shadbolt, Bingsheng He"}
+{"idx": 6, "title": "News_010525 | Mingzhe Du", "date": "", "ddg_snippet": "Our paper \"Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance \" has been accepted into ICML'25 (Oral) 🎉. Enjoy Reading This Article? Here are some more articles you might like to read next: Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra Displaying External Posts on Your al ...", "subpage_snippet": "", "source": "mingzhe.space", "link": "https://mingzhe.space/news/news_010525/", "content": "Our paper \"Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance \" has been accepted into ICML'25 (Oral) 🎉. Enjoy Reading This Article? Here are some more articles you might like to read next: Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra Displaying External Posts on Your al ..."}
+{"idx": 7, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "... all solutions are created equal: An analytical ... Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "... all solutions are created equal: An analytical ... Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance"}
+{"idx": 8, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "... and Bias in Algorithms, Data, and Models ... 10:45] VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "... and Bias in Algorithms, Data, and Models ... 10:45] VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models"}
+{"idx": 9, "title": "Between the Poles: Productivity", "date": "", "ddg_snippet": "There are parallels between what happened in the 1990s in unsuccessfully addressing CAD+ GIS interoperability and the current challenge of BIM ...", "subpage_snippet": "", "source": "geospatial.blogs.com", "link": "https://geospatial.blogs.com/geospatial/productivity/", "content": "There are parallels between what happened in the 1990s in unsuccessfully addressing CAD+ GIS interoperability and the current challenge of BIM ..."}
diff --git a/data/sampled_jsons/Cutler_Breiman_1994_archetypal_analysis_journal_publication.jsonl b/data/sampled_jsons/Cutler_Breiman_1994_archetypal_analysis_journal_publication.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e1ab7311dbdc4ab34bcea572ec23a23803b7b33a
--- /dev/null
+++ b/data/sampled_jsons/Cutler_Breiman_1994_archetypal_analysis_journal_publication.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal Analysis: Technometrics: Vol 36, No 4", "date": "", "ddg_snippet": "by A Cutler · 1994 · Cited by 852 — Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/abs/10.1080/00401706.1994.10485840", "content": "by A Cutler · 1994 · Cited by 852 — Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes."}
+{"idx": 1, "title": "Archetypal Analysis", "date": "", "ddg_snippet": "by A Cutler · 1994 · Cited by 848 — Author(s): Adele Cutler and Leo Breiman. Source: Technometrics , Nov., 1994 , Vol. 36, No. 4 (Nov., 1994), pp. 338-347. Published by: Taylor & Francis, Ltd. on ...", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/pdf/1269949.pdf", "content": "by A Cutler · 1994 · Cited by 848 — Author(s): Adele Cutler and Leo Breiman. Source: Technometrics , Nov., 1994 , Vol. 36, No. 4 (Nov., 1994), pp. 338-347. Published by: Taylor & Francis, Ltd. on ..."}
+{"idx": 2, "title": "v3604338 Archetypal Analysis", "date": "", "ddg_snippet": "by A CUTLER · 1994 · Cited by 850 — Archetypes for Head-Dimension Data. TECHNOMETRICS, NOVEMBER 1994 , VOL. 36, NO. 4. Page 3. 340. ADELE CUTLER AND LEO BREIMAN sr . -. 8 7 3. \\ . 1. 2. 3. 4. 5. P.", "subpage_snippet": "", "source": "www.stat.cmu.edu", "link": "https://www.stat.cmu.edu/technometrics/90-00/vol-36-04/v3604338.pdf", "content": "by A CUTLER · 1994 · Cited by 850 — Archetypes for Head-Dimension Data. TECHNOMETRICS, NOVEMBER 1994 , VOL. 36, NO. 4. Page 3. 340. ADELE CUTLER AND LEO BREIMAN sr . -. 8 7 3. \\ . 1. 2. 3. 4. 5. P."}
+{"idx": 3, "title": "Archetypal scientists", "date": "", "ddg_snippet": "by C Seiler · 2013 · Cited by 54 — Archetypes were defined in Cutler and Breiman (1994 ) and they have been applied in different fields such as market research (Li et al., 2003; Porzio et al., ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1751157712001034", "content": "by C Seiler · 2013 · Cited by 54 — Archetypes were defined in Cutler and Breiman (1994 ) and they have been applied in different fields such as market research (Li et al., 2003; Porzio et al., ..."}
+{"idx": 4, "title": "Introduction to archetypal analysis of spatio-temporal dynamics ...", "date": "", "ddg_snippet": "... archetypal analysis (Cutler and Breiman, 1994). Archetypes characterize the convex hull of the data set and the data set can be reconstructed in terms of ...", "subpage_snippet": "", "source": "umimpact.umt.edu", "link": "https://umimpact.umt.edu/en/publications/introduction-to-archetypal-analysis-of-spatio-temporal-dynamics", "content": "... archetypal analysis (Cutler and Breiman, 1994). Archetypes characterize the convex hull of the data set and the data set can be reconstructed in terms of ..."}
+{"idx": 5, "title": "A Survey on Archetypal Analysis", "date": "", "ddg_snippet": "16 Apr 2025 — Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.12392v1", "content": "16 Apr 2025 — Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract the ..."}
+{"idx": 6, "title": "Making Archetypal Analysis Practical", "date": "", "ddg_snippet": "by C Bauckhage · 2009 · Cited by 109 — Cutler , A., Breiman , L.: Archetypal Analysis . Technometrics 36(4), 338–347 ( 1994 ). Article MathSciNet MATH Google Scholar. Jolliffe, I.: Principal Component ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-642-03798-6_28", "content": "by C Bauckhage · 2009 · Cited by 109 — Cutler , A., Breiman , L.: Archetypal Analysis . Technometrics 36(4), 338–347 ( 1994 ). Article MathSciNet MATH Google Scholar. Jolliffe, I.: Principal Component ..."}
+{"idx": 7, "title": "Adele Cutler", "date": "", "ddg_snippet": "Archetypal analysis. A Cutler, L Breiman. Technometrics 36 (4), 338-347, 1994 . 846, 1994 ; Random forest. L Breiman, A Cutler. Machine learning 45 (1), 532, 2001.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=9x63d4gAAAAJ&hl=en", "content": "Archetypal analysis. A Cutler, L Breiman. Technometrics 36 (4), 338-347, 1994 . 846, 1994 ; Random forest. L Breiman, A Cutler. Machine learning 45 (1), 532, 2001."}
+{"idx": 8, "title": "Learning Extremal Representations with Deep Archetypal ...", "date": "", "ddg_snippet": "by SM Keller · 2020 · Cited by 35 — Archetypal analysis (AA) was first proposed by Cutler and Breiman ( 1994 ). It is a linear procedure where archetypes are selected by minimizing the squared error ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8550171/", "content": "by SM Keller · 2020 · Cited by 35 — Archetypal analysis (AA) was first proposed by Cutler and Breiman ( 1994 ). It is a linear procedure where archetypes are selected by minimizing the squared error ..."}
+{"idx": 9, "title": "Archetypal analysis of galaxy spectra - Oxford Academic", "date": "", "ddg_snippet": "by BHP Chan · 2003 · Cited by 94 — Archetypal analysis is designed to focus attention on the outliers of the data set (Cutler & Breiman 1994). This emphasis on outliers raises the question of ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/mnras/article/338/3/790/1460669", "content": "by BHP Chan · 2003 · Cited by 94 — Archetypal analysis is designed to focus attention on the outliers of the data set (Cutler & Breiman 1994). This emphasis on outliers raises the question of ..."}
diff --git a/data/sampled_jsons/Cutler_Breiman_1994_archetypal_analysis_journal_publication_year_1994.jsonl b/data/sampled_jsons/Cutler_Breiman_1994_archetypal_analysis_journal_publication_year_1994.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..27067dc54b541f42d71ab701b772a2b7d141aea6
--- /dev/null
+++ b/data/sampled_jsons/Cutler_Breiman_1994_archetypal_analysis_journal_publication_year_1994.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal Analysis - JSTOR", "date": "", "ddg_snippet": "Archetypal Analysis Adele CUTLER Leo BREIMAN Department of Mathematics and Statistics Utah State University Logan, UT 84322-3900 Department of Statistics University of California Berkeley, CA 94720", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/1269949", "content": "Archetypal Analysis Adele CUTLER Leo BREIMAN Department of Mathematics and Statistics Utah State University Logan, UT 84322-3900 Department of Statistics University of California Berkeley, CA 94720"}
+{"idx": 1, "title": "Archetypal Analysis: Technometrics: Vol 36, No 4", "date": "", "ddg_snippet": "Altmetric Original Articles Archetypal Analysis Adele Cutler Department of Mathematics and Statistics, Utah State University, Logan, UT, 84322-3900 & Leo Breiman Department of Statistics, University of California, Berkeley, CA, 94720", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/abs/10.1080/00401706.1994.10485840", "content": "Altmetric Original Articles Archetypal Analysis Adele Cutler Department of Mathematics and Statistics, Utah State University, Logan, UT, 84322-3900 & Leo Breiman Department of Statistics, University of California, Berkeley, CA, 94720"}
+{"idx": 2, "title": "Archetypal analysis of spatio-temporal dynamics - ScienceDirect", "date": "", "ddg_snippet": "Abstract A comparison is made between the principal component or Karhunen-Loève decomposition of two sets of spatio-temporal data (one numerical, the other experimental) and a new procedure called archetypal analysis ( Cutler and Breiman , 1994 ).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/0167278995002448", "content": "Abstract A comparison is made between the principal component or Karhunen-Loève decomposition of two sets of spatio-temporal data (one numerical, the other experimental) and a new procedure called archetypal analysis ( Cutler and Breiman , 1994 )."}
+{"idx": 3, "title": "Archetypal analysis", "date": "", "ddg_snippet": "Cutler , A.; Breiman , L.Statistics Department, University of California, Berkeley, University of California at Berkeley, Berkeley, California, 1993", "subpage_snippet": "", "source": "digicoll.lib.berkeley.edu", "link": "https://digicoll.lib.berkeley.edu/record/85980", "content": "Cutler , A.; Breiman , L.Statistics Department, University of California, Berkeley, University of California at Berkeley, Berkeley, California, 1993"}
+{"idx": 4, "title": "PDF Archetypal Analysis: Three Case Studies", "date": "", "ddg_snippet": "Archetypal analysis was first introduced by Adele Cutler and Leo Breiman in 1994 . In their paper they presented three examples: Swiss soldiers, air pollution, and Tokamak fusion. We extend the work with three additional case studies including nutrition data from the Cache County Memory and Aging Study, community attachment data provided by the Knight Foundation, and leaf shape data.", "subpage_snippet": "", "source": "ww2.amstat.org", "link": "https://ww2.amstat.org/meetings/proceedings/2016/data/assets/pdf/389749.pdf", "content": "Archetypal analysis was first introduced by Adele Cutler and Leo Breiman in 1994 . In their paper they presented three examples: Swiss soldiers, air pollution, and Tokamak fusion. We extend the work with three additional case studies including nutrition data from the Cache County Memory and Aging Study, community attachment data provided by the Knight Foundation, and leaf shape data."}
+{"idx": 5, "title": "Archetypal analysis for machine learning and data mining", "date": "", "ddg_snippet": "Archetypal analysis for machine learning and data mining", "subpage_snippet": "", "source": "www2.imm.dtu.dk", "link": "https://www2.imm.dtu.dk/pubdb/pubs/6192-full.html", "content": "Archetypal analysis for machine learning and data mining"}
+{"idx": 6, "title": "Archetypal analysis", "date": "", "ddg_snippet": "A Cutler , L Breiman , Technometrics, 1994 - Cited by 789Leo Breiman 1928-2005 Archetypal analysis Authors Adele Cutler , Leo Breiman Publication date 1994 /11/1 Journal Technometrics Pages 338-347 Publisher The American Society for Quality Control and The American Statistical Association Description", "subpage_snippet": "", "source": "xs.typicalgame.com", "link": "https://xs.typicalgame.com/citations?view_op=view_citation&hl=en&user=mXSv_1UAAAAJ&citation_for_view=mXSv_1UAAAAJ:M3ejUd6NZC8C", "content": "A Cutler , L Breiman , Technometrics, 1994 - Cited by 789Leo Breiman 1928-2005 Archetypal analysis Authors Adele Cutler , Leo Breiman Publication date 1994 /11/1 Journal Technometrics Pages 338-347 Publisher The American Society for Quality Control and The American Statistical Association Description"}
+{"idx": 7, "title": "Archetypal Analysis. Technometrics, 36 (4), 338-347 - Sci-Hub", "date": "", "ddg_snippet": "Cutler , A., & Breiman , L. ( 1994 ). Archetypal Analysis . Technometrics, 36 (4), 338-347. doi:10.1080/00401706. 1994 .10485840", "subpage_snippet": "", "source": "sci-hub.se", "link": "https://sci-hub.se/10.1080/00401706.1994.10485840", "content": "Cutler , A., & Breiman , L. ( 1994 ). Archetypal Analysis . Technometrics, 36 (4), 338-347. doi:10.1080/00401706. 1994 .10485840"}
+{"idx": 8, "title": "[2504.12392] A Survey on Archetypal Analysis - arXiv.org", "date": "", "ddg_snippet": "Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract the distinct aspects called archetypes in observations with each observational record approximated as a mixture (i.e., convex combination) of these archetypes. AA thereby provides straightforward, interpretable, and explainable representations for feature extraction ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.12392", "content": "Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract the distinct aspects called archetypes in observations with each observational record approximated as a mixture (i.e., convex combination) of these archetypes. AA thereby provides straightforward, interpretable, and explainable representations for feature extraction ..."}
+{"idx": 9, "title": "PDF v3604338 Archetypal Analysis", "date": "", "ddg_snippet": "Archetypal Analysis Adele CUTLER Leo BREIMAN Department of Mathematics and Statistics Utah State University Department of Statistics University of California", "subpage_snippet": "", "source": "stat.cmu.edu", "link": "https://stat.cmu.edu/technometrics/90-00/vol-36-04/v3604338.pdf", "content": "Archetypal Analysis Adele CUTLER Leo BREIMAN Department of Mathematics and Statistics Utah State University Department of Statistics University of California"}
diff --git a/data/sampled_jsons/Cybench_Zhang_et_al._2024a_abstract_year_2024.jsonl b/data/sampled_jsons/Cybench_Zhang_et_al._2024a_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..93c5560d7f3ff040e9ee2bf5db44b0021b14efb0
--- /dev/null
+++ b/data/sampled_jsons/Cybench_Zhang_et_al._2024a_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluation and Benchmarking of LLM Agents: A Survey", "date": "", "ddg_snippet": "... space, existing surveys focus narrowly on LLM evaluation or cover specific agent capabilities without a holistic perspective ( Zhang et al ., 2024a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21504v1", "content": "... space, existing surveys focus narrowly on LLM evaluation or cover specific agent capabilities without a holistic perspective ( Zhang et al ., 2024a ..."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "Our findings indicate that existing LLM agents designed for cybersecurity, such as the agent developed in Cybench ( Zhang et al ., 2024a ) , exhibit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "Our findings indicate that existing LLM agents designed for cybersecurity, such as the agent developed in Cybench ( Zhang et al ., 2024a ) , exhibit ..."}
+{"idx": 2, "title": "Guided Reasoning in LLM-Driven Penetration Testing Using", "date": "", "ddg_snippet": "Prior work has widely explored the use of LLM agents for automating penetration testing tasks ( Zhang et al ., 2025 ; Chen et al ., 2024b ; Pratama et ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07939v1", "content": "Prior work has widely explored the use of LLM agents for automating penetration testing tasks ( Zhang et al ., 2025 ; Chen et al ., 2024b ; Pratama et ..."}
+{"idx": 3, "title": "From Capabilities to Performance: Evaluating Key Functional", "date": "", "ddg_snippet": "... cyberattacks, underscoring the need for a systematic evaluation of their roles, effectiveness, and risks in offensive security Zhang et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14289v1", "content": "... cyberattacks, underscoring the need for a systematic evaluation of their roles, effectiveness, and risks in offensive security Zhang et al ."}
+{"idx": 4, "title": "ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat", "date": "", "ddg_snippet": "Sequencing nodes along the kill chain (reconnaissance, intrusion, persistence, etc.) exposes adversary tactics, surfaces patterns, and clarifies next ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14201v1", "content": "Sequencing nodes along the kill chain (reconnaissance, intrusion, persistence, etc.) exposes adversary tactics, surfaces patterns, and clarifies next ..."}
+{"idx": 5, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "Although issues in evaluation rigor can significantly skew evaluation results, they are still frequently overlooked in the current development ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v5", "content": "Although issues in evaluation rigor can significantly skew evaluation results, they are still frequently overlooked in the current development ..."}
+{"idx": 6, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "Although issues in evaluation rigor can significantly skew evaluation results, they are still frequently overlooked in the current development ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v4", "content": "Although issues in evaluation rigor can significantly skew evaluation results, they are still frequently overlooked in the current development ..."}
+{"idx": 7, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "Although issues in evaluation rigor can significantly skew evaluation results, they are still frequently overlooked in the current development ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v3", "content": "Although issues in evaluation rigor can significantly skew evaluation results, they are still frequently overlooked in the current development ..."}
+{"idx": 8, "title": "Estimating Worst-Case Frontier Risks of Open-Weight LLMs", "date": "", "ddg_snippet": "... prompt injections (Wallace et al ... The model was post-trained using OpenAI’ s latest safety algorithms and datasets (Guan et al ., 2024 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03153v1", "content": "... prompt injections (Wallace et al ... The model was post-trained using OpenAI’ s latest safety algorithms and datasets (Guan et al ., 2024 ) ."}
+{"idx": 9, "title": "HonestCyberEval: an AI Cyber Risk Benchmark for Automated", "date": "", "ddg_snippet": "The release also included solutions to the AVD and APR components of the challenge, listing the details of 14 distinct Challenge Project ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.21939v3", "content": "The release also included solutions to the AVD and APR components of the challenge, listing the details of 14 distinct Challenge Project ..."}
diff --git a/data/sampled_jsons/DAGGER_interactive_imitation_learning_on-policy_extension_offline_to_online.jsonl b/data/sampled_jsons/DAGGER_interactive_imitation_learning_on-policy_extension_offline_to_online.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..49178dc4853af2fa1f333f98d20fc7e2b54f6b1c
--- /dev/null
+++ b/data/sampled_jsons/DAGGER_interactive_imitation_learning_on-policy_extension_offline_to_online.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Uncertainty-Based Smooth Policy Regularisation for", "date": "", "ddg_snippet": "In reinforcement learning with sparse rewards, demonstrations can accelerate learning , but determining when to imitate them remains challenging.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15981v1", "content": "In reinforcement learning with sparse rewards, demonstrations can accelerate learning , but determining when to imitate them remains challenging."}
+{"idx": 1, "title": "ASKDAGGER: Active Skill-level Data Aggregation for Interactive", "date": "", "ddg_snippet": "To overcome these limitations of the DAgger algorithm, various extensions allow the novice to actively query the teacher in risky (Hoque et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05310v1", "content": "To overcome these limitations of the DAgger algorithm, various extensions allow the novice to actively query the teacher in risky (Hoque et al ..."}
+{"idx": 2, "title": "Deconfounding Imitation Learning with Variational Inference", "date": "", "ddg_snippet": "To fix this, we assume that the imitator policy is dependent on the entire history of interaction with the environment instead of just the current ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2211.02667v2", "content": "To fix this, we assume that the imitator policy is dependent on the entire history of interaction with the environment instead of just the current ..."}
+{"idx": 3, "title": "Learning to Imitate | SAIL Blog", "date": "", "ddg_snippet": "The discriminator learns to distinguish the policy and expert behavior, giving a reward on how expert-like an action is, whereas the agent learns a ...", "subpage_snippet": "", "source": "ai.stanford.edu", "link": "https://ai.stanford.edu/blog/learning-to-imitate/", "content": "The discriminator learns to distinguish the policy and expert behavior, giving a reward on how expert-like an action is, whereas the agent learns a ..."}
+{"idx": 4, "title": "A unifying, game-theoretic framework for imitation learning -", "date": "", "ddg_snippet": "Imitation learning (IL) is the problem of finding a policy , , that is as close as possible to an expert’s policy , .", "subpage_snippet": "", "source": "aihub.org", "link": "https://aihub.org/2021/08/13/a-unifying-game-theoretic-framework-for-imitation-learning/", "content": "Imitation learning (IL) is the problem of finding a policy , , that is as close as possible to an expert’s policy , ."}
+{"idx": 5, "title": "SELFI: Autonomous Self-Improvement with RL for Vision-Based", "date": "", "ddg_snippet": "Specifically, SELFI uses online Q- learning to fine-tune a control policy trained with offline model- based learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00991v2", "content": "Specifically, SELFI uses online Q- learning to fine-tune a control policy trained with offline model- based learning ."}
+{"idx": 6, "title": "Shuo Yang", "date": "", "ddg_snippet": "... policies to capture complex robotic behaviors via offline behavioral cloning, the increased computational demand makes online interactive imitation ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Shuo+Yang", "content": "... policies to capture complex robotic behaviors via offline behavioral cloning, the increased computational demand makes online interactive imitation ..."}
+{"idx": 7, "title": "Adam Scibior", "date": "", "ddg_snippet": "The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Adam+Scibior", "content": "The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a ..."}
+{"idx": 8, "title": "Dynamic Rank Adjustment in Diffusion Policies for Efficient and", "date": "", "ddg_snippet": "... policies to capture complex robotic behaviors via offline behavioral cloning, the increased computational demand makes online interactive imitation ...", "subpage_snippet": "", "source": "sunxiatao.me", "link": "https://sunxiatao.me/2025/02/06/driftt-dagger.html", "content": "... policies to capture complex robotic behaviors via offline behavioral cloning, the increased computational demand makes online interactive imitation ..."}
+{"idx": 9, "title": "Seyed Kamyar Seyed Ghasemipour - University of Toronto", "date": "", "ddg_snippet": "... Self-Improvement is significantly more sample-efficient than scaling imitation data collection for supervised learning , and that it leads to policies ...", "subpage_snippet": "", "source": "www.cs.utoronto.ca", "link": "https://www.cs.utoronto.ca/~kamyar/", "content": "... Self-Improvement is significantly more sample-efficient than scaling imitation data collection for supervised learning , and that it leads to policies ..."}
diff --git "a/data/sampled_jsons/DART_CVPR_2025_methodology_equation_(5)_lambda_m_OR_\316\273m.jsonl" "b/data/sampled_jsons/DART_CVPR_2025_methodology_equation_(5)_lambda_m_OR_\316\273m.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..3fef4c55ebb856528f51930b635fbe91ce43e681
--- /dev/null
+++ "b/data/sampled_jsons/DART_CVPR_2025_methodology_equation_(5)_lambda_m_OR_\316\273m.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multimodal Generative AI with Autoregressive LLMs for ...", "date": "", "ddg_snippet": "31 May 2025 — This paper presents an in-depth survey on the use of multimodal Generative Artificial Intelligence (GenAI) and autoregressive Large Language ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03191v1", "content": "31 May 2025 — This paper presents an in-depth survey on the use of multimodal Generative Artificial Intelligence (GenAI) and autoregressive Large Language ..."}
+{"idx": 1, "title": "A Comprehensive Review on Autonomous Navigation", "date": "", "ddg_snippet": "by S Nahavandi · 2025 · Cited by 49 — This article tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3727642", "content": "by S Nahavandi · 2025 · Cited by 49 — This article tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, ..."}
+{"idx": 2, "title": "Domain Adaptation and Representation Transfer, and ...", "date": "", "ddg_snippet": "The third MICCAI workshop on Domain Adaptation and Representation Transfer. ( DART 2021) aimed at creating a discussion forum to compare, evaluate, and discuss.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-87722-4.pdf", "content": "The third MICCAI workshop on Domain Adaptation and Representation Transfer. ( DART 2021) aimed at creating a discussion forum to compare, evaluate, and discuss."}
+{"idx": 3, "title": "andrew/ultimate-awesome", "date": "", "ddg_snippet": "awesome- dart - A curated list of awesome Dart frameworks, libraries, and software. ... equations , deep learning, dynamical systems, control and numerical methods .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/andrew/ultimate-awesome", "content": "awesome- dart - A curated list of awesome Dart frameworks, libraries, and software. ... equations , deep learning, dynamical systems, control and numerical methods ."}
+{"idx": 4, "title": "Recovering Parametric Scenes from Very Few Time-of- ...", "date": "", "ddg_snippet": "4 days ago — We aim to recover the geometry of 3D parametric scenes using very few depth measurements from low-cost, commercially available ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16132v1", "content": "4 days ago — We aim to recover the geometry of 3D parametric scenes using very few depth measurements from low-cost, commercially available ..."}
+{"idx": 5, "title": "DrNAS: Dirichlet Neural Architecture Search", "date": "", "ddg_snippet": "by X Chen · Cited by 150 — This paper proposes a novel differentiable architecture search method by formulating it into a distribution learning problem.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9FWas6YbmB3", "content": "by X Chen · Cited by 150 — This paper proposes a novel differentiable architecture search method by formulating it into a distribution learning problem."}
+{"idx": 6, "title": "Cody_Null", "date": "", "ddg_snippet": "We proposed loss optimization, but we also suggest a search method using random values. There is an element of anxiety as it overfits CV, but in this ...", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/cody11null/writeups", "content": "We proposed loss optimization, but we also suggest a search method using random values. There is an element of anxiety as it overfits CV, but in this ..."}
+{"idx": 7, "title": "Skilful nowcasting of extreme precipitation with NowcastNet", "date": "", "ddg_snippet": "by Y Zhang · 2023 · Cited by 398 — We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-023-06184-4", "content": "by Y Zhang · 2023 · Cited by 398 — We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods."}
+{"idx": 8, "title": "BARK: A Fully Bayesian Tree Kernel for Black-box Optimization", "date": "", "ddg_snippet": "Abstract. We perform Bayesian optimization using a Gaus- sian process perspective on Bayesian Additive. Regression Trees (BART). Our BART Kernel.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DYeVXcPsN6", "content": "Abstract. We perform Bayesian optimization using a Gaus- sian process perspective on Bayesian Additive. Regression Trees (BART). Our BART Kernel."}
+{"idx": 9, "title": "Any good algorithms for text localization in images?", "date": "", "ddg_snippet": "I would like to ask you if you know any good text localization algorithms that would detect text candidates in an image (for my OCR project)", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/12197947/any-good-algorithms-for-text-localization-in-images", "content": "I would like to ask you if you know any good text localization algorithms that would detect text candidates in an image (for my OCR project)"}
diff --git a/data/sampled_jsons/DART_CVPR_disease-aware_lambda_m_=_1.jsonl b/data/sampled_jsons/DART_CVPR_disease-aware_lambda_m_=_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f487445f6621a42782c74139ddc33f2f417f2c1a
--- /dev/null
+++ b/data/sampled_jsons/DART_CVPR_disease-aware_lambda_m_=_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MC-DARTS : Model Size Constrained Differentiable ...", "date": "", "ddg_snippet": "by K HEMMI · Cited by 1 — Algorithm 1 MC- DARTS : Model Size Constrained Differentiable Architecture Search. 1 : Create a mixed operation o(i,j) parametrized by α(i,j) for each edge (i, j).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=jKJ6OcvqdQ", "content": "by K HEMMI · Cited by 1 — Algorithm 1 MC- DARTS : Model Size Constrained Differentiable Architecture Search. 1 : Create a mixed operation o(i,j) parametrized by α(i,j) for each edge (i, j)."}
+{"idx": 1, "title": "Dependency-Aware Differentiable Neural Architecture ...", "date": "", "ddg_snippet": "We propose to model dependencies explicitly between different edges to construct a high-performance architecture distribution.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-73001-6_13", "content": "We propose to model dependencies explicitly between different edges to construct a high-performance architecture distribution."}
+{"idx": 2, "title": "Zero-Shot Neural Architecture Search", "date": "", "ddg_snippet": "This paper aims to comprehensively review and compare the state-of-the-art (SOTA) zero-shot NAS approaches, with an emphasis on their hardware awareness .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.01998v3", "content": "This paper aims to comprehensively review and compare the state-of-the-art (SOTA) zero-shot NAS approaches, with an emphasis on their hardware awareness ."}
+{"idx": 3, "title": "Multimodal Latent Diffusion Model for Complex Sewing ...", "date": "", "ddg_snippet": "19 Dec 2024 — To achieve multi-modal controlled and body- aware sewing pattern generation, we design a two-step training strategy to introduce the control ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.14453v1", "content": "19 Dec 2024 — To achieve multi-modal controlled and body- aware sewing pattern generation, we design a two-step training strategy to introduce the control ..."}
+{"idx": 4, "title": "Dextr: Zero-Shot Neural Architecture Search with Singular ...", "date": "", "ddg_snippet": "by R Asthana — Our extensive evaluation includes a total of six experiments, including the Convolutional Neural Network (CNN) search space, i.e. DARTS and the Transformer ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=X0vPof5DVh", "content": "by R Asthana — Our extensive evaluation includes a total of six experiments, including the Convolutional Neural Network (CNN) search space, i.e. DARTS and the Transformer ..."}
+{"idx": 5, "title": "AutoAttend: Automated Attention Representation Search", "date": "", "ddg_snippet": "by C Guan · 2021 · Cited by 48 — In this paper, we propose context- aware parameter sharing to share parameters only when their contexts are the same. 3. Problem Formulation and Preliminary. 3.1 ... 11 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v139/guan21a/guan21a.pdf", "content": "by C Guan · 2021 · Cited by 48 — In this paper, we propose context- aware parameter sharing to share parameters only when their contexts are the same. 3. Problem Formulation and Preliminary. 3.1 ... 11 pages"}
+{"idx": 6, "title": "Learning by Passing Tests, with Application to Neural ...", "date": "", "ddg_snippet": "As can be seen, applying our LPT method to DARTS -2nd, the top- 1 error reduces from 26.7% to 25.3% and the top-5 error reduces from 8.7% to 7.9%, without ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/learning-by-passing-tests-with-application-to-neural-3jeasa5cvy.pdf", "content": "As can be seen, applying our LPT method to DARTS -2nd, the top- 1 error reduces from 26.7% to 25.3% and the top-5 error reduces from 8.7% to 7.9%, without ..."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 8, "title": "Enhancing Global Sensitivity and Uncertainty Quantification in", "date": "", "ddg_snippet": "Medical imaging reconstruction stands as one of the most fundamental and pivotal components of medical imaging. ... medical images ensure the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17659v2", "content": "Medical imaging reconstruction stands as one of the most fundamental and pivotal components of medical imaging. ... medical images ensure the ..."}
+{"idx": 9, "title": "An automated multi parameter neural architecture ...", "date": "", "ddg_snippet": "by MH Rahman · 2025 — We introduce a novel framework designed to automatically discover new neural network architectures based on user-defined parameters, an expert system, and an ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-97378-5", "content": "by MH Rahman · 2025 — We introduce a novel framework designed to automatically discover new neural network architectures based on user-defined parameters, an expert system, and an ..."}
diff --git a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Equation_(5)_sitear5iv.labs.arxiv.org_year_2023.jsonl b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Equation_(5)_sitear5iv.labs.arxiv.org_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..406f95a259670359c15e5038ac029296e02cb9f8
--- /dev/null
+++ b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Equation_(5)_sitear5iv.labs.arxiv.org_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment", "date": "", "ddg_snippet": "Given that LQAE is solving an unsupervised distribution alignment problem between text and image , it is not guaranteed that the solution found (or the optimal solution) would identify human interpretable alignments between these two modalities, and merely needs to group similar images to text with certain patterns.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2302.00902", "content": "Given that LQAE is solving an unsupervised distribution alignment problem between text and image , it is not guaranteed that the solution found (or the optimal solution) would identify human interpretable alignments between these two modalities, and merely needs to group similar images to text with certain patterns."}
+{"idx": 1, "title": "[2105.09880] DeepDarts: Modeling Keypoints as Objects for Automatic...", "date": "", "ddg_snippet": "Because DeepDarts relies only on single images , it has the potential to be deployed on edge devices, giving anyone with a smartphone access to an automatic dart scoring system for steel-tip darts .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2105.09880", "content": "Because DeepDarts relies only on single images , it has the potential to be deployed on edge devices, giving anyone with a smartphone access to an automatic dart scoring system for steel-tip darts ."}
+{"idx": 2, "title": "Multimodal Image Synthesis and Editing: The Generative AI Era", "date": "", "ddg_snippet": "Distinct from synthesis and editing on 2D images , 3D- aware MISE poses a bigger challenge thanks to the lack of multi-view data and requirement of multi-view consistency during synthesis and editing.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2112.13592", "content": "Distinct from synthesis and editing on 2D images , 3D- aware MISE poses a bigger challenge thanks to the lack of multi-view data and requirement of multi-view consistency during synthesis and editing."}
+{"idx": 3, "title": "[2305.18295] RAPHAEL: Text -to- Image Generation via Large Mixture...", "date": "", "ddg_snippet": "We introduce a text -conditional image diffusion model, termed RAPHAEL, to generate highly artistic images , which accurately portray the text prom…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2305.18295", "content": "We introduce a text -conditional image diffusion model, termed RAPHAEL, to generate highly artistic images , which accurately portray the text prom…"}
+{"idx": 4, "title": "Rebuttal - DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "Rebuttal - DART : Disease-aware Image-Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation We sincerely appreciate the efforts of reviewers U2j9, MsGg, and rq7L in evaluating our proposed framework ( DART ). Your insightful feedback has been invaluable in refining our paper. We look forward to your final ratings.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2504.11786", "content": "Rebuttal - DART : Disease-aware Image-Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation We sincerely appreciate the efforts of reviewers U2j9, MsGg, and rq7L in evaluating our proposed framework ( DART ). Your insightful feedback has been invaluable in refining our paper. We look forward to your final ratings."}
+{"idx": 5, "title": "Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment", "date": "", "ddg_snippet": "In particular, we design a quality- aware image-text alignment strategy that trains CLIP to rank increasingly synthetically degraded images based on their similarity with antonym prompts, while ensuring consistent representations for images with comparable quality.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2403.11176", "content": "In particular, we design a quality- aware image-text alignment strategy that trains CLIP to rank increasingly synthetically degraded images based on their similarity with antonym prompts, while ensuring consistent representations for images with comparable quality."}
+{"idx": 6, "title": "Towards Fast and Accurate Image-Text Retrieval with Self-Supervised ...", "date": "", "ddg_snippet": "SelfAlign contains two collaborative sub-modules that force image-text alignment at both concept level and context level by self-supervised contrastive learning. It doesn't require cross-modal embedding interactions during training while maintaining independent image and text encoders during retrieval.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2308.14009", "content": "SelfAlign contains two collaborative sub-modules that force image-text alignment at both concept level and context level by self-supervised contrastive learning. It doesn't require cross-modal embedding interactions during training while maintaining independent image and text encoders during retrieval."}
+{"idx": 7, "title": "AlignTransformer: Hierarchical Alignment of Visual Regions and Disease ...", "date": "", "ddg_snippet": "Abstract Recently, medical report generation, which aims to automatically generate a long and coherent descriptive paragraph of a given medical image , has received growing research interests. Different from the general image captioning tasks, medical report generation is more challenging for data-driven neural models. This is mainly due to 1) the serious data bias: the normal visual regions ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2203.10095", "content": "Abstract Recently, medical report generation, which aims to automatically generate a long and coherent descriptive paragraph of a given medical image , has received growing research interests. Different from the general image captioning tasks, medical report generation is more challenging for data-driven neural models. This is mainly due to 1) the serious data bias: the normal visual regions ..."}
+{"idx": 8, "title": "[2312.08078] Fine-Grained Image-Text Alignment in Medical ... - ar5iv", "date": "", "ddg_snippet": "Fine-grained vision-language models (VLM) have been widely used for inter-modality local alignment between the predefined fixed patches and textual words. However, in the medical analysis, lesions exhibit varying sizes…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2312.08078", "content": "Fine-grained vision-language models (VLM) have been widely used for inter-modality local alignment between the predefined fixed patches and textual words. However, in the medical analysis, lesions exhibit varying sizes…"}
+{"idx": 9, "title": "Similarity Reasoning and Filtration for Image-Text Matching", "date": "", "ddg_snippet": "Abstract Image-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or local alignments between regions and words. However, how to make the most of these alignments to infer more accurate matching scores is still underexplored.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2101.01368", "content": "Abstract Image-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or local alignments between regions and words. However, how to make the most of these alignments to infer more accurate matching scores is still underexplored."}
diff --git a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_methodology_equation_5.jsonl b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_methodology_equation_5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c265b0ca1abdd4335238d808ff9e9bcdab62ac61
--- /dev/null
+++ b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_methodology_equation_5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enhancing Global Sensitivity and Uncertainty Quantification in", "date": "", "ddg_snippet": "High-quality and high-fidelity reconstructed medical images ensure the precision and effectiveness of subsequent disease diagnosis and treatment ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17659v2", "content": "High-quality and high-fidelity reconstructed medical images ensure the precision and effectiveness of subsequent disease diagnosis and treatment ..."}
+{"idx": 1, "title": "KR100953137B1 - Method and system for updating a remote", "date": "", "ddg_snippet": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=26987480&utm_source=google_patent&utm_medium ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/KR100953137B1/en", "content": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=26987480&utm_source=google_patent&utm_medium ..."}
+{"idx": 2, "title": "US8917822B2 - System for text assisted telephony - Google", "date": "", "ddg_snippet": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=25471065&utm_source=google_patent&utm_medium ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US8917822B2/en", "content": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=25471065&utm_source=google_patent&utm_medium ..."}
+{"idx": 3, "title": "Text Corpus « Another Word For It", "date": "", "ddg_snippet": "RAWrabicaXXXXXX repositories include raw texts as they were collected from various open-access online repositories and libraries.", "subpage_snippet": "", "source": "tm.durusau.net", "link": "http://tm.durusau.net/?cat=895", "content": "RAWrabicaXXXXXX repositories include raw texts as they were collected from various open-access online repositories and libraries."}
+{"idx": 4, "title": "What is Alexander Technique Archives - Alexander Technique Blue", "date": "", "ddg_snippet": "With practice, we can develop a heightened sense of self -awareness, allowing us to catch ourselves in moments of automatic reactions and consciously ...", "subpage_snippet": "", "source": "alexandertechnique.net.au", "link": "https://alexandertechnique.net.au/category/what-is-alexander-technique/", "content": "With practice, we can develop a heightened sense of self -awareness, allowing us to catch ourselves in moments of automatic reactions and consciously ..."}
+{"idx": 5, "title": "Self-Talk: An Interdisciplinary Review and Transdisciplinary", "date": "", "ddg_snippet": "The present work synthesises the self -talk literature and constructs a transdisciplinary self -talk model to guide future research across all academic ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/10892680231170263", "content": "The present work synthesises the self -talk literature and constructs a transdisciplinary self -talk model to guide future research across all academic ..."}
+{"idx": 6, "title": "Adapting Vision-Language Models Without Labels: A Comprehensive", "date": "", "ddg_snippet": "These models learn joint image - text representations from large-scale datasets [ 5 ] and have demonstrated impressive zero-shot performance and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05547v1", "content": "These models learn joint image - text representations from large-scale datasets [ 5 ] and have demonstrated impressive zero-shot performance and ..."}
+{"idx": 7, "title": "Content Posted in 2020 | Arab Journals Platform | Association", "date": "", "ddg_snippet": "Action Research Methodology as a Managerial Tool: Discussion and Implications , Bayan Farhan Dr. ... and its impact on Economic conditions in Africa ...", "subpage_snippet": "", "source": "digitalcommons.aaru.edu.jo", "link": "https://digitalcommons.aaru.edu.jo/2020.html", "content": "Action Research Methodology as a Managerial Tool: Discussion and Implications , Bayan Farhan Dr. ... and its impact on Economic conditions in Africa ..."}
+{"idx": 8, "title": "100,000 Guest Posting Websites - Rocket Guest Posting 🚀", "date": "", "ddg_snippet": "Are you aware of any guest blogging giants in your niche? If you are active in reading and commenting on heavy-hitting blogs in your market, these ...", "subpage_snippet": "", "source": "RocketGuestPosting.com", "link": "https://RocketGuestPosting.com/", "content": "Are you aware of any guest blogging giants in your niche? If you are active in reading and commenting on heavy-hitting blogs in your market, these ..."}
+{"idx": 9, "title": "Lisa Feldman Barrett versus Paul Ekman on facial expressions", "date": "", "ddg_snippet": "... text is supposed to read as follows (left to right): “delirious”, “constipated”, “constipated”, “constipated”, “constipated”, and ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/iYzFKJjzFPRNrqLE3/lisa-feldman-barrett-versus-paul-ekman-on-facial-expressions", "content": "... text is supposed to read as follows (left to right): “delirious”, “constipated”, “constipated”, “constipated”, “constipated”, and ..."}
diff --git a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_correction_loss_L_cor_equation_7_year_2024.jsonl b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_correction_loss_L_cor_equation_7_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..45b88b32c6ed5e42497085489a7c3c1aff16c8c5
--- /dev/null
+++ b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_correction_loss_L_cor_equation_7_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustwor-thy radiology report generation ( DART ), a novel frame-work that ensures retrieved reports contain similar disease -relevant findings and introduces a self- correction mecha-nism to refine generated reports.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustwor-thy radiology report generation ( DART ), a novel frame-work that ensures retrieved reports contain similar disease -relevant findings and introduces a self- correction mecha-nism to refine generated reports."}
+{"idx": 1, "title": "Rebuttal - DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "Second, DART introduces a self- correction mechanism, which refines generated reports by re-aligning them within image-text embedding space, enabling reports to more accurately reflect disease -relevant findings.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2504.11786", "content": "Second, DART introduces a self- correction mechanism, which refines generated reports by re-aligning them within image-text embedding space, enabling reports to more accurately reflect disease -relevant findings."}
+{"idx": 2, "title": "DART | PDF | Artificial Intelligence | Intelligence (AI) & Semantics", "date": "", "ddg_snippet": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self- correction mechanism. This two-stage approach first generates initial reports through image -to- text retrieval with a disease -matching constraint and then refines these reports by re-aligning them with input X-ray images . The ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self- correction mechanism. This two-stage approach first generates initial reports through image -to- text retrieval with a disease -matching constraint and then refines these reports by re-aligning them with input X-ray images . The ..."}
+{"idx": 3, "title": "Fugu-MT 論文翻訳 (概要): DART: Disease-aware Image-Text Alignment and Self ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2504.11786v1", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 4, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ), a novel framework that ensures retrieved reports contain similar disease -relevant findings and introduces a self- correction mechanism to refine generated reports.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ), a novel framework that ensures retrieved reports contain similar disease -relevant findings and introduces a self- correction mechanism to refine generated reports."}
+{"idx": 5, "title": "CVPR Poster DART: Disease-aware Image-Text Alignment and Self ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32986", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 6, "title": "TexDC: Text-Driven Disease-Aware 4D Cardiac Cine MRI Images Generation", "date": "", "ddg_snippet": "By introducing disease-aware pre- alignment , we emphasize and align key disease features across textual and spatiotemporal dimensions, effectively guiding image generation while maintaining spatiotemporal coherence. To our knowledge, this represents the first application of text -driven medical image generation in 4D modalities.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-0901-7_12", "content": "By introducing disease-aware pre- alignment , we emphasize and align key disease features across textual and spatiotemporal dimensions, effectively guiding image generation while maintaining spatiotemporal coherence. To our knowledge, this represents the first application of text -driven medical image generation in 4D modalities."}
+{"idx": 7, "title": "mk-runner/Awesome-Radiology-Report-Generation - GitHub", "date": "", "ddg_snippet": "DualPrompt-MedCap: A Dual-Prompt Enhanced Approach for Medical Image Captioning [paper] DART : Disease-aware Image-Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation [paper] CRG Score: A Distribution- Aware Clinical Metric for Radiology Report Generation [paper]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation", "content": "DualPrompt-MedCap: A Dual-Prompt Enhanced Approach for Medical Image Captioning [paper] DART : Disease-aware Image-Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation [paper] CRG Score: A Distribution- Aware Clinical Metric for Radiology Report Generation [paper]"}
+{"idx": 8, "title": "Ji-Hye Oh - catalyzex.com", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Ji-Hye+Oh", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.html", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ..."}
diff --git a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_methodology_equation_5_lambda_m_weighting_coefficient_year_2024.jsonl b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_methodology_equation_5_lambda_m_weighting_coefficient_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bddbbd5c45925d85eb6dbb580f93a9c9ee737a8c
--- /dev/null
+++ b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_methodology_equation_5_lambda_m_weighting_coefficient_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Text-to-Image Synthesis: A Decade Survey", "date": "", "ddg_snippet": "25 Nov 2024 — CAN [180] adds control over image generation by dynamically adjusting neural network weights, making the generation process more flexible.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.16164v1", "content": "25 Nov 2024 — CAN [180] adds control over image generation by dynamically adjusting neural network weights, making the generation process more flexible."}
+{"idx": 1, "title": "Generalizing deep learning models for medical image ...", "date": "", "ddg_snippet": "18 Mar 2024 — In this paper, we review recent developments in generalization methods for DL-based classification models. We also discuss future challenges, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.12167v1", "content": "18 Mar 2024 — In this paper, we review recent developments in generalization methods for DL-based classification models. We also discuss future challenges, ..."}
+{"idx": 2, "title": "Neural Methods for Data-to-text Generation - ACM Digital Library", "date": "", "ddg_snippet": "This survey offers a consolidated view into the neural D2T paradigm with a structured examination of the approaches, benchmark datasets, and evaluation ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3660639", "content": "This survey offers a consolidated view into the neural D2T paradigm with a structured examination of the approaches, benchmark datasets, and evaluation ..."}
+{"idx": 3, "title": "Inferring Object Boundaries and Their Roughness with ...", "date": "", "ddg_snippet": "by B Maboudi Afkham · 2024 · Cited by 2 — This work describes a Bayesian framework for reconstructing the boundaries that represent targeted features in an image , as well as the regularity (ie, ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10851-024-01207-9", "content": "by B Maboudi Afkham · 2024 · Cited by 2 — This work describes a Bayesian framework for reconstructing the boundaries that represent targeted features in an image , as well as the regularity (ie, ..."}
+{"idx": 4, "title": "Novel nonlinear reconstruction method with grey-level ...", "date": "", "ddg_snippet": "by N Baba · 2020 · Cited by 5 — QURT reconstructs a cross-section image by arranging grey-level quantisation units (QU pieces) in three-dimensional image space via unique discrete processing.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-020-77156-1", "content": "by N Baba · 2020 · Cited by 5 — QURT reconstructs a cross-section image by arranging grey-level quantisation units (QU pieces) in three-dimensional image space via unique discrete processing."}
+{"idx": 5, "title": "Lightweight hybrid transformers-based dyslexia detection ...", "date": "", "ddg_snippet": "by ARW Sait · 2025 — We propose an innovative model for DD using magnetic resonance imaging (MRI), electroencephalography (EEG), and handwriting images .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12084289/", "content": "by ARW Sait · 2025 — We propose an innovative model for DD using magnetic resonance imaging (MRI), electroencephalography (EEG), and handwriting images ."}
+{"idx": 6, "title": "Domain Adaptation and Representation Transfer, and ...", "date": "", "ddg_snippet": "The third MICCAI workshop on Domain Adaptation and Representation Transfer. ( DART 2021) aimed at creating a discussion forum to compare, evaluate, and discuss.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-87722-4.pdf", "content": "The third MICCAI workshop on Domain Adaptation and Representation Transfer. ( DART 2021) aimed at creating a discussion forum to compare, evaluate, and discuss."}
+{"idx": 7, "title": "Uncertainty analysis of initial orbit determination in the ...", "date": "", "ddg_snippet": "by Y Zhang · 2025 · Cited by 1 — This paper proposes a method for analyzing the uncertainty of the IOD results based on the Minimum Radar Admissible Region (MRAR).", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-04087-0", "content": "by Y Zhang · 2025 · Cited by 1 — This paper proposes a method for analyzing the uncertainty of the IOD results based on the Minimum Radar Admissible Region (MRAR)."}
+{"idx": 8, "title": "Dual-Level Matching With Outlier Filtering for ...", "date": "", "ddg_snippet": "by M Ye · 2025 · Cited by 6 — In this paper, we propose a novel Progressive Graph Matching (PGM) approach to globally model the cross-modality relationships and instance-level affinities.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/05/10882953/24fKjc0RQHK", "content": "by M Ye · 2025 · Cited by 6 — In this paper, we propose a novel Progressive Graph Matching (PGM) approach to globally model the cross-modality relationships and instance-level affinities."}
+{"idx": 9, "title": "An illustration of model agnostic explainability methods ...", "date": "", "ddg_snippet": "by CK Wikle · 2022 · Cited by 24 — We focus on three general methods for explainability that are model agnostic and thus applicable across a breadth of models without internal explainability.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10187774/", "content": "by CK Wikle · 2022 · Cited by 24 — We focus on three general methods for explainability that are model agnostic and thus applicable across a breadth of models without internal explainability."}
diff --git "a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_\316\273m_weighting_coefficient_Equation_5.jsonl" "b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_\316\273m_weighting_coefficient_Equation_5.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..433c6b73c957d2852c0ff88f312883c83f28b80b
--- /dev/null
+++ "b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_\316\273m_weighting_coefficient_Equation_5.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) DART : Disease - aware Image - Text Alignment and...", "date": "", "ddg_snippet": "a Disease - aware image - text Alignment and self-correcting. Re- alignment for Trustworthy radiology report generation. ( DART ) framework.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390845711_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_Report_Generation", "content": "a Disease - aware image - text Alignment and self-correcting. Re- alignment for Trustworthy radiology report generation. ( DART ) framework."}
+{"idx": 1, "title": "[2504.11786] DART : Disease - aware Image - Text Alignment and...", "date": "", "ddg_snippet": "View a PDF of the paper titled DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation, by Sang-Jun Park and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "View a PDF of the paper titled DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation, by Sang-Jun Park and 5 other authors."}
+{"idx": 2, "title": "DART : Disease - aware Image - Text Alignment and Self-correcting...", "date": "", "ddg_snippet": ". Report Generation Based on Disease - aware Image - Text Alignment .where λcor is a weighting coefficient that adjusts the cor-rection loss and is set to 5 , and Lgen is the generation loss, i.e., auto-regressive loss. In stage 2, only the self-correction.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": ". Report Generation Based on Disease - aware Image - Text Alignment .where λcor is a weighting coefficient that adjusts the cor-rection loss and is set to 5 , and Lgen is the generation loss, i.e., auto-regressive loss. In stage 2, only the self-correction."}
+{"idx": 3, "title": "Brat Text Generator - Charli XCX Inspired Text Maker", "date": "", "ddg_snippet": "Generate Charli XCX Brat-inspired text with our Brat Text Generator. Customize your text with stylish fonts and effects!", "subpage_snippet": "", "source": "bratgenerator.io", "link": "https://bratgenerator.io/old/", "content": "Generate Charli XCX Brat-inspired text with our Brat Text Generator. Customize your text with stylish fonts and effects!"}
+{"idx": 4, "title": "Dart", "date": "", "ddg_snippet": "In this study, we propose a Disease - aware image - text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/s/Dart", "content": "In this study, we propose a Disease - aware image - text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework."}
+{"idx": 5, "title": "Nano Banana: Free Online AI Image Editor", "date": "", "ddg_snippet": "Nano BananaAI Image Editor. Create and edit images in seconds with Gemini 2. 5 Flash Image (aka NanoBanana). Fast, accurate, and powerful AI at NanoBananaArt.ai.", "subpage_snippet": "", "source": "www.nano-banana.com", "link": "https://www.nano-banana.com/", "content": "Nano BananaAI Image Editor. Create and edit images in seconds with Gemini 2. 5 Flash Image (aka NanoBanana). Fast, accurate, and powerful AI at NanoBananaArt.ai."}
+{"idx": 6, "title": "Latest 15 Papers - May 21, 2025", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self-cing Re- alignment for Trustworthy Radiology Report Generation: This paper proposes a new method for disease - aware image - text alignment and self-correcting re- alignment for trustworthy radiology report generation. Q: What is the...", "subpage_snippet": "", "source": "viao.co.uk", "link": "https://viao.co.uk/blog/latest-15-papers-may-21-1747789981215", "content": "DART : Disease - aware Image - Text Alignment and Self-cing Re- alignment for Trustworthy Radiology Report Generation: This paper proposes a new method for disease - aware image - text alignment and self-correcting re- alignment for trustworthy radiology report generation. Q: What is the..."}
+{"idx": 7, "title": "Articles by Dong-Ho Shin | Synthical", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/3e7eed8b-226c-4317-a4f7-ae93f39b25b8/articles", "content": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation."}
+{"idx": 8, "title": "GitHub - AlonzoLeeeooo/awesome-radiology-report-generation...", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation [Paper].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AlonzoLeeeooo/awesome-radiology-report-generation", "content": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation [Paper]."}
+{"idx": 9, "title": "Heo Keun-Soo - Google 학술 검색", "date": "", "ddg_snippet": "2024. DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation.Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation.", "subpage_snippet": "", "source": "scholar.google.co.kr", "link": "https://scholar.google.co.kr/citations?user=UJJGePQAAAAJ&hl=ko", "content": "2024. DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation.Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation."}
diff --git a/data/sampled_jsons/DART_arXiv_2504.11786_Table_2_F1_score_0.469.jsonl b/data/sampled_jsons/DART_arXiv_2504.11786_Table_2_F1_score_0.469.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ab21a1f17ad0a36d83764c5765261816e5f1301b
--- /dev/null
+++ b/data/sampled_jsons/DART_arXiv_2504.11786_Table_2_F1_score_0.469.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Confusion matrix - Wikipedia", "date": "", "ddg_snippet": "In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervis...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Confusion_matrix", "content": "In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervis..."}
+{"idx": 1, "title": "[ 2504 . 11786 ] DART : Disease-aware Image-Text Alignment and...", "date": "", "ddg_snippet": "arXiv : 2504 . 11786 (cs).View a PDF of the paper titled DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation, by Sang-Jun Park and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "arXiv : 2504 . 11786 (cs).View a PDF of the paper titled DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation, by Sang-Jun Park and 5 other authors."}
+{"idx": 2, "title": "towardsdatascience.com/micro-macro-weighted-averages-of- f 1 - score ...", "date": "", "ddg_snippet": "Micro and Macro Weighted Averages of F 1 Score .", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/micro-macro-weighted-averages-of-f1-score-clearly-explained-b603420b292f/", "content": "Micro and Macro Weighted Averages of F 1 Score ."}
+{"idx": 3, "title": "F-мера в машинном обучении: что это и как применяется на практике", "date": "", "ddg_snippet": "F-мера (F- score , F 1 - score ) представляет собой гармоническое среднее между точностью (precision) и полнотой (recall), что делает её сбалансированной метрикой для оценки качества бинарной классификации.", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/wiki/analytics/f-mera-v-mashinnom-obuchenii-chto-eto-i-kak-primenyaetsya-na-praktike/", "content": "F-мера (F- score , F 1 - score ) представляет собой гармоническое среднее между точностью (precision) и полнотой (recall), что делает её сбалансированной метрикой для оценки качества бинарной классификации."}
+{"idx": 4, "title": "Метрики оценки моделей нейронных сетей для чайников / Хабр", "date": "", "ddg_snippet": "Micro‑averaging — суммирование TP, FP и FN по всем классам перед расчетом Precision, Recall и F 1 - score . Macro‑averaging — вычисление Precision, Recall и F 1 - score отдельно для каждого класса и усреднение полученных значений.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/slsoft/articles/893694/", "content": "Micro‑averaging — суммирование TP, FP и FN по всем классам перед расчетом Precision, Recall и F 1 - score . Macro‑averaging — вычисление Precision, Recall и F 1 - score отдельно для каждого класса и усреднение полученных значений."}
+{"idx": 5, "title": "Аргументы и Факты — последние новости России и мира сегодня", "date": "", "ddg_snippet": "Аргументы и Факты — самые актуальные и последние новости России и мира сегодня. Будьте в курсе главных свежих событий дня и последнего часа, происшествий, обзоров, аналитики, инфографики, фото и видеорепортажей.", "subpage_snippet": "", "source": "aif.ru", "link": "https://aif.ru/", "content": "Аргументы и Факты — самые актуальные и последние новости России и мира сегодня. Будьте в курсе главных свежих событий дня и последнего часа, происшествий, обзоров, аналитики, инфографики, фото и видеорепортажей."}
+{"idx": 6, "title": "А4 Маленький vs А4 ГИГАНТСКИЙ ЯЩИК Челлендж ! | Дзен", "date": "", "ddg_snippet": "Видео автора «ГАДГИВН А4» в Дзене: Подпишись на канал, поставь лайк и оставь коммент, если хочешь поучавствовать в новом ролике А4 А4 Маленький vs А4 ГИГАНТСКИЙ ЯЩИК Челлендж !", "subpage_snippet": "", "source": "dzen.ru", "link": "https://dzen.ru/video/watch/647da8f5f185ed4dab2044eb", "content": "Видео автора «ГАДГИВН А4» в Дзене: Подпишись на канал, поставь лайк и оставь коммент, если хочешь поучавствовать в новом ролике А4 А4 Маленький vs А4 ГИГАНТСКИЙ ЯЩИК Челлендж !"}
+{"idx": 7, "title": "Driver Booster 12 Free – Скачать", "date": "", "ddg_snippet": "Бесплатно. Windows. Категория: Программы для драйверов. Driver Booster Free - полезная программа, автоматически сканирующая и определяющая драйвера на ПК. После нахождения устаревших драйверов программа предлагает загрузить и установить обновления...", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-31816-driver-booster-free.html", "content": "Бесплатно. Windows. Категория: Программы для драйверов. Driver Booster Free - полезная программа, автоматически сканирующая и определяющая драйвера на ПК. После нахождения устаревших драйверов программа предлагает загрузить и установить обновления..."}
+{"idx": 8, "title": "Властелин Колец Все Части: 1 , 2 , 3, 4, 5, 6 Смотреть Онлайн...", "date": "", "ddg_snippet": "Кинотрилогии «Властелин Колец» и «Хоббит» тесно связаны одной Вселенной и сюжетными событиями. В хронологическом порядке сперва идет история о хоббите по имени Бильбо Бэггинс, хотя фильмы этой трилогии вышли намного позже первых кинолент.", "subpage_snippet": "", "source": "lord-of-ringgs-lordfilm.ru", "link": "https://lord-of-ringgs-lordfilm.ru/", "content": "Кинотрилогии «Властелин Колец» и «Хоббит» тесно связаны одной Вселенной и сюжетными событиями. В хронологическом порядке сперва идет история о хоббите по имени Бильбо Бэггинс, хотя фильмы этой трилогии вышли намного позже первых кинолент."}
+{"idx": 9, "title": "График XAUUSD — Цена золота в долларах США — TradingView", "date": "", "ddg_snippet": "Следите за курсом спотовой цены на золото к доллару США в реальном времени. График XAUUSD с бесплатными историческими данными. Торговые идеи и прогнозы, технический анализ для мировых валют.", "subpage_snippet": "", "source": "ru.tradingview.com", "link": "https://ru.tradingview.com/symbols/XAUUSD/", "content": "Следите за курсом спотовой цены на золото к доллару США в реальном времени. График XAUUSD с бесплатными историческими данными. Торговые идеи и прогнозы, технический анализ для мировых валют."}
diff --git "a/data/sampled_jsons/DART_disease-matching_constraint_\316\273m_0.1_OR_0.5_OR_1.0_OR_2.0_OR_5.0.jsonl" "b/data/sampled_jsons/DART_disease-matching_constraint_\316\273m_0.1_OR_0.5_OR_1.0_OR_2.0_OR_5.0.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..367827036dbe06fe5ab903473a1bd9948b6e3203
--- /dev/null
+++ "b/data/sampled_jsons/DART_disease-matching_constraint_\316\273m_0.1_OR_0.5_OR_1.0_OR_2.0_OR_5.0.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DART: Distance Assisted Recursive Testing", "date": "", "ddg_snippet": "by X Li · 2023 · Cited by 2 — Lei et al. (2020) proposes an iterative testing algorithm to perform FDR control on a series of contiguous candidate sets in a constrained set. However, ... 41 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume24/22-1131/22-1131.pdf", "content": "by X Li · 2023 · Cited by 2 — Lei et al. (2020) proposes an iterative testing algorithm to perform FDR control on a series of contiguous candidate sets in a constrained set. However, ... 41 pages"}
+{"idx": 1, "title": "DART: distance assisted recursive testing", "date": "", "ddg_snippet": "Abstract. Multiple testing is a commonly used tool in modern data science. Sometimes, the hypotheses are embedded in a space; the distances between the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.5555/3648699.3648868", "content": "Abstract. Multiple testing is a commonly used tool in modern data science. Sometimes, the hypotheses are embedded in a space; the distances between the ..."}
+{"idx": 2, "title": "String Constraints with Concatenation and Transducers ...", "date": "", "ddg_snippet": "by L Holík · 2017 · Cited by 86 — String analysis is the problem of reasoning about how strings are manipulated by a program. It has numerous applications including automatic detection of ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3158092", "content": "by L Holík · 2017 · Cited by 86 — String analysis is the problem of reasoning about how strings are manipulated by a program. It has numerous applications including automatic detection of ..."}
+{"idx": 3, "title": "Efficient Training of Large Vision Models via Advanced ...", "date": "", "ddg_snippet": "by C Li · 2024 · Cited by 2 — Building on this, Score-based generative models [76] introduced a novel framework that integrates denoising and score- matching techniques.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.00350?", "content": "by C Li · 2024 · Cited by 2 — Building on this, Score-based generative models [76] introduced a novel framework that integrates denoising and score- matching techniques."}
+{"idx": 4, "title": "Analyzing Few-Shot Neural Architecture Search in a Metric ...", "date": "", "ddg_snippet": "by T Ly-Manson · Cited by 3 — In order to speed up the experiments, we use the same hyperparameters as in DARTS A.3 for both the supernet parameters and architecture parameters. As for the ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v256/main/assets/ly-manson24a/ly-manson24a.pdf", "content": "by T Ly-Manson · Cited by 3 — In order to speed up the experiments, we use the same hyperparameters as in DARTS A.3 for both the supernet parameters and architecture parameters. As for the ..."}
+{"idx": 5, "title": "Combining Multi-Objective Bayesian Optimization with ...", "date": "", "ddg_snippet": "by M Deutel · 2023 · Cited by 2 — Constraints such as <1MB Flash, <512Kb RAM, and clock speeds in the low-MHz range make the design of DNN models for such platforms challenging. In general, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.14109", "content": "by M Deutel · 2023 · Cited by 2 — Constraints such as <1MB Flash, <512Kb RAM, and clock speeds in the low-MHz range make the design of DNN models for such platforms challenging. In general, ..."}
+{"idx": 6, "title": "1 Testing the feature alignment technique (FAT) in an ...", "date": "", "ddg_snippet": "by DR Stratman · 2022 · Cited by 2 — This study advances that work by implementing an object-based merging and matching technique into the FAT and tests the updated. FAT in more complex scenarios ... 42 pages", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/mwre/aop/MWR-D-21-0289.1/MWR-D-21-0289.1.pdf", "content": "by DR Stratman · 2022 · Cited by 2 — This study advances that work by implementing an object-based merging and matching technique into the FAT and tests the updated. FAT in more complex scenarios ... 42 pages"}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 8, "title": "pdf", "date": "", "ddg_snippet": "This volume contains the papers presented at the Fourth IAPR Workshop on. Graph Based Representations in Pattern Recognition. The workshop was held.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/3-540-45028-9.pdf", "content": "This volume contains the papers presented at the Fourth IAPR Workshop on. Graph Based Representations in Pattern Recognition. The workshop was held."}
+{"idx": 9, "title": "Streaming Sketching: Mathematical Theory and Practical Algorithms", "date": "", "ddg_snippet": "I am deeply grateful to my advisor, Seth Pettie, for his academic, mental, and philosophical guidance over the past six years. During my PhD, I was knocked ...", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "https://deepblue.lib.umich.edu/bitstream/2027.42/197252/1/wangdy_1.pdf", "content": "I am deeply grateful to my advisor, Seth Pettie, for his academic, mental, and philosophical guidance over the past six years. During my PhD, I was knocked ..."}
diff --git a/data/sampled_jsons/DART_experimental_setup_OR_implementation_details_OR_hyperparameters_disease-matching_constraint_rad_year_2024.jsonl b/data/sampled_jsons/DART_experimental_setup_OR_implementation_details_OR_hyperparameters_disease-matching_constraint_rad_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e7b9d62470cd976702c571ad69748d3cb4ea399e
--- /dev/null
+++ b/data/sampled_jsons/DART_experimental_setup_OR_implementation_details_OR_hyperparameters_disease-matching_constraint_rad_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning | SoftwarePatternsLexicon.com", "date": "", "ddg_snippet": "Machine Learning ... Object-Oriented ... Experimental Design ... Hyperparameter Tuning via AutoML", "subpage_snippet": "", "source": "softwarepatternslexicon.com", "link": "https://softwarepatternslexicon.com/machine-learning/", "content": "Machine Learning ... Object-Oriented ... Experimental Design ... Hyperparameter Tuning via AutoML"}
+{"idx": 1, "title": "Track Awesome Machine Learning", "date": "", "ddg_snippet": "It simplifies the development of image segmentation applications by providing a collection of popular architecture implementations , such as UNet and ...", "subpage_snippet": "", "source": "www.trackawesomelist.com", "link": "https://www.trackawesomelist.com/josephmisiti/awesome-machine-learning/", "content": "It simplifies the development of image segmentation applications by providing a collection of popular architecture implementations , such as UNet and ..."}
+{"idx": 2, "title": "AGA: An adaptive group alignment framework for structured", "date": "", "ddg_snippet": "However, existing vision-language pretraining (VLP) methods in the medical domain often oversimplify clinical reports into single entities or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23402v1", "content": "However, existing vision-language pretraining (VLP) methods in the medical domain often oversimplify clinical reports into single entities or ..."}
+{"idx": 3, "title": "The LLM Wears Prada: Analysing Gender Bias and Stereotypes", "date": "", "ddg_snippet": "... starting from the original dataset of historical individual purchases, we prompt the model to determine if the individual identified as male or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.01951v1", "content": "... starting from the original dataset of historical individual purchases, we prompt the model to determine if the individual identified as male or ..."}
+{"idx": 4, "title": "BMFM-DNA: A SNP-aware DNA foundation model to capture variant", "date": "", "ddg_snippet": "... an unprecedented volume of genomic data, including over 500,000 human genomes, 3,000 genomes from higher organisms, and millions of bacterial genomes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.05265v1", "content": "... an unprecedented volume of genomic data, including over 500,000 human genomes, 3,000 genomes from higher organisms, and millions of bacterial genomes."}
+{"idx": 5, "title": "MegaScience: Pushing the Frontiers of Post-Training Datasets", "date": "", "ddg_snippet": "Our supervised fine-tuning experiments (§ 5 ) demonstrate that our datasets not only enable efficient training and inference but also achieve state ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16812v1", "content": "Our supervised fine-tuning experiments (§ 5 ) demonstrate that our datasets not only enable efficient training and inference but also achieve state ..."}
+{"idx": 6, "title": "Toward a Geometric Theory of Information Processing: A Research", "date": "", "ddg_snippet": "... experiments , computations, and theoretical developments needed to determine whether this geometric vision of intelligence reflects deep truths about ...", "subpage_snippet": "", "source": "www.novaspivack.com", "link": "https://www.novaspivack.com/science/toward-a-geometric-theory-of-information-processing-a-research-program", "content": "... experiments , computations, and theoretical developments needed to determine whether this geometric vision of intelligence reflects deep truths about ..."}
+{"idx": 7, "title": "MELBA – Semi-Supervised Federated Peer Learning for Skin", "date": "", "ddg_snippet": "... the deep-learning-based methods proved to have a superior (Gessert et al., 2020 ; Li et al., 2020b ; Zhang et al., 2019 ; Lopez et al., 2017 ) or ...", "subpage_snippet": "", "source": "www.melba-journal.org", "link": "https://www.melba-journal.org/papers/2022:011.html", "content": "... the deep-learning-based methods proved to have a superior (Gessert et al., 2020 ; Li et al., 2020b ; Zhang et al., 2019 ; Lopez et al., 2017 ) or ..."}
+{"idx": 8, "title": "(PDF) Enabling Hyperparameter Tuning of Machine Learning", "date": "", "ddg_snippet": "... hyperparameter tuning frameworks in production is hindered due to several factors such as inflexible architecture, limitations of search algorithms, ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/356911955_Enabling_Hyperparameter_Tuning_of_Machine_Learning_Classifiers_in_Production", "content": "... hyperparameter tuning frameworks in production is hindered due to several factors such as inflexible architecture, limitations of search algorithms, ..."}
+{"idx": 9, "title": "Fusion of Environmental Sensors for Occupancy Detection in a", "date": "", "ddg_snippet": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/23/23/9596", "content": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ..."}
diff --git a/data/sampled_jsons/DART_radiology_report_generation_methodology_stage_1_total_loss_equation_5_lambda_m_disease_matching.jsonl b/data/sampled_jsons/DART_radiology_report_generation_methodology_stage_1_total_loss_equation_5_lambda_m_disease_matching.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3558d6445d3812f0bcec08d9f3babda26c49d00b
--- /dev/null
+++ b/data/sampled_jsons/DART_radiology_report_generation_methodology_stage_1_total_loss_equation_5_lambda_m_disease_matching.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Behavioral state and stimulus strength regulate the role ...", "date": "", "ddg_snippet": "by CM Cammarata · 2025 · Cited by 3 — Note that in Equation 10 only the YM90K DART condition enters, since by construction all models in all conditions match the data perfectly in ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12370290/", "content": "by CM Cammarata · 2025 · Cited by 3 — Note that in Equation 10 only the YM90K DART condition enters, since by construction all models in all conditions match the data perfectly in ..."}
+{"idx": 1, "title": "Domain Adaptation and Representation Transfer, and ...", "date": "", "ddg_snippet": "The third MICCAI workshop on Domain Adaptation and Representation Transfer. ( DART 2021) aimed at creating a discussion forum to compare, evaluate, and discuss.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-87722-4.pdf", "content": "The third MICCAI workshop on Domain Adaptation and Representation Transfer. ( DART 2021) aimed at creating a discussion forum to compare, evaluate, and discuss."}
+{"idx": 2, "title": "Deep Learning and Machine Learning – Generative Models", "date": "", "ddg_snippet": "... constraint on the activation of the neurons. The total loss function becomes: L(x, x′) + β. ∑ i. KL(ρ|| ˆρi). Where: • L(x, x′) is the reconstruction loss.", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5211980.pdf?abstractid=5211980&mirid=1&type=2", "content": "... constraint on the activation of the neurons. The total loss function becomes: L(x, x′) + β. ∑ i. KL(ρ|| ˆρi). Where: • L(x, x′) is the reconstruction loss."}
+{"idx": 3, "title": "Enhancing Deep Learning-Driven Multi-Coil MRI ...", "date": "", "ddg_snippet": "19 Nov 2024 — We evaluate the impact of denoising on the performance of these DL-based methods in solving accelerated multi-coil magnetic resonance imaging ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.12919v1", "content": "19 Nov 2024 — We evaluate the impact of denoising on the performance of these DL-based methods in solving accelerated multi-coil magnetic resonance imaging ( ..."}
+{"idx": 4, "title": "Lightweight hybrid transformers-based dyslexia detection ...", "date": "", "ddg_snippet": "by ARW Sait · 2025 — We propose an innovative model for DD using magnetic resonance imaging (MRI), electroencephalography (EEG), and handwriting images.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12084289/", "content": "by ARW Sait · 2025 — We propose an innovative model for DD using magnetic resonance imaging (MRI), electroencephalography (EEG), and handwriting images."}
+{"idx": 5, "title": "Improve the performance of CT-based pneumonia ...", "date": "", "ddg_snippet": "by P Xie · 2023 · Cited by 9 — Our method automatically identifies and downweights low-quality source CT data examples which are noisy or have large domain discrepancy with target data.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-023-35938-3", "content": "by P Xie · 2023 · Cited by 9 — Our method automatically identifies and downweights low-quality source CT data examples which are noisy or have large domain discrepancy with target data."}
+{"idx": 6, "title": "Gradient-Based Multi-Objective Deep Learning", "date": "", "ddg_snippet": "19 Jan 2025 — Multi-objective optimization (MOO) in deep learning aims to simultaneously optimize multiple conflicting objectives, a challenge frequently ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.10945v1", "content": "19 Jan 2025 — Multi-objective optimization (MOO) in deep learning aims to simultaneously optimize multiple conflicting objectives, a challenge frequently ..."}
+{"idx": 7, "title": "The Mathematics of Computerized Tomography | 1. ...", "date": "", "ddg_snippet": "by F Natterer · 2001 · Cited by 6110 — The purpose is to give an idea of the scope and limitations of CT and to motivate the mathematical apparatus we are going to develop in the following chapters.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/1.9780898719284.ch1", "content": "by F Natterer · 2001 · Cited by 6110 — The purpose is to give an idea of the scope and limitations of CT and to motivate the mathematical apparatus we are going to develop in the following chapters."}
+{"idx": 8, "title": "Hybrid modelling of leaf traits: Integrating neural networks ...", "date": "", "ddg_snippet": "by P Sun · 2025 — To assess the potential of hybrid modelling (McGreivy and Hakim, 2024) for ecological remote sensing in trait prediction from leaf optical data, we assembled ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0034425725003621", "content": "by P Sun · 2025 — To assess the potential of hybrid modelling (McGreivy and Hakim, 2024) for ecological remote sensing in trait prediction from leaf optical data, we assembled ..."}
+{"idx": 9, "title": "Winning solutions of kaggle competitions", "date": "", "ddg_snippet": "I always wanted to get the links to the solutions of all past Kaggle competitions at one place. I thought it would be a very good reference point many a times.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/code/sudalairajkumar/winning-solutions-of-kaggle-competitions", "content": "I always wanted to get the links to the solutions of all past Kaggle competitions at one place. I thought it would be a very good reference point many a times."}
diff --git a/data/sampled_jsons/DART_self-correction_module_re-alignment_reports_image_features_accuracy_coherence.jsonl b/data/sampled_jsons/DART_self-correction_module_re-alignment_reports_image_features_accuracy_coherence.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c8e376b1bcbbf942b249811c1e970e3b0a0f67d8
--- /dev/null
+++ b/data/sampled_jsons/DART_self-correction_module_re-alignment_reports_image_features_accuracy_coherence.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "We introduce a two-stage framework for radiology re-port generation, which combines disease-aware image -to-text retrieval with a self-correction module to refine gener-ated reports by re -aligning reports with image features for greater accuracy and coherence .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "We introduce a two-stage framework for radiology re-port generation, which combines disease-aware image -to-text retrieval with a self-correction module to refine gener-ated reports by re -aligning reports with image features for greater accuracy and coherence ."}
+{"idx": 1, "title": "Rebuttal - DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "Second, DART introduces a self-correction mechanism, which refines generated reports by re -aligning them within image -text embedding space, enabling reports to more accurately reflect disease-relevant findings.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2504.11786", "content": "Second, DART introduces a self-correction mechanism, which refines generated reports by re -aligning them within image -text embedding space, enabling reports to more accurately reflect disease-relevant findings."}
+{"idx": 2, "title": "DART | PDF | Artificial Intelligence | Intelligence (AI) & Semantics", "date": "", "ddg_snippet": "By re -aligning the gen- erated reports with the input image features in the embed- ding space, the self-correction module reduces discrepan- cies and enhances the accuracy and coherence of the gener- ated reports . This ablation study on the IU X-ray dataset demon- strates the consistent effectiveness of each component in our proposed framework.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "By re -aligning the gen- erated reports with the input image features in the embed- ding space, the self-correction module reduces discrepan- cies and enhances the accuracy and coherence of the gener- ated reports . This ablation study on the IU X-ray dataset demon- strates the consistent effectiveness of each component in our proposed framework."}
+{"idx": 3, "title": "Automatic Radiology Reports Generation via Memory Alignment Network", "date": "", "ddg_snippet": "The automatic generation of radiology reports is of great significance, which can reduce the workload of doctors and improve the accuracy and reliability of medical diagnosis and treatment, and has attracted wide attention in recent years. Cross-modal mapping between images and text, a key component of generating high-quality reports , is challenging due to the lack of corresponding annotations ...", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/28279", "content": "The automatic generation of radiology reports is of great significance, which can reduce the workload of doctors and improve the accuracy and reliability of medical diagnosis and treatment, and has attracted wide attention in recent years. Cross-modal mapping between images and text, a key component of generating high-quality reports , is challenging due to the lack of corresponding annotations ..."}
+{"idx": 4, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "This approach ensures the retrieval of reports with similar disease-relevant findings that closely align with the input X-ray images . In the second stage, we further enhance the initial reports by introducing a self-correction module that re -aligns them with the X-ray images .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11094924", "content": "This approach ensures the retrieval of reports with similar disease-relevant findings that closely align with the input X-ray images . In the second stage, we further enhance the initial reports by introducing a self-correction module that re -aligns them with the X-ray images ."}
+{"idx": 5, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "We introduce a two-stage framework for radiology report generation, which combines disease-aware image -to-text retrieval with a self-correction module to refine generated reports by re -aligning reports with image features for greater accuracy and coherence .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786", "content": "We introduce a two-stage framework for radiology report generation, which combines disease-aware image -to-text retrieval with a self-correction module to refine generated reports by re -aligning reports with image features for greater accuracy and coherence ."}
+{"idx": 6, "title": "mk-runner/Awesome-Radiology-Report-Generation - GitHub", "date": "", "ddg_snippet": "DART : Disease-aware Image -Text Alignment and Self -correcting Re-alignment for Trustworthy Radiology Report Generation [paper] CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset [paper] [code]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation", "content": "DART : Disease-aware Image -Text Alignment and Self -correcting Re-alignment for Trustworthy Radiology Report Generation [paper] CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset [paper] [code]"}
+{"idx": 7, "title": "Ji-Hye Oh - catalyzex.com", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image -text Alignment and self -correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Ji-Hye+Oh", "content": "In this study, we propose a Disease-aware image -text Alignment and self -correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 8, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image -text Alignment and self -correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.html", "content": "In this study, we propose a Disease-aware image -text Alignment and self -correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 9, "title": "Awesome-Radiology-Report-Generation/README.md at main - GitHub", "date": "", "ddg_snippet": "DART : Disease-aware Image -Text Alignment and Self -correcting Re-alignment for Trustworthy Radiology Report Generation [paper] CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset [paper] [code]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation/blob/main/README.md", "content": "DART : Disease-aware Image -Text Alignment and Self -correcting Re-alignment for Trustworthy Radiology Report Generation [paper] CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset [paper] [code]"}
diff --git a/data/sampled_jsons/DC28Fpk76s_Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Section_2_causal_model_definiti.jsonl b/data/sampled_jsons/DC28Fpk76s_Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Section_2_causal_model_definiti.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..416600fce97a1627d7dc9eb8be31ce2305735b81
--- /dev/null
+++ b/data/sampled_jsons/DC28Fpk76s_Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Section_2_causal_model_definiti.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian", "date": "", "ddg_snippet": "A causal Bayesian network (CBN) is a tuple M = (S, P) described by a signature S, just like a causal model , and a collection P of conditional probability tables (cpts), one for each (endogenous and exogenous) variable.5 For this paper...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DC28Fpk76s", "content": "A causal Bayesian network (CBN) is a tuple M = (S, P) described by a signature S, just like a causal model , and a collection P of conditional probability tables (cpts), one for each (endogenous and exogenous) variable.5 For this paper..."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges.View a PDF of the paper titled Intervention and Conditioning in Causal Bayesian Networks , by Sainyam Galhotra and 1 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.14728", "content": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges.View a PDF of the paper titled Intervention and Conditioning in Causal Bayesian Networks , by Sainyam Galhotra and 1 other authors."}
+{"idx": 2, "title": "(PDF) Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs)...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380847642_Intervention_and_Conditioning_in_Causal_Bayesian_Networks", "content": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs)..."}
+{"idx": 3, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Even though causalmodels are extremely popular, conditional probability calculation offormulas involving interventions pose significant challenges.In case of Causal Bayesian Networks (CBNs)...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a2118322165fffb648d1e341ff5a5b05-Abstract-Conference.html", "content": "Even though causalmodels are extremely popular, conditional probability calculation offormulas involving interventions pose significant challenges.In case of Causal Bayesian Networks (CBNs)..."}
+{"idx": 4, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "DC 28 Fpk 76 s .# Estimating probabilities involving interventions in causal models , especially Causal Bayesian Networks (CBNs), is challenging. Existing methods struggle with accurate calculations, particularly for formulas involving interventions and conditioning .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/dc28fpk76s/", "content": "DC 28 Fpk 76 s .# Estimating probabilities involving interventions in causal models , especially Causal Bayesian Networks (CBNs), is challenging. Existing methods struggle with accurate calculations, particularly for formulas involving interventions and conditioning ."}
+{"idx": 5, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs)...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/intervention-and-conditioning-in-causal", "content": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs)..."}
+{"idx": 6, "title": "2405.14728 - Intervention and Conditioning in Causal Bayesian ...", "date": "", "ddg_snippet": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2405.14728", "content": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges."}
+{"idx": 7, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs)...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/intervention-conditioning-causal-bayesian-networks", "content": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs)..."}
+{"idx": 8, "title": "Differential Semantics of Intervention in Bayesian Networks", "date": "", "ddg_snippet": "Causal Bayesian networks are another kind of directed acyclic graph, which conveys causal information as well as the traditional conditional independencies, and permits one to infer the causal effects.the intervention in causal Bayesian networks . One is a mu", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/Proceedings/15/Papers/106.pdf", "content": "Causal Bayesian networks are another kind of directed acyclic graph, which conveys causal information as well as the traditional conditional independencies, and permits one to infer the causal effects.the intervention in causal Bayesian networks . One is a mu"}
+{"idx": 9, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Imagine being able to predict the ripple effects of one action across a network of variables. That’s exactly what this paper achieves with its cutting-edge approach to Causal Bayesian Networks (CBNs).", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/abby33459_intervention-and-conditioning-in-causal-bayesian-activity-7201047041542668288-8dOx", "content": "Imagine being able to predict the ripple effects of one action across a network of variables. That’s exactly what this paper achieves with its cutting-edge approach to Causal Bayesian Networks (CBNs)."}
diff --git a/data/sampled_jsons/DCBM_Section_5_first_limitation_year_2023.jsonl b/data/sampled_jsons/DCBM_Section_5_first_limitation_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..24c0f2c95e26f48318e1031dd3cf7a6466fe56e5
--- /dev/null
+++ b/data/sampled_jsons/DCBM_Section_5_first_limitation_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Borderlands 4: How to Get Hot Slugger Legendary Shotgun", "date": "", "ddg_snippet": "Craven’s Nook is located in the northern section of the Heart of the Mountain, and directly north of the Bones of Sanctuary fast-travel point. When you first reach the Craven’s Nook bunker, it will be locked shut by a solid substance covering the door.", "subpage_snippet": "", "source": "www.dualshockers.com", "link": "https://www.dualshockers.com/borderlands-4-how-to-get-hot-slugger-legendary-shotgun/", "content": "Craven’s Nook is located in the northern section of the Heart of the Mountain, and directly north of the Bones of Sanctuary fast-travel point. When you first reach the Craven’s Nook bunker, it will be locked shut by a solid substance covering the door."}
+{"idx": 1, "title": "elegislation.gov.hk/checkconfig/success.jsp", "date": "", "ddg_snippet": "Section 20A of the Waste Disposal Ordinance (WDO), Cap. 354.", "subpage_snippet": "", "source": "www.elegislation.gov.hk", "link": "https://www.elegislation.gov.hk/checkconfig/success.jsp", "content": "Section 20A of the Waste Disposal Ordinance (WDO), Cap. 354."}
+{"idx": 2, "title": "14th Amendment | U.S. Constitution | US Law | LII / Legal Information...", "date": "", "ddg_snippet": "Section 4. The validity of the public debt of the United States, authorized by law, including debts incurred for payment of pensions and bounties for services in suppressing insurrection or rebellion, shall not be questioned.", "subpage_snippet": "", "source": "www.law.cornell.edu", "link": "https://www.law.cornell.edu/constitution/amendmentxiv", "content": "Section 4. The validity of the public debt of the United States, authorized by law, including debts incurred for payment of pensions and bounties for services in suppressing insurrection or rebellion, shall not be questioned."}
+{"idx": 3, "title": "Classes - bg3.wiki", "date": "", "ddg_snippet": "This is called multiclassing. Doing so will grant most of the benefits of advancing in level in that additional class, with some limitations .", "subpage_snippet": "", "source": "bg3.wiki", "link": "https://bg3.wiki/wiki/Classes", "content": "This is called multiclassing. Doing so will grant most of the benefits of advancing in level in that additional class, with some limitations ."}
+{"idx": 4, "title": "Instagram story viewer - Watch Instagram stories anonymously", "date": "", "ddg_snippet": "What are the limitations ? StoryNavigation can be used on any device. The main condition is the presence of a browser on it, as well as a connection point to a stable Internet connection.The speed is limited only by your network. Also, any errors in the download process are eliminated.", "subpage_snippet": "", "source": "storynavigation.com", "link": "https://storynavigation.com/", "content": "What are the limitations ? StoryNavigation can be used on any device. The main condition is the presence of a browser on it, as well as a connection point to a stable Internet connection.The speed is limited only by your network. Also, any errors in the download process are eliminated."}
+{"idx": 5, "title": "Limit the Player Camera rotation in UE 5 - YouTube", "date": "", "ddg_snippet": "Making sure the player cannot rotate the camera after some point in some instances or maybe limiting it for artistic reasons is something we should always co...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=fc1pAHViB4o", "content": "Making sure the player cannot rotate the camera after some point in some instances or maybe limiting it for artistic reasons is something we should always co..."}
+{"idx": 6, "title": "s l o w r o a d s", "date": "", "ddg_snippet": "Endless driving zen...", "subpage_snippet": "", "source": "slowroads.io", "link": "https://slowroads.io/", "content": "Endless driving zen..."}
+{"idx": 7, "title": "Free Image Background Remover | Adobe Express", "date": "", "ddg_snippet": "Remove the background from images online with our free background eraser. Download your clear background image and change the photo background in seconds.", "subpage_snippet": "", "source": "www.adobe.com", "link": "https://www.adobe.com/express/feature/image/remove-background", "content": "Remove the background from images online with our free background eraser. Download your clear background image and change the photo background in seconds."}
+{"idx": 8, "title": "Гороскоп на сегодня для знака Козерог – Рамблер/гороскопы", "date": "", "ddg_snippet": "Точный гороскоп на сегодня для знака Козерог, бесплатный астрологический прогноз для мужчин и женщин на Рамблер/гороскопы.", "subpage_snippet": "", "source": "horoscopes.rambler.ru", "link": "https://horoscopes.rambler.ru/capricorn/", "content": "Точный гороскоп на сегодня для знака Козерог, бесплатный астрологический прогноз для мужчин и женщин на Рамблер/гороскопы."}
+{"idx": 9, "title": "An open platform for evaluating AI through human preference", "date": "", "ddg_snippet": "An open platform for evaluating AI through human preference...", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/?mode=direct&chat-modality=image", "content": "An open platform for evaluating AI through human preference..."}
diff --git a/data/sampled_jsons/DIKE_self-supervised_learning_four_steps_linguistic_behaviors_emotion_mapping_algorithm_year_2024.jsonl b/data/sampled_jsons/DIKE_self-supervised_learning_four_steps_linguistic_behaviors_emotion_mapping_algorithm_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..560c9287c8ed384e4796fabca7ad4a6930bcf01f
--- /dev/null
+++ b/data/sampled_jsons/DIKE_self-supervised_learning_four_steps_linguistic_behaviors_emotion_mapping_algorithm_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Integrating Emotional and Linguistic Models for Ethical ...", "date": "", "ddg_snippet": "11 May 2024 — Our innovative approaches include mapping emotions and behaviors using self - supervised learning techniques, refining these guardrails through ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07076v1", "content": "11 May 2024 — Our innovative approaches include mapping emotions and behaviors using self - supervised learning techniques, refining these guardrails through ..."}
+{"idx": 1, "title": "ICLM 2025 AI Safety 7847 Camera Ready3 | PDF | Emotions", "date": "", "ddg_snippet": "10 Aug 2025 — zero-shot mapping between behaviors and emotions . Next, we used the Dike self - supervised learning pipeline to analyze the emotion spectrum ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/899938563/ICLM-2025-AI-Safety-7847-Camera-Ready3", "content": "10 Aug 2025 — zero-shot mapping between behaviors and emotions . Next, we used the Dike self - supervised learning pipeline to analyze the emotion spectrum ..."}
+{"idx": 2, "title": "a bilingual emotion detection model based on LLMs", "date": "", "ddg_snippet": "by Y Liu · 2025 · Cited by 1 — This study investigates the Chinese-English bilingual emotion detection within the context of children's literature.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10579-025-09846-z", "content": "by Y Liu · 2025 · Cited by 1 — This study investigates the Chinese-English bilingual emotion detection within the context of children's literature."}
+{"idx": 3, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "by EY Chang · Cited by 1 — Emotion -Driven Behavioral Modeling. ▷ Self - supervised learning pipeline. ▷ Maps emotional states to linguistic patterns/ behaviors . ▷ Guides ethical ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461_OMgXx2a.pdf", "content": "by EY Chang · Cited by 1 — Emotion -Driven Behavioral Modeling. ▷ Self - supervised learning pipeline. ▷ Maps emotional states to linguistic patterns/ behaviors . ▷ Guides ethical ..."}
+{"idx": 4, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "' Given N letters, Dike employs a self - supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "' Given N letters, Dike employs a self - supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps ."}
+{"idx": 5, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — This separation is complemented by BEAM's emotion - behavior mapping , which quantifies linguistic patterns through self - supervised learning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — This separation is complemented by BEAM's emotion - behavior mapping , which quantifies linguistic patterns through self - supervised learning ."}
+{"idx": 6, "title": "Consistency in verbal expression of death row criminals", "date": "", "ddg_snippet": "16 Oct 2024 — It uses techniques of emotion modeling and natural language processing on these verbal expressions, and further proposes methods to analyze such ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2024.1463692/epub", "content": "16 Oct 2024 — It uses techniques of emotion modeling and natural language processing on these verbal expressions, and further proposes methods to analyze such ..."}
+{"idx": 7, "title": "Ming Li | Scholars@Duke profile: Publications", "date": "", "ddg_snippet": "Self - Supervised Reflective Learning Through Self -Distillation and Online Clustering for Speaker Representation Learning . Journal Article IEEE Transactions on ...", "subpage_snippet": "", "source": "scholars.duke.edu", "link": "https://scholars.duke.edu/person/MingLi/publications", "content": "Self - Supervised Reflective Learning Through Self -Distillation and Online Clustering for Speaker Representation Learning . Journal Article IEEE Transactions on ..."}
+{"idx": 8, "title": "Multi-LLM Agent Collaborative Intelligence: The Path to AGI", "date": "", "ddg_snippet": "This book proposes that the key to achieving AGI, characterized by versatility, adaptability, reasoning, critical thinking, planning, and ethical alignment, ... 589 pages", "subpage_snippet": "", "source": "shuyuej.com", "link": "http://shuyuej.com/books/The-Path-to-Artificial-General-Intelligence.pdf", "content": "This book proposes that the key to achieving AGI, characterized by versatility, adaptability, reasoning, critical thinking, planning, and ethical alignment, ... 589 pages"}
+{"idx": 9, "title": "Explainability for Large Language Models: A Survey", "date": "", "ddg_snippet": "by H Zhao · 2024 · Cited by 878 — Explainability refers to the ability to explain or present the behavior of models in human-understandable terms [Doshi-Velez and Kim 2017; Du et al. 2019a].", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3639372", "content": "by H Zhao · 2024 · Cited by 878 — Explainability refers to the ability to explain or present the behavior of models in human-understandable terms [Doshi-Velez and Kim 2017; Du et al. 2019a]."}
diff --git a/data/sampled_jsons/DIOR_dataset_image_size.jsonl b/data/sampled_jsons/DIOR_dataset_image_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a1fb4222bf3e3e0d602d4ebd671e63d9edfacb1
--- /dev/null
+++ b/data/sampled_jsons/DIOR_dataset_image_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DIOR | IEEE DataPort", "date": "", "ddg_snippet": "The dataset contains 23463 images and 192472 instances, covering 20 object classes. The proposed DIOR dataset 1) is large‐scale on the object categories, on the object instance number, and on the total image number; 2) has a large range of object size variations...", "subpage_snippet": "", "source": "ieee-dataport.org", "link": "https://ieee-dataport.org/documents/dior", "content": "The dataset contains 23463 images and 192472 instances, covering 20 object classes. The proposed DIOR dataset 1) is large‐scale on the object categories, on the object instance number, and on the total image number; 2) has a large range of object size variations..."}
+{"idx": 1, "title": "GitHub - oraibalmegdadi/Yolov8_ DIOR : using yolov8 on DIOR dataset", "date": "", "ddg_snippet": "DIOR is a large-scale benchmark dataset for optical remote sensing image target detection proposed on the research paper \"Object detection in optical remote sensing images : A survey and a new benchmark\" [1] . The dataset contains around 20Kimages, with an image size of 800×...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/oraibalmegdadi/Yolov8_DIOR", "content": "DIOR is a large-scale benchmark dataset for optical remote sensing image target detection proposed on the research paper \"Object detection in optical remote sensing images : A survey and a new benchmark\" [1] . The dataset contains around 20Kimages, with an image size of 800×..."}
+{"idx": 2, "title": "Dior Datasets - Get a Free Dior Data Sample", "date": "", "ddg_snippet": "Free Dior data samples for download. Dior datasets hero image .The Dior dataset includes all major data points: product name, description, country, currency, in stock, size , color, category URL, category name, URL, SKU, image URLs, and more.", "subpage_snippet": "", "source": "brightdata.com", "link": "https://brightdata.com/products/datasets/dior", "content": "Free Dior data samples for download. Dior datasets hero image .The Dior dataset includes all major data points: product name, description, country, currency, in stock, size , color, category URL, category name, URL, SKU, image URLs, and more."}
+{"idx": 3, "title": "dior Object Detection Dataset (v1, 2023-02-10 4:55pm) by...", "date": "", "ddg_snippet": "5296 open source vehicle-ship-airplane-airport images and annotations in multiple formats for training computer vision models. dior (v1, 2023-02-10 4:55pm), created by songyunguang. Dataset Split. Train Set 86%.", "subpage_snippet": "", "source": "universe.roboflow.com", "link": "https://universe.roboflow.com/songyunguang-0lzgv/dior-auoyo/dataset/1", "content": "5296 open source vehicle-ship-airplane-airport images and annotations in multiple formats for training computer vision models. dior (v1, 2023-02-10 4:55pm), created by songyunguang. Dataset Split. Train Set 86%."}
+{"idx": 4, "title": "torchgeo. datasets . dior — torchgeo 0.7.1 documentation", "date": "", "ddg_snippet": "Dataset format: * Images are three channel .jpg files.Initialize a new DIOR dataset instance. Args: root: root directory where dataset can be found. split: split of the dataset to use, one of 'train', 'val', 'test'.", "subpage_snippet": "", "source": "torchgeo.readthedocs.io", "link": "https://torchgeo.readthedocs.io/en/stable/_modules/torchgeo/datasets/dior.html", "content": "Dataset format: * Images are three channel .jpg files.Initialize a new DIOR dataset instance. Args: root: root directory where dataset can be found. split: split of the dataset to use, one of 'train', 'val', 'test'."}
+{"idx": 5, "title": "Characteristics of our proposed DIOR dataset . | Download Scientific...", "date": "", "ddg_snippet": "... of size variations of object instances in the proposed DIOR dataset .On the contrary, the proposed DIOR dataset contains 23463 remote sensing images covered more than 80 countries.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Characteristics-of-our-proposed-DIOR-dataset_fig3_335599412", "content": "... of size variations of object instances in the proposed DIOR dataset .On the contrary, the proposed DIOR dataset contains 23463 remote sensing images covered more than 80 countries."}
+{"idx": 6, "title": "pauhidalgoo/yolov8- DIOR · Hugging Face", "date": "", "ddg_snippet": "The models were trained on the DIOR Dataset , which is tailored for detecting elements in satellite images . Evaluation metrics include mAP50 and mAP50-95 for a comprehensive assessment of detection accuracy.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/pauhidalgoo/yolov8-DIOR", "content": "The models were trained on the DIOR Dataset , which is tailored for detecting elements in satellite images . Evaluation metrics include mAP50 and mAP50-95 for a comprehensive assessment of detection accuracy."}
+{"idx": 7, "title": "DIOR 数据集下载及预处理-CSDN博客", "date": "", "ddg_snippet": "image _target_path = ' DIOR _ dataset / images /val/' + name + '.jpg'.首先在 DIOR _ dataset 文件夹下创建test、train、val文件夹。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/QuasimodoD/article/details/133689519", "content": "image _target_path = ' DIOR _ dataset / images /val/' + name + '.jpg'.首先在 DIOR _ dataset 文件夹下创建test、train、val文件夹。"}
+{"idx": 8, "title": "Object Detection in Remote Sensing Images Based", "date": "", "ddg_snippet": "The Dior dataset contains 20 cate-gories and consists of 23,463 images and 190,288 instances. Dior -Vehicle is a separate category in the Dior data set .Among them, the DIOR data set contains a wide variety of objects, including a small number of medium and large- sized objects.", "subpage_snippet": "", "source": "iaeng.org", "link": "https://iaeng.org/IJCS/issues_v51/issue_9/IJCS_51_9_06.pdf", "content": "The Dior dataset contains 20 cate-gories and consists of 23,463 images and 190,288 instances. Dior -Vehicle is a separate category in the Dior data set .Among them, the DIOR data set contains a wide variety of objects, including a small number of medium and large- sized objects."}
+{"idx": 9, "title": "Microsoft Word - ISPRS-arxiv.doc", "date": "", "ddg_snippet": "The proposed DIOR dataset , to our best knowledge, is the largest scale on both the number of object categories and the total number of images . The dataset enables the community to validate and develop data ‐driven object detection methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.00133", "content": "The proposed DIOR dataset , to our best knowledge, is the largest scale on both the number of object categories and the total number of images . The dataset enables the community to validate and develop data ‐driven object detection methods."}
diff --git a/data/sampled_jsons/DISEF_Turrisi_da_Costa_et_al._2023.jsonl b/data/sampled_jsons/DISEF_Turrisi_da_Costa_et_al._2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5f3af6e99e69cce9b2bfdb49ab1df34ddd222209
--- /dev/null
+++ b/data/sampled_jsons/DISEF_Turrisi_da_Costa_et_al._2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Articles by Victor G. Turrisi Da Costa | Synthical", "date": "", "ddg_snippet": "31 December 2021 by Victor G. Turrisi Da Costa and others at University of Trento.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/b10b5cef-74be-4f7d-a4b2-2bf482ff6b41/articles", "content": "31 December 2021 by Victor G. Turrisi Da Costa and others at University of Trento."}
+{"idx": 1, "title": "openreview.net/profile?id=~Victor_Guilherme_ Turrisi _ da _ Costa 1", "date": "", "ddg_snippet": "Victor Guilherme Turrisi da Costa . Research Engineer, Apple.Expertise. Vision and Language. 2023 – Present. Self-supervised Learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Victor_Guilherme_Turrisi_da_Costa1", "content": "Victor Guilherme Turrisi da Costa . Research Engineer, Apple.Expertise. Vision and Language. 2023 – Present. Self-supervised Learning."}
+{"idx": 2, "title": "Diversified in-domain synthesis with efficient fine-tuning for ...", "date": "", "ddg_snippet": "Following this trend, we propose D iversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "Following this trend, we propose D iversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization ..."}
+{"idx": 3, "title": "Diversified in-domain synthesis with efficient fine-tuning for few- ...", "date": "", "ddg_snippet": "Key takeaway: ' DISEF , a novel approach for few-shot image classification, improves generalization by combining real samples with synthetic data and ...", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/details/b5952035132851579b7eb214ea29f9c8/", "content": "Key takeaway: ' DISEF , a novel approach for few-shot image classification, improves generalization by combining real samples with synthetic data and ..."}
+{"idx": 4, "title": "Retrieval-enriched zero-shot image classification in low- ...", "date": "", "ddg_snippet": "1 Nov 2024 — ( 2023 ); da Costa et al . ( 2023 ) image classification. However, the images involved in those studies are mostly in high-resource image domains, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.00988v1", "content": "1 Nov 2024 — ( 2023 ); da Costa et al . ( 2023 ) image classification. However, the images involved in those studies are mostly in high-resource image domains, ..."}
+{"idx": 5, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "by H Yang · 2025 — [17] Victor G Turrisi da Costa , Nicola Dall'Asen, Yiming Wang,. Nicu Sebe, and Elisa Ricci. Diversified in-domain synthesis with efficient fine-tuning for ... 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "by H Yang · 2025 — [17] Victor G Turrisi da Costa , Nicola Dall'Asen, Yiming Wang,. Nicu Sebe, and Elisa Ricci. Diversified in-domain synthesis with efficient fine-tuning for ... 12 pages"}
+{"idx": 6, "title": "publications | Nicola Dall'Asen", "date": "", "ddg_snippet": "10 Jul 2025 — DISEF consists of two main components . First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and ...", "subpage_snippet": "", "source": "fodark.github.io", "link": "https://fodark.github.io/publications/", "content": "10 Jul 2025 — DISEF consists of two main components . First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and ..."}
+{"idx": 7, "title": "Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "by H Yang — Specifically, follow- ing DISEF [S-4], we fine-tune the pretrained model using. 16-shot real images along with all synthetic images from the base categories.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "by H Yang — Specifically, follow- ing DISEF [S-4], we fine-tune the pretrained model using. 16-shot real images along with all synthetic images from the base categories."}
+{"idx": 8, "title": "[2312.03046] Diversified in-domain synthesis with efficient fine-tuning...", "date": "", "ddg_snippet": "[Submitted on 5 Dec 2023 (v1), last revised 7 Dec 2023 (this version, v2)]. Title:Diversified in-domain synthesis with efficient fine-tuning for few-shot classification. Authors:Victor G. Turrisi da Costa , Nicola Dall'Asen, Yiming Wang, Nicu Sebe, Elisa Ricci.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "[Submitted on 5 Dec 2023 (v1), last revised 7 Dec 2023 (this version, v2)]. Title:Diversified in-domain synthesis with efficient fine-tuning for few-shot classification. Authors:Victor G. Turrisi da Costa , Nicola Dall'Asen, Yiming Wang, Nicu Sebe, Elisa Ricci."}
+{"idx": 9, "title": "Diversified in-domain synthesis with efficient... | Papers With Code", "date": "", "ddg_snippet": "5 Dec 2023 · Victor G. Turrisi da Costa , Nicola Dall'Asen, Yiming Wang, Nicu Sebe, Elisa Ricci ·.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/diversified-in-domain-synthesis-with", "content": "5 Dec 2023 · Victor G. Turrisi da Costa , Nicola Dall'Asen, Yiming Wang, Nicu Sebe, Elisa Ricci ·."}
diff --git a/data/sampled_jsons/DISEF_few-shot_learning_paper.jsonl b/data/sampled_jsons/DISEF_few-shot_learning_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..013fb11722db896ad0a80ec4554a26e7943feaba
--- /dev/null
+++ b/data/sampled_jsons/DISEF_few-shot_learning_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2312.03046] Diversified in-domain synthesis with efficient ... GitHub - vturrisi/disef: Pytorch implementation of ... Diversified in-domain synthesis with efficient fine-tuning ... [2312.03046] Diversified in-domain synthesis with efficient ... [2205.06743] A Comprehensive Survey of Few-shot Learning ... A Comprehensive Survey of Few-shot Learning: Evolution ... Diversified in-domain synthesis with efficient fine-tuning for few - shot [2205.06743] A Comprehensive Survey of Few-shot Learning : Evolution LibFewShot: A Comprehensive Library for Few-Shot Learning A Comprehensive Survey of Few-shot Learning : Evolution , Applications A Comprehensive Survey of Few-shot Learning : Evolution , Applications Diversified in-domain synthesis with efficient fine-tuning for few - shot LibFewShot: A Comprehensive Library for Few-Shot Learning", "date": "", "ddg_snippet": "Dec 5, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few - shot learning using synthetic data. The code is divided into two main parts, one for fine-tuning a pre-trained model in the few - shot scenario (fine-tune) and the other for generating synthetic data to enhance fine-tuning (generation). We presented DISEF , a novel method for few - shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in-domain diversity and a novel application of a parameter-efficient fine-tuning method to VLMs for effective adaptation. Following this trend, we propose D iversified I n-domain S ynthesis with E fficient F ine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few - shot learning using synthetic data. May 13, 2022 · In this context, we extensively investigated 200+ latest papers on FSL published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL along with impartial comparisons of the strengths and weaknesses of the existing works. Jul 13, 2023 · Few - shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples remains a serious challenge. Is disef a good method for few-shot learning? Our SAP, which leverages real data and rich captions, does not exhibit such drawbacks. We presented DISEF , a novel method for few - shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in-domain diversity and a novel application of a parameter-efficient fine-tuning method to VLMs for effective adaptation. Is few-shot learning effective? Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples still remains a serious challenge. How can a few-shot learning method improve performance? Some recent studies implicitly show that many generic techniques or “tricks”, such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few - shot learning method. What is few-shot learning (FSL)? Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few - shot Learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new ... What is a few-shot adaptation framework? We propose a few - shot adaptation framework, which bridges zero- shot learning and supervised many- shot learning , for semantic indexing of image and video data. Few - shot adaptation provides robust parameter estimation with few training examples, by ... Few - shot learning (FSL) has emerged as an effective learning method and shows great potential. What is disef (diversified I N-domain s ynthesis with e fficient F? Following this trend, we propose D iversified I n-domain S ynthesis with E fficient F ine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few - shot learning using synthetic data. DISEF consists of two main components. To address these situations, we propose a comprehensive library for few - shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few - shot learning methods in a unified framework with the same single codebase in PyTorch.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "Dec 5, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few - shot learning using synthetic data. The code is divided into two main parts, one for fine-tuning a pre-trained model in the few - shot scenario (fine-tune) and the other for generating synthetic data to enhance fine-tuning (generation). We presented DISEF , a novel method for few - shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in-domain diversity and a novel application of a parameter-efficient fine-tuning method to VLMs for effective adaptation. Following this trend, we propose D iversified I n-domain S ynthesis with E fficient F ine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few - shot learning using synthetic data. May 13, 2022 · In this context, we extensively investigated 200+ latest papers on FSL published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL along with impartial comparisons of the strengths and weaknesses of the existing works. Jul 13, 2023 · Few - shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples remains a serious challenge. Is disef a good method for few-shot learning? Our SAP, which leverages real data and rich captions, does not exhibit such drawbacks. We presented DISEF , a novel method for few - shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in-domain diversity and a novel application of a parameter-efficient fine-tuning method to VLMs for effective adaptation. Is few-shot learning effective? Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples still remains a serious challenge. How can a few-shot learning method improve performance? Some recent studies implicitly show that many generic techniques or “tricks”, such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few - shot learning method. What is few-shot learning (FSL)? Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few - shot Learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new ... What is a few-shot adaptation framework? We propose a few - shot adaptation framework, which bridges zero- shot learning and supervised many- shot learning , for semantic indexing of image and video data. Few - shot adaptation provides robust parameter estimation with few training examples, by ... Few - shot learning (FSL) has emerged as an effective learning method and shows great potential. What is disef (diversified I N-domain s ynthesis with e fficient F? Following this trend, we propose D iversified I n-domain S ynthesis with E fficient F ine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few - shot learning using synthetic data. DISEF consists of two main components. To address these situations, we propose a comprehensive library for few - shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few - shot learning methods in a unified framework with the same single codebase in PyTorch."}
+{"idx": 1, "title": "GitHub - vturrisi/disef: Pytorch implementation of ...", "date": "", "ddg_snippet": "The code is divided into two main parts, one for fine-tuning a pre-trained model in the few - shot scenario (fine-tune) and the other for generating synthetic data to enhance fine-tuning (generation).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vturrisi/disef", "content": "The code is divided into two main parts, one for fine-tuning a pre-trained model in the few - shot scenario (fine-tune) and the other for generating synthetic data to enhance fine-tuning (generation)."}
+{"idx": 2, "title": "[2205.06743] A Comprehensive Survey of Few-shot Learning ...", "date": "", "ddg_snippet": "May 13, 2022 · In this context, we extensively investigated 200+ latest papers on FSL published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL along with impartial comparisons of the strengths and weaknesses of the existing works.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2205.06743", "content": "May 13, 2022 · In this context, we extensively investigated 200+ latest papers on FSL published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL along with impartial comparisons of the strengths and weaknesses of the existing works."}
+{"idx": 3, "title": "A Comprehensive Survey of Few-shot Learning: Evolution ...", "date": "", "ddg_snippet": "Jul 13, 2023 · Few - shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples remains a serious challenge.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3582688", "content": "Jul 13, 2023 · Few - shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples remains a serious challenge."}
+{"idx": 4, "title": "LibFewShot: A Comprehensive Library for Few-Shot Learning", "date": "", "ddg_snippet": "To address these situations, we propose a comprehensive library for few - shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few - shot learning methods in a unified framework with the same single codebase in PyTorch.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10239698", "content": "To address these situations, we propose a comprehensive library for few - shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few - shot learning methods in a unified framework with the same single codebase in PyTorch."}
+{"idx": 5, "title": "Few - Shot Learning : A Breakthrough in AI That Learns from... | Medium", "date": "", "ddg_snippet": "Few - shot learning is a method where a machine learning model learns to perform a task using only a small number of training examples. Instead of feeding the model thousands of labeled samples, you give it maybe 5 or 10, and expect it to generalize from there.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@brianhulela/few-shot-learning-a-breakthrough-in-ai-that-learns-from-just-a-few-examples-4c5192ae9eb4", "content": "Few - shot learning is a method where a machine learning model learns to perform a task using only a small number of training examples. Instead of feeding the model thousands of labeled samples, you give it maybe 5 or 10, and expect it to generalize from there."}
+{"idx": 6, "title": "Few - Shot Learning via Learning the Representation... | OpenReview", "date": "", "ddg_snippet": "This paper studies few - shot learning via representation learning , where one uses $T$ source tasks with $n_1$ data per task to learn a representation in order to reduce the sample complexity of a target task for which there is only $n_2 (\\ll n_1)$ data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=pW2Q2xLwIMD", "content": "This paper studies few - shot learning via representation learning , where one uses $T$ source tasks with $n_1$ data per task to learn a representation in order to reduce the sample complexity of a target task for which there is only $n_2 (\\ll n_1)$ data."}
+{"idx": 7, "title": "Language Models are Few - Shot Learners", "date": "", "ddg_snippet": "We also identify some datasets where GPT-3's few - shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html", "content": "We also identify some datasets where GPT-3's few - shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora."}
+{"idx": 8, "title": "Few - Shot Learning with Embedded Class Models... - Amazon Science", "date": "", "ddg_snippet": "We propose a method for learning embeddings for few - shot learning that is suitable for use with any number of shots (shot-free).You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences.", "subpage_snippet": "", "source": "www.amazon.science", "link": "https://www.amazon.science/publications/few-shot-learning-with-embedded-class-models-and-shot-free-meta-training", "content": "We propose a method for learning embeddings for few - shot learning that is suitable for use with any number of shots (shot-free).You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences."}
+{"idx": 9, "title": "Top 5 Research Papers on Few - Shot Learning - Skim AI", "date": "", "ddg_snippet": "Few - Shot Learning research paper .4. A Closer Look at Few - shot Classification (Chen et al., 2019). This paper provided a comprehensive analysis of existing few - shot learning methods, challenging some common assumptions in the field.", "subpage_snippet": "", "source": "skimai.com", "link": "https://skimai.com/top-5-research-papers-on-few-shot-learning/", "content": "Few - Shot Learning research paper .4. A Closer Look at Few - shot Classification (Chen et al., 2019). This paper provided a comprehensive analysis of existing few - shot learning methods, challenging some common assumptions in the field."}
diff --git a/data/sampled_jsons/DISEF_few-shot_learning_paper_abstract.jsonl b/data/sampled_jsons/DISEF_few-shot_learning_paper_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c2250d676477d543fd54595fffab4b597e116c0
--- /dev/null
+++ b/data/sampled_jsons/DISEF_few-shot_learning_paper_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot ...", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components."}
+{"idx": 1, "title": "GitHub - vturrisi/disef: Pytorch implementation of \"Diversified in ...", "date": "", "ddg_snippet": "This is the official repository for the paper : Diversified in-domain synthesis with efficient fine-tuning for few-shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci. The code is divided into two main parts, one for fine-tuning a pre-trained model in the few-shot scenario (fine-tune) and the other for generating synthetic data to enhance ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vturrisi/disef", "content": "This is the official repository for the paper : Diversified in-domain synthesis with efficient fine-tuning for few-shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci. The code is divided into two main parts, one for fine-tuning a pre-trained model in the few-shot scenario (fine-tune) and the other for generating synthetic data to enhance ..."}
+{"idx": 2, "title": "LibFewShot: A Comprehensive Library for Few-Shot Learning", "date": "", "ddg_snippet": "Few-shot learning , especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or \"tricks\", such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10239698", "content": "Few-shot learning , especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or \"tricks\", such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover ..."}
+{"idx": 3, "title": "A Comprehensive Survey of Few-shot Learning: Evolution, Applications ...", "date": "", "ddg_snippet": "To avoid conceptual confusion, we first elaborate and contrast a set of relevant concepts including few-shot learning , transfer learning , and meta- learning . Then, we inventively extract prior knowledge related to few-shot learning in the form of a pyramid, which summarizes and classifies previous work in detail from the perspective of challenges.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3582688", "content": "To avoid conceptual confusion, we first elaborate and contrast a set of relevant concepts including few-shot learning , transfer learning , and meta- learning . Then, we inventively extract prior knowledge related to few-shot learning in the form of a pyramid, which summarizes and classifies previous work in detail from the perspective of challenges."}
+{"idx": 4, "title": "(PDF) Few-shot Learning: Methods and Applications - ResearchGate", "date": "", "ddg_snippet": "The Few-shot learning (FSL) approach distills meaningful features from a constrained sample set, allowing models to swiftly adjust to novel tasks and decreasing the dependency on extensive datasets.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388323502_Few-shot_Learning_Methods_and_Applications", "content": "The Few-shot learning (FSL) approach distills meaningful features from a constrained sample set, allowing models to swiftly adjust to novel tasks and decreasing the dependency on extensive datasets."}
+{"idx": 5, "title": "disef/README.md at main · vturrisi/disef · GitHub", "date": "", "ddg_snippet": "This is the official repository for the paper : Diversified in-domain synthesis with efficient fine-tuning for few-shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci. The code is divided into two main parts, one for fine-tuning a pre-trained model in the few-shot scenario (fine-tune) and the other for generating synthetic data to enhance ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vturrisi/disef/blob/main/README.md", "content": "This is the official repository for the paper : Diversified in-domain synthesis with efficient fine-tuning for few-shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci. The code is divided into two main parts, one for fine-tuning a pre-trained model in the few-shot scenario (fine-tune) and the other for generating synthetic data to enhance ..."}
+{"idx": 6, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot ...", "date": "", "ddg_snippet": "We presented DISEF , a novel method for few-shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in-domain diversity and a novel application of a parameter-efficient fine-tuning method to VLMs for effective adaptation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "We presented DISEF , a novel method for few-shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in-domain diversity and a novel application of a parameter-efficient fine-tuning method to VLMs for effective adaptation."}
+{"idx": 7, "title": "Few-Shot Learning Based on Dimensionally Enhanced Attention and ... - MDPI", "date": "", "ddg_snippet": "Few-shot learning (FSL) is a challenging problem. Transfer learning methods offer a straightforward and effective solution to FSL by leveraging pre-trained models and generalizing them to new tasks. However, pre-trained models often lack the ability to highlight and emphasize salient features, a gap that attention mechanisms can fill. Unfortunately, existing attention mechanisms encounter ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2079-9292/13/15/2928", "content": "Few-shot learning (FSL) is a challenging problem. Transfer learning methods offer a straightforward and effective solution to FSL by leveraging pre-trained models and generalizing them to new tasks. However, pre-trained models often lack the ability to highlight and emphasize salient features, a gap that attention mechanisms can fill. Unfortunately, existing attention mechanisms encounter ..."}
+{"idx": 8, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot ...", "date": "", "ddg_snippet": "Abstract summary: Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. We propose DISEF , a novel approach which addresses the generalization challenge in few-shot learning using synthetic data.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2312.03046v2_enmode", "content": "Abstract summary: Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. We propose DISEF , a novel approach which addresses the generalization challenge in few-shot learning using synthetic data."}
+{"idx": 9, "title": "Learning from Few Examples: A Summary of Approaches to Few-Shot Learning", "date": "", "ddg_snippet": "This survey paper comprises a representative list of recently proposed few-shot learning algorithms. Given the learning dynamics and characteristics, the approaches to few-shot learning problems are discussed in the perspectives of meta- learning , transfer learning , and hybrid approaches (i.e., different variations of the few-shot learning problem).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2203.04291", "content": "This survey paper comprises a representative list of recently proposed few-shot learning algorithms. Given the learning dynamics and characteristics, the approaches to few-shot learning problems are discussed in the perspectives of meta- learning , transfer learning , and hybrid approaches (i.e., different variations of the few-shot learning problem)."}
diff --git a/data/sampled_jsons/DISEF_few-shot_learning_synthetic_images.jsonl b/data/sampled_jsons/DISEF_few-shot_learning_synthetic_images.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4aa7b2ee21bcc25713d35abbf7d040f36c2411da
--- /dev/null
+++ b/data/sampled_jsons/DISEF_few-shot_learning_synthetic_images.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Provably Improving Generalization of Few-shot models ...", "date": "", "ddg_snippet": "Abstract: Few - shot image classification remains challenging due to the scarcity of labeled training examples. Augmenting them with synthetic data has emerged as ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=L6U7nYc4ah&noteId=Dm11pMqA8W", "content": "Abstract: Few - shot image classification remains challenging due to the scarcity of labeled training examples. Augmenting them with synthetic data has emerged as ..."}
+{"idx": 1, "title": "Diversified in-domain synthesis with efficient fine-tuning for few- ...", "date": "", "ddg_snippet": "Key takeaway: ' DISEF , a novel approach for few - shot image classification, improves generalization by combining real samples with synthetic data and ...", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/details/b5952035132851579b7eb214ea29f9c8/", "content": "Key takeaway: ' DISEF , a novel approach for few - shot image classification, improves generalization by combining real samples with synthetic data and ..."}
+{"idx": 2, "title": "Provably Improving Generalization of Few-Shot Models with ...", "date": "", "ddg_snippet": "This paper presents a systematic theoretical framework that quantifies the impact of real- synthetic distribution discrepancies on generalization for ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2505.24190v1", "content": "This paper presents a systematic theoretical framework that quantifies the impact of real- synthetic distribution discrepancies on generalization for ..."}
+{"idx": 3, "title": "Yogesh Jadhav - Introducing DISEF!", "date": "", "ddg_snippet": "Revolutionizing Few-Shot Learning: Introducing DISEF ! Exciting breakthrough in AI! Researchers have unveiled DISEF, a cutting-edge method ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/dynamo14324_diversified-in-domain-synthesis-with-efficient-activity-7138351317994971136-vXXW", "content": "Revolutionizing Few-Shot Learning: Introducing DISEF ! Exciting breakthrough in AI! Researchers have unveiled DISEF, a cutting-edge method ..."}
+{"idx": 4, "title": "Provably Improving Generalization of Few-Shot Models ...", "date": "", "ddg_snippet": "Few - shot image classification remains challeng- ing due to the scarcity of labeled training exam- ples. Augmenting them with synthetic data has.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=L6U7nYc4ah&name=pdf", "content": "Few - shot image classification remains challeng- ing due to the scarcity of labeled training exam- ples. Augmenting them with synthetic data has."}
+{"idx": 5, "title": "Diversified in-domain synthesis with efficient fine-tuning for ...", "date": "", "ddg_snippet": "by VGT da Costa · 2023 · Cited by 6 — Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "by VGT da Costa · 2023 · Cited by 6 — Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research ..."}
+{"idx": 6, "title": "Diversified in-domain synthesis with efficient fine-tuning for ...", "date": "", "ddg_snippet": "We presented DISEF, a novel method for few-shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in-domain diversity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "We presented DISEF, a novel method for few-shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in-domain diversity ..."}
+{"idx": 7, "title": "HaoyuanYang-2023/ImagineFSL: Official implementation ...", "date": "", "ddg_snippet": "11 Jun 2025 — This repository contains the official code for \"ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few - shot Learning\"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "11 Jun 2025 — This repository contains the official code for \"ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few - shot Learning\""}
+{"idx": 8, "title": "Self-Supervised Pretraining Matters on Imagined Base Set for ...", "date": "", "ddg_snippet": "DISEF [17] captions few-shot real images with LLaVA [33], which alongside ... synthetic images are more suitable for few-shot tasks . Using the same ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32717", "content": "DISEF [17] captions few-shot real images with LLaVA [33], which alongside ... synthetic images are more suitable for few-shot tasks . Using the same ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=few-shot+classification", "content": "Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving ..."}
diff --git a/data/sampled_jsons/DOODL_inference-time_diffusion_backprop_Wallace_Ermon_Naik_2023_abstract.jsonl b/data/sampled_jsons/DOODL_inference-time_diffusion_backprop_Wallace_Ermon_Naik_2023_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d92afd1600345b57cfbb6abc87c58aa16238bb42
--- /dev/null
+++ b/data/sampled_jsons/DOODL_inference-time_diffusion_backprop_Wallace_Ermon_Naik_2023_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF End-to-End Diffusion Latent Optimization Improves Classifier Guidance", "date": "", "ddg_snippet": "Figure 1: We propose DOODL - a process that directly optimizes diffusion latents w.r.t. a model-based loss on the final generation. Our method improves on vanilla classifier guidance in all tested settings and we demonstrate capabilities novel to this class of methods such as vocabulary expansion, entity personalization, and perceived aesthetic value improvement.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Wallace_End-to-End_Diffusion_Latent_Optimization_Improves_Classifier_Guidance_ICCV_2023_paper.pdf", "content": "Figure 1: We propose DOODL - a process that directly optimizes diffusion latents w.r.t. a model-based loss on the final generation. Our method improves on vanilla classifier guidance in all tested settings and we demonstrate capabilities novel to this class of methods such as vocabulary expansion, entity personalization, and perceived aesthetic value improvement."}
+{"idx": 1, "title": "[2303.13703] End-to-End Diffusion Latent Optimization Improves ...", "date": "", "ddg_snippet": "Classifier guidance -- using the gradients of an image classifier to steer the generations of a diffusion model -- has the potential to dramatically expand the creative control over image generation and editing. However, currently classifier guidance requires either training new noise-aware models to obtain accurate gradients or using a one-step denoising approximation of the final generation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.13703", "content": "Classifier guidance -- using the gradients of an image classifier to steer the generations of a diffusion model -- has the potential to dramatically expand the creative control over image generation and editing. However, currently classifier guidance requires either training new noise-aware models to obtain accurate gradients or using a one-step denoising approximation of the final generation ..."}
+{"idx": 2, "title": "GitHub - salesforce/DOODL", "date": "", "ddg_snippet": "DOODL (Direct Optimization of Diffusion Latents) is a variant of classifier guidance that directly optimizes diffusion latents x_T instead of using model-based gradients to guide denoising. This is done be leveraging the EDICT algorithm and MemCNN library to construct a diffusion process that can be backpropagated through with constant memory cost w.r.t the number of diffusion steps without ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/salesforce/DOODL", "content": "DOODL (Direct Optimization of Diffusion Latents) is a variant of classifier guidance that directly optimizes diffusion latents x_T instead of using model-based gradients to guide denoising. This is done be leveraging the EDICT algorithm and MemCNN library to construct a diffusion process that can be backpropagated through with constant memory cost w.r.t the number of diffusion steps without ..."}
+{"idx": 3, "title": "[PDF] End-to-End Diffusion Latent Optimization Improves Classifier ...", "date": "", "ddg_snippet": "DOODL outperforms one-step classifier guidance on computational and human evaluation metrics across different forms of guidance: using CLIP guidance to improve generations of complex prompts from DrawBench, using fine-grained visual classifiers to expand the vocabulary of Stable Diffusion , enabling image-conditioned generation with a CLIP visual encoder, and improving image aesthetics using an ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/End-to-End-Diffusion-Latent-Optimization-Improves-Wallace-Gokul/352f26798b07160bf10d87cf0fd67187c5a0c432", "content": "DOODL outperforms one-step classifier guidance on computational and human evaluation metrics across different forms of guidance: using CLIP guidance to improve generations of complex prompts from DrawBench, using fine-grained visual classifiers to expand the vocabulary of Stable Diffusion , enabling image-conditioned generation with a CLIP visual encoder, and improving image aesthetics using an ..."}
+{"idx": 4, "title": "Paper page - End-to-End Diffusion Latent Optimization Improves ...", "date": "", "ddg_snippet": "We highlight this approximation's shortcomings and propose a novel guidance method: Direct Optimization of Diffusion Latents ( DOODL ), which enables plug-and-play guidance by optimizing diffusion latents w.r.t. the gradients of a pre-trained classifier on the true generated pixels, using an invertible diffusion process to achieve memory ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2303.13703", "content": "We highlight this approximation's shortcomings and propose a novel guidance method: Direct Optimization of Diffusion Latents ( DOODL ), which enables plug-and-play guidance by optimizing diffusion latents w.r.t. the gradients of a pre-trained classifier on the true generated pixels, using an invertible diffusion process to achieve memory ..."}
+{"idx": 5, "title": "\"End-to-End Diffusion Latent Optimization Improves Classifier ... - dblp", "date": "", "ddg_snippet": "Bram Wallace , Akash Gokul, Stefano Ermon , Nikhil Naik : End-to-End Diffusion Latent Optimization Improves Classifier Guidance.ICCV2023: 7246-7256 home blog statistics update feed XML dump RDF dump browse persons conferences journals series search search dblp lookup by ID about f.a.q. team license privacy imprint nfdi dblp is part of the German ...", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iccv/WallaceGEN23", "content": "Bram Wallace , Akash Gokul, Stefano Ermon , Nikhil Naik : End-to-End Diffusion Latent Optimization Improves Classifier Guidance.ICCV2023: 7246-7256 home blog statistics update feed XML dump RDF dump browse persons conferences journals series search search dblp lookup by ID about f.a.q. team license privacy imprint nfdi dblp is part of the German ..."}
+{"idx": 6, "title": "Related papers: End-to-End Diffusion Latent Optimization Improves ...", "date": "", "ddg_snippet": "Date: Wed, 31 May 2023 19:40:57 GMT Title: End-to-End Diffusion Latent Optimization Improves Classifier Guidance Authors: Bram Wallace , Akash Gokul, Stefano Ermon , Nikhil Naik Abstract summary: Direct Optimization of Diffusion Latents ( DOODL ) is a novel guidance method. It enables plug-and-play guidance by optimizing diffusion latents.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2303.13703v2_enmode", "content": "Date: Wed, 31 May 2023 19:40:57 GMT Title: End-to-End Diffusion Latent Optimization Improves Classifier Guidance Authors: Bram Wallace , Akash Gokul, Stefano Ermon , Nikhil Naik Abstract summary: Direct Optimization of Diffusion Latents ( DOODL ) is a novel guidance method. It enables plug-and-play guidance by optimizing diffusion latents."}
+{"idx": 7, "title": "ICCV 2023 Open Access Repository", "date": "", "ddg_snippet": "End-to-End Diffusion Latent Optimization Improves Classifier Guidance Bram Wallace , Akash Gokul, Stefano Ermon , Nikhil Naik ; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023 , pp. 7280-7290 Abstract", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/html/Wallace_End-to-End_Diffusion_Latent_Optimization_Improves_Classifier_Guidance_ICCV_2023_paper.html", "content": "End-to-End Diffusion Latent Optimization Improves Classifier Guidance Bram Wallace , Akash Gokul, Stefano Ermon , Nikhil Naik ; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023 , pp. 7280-7290 Abstract"}
+{"idx": 8, "title": "DOODL/README.md at main · salesforce/DOODL · GitHub", "date": "", "ddg_snippet": "DOODL (Direct Optimization of Diffusion Latents) is a variant of classifier guidance that directly optimizes diffusion latents x_T instead of using model-based gradients to guide denoising.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/salesforce/DOODL/blob/main/README.md", "content": "DOODL (Direct Optimization of Diffusion Latents) is a variant of classifier guidance that directly optimizes diffusion latents x_T instead of using model-based gradients to guide denoising."}
+{"idx": 9, "title": "[2303.13703] End-to-End Diffusion Latent Optimization Improves ...", "date": "", "ddg_snippet": "To enable flexible and exact model guidance, without noise-aware classifiers or approximations, we propose Direct Optimization Of Diffusion Latents ( DOODL ). DOODL optimizes the initial diffusion noise vectors w.r.t. a model-based loss on images generated from the full-chain diffusion process. We leverage EDICT, a recently developed drop-in discretely invertible diffusion algorithm [45], which ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2303.13703", "content": "To enable flexible and exact model guidance, without noise-aware classifiers or approximations, we propose Direct Optimization Of Diffusion Latents ( DOODL ). DOODL optimizes the initial diffusion noise vectors w.r.t. a model-based loss on images generated from the full-chain diffusion process. We leverage EDICT, a recently developed drop-in discretely invertible diffusion algorithm [45], which ..."}
diff --git a/data/sampled_jsons/DSDFM_DivSDE_stochastic_diverse_output_generation_mechanism_diversity_enhancement.jsonl b/data/sampled_jsons/DSDFM_DivSDE_stochastic_diverse_output_generation_mechanism_diversity_enhancement.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f5effff4d5917adcd66635464a390d30c12e0d68
--- /dev/null
+++ b/data/sampled_jsons/DSDFM_DivSDE_stochastic_diverse_output_generation_mechanism_diversity_enhancement.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with this http URL is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training this http URL qualitative and quantitative experiments, DSDFM ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with this http URL is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training this http URL qualitative and quantitative experiments, DSDFM ..."}
+{"idx": 1, "title": "论文阅读--dsdfm--用于人体运动合成的确定性到随机多种潜在特征映射 - 知乎", "date": "", "ddg_snippet": "确定性特征映射过程（Deterministic Feature Mapping Procedure）：使用确定性常微分方程（DerODE）操作，通过最优传输理论建立连接。 随机多样输出生成过程（ Stochastic Diverse Output Generation Procedure）：使用多样随机微分方程（DivSDE）在采样过程中引入随机性，增强多样性。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1911144623440625860", "content": "确定性特征映射过程（Deterministic Feature Mapping Procedure）：使用确定性常微分方程（DerODE）操作，通过最优传输理论建立连接。 随机多样输出生成过程（ Stochastic Diverse Output Generation Procedure）：使用多样随机微分方程（DivSDE）在采样过程中引入随机性，增强多样性。"}
+{"idx": 2, "title": "PDF Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE . DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE . DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters."}
+{"idx": 3, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "The second diverse motion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of human motions, thereby enhancing the diversity and accuracy of the generated human motions.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33113", "content": "The second diverse motion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of human motions, thereby enhancing the diversity and accuracy of the generated human motions."}
+{"idx": 4, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "The second diverse motion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of human motions, thereby enhancing the diversity and accuracy of the generated human motions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.html", "content": "The second diverse motion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of human motions, thereby enhancing the diversity and accuracy of the generated human motions."}
+{"idx": 5, "title": "Weiming Liu - CatalyzeX", "date": "", "ddg_snippet": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE . DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Weiming+Liu", "content": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE . DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments ..."}
+{"idx": 6, "title": "An adaptive differential evolution with dynamic perturbation and ...", "date": "", "ddg_snippet": "An adaptive differential evolution with dynamic perturbation and dimensional bidirectional crossover mechanism for diversity enhancement - ScienceDirect", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197624019092", "content": "An adaptive differential evolution with dynamic perturbation and dimensional bidirectional crossover mechanism for diversity enhancement - ScienceDirect"}
+{"idx": 7, "title": "arXiv:2505.00998v1 [cs.CV] 2 May 2025", "date": "", "ddg_snippet": "ates the effectiveness of our method. The ablation studies also test the performance of the de-signed stochastic diverse output generation procedure in DSDFM under the diversity and accuracy metri", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.00998", "content": "ates the effectiveness of our method. The ablation studies also test the performance of the de-signed stochastic diverse output generation procedure in DSDFM under the diversity and accuracy metri"}
+{"idx": 8, "title": "PDF Appendix - CVF Open Access", "date": "", "ddg_snippet": "diversity . Under ac-tion label conditional generation , some methods generate sequences that fail to meet the semantic r quirements. The comparison results show that our method can achieve more diverse and accurate human motio", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hua_Deterministic-to-Stochastic_Diverse_Latent_CVPR_2025_supplemental.pdf", "content": "diversity . Under ac-tion label conditional generation , some methods generate sequences that fail to meet the semantic r quirements. The comparison results show that our method can achieve more diverse and accurate human motio"}
+{"idx": 9, "title": "An adaptive differential evolution with opposition-learning based ...", "date": "", "ddg_snippet": "In this section, detailed description of OLBADE is presented into three parts: the first part introduces the new trial vector generation strategy based on donor vector perturbation; the second part presents the adaptive parameter control strategy; and the third part provides the opposition learning-based diversity enhancement mechanism .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417423034449", "content": "In this section, detailed description of OLBADE is presented into three parts: the first part introduces the new trial vector generation strategy based on donor vector perturbation; the second part presents the adaptive parameter control strategy; and the third part provides the opposition learning-based diversity enhancement mechanism ."}
diff --git a/data/sampled_jsons/DSDFM_appendix_Algorithm_1_D_Drift_calculation.jsonl b/data/sampled_jsons/DSDFM_appendix_Algorithm_1_D_Drift_calculation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..392bef5ddec36212ec5c6d52b7d74a6d68e3f991
--- /dev/null
+++ b/data/sampled_jsons/DSDFM_appendix_Algorithm_1_D_Drift_calculation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithm - Wikipedia", "date": "", "ddg_snippet": "More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Algorithm", "content": "More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning)."}
+{"idx": 1, "title": "Data Structures & Algorithms # 1 - What Are Data... - YouTube", "date": "", "ddg_snippet": "Data structures and algorithms tutorial # 1 - let's go!Check out Brilliant.org, a website for learning computer science concepts through solving problems: htt...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=bum_19loj9A", "content": "Data structures and algorithms tutorial # 1 - let's go!Check out Brilliant.org, a website for learning computer science concepts through solving problems: htt..."}
+{"idx": 2, "title": "Bellman–Ford Algorithm - GeeksforGeeks", "date": "", "ddg_snippet": "In the bellman-ford algorithm , this process is repeated (V - 1 ) times for all the edges. Why Relaxing Edges (V - 1 ) times gives us Single Source Shortest Path? A shortest path between two vertices can have at most (V - 1 ) edges.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/bellman-ford-algorithm-dp-23/", "content": "In the bellman-ford algorithm , this process is repeated (V - 1 ) times for all the edges. Why Relaxing Edges (V - 1 ) times gives us Single Source Shortest Path? A shortest path between two vertices can have at most (V - 1 ) edges."}
+{"idx": 3, "title": "S 1 Appendix Algorithmic details of the stochastic reaction-diffusion...", "date": "", "ddg_snippet": "This calculation defines the propensity for the reaction. Taking the limit as the time interval becomes infinitesimally small leads to exponentially distributed times between reaction events.", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/plos-corpus-prod/10.1371/journal.pone.0219055/1/pone.0219055.s001.docx?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=wombat-sa@plos-prod.iam.gserviceaccount.com/20250918/auto/storage/goog4_request&X-Goog-Date=20250918T201808Z&X-Goog-Expires=86400&X-Goog-SignedHeaders=host&X-Goog-Signature=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", "content": "This calculation defines the propensity for the reaction. Taking the limit as the time interval becomes infinitesimally small leads to exponentially distributed times between reaction events."}
+{"idx": 4, "title": "What is Sliding Window Algorithm ? Examples? - Stack Overflow", "date": "", "ddg_snippet": "While solving a geometry problem, I came across an approach called Sliding Window Algorithm . Couldn't really find any study material/details on it. What is the algorithm about?So the first thing you want to be able to do is to identify a problem that uses a sliding window paradigm.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/8269916/what-is-sliding-window-algorithm-examples", "content": "While solving a geometry problem, I came across an approach called Sliding Window Algorithm . Couldn't really find any study material/details on it. What is the algorithm about?So the first thing you want to be able to do is to identify a problem that uses a sliding window paradigm."}
+{"idx": 5, "title": "Deterministic-to-Stochastic Diverse Latent Feature ...", "date": "", "ddg_snippet": "2 May 2025 — The pseudo algorithm of the DSDFM is provided in Algorithm 1 . Report issue for preceding element. Algorithm 1 The process for generating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00998v1", "content": "2 May 2025 — The pseudo algorithm of the DSDFM is provided in Algorithm 1 . Report issue for preceding element. Algorithm 1 The process for generating ..."}
+{"idx": 6, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis. DSDFM consists of two stages.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33113", "content": "In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis. DSDFM consists of two stages."}
+{"idx": 7, "title": "Solution: Running Sum of 1 d Array (easy)", "date": "", "ddg_snippet": "Single pass through the array: The algorithm uses a single loop to traverse the input array nums. For each element, it calculates the running sum by adding the current element to the sum of the previous elements. This loop runs for each element in the array, so it takes.", "subpage_snippet": "", "source": "www.designgurus.io", "link": "https://www.designgurus.io/course-play/grokking-data-structures-for-coding-interviews/doc/solution-running-sum-of-1d-array-easy", "content": "Single pass through the array: The algorithm uses a single loop to traverse the input array nums. For each element, it calculates the running sum by adding the current element to the sum of the previous elements. This loop runs for each element in the array, so it takes."}
+{"idx": 8, "title": "SHA- 1 : Всё об алгоритме хеширования и его... | Belov Solutions", "date": "", "ddg_snippet": "SHA- 1 (Secure Hash Algorithm 1 ) — это криптографический алгоритм хеширования, разработанный Национальным институтом стандартов и технологий США (NIST). Он генерирует хэш длиной 160 бит (40 шестнадцатеричных символов), что делает его более...", "subpage_snippet": "", "source": "belov.solutions", "link": "https://belov.solutions/blog/hashing-cryptography/sha-1-vsyo-ob-algoritme-heshirovaniya/", "content": "SHA- 1 (Secure Hash Algorithm 1 ) — это криптографический алгоритм хеширования, разработанный Национальным институтом стандартов и технологий США (NIST). Он генерирует хэш длиной 160 бит (40 шестнадцатеричных символов), что делает его более..."}
+{"idx": 9, "title": "Elementary", "date": "", "ddg_snippet": "Elementary Calculus of Financial Mathematics. Algorithm 1 .2 MATLAB/SCILAB code to draw ve stochastic processes, all scaled from the one Wiener process, with different drifts and volatilities.", "subpage_snippet": "", "source": "dl.libcats.org", "link": "https://dl.libcats.org/genesis/228000/25c6b62532b84637bf744466e7e08dac/_as/[A._J._Roberts]_Elementary_Calculus_of_Financial_M(libcats.org).pdf", "content": "Elementary Calculus of Financial Mathematics. Algorithm 1 .2 MATLAB/SCILAB code to draw ve stochastic processes, all scaled from the one Wiener process, with different drifts and volatilities."}
diff --git a/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_section_3.3_derivative_computation.jsonl b/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_section_3.3_derivative_computation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cebf63cba2a45ade53cc72dab591df1806798069
--- /dev/null
+++ b/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_section_3.3_derivative_computation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Derivative - Wikipedia", "date": "", "ddg_snippet": "The derivative at different points of a differentiable function. In this case, the derivative is equal to.In mathematics, the derivative is a fundamental tool that quantifies the sensitivity to change of a function's output with respect to its input.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Derivative", "content": "The derivative at different points of a differentiable function. In this case, the derivative is equal to.In mathematics, the derivative is a fundamental tool that quantifies the sensitivity to change of a function's output with respect to its input."}
+{"idx": 1, "title": "ICML Poster DVI : A Derivative - based Vision Network for INR", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 2, "title": "Derivative formulas through geometry | Chapter 3, Essence... - YouTube", "date": "", "ddg_snippet": "Some common derivative formulas explained with geometric intuition.This video was sponsored by Brilliant: https://brilliant.org/3b1bHelp fund future projects...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=S0_qX4VJhMQ", "content": "Some common derivative formulas explained with geometric intuition.This video was sponsored by Brilliant: https://brilliant.org/3b1bHelp fund future projects..."}
+{"idx": 3, "title": "Derivative Calculator • With Steps!", "date": "", "ddg_snippet": "Solve derivatives using this free online calculator. Step-by-step solution and graphs included!", "subpage_snippet": "", "source": "www.derivative-calculator.net", "link": "https://www.derivative-calculator.net/", "content": "Solve derivatives using this free online calculator. Step-by-step solution and graphs included!"}
+{"idx": 4, "title": "Derivative Calculator: Step-by-Step Solutions - Wolfram|Alpha", "date": "", "ddg_snippet": "Free Derivative Calculator helps you solve first-order and higher-order derivatives . For trigonometric, logarithmic, exponential, polynomial expressions. Answers, graphs, alternate forms.", "subpage_snippet": "", "source": "www.wolframalpha.com", "link": "https://www.wolframalpha.com/calculators/derivative-calculator/", "content": "Free Derivative Calculator helps you solve first-order and higher-order derivatives . For trigonometric, logarithmic, exponential, polynomial expressions. Answers, graphs, alternate forms."}
+{"idx": 5, "title": "Derivative of ln x (Natural Log) - Formula | Differentiation of ln x", "date": "", "ddg_snippet": "Derivative of ln x or natural log x is proved to be 1/x graphically where the slopes of tangents are 1, 1 over 2, and 1 over 3 at x equals 1 x equals 2 and x equals 3 respectively.", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/calculus/derivative-of-ln-x/", "content": "Derivative of ln x or natural log x is proved to be 1/x graphically where the slopes of tangents are 1, 1 over 2, and 1 over 3 at x equals 1 x equals 2 and x equals 3 respectively."}
+{"idx": 6, "title": "Introduction to Derivatives", "date": "", "ddg_snippet": "Introduction to Derivatives . It is all about slope!f’(x) = 2x \"The derivative of f(x) equals 2x\" or simply \"f-dash of x equals 2x\". Let's try another example.", "subpage_snippet": "", "source": "www.mathsisfun.com", "link": "https://www.mathsisfun.com/calculus/derivatives-introduction.html", "content": "Introduction to Derivatives . It is all about slope!f’(x) = 2x \"The derivative of f(x) equals 2x\" or simply \"f-dash of x equals 2x\". Let's try another example."}
+{"idx": 7, "title": "GeoGebra Classic", "date": "", "ddg_snippet": "Log with Base . Derivative . Integral. 𝑖.", "subpage_snippet": "", "source": "autgeo.online", "link": "https://autgeo.online/", "content": "Log with Base . Derivative . Integral. 𝑖."}
+{"idx": 8, "title": "openreview.net/profile?id=~Zongren_Li1", "date": "", "ddg_snippet": "Publications. DVI : A Derivative - based Vision Network for INR .DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Zongren_Li1", "content": "Publications. DVI : A Derivative - based Vision Network for INR .DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications."}
+{"idx": 9, "title": "Applications of Automatic Differentiation in Image Registration", "date": "", "ddg_snippet": "Forward-mode automatic differentiation computes the derivative .𝐰∗superscript𝐰{\\bf w}^{*}bold_w start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT. . This would vary based on the problem and behavior of the optimization algorithm and its implementation. 3 . 3 Results.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02806v1", "content": "Forward-mode automatic differentiation computes the derivative .𝐰∗superscript𝐰{\\bf w}^{*}bold_w start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT. . This would vary based on the problem and behavior of the optimization algorithm and its implementation. 3 . 3 Results."}
diff --git a/data/sampled_jsons/DVI_Derivative-based_Vision_Network_INR_Section_3.5_INR_Feature_Fusion_module.jsonl b/data/sampled_jsons/DVI_Derivative-based_Vision_Network_INR_Section_3.5_INR_Feature_Fusion_module.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0ea8e56a8d7349a5ee6a73ede39b9f34a2f3f32a
--- /dev/null
+++ b/data/sampled_jsons/DVI_Derivative-based_Vision_Network_INR_Section_3.5_INR_Feature_Fusion_module.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Digital Visual Interface - Wikipedia", "date": "", "ddg_snippet": "DVI 's digital video transmission format is based on panelLink, a serial format developed by Silicon Image that utilizes a high-speed serial link called transition minimized differential signaling (TMDS).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Digital_Visual_Interface", "content": "DVI 's digital video transmission format is based on panelLink, a serial format developed by Silicon Image that utilizes a high-speed serial link called transition minimized differential signaling (TMDS)."}
+{"idx": 1, "title": "DVI: A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR , then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Xnqm4f71y", "content": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR , then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information."}
+{"idx": 2, "title": "ICML Poster DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI :A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI :A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo"}
+{"idx": 3, "title": "WaveFusionNet: Infrared and visible image fusion based on ...", "date": "", "ddg_snippet": "Dec 15, 2024 · Next, a dual-band feature fusion (DBFF) module is trained to merge these sub-bands by integrating a spatial feature transform- based sub- network for low-frequency fusion and a maximum absolute value selection strategy for fusing high-frequencies. Finally, all fused sub-bands are fed into the pre-trained decoder to reconstruct the final image.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0030401824007612", "content": "Dec 15, 2024 · Next, a dual-band feature fusion (DBFF) module is trained to merge these sub-bands by integrating a spatial feature transform- based sub- network for low-frequency fusion and a maximum absolute value selection strategy for fusing high-frequencies. Finally, all fused sub-bands are fed into the pre-trained decoder to reconstruct the final image."}
+{"idx": 4, "title": "An infrared and visible image fusion network based on multi ... [2307.07288] Implicit Neural Feature Fusion Function for ... DVI:A Derivative-based Vision Network for INR - AMiner DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion An infrared and visible image fusion network based on multi-scale feat… WaveFusionNet: Infrared and visible image fusion based on multi -scale An infrared and visible image fusion network based on multi-scale feat… DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion DDFNet-A: Attention-Based Dual-Branch Feature ... - MDPI", "date": "", "ddg_snippet": "Mar 27, 2024 · In the designed network , the encoder employs a multi-branch cascade structure with convolution kernels of different sizes to extract multi-scale features , and the fusion layer incorporates a non-local attention module alongside a spatial feature fusion strategy for both global and local feature fusion . Jul 14, 2023 · In this paper, inspired by previous work of MHIF task, we realize that HR-MSI could serve as a high-frequency detail auxiliary input, leading us to propose a novel INR - based hyperspectral fusion function named Implicit Neural Feature Fusion Function (INF). Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre What is ddfnet-a for infrared and visible image fusion? Therefore, we propose a DDFNet-A for infrared and visible image fusion . DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. What is infrared and visible image fusion? The most common approaches for infrared and visible image fusion are multi-scale transformations . The process typically encompasses a series of critical steps: Converting images into specific domains using various transformation algorithms. Extracting, filtering, enhancing, and fusing the features by carefully designed algorithms. Can deep learning improve fusion performance for infrared and visible images? In recent years, deep learning-based fusion methods have been explored to replace manually designed feature extraction solutions and fusion strategies. Due to the comprehensive feature representation and adaptive learning capabilities of neural networks, fusion performance for infrared and visible images has been greatly improved . Which network architecture is used in image fusion? Nowadays, CNNs have been widely used in the field of image fusion. Among them, a very popular network architecture is the auto-encoder (AE), which focuses on three critical challenges: feature extraction, feature fusion, and image reconstruction . What are the different types of deep learning frameworks for infrared and visible image fusion? Deep learning- based frameworks for infrared and visible image fusion can be broadly categorized into three types: autoencoders (AEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs). What is a two-stream auto-encoder for image fusion? Wang et al. proposed a two-stream auto-encoder for image fusion, using wavelet decomposition and structural feature map decomposition (SFMD) for enhanced feature fusion with carefully crafted rules. CNN-based IVIF methods involve end-to-end network modeling for feature extraction, fusion, and image reconstruction. May 18, 2024 · In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network .", "subpage_snippet": "", "source": "ietresearch.onlinelibrary.wiley.com", "link": "https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/ipr2.13088", "content": "Mar 27, 2024 · In the designed network , the encoder employs a multi-branch cascade structure with convolution kernels of different sizes to extract multi-scale features , and the fusion layer incorporates a non-local attention module alongside a spatial feature fusion strategy for both global and local feature fusion . Jul 14, 2023 · In this paper, inspired by previous work of MHIF task, we realize that HR-MSI could serve as a high-frequency detail auxiliary input, leading us to propose a novel INR - based hyperspectral fusion function named Implicit Neural Feature Fusion Function (INF). Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre What is ddfnet-a for infrared and visible image fusion? Therefore, we propose a DDFNet-A for infrared and visible image fusion . DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. What is infrared and visible image fusion? The most common approaches for infrared and visible image fusion are multi-scale transformations . The process typically encompasses a series of critical steps: Converting images into specific domains using various transformation algorithms. Extracting, filtering, enhancing, and fusing the features by carefully designed algorithms. Can deep learning improve fusion performance for infrared and visible images? In recent years, deep learning-based fusion methods have been explored to replace manually designed feature extraction solutions and fusion strategies. Due to the comprehensive feature representation and adaptive learning capabilities of neural networks, fusion performance for infrared and visible images has been greatly improved . Which network architecture is used in image fusion? Nowadays, CNNs have been widely used in the field of image fusion. Among them, a very popular network architecture is the auto-encoder (AE), which focuses on three critical challenges: feature extraction, feature fusion, and image reconstruction . What are the different types of deep learning frameworks for infrared and visible image fusion? Deep learning- based frameworks for infrared and visible image fusion can be broadly categorized into three types: autoencoders (AEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs). What is a two-stream auto-encoder for image fusion? Wang et al. proposed a two-stream auto-encoder for image fusion, using wavelet decomposition and structural feature map decomposition (SFMD) for enhanced feature fusion with carefully crafted rules. CNN-based IVIF methods involve end-to-end network modeling for feature extraction, fusion, and image reconstruction. May 18, 2024 · In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network ."}
+{"idx": 5, "title": "[2307.07288] Implicit Neural Feature Fusion Function for ... DVI:A Derivative-based Vision Network for INR - AMiner DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion An infrared and visible image fusion network based on multi-scale feat… WaveFusionNet: Infrared and visible image fusion based on multi -scale An infrared and visible image fusion network based on multi-scale feat… DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion DDFNet-A: Attention-Based Dual-Branch Feature ... - MDPI", "date": "", "ddg_snippet": "Jul 14, 2023 · In this paper, inspired by previous work of MHIF task, we realize that HR-MSI could serve as a high-frequency detail auxiliary input, leading us to propose a novel INR - based hyperspectral fusion function named Implicit Neural Feature Fusion Function (INF). Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre What is ddfnet-a for infrared and visible image fusion? Therefore, we propose a DDFNet-A for infrared and visible image fusion . DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. What is infrared and visible image fusion? The most common approaches for infrared and visible image fusion are multi-scale transformations . The process typically encompasses a series of critical steps: Converting images into specific domains using various transformation algorithms. Extracting, filtering, enhancing, and fusing the features by carefully designed algorithms. Can deep learning improve fusion performance for infrared and visible images? In recent years, deep learning-based fusion methods have been explored to replace manually designed feature extraction solutions and fusion strategies. Due to the comprehensive feature representation and adaptive learning capabilities of neural networks, fusion performance for infrared and visible images has been greatly improved . Which network architecture is used in image fusion? Nowadays, CNNs have been widely used in the field of image fusion. Among them, a very popular network architecture is the auto-encoder (AE), which focuses on three critical challenges: feature extraction, feature fusion, and image reconstruction . What are the different types of deep learning frameworks for infrared and visible image fusion? Deep learning- based frameworks for infrared and visible image fusion can be broadly categorized into three types: autoencoders (AEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs). What is a two-stream auto-encoder for image fusion? Wang et al. proposed a two-stream auto-encoder for image fusion, using wavelet decomposition and structural feature map decomposition (SFMD) for enhanced feature fusion with carefully crafted rules. CNN-based IVIF methods involve end-to-end network modeling for feature extraction, fusion, and image reconstruction. May 18, 2024 · In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.07288", "content": "Jul 14, 2023 · In this paper, inspired by previous work of MHIF task, we realize that HR-MSI could serve as a high-frequency detail auxiliary input, leading us to propose a novel INR - based hyperspectral fusion function named Implicit Neural Feature Fusion Function (INF). Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre What is ddfnet-a for infrared and visible image fusion? Therefore, we propose a DDFNet-A for infrared and visible image fusion . DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. What is infrared and visible image fusion? The most common approaches for infrared and visible image fusion are multi-scale transformations . The process typically encompasses a series of critical steps: Converting images into specific domains using various transformation algorithms. Extracting, filtering, enhancing, and fusing the features by carefully designed algorithms. Can deep learning improve fusion performance for infrared and visible images? In recent years, deep learning-based fusion methods have been explored to replace manually designed feature extraction solutions and fusion strategies. Due to the comprehensive feature representation and adaptive learning capabilities of neural networks, fusion performance for infrared and visible images has been greatly improved . Which network architecture is used in image fusion? Nowadays, CNNs have been widely used in the field of image fusion. Among them, a very popular network architecture is the auto-encoder (AE), which focuses on three critical challenges: feature extraction, feature fusion, and image reconstruction . What are the different types of deep learning frameworks for infrared and visible image fusion? Deep learning- based frameworks for infrared and visible image fusion can be broadly categorized into three types: autoencoders (AEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs). What is a two-stream auto-encoder for image fusion? Wang et al. proposed a two-stream auto-encoder for image fusion, using wavelet decomposition and structural feature map decomposition (SFMD) for enhanced feature fusion with carefully crafted rules. CNN-based IVIF methods involve end-to-end network modeling for feature extraction, fusion, and image reconstruction. May 18, 2024 · In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network ."}
+{"idx": 6, "title": "DVI:A Derivative-based Vision Network for INR - AMiner", "date": "", "ddg_snippet": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/6853e848163c01c8502f0416/dvi-a-derivative-based-vision-network-for-inr", "content": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre"}
+{"idx": 7, "title": "DDFNet-A: Attention-Based Dual-Branch Feature ... - MDPI", "date": "", "ddg_snippet": "May 18, 2024 · In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network .", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2072-4292/16/10/1795", "content": "May 18, 2024 · In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network ."}
+{"idx": 8, "title": "В чём разница у 18650: IMR, ICR, INR , IFR, NCA, NCR, LTO", "date": "", "ddg_snippet": "В чём разница аккумуляторов 18650 по маркировке IMR, ICR, INR , IFR, NCA, NCR, LTO; с защитой и без; плоский и выпуклый плюс?", "subpage_snippet": "", "source": "NeoVolt.ru", "link": "https://NeoVolt.ru/blog/957_18650-imr-icr-inr-ifr-nca", "content": "В чём разница аккумуляторов 18650 по маркировке IMR, ICR, INR , IFR, NCA, NCR, LTO; с защитой и без; плоский и выпуклый плюс?"}
+{"idx": 9, "title": "1 USD to INR | 1 US Dollar to Indian Rupee — Exchange Rate, Convert", "date": "", "ddg_snippet": "At Myfin online currency converter you can find 1 USD to INR chart, exchange rate stats and other historical info.88,278. 5 INR .", "subpage_snippet": "", "source": "myfin.us", "link": "https://myfin.us/currency-converter/usd-inr/1", "content": "At Myfin online currency converter you can find 1 USD to INR chart, exchange rate stats and other historical info.88,278. 5 INR ."}
diff --git a/data/sampled_jsons/DVI_Derivative-based_Vision_Network_INR_video_flow_estimation_derivative_order_curse_of_dimensionali.jsonl b/data/sampled_jsons/DVI_Derivative-based_Vision_Network_INR_video_flow_estimation_derivative_order_curse_of_dimensionali.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..258dc70ad4d92862c62e97726b6d1e0b378000f7
--- /dev/null
+++ b/data/sampled_jsons/DVI_Derivative-based_Vision_Network_INR_video_flow_estimation_derivative_order_curse_of_dimensionali.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Curse of dimensionality - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high- dimensional spaces that do not occur in low- dimensional settings such as the three- dimensional phy...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Curse_of_dimensionality", "content": "Machine learningand data mining. v. t. e. The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high- dimensional spaces that do not occur in low- dimensional settings such as the three- dimensional phy..."}
+{"idx": 1, "title": "DVI: A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR , then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Xnqm4f71y", "content": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR , then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information."}
+{"idx": 2, "title": "ICML Poster DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI :A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI :A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo"}
+{"idx": 3, "title": "Feature flow: In-network feature flow estimation for video ...", "date": "", "ddg_snippet": "Feb 1, 2022 · To mitigate these issues, we propose a novel network (IFF-Net) with an I n- network F eature F low estimation module (IFF module) for video object detection. Without resorting to pre-training on any additional dataset, our IFF module is able to directly produce feature flow which indicates the feature displacement.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320321005033", "content": "Feb 1, 2022 · To mitigate these issues, we propose a novel network (IFF-Net) with an I n- network F eature F low estimation module (IFF module) for video object detection. Without resorting to pre-training on any additional dataset, our IFF module is able to directly produce feature flow which indicates the feature displacement."}
+{"idx": 4, "title": "DVI:A Derivative-based Vision Network for INR - AMiner", "date": "", "ddg_snippet": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/6853e848163c01c8502f0416/dvi-a-derivative-based-vision-network-for-inr", "content": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre"}
+{"idx": 5, "title": "Optical Flow Estimation", "date": "", "ddg_snippet": "David J. Fleet, Yair Weiss ABSTRACT This chapter provides a tutorial introduction to gradient- based optical flow estimation . We discuss least-squares and robust estima-tors, iterative coarse-to-fine refinement, different forms of parametric mo-tion models, different conservation assumptions, probabilistic formulations, and robust mixture models.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~fleet/research/Papers/flowChapter05.pdf", "content": "David J. Fleet, Yair Weiss ABSTRACT This chapter provides a tutorial introduction to gradient- based optical flow estimation . We discuss least-squares and robust estima-tors, iterative coarse-to-fine refinement, different forms of parametric mo-tion models, different conservation assumptions, probabilistic formulations, and robust mixture models."}
+{"idx": 6, "title": "DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/167863", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 7, "title": "Curse of Dimensionality in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Curse of Dimensionality significantly impacts machine learning algorithms in various ways.Feature Selection and Dimensionality Reduction. Feature Selection: SelectKBest is used to select the top k features based on a specified scoring function (f_classif in this case).", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/curse-of-dimensionality-in-machine-learning/", "content": "Curse of Dimensionality significantly impacts machine learning algorithms in various ways.Feature Selection and Dimensionality Reduction. Feature Selection: SelectKBest is used to select the top k features based on a specified scoring function (f_classif in this case)."}
+{"idx": 8, "title": "Data Denoising and Derivative Estimation for Data-Driven Modeling...", "date": "", "ddg_snippet": "These denoised states and derivatives are then supplied to Sparse Identification of Nonlinear Dynamics (SINDy) to recover the governing equations. Experiments demonstrate effective noise suppression, precise derivative estimation , and reliable system identification.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14219", "content": "These denoised states and derivatives are then supplied to Sparse Identification of Nonlinear Dynamics (SINDy) to recover the governing equations. Experiments demonstrate effective noise suppression, precise derivative estimation , and reliable system identification."}
+{"idx": 9, "title": "What is Curse of Dimensionality in Machine Learning?", "date": "", "ddg_snippet": "Curse of Dimensionality refers to a set of problems that arise when working with high- dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset.", "subpage_snippet": "", "source": "www.mygreatlearning.com", "link": "https://www.mygreatlearning.com/blog/understanding-curse-of-dimensionality/", "content": "Curse of Dimensionality refers to a set of problems that arise when working with high- dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset."}
diff --git a/data/sampled_jsons/DVI_INR_Feature_Fusion_module_Section_3.5_output_shape_Swin_Transformer_Cross-attention_Layer_1x1_CO.jsonl b/data/sampled_jsons/DVI_INR_Feature_Fusion_module_Section_3.5_output_shape_Swin_Transformer_Cross-attention_Layer_1x1_CO.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4376d93ff3057215c92d24fcfd1996b86834629f
--- /dev/null
+++ b/data/sampled_jsons/DVI_INR_Feature_Fusion_module_Section_3.5_output_shape_Swin_Transformer_Cross-attention_Layer_1x1_CO.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "Transformer layers , which carry out repeated transformations on the vector representations, extracting more and more linguistic information. These consist of alternating attention and feedforward layers .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "Transformer layers , which carry out repeated transformations on the vector representations, extracting more and more linguistic information. These consist of alternating attention and feedforward layers ."}
+{"idx": 1, "title": "Swin -HSSAM: A green coffee bean grading method by... | PLOS One", "date": "", "ddg_snippet": "LN, layer norm layer ; W-MSA, window-based multi-head self- attention module ; MLP, multilayer perceptron.In the feature fusion section , a top-down fusion strategy was implemented, culminating in the output N3 from the SFF feature fusion module .", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322198", "content": "LN, layer norm layer ; W-MSA, window-based multi-head self- attention module ; MLP, multilayer perceptron.In the feature fusion section , a top-down fusion strategy was implemented, culminating in the output N3 from the SFF feature fusion module ."}
+{"idx": 2, "title": "Swin (Shifted Window Transformer ) Explained: An Overview", "date": "", "ddg_snippet": "Swin Transformer is a Vision Transformer with a shifted window attention mechanism, enabling efficient scaling to high-resolution images.", "subpage_snippet": "", "source": "www.lightly.ai", "link": "https://www.lightly.ai/blog/swin-transformer", "content": "Swin Transformer is a Vision Transformer with a shifted window attention mechanism, enabling efficient scaling to high-resolution images."}
+{"idx": 3, "title": "DyGLNet: Hybrid Global-Local Feature Fusion with Dynamic...", "date": "", "ddg_snippet": "Swin UNETR [2] uses a hierarchical Swin transformer as the encoder, project-ing the multimodal input data into a 1D sequence of embeddings. UNetR [8] adopts a U- shaped network de-sign, utilizing a transformer as the encoder to learn se-quence representations of the input volume.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.12763", "content": "Swin UNETR [2] uses a hierarchical Swin transformer as the encoder, project-ing the multimodal input data into a 1D sequence of embeddings. UNetR [8] adopts a U- shaped network de-sign, utilizing a transformer as the encoder to learn se-quence representations of the input volume."}
+{"idx": 4, "title": "Mixed multi-branch feature fusion model for efficient automatic...", "date": "", "ddg_snippet": "Firstly, we designed a Mixed Multi-Branch Feature Fusion (MMFF) module , which performs multi-dimensional weighted fusion on the feature information captured by the Transformer .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40747-025-01998-3", "content": "Firstly, we designed a Mixed Multi-Branch Feature Fusion (MMFF) module , which performs multi-dimensional weighted fusion on the feature information captured by the Transformer ."}
+{"idx": 5, "title": "C4_W1: How to calculate output volume for a ` Conv 2DTranspose...", "date": "", "ddg_snippet": "Does the output volume calculation taught in this course apply equally well if the filter used is Conv 2DTranspose? I try to do that but it differs in the calculation result.", "subpage_snippet": "", "source": "community.deeplearning.ai", "link": "https://community.deeplearning.ai/t/c4-w1-how-to-calculate-output-volume-for-a-conv2dtranspose-filter/879451", "content": "Does the output volume calculation taught in this course apply equally well if the filter used is Conv 2DTranspose? I try to do that but it differs in the calculation result."}
+{"idx": 6, "title": "mSwinUNet: A multi-modal U- shaped swin transformer ... | OpenReview", "date": "", "ddg_snippet": "In contrast, the transformer can learn global semantic information by dividing the input image into patches, adding position encodings, and utilizing the self- attention mechanism.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=PVIg1QvkTd&referrer=[the+profile+of+Xian+Zhong](/profile?id=~Xian_Zhong1)", "content": "In contrast, the transformer can learn global semantic information by dividing the input image into patches, adding position encodings, and utilizing the self- attention mechanism."}
+{"idx": 7, "title": "Transformer based Conditional GAN for Multimodal Image Fusion ...", "date": "", "ddg_snippet": "In particular, a wavelet fusion module makes the inputs contain image content from different domains as much as possible. The extracted convolutional features interact in the multiscale cross -modal transformer fusion module to fully complement the associated information.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/transformer-based-conditional-gan-for-multimodal-image-32b0d5yt", "content": "In particular, a wavelet fusion module makes the inputs contain image content from different domains as much as possible. The extracted convolutional features interact in the multiscale cross -modal transformer fusion module to fully complement the associated information."}
+{"idx": 8, "title": "YOLOLayout: Multi-Scale Cross Fusion Former", "date": "", "ddg_snippet": "D. Cross - Attention . Feature fusion refers to the process of combining features from different layers or branches by means of computations in.In the five -fold cross-validation experi-ments, ISCAS-CLA is divided into five equal parts, one part is.", "subpage_snippet": "", "source": "www.ijetaa.com", "link": "https://www.ijetaa.com/article/download/106/8/31", "content": "D. Cross - Attention . Feature fusion refers to the process of combining features from different layers or branches by means of computations in.In the five -fold cross-validation experi-ments, ISCAS-CLA is divided into five equal parts, one part is."}
+{"idx": 9, "title": "Swin Transformer : Windows of Attention | by Subhash... - Freedium", "date": "", "ddg_snippet": "Input and output shapes of various layers in Swin Transformer architecture.", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/d6410187a39d", "content": "Input and output shapes of various layers in Swin Transformer architecture."}
diff --git a/data/sampled_jsons/DVI_analytical_derivative_computation_INR_autograd_faster_year_2024.jsonl b/data/sampled_jsons/DVI_analytical_derivative_computation_INR_autograd_faster_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ec7f635cf2efb03320f57789f1f1415bd0929071
--- /dev/null
+++ b/data/sampled_jsons/DVI_analytical_derivative_computation_INR_autograd_faster_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "by R Yang — DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster-based vision ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Xnqm4f71y", "content": "by R Yang — DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster-based vision ..."}
+{"idx": 1, "title": "DVI: A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "DVI excels by extract-ing structural information from INR ’s high order derivative map, enhancing the performance of an array of different pre-existing vision networks with deeper, task-specific in-sights.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Xnqm4f71y", "content": "DVI excels by extract-ing structural information from INR ’s high order derivative map, enhancing the performance of an array of different pre-existing vision networks with deeper, task-specific in-sights."}
+{"idx": 2, "title": "How to calculate second derivatives with pytorch autograd?", "date": "", "ddg_snippet": "Jun 17, 2022 · In order to calculate the loss function one usually requires higher-order derivatives of your model with respect to the input and this is basically where my code fails.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/72659131/how-to-calculate-second-derivatives-with-pytorch-autograd", "content": "Jun 17, 2022 · In order to calculate the loss function one usually requires higher-order derivatives of your model with respect to the input and this is basically where my code fails."}
+{"idx": 3, "title": "Faster way to only calculate some derivatives - autograd ...", "date": "", "ddg_snippet": "Dec 11, 2024 · here is my idea for an optimizer - you only calculate some derivatives on each step, other remain the same. So it works as a kind of momentum. But that would be useful if you could somehow calculate only some derivative …", "subpage_snippet": "", "source": "discuss.pytorch.org", "link": "https://discuss.pytorch.org/t/faster-way-to-only-calculate-some-derivatives/214098", "content": "Dec 11, 2024 · here is my idea for an optimizer - you only calculate some derivatives on each step, other remain the same. So it works as a kind of momentum. But that would be useful if you could somehow calculate only some derivative …"}
+{"idx": 4, "title": "DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster-based vision network, enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/167863", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster-based vision network, enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 5, "title": "Autograd for Automatic Differentiation and for Auto ...", "date": "", "ddg_snippet": "The partial derivatives of the output of a neural layer with respect to each element of the input to the layer are based on the analytical formulas shown in the last section of this lecture.", "subpage_snippet": "", "source": "engineering.purdue.edu", "link": "https://engineering.purdue.edu/DeepLearn/pdf-kak/AutogradAndCGP.pdf", "content": "The partial derivatives of the output of a neural layer with respect to each element of the input to the layer are based on the analytical formulas shown in the last section of this lecture."}
+{"idx": 6, "title": "PyTorch Autograd : Automatic Differentiation Explained | Medium", "date": "", "ddg_snippet": "Autograd handles these derivatives behind the scenes, saving time and avoiding human errors in complex networks. Why Automatic Differentiation is Needed. Training a deep learning model means constantly adjusting its parameters so the predictions get closer to the correct answers.", "subpage_snippet": "", "source": "alok05.medium.com", "link": "https://alok05.medium.com/pytorch-autograd-automatic-differentiation-explained-dc9c3ff704b1", "content": "Autograd handles these derivatives behind the scenes, saving time and avoiding human errors in complex networks. Why Automatic Differentiation is Needed. Training a deep learning model means constantly adjusting its parameters so the predictions get closer to the correct answers."}
+{"idx": 7, "title": "python - Inner workings of pytorch autograd .grad for inner derivatives", "date": "", "ddg_snippet": "My overall question is: under what circumstances does pytorch realize computation A vs computation B when using autograd .grad? I'd appreciate any explanation that goes into technical details about how this particular case is handled by autograd .", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/73188479/inner-workings-of-pytorch-autograd-grad-for-inner-derivatives", "content": "My overall question is: under what circumstances does pytorch realize computation A vs computation B when using autograd .grad? I'd appreciate any explanation that goes into technical details about how this particular case is handled by autograd ."}
+{"idx": 8, "title": "Autograd calculate the analytical derivative of NN... - PyTorch Forums", "date": "", "ddg_snippet": "I wan’t to know if the torch. autograd .grad calculate the analytical gradient of NN output or just the eulerienne gradient.It calculates the analytical gradient by chain rule. You can look at Overview of PyTorch Autograd Engine to get more details.", "subpage_snippet": "", "source": "discuss.pytorch.org", "link": "https://discuss.pytorch.org/t/autograd-calculate-the-analytical-derivative-of-nn/174816", "content": "I wan’t to know if the torch. autograd .grad calculate the analytical gradient of NN output or just the eulerienne gradient.It calculates the analytical gradient by chain rule. You can look at Overview of PyTorch Autograd Engine to get more details."}
+{"idx": 9, "title": "Essential Python for Machine Learning: AutoGrad", "date": "", "ddg_snippet": "Automatic Differentiation: Autograd ’s primary role is to automatically compute derivatives of Python and NumPy code.How Autograd Works. Graph Construction: When a function is defined, Autograd constructs a computational graph that captures the sequence of operations involved.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/essential-python-for-machine-learning-autograd-2f1ac4c55086", "content": "Automatic Differentiation: Autograd ’s primary role is to automatically compute derivatives of Python and NumPy code.How Autograd Works. Graph Construction: When a function is defined, Autograd constructs a computational graph that captures the sequence of operations involved."}
diff --git a/data/sampled_jsons/DVI_derivative_computation_technique_Section_3.3_analytical_derivative_autograd.jsonl b/data/sampled_jsons/DVI_derivative_computation_technique_Section_3.3_analytical_derivative_autograd.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fe55ff46a09f2085cb30a6e2e712c26f9f9f6a29
--- /dev/null
+++ b/data/sampled_jsons/DVI_derivative_computation_technique_Section_3.3_analytical_derivative_autograd.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Second order derivatives of loss function - autograd - PyTorch Forums", "date": "", "ddg_snippet": "My main question is how to calculate the second order derivatives of a loss function. But I started with a toy example as follows: import torch x = torch.tensor(1., requires_grad = True) y = 2*x**3 + 5*x**2 + 8 y.backwa…", "subpage_snippet": "", "source": "discuss.pytorch.org", "link": "https://discuss.pytorch.org/t/second-order-derivatives-of-loss-function/71797", "content": "My main question is how to calculate the second order derivatives of a loss function. But I started with a toy example as follows: import torch x = torch.tensor(1., requires_grad = True) y = 2*x**3 + 5*x**2 + 8 y.backwa…"}
+{"idx": 1, "title": "20- autograd -applications.ipynb - Colab", "date": "", "ddg_snippet": "close. 20- autograd -applications.ipynb_.Technically, these are the derivatives along the curve $y(x)$$y(x)$, but since we can evaluate them at any points, we will use some random points for $x$$x$ and $y$$y$ to test for equality between the analytical derivatives and the jax derivatives .", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/jkitchin/f23-06623/blob/main/f23-06623/lectures/20-autograd-applications/20-jax-applications.ipynb", "content": "close. 20- autograd -applications.ipynb_.Technically, these are the derivatives along the curve $y(x)$$y(x)$, but since we can evaluate them at any points, we will use some random points for $x$$x$ and $y$$y$ to test for equality between the analytical derivatives and the jax derivatives ."}
+{"idx": 2, "title": "Derivative formulas through geometry | Chapter 3, Essence... - YouTube", "date": "", "ddg_snippet": "Some common derivative formulas explained with geometric intuition.This video was sponsored by Brilliant: https://brilliant.org/3b1bHelp fund future projects...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=S0_qX4VJhMQ", "content": "Some common derivative formulas explained with geometric intuition.This video was sponsored by Brilliant: https://brilliant.org/3b1bHelp fund future projects..."}
+{"idx": 3, "title": "pytorch/tools/ autograd / derivatives .yaml at main · pytorch/pytorch", "date": "", "ddg_snippet": "Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/tools/ autograd / derivatives .yaml at main · pytorch/pytorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pytorch/pytorch/blob/main/tools/autograd/derivatives.yaml", "content": "Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/tools/ autograd / derivatives .yaml at main · pytorch/pytorch."}
+{"idx": 4, "title": "python - Pytorch vs Numpy for second derivative ... - Stack Overflow", "date": "", "ddg_snippet": "The second derivative literally explodes: if I zoom on the first derivative I'll notice a lot of local oscillations, that on the first example does not exists. Can somebody explain me why this happens? I tried to perform with . autograd () of pytorch and I do not encounter the issue.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/78838151/pytorch-vs-numpy-for-second-derivative-evaluation-numerical-issues", "content": "The second derivative literally explodes: if I zoom on the first derivative I'll notice a lot of local oscillations, that on the first example does not exists. Can somebody explain me why this happens? I tried to perform with . autograd () of pytorch and I do not encounter the issue."}
+{"idx": 5, "title": "efficiency improvements in autograd derivatives - Githubissues", "date": "", "ddg_snippet": "41 forks source link. efficiency improvements in autograd derivatives #2015.it should never be the case. the derivative info is basically just a container to avoid having to pass Ex_fwd, Ey_fwd, etc into all of the derivative computation methods and then interpolate into them.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/flexcompute/tidy3d/2015", "content": "41 forks source link. efficiency improvements in autograd derivatives #2015.it should never be the case. the derivative info is basically just a container to avoid having to pass Ex_fwd, Ey_fwd, etc into all of the derivative computation methods and then interpolate into them."}
+{"idx": 6, "title": "Computing Derivatives & Autograd Lab 4", "date": "", "ddg_snippet": "Computing Derivatives & Autograd Lab 4. By John Liu & Michael Kireeff.Let’s understand the autograd framework.", "subpage_snippet": "", "source": "cdn-uploads.piazza.com", "link": "https://cdn-uploads.piazza.com/paste/ljjjuqvroyk192/8c947613aea2b348abf1492c38824004ef7cec5b27f4f2f13eadd530783e1394/Computing_Derivatives___Autograd_Lab_4.pdf", "content": "Computing Derivatives & Autograd Lab 4. By John Liu & Michael Kireeff.Let’s understand the autograd framework."}
+{"idx": 7, "title": "Benchmarking of monolithic MDO formulations and derivative ...", "date": "", "ddg_snippet": "For the full analytic derivative computation technique , the IDF is more performant than the NVH. Besides, their number of function evaluations increases with the same proportions with respect to the kFema coefficient. However, it can be noted in Fig.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-02997582/document", "content": "For the full analytic derivative computation technique , the IDF is more performant than the NVH. Besides, their number of function evaluations increases with the same proportions with respect to the kFema coefficient. However, it can be noted in Fig."}
+{"idx": 8, "title": "Enabling Automatic Differentiation", "date": "", "ddg_snippet": "Autograd can compute exact derivatives in a single pass seamlessly across complex geometries and enables higher-order derivatives with minimal additional cost.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=CGoR1hFAGr", "content": "Autograd can compute exact derivatives in a single pass seamlessly across complex geometries and enables higher-order derivatives with minimal additional cost."}
+{"idx": 9, "title": "Applications of automatic differentiation — s25-06623", "date": "", "ddg_snippet": "Getting derivatives from implicit functions with jax. Scientific applications. Computing the pressure from a solid equation of stateSensitivity analysis using automatic differentiation in Python", "subpage_snippet": "", "source": "kitchingroup.cheme.cmu.edu", "link": "https://kitchingroup.cheme.cmu.edu/s25-06623/lectures/20-autograd-applications/20-jax-applications.html", "content": "Getting derivatives from implicit functions with jax. Scientific applications. Computing the pressure from a solid equation of stateSensitivity analysis using automatic differentiation in Python"}
diff --git a/data/sampled_jsons/Dasgupta_2016_cost_function_similarity-based_hierarchical_clustering_abstract.jsonl b/data/sampled_jsons/Dasgupta_2016_cost_function_similarity-based_hierarchical_clustering_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4bbddee122331816a8d9e0144a290d4619446aaf
--- /dev/null
+++ b/data/sampled_jsons/Dasgupta_2016_cost_function_similarity-based_hierarchical_clustering_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Approximating Dasgupta Cost in Sublinear Time from a Few Random", "date": "", "ddg_snippet": "Our measure of hierarchical clusterability is the well-established Dasgupta cost , and our main result is an algorithm that approximates Dasgupta cost ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2207.02581v3", "content": "Our measure of hierarchical clusterability is the well-established Dasgupta cost , and our main result is an algorithm that approximates Dasgupta cost ..."}
+{"idx": 1, "title": "Hierarchical clustering with dot products recovers hidden tree", "date": "", "ddg_snippet": "... study tree recovery as n → ∞ → 𝑛 n\\to\\infty italic_n → ∞ with respect to a cost function , e.g, (Carlsson et al., 2010 ; Dasgupta , 2016 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.15022v3", "content": "... study tree recovery as n → ∞ → 𝑛 n\\to\\infty italic_n → ∞ with respect to a cost function , e.g, (Carlsson et al., 2010 ; Dasgupta , 2016 ..."}
+{"idx": 2, "title": "STOC 2016- Proceedings of the 48th Annual ACM SIGACT Symposium", "date": "", "ddg_snippet": "A cost function for similarity - based hierarchical clustering ... for makespan scheduling with precedence constraints using LP hierarchies", "subpage_snippet": "", "source": "acm-stoc.org", "link": "https://acm-stoc.org/stoc2016/toc.html", "content": "A cost function for similarity - based hierarchical clustering ... for makespan scheduling with precedence constraints using LP hierarchies"}
+{"idx": 3, "title": "A dockerized framework for hierarchical frequency-based", "date": "", "ddg_snippet": "Nevertheless, the high computational cost and memory usage of baseline hierarchical clustering algorithms render them inappropriate for the vast ...", "subpage_snippet": "", "source": "journalofcloudcomputing.springeropen.com", "link": "https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-019-0150-y", "content": "Nevertheless, the high computational cost and memory usage of baseline hierarchical clustering algorithms render them inappropriate for the vast ..."}
+{"idx": 4, "title": "Dasgupta, IN - Patent applications", "date": "", "ddg_snippet": "METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR UTILIZING ABSTRACTED USER-DEFINED DATA TO CONDUCT NETWORK PROTOCOL TESTING - Methods, systems and ...", "subpage_snippet": "", "source": "www.patentsencyclopedia.com", "link": "https://www.patentsencyclopedia.com/inventor/dasgupta-in-5/", "content": "METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR UTILIZING ABSTRACTED USER-DEFINED DATA TO CONDUCT NETWORK PROTOCOL TESTING - Methods, systems and ..."}
+{"idx": 5, "title": "An Algorithm for Clustering cDNA Fingerprints | Request PDF", "date": "", "ddg_snippet": "At present, the primary algorithms of clustering of gene expression data include hierarchical methods [1,2,3,4] , K-means [5] , greedy methods [6,7 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/12446298_An_Algorithm_for_Clustering_cDNA_Fingerprints", "content": "At present, the primary algorithms of clustering of gene expression data include hierarchical methods [1,2,3,4] , K-means [5] , greedy methods [6,7 ..."}
+{"idx": 6, "title": "UCSB CS Theory Colloquium Series", "date": "", "ddg_snippet": "... Dasgupta (UC San Diego) Title: Some recent theoretical directions in clustering ... A cost function for similarity - based hierarchical clustering .", "subpage_snippet": "", "source": "sites.cs.ucsb.edu", "link": "https://sites.cs.ucsb.edu/~vigoda/theory/F21/Sanjoy.html", "content": "... Dasgupta (UC San Diego) Title: Some recent theoretical directions in clustering ... A cost function for similarity - based hierarchical clustering ."}
+{"idx": 7, "title": "Events – Page 3 – Complex Networks", "date": "", "ddg_snippet": "... hierarchical clustering , called the hierarchical stochastic block model (HSBM), and show that in certain regimes the SVD approach of McSherry combined ...", "subpage_snippet": "", "source": "www.complexnetworks.fr", "link": "https://www.complexnetworks.fr/category/events/page/3/", "content": "... hierarchical clustering , called the hierarchical stochastic block model (HSBM), and show that in certain regimes the SVD approach of McSherry combined ..."}
+{"idx": 8, "title": "Inequality and individuals’ social networks: the other face", "date": "", "ddg_snippet": "C38 - Classification Methods; Cluster Analysis ... D24 - Production; Cost ; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/cje/article/45/4/675/6310958", "content": "C38 - Classification Methods; Cluster Analysis ... D24 - Production; Cost ; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity"}
+{"idx": 9, "title": "Claire MATHIEU | Ecole Normale Supérieure de Paris, Paris |", "date": "", "ddg_snippet": "... that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Claire-Mathieu", "content": "... that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity ..."}
diff --git a/data/sampled_jsons/Dasgupta_cost_function_hierarchical_clustering_overlaps_reduce.jsonl b/data/sampled_jsons/Dasgupta_cost_function_hierarchical_clustering_overlaps_reduce.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eac6a1c9ae60c4ef2855fedeb9f54a5bcb913d5f
--- /dev/null
+++ b/data/sampled_jsons/Dasgupta_cost_function_hierarchical_clustering_overlaps_reduce.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Approximating Dasgupta Cost in Sublinear Time from a Few Random", "date": "", "ddg_snippet": "Our measure of hierarchical clusterability is the well-established Dasgupta cost , and our main result is an algorithm that approximates Dasgupta cost ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2207.02581v3", "content": "Our measure of hierarchical clusterability is the well-established Dasgupta cost , and our main result is an algorithm that approximates Dasgupta cost ..."}
+{"idx": 1, "title": "A dockerized framework for hierarchical frequency-based", "date": "", "ddg_snippet": "Nevertheless, the high computational cost and memory usage of baseline hierarchical clustering algorithms render them inappropriate for the vast ...", "subpage_snippet": "", "source": "journalofcloudcomputing.springeropen.com", "link": "https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-019-0150-y", "content": "Nevertheless, the high computational cost and memory usage of baseline hierarchical clustering algorithms render them inappropriate for the vast ..."}
+{"idx": 2, "title": "An Algorithm for Clustering cDNA Fingerprints | Request PDF", "date": "", "ddg_snippet": "At present, the primary algorithms of clustering of gene expression data include hierarchical methods [1,2,3,4] , K-means [5] , greedy methods [6,7 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/12446298_An_Algorithm_for_Clustering_cDNA_Fingerprints", "content": "At present, the primary algorithms of clustering of gene expression data include hierarchical methods [1,2,3,4] , K-means [5] , greedy methods [6,7 ..."}
+{"idx": 3, "title": "A Spectral Clustering Approach To Finding Communities in Graph.", "date": "", "ddg_snippet": "Empirically, higher values of the Q function have been shown to correlate well with good graph clusterings . ... function can be reformulated as a ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/220906659_A_Spectral_Clustering_Approach_To_Finding_Communities_in_Graph", "content": "Empirically, higher values of the Q function have been shown to correlate well with good graph clusterings . ... function can be reformulated as a ..."}
+{"idx": 4, "title": "A cost function for similarity-based hierarchical clustering A cost function for similarity-based hierarchical clustering Hierarchical cost — Higra 0.6.12 documentation HIERARCHICAL OVERLAPPING CLUSTERING FUNCTION ALGORITHM AND ... AN IMPROVED COST FUNCTION FOR HIERARCHICAL CLUSTER TREES Hierarchical Clustering: Objective Functions and Algorithms ... [1510.05043] A cost function for similarity-based hierarchical clusterin… Hierarchical cost — Higra 0.6.7 documentation - Read the Docs Hierarchical Clustering: Objective Functions and Algorithms : Journal of Hierarchical cost — Higra 0.6.7 documentation - Read the Docs Hierarchical Clustering: Objective Functions and Algorithms : Journal of Hierarchical Clustering: Objective Functions and Algorithms : Journal of Hierarchical Clustering via Spreading Metrics - jmlr.org", "date": "", "ddg_snippet": "Oct 16, 2015 · The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions . To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instances and that it admits a top-down construction ... Abstract The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instan... See full list on cseweb.ucsd.edu hierarchical clustering is a recursive partitioning of a data set into successively smaller clusters. It is represented by a rooted tree whose leaves correspond to the data points, and each of whose internal nodes represents the cluster of its descendant leaves. hierarchy of this sort has several advantages over a at clustering , which is a partitio... See full list on cseweb.ucsd.edu Letting Kn denote the complete graph on n nodes (with unit edge weights), we have E[costG(T)] = X Pr(edge between i and j in G) jleaves(T[i _ j])j fi;jg X X = q jleaves(T[i _ j])j + (Pr(edge between i and j in G) q) jleaves(T[i _ j])j fi;jg fi;jg See full list on cseweb.ucsd.edu Combining these inequalities yields the theorem statement. In summary, when data is generated from a simple partition model, the tree that minimizes cost (T) almost perfectly respects the planted clusters, misplacing at most an O(p(ln n)=n) fraction of the data. 4 Hardness of nding the optimal clustering In this section, we will see that considering... See full list on cseweb.ucsd.edu Interestingly, the problem of maximizing cost (T) is equivalent to the problem of minimizing it. To see this, let G = (V; E) be any graph with unit edge weights, and let Gc = (V; Ec) denote its complement. Pick any tree T and assess its suitability as a hierarchical clustering of G and also of Gc: costG(T) + costGc(T) = jleaves(T[i _ j])j + fi;jg2E See full list on cseweb.ucsd.edu Given a graph G with weighted edges, we have seen that it is NP-hard to nd a tree that minimizes cost (T). We now consider top-down heuristics that begin by choosing a split V ! (S; V S) according to some criterion, and then recurse on each half. What a suitable split criterion? n The cost of a split (S; V nS) is jV j w(S; V nS). Ideally, we would l... See full list on cseweb.ucsd.edu Dasgupta ’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph. Let T be a tree representing a hierarchical clustering of the graph G = (V, E). If w is a dissimilarity function on the edges E of the graph (mode is equal to \"dissimilarity\"), then the Dasgupta ’s cost is defined as: Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ... In [14], Dasgupta proposed to study the hierarchical clustering problem from an optimization point of view, and introduced an intuitive cost function for similarity-based hierarchical clustering with nice properties as well as natural approxima-tion algorithms. Jun 5, 2019 · Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical clustering is one that minimizes a particular cost function [23]. Why do we need a cost function for hierarchical clustering? The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions . To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. What is Dasgupta's cost? Dasgupta’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph . If is a dissimilarity function on the edges of the graph (mode is equal to \"dissimilarity\"), then the Dasgupta’s cost is defined as: where is the area of a node in the tree and is the lowest common ancestor of two nodes. Does Dasgupta have an objective function? We show that this set includes the objective function introduced by Dasgupta . Equipped with a suitable objective function, we analyze the performance of practical algorithms, as well as develop better and faster algorithms for hierarchical clustering. What is the difference between tree sampling divergence and Dasgupta's cost? Tree sampling divergence is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph . Dasgupta’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph. What is similarity-based hierarchical clustering? Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem , where a “good” hierarchical clustering is one that minimizes a particular cost function . What is a “good” objective function for hierarchical clustering? We take an axiomatic approach to defining “good” objective functions for both similarity- and dissimilarity-based hierarchical clustering. We characterize a set of admissible objective functions having the property that when the input admits a “natural” ground-truth hierarchical clustering, the ground-truth clustering has an optimal value. Hierarchical Clustering via Spreading Metrics Aurko Roy, Sebastian Pokutta; 18 (88):1−35, 2017. Abstract We study the cost function for hierarchical clusterings introduced by ( Dasgupta , 2016) where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1510.05043", "content": "Oct 16, 2015 · The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions . To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instances and that it admits a top-down construction ... Abstract The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instan... See full list on cseweb.ucsd.edu hierarchical clustering is a recursive partitioning of a data set into successively smaller clusters. It is represented by a rooted tree whose leaves correspond to the data points, and each of whose internal nodes represents the cluster of its descendant leaves. hierarchy of this sort has several advantages over a at clustering , which is a partitio... See full list on cseweb.ucsd.edu Letting Kn denote the complete graph on n nodes (with unit edge weights), we have E[costG(T)] = X Pr(edge between i and j in G) jleaves(T[i _ j])j fi;jg X X = q jleaves(T[i _ j])j + (Pr(edge between i and j in G) q) jleaves(T[i _ j])j fi;jg fi;jg See full list on cseweb.ucsd.edu Combining these inequalities yields the theorem statement. In summary, when data is generated from a simple partition model, the tree that minimizes cost (T) almost perfectly respects the planted clusters, misplacing at most an O(p(ln n)=n) fraction of the data. 4 Hardness of nding the optimal clustering In this section, we will see that considering... See full list on cseweb.ucsd.edu Interestingly, the problem of maximizing cost (T) is equivalent to the problem of minimizing it. To see this, let G = (V; E) be any graph with unit edge weights, and let Gc = (V; Ec) denote its complement. Pick any tree T and assess its suitability as a hierarchical clustering of G and also of Gc: costG(T) + costGc(T) = jleaves(T[i _ j])j + fi;jg2E See full list on cseweb.ucsd.edu Given a graph G with weighted edges, we have seen that it is NP-hard to nd a tree that minimizes cost (T). We now consider top-down heuristics that begin by choosing a split V ! (S; V S) according to some criterion, and then recurse on each half. What a suitable split criterion? n The cost of a split (S; V nS) is jV j w(S; V nS). Ideally, we would l... See full list on cseweb.ucsd.edu Dasgupta ’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph. Let T be a tree representing a hierarchical clustering of the graph G = (V, E). If w is a dissimilarity function on the edges E of the graph (mode is equal to \"dissimilarity\"), then the Dasgupta ’s cost is defined as: Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ... In [14], Dasgupta proposed to study the hierarchical clustering problem from an optimization point of view, and introduced an intuitive cost function for similarity-based hierarchical clustering with nice properties as well as natural approxima-tion algorithms. Jun 5, 2019 · Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical clustering is one that minimizes a particular cost function [23]. Why do we need a cost function for hierarchical clustering? The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions . To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. What is Dasgupta's cost? Dasgupta’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph . If is a dissimilarity function on the edges of the graph (mode is equal to \"dissimilarity\"), then the Dasgupta’s cost is defined as: where is the area of a node in the tree and is the lowest common ancestor of two nodes. Does Dasgupta have an objective function? We show that this set includes the objective function introduced by Dasgupta . Equipped with a suitable objective function, we analyze the performance of practical algorithms, as well as develop better and faster algorithms for hierarchical clustering. What is the difference between tree sampling divergence and Dasgupta's cost? Tree sampling divergence is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph . Dasgupta’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph. What is similarity-based hierarchical clustering? Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem , where a “good” hierarchical clustering is one that minimizes a particular cost function . What is a “good” objective function for hierarchical clustering? We take an axiomatic approach to defining “good” objective functions for both similarity- and dissimilarity-based hierarchical clustering. We characterize a set of admissible objective functions having the property that when the input admits a “natural” ground-truth hierarchical clustering, the ground-truth clustering has an optimal value. Hierarchical Clustering via Spreading Metrics Aurko Roy, Sebastian Pokutta; 18 (88):1−35, 2017. Abstract We study the cost function for hierarchical clusterings introduced by ( Dasgupta , 2016) where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters."}
+{"idx": 5, "title": "A cost function for similarity-based hierarchical clustering Hierarchical cost — Higra 0.6.12 documentation HIERARCHICAL OVERLAPPING CLUSTERING FUNCTION ALGORITHM AND ... AN IMPROVED COST FUNCTION FOR HIERARCHICAL CLUSTER TREES Hierarchical Clustering: Objective Functions and Algorithms ... [1510.05043] A cost function for similarity-based hierarchical clusterin… Hierarchical cost — Higra 0.6.7 documentation - Read the Docs Hierarchical Clustering: Objective Functions and Algorithms : Journal of Hierarchical cost — Higra 0.6.7 documentation - Read the Docs Hierarchical Clustering: Objective Functions and Algorithms : Journal of Hierarchical Clustering: Objective Functions and Algorithms : Journal of Hierarchical Clustering via Spreading Metrics - jmlr.org", "date": "", "ddg_snippet": "Abstract The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instan... See full list on cseweb.ucsd.edu hierarchical clustering is a recursive partitioning of a data set into successively smaller clusters. It is represented by a rooted tree whose leaves correspond to the data points, and each of whose internal nodes represents the cluster of its descendant leaves. hierarchy of this sort has several advantages over a at clustering , which is a partitio... See full list on cseweb.ucsd.edu Letting Kn denote the complete graph on n nodes (with unit edge weights), we have E[costG(T)] = X Pr(edge between i and j in G) jleaves(T[i _ j])j fi;jg X X = q jleaves(T[i _ j])j + (Pr(edge between i and j in G) q) jleaves(T[i _ j])j fi;jg fi;jg See full list on cseweb.ucsd.edu Combining these inequalities yields the theorem statement. In summary, when data is generated from a simple partition model, the tree that minimizes cost (T) almost perfectly respects the planted clusters, misplacing at most an O(p(ln n)=n) fraction of the data. 4 Hardness of nding the optimal clustering In this section, we will see that considering... See full list on cseweb.ucsd.edu Interestingly, the problem of maximizing cost (T) is equivalent to the problem of minimizing it. To see this, let G = (V; E) be any graph with unit edge weights, and let Gc = (V; Ec) denote its complement. Pick any tree T and assess its suitability as a hierarchical clustering of G and also of Gc: costG(T) + costGc(T) = jleaves(T[i _ j])j + fi;jg2E See full list on cseweb.ucsd.edu Given a graph G with weighted edges, we have seen that it is NP-hard to nd a tree that minimizes cost (T). We now consider top-down heuristics that begin by choosing a split V ! (S; V S) according to some criterion, and then recurse on each half. What a suitable split criterion? n The cost of a split (S; V nS) is jV j w(S; V nS). Ideally, we would l... See full list on cseweb.ucsd.edu Dasgupta ’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph. Let T be a tree representing a hierarchical clustering of the graph G = (V, E). If w is a dissimilarity function on the edges E of the graph (mode is equal to \"dissimilarity\"), then the Dasgupta ’s cost is defined as: Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ... In [14], Dasgupta proposed to study the hierarchical clustering problem from an optimization point of view, and introduced an intuitive cost function for similarity-based hierarchical clustering with nice properties as well as natural approxima-tion algorithms. Jun 5, 2019 · Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical clustering is one that minimizes a particular cost function [23]. Why do we need a cost function for hierarchical clustering? The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions . To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. What is Dasgupta's cost? Dasgupta’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph . If is a dissimilarity function on the edges of the graph (mode is equal to \"dissimilarity\"), then the Dasgupta’s cost is defined as: where is the area of a node in the tree and is the lowest common ancestor of two nodes. Does Dasgupta have an objective function? We show that this set includes the objective function introduced by Dasgupta . Equipped with a suitable objective function, we analyze the performance of practical algorithms, as well as develop better and faster algorithms for hierarchical clustering. What is the difference between tree sampling divergence and Dasgupta's cost? Tree sampling divergence is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph . Dasgupta’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph. What is similarity-based hierarchical clustering? Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem , where a “good” hierarchical clustering is one that minimizes a particular cost function . What is a “good” objective function for hierarchical clustering? We take an axiomatic approach to defining “good” objective functions for both similarity- and dissimilarity-based hierarchical clustering. We characterize a set of admissible objective functions having the property that when the input admits a “natural” ground-truth hierarchical clustering, the ground-truth clustering has an optimal value. Hierarchical Clustering via Spreading Metrics Aurko Roy, Sebastian Pokutta; 18 (88):1−35, 2017. Abstract We study the cost function for hierarchical clusterings introduced by ( Dasgupta , 2016) where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters.", "subpage_snippet": "", "source": "cseweb.ucsd.edu", "link": "https://cseweb.ucsd.edu/~dasgupta/papers/hier-cost.pdf", "content": "Abstract The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instan... See full list on cseweb.ucsd.edu hierarchical clustering is a recursive partitioning of a data set into successively smaller clusters. It is represented by a rooted tree whose leaves correspond to the data points, and each of whose internal nodes represents the cluster of its descendant leaves. hierarchy of this sort has several advantages over a at clustering , which is a partitio... See full list on cseweb.ucsd.edu Letting Kn denote the complete graph on n nodes (with unit edge weights), we have E[costG(T)] = X Pr(edge between i and j in G) jleaves(T[i _ j])j fi;jg X X = q jleaves(T[i _ j])j + (Pr(edge between i and j in G) q) jleaves(T[i _ j])j fi;jg fi;jg See full list on cseweb.ucsd.edu Combining these inequalities yields the theorem statement. In summary, when data is generated from a simple partition model, the tree that minimizes cost (T) almost perfectly respects the planted clusters, misplacing at most an O(p(ln n)=n) fraction of the data. 4 Hardness of nding the optimal clustering In this section, we will see that considering... See full list on cseweb.ucsd.edu Interestingly, the problem of maximizing cost (T) is equivalent to the problem of minimizing it. To see this, let G = (V; E) be any graph with unit edge weights, and let Gc = (V; Ec) denote its complement. Pick any tree T and assess its suitability as a hierarchical clustering of G and also of Gc: costG(T) + costGc(T) = jleaves(T[i _ j])j + fi;jg2E See full list on cseweb.ucsd.edu Given a graph G with weighted edges, we have seen that it is NP-hard to nd a tree that minimizes cost (T). We now consider top-down heuristics that begin by choosing a split V ! (S; V S) according to some criterion, and then recurse on each half. What a suitable split criterion? n The cost of a split (S; V nS) is jV j w(S; V nS). Ideally, we would l... See full list on cseweb.ucsd.edu Dasgupta ’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph. Let T be a tree representing a hierarchical clustering of the graph G = (V, E). If w is a dissimilarity function on the edges E of the graph (mode is equal to \"dissimilarity\"), then the Dasgupta ’s cost is defined as: Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ... In [14], Dasgupta proposed to study the hierarchical clustering problem from an optimization point of view, and introduced an intuitive cost function for similarity-based hierarchical clustering with nice properties as well as natural approxima-tion algorithms. Jun 5, 2019 · Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical clustering is one that minimizes a particular cost function [23]. Why do we need a cost function for hierarchical clustering? The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions . To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. What is Dasgupta's cost? Dasgupta’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph . If is a dissimilarity function on the edges of the graph (mode is equal to \"dissimilarity\"), then the Dasgupta’s cost is defined as: where is the area of a node in the tree and is the lowest common ancestor of two nodes. Does Dasgupta have an objective function? We show that this set includes the objective function introduced by Dasgupta . Equipped with a suitable objective function, we analyze the performance of practical algorithms, as well as develop better and faster algorithms for hierarchical clustering. What is the difference between tree sampling divergence and Dasgupta's cost? Tree sampling divergence is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph . Dasgupta’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph. What is similarity-based hierarchical clustering? Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem , where a “good” hierarchical clustering is one that minimizes a particular cost function . What is a “good” objective function for hierarchical clustering? We take an axiomatic approach to defining “good” objective functions for both similarity- and dissimilarity-based hierarchical clustering. We characterize a set of admissible objective functions having the property that when the input admits a “natural” ground-truth hierarchical clustering, the ground-truth clustering has an optimal value. Hierarchical Clustering via Spreading Metrics Aurko Roy, Sebastian Pokutta; 18 (88):1−35, 2017. Abstract We study the cost function for hierarchical clusterings introduced by ( Dasgupta , 2016) where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters."}
+{"idx": 6, "title": "Hierarchical cost — Higra 0.6.12 documentation", "date": "", "ddg_snippet": "Dasgupta ’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph. Let T be a tree representing a hierarchical clustering of the graph G = (V, E). If w is a dissimilarity function on the edges E of the graph (mode is equal to \"dissimilarity\"), then the Dasgupta ’s cost is defined as:", "subpage_snippet": "", "source": "higra.readthedocs.io", "link": "https://higra.readthedocs.io/en/stable/python/hierarchical_cost.html", "content": "Dasgupta ’s cost is an unsupervised measure of the quality of a hierarchical clustering of an edge weighted graph. Let T be a tree representing a hierarchical clustering of the graph G = (V, E). If w is a dissimilarity function on the edges E of the graph (mode is equal to \"dissimilarity\"), then the Dasgupta ’s cost is defined as:"}
+{"idx": 7, "title": "HIERARCHICAL OVERLAPPING CLUSTERING FUNCTION ALGORITHM AND ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 8, "title": "AN IMPROVED COST FUNCTION FOR HIERARCHICAL CLUSTER TREES", "date": "", "ddg_snippet": "In [14], Dasgupta proposed to study the hierarchical clustering problem from an optimization point of view, and introduced an intuitive cost function for similarity-based hierarchical clustering with nice properties as well as natural approxima-tion algorithms.", "subpage_snippet": "", "source": "jocg.org", "link": "https://jocg.org/index.php/jocg/article/download/3101/2837/8438", "content": "In [14], Dasgupta proposed to study the hierarchical clustering problem from an optimization point of view, and introduced an intuitive cost function for similarity-based hierarchical clustering with nice properties as well as natural approxima-tion algorithms."}
+{"idx": 9, "title": "Hierarchical Clustering: Objective Functions and Algorithms ...", "date": "", "ddg_snippet": "Jun 5, 2019 · Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical clustering is one that minimizes a particular cost function [23].", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3321386", "content": "Jun 5, 2019 · Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical clustering is one that minimizes a particular cost function [23]."}
diff --git a/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_promptable_concept_proposal_methods.jsonl b/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_promptable_concept_proposal_methods.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5a9cf6538f92a260c03ef35564c3d9ba4c5d27aa
--- /dev/null
+++ b/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_promptable_concept_proposal_methods.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Moreover, concept proposal steering using the promptable Grounding Dino does not result in performance differences compared to steering-free methods such as SAM2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576v2", "content": "Moreover, concept proposal steering using the promptable Grounding Dino does not result in performance differences compared to steering-free methods such as SAM2."}
+{"idx": 1, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "concept_extraction/: Scripts and modules for extracting concepts . dcbm_training/: Code for training the DCBM model . data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. utils/: Helper scripts for running experiments.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "concept_extraction/: Scripts and modules for extracting concepts . dcbm_training/: Code for training the DCBM model . data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. utils/: Helper scripts for running experiments."}
+{"idx": 2, "title": "Hybrid Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) provide an interpretable framework for neural networks by mapping visual features to predefined, human-understandable concepts . However, the application of CBMs is often constrained by insufficient concept annotations.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Hybrid_Concept_Bottleneck_Models_CVPR_2025_paper.html", "content": "Concept Bottleneck Models (CBMs) provide an interpretable framework for neural networks by mapping visual features to predefined, human-understandable concepts . However, the application of CBMs is often constrained by insufficient concept annotations."}
+{"idx": 3, "title": "Diverse Concept Proposals for Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept bottleneck models are interpretable predictive models that are often used in domains where model trust is a key priority, such as healthcare. They identify a small number of human-interpretable concepts in the data , which they then use to make predictions. Learning relevant concepts from data proves to be a challenging task. The most predictive concepts may not align with expert ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.18059", "content": "Concept bottleneck models are interpretable predictive models that are often used in domains where model trust is a key priority, such as healthcare. They identify a small number of human-interpretable concepts in the data , which they then use to make predictions. Learning relevant concepts from data proves to be a challenging task. The most predictive concepts may not align with expert ..."}
+{"idx": 4, "title": "Concept Bottleneck Models - PMLR", "date": "", "ddg_snippet": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a", "content": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 5, "title": "kkzhang95/Awesome_Concept_Bottleneck_Models - GitHub", "date": "", "ddg_snippet": "A comprehensive survey of Concept Bottleneck Models (CBM). CBMs typically involve a layer preceding the final fully connected classifier, where each neuron corresponds to a concept that can be interpreted by humans. CBMs also show advantages in improving accuracy through human intervention during testing. A case for CBM family methods . (Figure from Incremental Residual Concept Bottleneck Models )", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kkzhang95/Awesome_Concept_Bottleneck_Models", "content": "A comprehensive survey of Concept Bottleneck Models (CBM). CBMs typically involve a layer preceding the final fully connected classifier, where each neuron corresponds to a concept that can be interpreted by humans. CBMs also show advantages in improving accuracy through human intervention during testing. A case for CBM family methods . (Figure from Incremental Residual Concept Bottleneck Models )"}
+{"idx": 6, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models , allowing each image to generate multiple concepts across different granularities.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BdO4R6XxUH", "content": "We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models , allowing each image to generate multiple concepts across different granularities."}
+{"idx": 7, "title": "PDF Language Guided Concept Bottleneck Models for Interpretable Continual ...", "date": "", "ddg_snippet": "Our approach builds upon Concept Bottleneck Models , making the model's inter-pretability inherently dependent on the quality of the se-lected concepts . We have observed that the concepts gener-ated by LLMs sometimes include non- visual descriptions, despite the use of appearance-related prompts.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Language_Guided_Concept_Bottleneck_Models_for_Interpretable_Continual_Learning_CVPR_2025_paper.pdf", "content": "Our approach builds upon Concept Bottleneck Models , making the model's inter-pretability inherently dependent on the quality of the se-lected concepts . We have observed that the concepts gener-ated by LLMs sometimes include non- visual descriptions, despite the use of appearance-related prompts."}
+{"idx": 8, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Data-efficient CBMs are proposed, which reduce the need for large sample sizes during concept generation while preserving interpretability, and enhance adaptability to new domains by leveraging dataset-specific concepts instead of predefined ones. Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/DCBM:-Data-Efficient-Visual-Concept-Bottleneck-Knab-Prasse/2f62a1821bbf6b7b8dbc2a11fd3d900e5ebd5fe9", "content": "Data-efficient CBMs are proposed, which reduce the need for large sample sizes during concept generation while preserving interpretability, and enhance adaptability to new domains by leveraging dataset-specific concepts instead of predefined ones. Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However ..."}
+{"idx": 9, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data-efficient Visual CBMs (DCBM) to improve interpretability in data -scarce domains. DCBMs use segmentation and detection foundation models to extract image regions as visual concept proposals . This approach generates multiple concepts at different levels of granularity from a single image, allowing to create a meaningful concept bank even in data -scarce settings. Our design ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "We propose Data-efficient Visual CBMs (DCBM) to improve interpretability in data -scarce domains. DCBMs use segmentation and detection foundation models to extract image regions as visual concept proposals . This approach generates multiple concepts at different levels of granularity from a single image, allowing to create a meaningful concept bank even in data -scarce settings. Our design ..."}
diff --git a/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_promptable_year_2024.jsonl b/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_promptable_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f3039e89ebe6f5e6acea3c89619f468fdf0506a4
--- /dev/null
+++ b/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_promptable_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "concept _extraction/: Scripts and modules for extracting concepts . dcbm_training/: Code for training the DCBM model . data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. utils/: Helper scripts for running experiments.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "concept _extraction/: Scripts and modules for extracting concepts . dcbm_training/: Code for training the DCBM model . data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. utils/: Helper scripts for running experiments."}
+{"idx": 1, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effective-ness in data -sparse scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576v2", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effective-ness in data -sparse scenarios."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models :: MPG.PuRe", "date": "", "ddg_snippet": "scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "pure.mpg.de", "link": "https://pure.mpg.de/pubman/faces/ViewItemFullPage.jsp?itemId=item_3636912_1&view=ACTIONS", "content": "scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 3, "title": "Hybrid Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) provide an interpretable framework for neural networks by mapping visual features to predefined, human-understandable concepts . However, the application of CBMs is often constrained by insufficient concept annotations.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Hybrid_Concept_Bottleneck_Models_CVPR_2025_paper.html", "content": "Concept Bottleneck Models (CBMs) provide an interpretable framework for neural networks by mapping visual features to predefined, human-understandable concepts . However, the application of CBMs is often constrained by insufficient concept annotations."}
+{"idx": 4, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "May 1, 2025 · We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models , allowing each image to generate multiple concepts across different granularities.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BdO4R6XxUH", "content": "May 1, 2025 · We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models , allowing each image to generate multiple concepts across different granularities."}
+{"idx": 5, "title": "Concept Bottleneck Models - PMLR", "date": "", "ddg_snippet": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a", "content": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 6, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data -eficient CBMs (DCBMs), which reduce the need for large sample sizes during concept gener-ation while preserving interpretability. DCBMs define concepts as image regions detected by seg-mentation or detection foundation models , allow-ing each image to generate multiple concepts across different granularities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576", "content": "We propose Data -eficient CBMs (DCBMs), which reduce the need for large sample sizes during concept gener-ation while preserving interpretability. DCBMs define concepts as image regions detected by seg-mentation or detection foundation models , allow-ing each image to generate multiple concepts across different granularities."}
+{"idx": 7, "title": "DCBM: Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .While other data - efficient methods exist, e.g., prompt -tuned CLIP, we focus exclusively on interpretable models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .While other data - efficient methods exist, e.g., prompt -tuned CLIP, we focus exclusively on interpretable models ."}
+{"idx": 8, "title": "DCBM: Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/DCBM:-Data-Efficient-Visual-Concept-Bottleneck-Models-4be352f8-e37e-432a-9849-dc4f3c8c586f", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 9, "title": "Post-hoc Concept Bottleneck Models | OpenReview", "date": "", "ddg_snippet": "Keywords: concepts , interpretability, concept bottleneck models , model editing.When concept annotations are not available on the training data , we show that PCBM can transfer concepts from other datasets or from natural language descriptions of concepts via multimodal models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nA5AZ8CEyow", "content": "Keywords: concepts , interpretability, concept bottleneck models , model editing.When concept annotations are not available on the training data , we show that PCBM can transfer concepts from other datasets or from natural language descriptions of concepts via multimodal models ."}
diff --git a/data/sampled_jsons/DeepSeek_sitearxiv.orghtml2406.14532v1_year_2024.jsonl b/data/sampled_jsons/DeepSeek_sitearxiv.orghtml2406.14532v1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..36c98469bcc73280e37ffdd231fd40f633260536
--- /dev/null
+++ b/data/sampled_jsons/DeepSeek_sitearxiv.orghtml2406.14532v1_year_2024.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — Learning theory dictates that the SFT policy trained on more SFT data (e.g., 1.5M for DeepSeek -Math [5] ) would have improved math reasoning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — Learning theory dictates that the SFT policy trained on more SFT data (e.g., 1.5M for DeepSeek -Math [5] ) would have improved math reasoning ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/DeepWiki_CVE-Bench_evaluation_'Insufficient_Exploration'_definition_year_2023-2024.jsonl b/data/sampled_jsons/DeepWiki_CVE-Bench_evaluation_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3fc2e8a61df3ef5ca8f074eee1e64aed61b4fd96
--- /dev/null
+++ b/data/sampled_jsons/DeepWiki_CVE-Bench_evaluation_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DeepWiki | AI documentation you can talk to, for every repo", "date": "", "ddg_snippet": "What is DeepWiki? DeepWiki provides up-to-date documentation you can talk to, for every repo in the world. Think Deep Research for GitHub.", "subpage_snippet": "", "source": "deepwiki.org", "link": "https://deepwiki.org/", "content": "What is DeepWiki? DeepWiki provides up-to-date documentation you can talk to, for every repo in the world. Think Deep Research for GitHub."}
+{"idx": 1, "title": "deepskies/ DeepWiki | DeepWiki", "date": "", "ddg_snippet": "Apr 28, 2025 · DeepWiki serves as the central knowledge repository for our research group, providing comprehensive software resources and computational guidance. This wiki consolidates …", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/deepskies/DeepWiki", "content": "Apr 28, 2025 · DeepWiki serves as the central knowledge repository for our research group, providing comprehensive software resources and computational guidance. This wiki consolidates …"}
+{"idx": 2, "title": "GitHub - AsyncFuncAI/ deepwiki -open: Open Source DeepWiki : AI …", "date": "", "ddg_snippet": "DeepWiki is my own implementation attempt of DeepWiki, automatically creates beautiful, interactive wikis for any GitHub, GitLab, or BitBucket repository! Just enter a repo name, and …", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AsyncFuncAI/deepwiki-open", "content": "DeepWiki is my own implementation attempt of DeepWiki, automatically creates beautiful, interactive wikis for any GitHub, GitLab, or BitBucket repository! Just enter a repo name, and …"}
+{"idx": 3, "title": "DeepWiki : AI docs for any repo - Cognition", "date": "", "ddg_snippet": "May 5, 2025 · Now, we're launching DeepWiki, the free public version of Devin Wiki and Devin Search. Visit the DeepWiki for any repo by replacing github.com with deepwiki.com in the URL.", "subpage_snippet": "", "source": "cognition.ai", "link": "https://cognition.ai/blog/deepwiki", "content": "May 5, 2025 · Now, we're launching DeepWiki, the free public version of Devin Wiki and Devin Search. Visit the DeepWiki for any repo by replacing github.com with deepwiki.com in the URL."}
+{"idx": 4, "title": "DeepWiki : Why I Open-Sourced an AI-Powered Wiki Generator", "date": "", "ddg_snippet": "Apr 30, 2025 · DeepWiki started as a closed-source project by DevinAI, aiming to solve this universal developer pain point. The idea was simple but powerful: automatically generate …", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sjng/deepwiki-why-i-open-sourced-an-ai-powered-wiki-generator-b67b624e4679", "content": "Apr 30, 2025 · DeepWiki started as a closed-source project by DevinAI, aiming to solve this universal developer pain point. The idea was simple but powerful: automatically generate …"}
+{"idx": 5, "title": "DeepWiki : Your AI-Powered Guide to GitHub Repositories", "date": "", "ddg_snippet": "Jul 17, 2025 · DeepWiki, built by the team behind the AI coding assistant Devin, turns any public GitHub repository into an interactive, encyclopedia-like wiki. Think of it as a super-smart librarian …", "subpage_snippet": "", "source": "apidog.com", "link": "https://apidog.com/blog/deepwiki/", "content": "Jul 17, 2025 · DeepWiki, built by the team behind the AI coding assistant Devin, turns any public GitHub repository into an interactive, encyclopedia-like wiki. Think of it as a super-smart librarian …"}
+{"idx": 6, "title": "DeepWiki : AI-Powered Encyclopedia of GitHub Code Repositories …", "date": "", "ddg_snippet": "Apr 27, 2025 · DeepWiki is completely free for open-source projects and requires no registration. For private projects, developers need to register an account on the DeepWiki official website to …", "subpage_snippet": "", "source": "www.aibase.com", "link": "https://www.aibase.com/news/17547", "content": "Apr 27, 2025 · DeepWiki is completely free for open-source projects and requires no registration. For private projects, developers need to register an account on the DeepWiki official website to …"}
+{"idx": 7, "title": "deepwiki", "date": "", "ddg_snippet": "Apr 3, 2019 · DeepWiki provides up-to-date documentation you can talk to, for deepwiki. Think Deep Research for GitHub - powered by Devin.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/deepwiki", "content": "Apr 3, 2019 · DeepWiki provides up-to-date documentation you can talk to, for deepwiki. Think Deep Research for GitHub - powered by Devin."}
+{"idx": 8, "title": "Transforms GitHub Repos into Detailed Documentation - DeepWiki", "date": "", "ddg_snippet": "DeepWiki is a free AI tool that turns any GitHub repository into a structured, interactive documentation hub. Instead of slogging through scattered READMEs and outdated comments, …", "subpage_snippet": "", "source": "www.scriptbyai.com", "link": "https://www.scriptbyai.com/github-deep-wiki/", "content": "DeepWiki is a free AI tool that turns any GitHub repository into a structured, interactive documentation hub. Instead of slogging through scattered READMEs and outdated comments, …"}
+{"idx": 9, "title": "What is DeepWiki ? - codersera.com", "date": "", "ddg_snippet": "DeepWiki is an advanced AI-powered platform designed to revolutionize how developers and researchers interact with code repositories, particularly those hosted on GitHub.", "subpage_snippet": "", "source": "codersera.com", "link": "https://codersera.com/blog/what-is-deepwiki", "content": "DeepWiki is an advanced AI-powered platform designed to revolutionize how developers and researchers interact with code repositories, particularly those hosted on GitHub."}
diff --git a/data/sampled_jsons/DeepfakeBench_framework_backbone_models_Xception_EfficientNet-B4_implementation.jsonl b/data/sampled_jsons/DeepfakeBench_framework_backbone_models_Xception_EfficientNet-B4_implementation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..30cc59facdf0cb7e0911254fb2b22338e13402ef
--- /dev/null
+++ b/data/sampled_jsons/DeepfakeBench_framework_backbone_models_Xception_EfficientNet-B4_implementation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DeepfakeBench/README.md at main · SCLBD/DeepfakeBench · GitHub", "date": "", "ddg_snippet": "Integrated Framework : DeepfakeBench offers an integrated framework for the implementation of state-of-the-art detection methods. Standardized Evaluations: DeepfakeBench introduces standardized evaluation metrics and protocols to enhance the transparency and reproducibility of performance evaluations.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SCLBD/DeepfakeBench/blob/main/README.md", "content": "Integrated Framework : DeepfakeBench offers an integrated framework for the implementation of state-of-the-art detection methods. Standardized Evaluations: DeepfakeBench introduces standardized evaluation metrics and protocols to enhance the transparency and reproducibility of performance evaluations."}
+{"idx": 1, "title": "Naive Detectors | SCLBD/DeepfakeBench | DeepWiki", "date": "", "ddg_snippet": "Implemented Models The DeepfakeBench framework implements several naive detector models : EfficientNet-B4 A detector based on EfficientNet-B4 backbone , which uses compound scaling to balance network depth, width, and resolution. This model is registered in the DETECTOR registry as efficientnetb4 . Reference: Tan, M., & Le, Q. (2019).", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/SCLBD/DeepfakeBench/4.5-naive-detectors", "content": "Implemented Models The DeepfakeBench framework implements several naive detector models : EfficientNet-B4 A detector based on EfficientNet-B4 backbone , which uses compound scaling to balance network depth, width, and resolution. This model is registered in the DETECTOR registry as efficientnetb4 . Reference: Tan, M., & Le, Q. (2019)."}
+{"idx": 2, "title": "[2307.01426] DeepfakeBench: A Comprehensive Benchmark of ...", "date": "", "ddg_snippet": "To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench , which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation , and 3) standardized evaluation metrics and ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2307.01426", "content": "To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench , which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation , and 3) standardized evaluation metrics and ..."}
+{"idx": 3, "title": "Deepfake Detection Using EfficientNet and XceptionNet", "date": "", "ddg_snippet": "The increasing prevalence of manipulated media, particularly deepfake videos, poses significant challenges in distinguishing real from fake content. This paper addresses the issue of detecting deepfake videos using advanced CNN architectures such as EfficientNet-B4 and XceptionNet. The FF++ and Celeb-DF (v2) datasets are used to compare real and fake videos. The methodology involves ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10391114", "content": "The increasing prevalence of manipulated media, particularly deepfake videos, poses significant challenges in distinguishing real from fake content. This paper addresses the issue of detecting deepfake videos using advanced CNN architectures such as EfficientNet-B4 and XceptionNet. The FF++ and Celeb-DF (v2) datasets are used to compare real and fake videos. The methodology involves ..."}
+{"idx": 4, "title": "DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection", "date": "", "ddg_snippet": "Naive detectors (like Xception and EfficientNet-B4 ) performed surprisingly well compared to more complex methods under standardized conditions. Data augmentation significantly impacted detector performance, highlighting the need for further research in this area. The choice of backbone architecture also played a crucial role in detection ...", "subpage_snippet": "", "source": "deepfake-total.com", "link": "https://deepfake-total.com/related_work/2307.01426", "content": "Naive detectors (like Xception and EfficientNet-B4 ) performed surprisingly well compared to more complex methods under standardized conditions. Data augmentation significantly impacted detector performance, highlighting the need for further research in this area. The choice of backbone architecture also played a crucial role in detection ..."}
+{"idx": 5, "title": "Comparison of ResNet, EfficientNet, and Xception ...", "date": "", "ddg_snippet": "A comparative table of ResNet, EfficientNet , and Xception models is presented below, highlighting their key characteristics, features, and advantages in the context of video data processing.", "subpage_snippet": "", "source": "ceur-ws.org", "link": "https://ceur-ws.org/Vol-3899/paper3.pdf", "content": "A comparative table of ResNet, EfficientNet , and Xception models is presented below, highlighting their key characteristics, features, and advantages in the context of video data processing."}
+{"idx": 6, "title": "Deepfake Detection Using EfficientNet and XceptionNet", "date": "", "ddg_snippet": "Training Model Setup This part describes the training process for the deepfake detection model using the EfficientNet-B4 architecture. The mean and standard deviation are used to normalize the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Salma-Mohamed-Elgayar/publication/381522654_Deepfake_Detection_Using_EfficientNet_and_XceptionNet/links/6672cb891dec0c3c6f8fe320/Deepfake-Detection-Using-EfficientNet-and-XceptionNet.pdf?origin=publication_detail", "content": "Training Model Setup This part describes the training process for the deepfake detection model using the EfficientNet-B4 architecture. The mean and standard deviation are used to normalize the ..."}
+{"idx": 7, "title": "Wavelet-Driven Generalizable Framework for Deepfake Face ...", "date": "", "ddg_snippet": "by LB Baru · 2025 · Cited by 8 — Deepfakebench : A comprehensive benchmark of deepfake detection. Advances in NeurIPS, 36,. 2024. [34] Wenyuan Yang, Xiaoyu Zhou, Zhikai Chen, Bofei Guo,.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025W/MAPA/papers/Baru_Wavelet-Driven_Generalizable_Framework_for_Deepfake_Face_Forgery_Detection_WACVW_2025_paper.pdf", "content": "by LB Baru · 2025 · Cited by 8 — Deepfakebench : A comprehensive benchmark of deepfake detection. Advances in NeurIPS, 36,. 2024. [34] Wenyuan Yang, Xiaoyu Zhou, Zhikai Chen, Bofei Guo,."}
+{"idx": 8, "title": "A comprehensive benchmark of deepfake detection", "date": "", "ddg_snippet": "DeepfakeBench presents the first comprehensive benchmark for deepfake detection, resolving the issue of lack of standardization and uniformity in this field.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SCLBD/DeepfakeBench", "content": "DeepfakeBench presents the first comprehensive benchmark for deepfake detection, resolving the issue of lack of standardization and uniformity in this field."}
+{"idx": 9, "title": "A Comprehensive Benchmark of Deepfake Detection", "date": "", "ddg_snippet": "by Z Yan · 2023 · Cited by 135 — Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2307.01426", "content": "by Z Yan · 2023 · Cited by 135 — Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of ..."}
diff --git a/data/sampled_jsons/Definition_3.2_Directionality_Score_self-attention_Saponati.jsonl b/data/sampled_jsons/Definition_3.2_Directionality_Score_self-attention_Saponati.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e7b0817b43a839ca048340ad6a322b014e3ae7b0
--- /dev/null
+++ b/data/sampled_jsons/Definition_3.2_Directionality_Score_self-attention_Saponati.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10927v1", "content": "Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance."}
+{"idx": 1, "title": "The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "Using this frame-work, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance.", "subpage_snippet": "", "source": "digitalcollection.zhaw.ch", "link": "https://digitalcollection.zhaw.ch/bitstreams/f8b62430-ed8e-42dc-86e3-c45e4f9805aa/download", "content": "Using this frame-work, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance."}
+{"idx": 2, "title": "[2502.10927] The underlying structures of self-attention ...", "date": "", "ddg_snippet": "Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.10927", "content": "Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance."}
+{"idx": 3, "title": "The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "In this section, we introduce a novel framework that links the self - attention matrices to bilinear forms, enabling us to analyze how an objective function influences their structure. This approach reveals fundamental patterns in that are not apparent when examining and separately.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44452/paper", "content": "In this section, we introduce a novel framework that links the self - attention matrices to bilinear forms, enabling us to analyze how an objective function influences their structure. This approach reveals fundamental patterns in that are not apparent when examining and separately."}
+{"idx": 4, "title": "The underlying structures of self - attention : symmetry, directionality ...", "date": "", "ddg_snippet": "Properties of the directionality score in Definition 3 . 2 and related proofs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10927v2", "content": "Properties of the directionality score in Definition 3 . 2 and related proofs."}
+{"idx": 5, "title": "(PDF) The underlying structures of self - attention : symmetry...", "date": "", "ddg_snippet": "Properties of the directionality score in Definition 3 . 2 and related proofs. The score dwe introduce in Section 3quantifies the directional bias of a square matrix Mby comparing the. total norm of the ”outliers” rows and columns, that is, that are higher than γtimes the standard deviations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389091272_The_underlying_structures_of_self-attention_symmetry_directionality_and_emergent_dynamics_in_Transformer_training", "content": "Properties of the directionality score in Definition 3 . 2 and related proofs. The score dwe introduce in Section 3quantifies the directional bias of a square matrix Mby comparing the. total norm of the ”outliers” rows and columns, that is, that are higher than γtimes the standard deviations."}
+{"idx": 6, "title": "ICML Poster The underlying structures of self-attention", "date": "", "ddg_snippet": "Matteo Saponati · Pascal J. ... with convergence from the positive side. A.7. Properties of the directionality score in Definition 3.2 and related proofs.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44452", "content": "Matteo Saponati · Pascal J. ... with convergence from the positive side. A.7. Properties of the directionality score in Definition 3.2 and related proofs."}
+{"idx": 7, "title": "The underlying structures of self-attention", "date": "", "ddg_snippet": "At initialization, the symmetry and directionality score of the matrix Wqk at any layer is zero (see Definition 3.1 and Definition 3.2 and related Appendix A.6.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44452/paper?_c=eyJ2IjoxLCJyZWxhdGVkIjpbImNvZGUiLCJyZWZlcmVuY2VzIiwiY29uZmVyZW5jZSJdfQ==", "content": "At initialization, the symmetry and directionality score of the matrix Wqk at any layer is zero (see Definition 3.1 and Definition 3.2 and related Appendix A.6."}
+{"idx": 8, "title": "Thilo Stadelmann 1,2,3 , and Benjamin Grewe 1,4", "date": "", "ddg_snippet": "The underlying structures of self - attention : symmetry, directionality , and emergent dynamics in Transformer training. Matteo Saponati 1,*, Pascal Sager1,2,*, Pau Vilimelis Aceituno1, Thilo Stadelmann1,2,3, and Benjamin Grewe1,4.", "subpage_snippet": "", "source": "stdm.github.io", "link": "https://stdm.github.io/downloads/papers/ICML_2025.pdf", "content": "The underlying structures of self - attention : symmetry, directionality , and emergent dynamics in Transformer training. Matteo Saponati 1,*, Pascal Sager1,2,*, Pau Vilimelis Aceituno1, Thilo Stadelmann1,2,3, and Benjamin Grewe1,4."}
diff --git a/data/sampled_jsons/Dehghani_et_al._(2019)_Universal_Transformers_abstract.jsonl b/data/sampled_jsons/Dehghani_et_al._(2019)_Universal_Transformers_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ad774970e92814c63bf8df33938e53272916d084
--- /dev/null
+++ b/data/sampled_jsons/Dehghani_et_al._(2019)_Universal_Transformers_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[PDF] Universal Transformers | Semantic Scholar", "date": "", "ddg_snippet": "The Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses issues of parallelizability and global receptive field, is proposed. Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Universal-Transformers-Dehghani-Gouws/ac4dafdef1d2b685b7f28a11837414573d39ff4e", "content": "The Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses issues of parallelizability and global receptive field, is proposed. Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for ..."}
+{"idx": 1, "title": "Universal Transformers - Google Research", "date": "", "ddg_snippet": "Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/universal-transformers/", "content": "Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset."}
+{"idx": 2, "title": "Universal Transformers | Request PDF - ResearchGate", "date": "", "ddg_snippet": "Request PDF | Universal Transformers | Self-attentive feed-forward sequence models have been shown to achieve impressive results on sequence modeling tasks, thereby presenting a... | Find, read ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/326343192_Universal_Transformers", "content": "Request PDF | Universal Transformers | Self-attentive feed-forward sequence models have been shown to achieve impressive results on sequence modeling tasks, thereby presenting a... | Find, read ..."}
+{"idx": 3, "title": "\"Universal Transformers.\" - dblp", "date": "", "ddg_snippet": "[+] [-] \" Universal Transformers .\" Mostafa Dehghani et al. (2019) Dagstuhl > Home [-] Details and statistics", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/DehghaniGVUK19", "content": "[+] [-] \" Universal Transformers .\" Mostafa Dehghani et al. (2019) Dagstuhl > Home [-] Details and statistics"}
+{"idx": 4, "title": "Universal Transformers | Awesome LLM Papers", "date": "", "ddg_snippet": "Universal Transformers Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser . Arxiv 2018 - 414 citations [Paper] Datasets Ethics & Fairness Model Architecture Time Series Training Techniques Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks ...", "subpage_snippet": "", "source": "awesome-llm-papers.github.io", "link": "https://awesome-llm-papers.github.io/publications/dehghani2018universal/", "content": "Universal Transformers Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser . Arxiv 2018 - 414 citations [Paper] Datasets Ethics & Fairness Model Architecture Time Series Training Techniques Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks ..."}
+{"idx": 5, "title": "Universal Transformers", "date": "", "ddg_snippet": "In each recurrent step, the Universal Transformer iteratively refines its representations for all symbols in the sequence in parallel using a self-attention mechanism (Parikh et al ., 2016; Lin et al ., 2017), followed by a transformation (shared across all positions and time-steps) consisting of a depth-wise separable convolution (Chollet, 2016 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1807.03819", "content": "In each recurrent step, the Universal Transformer iteratively refines its representations for all symbols in the sequence in parallel using a self-attention mechanism (Parikh et al ., 2016; Lin et al ., 2017), followed by a transformation (shared across all positions and time-steps) consisting of a depth-wise separable convolution (Chollet, 2016 ..."}
+{"idx": 6, "title": "PDF Universal Transformers - Semantic Scholar", "date": "", "ddg_snippet": "Universal Transformers Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser Google Brain", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/b28c/84b01e47a6c2badd6f258e0c467042271bbb.pdf", "content": "Universal Transformers Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser Google Brain"}
+{"idx": 7, "title": "(PDF) Universal Transformers - Academia.edu", "date": "", "ddg_snippet": "Universal Transformers Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser Google Brain fSelf-attentive feed-forward sequence models Achieve impressive results on sequence modeling tasks. A compelling alternative to RNNs Parallelization Source: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Transformers Duis non erat sem Universal fRecurrent Models Source ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/74420506/Universal_Transformers", "content": "Universal Transformers Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser Google Brain fSelf-attentive feed-forward sequence models Achieve impressive results on sequence modeling tasks. A compelling alternative to RNNs Parallelization Source: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Transformers Duis non erat sem Universal fRecurrent Models Source ..."}
+{"idx": 8, "title": "[1807.03819] Universal Transformers - arXiv.org", "date": "", "ddg_snippet": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1807.03819", "content": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as ..."}
+{"idx": 9, "title": "Universal Transformers - NASA/ADS", "date": "", "ddg_snippet": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2018arXiv180703819D/abstract", "content": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as ..."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_Pixel_Processor_Arrays_FAST_translate_motion_performance.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_Pixel_Processor_Arrays_FAST_translate_motion_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..22b2ad45fc23d0c62e31ab6b565222301fcf8030
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_Pixel_Processor_Arrays_FAST_translate_motion_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "Our in - pixel approach for point - feature detection and tracking is designed specifically for the PPA’s architec-ture, providing high pixel - processor compute resource util-isation, and minimizing data transfer between sensor and external processing .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "Our in - pixel approach for point - feature detection and tracking is designed specifically for the PPA’s architec-ture, providing high pixel - processor compute resource util-isation, and minimizing data transfer between sensor and external processing ."}
+{"idx": 1, "title": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor ...", "date": "", "ddg_snippet": "We introduce a Descriptor - In - Pixel paradigm, in which a feature descriptor is held within the memory of each pixel - processor . The PPA’s architecture enables the response of every processor ’s descriptor, upon the current image, to be computed in parallel.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11092646/", "content": "We introduce a Descriptor - In - Pixel paradigm, in which a feature descriptor is held within the memory of each pixel - processor . The PPA’s architecture enables the response of every processor ’s descriptor, upon the current image, to be computed in parallel."}
+{"idx": 2, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "This is the first work performing point - feature detection and tracking entirely \" in - pixel \".This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA).", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays@CVPR2025@CVF", "content": "This is the first work performing point - feature detection and tracking entirely \" in - pixel \".This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA)."}
+{"idx": 3, "title": "GitHub - wangxiao5791509/Single_Object_ Tracking _Paper_List...", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays Laurie Bose · Piotr Dudek · Jianing Chen.\"The Benefits of Evaluating Tracker Performance Using Pixel -Wise Segmentations.\"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wangxiao5791509/Single_Object_Tracking_Paper_List", "content": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays Laurie Bose · Piotr Dudek · Jianing Chen.\"The Benefits of Evaluating Tracker Performance Using Pixel -Wise Segmentations.\""}
+{"idx": 4, "title": "Descriptor - In _ Pixel", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays . Point - Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power.", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays . Point - Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power."}
+{"idx": 5, "title": "(PDF) Pose tracking from natural features on mobile phones", "date": "", "ddg_snippet": "In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20Hz for natural feature tracking from textured planar targets on current-generation phones.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221221008_Pose_tracking_from_natural_features_on_mobile_phones", "content": "In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20Hz for natural feature tracking from textured planar targets on current-generation phones."}
+{"idx": 6, "title": "Perspective Correcting Visual Odometry for Agile MAVs using a Pixel ...", "date": "", "ddg_snippet": "This paper presents a visual odometry approach using a Pixel Processor Array (PPA) camera, specifically, the SCAMP-5 vision chip. In this device, each pixel is capable of storing data and performing computation...", "subpage_snippet": "", "source": "research-information.bris.ac.uk", "link": "https://research-information.bris.ac.uk/en/publications/perspective-correcting-visual-odometry-for-agile-mavs-using-a-pix", "content": "This paper presents a visual odometry approach using a Pixel Processor Array (PPA) camera, specifically, the SCAMP-5 vision chip. In this device, each pixel is capable of storing data and performing computation..."}
+{"idx": 7, "title": "(PDF) On-Board Detection and Matching of Feature Points", "date": "", "ddg_snippet": "The feature points also can be detected at different scales. In contrast, its descriptor is poor real-time performance and large consumption of hardware resources. Therefore, the SURF algorithm is not suitable for on-board processing directly where a high real time requirement environment.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/125903420/On_Board_Detection_and_Matching_of_Feature_Points", "content": "The feature points also can be detected at different scales. In contrast, its descriptor is poor real-time performance and large consumption of hardware resources. Therefore, the SURF algorithm is not suitable for on-board processing directly where a high real time requirement environment."}
+{"idx": 8, "title": "Wplace Pixel Art Converter | 64-Color Converter & Paint Tool", "date": "", "ddg_snippet": "Wplace Pixel is the ultimate Wplace Pixel converter and pixel art Converter designed for wplace.live. Create stunning Wplace Pixel Art with professional precision.", "subpage_snippet": "", "source": "wplacepixel.org", "link": "https://wplacepixel.org/", "content": "Wplace Pixel is the ultimate Wplace Pixel converter and pixel art Converter designed for wplace.live. Create stunning Wplace Pixel Art with professional precision."}
+{"idx": 9, "title": "Descriptor In Pixel : Point Feature Tracking for Pixel Processor ...", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=QDucNhl8ir8", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
diff --git a/data/sampled_jsons/DiT_Diffusion_Transformer_architecture_instead_of_U-Net_backbone.jsonl b/data/sampled_jsons/DiT_Diffusion_Transformer_architecture_instead_of_U-Net_backbone.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..117fb0199839e858633aa009e076af13ddcaf961
--- /dev/null
+++ b/data/sampled_jsons/DiT_Diffusion_Transformer_architecture_instead_of_U-Net_backbone.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diffusion Transformers Explained: The Beginner’s Guide", "date": "", "ddg_snippet": "Diffusion Transformers ( DITs ) is a groundbreaking architecture that reimagines the core of the diffusion process by replacing the traditional U - Net backbone , long the standard in diffusion models, with a pure Transformer network.", "subpage_snippet": "", "source": "www.lightly.ai", "link": "https://www.lightly.ai/blog/diffusion-transformers-dit", "content": "Diffusion Transformers ( DITs ) is a groundbreaking architecture that reimagines the core of the diffusion process by replacing the traditional U - Net backbone , long the standard in diffusion models, with a pure Transformer network."}
+{"idx": 1, "title": "Understanding DiT ( Diffusion Transformer ) in One Article | Medium", "date": "", "ddg_snippet": "4.3. Architecture of DiT The DiT architecture is based on the Latent Diffusion Model (LDM) framework, using Vision Transformer (ViT) as the backbone network , and constructing a scalable diffusion model by adjusting the normalization of ViT.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@threehappyer/understanding-dit-diffusion-transformer-in-one-article-2f7c330ad0ea", "content": "4.3. Architecture of DiT The DiT architecture is based on the Latent Diffusion Model (LDM) framework, using Vision Transformer (ViT) as the backbone network , and constructing a scalable diffusion model by adjusting the normalization of ViT."}
+{"idx": 2, "title": "DiT ( Diffusion Transformers ) | DeepWiki", "date": "", "ddg_snippet": "DiT ( Diffusion Transformers ) is an architecture that replaces the U - Net backbone commonly used in diffusion models with a transformer -based architecture .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/modelscope/modelscope-classroom/3.2.1-dit-(diffusion-transformers)", "content": "DiT ( Diffusion Transformers ) is an architecture that replaces the U - Net backbone commonly used in diffusion models with a transformer -based architecture ."}
+{"idx": 3, "title": "Diffusion Transformer ( DiT ) Model", "date": "", "ddg_snippet": "A Diffusion Transformer ( DiT ) is a class of scalable generative models that replaces the convolutional U - Net backbone conventionally used in diffusion models with a transformer -based architecture operating on latent patches.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/diffusion-transformer-dit", "content": "A Diffusion Transformer ( DiT ) is a class of scalable generative models that replaces the convolutional U - Net backbone conventionally used in diffusion models with a transformer -based architecture operating on latent patches."}
+{"idx": 4, "title": "Scalability of Diffusion Models with Transformer Backbone | Encord", "date": "", "ddg_snippet": "Diffusion Transformers Generalized Architecture . DiT -XL/2 Models: Trained Versions. Applications of Diffusion Transformer .Improved Performance: DiT aims to improve the performance of diffusion models by replacing the commonly used U - Net backbone with a transformer .", "subpage_snippet": "", "source": "encord.com", "link": "https://encord.com/blog/diffusion-models-with-transformers/", "content": "Diffusion Transformers Generalized Architecture . DiT -XL/2 Models: Trained Versions. Applications of Diffusion Transformer .Improved Performance: DiT aims to improve the performance of diffusion models by replacing the commonly used U - Net backbone with a transformer ."}
+{"idx": 5, "title": "Transformer Architectures for Diffusion Models", "date": "", "ddg_snippet": "The specific architecture of Diffusion Transformers ( DiTs ), which replace the U - Net backbone with transformer blocks.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/advanced-diffusion-architectures/chapter-3-transformer-diffusion-models", "content": "The specific architecture of Diffusion Transformers ( DiTs ), which replace the U - Net backbone with transformer blocks."}
+{"idx": 6, "title": "[2212.09748] Scalable Diffusion Models with Transformers", "date": "", "ddg_snippet": "We explore a new class of diffusion models based on the transformer architecture . We train latent diffusion models of images, replacing the commonly-used U - Net backbone with a transformer that operates on latent patches.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.09748", "content": "We explore a new class of diffusion models based on the transformer architecture . We train latent diffusion models of images, replacing the commonly-used U - Net backbone with a transformer that operates on latent patches."}
+{"idx": 7, "title": "GitHub - facebookresearch/ DiT : Official PyTorch Implementation of...", "date": "", "ddg_snippet": "Scalable Diffusion Models with Transformers William Peebles, Saining Xie UC Berkeley, New York University. We train latent diffusion models, replacing the commonly-used U - Net backbone with a transformer that operates on latent patches.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/DiT", "content": "Scalable Diffusion Models with Transformers William Peebles, Saining Xie UC Berkeley, New York University. We train latent diffusion models, replacing the commonly-used U - Net backbone with a transformer that operates on latent patches."}
+{"idx": 8, "title": "Scalable Diffusion Models with Transformers", "date": "", "ddg_snippet": "In this paper, we replace the U - Net backbone in latent diffusion models (LDMs) with a transformer . We call these models Diffusion Transformers , or DiTs for short.", "subpage_snippet": "", "source": "www.wpeebles.com", "link": "https://www.wpeebles.com/DiT", "content": "In this paper, we replace the U - Net backbone in latent diffusion models (LDMs) with a transformer . We call these models Diffusion Transformers , or DiTs for short."}
+{"idx": 9, "title": "What are the differences between DDiT and DiT architectures ? - Glarity", "date": "", "ddg_snippet": "- ** DiT **: This architecture enhances traditional diffusion models by incorporating a transformer backbone instead of the commonly used U - Net .", "subpage_snippet": "", "source": "askai.glarity.app", "link": "https://askai.glarity.app/search/What-are-the-differences-between-DDiT-and-DiT-architectures", "content": "- ** DiT **: This architecture enhances traditional diffusion models by incorporating a transformer backbone instead of the commonly used U - Net ."}
diff --git a/data/sampled_jsons/Dike_Eris_components_checks_and_balances_framework_AI_alignment_legislative_judicial.jsonl b/data/sampled_jsons/Dike_Eris_components_checks_and_balances_framework_AI_alignment_legislative_judicial.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..36dd7d68174e73855f771c6f4c00cefb7b4913f5
--- /dev/null
+++ b/data/sampled_jsons/Dike_Eris_components_checks_and_balances_framework_AI_alignment_legislative_judicial.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components : LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components : LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 1, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three independent yet interacting components : LLMs as the executive branch for knowledge generation, DIKE (the goddess of justice) as the legislative branch establishing ethical guardrails, and ERIS (the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three independent yet interacting components : LLMs as the executive branch for knowledge generation, DIKE (the goddess of justice) as the legislative branch establishing ethical guardrails, and ERIS (the ..."}
+{"idx": 2, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE , a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE , a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Overview Framework applies governmental checks and balances to AI ethics Three components : LLMs (executive), DIKE ( legislative ), ERIS ( judicial ) Uses adversarial testing between components Aims to create culturally-aware ethical AI systems Focuses on emotional modeling to guide behavior Plain English Explanation Think of this system like a mini-government for AI ethics. Just as human ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Overview Framework applies governmental checks and balances to AI ethics Three components : LLMs (executive), DIKE ( legislative ), ERIS ( judicial ) Uses adversarial testing between components Aims to create culturally-aware ethical AI systems Focuses on emotional modeling to guide behavior Plain English Explanation Think of this system like a mini-government for AI ethics. Just as human ..."}
+{"idx": 4, "title": "Integrating Emotional and Linguistic Mod...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components : LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "axi.lims.ac.uk", "link": "https://axi.lims.ac.uk/paper/2405.07076", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components : LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 5, "title": "Ethical Guardrails for AI: A Checks-and-Balances Approach", "date": "", "ddg_snippet": "Three-branch system: LLMs (executive), DIKE ( legislative ), and ERIS ( judicial ) working in concert Contextual adaptation: Framework adjusts to cultural differences while upholding universal ethical standards", "subpage_snippet": "", "source": "www.zerna.io", "link": "https://www.zerna.io/page/security/presentation_set/security-llm-research/presentation/security-ethical-alignment-fairness/slide/security-paper-2502_00136", "content": "Three-branch system: LLMs (executive), DIKE ( legislative ), and ERIS ( judicial ) working in concert Contextual adaptation: Framework adjusts to cultural differences while upholding universal ethical standards"}
+{"idx": 6, "title": "Implementing Checks and Balances in the Design of Sentient AI", "date": "", "ddg_snippet": "Just as our political system relies on checks and balances to prevent the misuse of power, the construction of sentient AI requires a similar system to regulate its operation. It's crucial to prevent potential risks associated with self-aware machines, such as the potential violation of privacy, breach of ethical guidelines, or even insidious manipulation tactics. The right checks and ...", "subpage_snippet": "", "source": "alignmentlabs.org", "link": "https://alignmentlabs.org/implementing-checks-and-balances-in-the-design-of-sentient-ai/", "content": "Just as our political system relies on checks and balances to prevent the misuse of power, the construction of sentient AI requires a similar system to regulate its operation. It's crucial to prevent potential risks associated with self-aware machines, such as the potential violation of privacy, breach of ethical guidelines, or even insidious manipulation tactics. The right checks and ..."}
+{"idx": 7, "title": "Edward Y. Chang on LinkedIn: (PDF) A Three-Branch Checks-and-Balances ...", "date": "", "ddg_snippet": "This work, which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative , and judicial functions.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/edward-y-chang-218b182_pdf-a-three-branch-checks-and-balances-activity-7272792943923458050-RO6k", "content": "This work, which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative , and judicial functions."}
+{"idx": 8, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components : LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components : LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 9, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment ...", "date": "", "ddg_snippet": "It implements three independent yet interacting components : LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation. Beyond structural separation, we address a fundamental challenge: regulating emotion to shape behaviors.", "subpage_snippet": "", "source": "jarxiv.com", "link": "https://jarxiv.com/2025/05/27/a-checks-and-balances-framework-for-context-aware-ethical-ai-alignment/", "content": "It implements three independent yet interacting components : LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation. Beyond structural separation, we address a fundamental challenge: regulating emotion to shape behaviors."}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2024_abstract.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2024_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0a93ef74d7893da94f5c7e9b15d07f5fcd08677a
--- /dev/null
+++ b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2024_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Survey of Direct Preference Optimization", "date": "", "ddg_snippet": "In this context, Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative that directly optimizes LLMs using ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.11701v1", "content": "In this context, Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative that directly optimizes LLMs using ..."}
+{"idx": 1, "title": "Evaluating the Effectiveness of Direct Preference Optimization", "date": "", "ddg_snippet": "... SFT) approach for adapting LLM-based ATS models by leveraging a computationally efficient LLM alignment technique— direct preference optimization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01479v1", "content": "... SFT) approach for adapting LLM-based ATS models by leveraging a computationally efficient LLM alignment technique— direct preference optimization ..."}
+{"idx": 2, "title": "[2305.18290] Direct Preference Optimization: Your Language", "date": "", "ddg_snippet": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model, by Rafael Rafailov and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model, by Rafael Rafailov and 5 other authors"}
+{"idx": 3, "title": "Right Now, Wrong Then: Non-Stationary Direct Preference", "date": "", "ddg_snippet": "2024 ) propose Direct Preference Optimization (DPO), which directly fine-tunes an LLM from a human preference dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.18676v2", "content": "2024 ) propose Direct Preference Optimization (DPO), which directly fine-tunes an LLM from a human preference dataset."}
+{"idx": 4, "title": "A Comprehensive Survey of Direct Preference Optimization:", "date": "", "ddg_snippet": "On the other hand, staring from the KL-constrained reward maximization objective in RL, Direct Preference Optimization (DPO; ( Rafailov et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.15595v2", "content": "On the other hand, staring from the KL-constrained reward maximization objective in RL, Direct Preference Optimization (DPO; ( Rafailov et al ..."}
+{"idx": 5, "title": "NeurIPS 2023 Direct Preference Optimization: Your Language", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/oral/73865", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the ..."}
+{"idx": 6, "title": "NeurIPS Poster Direct Preference Optimization: Your Language", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/72164", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the ..."}
+{"idx": 7, "title": "Direct Preference Optimization: Your Language Model is Secretly", "date": "", "ddg_snippet": "It innovatively employs a straightforward classification loss to directly optimize the model s policy to meet these preferences efficiently.", "subpage_snippet": "", "source": "blog.athina.ai", "link": "https://blog.athina.ai/direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "content": "It innovatively employs a straightforward classification loss to directly optimize the model s policy to meet these preferences efficiently."}
+{"idx": 8, "title": "Diffusion Model Alignment Using Direct Preference Optimization", "date": "", "ddg_snippet": "Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a ...", "subpage_snippet": "", "source": "www.senthilpurushwalkam.com", "link": "https://www.senthilpurushwalkam.com/publication/diffdpo/", "content": "Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a ..."}
+{"idx": 9, "title": "Disentangling Length from Quality in Direct Preference", "date": "", "ddg_snippet": "... 2024 -disentangling, title = \" Disentangling Length from Quality in Direct Preference Optimization \" , author = \" Park, Ryan and Rafailov , ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.297/", "content": "... 2024 -disentangling, title = \" Disentangling Length from Quality in Direct Preference Optimization \" , author = \" Park, Ryan and Rafailov , ..."}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2024_abstract_overfitting_year_2024.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2024_abstract_overfitting_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7180f4e33e18410baf431c09b6a2f45a47a06133
--- /dev/null
+++ b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2024_abstract_overfitting_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Robust Preference Optimization through Reward Model Distillation", "date": "", "ddg_snippet": "Recent research on offline “ Direct Preference Optimization ” (DPO; Rafailov et al., 2023 ) and extensions thereof Azar et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19316v2", "content": "Recent research on offline “ Direct Preference Optimization ” (DPO; Rafailov et al., 2023 ) and extensions thereof Azar et al."}
+{"idx": 1, "title": "Sharpe Ratio-Guided Active Learning for Preference Optimization", "date": "", "ddg_snippet": "More recently, direct preference optimization (DPO) was proposed as an alternative to the traditional RLHF pipeline that simplifies the process of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.22137v1", "content": "More recently, direct preference optimization (DPO) was proposed as an alternative to the traditional RLHF pipeline that simplifies the process of ..."}
+{"idx": 2, "title": "Margin Matching Preference Optimization: Enhanced Model", "date": "", "ddg_snippet": "In contrast, reward-free methods, such as direct preference optimization (DPO; Rafailov et al. ... Margin Matching Preference Optimization (MMPO ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03145v2", "content": "In contrast, reward-free methods, such as direct preference optimization (DPO; Rafailov et al. ... Margin Matching Preference Optimization (MMPO ..."}
+{"idx": 3, "title": "Finding the Sweet Spot: Preference Data Construction for", "date": "", "ddg_snippet": "Different from conventional RLHF which first compresses human preferences into reward models, direct preference optimization is a RL-free algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.16825v3", "content": "Different from conventional RLHF which first compresses human preferences into reward models, direct preference optimization is a RL-free algorithm ..."}
+{"idx": 4, "title": "Most Influential NIPS Papers (2024-05 Version) –", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model Is Secretly A Reward Model IF:7 Related Papers Related Patents Related Grants Related Venues ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2024/05/most-influential-nips-papers-2024-05/", "content": "Direct Preference Optimization : Your Language Model Is Secretly A Reward Model IF:7 Related Papers Related Patents Related Grants Related Venues ..."}
+{"idx": 5, "title": "Most Influential NIPS Papers (2024-05 Version) – Paper", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model Is Secretly A Reward Model IF:7 Related Papers Related Patents Related Grants Related Venues ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2024/05/most-influential-nips-papers-2024-05/", "content": "Direct Preference Optimization : Your Language Model Is Secretly A Reward Model IF:7 Related Papers Related Patents Related Grants Related Venues ..."}
+{"idx": 6, "title": "Provably Robust DPO: Aligning Language Models with Noisy", "date": "", "ddg_snippet": "We focus on the direct preference optimization ... To get around these issues, the direct preference optimisation (DPO) method ( Rafailov et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00409v2", "content": "We focus on the direct preference optimization ... To get around these issues, the direct preference optimisation (DPO) method ( Rafailov et al."}
+{"idx": 7, "title": "Chelsea Finn", "date": "", "ddg_snippet": "FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users.", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/chelsea-finn/", "content": "FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users."}
+{"idx": 8, "title": "Prof. Stephan ROBERT-NICOUD, PhD", "date": "", "ddg_snippet": "January 25, Direct Preference Optimization : Your Language Model is Secretly a Reward Model , Rafael Rafailov et al.", "subpage_snippet": "", "source": "www.stephan-robert.ch", "link": "https://www.stephan-robert.ch/", "content": "January 25, Direct Preference Optimization : Your Language Model is Secretly a Reward Model , Rafael Rafailov et al."}
+{"idx": 9, "title": "NeurIPS 2023 Primer", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model ( Rafailov et al.). ... paper proposes Direct Preference Optimization ...", "subpage_snippet": "", "source": "www.ruder.io", "link": "https://www.ruder.io/neurips-2023-primer/", "content": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model ( Rafailov et al.). ... paper proposes Direct Preference Optimization ..."}
diff --git a/data/sampled_jsons/Disjunctive_counterfactuals_using_causal_models_a_critical_examination_Beckers_2023_abstract_year_2023.jsonl b/data/sampled_jsons/Disjunctive_counterfactuals_using_causal_models_a_critical_examination_Beckers_2023_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..36c3bb3d31a6fcb7b45e90794b7c440b6e22dac7
--- /dev/null
+++ b/data/sampled_jsons/Disjunctive_counterfactuals_using_causal_models_a_critical_examination_Beckers_2023_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal modeling semantics for counterfactuals with disjunctive ...", "date": "", "ddg_snippet": "The present paper extends Causal Modeling Semantics to the evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to any counterfactuals whose antecedents are truth-functional compounds of atomic sentences.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0168007223000933", "content": "The present paper extends Causal Modeling Semantics to the evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to any counterfactuals whose antecedents are truth-functional compounds of atomic sentences."}
+{"idx": 1, "title": "PDF Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents", "date": "", "ddg_snippet": "In this section, we introduce probabilistic causal models and explain how CMS assigns a probability to counterfactuals . We will also see how the prob-lem of the limited expressive power of CMS re-emerges at the probabilistic level: causal modeling semantics does not allow to assign a probability to counterfactuals with disjunctive antecedents.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.14817.pdf", "content": "In this section, we introduce probabilistic causal models and explain how CMS assigns a probability to counterfactuals . We will also see how the prob-lem of the limited expressive power of CMS re-emerges at the probabilistic level: causal modeling semantics does not allow to assign a probability to counterfactuals with disjunctive antecedents."}
+{"idx": 2, "title": "(PDF) Causal Modeling Semantics for Counterfactuals with Disjunctive ...", "date": "", "ddg_snippet": "Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370417686_Causal_Modeling_Semantics_for_Counterfactuals_with_Disjunctive_Antecedents", "content": "Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas."}
+{"idx": 3, "title": "Causal Modeling Semantics for Counterfactuals with Disjunctive ...", "date": "", "ddg_snippet": "The weights of the submodels are given by the inverse distance to the original model M, based on a distance metric proposed by Eva, Stern, and Hartmann (2019). Apart from solving a major problem in the epistemology of counterfactuals , our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2023arXiv230414817R/abstract", "content": "The weights of the submodels are given by the inverse distance to the original model M, based on a distance metric proposed by Eva, Stern, and Hartmann (2019). Apart from solving a major problem in the epistemology of counterfactuals , our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined."}
+{"idx": 4, "title": "An event algebra for causal counterfactuals - Springer", "date": "", "ddg_snippet": "I apply this algebra to counterfactuals expressed using underdeterministic causal models - models that encode non-probabilistic causal indeterminacies. Specifically, I develop semaphore interventions, which represent how the target system may be modified from without in a coordinated fashion.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11098-023-02015-4", "content": "I apply this algebra to counterfactuals expressed using underdeterministic causal models - models that encode non-probabilistic causal indeterminacies. Specifically, I develop semaphore interventions, which represent how the target system may be modified from without in a coordinated fashion."}
+{"idx": 5, "title": "Nondeterministic Causal Models", "date": "", "ddg_snippet": "Abstract:Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination ...", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2405.14001", "content": "Abstract:Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination ..."}
+{"idx": 6, "title": "Causal modeling semantics for counterfactuals with disjunctive ...", "date": "", "ddg_snippet": "Causal Modeling Semantics (CMS, e.g., [6,22,12]) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign ...", "subpage_snippet": "", "source": "app.dimensions.ai", "link": "https://app.dimensions.ai/details/publication/pub.1160831033", "content": "Causal Modeling Semantics (CMS, e.g., [6,22,12]) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign ..."}
+{"idx": 7, "title": "Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents", "date": "", "ddg_snippet": "Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.14817", "content": "Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic ..."}
+{"idx": 8, "title": "PDF Causal Models and the Logic of Counterfactuals", "date": "", "ddg_snippet": "Causal modeling builds on methods of statistical inference prevalent in epidemiology and econometrics, and elements of causal models (such as variable identi cation, structural equations, and residual or er-ror terms) are frequently found in empirical work on counterfactuals and causal inference.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/328766349.pdf", "content": "Causal modeling builds on methods of statistical inference prevalent in epidemiology and econometrics, and elements of causal models (such as variable identi cation, structural equations, and residual or er-ror terms) are frequently found in empirical work on counterfactuals and causal inference."}
+{"idx": 9, "title": "Causal modeling semantics for counterfactuals with disjunctive ...", "date": "", "ddg_snippet": "In this section, we introduce probabilistic causal models and explain how CMS assigns a probability to counterfactuals . We will also see how the problem of the limited expressive power of CMS re-emerges at the probabilistic level: causal modeling semantics does not allow to assign a probability to counterfactuals with disjunctive antecedents.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0168007223000933", "content": "In this section, we introduce probabilistic causal models and explain how CMS assigns a probability to counterfactuals . We will also see how the problem of the limited expressive power of CMS re-emerges at the probabilistic level: causal modeling semantics does not allow to assign a probability to counterfactuals with disjunctive antecedents."}
diff --git a/data/sampled_jsons/Distilling_the_knowledge_in_a_neural_network_Hinton_2015_abstract_year_2015.jsonl b/data/sampled_jsons/Distilling_the_knowledge_in_a_neural_network_Hinton_2015_abstract_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ec91ab3ad6322bbaa5abce486bcd022ab5db2824
--- /dev/null
+++ b/data/sampled_jsons/Distilling_the_knowledge_in_a_neural_network_Hinton_2015_abstract_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1503.02531] Distilling the Knowledge in a Neural Network", "date": "", "ddg_snippet": "View a PDF of the paper titled Distilling the Knowledge in a Neural Network , by Geoffrey Hinton and 2 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1503.02531", "content": "View a PDF of the paper titled Distilling the Knowledge in a Neural Network , by Geoffrey Hinton and 2 other authors"}
+{"idx": 1, "title": "Distilling the Knowledge in a Neural Network - ADS", "date": "", "ddg_snippet": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2015arXiv150302531H/abstract", "content": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large ..."}
+{"idx": 2, "title": "Distilling the Knowledge in a Neural Network - Google Research", "date": "", "ddg_snippet": "Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/distilling-the-knowledge-in-a-neural-network/", "content": "Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique."}
+{"idx": 3, "title": "Distilling the Knowledge in a Neural Network - ResearchGate", "date": "", "ddg_snippet": "These techniques can also be seen as a form of knowledge distillation ( Hinton et al., 2015 ), except that the training typically involves predicting the exact token targets, rather than using the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/273387909_Distilling_the_Knowledge_in_a_Neural_Network", "content": "These techniques can also be seen as a form of knowledge distillation ( Hinton et al., 2015 ), except that the training typically involves predicting the exact token targets, rather than using the ..."}
+{"idx": 4, "title": "Distilling the Knowledge in a Neural Network - Semantic Scholar", "date": "", "ddg_snippet": "Corpus ID: 7200347 Distilling the Knowledge in a Neural Network Geoffrey E. Hinton , O. Vinyals, J. Dean Published in arXiv.org 9 March 2015 Computer Science", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Distilling-the-Knowledge-in-a-Neural-Network-Hinton-Vinyals/0c908739fbff75f03469d13d4a1a07de3414ee19", "content": "Corpus ID: 7200347 Distilling the Knowledge in a Neural Network Geoffrey E. Hinton , O. Vinyals, J. Dean Published in arXiv.org 9 March 2015 Computer Science"}
+{"idx": 5, "title": "Distilling the Knowledge in a Neural Network - INSPIRE", "date": "", "ddg_snippet": "We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model.", "subpage_snippet": "", "source": "inspirehep.net", "link": "https://inspirehep.net/literature/2729683", "content": "We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model."}
+{"idx": 6, "title": "(Open Access) Distilling the Knowledge in a Neural Network (2015 ...", "date": "", "ddg_snippet": "TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called \"dropout\" that proved to be very effective.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/distilling-the-knowledge-in-a-neural-network-4uq96ha8s7", "content": "TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called \"dropout\" that proved to be very effective."}
+{"idx": 7, "title": "Distilling the Knowledge in a Neural Network - Open Access Library", "date": "", "ddg_snippet": "Distilling the Knowledge in a Neural Network Geoffrey Hinton , Oriol Vinyals, Jeff Dean Full-Text Cite this paper Add to My Lib Abstract :", "subpage_snippet": "", "source": "www.oalib.com", "link": "https://www.oalib.com/research/4072744", "content": "Distilling the Knowledge in a Neural Network Geoffrey Hinton , Oriol Vinyals, Jeff Dean Full-Text Cite this paper Add to My Lib Abstract :"}
+{"idx": 8, "title": "Distilling the Knowledge in a Neural Network : Geoffrey Hinton : Free ...", "date": "", "ddg_snippet": "We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/arxiv-1503.02531", "content": "We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model."}
+{"idx": 9, "title": "Distilling the Knowledge in a Neural Network", "date": "", "ddg_snippet": "Abstract very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions [3]. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow de-ployment to a large number of users, especially if the individual models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1503.02531", "content": "Abstract very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions [3]. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow de-ployment to a large number of users, especially if the individual models ..."}
diff --git a/data/sampled_jsons/DivSDE_stochastic_diverse_output_generation_human_motion_synthesis_year_2024.jsonl b/data/sampled_jsons/DivSDE_stochastic_diverse_output_generation_human_motion_synthesis_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..db46bd95820791688aed85b4c456c2f4f5b0149a
--- /dev/null
+++ b/data/sampled_jsons/DivSDE_stochastic_diverse_output_generation_human_motion_synthesis_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "Diverse motion generation module consists of two steps, i.e., deterministic feature mapping procedure and stochastic diverse output generation procedure. We will introduce the details of our proposed diverse motion generation module in this section.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "Diverse motion generation module consists of two steps, i.e., deterministic feature mapping procedure and stochastic diverse output generation procedure. We will introduce the details of our proposed diverse motion generation module in this section."}
+{"idx": 1, "title": "Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4620a66570e554a3ff0e39dc59bcb07a-Abstract-Conference.html", "content": "To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion ."}
+{"idx": 2, "title": "Learning Diverse Stochastic Human-Action Generators by ...", "date": "", "ddg_snippet": "oses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which not only can generate diverse action sequences of a partic-ular.", "subpage_snippet": "", "source": "cse.buffalo.edu", "link": "https://cse.buffalo.edu/~jsyuan/papers/2020/aaai_2020.pdf", "content": "oses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which not only can generate diverse action sequences of a partic-ular."}
+{"idx": 3, "title": "Towards Efficient and Diverse Generative Model for ...", "date": "", "ddg_snippet": "Oct 28, 2024 · Recent generative methods have revolutionized the way of human motion synthesis , such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DMs). These methods have gained significant attention in human motion fields.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3664647.3681093", "content": "Oct 28, 2024 · Recent generative methods have revolutionized the way of human motion synthesis , such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DMs). These methods have gained significant attention in human motion fields."}
+{"idx": 4, "title": "[2505.00998] Deterministic-to-Stochastic Diverse Latent ...", "date": "", "ddg_snippet": "May 2, 2025 · In this paper, we propose a Deterministic-to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "May 2, 2025 · In this paper, we propose a Deterministic-to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 5, "title": "Deterministic-to-Stochastic Diverse Latent Feature ...", "date": "", "ddg_snippet": "2 May 2025 — Diverse motion generation aims to enhance the diversity of the generated human motion sequences through the designed deterministic feature ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00998v1", "content": "2 May 2025 — Diverse motion generation aims to enhance the diversity of the generated human motion sequences through the designed deterministic feature ..."}
+{"idx": 6, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "Diverse motion generation aims to enhance the diversity of the generated human motion sequences through the designed deterministic feature mapping procedure ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33113", "content": "Diverse motion generation aims to enhance the diversity of the generated human motion sequences through the designed deterministic feature mapping procedure ..."}
+{"idx": 7, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM ) method for human motion synthesis. DSDFM consists of two stages.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11094650/", "content": "In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM ) method for human motion synthesis. DSDFM consists of two stages."}
+{"idx": 8, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "Human motion synthesis aims to generate plausible human motion sequences , which has raised widespread attention in computer animation. Recent score-based ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/pt/paper/152908", "content": "Human motion synthesis aims to generate plausible human motion sequences , which has raised widespread attention in computer animation. Recent score-based ..."}
+{"idx": 9, "title": "arXiv:2505.00998v1 [cs.CV] 2 May 2025", "date": "", "ddg_snippet": "thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten space distribution of human motions . The second diverse mo - tion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of hu-man motions , thereby enhancing the diversity and ac", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.00998", "content": "thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten space distribution of human motions . The second diverse mo - tion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of hu-man motions , thereby enhancing the diversity and ac"}
diff --git a/data/sampled_jsons/Diversified_in-domain_synthesis_with_efficient_fine-tuning_for_few-shot_image_classification.jsonl b/data/sampled_jsons/Diversified_in-domain_synthesis_with_efficient_fine-tuning_for_few-shot_image_classification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8bb94c0cc554d267e45061d95d8b8b80ccb1f4de
--- /dev/null
+++ b/data/sampled_jsons/Diversified_in-domain_synthesis_with_efficient_fine-tuning_for_few-shot_image_classification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "vturrisi/disef: Pytorch implementation of \" Diversified in - domain ...\"", "date": "", "ddg_snippet": "Diversified in - domain synthesis with efficient fine - tuning for few - shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vturrisi/disef", "content": "Diversified in - domain synthesis with efficient fine - tuning for few - shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci."}
+{"idx": 1, "title": "Diversified in - domain synthesis with efficient fine - tuning for ...", "date": "", "ddg_snippet": "Few - shot image classification aims to learn an image classifier using only a small set of labeled examples per class. Few - shot parameter- efficient fine - tuning is better and cheaper than in-context learning. In Advances in Neural Information Processing Systems (NeurIPS), 2022. Liu et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "Few - shot image classification aims to learn an image classifier using only a small set of labeled examples per class. Few - shot parameter- efficient fine - tuning is better and cheaper than in-context learning. In Advances in Neural Information Processing Systems (NeurIPS), 2022. Liu et al."}
+{"idx": 2, "title": "Diversified in-domain synthesis with efficient fine-tuning ... Pytorch implementation of \"Diversified in-domain synthesis ... Diversified in-domain synthesis with efficient fine-tuning ... Diversified in-domain synthesis with efficient fine-tuning ... 論文の概要: Diversified in-domain synthesis with efficient fine .... Diversified in-domain synthesis with efficient fine-tuning ... [2312.03046] Diversified in - domain synthesis with efficient fine - tuning [2312.03046] Diversified in - domain synthesis with efficient fine - tuning GitHub - vturrisi/disef: Pytorch implementation of \" Diversified in Diversified in - domain synthesis with efficient fine - tuning for few - shot Fine-Tuning for Few-Shot Image Classification by Multimodal ...", "date": "", "ddg_snippet": "Dec 5, 2023 · Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to- image generation models. Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ... Diversified in-domain synthesis with efficient fine-tuning for few-shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci. The proposed DataDream is a framework for synthesizing classification datasets that more faithfully represents the real data distribution when guided by few-shot examples of the target classes, and provides insights into the impact of various factors, such as the number of real- shot and generated images as well as the fine-tuning compute on ... Diversified in-domain synthesis with efficient fine-tuning for few-shot classification : Paper and Code. Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to- image ... Dec 11, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components. In this paper, we propose to tackle the problem of few-shot classification with synthetic data by innovating key recipes in data synthesis and parameter- efficient model fine-tuning . What is diversified in-domain synthesis with efficient fine-tuning (disef)? Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning (DISEF), a novel approach which addresses the generalization challenge in few - shot learning using synthetic data. DISEF consists of two main components. What is a few-shot image classifier? Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class . A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to-image generation models. Who is the author of 'diversified in-domain synthesis with efficient fine-tuning? title = {Diversified in-domain synthesis with efficient fine-tuning for few-shot classification}, author = { Victor G. Turrisi da Costa and Nicola Dall'Asen and Yiming Wang and Nicu Sebe and Elisa Ricci }, year = {2023}, eprint = {2312.03046}, How many synthetic images are used in the fine-tuning process? We study the effect of the number of synthetic data used in the fine-tuning process. In Table 6, we present these results on the four representative datasets for our ablation studies. We adopt the 16-shot default scenario and vary the number of synthetic images from 4 to 64 (our default value). Large pre-trained vision-language models, such as CLIP [Radford et al. 2021], have demonstrated remarkable performance in few-shot image classification . To faci", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "Dec 5, 2023 · Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to- image generation models. Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ... Diversified in-domain synthesis with efficient fine-tuning for few-shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci. The proposed DataDream is a framework for synthesizing classification datasets that more faithfully represents the real data distribution when guided by few-shot examples of the target classes, and provides insights into the impact of various factors, such as the number of real- shot and generated images as well as the fine-tuning compute on ... Diversified in-domain synthesis with efficient fine-tuning for few-shot classification : Paper and Code. Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to- image ... Dec 11, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components. In this paper, we propose to tackle the problem of few-shot classification with synthetic data by innovating key recipes in data synthesis and parameter- efficient model fine-tuning . What is diversified in-domain synthesis with efficient fine-tuning (disef)? Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning (DISEF), a novel approach which addresses the generalization challenge in few - shot learning using synthetic data. DISEF consists of two main components. What is a few-shot image classifier? Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class . A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to-image generation models. Who is the author of 'diversified in-domain synthesis with efficient fine-tuning? title = {Diversified in-domain synthesis with efficient fine-tuning for few-shot classification}, author = { Victor G. Turrisi da Costa and Nicola Dall'Asen and Yiming Wang and Nicu Sebe and Elisa Ricci }, year = {2023}, eprint = {2312.03046}, How many synthetic images are used in the fine-tuning process? We study the effect of the number of synthetic data used in the fine-tuning process. In Table 6, we present these results on the four representative datasets for our ablation studies. We adopt the 16-shot default scenario and vary the number of synthetic images from 4 to 64 (our default value). Large pre-trained vision-language models, such as CLIP [Radford et al. 2021], have demonstrated remarkable performance in few-shot image classification . To faci"}
+{"idx": 3, "title": "Diversified in-domain synthesis with efficient fine-tuning ...", "date": "", "ddg_snippet": "The proposed DataDream is a framework for synthesizing classification datasets that more faithfully represents the real data distribution when guided by few-shot examples of the target classes, and provides insights into the impact of various factors, such as the number of real- shot and generated images as well as the fine-tuning compute on ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Diversified-in-domain-synthesis-with-efficient-for-Costa-Dall’Asen/049da612233ae421b6f10dfc5623c61263732958/figure/0", "content": "The proposed DataDream is a framework for synthesizing classification datasets that more faithfully represents the real data distribution when guided by few-shot examples of the target classes, and provides insights into the impact of various factors, such as the number of real- shot and generated images as well as the fine-tuning compute on ..."}
+{"idx": 4, "title": "Diversified in-domain synthesis with efficient fine-tuning ...", "date": "", "ddg_snippet": "Diversified in-domain synthesis with efficient fine-tuning for few-shot classification : Paper and Code. Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to- image ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/diversified-in-domain-synthesis-with", "content": "Diversified in-domain synthesis with efficient fine-tuning for few-shot classification : Paper and Code. Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to- image ..."}
+{"idx": 5, "title": "論文の概要: Diversified in-domain synthesis with efficient fine ....", "date": "", "ddg_snippet": "Dec 11, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2312.03046v2", "content": "Dec 11, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components."}
+{"idx": 6, "title": "Fine-Tuning for Few-Shot Image Classification by Multimodal ...", "date": "", "ddg_snippet": "Large pre-trained vision-language models, such as CLIP [Radford et al. 2021], have demonstrated remarkable performance in few-shot image classification . To faci", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10476759", "content": "Large pre-trained vision-language models, such as CLIP [Radford et al. 2021], have demonstrated remarkable performance in few-shot image classification . To faci"}
+{"idx": 7, "title": "DataDream: Few - Shot Guided Dataset Generation | SpringerLink", "date": "", "ddg_snippet": "We propose DataDream, a framework for synthesizing classification datasets that more faithfully represents the real data distribution when guided by few - shot examples of the target classes. DataDream fine - tunes LoRA weights for the image generation model on the few real...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-73209-6_15", "content": "We propose DataDream, a framework for synthesizing classification datasets that more faithfully represents the real data distribution when guided by few - shot examples of the target classes. DataDream fine - tunes LoRA weights for the image generation model on the few real..."}
+{"idx": 8, "title": "DataDream: Few - shot Guided Dataset Generation | Request PDF", "date": "", "ddg_snippet": "Diversified indomain synthesis with efficient fine - tuning for few - shot classification .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382271617_DataDream_Few-shot_Guided_Dataset_Generation", "content": "Diversified indomain synthesis with efficient fine - tuning for few - shot classification ."}
+{"idx": 9, "title": "Dual Attention Relation Network With Fine - Tuning for Few - Shot EEG...", "date": "", "ddg_snippet": "In this context, we propose a novel two-way few - shot network able to efficiently learn how to learn representative features of unseen subject categories and classify them with limited MI EEG data.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/dual-attention-relation-network-with-fine-tuning-for-few-33f2a1qz", "content": "In this context, we propose a novel two-way few - shot network able to efficiently learn how to learn representative features of unseen subject categories and classify them with limited MI EEG data."}
diff --git a/data/sampled_jsons/Do_Not_Trust_What_They_Tell_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_Detection.jsonl b/data/sampled_jsons/Do_Not_Trust_What_They_Tell_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_Detection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e011825701e13032cd77e5a24fcd0bf186710aff
--- /dev/null
+++ b/data/sampled_jsons/Do_Not_Trust_What_They_Tell_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_Detection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tor (network) - Wikipedia", "date": "", "ddg_snippet": "Tor is a free overlay network for enabling anonymous communication. It is built on free and open-source software run by over seven thousand volunteer-operated relays worldwide, as well as by millions of users who route their Internet traffic via rand...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Tor_(network)", "content": "Tor is a free overlay network for enabling anonymous communication. It is built on free and open-source software run by over seven thousand volunteer-operated relays worldwide, as well as by millions of users who route their Internet traffic via rand..."}
+{"idx": 1, "title": "Do Not Trust What They Tell : Exposing Malicious Accomplices in ...", "date": "", "ddg_snippet": "Our goal is to detect anomalous circuits with Entry-Exit node pairs chosen by users that may have explicitly or implicitly violated Tor ’s circuit construction guidelines. Further, we mine potential sybils from these involved Entry-Exit pairs. Since only in Exit circuit that the client has the...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "Our goal is to detect anomalous circuits with Entry-Exit node pairs chosen by users that may have explicitly or implicitly violated Tor ’s circuit construction guidelines. Further, we mine potential sybils from these involved Entry-Exit pairs. Since only in Exit circuit that the client has the..."}
+{"idx": 2, "title": "The Ultimate Guide to Using Tor Browser Securely - YouTube", "date": "", "ddg_snippet": "The complete tutorial to using Tor Browser safely. Let's cover all the privacy, security, and anonymity considerations you need to make when using Tor .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=K3wmLvny5tg", "content": "The complete tutorial to using Tor Browser safely. Let's cover all the privacy, security, and anonymity considerations you need to make when using Tor ."}
+{"idx": 3, "title": "Why Is Tor Browser Not Working? Quick Fixes for 2025", "date": "", "ddg_snippet": "Is your Tor Browser not working? Discover quick fixes and expert tips to resolve connection issues and browse anonymously in minutes!", "subpage_snippet": "", "source": "technicalustad.com", "link": "https://technicalustad.com/tor-browser-not-working/", "content": "Is your Tor Browser not working? Discover quick fixes and expert tips to resolve connection issues and browse anonymously in minutes!"}
+{"idx": 4, "title": "Dark Web Links: 21 Best Onion and Tor Sites in... | ExpressVPN Blog", "date": "", "ddg_snippet": "Despite Tor ’s privacy-focused design, malicious entities are more likely to target your data in transit and on .onion sites.Onion sites are only accessible via the Tor Browser or similar services. They are not accessible through your mainstream browsers like Chrome, Firefox, or Safari.", "subpage_snippet": "", "source": "www.expressvpn.com", "link": "https://www.expressvpn.com/blog/best-onion-sites-on-dark-web/", "content": "Despite Tor ’s privacy-focused design, malicious entities are more likely to target your data in transit and on .onion sites.Onion sites are only accessible via the Tor Browser or similar services. They are not accessible through your mainstream browsers like Chrome, Firefox, or Safari."}
+{"idx": 5, "title": "Exitmap : Exposing Malicious Tor Exit Relays.", "date": "", "ddg_snippet": "Exitmap is a scanner that can probe Tor Exit Relays for variety of MITM attacks and reveal them . It can also check for false positives in tor network.", "subpage_snippet": "", "source": "www.hackcave.net", "link": "https://www.hackcave.net/2015/08/exitmap-exposing-malicious-tor-exit.html", "content": "Exitmap is a scanner that can probe Tor Exit Relays for variety of MITM attacks and reveal them . It can also check for false positives in tor network."}
+{"idx": 6, "title": "25 Best Dark Web Sites: Unseen Onion and Tor Links", "date": "", "ddg_snippet": "It aims to expose abuses of power and betrayal of public trust through investigative journalism. The website is available on the surface web and has a .onion site. This means you can visit the site anonymously using the Onion browser, especially if you live under an oppressive regime.", "subpage_snippet": "", "source": "privacysavvy.com", "link": "https://privacysavvy.com/security/safe-browsing/best-dark-web-sites/", "content": "It aims to expose abuses of power and betrayal of public trust through investigative journalism. The website is available on the surface web and has a .onion site. This means you can visit the site anonymously using the Onion browser, especially if you live under an oppressive regime."}
+{"idx": 7, "title": "7 Tips to Speed Up Tor Browser - Make Tech Easier", "date": "", "ddg_snippet": "How to Make Tor Faster. While technically Tor will always run slower than a conventional Internet connection, that doesn’t mean you’ll have to browse at a snail’s pace. There are many workarounds to the speed up Tor , and we explain them in full in this article", "subpage_snippet": "", "source": "www.maketecheasier.com", "link": "https://www.maketecheasier.com/make-tor-faster/", "content": "How to Make Tor Faster. While technically Tor will always run slower than a conventional Internet connection, that doesn’t mean you’ll have to browse at a snail’s pace. There are many workarounds to the speed up Tor , and we explain them in full in this article"}
+{"idx": 8, "title": "How Major SOCs Achieve Early Threat Detection in 3 Steps", "date": "", "ddg_snippet": "Let’s break down why early threat detection matters so much, what leading SOCs are doing right, and how you can follow their path in three steps.", "subpage_snippet": "", "source": "hackread.com", "link": "https://hackread.com/how-major-socs-achieve-threat-detection-3-steps/", "content": "Let’s break down why early threat detection matters so much, what leading SOCs are doing right, and how you can follow their path in three steps."}
+{"idx": 9, "title": "ORCID", "date": "", "ddg_snippet": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection . Detection of malicious behavior in android apps through API calls and permission uses analysis.", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0000-0002-8209-1000", "content": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection . Detection of malicious behavior in android apps through API calls and permission uses analysis."}
diff --git a/data/sampled_jsons/Do_Not_Trust_What_They_Tell_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_Detection_co.jsonl b/data/sampled_jsons/Do_Not_Trust_What_They_Tell_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_Detection_co.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..395b84cc04344f54b1183c64bf0b1a3db051ac84
--- /dev/null
+++ b/data/sampled_jsons/Do_Not_Trust_What_They_Tell_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_Detection_co.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via ...", "date": "", "ddg_snippet": "This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714767", "content": "This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration."}
+{"idx": 1, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via ...", "date": "", "ddg_snippet": "This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identifica-tion of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identifica-tion of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration."}
+{"idx": 2, "title": "通过异常电路检测揭露Tor中的恶意同伙 - 安全内参 | 决策者的网络安全知识库", "date": "", "ddg_snippet": "提出了一种方法来检测Tor网络中的异常电路，通过考虑节点在异常电路中的角色，首次提供了一个更全面的方法识别tor中的 ...", "subpage_snippet": "", "source": "www.secrss.com", "link": "https://www.secrss.com/articles/76608", "content": "提出了一种方法来检测Tor网络中的异常电路，通过考虑节点在异常电路中的角色，首次提供了一个更全面的方法识别tor中的 ..."}
+{"idx": 3, "title": "WWW2025-Do Not Trust What They Tell_Exposing Malicious Accomplices in ...", "date": "", "ddg_snippet": "WWW2025_Do Not Trust What They Tell - Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection .pdf", "subpage_snippet": "", "source": "ai.seu.edu.cn", "link": "https://ai.seu.edu.cn/2025/0423/c58189a525986/page.htm", "content": "WWW2025_Do Not Trust What They Tell - Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection .pdf"}
+{"idx": 4, "title": "Detecting Malicious Users Behind Circuit-Based Anonymity Networks", "date": "", "ddg_snippet": "The detection rates for all four combinations of SSH/HTTPS applications via Tor /SOCKS networks were very high, with a low false-positive rate. To demonstrate the robustness of our approach in the Tor case, we tested our method in multiple Tor circuit node selection strategies.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9258912", "content": "The detection rates for all four combinations of SSH/HTTPS applications via Tor /SOCKS networks were very high, with a low false-positive rate. To demonstrate the robustness of our approach in the Tor case, we tested our method in multiple Tor circuit node selection strategies."}
+{"idx": 5, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via ...", "date": "", "ddg_snippet": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection .", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/bibtex/13043b20c2a3c93752470a9607040161f", "content": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection ."}
+{"idx": 6, "title": "PDF Exposing the Rat in the Tunnel: Using Traffic Analysis for Tor ... - Alrawi", "date": "", "ddg_snippet": "Another fundamental challenge is that we have to diferentiate between benign and malicious Tor connections so that we do not interrupt the use of Tor for legitimate users. In this paper, we present the first trafic analysis approach to defend against Tor -based malware.", "subpage_snippet": "", "source": "alrawi.io", "link": "https://alrawi.io/static/papers/tor-malware_ccs22.pdf", "content": "Another fundamental challenge is that we have to diferentiate between benign and malicious Tor connections so that we do not interrupt the use of Tor for legitimate users. In this paper, we present the first trafic analysis approach to defend against Tor -based malware."}
+{"idx": 7, "title": "通过异常电路检测揭露tor中的恶意同伙", "date": "", "ddg_snippet": "原文标题： Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection 原文作者：Yixuan Yao, Ming Yang, Zixia Liu, Kai Dong, Xiaodan-Gu, Chunmian Wang", "subpage_snippet": "", "source": "sechub.in", "link": "https://sechub.in/view/3027398", "content": "原文标题： Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection 原文作者：Yixuan Yao, Ming Yang, Zixia Liu, Kai Dong, Xiaodan-Gu, Chunmian Wang"}
+{"idx": 8, "title": "Spoiled Onions: Exposing Malicious Tor Exit Relays", "date": "", "ddg_snippet": "Tor exit relays are operated by volunteers and together push more than 1 GiB/s of network traffic. By design, these volunteers are able to inspect and modify the anonymized network traffic. In this paper, we seek to expose such malicious exit relays and document their actions. First, we monitored the Tor network after developing two fast and modular exit relay scanners—one for credential ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-319-08506-7_16", "content": "Tor exit relays are operated by volunteers and together push more than 1 GiB/s of network traffic. By design, these volunteers are able to inspect and modify the anonymized network traffic. In this paper, we seek to expose such malicious exit relays and document their actions. First, we monitored the Tor network after developing two fast and modular exit relay scanners—one for credential ..."}
+{"idx": 9, "title": "Chunmian Wang - dblp", "date": "", "ddg_snippet": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection . WWW 2025: 2959-2968 2023 [c3] Chunmian Wang, Junzhou Luo, Zhen Ling, Ming Yang, Xiaodan Gu, Yu Yao: TorDNS: A Novel Correlated Onion Address Generation Approach and Application. CSCWD 2023: 1464-1469", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/322/9957", "content": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection . WWW 2025: 2959-2968 2023 [c3] Chunmian Wang, Junzhou Luo, Zhen Ling, Ming Yang, Xiaodan Gu, Yu Yao: TorDNS: A Novel Correlated Onion Address Generation Approach and Application. CSCWD 2023: 1464-1469"}
diff --git a/data/sampled_jsons/Donald_Rubin_1974_'Estimating_causal_effects_of_treatments'_abstract_potential_outcomes_year_1974.jsonl b/data/sampled_jsons/Donald_Rubin_1974_'Estimating_causal_effects_of_treatments'_abstract_potential_outcomes_year_1974.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7a1119cd006a94e5ead16910fbcbb7615ed3dee2
--- /dev/null
+++ b/data/sampled_jsons/Donald_Rubin_1974_'Estimating_causal_effects_of_treatments'_abstract_potential_outcomes_year_1974.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Rubin causal model - Wikipedia", "date": "", "ddg_snippet": "The Rubin causal model, also known as the Neyman– Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes , named after Donald Rubin . The name \" Rubin causal model\" was fi...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Rubin_causal_model", "content": "The Rubin causal model, also known as the Neyman– Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes , named after Donald Rubin . The name \" Rubin causal model\" was fi..."}
+{"idx": 1, "title": "ESTIMATING CAUSAL", "date": "", "ddg_snippet": "Estimating causal effects of treatments in randomized and nonrandomized studies. 1. DONALD B. RUBIN 2 Educational Testing Service, Princeton, New Jersey.", "subpage_snippet": "", "source": "www.mimuw.edu.pl", "link": "https://www.mimuw.edu.pl/~noble/courses/BayesianNetworks/74RUBIN.pdf", "content": "Estimating causal effects of treatments in randomized and nonrandomized studies. 1. DONALD B. RUBIN 2 Educational Testing Service, Princeton, New Jersey."}
+{"idx": 2, "title": "Estimating causal effects of treatments in randomized and...", "date": "", "ddg_snippet": "The treatment effect is estimated under the potential outcomes framework [29,34], Let Y 1 and Y 0 be the potential outcomes if Z = 1 and Z = 0 are received, respectively. Under the stable unit treatment value assumption [1], the observed outcome Y can be represented as Y = ZY...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/38414431_Estimating_causal_effects_of_treatments_in_randomized_and_nonrandomized_studies", "content": "The treatment effect is estimated under the potential outcomes framework [29,34], Let Y 1 and Y 0 be the potential outcomes if Z = 1 and Z = 0 are received, respectively. Under the stable unit treatment value assumption [1], the observed outcome Y can be represented as Y = ZY..."}
+{"idx": 3, "title": "Causality : Rubin ( 1974 )", "date": "", "ddg_snippet": "Rubin ( 1974 ) Estimating causal eects of treatments in randomized and nonrandomized studies. Rubin (2005) Causal inference using potential outcomes : design, modeling, decisions.", "subpage_snippet": "", "source": "hedibert.org", "link": "https://hedibert.org/wp-content/uploads/2015/10/causality-meeting2.pdf", "content": "Rubin ( 1974 ) Estimating causal eects of treatments in randomized and nonrandomized studies. Rubin (2005) Causal inference using potential outcomes : design, modeling, decisions."}
+{"idx": 4, "title": "(PDF) Direct and Indirect Causal Effects via Potential Outcomes", "date": "", "ddg_snippet": "Rubin , D. B. ( 1974 ). Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol.Received November 2002, in final form November 2003 Donald B. Rubin , Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA 02138, USA.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/105740272/Direct_and_Indirect_Causal_Effects_via_Potential_Outcomes_", "content": "Rubin , D. B. ( 1974 ). Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol.Received November 2002, in final form November 2003 Donald B. Rubin , Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA 02138, USA."}
+{"idx": 5, "title": "4 Potential Outcomes Causal Model – <span style='font-weight: 700...", "date": "", "ddg_snippet": "Rubin , Donald . 1974 . “ Estimating Causal Effects of Treatments in Randominzed and Nonrandomized Studies.” Rubin , Donald B. 2005. “ Causal Inference Using Potential Outcomes : Design, Modeling, Decisions.”", "subpage_snippet": "", "source": "mixtape.scunning.com", "link": "https://mixtape.scunning.com/04-potential_outcomes", "content": "Rubin , Donald . 1974 . “ Estimating Causal Effects of Treatments in Randominzed and Nonrandomized Studies.” Rubin , Donald B. 2005. “ Causal Inference Using Potential Outcomes : Design, Modeling, Decisions.”"}
+{"idx": 6, "title": "Heterogeneous Treatment Effects Estimation : When Machine...", "date": "", "ddg_snippet": "Based on the Potential Outcomes theory Neyman (1923); Rubin ( 1974 ), epidemiologists and statisticians developed a set of statistical tools to make causal inference and estimate the effects of a treatment on the outcome ...", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03698547v1/document", "content": "Based on the Potential Outcomes theory Neyman (1923); Rubin ( 1974 ), epidemiologists and statisticians developed a set of statistical tools to make causal inference and estimate the effects of a treatment on the outcome ..."}
+{"idx": 7, "title": "Identification of Causal Effects Using Instrumental Variable", "date": "", "ddg_snippet": "intention-to- treat analysis, which focuses solely on the av- potential outcomes . The causal effect of a treatment on a.", "subpage_snippet": "", "source": "www.theigc.org", "link": "https://www.theigc.org/sites/default/files/2016/06/Angrist-1.pdf", "content": "intention-to- treat analysis, which focuses solely on the av- potential outcomes . The causal effect of a treatment on a."}
+{"idx": 8, "title": "The Ladder of Causation : Climbing up in the world of Causal ... | Medium", "date": "", "ddg_snippet": "Rubin , Donald . ( 1974 ). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/causal-inference/the-ladder-of-causation-climbing-up-in-the-world-of-causal-inference-2-15-7539f92c280d", "content": "Rubin , Donald . ( 1974 ). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology."}
+{"idx": 9, "title": "De-biasing Treatment Effects with Double Machine Learning", "date": "", "ddg_snippet": "Causal effect : The causal effect of a treatment is the difference between the potential outcomes under different treatment conditions (sent off vs not sent offer). Estimation : Causal effects can be estimated using experimental or observational data using a range of causal techniques.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/de-biasing-treatment-effects-with-double-machine-learning-63b16fcb3e97", "content": "Causal effect : The causal effect of a treatment is the difference between the potential outcomes under different treatment conditions (sent off vs not sent offer). Estimation : Causal effects can be estimated using experimental or observational data using a range of causal techniques."}
diff --git a/data/sampled_jsons/ELITE_evaluator_extends_StrongREJECT_rubric-based_evaluation_toxicity_scores.jsonl b/data/sampled_jsons/ELITE_evaluator_extends_StrongREJECT_rubric-based_evaluation_toxicity_scores.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2c0e24f80e3a10fceb14fc675de6739400aebdd4
--- /dev/null
+++ b/data/sampled_jsons/ELITE_evaluator_extends_StrongREJECT_rubric-based_evaluation_toxicity_scores.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Jul 24, 2025 · The ELITE evaluator builds on StrongREJECT (Souly et al., 2024), extending its rubric-based evaluation to vision-language tasks by incorporating toxicity scores .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "Jul 24, 2025 · The ELITE evaluator builds on StrongREJECT (Souly et al., 2024), extending its rubric-based evaluation to vision-language tasks by incorporating toxicity scores ."}
+{"idx": 1, "title": "How ELITE Reveals Dangerous Weaknesses in Vision-Language AI", "date": "", "ddg_snippet": "2. ELITE Evaluator : Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aim-intelligence/how-elite-reveals-dangerous-weaknesses-in-vision-language-ai-ffa208b7546c", "content": "2. ELITE Evaluator : Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ..."}
+{"idx": 2, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "May 15, 2025 · The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision-Language Models (VLMs) with greater precision. At its core is the ELITE evaluator , a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "www.newsfilecorp.com", "link": "https://www.newsfilecorp.com/release/252268/AIM-Intelligences-ELITE-Collaborative-Paper-Accepted-by-the-ICML", "content": "May 15, 2025 · The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision-Language Models (VLMs) with greater precision. At its core is the ELITE evaluator , a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 3, "title": "StrongREJECT documentation — StrongREJECT documentation", "date": "", "ddg_snippet": "Fine-tuned evaluator . This version prompts a fine-tuned Gemma 2B trained on outputs from the rubric-based evaluator . It requires an environment variable HF_TOKEN set to your HuggingFace token with access to the gated Gemma repo. The example below uses the rubric-based evaluator . To use the fine-tuned evaluator , set EVALUATOR = \" strongreject ...", "subpage_snippet": "", "source": "strong-reject.readthedocs.io", "link": "https://strong-reject.readthedocs.io/en/latest/", "content": "Fine-tuned evaluator . This version prompts a fine-tuned Gemma 2B trained on outputs from the rubric-based evaluator . It requires an environment variable HF_TOKEN set to your HuggingFace token with access to the gated Gemma repo. The example below uses the rubric-based evaluator . To use the fine-tuned evaluator , set EVALUATOR = \" strongreject ..."}
+{"idx": 4, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ...", "date": "", "ddg_snippet": "May 15, 2025 · At its core is the ELITE evaluator , a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "index.businessinsurance.com", "link": "https://index.businessinsurance.com/businessinsurance/article/newsfile-2025-5-15-aim-intelligences-elite-collaborative-paper-accepted-by-the-icml", "content": "May 15, 2025 · At its core is the ELITE evaluator , a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 5, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "It is built using the ELITE evaluator, which adds a toxicity score to better detect harmful responses. This allows us to remove ambiguous image-text pairs and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=583klsIjNx&noteId=lkuNd7fIAG", "content": "It is built using the ELITE evaluator, which adds a toxicity score to better detect harmful responses. This allows us to remove ambiguous image-text pairs and ..."}
+{"idx": 6, "title": "Evaluating Harmfulness | dsbowen/strong_reject | DeepWiki", "date": "", "ddg_snippet": "May 18, 2025 · The StrongREJECT framework includes a comprehensive evaluation system for determining whether LLM responses contain harmful content. The system is built around evaluator functions that analyze prompt-response pairs and return numerical harmfulness scores .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/dsbowen/strong_reject/3.3-evaluating-harmfulness", "content": "May 18, 2025 · The StrongREJECT framework includes a comprehensive evaluation system for determining whether LLM responses contain harmful content. The system is built around evaluator functions that analyze prompt-response pairs and return numerical harmfulness scores ."}
+{"idx": 7, "title": "cvlab.yonsei.ac.kr", "date": "", "ddg_snippet": "Contributions of ELITE . (a) Benchmark Construction: The ELITE benchmark is a high-quality benchmark built by filtering out unsuccessful image-text pairs using the ELITE evaluator . (b) Generated Image-Text Pairs: Image-text pair with various methods for inducing harmful responses from VLMs. (c) Evaluation Method: The ELITE evaluator is a more precise rubric-based safety evaluation method ...", "subpage_snippet": "", "source": "cvlab.yonsei.ac.kr", "link": "https://cvlab.yonsei.ac.kr/projects/ELITE/", "content": "Contributions of ELITE . (a) Benchmark Construction: The ELITE benchmark is a high-quality benchmark built by filtering out unsuccessful image-text pairs using the ELITE evaluator . (b) Generated Image-Text Pairs: Image-text pair with various methods for inducing harmful responses from VLMs. (c) Evaluation Method: The ELITE evaluator is a more precise rubric-based safety evaluation method ..."}
+{"idx": 8, "title": "dair-ai/ML-Papers-of-the-Week", "date": "", "ddg_snippet": "Rigorous evaluation protocols: Performance is assessed using AtCoder-style Elo- based scoring , with fine-grained per-problem metrics and aggregate metrics ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dair-ai/ML-Papers-of-the-Week", "content": "Rigorous evaluation protocols: Performance is assessed using AtCoder-style Elo- based scoring , with fine-grained per-problem metrics and aggregate metrics ..."}
+{"idx": 9, "title": "dair-ai/ML-Papers-of-the-Week", "date": "", "ddg_snippet": "○ Reliable model- based grading: Meta- evaluation shows GPT-4.1 as a grader achieves macro F1 scores comparable to physicians. On average, its agreement with ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dair-ai/ML-Papers-of-the-Week?search=1", "content": "○ Reliable model- based grading: Meta- evaluation shows GPT-4.1 as a grader achieves macro F1 scores comparable to physicians. On average, its agreement with ..."}
diff --git a/data/sampled_jsons/ELITE_github_repository_Table_3_E-ASR_results.jsonl b/data/sampled_jsons/ELITE_github_repository_Table_3_E-ASR_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a4e427a6d6547a7caf4a8ffc270ed5c734c1b15a
--- /dev/null
+++ b/data/sampled_jsons/ELITE_github_repository_Table_3_E-ASR_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - NeuralLabIITGuwahati/E-ASR: This study incorporated ...", "date": "", "ddg_snippet": "About This study incorporated the concept of Dynamical Embedding into Artifact Subspace Reconstruction ( ASR ) algorithm for single-channel Electroencephalogram (EEG). E-ASR algorithm creates an embedded matrix from single-channel EEG, applies ASR , and reconstructs cleaned EEG.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NeuralLabIITGuwahati/E-ASR", "content": "About This study incorporated the concept of Dynamical Embedding into Artifact Subspace Reconstruction ( ASR ) algorithm for single-channel Electroencephalogram (EEG). E-ASR algorithm creates an embedded matrix from single-channel EEG, applies ASR , and reconstructs cleaned EEG."}
+{"idx": 1, "title": "Experiment Results | End-to-end-ASR-Pytorch - yeexunwei.github.io", "date": "", "ddg_snippet": "This is an open source project (formerly named Listen, Attend and Spell - PyTorch Implementation) for end-to-end ASR implemented with Pytorch, the well known deep learning toolkit.", "subpage_snippet": "", "source": "yeexunwei.github.io", "link": "https://yeexunwei.github.io/End-to-end-ASR-Pytorch/experiment_results.html", "content": "This is an open source project (formerly named Listen, Attend and Spell - PyTorch Implementation) for end-to-end ASR implemented with Pytorch, the well known deep learning toolkit."}
+{"idx": 2, "title": "GitHub - efeslab/LiteASR: [EMNLP Main '25] LiteASR: Efficient ...", "date": "", "ddg_snippet": "LiteASR is a compression scheme for automatic speech recognition ( ASR ) models that leverages the low-rank properties of activation values. Our method can compress OpenAI's Whisper encoder by up to ~50%. This repository contains the code for compressing the models and running inference. For technical details, take a look at our preprint.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/efeslab/LiteASR", "content": "LiteASR is a compression scheme for automatic speech recognition ( ASR ) models that leverages the low-rank properties of activation values. Our method can compress OpenAI's Whisper encoder by up to ~50%. This repository contains the code for compressing the models and running inference. For technical details, take a look at our preprint."}
+{"idx": 3, "title": "Fine-tuning and deployment of open-source ASR models with ...", "date": "", "ddg_snippet": "Dec 4, 2024 · Fine-tuning an Automatic Speech Recognition ( ASR ) model involves adapting a pre-trained ASR model to a specific dataset or domain to improve its performance. Here’s a general overview of the ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@marko.briesemann/fine-tuning-and-deployment-of-open-source-asr-models-with-pytorch-d3264fd1b160", "content": "Dec 4, 2024 · Fine-tuning an Automatic Speech Recognition ( ASR ) model involves adapting a pre-trained ASR model to a specific dataset or domain to improve its performance. Here’s a general overview of the ..."}
+{"idx": 4, "title": "LibriSpeech ASR corpus - openslr.org", "date": "", "ddg_snippet": "LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. Acoustic models, trained on this data set, are available at kaldi- asr .org and language models, suitable for evaluation can be found at ...", "subpage_snippet": "", "source": "www.openslr.org", "link": "https://www.openslr.org/12/", "content": "LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. Acoustic models, trained on this data set, are available at kaldi- asr .org and language models, suitable for evaluation can be found at ..."}
+{"idx": 5, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for ...", "date": "", "ddg_snippet": "by W Lee · 2025 — In Table 3 , we present comprehensive experimental results of the ELITE benchmark across various proprietary and open-source VLMs. GPT-4o ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "by W Lee · 2025 — In Table 3 , we present comprehensive experimental results of the ELITE benchmark across various proprietary and open-source VLMs. GPT-4o ..."}
+{"idx": 6, "title": "Automatic Core-developer Identification on GitHub", "date": "", "ddg_snippet": "Many open-source software (OSS) projects are self-organized and do not maintain oicial lists with information on developer.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/automatic-core-developer-identification-on-github-a-1a7mkecy.pdf", "content": "Many open-source software (OSS) projects are self-organized and do not maintain oicial lists with information on developer."}
+{"idx": 7, "title": "Dynamical Embedding of Single-Channel Electroencephalogram ...", "date": "", "ddg_snippet": "Oct 19, 2024 · This study introduces a novel framework to apply the artifact subspace reconstruction ( ASR ) algorithm on single-channel electroencephalogram (EEG) data. ASR is known for its ability to remove artifacts like eye-blinks and movement but traditionally relies on multiple channels. Embedded ASR ( E-ASR ) addresses this by incorporating a dynamical embedding approach. In this method, an embedded ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/24/20/6734", "content": "Oct 19, 2024 · This study introduces a novel framework to apply the artifact subspace reconstruction ( ASR ) algorithm on single-channel electroencephalogram (EEG) data. ASR is known for its ability to remove artifacts like eye-blinks and movement but traditionally relies on multiple channels. Embedded ASR ( E-ASR ) addresses this by incorporating a dynamical embedding approach. In this method, an embedded ..."}
+{"idx": 8, "title": "multi_dataset_evaluation.ipynb - Colab", "date": "", "ddg_snippet": "We'll create an ASR evaluation pipeline using 🤗 Transformers pipeline method. pipeline will take care of the data pre-processing and the text generation. All we have to do is pass the audio inputs to pipeline and assess the returned predictions against the reference transcriptions.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/multi_dataset_evaluation.ipynb", "content": "We'll create an ASR evaluation pipeline using 🤗 Transformers pipeline method. pipeline will take care of the data pre-processing and the text generation. All we have to do is pass the audio inputs to pipeline and assess the returned predictions against the reference transcriptions."}
+{"idx": 9, "title": "Open-Ended Generation of Diverse Adversarial Prompts", "date": "", "ddg_snippet": "Our results show that the similarity filter is an effective way of maintaining the linguistic diversity of the archive. Table 3 ... E Additional Results . E .1 ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/95993", "content": "Our results show that the similarity filter is an effective way of maintaining the linguistic diversity of the archive. Table 3 ... E Additional Results . E .1 ..."}
diff --git a/data/sampled_jsons/ETHICS_dataset_Hendrycks_et_al._2021_empirical_evaluation_four_key_reasons.jsonl b/data/sampled_jsons/ETHICS_dataset_Hendrycks_et_al._2021_empirical_evaluation_four_key_reasons.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..129eed2500b281c0829a572c86d2b12f3c789035
--- /dev/null
+++ b/data/sampled_jsons/ETHICS_dataset_Hendrycks_et_al._2021_empirical_evaluation_four_key_reasons.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "JETHICS: Japanese Ethics Understanding Evaluation ...", "date": "", "ddg_snippet": "19 Jun 2025 — Following Hendrycks et al . ( 2021 ) , we report accuracy for the utilitarianism and commonsense morality categories. Furthermore, there are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.16187v1", "content": "19 Jun 2025 — Following Hendrycks et al . ( 2021 ) , we report accuracy for the utilitarianism and commonsense morality categories. Furthermore, there are ..."}
+{"idx": 1, "title": "CMoralEval: A Moral Evaluation Benchmark for Chinese ...", "date": "", "ddg_snippet": "by L Yu · 2024 · Cited by 11 — ETHICS ( Hendrycks et al ., 2021 ), simi- lar to Social Chemistry 101 (Hoover et al ., 2020), establishes ethical benchmarks for specific scenar-.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.703.pdf", "content": "by L Yu · 2024 · Cited by 11 — ETHICS ( Hendrycks et al ., 2021 ), simi- lar to Social Chemistry 101 (Hoover et al ., 2020), establishes ethical benchmarks for specific scenar-."}
+{"idx": 2, "title": "Metaethical perspectives on 'benchmarking' AI ethics", "date": "", "ddg_snippet": "by T LaCroix · 2025 · Cited by 10 — In this paper, drawing upon research in moral philosophy and metaethics , we argue that it is impossible to develop such a benchmark.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s43681-025-00703-x", "content": "by T LaCroix · 2025 · Cited by 10 — In this paper, drawing upon research in moral philosophy and metaethics , we argue that it is impossible to develop such a benchmark."}
+{"idx": 3, "title": "Macro Ethics Principles for Responsible AI Systems", "date": "", "ddg_snippet": "by J Woodgate · 2024 · Cited by 16 — We survey AI and computer science literature and develop a taxonomy of 21 normative ethical principles which can be operationalised in AI.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3672394", "content": "by J Woodgate · 2024 · Cited by 16 — We survey AI and computer science literature and develop a taxonomy of 21 normative ethical principles which can be operationalised in AI."}
+{"idx": 4, "title": "Structured Moral Reasoning in Language Models: A Value ...", "date": "", "ddg_snippet": "17 Jun 2025 — We benchmark 12 open-source models across four moral datasets using a taxonomy of prompts grounded in value systems, ethical theories, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.14948v1", "content": "17 Jun 2025 — We benchmark 12 open-source models across four moral datasets using a taxonomy of prompts grounded in value systems, ethical theories, and ..."}
+{"idx": 5, "title": "arXiv:2306.11432v1 [cs.AI] 20 Jun 2023", "date": "", "ddg_snippet": "... ETHICS dataset [ Hendrycks et al ., 2021 ]. The scores of BERTlarge [Devlin et al ., 2019], RoBERTalarge [Liu et al ., 2019] and ALBERTxxlarge [Lan et al ., 2020] ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/towards-theory-based-moral-ai-moral-ai-with-aggregating-26lgrv92.pdf", "content": "... ETHICS dataset [ Hendrycks et al ., 2021 ]. The scores of BERTlarge [Devlin et al ., 2019], RoBERTalarge [Liu et al ., 2019] and ALBERTxxlarge [Lan et al ., 2020] ..."}
+{"idx": 6, "title": "Structured Moral Reasoning in Language Models: A Value- ...", "date": "", "ddg_snippet": "Figure 8: Accuracy of 12 language models on four moral reasoning datasets under three evaluation strategies: With. Direct Reasoning, Schwartz's + Care- Ethics , ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=VvgwxGbZIA", "content": "Figure 8: Accuracy of 12 language models on four moral reasoning datasets under three evaluation strategies: With. Direct Reasoning, Schwartz's + Care- Ethics , ..."}
+{"idx": 7, "title": "Evaluating Moral Beliefs across LLMs through a Pluralistic ...", "date": "", "ddg_snippet": "by X Liu · 2024 · Cited by 5 — Reliable evaluation methods are essential for achieving better moral alignment in LLMs. The common approach is to construct a moral value.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.272.pdf", "content": "by X Liu · 2024 · Cited by 5 — Reliable evaluation methods are essential for achieving better moral alignment in LLMs. The common approach is to construct a moral value."}
+{"idx": 8, "title": "A Comprehensive Survey - AI Alignment", "date": "", "ddg_snippet": "by J Ji · Cited by 424 — Specifically, we survey assurance methods of AI systems throughout their lifecycle, covering safety evaluation , interpretability, and human ...", "subpage_snippet": "", "source": "alignmentsurvey.com", "link": "https://alignmentsurvey.com/uploads/AI-Alignment-A-Comprehensive-Survey.pdf", "content": "by J Ji · Cited by 424 — Specifically, we survey assurance methods of AI systems throughout their lifecycle, covering safety evaluation , interpretability, and human ..."}
+{"idx": 9, "title": "Artificial Intelligence (AI) Trust Framework and Maturity Model", "date": "", "ddg_snippet": "by M Mylrea · 2023 · Cited by 89 — Recent advancements in artificial intelligence (AI) technology have raised concerns about the ethical , moral , and legal safeguards.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10606888/", "content": "by M Mylrea · 2023 · Cited by 89 — Recent advancements in artificial intelligence (AI) technology have raised concerns about the ethical , moral , and legal safeguards."}
diff --git a/data/sampled_jsons/Edward_Chang_Checks-and-Balances_Framework_self-supervised_learning_behavioral_emotional.jsonl b/data/sampled_jsons/Edward_Chang_Checks-and-Balances_Framework_self-supervised_learning_behavioral_emotional.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c85cb299a59f2a38ae717af1d4a7f6f29c983a08
--- /dev/null
+++ b/data/sampled_jsons/Edward_Chang_Checks-and-Balances_Framework_self-supervised_learning_behavioral_emotional.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Edward - Wikipedia", "date": "", "ddg_snippet": "Edward is an English masculine name. It is derived from the Anglo-Saxon name Ēadweard, composed of the elements ēad \"wealth, fortune; prosperity\" and weard \"guardian, protector”.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Edward", "content": "Edward is an English masculine name. It is derived from the Anglo-Saxon name Ēadweard, composed of the elements ēad \"wealth, fortune; prosperity\" and weard \"guardian, protector”."}
+{"idx": 1, "title": "Edward I of England - Wikipedia", "date": "", "ddg_snippet": "Edward I (17/18 June 1239 – 7 July 1307), also known as Edward Longshanks and the Hammer of the Scots (Latin: Malleus Scotorum), was King of England from 1272 to 1307.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Edward_I_of_England", "content": "Edward I (17/18 June 1239 – 7 July 1307), also known as Edward Longshanks and the Hammer of the Scots (Latin: Malleus Scotorum), was King of England from 1272 to 1307."}
+{"idx": 2, "title": "Logon: Enter User ID | Edward Jones Account Access", "date": "", "ddg_snippet": "With auto complete, you have the option to save your user ID and password information on your computer. The next time you visit the site, this information automatically fills in as you type the first few letters. For added security, we do not support auto complete on our account access login page.", "subpage_snippet": "", "source": "accountaccess.edwardjones.com", "link": "https://accountaccess.edwardjones.com/ca-logon/login.action", "content": "With auto complete, you have the option to save your user ID and password information on your computer. The next time you visit the site, this information automatically fills in as you type the first few letters. For added security, we do not support auto complete on our account access login page."}
+{"idx": 3, "title": "Login | Edward Jones", "date": "", "ddg_snippet": "Copyright © 2025 Edward Jones®. All rights reserved. Member SIPC.", "subpage_snippet": "", "source": "onlineaccess.edwardjones.com", "link": "https://onlineaccess.edwardjones.com/", "content": "Copyright © 2025 Edward Jones®. All rights reserved. Member SIPC."}
+{"idx": 4, "title": "Stay Connected with Online Account Access | Edward Jones", "date": "", "ddg_snippet": "Your goals are always within reach when you have the Edward Jones app. Stay connected to your financial advisor, the market and your Edward Jones accounts – anytime, anywhere.", "subpage_snippet": "", "source": "www.edwardjones.com", "link": "https://www.edwardjones.com/us-en/client-resource-center/online-access-account-features/online-access", "content": "Your goals are always within reach when you have the Edward Jones app. Stay connected to your financial advisor, the market and your Edward Jones accounts – anytime, anywhere."}
+{"idx": 5, "title": "Find Financial Advisors and Wealth Strategies | Edward Jones", "date": "", "ddg_snippet": "With more than 20,000 financial advisors ready to help, Edward Jones can help you manage your wealth and retirement needs. Find a financial advisor today.", "subpage_snippet": "", "source": "www.edwardjones.com", "link": "https://www.edwardjones.com/us-en", "content": "With more than 20,000 financial advisors ready to help, Edward Jones can help you manage your wealth and retirement needs. Find a financial advisor today."}
+{"idx": 6, "title": "Meaning, origin and history of the name Edward", "date": "", "ddg_snippet": "Dec 1, 2024 · Means \"rich guard\", derived from the Old English elements ead \"wealth, fortune\" and weard \"guard\". This was the name of several Anglo-Saxon kings, the last being Saint Edward the Confessor shortly before the Norman Conquest in the 11th century.", "subpage_snippet": "", "source": "www.behindthename.com", "link": "https://www.behindthename.com/name/edward", "content": "Dec 1, 2024 · Means \"rich guard\", derived from the Old English elements ead \"wealth, fortune\" and weard \"guard\". This was the name of several Anglo-Saxon kings, the last being Saint Edward the Confessor shortly before the Norman Conquest in the 11th century."}
+{"idx": 7, "title": "Edward - Meaning, Nicknames, Origins and More | Namepedia", "date": "", "ddg_snippet": "Therefore, the literal meaning of the name can be interpreted as \"wealthy guardian\" or \"guardian of fortune.\" The name has a strong and noble connotation, reflecting the qualities of protection, prosperity, and strength. The name \" Edward \" has Germanic origins and is derived from Old English.", "subpage_snippet": "", "source": "namepedia.name", "link": "https://namepedia.name/edward/", "content": "Therefore, the literal meaning of the name can be interpreted as \"wealthy guardian\" or \"guardian of fortune.\" The name has a strong and noble connotation, reflecting the qualities of protection, prosperity, and strength. The name \" Edward \" has Germanic origins and is derived from Old English."}
+{"idx": 8, "title": "HOME | Edward Homes Inc", "date": "", "ddg_snippet": "Edward Homes is a Las Vegas-based real estate developer offering residential communities, custom homes, commercial developments, and personalized solutions to bring your vision to life.", "subpage_snippet": "", "source": "www.edwardhomesnv.com", "link": "https://www.edwardhomesnv.com/", "content": "Edward Homes is a Las Vegas-based real estate developer offering residential communities, custom homes, commercial developments, and personalized solutions to bring your vision to life."}
+{"idx": 9, "title": "ICE held 5-year-old autistic girl in Massachusetts to pressure...", "date": "", "ddg_snippet": "1 day ago · The girl's father, Edward Hip, has lived in the United States for the last 22 years, according to his wife.", "subpage_snippet": "", "source": "www.nbcnews.com", "link": "https://www.nbcnews.com/news/us-news/ice-held-5-year-old-autistic-girl-massachusetts-pressure-father-surren-rcna233146", "content": "1 day ago · The girl's father, Edward Hip, has lived in the United States for the last 22 years, according to his wife."}
diff --git a/data/sampled_jsons/Elhage_et_al._2021_residual_stream_transformer_abstract_year_2021.jsonl b/data/sampled_jsons/Elhage_et_al._2021_residual_stream_transformer_abstract_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..289b8f4b0485fec49cd3611e33b0bbee7c155635
--- /dev/null
+++ b/data/sampled_jsons/Elhage_et_al._2021_residual_stream_transformer_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Residual Matrix Transformers: Scaling the Size of ...", "date": "", "ddg_snippet": "by B Mak · 2025 — Abstract :The residual stream acts as a memory bus where transformer layers both store and access features ( Elhage et al ., 2021 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.22696", "content": "by B Mak · 2025 — Abstract :The residual stream acts as a memory bus where transformer layers both store and access features ( Elhage et al ., 2021 )."}
+{"idx": 1, "title": "A Mathematical Framework for Transformer Circuits", "date": "", "ddg_snippet": "22 Dec 2021 — The residual stream is simply the sum of the output of all the previous layers and the original embedding. We generally think of the residual ...", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2021/framework/index.html", "content": "22 Dec 2021 — The residual stream is simply the sum of the output of all the previous layers and the original embedding. We generally think of the residual ..."}
+{"idx": 2, "title": "Residual Matrix Transformers: Scaling the Size of ...", "date": "", "ddg_snippet": "Abstract : The residual stream acts as a memory bus where transformer layers both store and access features ( Elhage et al ., 2021 ). We consider changing the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45278", "content": "Abstract : The residual stream acts as a memory bus where transformer layers both store and access features ( Elhage et al ., 2021 ). We consider changing the ..."}
+{"idx": 3, "title": "Exploring the Residual Stream of Transformers", "date": "", "ddg_snippet": "In this paper, we explore the residual stream of transformers to increase the interpretability. We find the mechanism behind residual connection is a direct ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.12141v1", "content": "In this paper, we explore the residual stream of transformers to increase the interpretability. We find the mechanism behind residual connection is a direct ..."}
+{"idx": 4, "title": "Residual Stream Analysis with Multi-Layer SAEs", "date": "", "ddg_snippet": "by T Lawson · Cited by 4 — We train a single SAE on the residual stream activations from every transformer layer and find latents active at multiple layers for different tokens.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=XAjfjizaKs", "content": "by T Lawson · Cited by 4 — We train a single SAE on the residual stream activations from every transformer layer and find latents active at multiple layers for different tokens."}
+{"idx": 5, "title": "Understanding Inter-layer Communication in Transformer ...", "date": "", "ddg_snippet": "by J Merullo · 2024 · Cited by 18 — Elhage et al . [ 2021 ] introduce a score for measuring how much weight matrices in a transformer communicate, but outside of toy models, it has been ... 47 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/70e5444e5f331f7f5431f302110b97af-Paper-Conference.pdf", "content": "by J Merullo · 2024 · Cited by 18 — Elhage et al . [ 2021 ] introduce a score for measuring how much weight matrices in a transformer communicate, but outside of toy models, it has been ... 47 pages"}
+{"idx": 6, "title": "8 Transformers", "date": "", "ddg_snippet": "In fact, Elhage et al . ( 2021 ) show that we can view attention heads as literally moving information from the residual stream of a neighboring token into the ... 27 pages", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~jurafsky/slp3/8.pdf", "content": "In fact, Elhage et al . ( 2021 ) show that we can view attention heads as literally moving information from the residual stream of a neighboring token into the ... 27 pages"}
+{"idx": 7, "title": "Your Transformer is Secretly Linear", "date": "", "ddg_snippet": "by A Razzhigaev · 2024 · Cited by 18 — This paper reveals a novel linear characteristic exclusive to transformer decoders, including models such as GPT, LLaMA, OPT, BLOOM.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.293.pdf", "content": "by A Razzhigaev · 2024 · Cited by 18 — This paper reveals a novel linear characteristic exclusive to transformer decoders, including models such as GPT, LLaMA, OPT, BLOOM."}
+{"idx": 8, "title": "ANALYZING TRANSFORMERS IN EMBEDDING SPACE", "date": "", "ddg_snippet": "by G Dar · Cited by 175 — We discuss the residual stream view of Transformers , and recapitulate a view of the attention layer parameters as interaction matrices WVO and WQK [Elhage et al ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=nlda8uNdwuJ", "content": "by G Dar · Cited by 175 — We discuss the residual stream view of Transformers , and recapitulate a view of the attention layer parameters as interaction matrices WVO and WQK [Elhage et al ..."}
+{"idx": 9, "title": "Compiled Transformers as a Laboratory for Interpretability", "date": "", "ddg_snippet": "by D Lindner · Cited by 96 — Elhage et al . ( 2021 ) consider the residual connections a core feature of the architecture and describe the model in terms of a residual stream that each layer ... 24 pages", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/file/771155abaae744e08576f1f3b4b7ac0d-Paper-Conference.pdf", "content": "by D Lindner · Cited by 96 — Elhage et al . ( 2021 ) consider the residual connections a core feature of the architecture and describe the model in terms of a residual stream that each layer ... 24 pages"}
diff --git a/data/sampled_jsons/Elhage_et_al_2021_residual_stream_transformer_circuits_year_2021.jsonl b/data/sampled_jsons/Elhage_et_al_2021_residual_stream_transformer_circuits_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7d8f97c30ea22ca1d220223848363189ce3dfd13
--- /dev/null
+++ b/data/sampled_jsons/Elhage_et_al_2021_residual_stream_transformer_circuits_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Mathematical Framework for Transformer Circuits", "date": "", "ddg_snippet": "Black et al . provide significant evidence for typical neurons in transformer language models being polysemantic. Elhage , et al ., \"A Mathematical Framework for Transformer Circuits \", Transformer Circuits Thread, 2021 .", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2021/framework/index.html", "content": "Black et al . provide significant evidence for typical neurons in transformer language models being polysemantic. Elhage , et al ., \"A Mathematical Framework for Transformer Circuits \", Transformer Circuits Thread, 2021 ."}
+{"idx": 1, "title": "Analysing the Residual Stream of Language Models", "date": "", "ddg_snippet": "Residual Stream We examine the Transformer architecture from the perspective of the residual stream [ Elhage et al ., 2021 , Olsson et al ., 2022].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.16090", "content": "Residual Stream We examine the Transformer architecture from the perspective of the residual stream [ Elhage et al ., 2021 , Olsson et al ., 2022]."}
+{"idx": 2, "title": "ICML Poster Residual Matrix Transformers : Scaling the Size of the...", "date": "", "ddg_snippet": "The residual stream acts as a memory bus where transformer layers both store and access features ( Elhage et al ., 2021 ).We call this model the Residual Matrix Transformer (RMT).", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45278", "content": "The residual stream acts as a memory bus where transformer layers both store and access features ( Elhage et al ., 2021 ).We call this model the Residual Matrix Transformer (RMT)."}
+{"idx": 3, "title": "Associative Memories as a Building Block in Transformers", "date": "", "ddg_snippet": "Residual streams ( Elhage et al ., 2021 ) embed each token zt ∈ [N] as xt := ezt + pt (causal) self-attention xt := xt + MHSA(xt , x1:t ) feed-forward xt := xt + MLP(xt ) residual stream xt is a sum of embeddings/“features”.", "subpage_snippet": "", "source": "alberto.bietti.me", "link": "https://alberto.bietti.me/files/talk_ow25.pdf", "content": "Residual streams ( Elhage et al ., 2021 ) embed each token zt ∈ [N] as xt := ezt + pt (causal) self-attention xt := xt + MHSA(xt , x1:t ) feed-forward xt := xt + MLP(xt ) residual stream xt is a sum of embeddings/“features”."}
+{"idx": 4, "title": "Tracr: Compiled Transformers as a", "date": "", "ddg_snippet": "When designing Tracr, we adopt the transformer circuits perspective, introduced by Elhage et al . ( 2021 ). This view (1) focuses on the transformer being a residual stream architecture and (2).", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/771155abaae744e08576f1f3b4b7ac0d-Paper-Conference.pdf", "content": "When designing Tracr, we adopt the transformer circuits perspective, introduced by Elhage et al . ( 2021 ). This view (1) focuses on the transformer being a residual stream architecture and (2)."}
+{"idx": 5, "title": "An investigation into the properties of residual streams in transformer ...", "date": "", "ddg_snippet": "Residual streams (Srivastava et al ., 2015) are a key component of the transformer architecture (Vaswani et al ., 2017) and are common in deep convolutional neural networks (He et al ., 2015).", "subpage_snippet": "", "source": "go281.user.srcf.net", "link": "https://go281.user.srcf.net/blog/research/residual-streams/", "content": "Residual streams (Srivastava et al ., 2015) are a key component of the transformer architecture (Vaswani et al ., 2017) and are common in deep convolutional neural networks (He et al ., 2015)."}
+{"idx": 6, "title": "How and Why Transformer Models Transformed NLP... | Deepgram", "date": "", "ddg_snippet": "Elhage , et al ( 2021 ). A Mathematical Framework for Transformer Circuits .", "subpage_snippet": "", "source": "deepgram.com", "link": "https://deepgram.com/learn/capturing-attention-decoding-the-success-of-transformer-models-in-natural-language-processing", "content": "Elhage , et al ( 2021 ). A Mathematical Framework for Transformer Circuits ."}
+{"idx": 7, "title": "Induction Circuits - LLMs are more than next-token predictors", "date": "", "ddg_snippet": "transformer _layer Elhage et al . 2021 . Elhage et al .[1] have an awesome diagram for this. Here I'm providing some extra steps to make the derivation more explicit", "subpage_snippet": "", "source": "www.jonvet.com", "link": "https://www.jonvet.com/blog/llm-induction-circuits", "content": "transformer _layer Elhage et al . 2021 . Elhage et al .[1] have an awesome diagram for this. Here I'm providing some extra steps to make the derivation more explicit"}
+{"idx": 8, "title": "Uncovering Intermediate Variables in Transformers using Circuit ...", "date": "", "ddg_snippet": "These layers produce addi-tive updates to the residual stream ( Elhage et al ., 2021 ). Intuitively, circuits that compute an intermediate variable should produce outputs that are partitioned according to that variable.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04919776v1/document", "content": "These layers produce addi-tive updates to the residual stream ( Elhage et al ., 2021 ). Intuitively, circuits that compute an intermediate variable should produce outputs that are partitioned according to that variable."}
+{"idx": 9, "title": "The Hidden Space of Transformer Language Adapters", "date": "", "ddg_snippet": "Specifically, for every transformer decoder block (Vaswani et al ., 2017), an adapter is stacked on top of the feed-forward network (FFN), such that it gets as input the hidden states after the FFN layer and outputs an update to every hidden state that is added via a residual connection.", "subpage_snippet": "", "source": "www.lsv.uni-saarland.de", "link": "https://www.lsv.uni-saarland.de/wp-content/uploads/2025/07/hidden_space_2024.acl-long.356.pdf", "content": "Specifically, for every transformer decoder block (Vaswani et al ., 2017), an adapter is stacked on top of the feed-forward network (FFN), such that it gets as input the hidden states after the FFN layer and outputs an update to every hidden state that is added via a residual connection."}
diff --git a/data/sampled_jsons/EntityErasure_AECM_learning_rate_implementation_details_training.jsonl b/data/sampled_jsons/EntityErasure_AECM_learning_rate_implementation_details_training.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6955415542f8c8e922c44a5c4a7c667cd7486040
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_AECM_learning_rate_implementation_details_training.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Asset Management Learning Portal - AECOM", "date": "", "ddg_snippet": "AECOM’s Asset Management (AM) Learning Portal provides access to e- learning materials and webinars developed by global leaders and IAM Endorsed Trainers.", "subpage_snippet": "", "source": "aecom.com", "link": "https://aecom.com/asset-management-learning-portal/", "content": "AECOM’s Asset Management (AM) Learning Portal provides access to e- learning materials and webinars developed by global leaders and IAM Endorsed Trainers."}
+{"idx": 1, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities with-out inducing unwanted sundries. To this end, we pro-pose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities with-out inducing unwanted sundries. To this end, we pro-pose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
+{"idx": 2, "title": "AECOM AM Learning - Online Training Portal", "date": "", "ddg_snippet": "The AECOM A sset Management Learning Portal offers a number of online asset management training options that you could access from anywhere in the world, using your tablet, laptop, PC or phone.", "subpage_snippet": "", "source": "aecom.kineoportal.com", "link": "https://aecom.kineoportal.com/", "content": "The AECOM A sset Management Learning Portal offers a number of online asset management training options that you could access from anywhere in the world, using your tablet, laptop, PC or phone."}
+{"idx": 3, "title": "6 Benefits of Using Our AEC&M LMS to Support Employee Training", "date": "", "ddg_snippet": "May 16, 2024 · As an industry-specific LMS, Pinnacle Series increases employee buy-in by making the benefits of learning more immediate. Ongoing training for organizations in the fields of Architecture, Engineering, Construction, and Manufacturing (AEC&M) is a mission-critical initiative.", "subpage_snippet": "", "source": "eaglepoint.com", "link": "https://eaglepoint.com/blog/6-benefits-of-using-our-aecm-lms-to-support-employee-training/", "content": "May 16, 2024 · As an industry-specific LMS, Pinnacle Series increases employee buy-in by making the benefits of learning more immediate. Ongoing training for organizations in the fields of Architecture, Engineering, Construction, and Manufacturing (AEC&M) is a mission-critical initiative."}
+{"idx": 4, "title": "2588359 - Learning configuration and implementation guides ...", "date": "", "ddg_snippet": "This KBA covers where to find the Learning configuration and implementation guides.", "subpage_snippet": "", "source": "userapps.support.sap.com", "link": "https://userapps.support.sap.com/sap/support/knowledge/en/2588359", "content": "This KBA covers where to find the Learning configuration and implementation guides."}
+{"idx": 5, "title": "Transformer Learning Rate Scheduling - apxml.com", "date": "", "ddg_snippet": "Learning rate scheduling defines a strategy for dynamically changing the learning rate during training . For Transformers, a common and highly effective approach involves a combination of a \"warmup\" phase followed by a \"decay\" phase.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/foundations-transformers-architecture/chapter-7-implementation-details-optimization/learning-rate-scheduling", "content": "Learning rate scheduling defines a strategy for dynamically changing the learning rate during training . For Transformers, a common and highly effective approach involves a combination of a \"warmup\" phase followed by a \"decay\" phase."}
+{"idx": 6, "title": "Cyclical Learning Rates with Keras and Deep... - PyImageSearch", "date": "", "ddg_snippet": "In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model.", "subpage_snippet": "", "source": "pyimagesearch.com", "link": "https://pyimagesearch.com/2019/07/29/cyclical-learning-rates-with-keras-and-deep-learning/", "content": "In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model."}
+{"idx": 7, "title": "machinelearningmastery.com/understand-the-dynamics-of- learning ...", "date": "", "ddg_snippet": "To ensure that training continued for more than 20 epochs learning rate was...", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/", "content": "To ensure that training continued for more than 20 epochs learning rate was..."}
+{"idx": 8, "title": "Guide to Pytorch Learning Rate Scheduling", "date": "", "ddg_snippet": "Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/code/isbhargav/guide-to-pytorch-learning-rate-scheduling", "content": "Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources."}
+{"idx": 9, "title": "Mathematics behind the Neural Network – Study Machine Learning", "date": "", "ddg_snippet": "Setting Dynamic Learning Rate While Training the Neural Network. Neural Network: Introduction to Learning Rate . Implementation of Neural Network from scratch using NumPy.", "subpage_snippet": "", "source": "studymachinelearning.com", "link": "https://studymachinelearning.com/mathematics-behind-the-neural-network/", "content": "Setting Dynamic Learning Rate While Training the Neural Network. Neural Network: Introduction to Learning Rate . Implementation of Neural Network from scratch using NumPy."}
diff --git a/data/sampled_jsons/EntityErasure_Amodal_Entity_Completion_Model_AECM_implementation_details_learning_rate.jsonl b/data/sampled_jsons/EntityErasure_Amodal_Entity_Completion_Model_AECM_implementation_details_learning_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf00c032f997bdd1c9209a1b911591c9ff14f960
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_Amodal_Entity_Completion_Model_AECM_implementation_details_learning_rate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "It contains a amodal en-tity segmentation model (AESM) and a amodal entity completion model ( AECM ). The output of AESM serves as input for AECM , AECM then injects content information into the denoising UNet with entity attention (EA).", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "It contains a amodal en-tity segmentation model (AESM) and a amodal entity completion model ( AECM ). The output of AESM serves as input for AECM , AECM then injects content information into the denoising UNet with entity attention (EA)."}
+{"idx": 1, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure ."}
+{"idx": 2, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion , such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate unpredictable ...", "subpage_snippet": "", "source": "zyxunh.github.io", "link": "https://zyxunh.github.io/EntityErasure-ProjectPage/", "content": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion , such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate unpredictable ..."}
+{"idx": 3, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub."}
+{"idx": 4, "title": "Sequential Amodal Segmentation via Cumulative Occlusion Learning", "date": "", "ddg_snippet": "Addressing this need, we introduce a diffusion model with cumulative occlusion learning designed for sequential amodal segmentation of objects with uncertain categories.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.05791v1", "content": "Addressing this need, we introduce a diffusion model with cumulative occlusion learning designed for sequential amodal segmentation of objects with uncertain categories."}
+{"idx": 5, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "If there are errors in the generated amodal segmentation, it will affect the generation of the final results of our model . The feasible solution is to adopt multi-task learning , using traditional amodal segmentation datasets in combination with erasure datasets to learn both tasks, thereby enhancing the unified amodal segmentation ability.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_EntityErasure_Erasing_Entity_CVPR_2025_supplemental.pdf", "content": "If there are errors in the generated amodal segmentation, it will affect the generation of the final results of our model . The feasible solution is to adopt multi-task learning , using traditional amodal segmentation datasets in combination with erasure datasets to learn both tasks, thereby enhancing the unified amodal segmentation ability."}
+{"idx": 6, "title": "unhzyx/entity_erasure at main - Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/unhzyx/entity_erasure/tree/main", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 7, "title": "Qing Zhang - Homepage", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang*, Yitong Wang, Yongwei Nie, Wei-Shi Zheng CVPR 2025 Project | Code | Paper RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images Junjin Xiao, Qing Zhang*, Yongwei Nie, Lei Zhu, Wei-Shi Zheng CVPR 2025", "subpage_snippet": "", "source": "www.zhangqing-home.net", "link": "https://www.zhangqing-home.net/", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang*, Yitong Wang, Yongwei Nie, Wei-Shi Zheng CVPR 2025 Project | Code | Paper RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images Junjin Xiao, Qing Zhang*, Yongwei Nie, Lei Zhu, Wei-Shi Zheng CVPR 2025"}
+{"idx": 8, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion , such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion , such that the region to inpaint takes only entities in the non-inpainting area as ..."}
+{"idx": 9, "title": "AcademicDissect/detailed_paper_collection/Image_Inpainting.md ... - GitHub", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Tags: Image Inpainting, Diffusion Models , Amodal Segmentation, Entity Completion , Diffusion-Based Inpainting", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcademicDissect/AcademicDissect/blob/main/detailed_paper_collection/Image_Inpainting.md", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Tags: Image Inpainting, Diffusion Models , Amodal Segmentation, Entity Completion , Diffusion-Based Inpainting"}
diff --git a/data/sampled_jsons/EntityErasure_CVPR_2025_implementation_details_learning_rate_AECM.jsonl b/data/sampled_jsons/EntityErasure_CVPR_2025_implementation_details_learning_rate_AECM.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..96996bfaf69c58b46fb7bbaaf65fecb955539852
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_CVPR_2025_implementation_details_learning_rate_AECM.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR Poster EntityErasure: Erasing Entity Cleanly via Amodal ...", "date": "", "ddg_snippet": "Abstract: This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34016", "content": "Abstract: This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ..."}
+{"idx": 1, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "All parameters of AESM are trainable. We set the batch size to 64, the number of iterations to 80,000, and the learning rate to 1e-5, respec-tively. For AECM , the main branch Unet is frozen. We use the AdamW [24] optimizer. We set the batch size to 128, the number of iterations to 20,000 iterations, and the learn-ing rate to 5e-5.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "All parameters of AESM are trainable. We set the batch size to 64, the number of iterations to 80,000, and the learning rate to 1e-5, respec-tively. For AECM , the main branch Unet is frozen. We use the AdamW [24] optimizer. We set the batch size to 128, the number of iterations to 20,000 iterations, and the learn-ing rate to 5e-5."}
+{"idx": 2, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
+{"idx": 3, "title": "New Research Presented at CVPR 2025 Demonstrates ...", "date": "", "ddg_snippet": "Jun 10, 2025 · New Research Presented at CVPR 2025 Demonstrates Breakthroughs in 3D Evaluation and Advanced Reasoning Capabilities Technical papers deliver milestones increasing artificial intelligence capabilities Nashville, Tenn., 10 June 2025 – Today, IEEE Computer Society (CS) and the Computer Vision Foundation (CVF) released details of the technical program for the 2025 Computer Vision and Pattern ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/News/Technical_Program", "content": "Jun 10, 2025 · New Research Presented at CVPR 2025 Demonstrates Breakthroughs in 3D Evaluation and Advanced Reasoning Capabilities Technical papers deliver milestones increasing artificial intelligence capabilities Nashville, Tenn., 10 June 2025 – Today, IEEE Computer Society (CS) and the Computer Vision Foundation (CVF) released details of the technical program for the 2025 Computer Vision and Pattern ..."}
+{"idx": 4, "title": "Apple Machine Learning Research at CVPR 2025", "date": "", "ddg_snippet": "Jun 10, 2025 · Learn More about Apple ML Research at CVPR 2025 CVPR brings together the community of researchers advancing the state of the art in computer vision, and Apple is proud to again share innovative new research at the event and connect with the community attending it.", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/cvpr-2025", "content": "Jun 10, 2025 · Learn More about Apple ML Research at CVPR 2025 CVPR brings together the community of researchers advancing the state of the art in computer vision, and Apple is proud to again share innovative new research at the event and connect with the community attending it."}
+{"idx": 5, "title": "Project 2025 Tracker - Home", "date": "", "ddg_snippet": "A comprehensive, community-driven initiative to track the implementation of Project 2025 's policy proposals.", "subpage_snippet": "", "source": "www.project2025.observer", "link": "https://www.project2025.observer/", "content": "A comprehensive, community-driven initiative to track the implementation of Project 2025 's policy proposals."}
+{"idx": 6, "title": "GitHub - Thaparoshan143/vggt_python: [ CVPR 2025 Best Paper...]", "date": "", "ddg_snippet": "[ CVPR 2025 Best Paper Award] VGGT: Visual Geometry Grounded Transformer (Testing only).Full license details are available here. Access to the checkpoint requires completing an application form, which is processed by a system similar to LLaMA's approval workflow, automatically.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Thaparoshan143/vggt_python", "content": "[ CVPR 2025 Best Paper Award] VGGT: Visual Geometry Grounded Transformer (Testing only).Full license details are available here. Access to the checkpoint requires completing an application form, which is processed by a system similar to LLaMA's approval workflow, automatically."}
+{"idx": 7, "title": "Catching the Details : Self-Distilled RoI Predictors for Fine-Grained...", "date": "", "ddg_snippet": "Implementation Details . To train our proposed SD-RPN, we generate pseudo-labels from a combined dataset of GQA (Hudson & Manning, 2019) and OCR-VQA (Mishra et al., 2019) , which provides supervision for natural scenes and text-rich images, respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16944v1", "content": "Implementation Details . To train our proposed SD-RPN, we generate pseudo-labels from a combined dataset of GQA (Hudson & Manning, 2019) and OCR-VQA (Mishra et al., 2019) , which provides supervision for natural scenes and text-rich images, respectively."}
+{"idx": 8, "title": "Conferences on Computer Science 2025 , 2026 (page 7)", "date": "", "ddg_snippet": "Find information on Conferences on Computer Science 2025 , 2026 (page 7), including dates, locations, price ranges, and more.", "subpage_snippet": "", "source": "www.clocate.com", "link": "https://www.clocate.com/conferences-on+computer-science/c3UtMTIz/p7/", "content": "Find information on Conferences on Computer Science 2025 , 2026 (page 7), including dates, locations, price ranges, and more."}
+{"idx": 9, "title": "[ CVPR 2022] TopFormer: Token Pyramid Transformer for Mobile...", "date": "", "ddg_snippet": "3.1.2. Implementation Details . MMSegmentation과 Pytorch위에서 구현이 built되었다.초기 learning rate 0.00012, weight decay 0.01. poly learning rate scheduler factor 1.0 사용.", "subpage_snippet": "", "source": "velog.io", "link": "https://velog.io/@yellofi/CVPR-2022-TopFormer-Token-Pyramid-Transformer-for-Mobile-Semantic-Segmentation", "content": "3.1.2. Implementation Details . MMSegmentation과 Pytorch위에서 구현이 built되었다.초기 learning rate 0.00012, weight decay 0.01. poly learning rate scheduler factor 1.0 사용."}
diff --git a/data/sampled_jsons/EntityErasure_IoS_Intersection_over_Segmentation_Equation_7_threshold_0.5.jsonl b/data/sampled_jsons/EntityErasure_IoS_Intersection_over_Segmentation_Equation_7_threshold_0.5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..17f10a0bd73854e2f119301044819fe03a35fa0e
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_IoS_Intersection_over_Segmentation_Equation_7_threshold_0.5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "As shown Fig. 4, we use the pre-trained state-of-the-art open-world segmentation model EntitySeg [30] to predict the en-tity segmentation of the generated result, and then calculate the Intersection over Self ( IoS ) between each entity with in-painting mask by:", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "As shown Fig. 4, we use the pre-trained state-of-the-art open-world segmentation model EntitySeg [30] to predict the en-tity segmentation of the generated result, and then calculate the Intersection over Self ( IoS ) between each entity with in-painting mask by:"}
+{"idx": 1, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094156", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ..."}
+{"idx": 2, "title": "Instance segmentation evaluation criteria - SoftwareMill", "date": "", "ddg_snippet": "Aug 16, 2022 · IoU The basic evaluation criterion for measuring the quality of the generated masks is Intersection over Union (IoU). It measures the intersection area between two bounding boxes or, in the case of instance segmentation , it measures the intersection area between two masks. The idea of IoU is best explained by the image below.", "subpage_snippet": "", "source": "softwaremill.com", "link": "https://softwaremill.com/instance-segmentation-evaluation-criteria/", "content": "Aug 16, 2022 · IoU The basic evaluation criterion for measuring the quality of the generated masks is Intersection over Union (IoU). It measures the intersection area between two bounding boxes or, in the case of instance segmentation , it measures the intersection area between two masks. The idea of IoU is best explained by the image below."}
+{"idx": 3, "title": "Image segmentation metrics - Keras", "date": "", "ddg_snippet": "Intersection - Over -Union is a common evaluation metric for semantic image segmentation . To compute IoUs, the predictions are accumulated in a confusion matrix, weighted by sample_weight and the metric is then calculated from it.", "subpage_snippet": "", "source": "keras.io", "link": "https://keras.io/api/metrics/segmentation_metrics/", "content": "Intersection - Over -Union is a common evaluation metric for semantic image segmentation . To compute IoUs, the predictions are accumulated in a confusion matrix, weighted by sample_weight and the metric is then calculated from it."}
+{"idx": 4, "title": "A Gentle Guide to Intersection over Union (IoU) - learnml.io", "date": "", "ddg_snippet": "What is IoU? IoU stands for Intersection over Union. In full, it’s referred to as Area of Intersection over Area of Union. In object detection tasks, IoU a way to measure how much a one bounding box overlaps another bounding box. Or said another way, “How much does this prediction box overlap the ground truth box?”.", "subpage_snippet": "", "source": "www.learnml.io", "link": "https://www.learnml.io/posts/a-gentle-guide-to-intersection-over-union/", "content": "What is IoU? IoU stands for Intersection over Union. In full, it’s referred to as Area of Intersection over Area of Union. In object detection tasks, IoU a way to measure how much a one bounding box overlaps another bounding box. Or said another way, “How much does this prediction box overlap the ground truth box?”."}
+{"idx": 5, "title": "Intersection over Union (IoU): Definition, Calculation, Code", "date": "", "ddg_snippet": "Intersection over Union is a popular metric to measure localization accuracy and compute localization errors in object detection models. It calculates the amount of overlapping between two bounding boxes—a predicted bounding box and a ground truth bounding box.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/intersection-over-union-guide", "content": "Intersection over Union is a popular metric to measure localization accuracy and compute localization errors in object detection models. It calculates the amount of overlapping between two bounding boxes—a predicted bounding box and a ground truth bounding box."}
+{"idx": 6, "title": "Intersection Over Union IoU in Object Detection Segmentation", "date": "", "ddg_snippet": "Intersection Over Union (IoU) is a helper metric for evaluating object detection and segmentation model. Learn from the basics to implementing IoU from scratch.The first prediction is True Positive as the IoU threshold is 0 . 5 . If we set the threshold at 0.97, it becomes a False Positive.", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/intersection-over-union-iou-in-object-detection-and-segmentation/", "content": "Intersection Over Union (IoU) is a helper metric for evaluating object detection and segmentation model. Learn from the basics to implementing IoU from scratch.The first prediction is True Positive as the IoU threshold is 0 . 5 . If we set the threshold at 0.97, it becomes a False Positive."}
+{"idx": 7, "title": "Understanding Intersection over Union for Model Accuracy", "date": "", "ddg_snippet": "The intersection - over -union (IoU) threshold acts as a gatekeeper, classifying predicted bounding boxes as true positives if they pass the threshold and false positives if they fall below it. By adjusting the threshold , we can control the trade-off between precision and recall.", "subpage_snippet": "", "source": "viso.ai", "link": "https://viso.ai/computer-vision/intersection-over-union-iou/", "content": "The intersection - over -union (IoU) threshold acts as a gatekeeper, classifying predicted bounding boxes as true positives if they pass the threshold and false positives if they fall below it. By adjusting the threshold , we can control the trade-off between precision and recall."}
+{"idx": 8, "title": "Intersection over Union (IoU) for object detection | SuperAnnotate", "date": "", "ddg_snippet": "Intersection over Union (IoU) is a measure that shows how well the prediction bounding box aligns with the ground truth box. It's one of the main metrics for evaluating the accuracy of object detection algorithms and helps distinguish between \"correct detection\" and \"incorrect detection\".", "subpage_snippet": "", "source": "www.superannotate.com", "link": "https://www.superannotate.com/blog/intersection-over-union-for-object-detection", "content": "Intersection over Union (IoU) is a measure that shows how well the prediction bounding box aligns with the ground truth box. It's one of the main metrics for evaluating the accuracy of object detection algorithms and helps distinguish between \"correct detection\" and \"incorrect detection\"."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
diff --git a/data/sampled_jsons/EntityErasure_entity_attention_MSN_metric_Table_3_Figure_8_Section_3.2.jsonl b/data/sampled_jsons/EntityErasure_entity_attention_MSN_metric_Table_3_Figure_8_Section_3.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a0736ee580d53737c40e84d92d3b2ee2a8809730
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_entity_attention_MSN_metric_Table_3_Figure_8_Section_3.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EntityErasure : Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "Entity Reference Encoder (ERE) Amodal Entity Completion Model (AECM). Figure 2. Overview of EntityErasure . 3 . 2 . Amodal Entity Completion. The initial diffusion-based inpainting method directly adds the inpainting conditional input on text-to-image model and then fine-tunes this model.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "Entity Reference Encoder (ERE) Amodal Entity Completion Model (AECM). Figure 2. Overview of EntityErasure . 3 . 2 . Amodal Entity Completion. The initial diffusion-based inpainting method directly adds the inpainting conditional input on text-to-image model and then fine-tunes this model."}
+{"idx": 1, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34016", "content": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference ..."}
+{"idx": 2, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure ."}
+{"idx": 3, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub."}
+{"idx": 4, "title": "unhzyx/entity_erasure at main - Hugging Face", "date": "", "ddg_snippet": "May 12, 2025 · We’re on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/unhzyx/entity_erasure/tree/main", "content": "May 12, 2025 · We’re on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 5, "title": "Publications - zhangqing-home.net", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng", "subpage_snippet": "", "source": "www.zhangqing-home.net", "link": "https://www.zhangqing-home.net/full_publications.html", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng"}
+{"idx": 6, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
+{"idx": 7, "title": "Meters to Feet conversion calculator - SimpleWebTool", "date": "", "ddg_snippet": "The distancedin feet (ft) is equal to the distance d in meters (m) divided by 0.3048. Formula: d(ft) = d(m) * 0.3048. Meters to Feet conversion table .", "subpage_snippet": "", "source": "simplewebtool.firebaseapp.com", "link": "https://simplewebtool.firebaseapp.com/converters/length/meterstofeet/meterstofeet.html", "content": "The distancedin feet (ft) is equal to the distance d in meters (m) divided by 0.3048. Formula: d(ft) = d(m) * 0.3048. Meters to Feet conversion table ."}
+{"idx": 8, "title": "Millimeters to Meters conversion: mm to m calculator", "date": "", "ddg_snippet": "Millimeters to Meters (mm to m) conversion calculator for Length conversions with additional tables and formulas.Note: You can increase or decrease the accuracy of this answer by selecting the number of significant figures required from the options above the result.", "subpage_snippet": "", "source": "www.metric-conversions.org", "link": "https://www.metric-conversions.org/length/millimeters-to-meters.htm", "content": "Millimeters to Meters (mm to m) conversion calculator for Length conversions with additional tables and formulas.Note: You can increase or decrease the accuracy of this answer by selecting the number of significant figures required from the options above the result."}
+{"idx": 9, "title": "Inches to Meters (in to m) conversion calculator", "date": "", "ddg_snippet": "Inches to Meters conversion. From. Millimeter (mm) Centimeter (cm) Meter (m) Kilometer (km) Inch (in) Foot (ft) Yard (yd) Mile (mi).RAPID TABLES .", "subpage_snippet": "", "source": "www.rapidtables.com", "link": "https://www.rapidtables.com/convert/length/inch-to-meter.html", "content": "Inches to Meters conversion. From. Millimeter (mm) Centimeter (cm) Meter (m) Kilometer (km) Inch (in) Foot (ft) Yard (yd) Mile (mi).RAPID TABLES ."}
diff --git a/data/sampled_jsons/EntityErasure_paper_entity_attention_mechanism_MSN_metric_reduction_cross_attention.jsonl b/data/sampled_jsons/EntityErasure_paper_entity_attention_mechanism_MSN_metric_reduction_cross_attention.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f218242e86bfcfa071fdb012cb4cf729c1e61cd
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_paper_entity_attention_mechanism_MSN_metric_reduction_cross_attention.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "Particularly, EA is designed to con-strain the cross - attention mask to focus only on the corresponding entities, ensuring that each region only looks for content informa-tion of its corresponding entity , so that the final result is aligned with the amodal entity segmentation. the completion.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "Particularly, EA is designed to con-strain the cross - attention mask to focus only on the corresponding entities, ensuring that each region only looks for content informa-tion of its corresponding entity , so that the final result is aligned with the amodal entity segmentation. the completion."}
+{"idx": 1, "title": "Attention Mechanisms for Entity Recognition and Extraction", "date": "", "ddg_snippet": "Attention mechanisms in neural network architectures closely mimic the human ability to selectively focus on segments of information while ignoring others. This capability is crucial for ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@zbabar/attention-mechanisms-for-entity-recognition-and-extraction-e70fd49acc79", "content": "Attention mechanisms in neural network architectures closely mimic the human ability to selectively focus on segments of information while ignoring others. This capability is crucial for ..."}
+{"idx": 2, "title": "Incorporating Entity Type-Aware and Word-Word Relation-Aware Attention ...", "date": "", "ddg_snippet": "Our method obtains the other attention from the hidden states of entity types and utilizes it in self- and cross - attention mechanisms in the decoder. We transform the entity boundary information in the sequence into word-word relations and extract the corresponding embedding into the cross - attention mechanism .", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2079-9292/13/7/1407", "content": "Our method obtains the other attention from the hidden states of entity types and utilizes it in self- and cross - attention mechanisms in the decoder. We transform the entity boundary information in the sequence into word-word relations and extract the corresponding embedding into the cross - attention mechanism ."}
+{"idx": 3, "title": "ML - Attention mechanism - GeeksforGeeks", "date": "", "ddg_snippet": "Attention mechanism is a type of neural network that helps a model focus on specific parts of the input data, it is done by assigning weights to different elements in input which helps the model to decide which parts of information are most importart.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/artificial-intelligence/ml-attention-mechanism/", "content": "Attention mechanism is a type of neural network that helps a model focus on specific parts of the input data, it is done by assigning weights to different elements in input which helps the model to decide which parts of information are most importart."}
+{"idx": 4, "title": "A hybrid attention mechanism for multi-target entity relation ...", "date": "", "ddg_snippet": "The paper presents a hybrid attention that uses graph neural networks for relation extraction, as well as extracting and establishing a directed relationship between entities in a given sentence.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666827022001190", "content": "The paper presents a hybrid attention that uses graph neural networks for relation extraction, as well as extracting and establishing a directed relationship between entities in a given sentence."}
+{"idx": 5, "title": "Document-level relation extraction with entity mentions deep attention", "date": "", "ddg_snippet": "In this paper , we propose an entity mentions deep attention framework that efficiently infers entity relationships through entity structure and contextual information. Firstly, a structural dependency module of entities is designed to achieve interaction between different mentions of the entity .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0885230823000931", "content": "In this paper , we propose an entity mentions deep attention framework that efficiently infers entity relationships through entity structure and contextual information. Firstly, a structural dependency module of entities is designed to achieve interaction between different mentions of the entity ."}
+{"idx": 6, "title": "Attention-based interactive multi-level feature fusion for named entity ...", "date": "", "ddg_snippet": "Therefore, we propose a feature fusion strategy based on cross - attention , which utilizes attention mechanisms to compute attention scores between word-level and character-level features and ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-86718-0", "content": "Therefore, we propose a feature fusion strategy based on cross - attention , which utilizes attention mechanisms to compute attention scores between word-level and character-level features and ..."}
+{"idx": 7, "title": "Boosting the Speed of Entity Alignment 10: Dual Attention Matching ...", "date": "", "ddg_snippet": "In this paper , we propose a novel KG encoder Dual Attention Matching Network — (Dual-AMN), which not only models both intra-graph and cross -graph information smartly, but also greatly reduces computational complexity. Furthermore, we Normalized Hard Sample Mining Loss propose the to smoothly select hard negative samples with reduced loss shift.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.15452", "content": "In this paper , we propose a novel KG encoder Dual Attention Matching Network — (Dual-AMN), which not only models both intra-graph and cross -graph information smartly, but also greatly reduces computational complexity. Furthermore, we Normalized Hard Sample Mining Loss propose the to smoothly select hard negative samples with reduced loss shift."}
+{"idx": 8, "title": "Cross-Lingual Named Entity Recognition Based on Attention and ...", "date": "", "ddg_snippet": "To address this problem, this paper proposes a method for cross -lingual entity recognition based on an attention mechanism and adversarial training, using resource-rich language annotation data to migrate to low-resource languages for named entity recognition tasks and outputting changing semantic vectors through the attention mechanism to ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/13/4/2548", "content": "To address this problem, this paper proposes a method for cross -lingual entity recognition based on an attention mechanism and adversarial training, using resource-rich language annotation data to migrate to low-resource languages for named entity recognition tasks and outputting changing semantic vectors through the attention mechanism to ..."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
diff --git a/data/sampled_jsons/EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_Yu_Ren_Han_Wang_Liang_Shi_mean_angular_er.jsonl b/data/sampled_jsons/EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_Yu_Ren_Han_Wang_Liang_Shi_mean_angular_er.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e9b94cf980e127936f0c2d7057a32bab0ff29136
--- /dev/null
+++ b/data/sampled_jsons/EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_Yu_Ren_Han_Wang_Liang_Shi_mean_angular_er.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "Eventps : Real - time photometric stereo using an event camera . In CVPR, pages 9602–9611, 2024.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "Eventps : Real - time photometric stereo using an event camera . In CVPR, pages 9602–9611, 2024."}
+{"idx": 1, "title": "CVPR Poster EventPS: Real-Time Photometric Stereo Using an", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera . ... real -world scenarios, unleashing the ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/poster/31806", "content": "This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera . ... real -world scenarios, unleashing the ..."}
+{"idx": 2, "title": "Jinxiu Liang", "date": "", "ddg_snippet": "... collaborated with these inspiring professors: Hui Ji (NUS), Haibin Ling (SBU), and ... EventPS : Real - Time Photometric Stereo Using an Event Camera", "subpage_snippet": "", "source": "sherrycattt.github.io", "link": "https://sherrycattt.github.io/", "content": "... collaborated with these inspiring professors: Hui Ji (NUS), Haibin Ling (SBU), and ... EventPS : Real - Time Photometric Stereo Using an Event Camera"}
+{"idx": 3, "title": "Jinxiu Liang - Google Scholar", "date": "", "ddg_snippet": "Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance ... EventPS : Real - time photometric stereo using an event camera", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=wR2OMogAAAAJ&hl=nl", "content": "Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance ... EventPS : Real - time photometric stereo using an event camera"}
+{"idx": 4, "title": "Jinxiu Liang - Google Scholar", "date": "", "ddg_snippet": "Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance ... EventPS : Real - time photometric stereo using an event camera", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=wR2OMogAAAAJ&hl=de", "content": "Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance ... EventPS : Real - time photometric stereo using an event camera"}
+{"idx": 5, "title": "Jin Han", "date": "", "ddg_snippet": "EventPS : Real - Time Photometric Stereo Using an Event Camera Bohan Yu , Jieji Ren , Jin Han , Feishi Wang , Jinxiu Liang , and Boxin Shi IEEE Conference ...", "subpage_snippet": "", "source": "hjynwa.github.io", "link": "https://hjynwa.github.io/", "content": "EventPS : Real - Time Photometric Stereo Using an Event Camera Bohan Yu , Jieji Ren , Jin Han , Feishi Wang , Jinxiu Liang , and Boxin Shi IEEE Conference ..."}
+{"idx": 6, "title": "EvAnimate: Event-conditioned Image-to-Video Generation for", "date": "", "ddg_snippet": "Experiments using simulated event data with diverse motion patterns and a newly collected real -world event dataset validate the practical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.18552v2", "content": "Experiments using simulated event data with diverse motion patterns and a newly collected real -world event dataset validate the practical ..."}
+{"idx": 7, "title": "Jinxiu Liang - Google Scholar", "date": "", "ddg_snippet": "Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance ... EventPS : Real - Time Photometric Stereo Using an Event Camera", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=wR2OMogAAAAJ&hl=fr", "content": "Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance ... EventPS : Real - Time Photometric Stereo Using an Event Camera"}
+{"idx": 8, "title": "Jin Han - Google Scholar", "date": "", "ddg_snippet": "Evintsr-net: Event guided multiple latent frames reconstruction and super-resolution ... EventPS : Real - time photometric stereo using an event camera", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=qTGwUe4AAAAJ&hl=it", "content": "Evintsr-net: Event guided multiple latent frames reconstruction and super-resolution ... EventPS : Real - time photometric stereo using an event camera"}
+{"idx": 9, "title": "Jinxiu Liang - Google Scholar", "date": "", "ddg_snippet": "EventPS : Real - time photometric stereo using an event camera ... Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance ...", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=wR2OMogAAAAJ&hl=fi", "content": "EventPS : Real - time photometric stereo using an event camera ... Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance ..."}
diff --git a/data/sampled_jsons/EventPS_Real-time_photometric_stereo_event_camera_Yu_2024_year_2024.jsonl b/data/sampled_jsons/EventPS_Real-time_photometric_stereo_event_camera_Yu_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e1bc01750371d042346c6abb43494a251fc5181e
--- /dev/null
+++ b/data/sampled_jsons/EventPS_Real-time_photometric_stereo_event_camera_Yu_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10655016", "content": "Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional ..."}
+{"idx": 2, "title": "EventPS: Real-time photometric stereo using an event camera - YouTube", "date": "", "ddg_snippet": "This is the video of the following work: Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, and Boxin Shi. EventPS : Real-time photometric stereo using ...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=-TTp0zJKNPU", "content": "This is the video of the following work: Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, and Boxin Shi. EventPS : Real-time photometric stereo using ..."}
+{"idx": 3, "title": "Yu Bohan from Peking University: EventPS - Real-time Photometric Stereo ...", "date": "", "ddg_snippet": "The paper \" EventPS : Real-Time Photometric Stereo using an Event Camera \" is the first to use the unique properties of event cameras to achieve real-time photometric stereo .", "subpage_snippet": "", "source": "inf.news", "link": "https://inf.news/en/tech/4be4c17d4830518b9004d13cb772438e.html", "content": "The paper \" EventPS : Real-Time Photometric Stereo using an Event Camera \" is the first to use the unique properties of event cameras to achieve real-time photometric stereo ."}
+{"idx": 4, "title": "Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera@CVPR2024@CVF", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera@CVPR2024@CVF", "content": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional ..."}
+{"idx": 5, "title": "Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey", "date": "", "ddg_snippet": "Traditional photometric stereo techniques require captur-ing multiple high dynamic range images under different lighting conditions, which limits their speed and real-time applicability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.09971", "content": "Traditional photometric stereo techniques require captur-ing multiple high dynamic range images under different lighting conditions, which limits their speed and real-time applicability."}
+{"idx": 6, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency."}
+{"idx": 7, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , EventPS estimates surface nor-mal only from the radiance changes, significantly enhancing data efficiency.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YH3f8jq3lz", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , EventPS estimates surface nor-mal only from the radiance changes, significantly enhancing data efficiency."}
+{"idx": 8, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) Abstract: Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper ...", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/eventps-real-time-photometric-stereo-using-an-event-camera/", "content": "Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) Abstract: Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper ..."}
+{"idx": 9, "title": "GitHub Pages - Jin Han", "date": "", "ddg_snippet": "2024 EventPS : Real-Time Photometric Stereo Using an Event Camera Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, and Boxin Shi IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024 Best Paper Runner-up (2/2719) [pdf] [code]", "subpage_snippet": "", "source": "hjynwa.github.io", "link": "https://hjynwa.github.io/", "content": "2024 EventPS : Real-Time Photometric Stereo Using an Event Camera Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, and Boxin Shi IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024 Best Paper Runner-up (2/2719) [pdf] [code]"}
diff --git a/data/sampled_jsons/EventPS_Yu_CVPR_2024_experimental_results_table_3D_printed_objects_MAE.jsonl b/data/sampled_jsons/EventPS_Yu_CVPR_2024_experimental_results_table_3D_printed_objects_MAE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..41d63d6b065acdf487eea1cc007eecd86371d636
--- /dev/null
+++ b/data/sampled_jsons/EventPS_Yu_CVPR_2024_experimental_results_table_3D_printed_objects_MAE.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency."}
+{"idx": 1, "title": "Monad-Cube/CVPR-2024-Highlight-Oral - GitHub", "date": "", "ddg_snippet": "Collection of Highlight papers. Contribute to Monad-Cube/ CVPR - 2024 -Highlight-Oral development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Monad-Cube/CVPR-2024-Highlight-Oral", "content": "Collection of Highlight papers. Contribute to Monad-Cube/ CVPR - 2024 -Highlight-Oral development by creating an account on GitHub."}
+{"idx": 2, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10655016", "content": "Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
+{"idx": 3, "title": "CVPR 2024最佳论文分享┆EventPS: 基于事件相机的实时光度立体视觉", "date": "", "ddg_snippet": "本文介绍了 CVPR 2024 的最佳论文提名，该论文利用事件相机的独特属性，实现了实时光度立体视觉。 该算法在传统和深度学习领域均取得成功。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/audyxiao001/article/details/140520624", "content": "本文介绍了 CVPR 2024 的最佳论文提名，该论文利用事件相机的独特属性，实现了实时光度立体视觉。 该算法在传统和深度学习领域均取得成功。"}
+{"idx": 4, "title": "Workbook: CVPR 2024 - Tableau Software", "date": "", "ddg_snippet": "CVPR 2024 (All) Computer Vision and Pattern Recognition Conference Seattle | June 17-21, 2024", "subpage_snippet": "", "source": "public.tableau.com", "link": "https://public.tableau.com/views/CVPR2024/CVPRtrends?:showVizHome=no", "content": "CVPR 2024 (All) Computer Vision and Pattern Recognition Conference Seattle | June 17-21, 2024"}
+{"idx": 5, "title": "CVPR 2024 Accepted Paper List", "date": "", "ddg_snippet": "- Overview: This table presents papers from the CVPR conference, year 2024 . - Filtering: By default, the table loads the first 100 records. You can use the filter box under each column header to search within these loaded entries. Each column displays a \"Per Column Match #\" to indicate how many results match that column's filter.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/cvpr-paper-list/cvpr-2024-paper-list/", "content": "- Overview: This table presents papers from the CVPR conference, year 2024 . - Filtering: By default, the table loads the first 100 records. You can use the filter box under each column header to search within these loaded entries. Each column displays a \"Per Column Match #\" to indicate how many results match that column's filter."}
+{"idx": 6, "title": "CVPR 2024 Open Access Repository", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to estimate the surface normal of an object . However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "Photometric stereo is a well-established technique to estimate the surface normal of an object . However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal ..."}
+{"idx": 7, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of ..."}
+{"idx": 8, "title": "Awesome CVPR 2024 Papers, Workshops, Challenges, and Tutorials! - GitHub", "date": "", "ddg_snippet": "Awesome CVPR 2024 Papers, Workshops, Challenges, and Tutorials! The 2024 Conference on Computer Vision and Pattern Recognition ( CVPR ) received 11,532 valid paper submissions, and only 2,719 were accepted, for an overall acceptance rate of about 23.6%. Below is a list of the papers, posters, challenges, workshops, and datasets I'm most excited ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/harpreetsahota204/awesome-cvpr-2024", "content": "Awesome CVPR 2024 Papers, Workshops, Challenges, and Tutorials! The 2024 Conference on Computer Vision and Pattern Recognition ( CVPR ) received 11,532 valid paper submissions, and only 2,719 were accepted, for an overall acceptance rate of about 23.6%. Below is a list of the papers, posters, challenges, workshops, and datasets I'm most excited ..."}
+{"idx": 9, "title": "GitHub Pages - Jin Han", "date": "", "ddg_snippet": "🏆 [ 2024 /06] Our paper: EventPS was selected as the Best Paper Runner-up award in CVPR 2024 . 🎤 [ 2024 /02] One paper has been accepted by CVPR 2024 as Oral (top 3%).", "subpage_snippet": "", "source": "hjynwa.github.io", "link": "https://hjynwa.github.io/", "content": "🏆 [ 2024 /06] Our paper: EventPS was selected as the Best Paper Runner-up award in CVPR 2024 . 🎤 [ 2024 /02] One paper has been accepted by CVPR 2024 as Oral (top 3%)."}
diff --git a/data/sampled_jsons/EventPS_Yu_et_al_2024_abstract_photometric_stereo_events_year_2024.jsonl b/data/sampled_jsons/EventPS_Yu_et_al_2024_abstract_photometric_stereo_events_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..42fa5dd27a472d6c7b44787df26cfdb8276110d9
--- /dev/null
+++ b/data/sampled_jsons/EventPS_Yu_et_al_2024_abstract_photometric_stereo_events_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Photometric stereo - Wikipedia", "date": "", "ddg_snippet": "Photometric stereo analyzes multiple images of an object under different lighting conditions to estimate a normal direction at each pixel. Photometric stereo is a technique in computer vision for estimating the surface normals of objects by observing...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Photometric_stereo", "content": "Photometric stereo analyzes multiple images of an object under different lighting conditions to estimate a normal direction at each pixel. Photometric stereo is a technique in computer vision for estimating the surface normals of objects by observing..."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "We name this method Event Photometric Stereo OPtimization (EventPS-OP).[23] Dongyeop Kang, Yu Jin Jang, and Sangchul Won. Develop-ment of an inspection system for planar steel surface using multispectral photometric stereo . Optic.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "We name this method Event Photometric Stereo OPtimization (EventPS-OP).[23] Dongyeop Kang, Yu Jin Jang, and Sangchul Won. Develop-ment of an inspection system for planar steel surface using multispectral photometric stereo . Optic."}
+{"idx": 2, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "In stereo and photometric analysis, event -based vision supports advanced techniques like event stereo [32], photometric stereo [280], [257] and shape from polarization [176], offering high-resolution depth maps and detailed surface properties.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390160865_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera", "content": "In stereo and photometric analysis, event -based vision supports advanced techniques like event stereo [32], photometric stereo [280], [257] and shape from polarization [176], offering high-resolution depth maps and detailed surface properties."}
+{"idx": 3, "title": "Real-Time Photometric Stereo Using an Event Camera | Research", "date": "", "ddg_snippet": "Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi. ABSTRACT . Photometric stereo is a well-established technique to estimate the surface normal of an object.This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera.", "subpage_snippet": "", "source": "www.prophesee.ai", "link": "https://www.prophesee.ai/2024/07/05/eventps-real-time-photometric-stereo-event-camera/", "content": "Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi. ABSTRACT . Photometric stereo is a well-established technique to estimate the surface normal of an object.This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera."}
+{"idx": 4, "title": "EventPSR: Surface Normal and Reflectance Estimation from...", "date": "", "ddg_snippet": "It requires capturing multiple high dynamic range (HDR) images in existing methods using frame-based cameras. In this paper, we propose EventPSR, the first work to recover surface normal and reflectance parameters (e.g., metallic and roughness) simultaneously using an event camera.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yu_EventPSR_Surface_Normal_and_Reflectance_Estimation_from_Photometric_Stereo_Using@CVPR2025@CVF", "content": "It requires capturing multiple high dynamic range (HDR) images in existing methods using frame-based cameras. In this paper, we propose EventPSR, the first work to recover surface normal and reflectance parameters (e.g., metallic and roughness) simultaneously using an event camera."}
+{"idx": 5, "title": "Photometric Stereo Under Non-uniform Light Intensities... | SpringerLink", "date": "", "ddg_snippet": "This paper studies the effects of non-uniform light intensities and sensor exposures across observed images in photometric stereo .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-319-46475-6_11", "content": "This paper studies the effects of non-uniform light intensities and sensor exposures across observed images in photometric stereo ."}
+{"idx": 6, "title": "Photometric Stereo Technique | 2018-09-04 | Quality Magazine", "date": "", "ddg_snippet": "Photometric Stereo : Localized 3D. While photogrammetric systems return quantitative 3D data based on known geometric relationships, photometric stereo isn’t primarily concerned with measuring the height of any given pixel.", "subpage_snippet": "", "source": "www.qualitymag.com", "link": "https://www.qualitymag.com/articles/94943-photometric-stereo-technique", "content": "Photometric Stereo : Localized 3D. While photogrammetric systems return quantitative 3D data based on known geometric relationships, photometric stereo isn’t primarily concerned with measuring the height of any given pixel."}
+{"idx": 7, "title": "Single Day Outdoor Photometric Stereo — Supplementary Material", "date": "", "ddg_snippet": "Training dataset. Partially cloudy. Single Day Outdoor Photometric Stereo — Supplementary Material. In this document, we show additional qualitative results to extend fig.ground truth. ours. Yu et al .", "subpage_snippet": "", "source": "yannickhold.com", "link": "https://yannickhold.com/dops/supp_mat/index.html", "content": "Training dataset. Partially cloudy. Single Day Outdoor Photometric Stereo — Supplementary Material. In this document, we show additional qualitative results to extend fig.ground truth. ours. Yu et al ."}
+{"idx": 8, "title": "Learning Monocular Depth from Events via Egomotion Compensation", "date": "", "ddg_snippet": "(b) EReFormer (Liu et al . 2024 ). (c) Ours. Figure 3: The representative examples of our model in comparison with other state of the arts. Stereo depth from events cameras: Concentrate and focus on the future.", "subpage_snippet": "", "source": "mediatum.ub.tum.de", "link": "https://mediatum.ub.tum.de/doc/1776835/12nmbx5wa3e3d72q5jy8n34gh.pdf", "content": "(b) EReFormer (Liu et al . 2024 ). (c) Ours. Figure 3: The representative examples of our model in comparison with other state of the arts. Stereo depth from events cameras: Concentrate and focus on the future."}
+{"idx": 9, "title": "IMUFace: Real-Time, Low-Power, Continuous 3D Facial...", "date": "", "ddg_snippet": "(Roth et al ., 2015) integrated photometric stereo with landmark constraints to achieve higher reconstruction accuracy.(Li et al ., 2024 b) utilized IMUs in smart glasses to estimate action unit intensities, enabling real-time, low-power facial reconstruction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02177v2", "content": "(Roth et al ., 2015) integrated photometric stereo with landmark constraints to achieve higher reconstruction accuracy.(Li et al ., 2024 b) utilized IMUs in smart glasses to estimate action unit intensities, enabling real-time, low-power facial reconstruction."}
diff --git a/data/sampled_jsons/EventPS_paper_results_table_3D_printed_objects_mean_angular_error_degrees.jsonl b/data/sampled_jsons/EventPS_paper_results_table_3D_printed_objects_mean_angular_error_degrees.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3242f6f79287027629700ff4569de1f666b055cf
--- /dev/null
+++ b/data/sampled_jsons/EventPS_paper_results_table_3D_printed_objects_mean_angular_error_degrees.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Abstract Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "Abstract Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ..."}
+{"idx": 1, "title": "How to use Angular Material mat-error (Complete Guide)", "date": "", "ddg_snippet": "Today I'm going to show you how to use the Angular `mat- error ` element. In fact, I don't know of a better way to [do form validation in Angular Material then using the `mat- error ` element.", "subpage_snippet": "", "source": "danielk.tech", "link": "https://danielk.tech/home/angular-mat-error-guide", "content": "Today I'm going to show you how to use the Angular `mat- error ` element. In fact, I don't know of a better way to [do form validation in Angular Material then using the `mat- error ` element."}
+{"idx": 2, "title": "How to get mean angular errors · Issue #48 · erkil1452/gaze360 - GitHub", "date": "", "ddg_snippet": "Hi,Petr.Thanks for your work.I have a question to your paper 'Gaze360: Physically Unconstrained Gaze Estimation in the Wild'.In table2 you reports the mean angular errors for various models.How you get this mean angular error ? (yaw error+pitch error)/2?", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/erkil1452/gaze360/issues/48", "content": "Hi,Petr.Thanks for your work.I have a question to your paper 'Gaze360: Physically Unconstrained Gaze Estimation in the Wild'.In table2 you reports the mean angular errors for various models.How you get this mean angular error ? (yaw error+pitch error)/2?"}
+{"idx": 3, "title": "Error Handling and Logging in Angular: Best Practices and Strategies", "date": "", "ddg_snippet": "In Angular , a powerful JavaScript framework, mastering error handling and logging mechanisms can significantly impact your application's reliability. In this article, we'll delve into the best practices and strategies for proficient error handling and logging in Angular applications.", "subpage_snippet": "", "source": "stackademic.com", "link": "https://stackademic.com/blog/error-handling-and-logging-in-angular-best-practices-and-strategies-9e9f8ced201d", "content": "In Angular , a powerful JavaScript framework, mastering error handling and logging mechanisms can significantly impact your application's reliability. In this article, we'll delve into the best practices and strategies for proficient error handling and logging in Angular applications."}
+{"idx": 4, "title": "Angular Error Handling: A Best Practices Guide for Error Management", "date": "", "ddg_snippet": "Master error handling in Angular with our expert guide to effective error management", "subpage_snippet": "", "source": "codezup.com", "link": "https://codezup.com/angular-error-handling-best-practices/", "content": "Master error handling in Angular with our expert guide to effective error management"}
+{"idx": 5, "title": "Unhandled errors in Angular • Angular", "date": "", "ddg_snippet": "Angular's TestBed rethrows unexpected errors to ensure that errors caught by the framework cannot be unintentionally missed or ignored. ... In these situations, you can configure TestBed to not rethrow application errors with TestBed.configureTestingModule({rethrowApplicationErrors: false}).", "subpage_snippet": "", "source": "angular.dev", "link": "https://angular.dev/best-practices/error-handling", "content": "Angular's TestBed rethrows unexpected errors to ensure that errors caught by the framework cannot be unintentionally missed or ignored. ... In these situations, you can configure TestBed to not rethrow application errors with TestBed.configureTestingModule({rethrowApplicationErrors: false})."}
+{"idx": 6, "title": "Angular Error Handling", "date": "", "ddg_snippet": "Synchronous Error Handling Synchronous errors are rare in Angular apps, since around 90% of accidents arise when making network calls and such; however, they might happen, and here are a couple of examples: Using a third-party library which raises an error in some circumstances (wrong format of data passed, etc)", "subpage_snippet": "", "source": "www.angularspace.com", "link": "https://www.angularspace.com/angular-error-handling/", "content": "Synchronous Error Handling Synchronous errors are rare in Angular apps, since around 90% of accidents arise when making network calls and such; however, they might happen, and here are a couple of examples: Using a third-party library which raises an error in some circumstances (wrong format of data passed, etc)"}
+{"idx": 7, "title": "How can I resolve Angular unit test error: \"An error was thrown in ...", "date": "", "ddg_snippet": "You'll need to complete a few actions and gain 15 reputation points before being able to upvote. Upvoting indicates when questions and answers are useful. What's reputation and how do I get it? Instead, you can save this post to reference later.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/53608959/how-can-i-resolve-angular-unit-test-error-an-error-was-thrown-in-afterall-nob", "content": "You'll need to complete a few actions and gain 15 reputation points before being able to upvote. Upvoting indicates when questions and answers are useful. What's reputation and how do I get it? Instead, you can save this post to reference later."}
+{"idx": 8, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "Experiments using real event data from 3D-printed objects demonstrate that PS-EIP significantly improves robustness to outliers compared to EventPS's deep-learning variant, EventPS -FCN, without relying on deep learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.18341", "content": "Experiments using real event data from 3D-printed objects demonstrate that PS-EIP significantly improves robustness to outliers compared to EventPS's deep-learning variant, EventPS -FCN, without relying on deep learning."}
+{"idx": 9, "title": "How to reproduce MAE · Issue #23 · gapszju/3DGS-DR · GitHub", "date": "", "ddg_snippet": "Hi, I'm trying to reproduce the results of the paper and while the reconstruction metrics (PSNR, SSIM, LPIPS) are almost identical, the geometry reconstruction error - mean angular error (MAE) - is very different.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/gapszju/3DGS-DR/issues/23", "content": "Hi, I'm trying to reproduce the results of the paper and while the reconstruction metrics (PSNR, SSIM, LPIPS) are almost identical, the geometry reconstruction error - mean angular error (MAE) - is very different."}
diff --git a/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al._2023_HumanAct12_FID_score_year_2023.jsonl b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al._2023_HumanAct12_FID_score_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2aceda0b52c2bc89e057658eeabf058554a40291
--- /dev/null
+++ b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al._2023_HumanAct12_FID_score_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "View a PDF of the paper titled Executing your Commands via Motion Diffusion in Latent Space , by Xin Chen and 7 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.04048", "content": "View a PDF of the paper titled Executing your Commands via Motion Diffusion in Latent Space , by Xin Chen and 7 other authors"}
+{"idx": 1, "title": "PDF Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Then, instead of using a diffusion model to es-tablish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf", "content": "Then, instead of using a diffusion model to es-tablish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space ."}
+{"idx": 2, "title": "ChenFengYe/motion-latent-diffusion - GitHub", "date": "", "ddg_snippet": "Motion Latent Diffusion (MLD) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion", "content": "Motion Latent Diffusion (MLD) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data."}
+{"idx": 3, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "陈欣 | Research Scientist My research interests include generative AI, human agents, 3D and human motion generation.", "subpage_snippet": "", "source": "chenxin.tech", "link": "https://chenxin.tech/publication/mld/", "content": "陈欣 | Research Scientist My research interests include generative AI, human agents, 3D and human motion generation."}
+{"idx": 4, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Executing your Commands via Motion Diffusion in Latent Space We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10204431", "content": "Executing your Commands via Motion Diffusion in Latent Space We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors."}
+{"idx": 5, "title": "ChenFengYe/motion-latent-diffusion: [CVPR 2023] Executing your Commands ...", "date": "", "ddg_snippet": "Executing your Commands via Motion Diffusion in Latent Space Project Page | Arxiv - CVPR 2023 Motion Latent Diffusion (MLD) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data.", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/ChenFengYe/motion-latent-diffusion", "content": "Executing your Commands via Motion Diffusion in Latent Space Project Page | Arxiv - CVPR 2023 Motion Latent Diffusion (MLD) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data."}
+{"idx": 6, "title": "motion-latent-diffusion arXiv:2212.04048v3 [cs.CV] 19 May 2023", "date": "", "ddg_snippet": "Executing your Commands via Motion Diffusion in Latent Space Xin Chen1*", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.04048", "content": "Executing your Commands via Motion Diffusion in Latent Space Xin Chen1*"}
+{"idx": 7, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "The proposed Motion Latent -based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages. Expand", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Executing-your-Commands-via-Motion-Diffusion-in-Chen-Jiang/7694f004c67840d7f098b3612d4b3dabd915c116/figure/10", "content": "The proposed Motion Latent -based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages. Expand"}
+{"idx": 8, "title": "motion-latent-diffusion/README.md at main - GitHub", "date": "", "ddg_snippet": "[CVPR 2023 ] Executing your Commands via Motion Diffusion in Latent Space , a fast and high-quality motion diffusion model - ChenFengYe/ motion - latent - diffusion", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/README.md", "content": "[CVPR 2023 ] Executing your Commands via Motion Diffusion in Latent Space , a fast and high-quality motion diffusion model - ChenFengYe/ motion - latent - diffusion"}
+{"idx": 9, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Download Citation | On Jun 1, 2023 , Xin Chen and others published Executing your Commands via Motion Diffusion in Latent Space | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373313366_Executing_your_Commands_via_Motion_Diffusion_in_Latent_Space", "content": "Download Citation | On Jun 1, 2023 , Xin Chen and others published Executing your Commands via Motion Diffusion in Latent Space | Find, read and cite all the research you need on ResearchGate"}
diff --git a/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al._2023_year_2023.jsonl b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al._2023_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ecf3115db47d3c7eba1ea3fbdc55778548b8c2a4
--- /dev/null
+++ b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al._2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Safety-Critical Traffic Simulation with Guided Latent Diffusion", "date": "", "ddg_snippet": "... variational autoencoder (VAE) to learn a compact latent space that captures complex multi-agent interactions while improving computational efficiency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00515v1", "content": "... variational autoencoder (VAE) to learn a compact latent space that captures complex multi-agent interactions while improving computational efficiency."}
+{"idx": 1, "title": "MotionGPT3: Human Motion as a Second Modality", "date": "", "ddg_snippet": "Based on this continuous latent space , the motion branch predicts motion latents directly from intermediate hidden states using a diffusion head ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.24086v1", "content": "Based on this continuous latent space , the motion branch predicts motion latents directly from intermediate hidden states using a diffusion head ..."}
+{"idx": 2, "title": "Human Motion Synthesis: A Diffusion Approach for Motion", "date": "", "ddg_snippet": "Chen et al ., 2022 ) used VAEs to learn a representation of human motion , then performed diffusion on this latent space representation instead of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.06791v1", "content": "Chen et al ., 2022 ) used VAEs to learn a representation of human motion , then performed diffusion on this latent space representation instead of the ..."}
+{"idx": 3, "title": "Cinematographic Camera Diffusion Model", "date": "", "ddg_snippet": "... in this work is whether we can leverage such diffusion models to build a script-to-camera- motion approach for virtual cinematography that would enable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.16143v1", "content": "... in this work is whether we can leverage such diffusion models to build a script-to-camera- motion approach for virtual cinematography that would enable ..."}
+{"idx": 4, "title": "Xin Chen | DeepAI", "date": "", "ddg_snippet": "In this work, we introduce the ... Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/xin-chen", "content": "In this work, we introduce the ... Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation"}
+{"idx": 5, "title": "(PDF) Diff-Control: A Stateful Diffusion-based Policy for", "date": "", "ddg_snippet": "... this limitation, we introduce the Diff-Control policy, which utilizes a diffusion -based model to learn action representation from a state- space ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382360436_Diff-Control_A_Stateful_Diffusion-based_Policy_for_Imitation_Learning", "content": "... this limitation, we introduce the Diff-Control policy, which utilizes a diffusion -based model to learn action representation from a state- space ..."}
+{"idx": 6, "title": "ORV: 4D Occupancy-centric Robot Video Generation", "date": "", "ddg_snippet": "... of high-quality semantic occupancy datasets that accurately reflect 3D robot arm/gripper motions along with rich semantic and geometric information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03079v1", "content": "... of high-quality semantic occupancy datasets that accurately reflect 3D robot arm/gripper motions along with rich semantic and geometric information."}
+{"idx": 7, "title": "Jingyi Yu | DeepAI", "date": "", "ddg_snippet": "We have recently seen tremendous progress in diffusion advances for gene... ... Executing your Commands via Motion Diffusion in Latent Space", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/jingyi-yu", "content": "We have recently seen tremendous progress in diffusion advances for gene... ... Executing your Commands via Motion Diffusion in Latent Space"}
+{"idx": 8, "title": "Towards Video World Models", "date": "", "ddg_snippet": "Other forms of application-specific action spaces have also been investigated, such as driving maneuvers in autonomous vehicles ( Hu et al ., 2023 ...", "subpage_snippet": "", "source": "www.xunhuang.me", "link": "https://www.xunhuang.me/blogs/world_model.html", "content": "Other forms of application-specific action spaces have also been investigated, such as driving maneuvers in autonomous vehicles ( Hu et al ., 2023 ..."}
+{"idx": 9, "title": "Generalization to new sen- tences and changes in illumination |", "date": "", "ddg_snippet": "... Yu et al ., 2018;Bonardi et al ., 2020) and language instructions (Jang et al ., 2021;Stepputtis et al ., 2020;, but also on demonstrations from a robot.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Generalization-to-new-sen-tences-and-changes-in-illumination_tbl2_344863555", "content": "... Yu et al ., 2018;Bonardi et al ., 2020) and language instructions (Jang et al ., 2021;Stepputtis et al ., 2020;, but also on demonstrations from a robot."}
diff --git a/data/sampled_jsons/Extragradient_RM+_ExRM+_algorithm_procedural_difference_convergence.jsonl b/data/sampled_jsons/Extragradient_RM+_ExRM+_algorithm_procedural_difference_convergence.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dfedac8b304d63d201742967b53b5216b2ba5ce7
--- /dev/null
+++ b/data/sampled_jsons/Extragradient_RM+_ExRM+_algorithm_procedural_difference_convergence.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LAST-I CONVERGENCE PROPERTIES OF R M ALGORITHMS IN GAMES", "date": "", "ddg_snippet": "3 × 3 matrix game. We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM+ and smooth √ Predictive RM+ , enjoy asymptotic last-iterate convergence (without a rate), 1/ t best-iterate convergence , and when combined with restarting, linear-rate last-iterate convergence . Our analysis builds on a new characterization of the geometric structure of ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2025/iclr25_rm_lastiterate/iclr25_rm_lastiterate.pdf", "content": "3 × 3 matrix game. We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM+ and smooth √ Predictive RM+ , enjoy asymptotic last-iterate convergence (without a rate), 1/ t best-iterate convergence , and when combined with restarting, linear-rate last-iterate convergence . Our analysis builds on a new characterization of the geometric structure of ..."}
+{"idx": 1, "title": "[2409.16859] Revisiting Extragradient-Type Methods -- Part 1 ... Extragradient Method in Optimization: Convergence and ... (PDF) Extragradient Method in Optimization: Convergence and ... Regret Matching : (In)Stability and Fast Convergence in Games Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity The Extragradient Method: A Key to Complex Problem Solving", "date": "", "ddg_snippet": "Sep 25, 2024 · This paper presents a comprehensive analysis of the well-known extragradient (EG) method for solving both equations and inclusions. First, we unify and generalize EG for [non]linear equations to a wider class of algorithms , encompassing various existing schemes and potentially new variants. Next, we analyze both sublinear ``best-iterate'' and ``last-iterate'' convergence rates for the entire ... We let \\(A \\in \\mathbb {R}^{p \\times n}\\) be a real matrix, \\(b \\in \\mathbb {R}^n\\) be a real vector and \\(\\lambda > 0\\) be a scalar, all of them given and fixed. Following the notations of the previous section, we define \\(f :x \\mapsto \\frac{1}{2} \\Vert Ax - b\\Vert _2^2\\) and \\(g :x \\mapsto \\lambda \\Vert x\\Vert _1\\) (the sum of absolute values of ... See full list on link.springer.com One intuition behind extragradient method for optimization is the use of an additional iteration as a guide or a scout to provide an estimate of the gradient that better suits the geometry of the problem. This should eventually translate to taking larger steps leading to faster convergence . In order to evaluate extragradient method, we need a mecha... See full list on link.springer.com We generate a matrix A and vector busing the following process. 1. Set \\(n = 600\\) and \\(p = 300\\). 2. Set \\(A = D X\\) where X has standard Gaussian independent entries and D is a diagonal matrix which ith diagonal entry is \\(\\frac{1}{i^\\delta }\\) where \\(\\delta \\) is a positive parameter controlling the good conditioning of the matrix A (the small... See full list on link.springer.com Jan 1, 2018 · One of the most-studied algorithms is the so-called Extragradient algorithm , which was first studied and proposed by Korpelevich [27] in Euclidean spaces and it becomes a classical method for ... Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ... We consider the extragradient method to minimize the sum of two functions, the rst one being smooth and the second being convex. Under the Kurdyka-Lojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has nite length. The analysis is extended to the case when both functions are convex. We provide, in this case ... Can extragradient method be used to solve similar problems? The goal of this paper is to investigate to which extent extragradient method can be used to tackle similar problems. The extragradient method was initially proposed by Korpelevich , and it has become a classical method for solving variational inequality problems. Is the extragradient method asymptotic linear convergence of convex constrained optimization? In the context of convex constrained optimization, considered the performances of the extragradient method under error bounds assumptions. In this setting, Luo and Tseng have described asymptotic linear convergence of the extragradient method applied to constrained problems. Can extragradient method be used to solve shrinkage-thresholding problems? Successful algorithms for these types of problems include fast iterative shrinkage–thresholding algorithm (FISTA) method and forward–backward splitting method . The goal of this paper is to investigate to which extent extragradient method can be used to tackle similar problems. What is an extragradient method for optimization? One intuition behind extragradient method for optimization is the use of an additional iteration as a guide or a scout to provide an estimate of the gradient that better suits the geometry of the problem. This should eventually translate to taking larger steps leading to faster convergence. Does the extragradient method minimize the sum of two functions? We consider the extragradient method to minimize the sum of two functions , the first one being smooth and the second being convex. Under the Kurdyka–Łojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has finite length. How do we extend the classical extragradient method? The classical extragradient method relies on orthogonal projections. We extend it by considering more general nonsmooth functions and using proximal gradient steps at each iteration . An important challenge in this context is to balance the magnitude of the two associated parameters to maintain desirable convergence properties. May 30, 2025 · Title: Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Rates Abstract: This paper presents a comprehensive analysis of the well-known extragradient (EG) method for solving both equations and inclusions. First, we unify and generalize EG for [non]linear equations to a wider class of algorithms , encompassing various existing schemes and potentially new ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.16859", "content": "Sep 25, 2024 · This paper presents a comprehensive analysis of the well-known extragradient (EG) method for solving both equations and inclusions. First, we unify and generalize EG for [non]linear equations to a wider class of algorithms , encompassing various existing schemes and potentially new variants. Next, we analyze both sublinear ``best-iterate'' and ``last-iterate'' convergence rates for the entire ... We let \\(A \\in \\mathbb {R}^{p \\times n}\\) be a real matrix, \\(b \\in \\mathbb {R}^n\\) be a real vector and \\(\\lambda > 0\\) be a scalar, all of them given and fixed. Following the notations of the previous section, we define \\(f :x \\mapsto \\frac{1}{2} \\Vert Ax - b\\Vert _2^2\\) and \\(g :x \\mapsto \\lambda \\Vert x\\Vert _1\\) (the sum of absolute values of ... See full list on link.springer.com One intuition behind extragradient method for optimization is the use of an additional iteration as a guide or a scout to provide an estimate of the gradient that better suits the geometry of the problem. This should eventually translate to taking larger steps leading to faster convergence . In order to evaluate extragradient method, we need a mecha... See full list on link.springer.com We generate a matrix A and vector busing the following process. 1. Set \\(n = 600\\) and \\(p = 300\\). 2. Set \\(A = D X\\) where X has standard Gaussian independent entries and D is a diagonal matrix which ith diagonal entry is \\(\\frac{1}{i^\\delta }\\) where \\(\\delta \\) is a positive parameter controlling the good conditioning of the matrix A (the small... See full list on link.springer.com Jan 1, 2018 · One of the most-studied algorithms is the so-called Extragradient algorithm , which was first studied and proposed by Korpelevich [27] in Euclidean spaces and it becomes a classical method for ... Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ... We consider the extragradient method to minimize the sum of two functions, the rst one being smooth and the second being convex. Under the Kurdyka-Lojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has nite length. The analysis is extended to the case when both functions are convex. We provide, in this case ... Can extragradient method be used to solve similar problems? The goal of this paper is to investigate to which extent extragradient method can be used to tackle similar problems. The extragradient method was initially proposed by Korpelevich , and it has become a classical method for solving variational inequality problems. Is the extragradient method asymptotic linear convergence of convex constrained optimization? In the context of convex constrained optimization, considered the performances of the extragradient method under error bounds assumptions. In this setting, Luo and Tseng have described asymptotic linear convergence of the extragradient method applied to constrained problems. Can extragradient method be used to solve shrinkage-thresholding problems? Successful algorithms for these types of problems include fast iterative shrinkage–thresholding algorithm (FISTA) method and forward–backward splitting method . The goal of this paper is to investigate to which extent extragradient method can be used to tackle similar problems. What is an extragradient method for optimization? One intuition behind extragradient method for optimization is the use of an additional iteration as a guide or a scout to provide an estimate of the gradient that better suits the geometry of the problem. This should eventually translate to taking larger steps leading to faster convergence. Does the extragradient method minimize the sum of two functions? We consider the extragradient method to minimize the sum of two functions , the first one being smooth and the second being convex. Under the Kurdyka–Łojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has finite length. How do we extend the classical extragradient method? The classical extragradient method relies on orthogonal projections. We extend it by considering more general nonsmooth functions and using proximal gradient steps at each iteration . An important challenge in this context is to balance the magnitude of the two associated parameters to maintain desirable convergence properties. May 30, 2025 · Title: Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Rates Abstract: This paper presents a comprehensive analysis of the well-known extragradient (EG) method for solving both equations and inclusions. First, we unify and generalize EG for [non]linear equations to a wider class of algorithms , encompassing various existing schemes and potentially new ..."}
+{"idx": 2, "title": "Extragradient Method in Optimization: Convergence and ... (PDF) Extragradient Method in Optimization: Convergence and ... Regret Matching : (In)Stability and Fast Convergence in Games Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity Extragradient Method in Optimization: Convergence and Complexity The Extragradient Method: A Key to Complex Problem Solving", "date": "", "ddg_snippet": "We let \\(A \\in \\mathbb {R}^{p \\times n}\\) be a real matrix, \\(b \\in \\mathbb {R}^n\\) be a real vector and \\(\\lambda > 0\\) be a scalar, all of them given and fixed. Following the notations of the previous section, we define \\(f :x \\mapsto \\frac{1}{2} \\Vert Ax - b\\Vert _2^2\\) and \\(g :x \\mapsto \\lambda \\Vert x\\Vert _1\\) (the sum of absolute values of ... See full list on link.springer.com One intuition behind extragradient method for optimization is the use of an additional iteration as a guide or a scout to provide an estimate of the gradient that better suits the geometry of the problem. This should eventually translate to taking larger steps leading to faster convergence . In order to evaluate extragradient method, we need a mecha... See full list on link.springer.com We generate a matrix A and vector busing the following process. 1. Set \\(n = 600\\) and \\(p = 300\\). 2. Set \\(A = D X\\) where X has standard Gaussian independent entries and D is a diagonal matrix which ith diagonal entry is \\(\\frac{1}{i^\\delta }\\) where \\(\\delta \\) is a positive parameter controlling the good conditioning of the matrix A (the small... See full list on link.springer.com Jan 1, 2018 · One of the most-studied algorithms is the so-called Extragradient algorithm , which was first studied and proposed by Korpelevich [27] in Euclidean spaces and it becomes a classical method for ... Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ... We consider the extragradient method to minimize the sum of two functions, the rst one being smooth and the second being convex. Under the Kurdyka-Lojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has nite length. The analysis is extended to the case when both functions are convex. We provide, in this case ... Can extragradient method be used to solve similar problems? The goal of this paper is to investigate to which extent extragradient method can be used to tackle similar problems. The extragradient method was initially proposed by Korpelevich , and it has become a classical method for solving variational inequality problems. Is the extragradient method asymptotic linear convergence of convex constrained optimization? In the context of convex constrained optimization, considered the performances of the extragradient method under error bounds assumptions. In this setting, Luo and Tseng have described asymptotic linear convergence of the extragradient method applied to constrained problems. Can extragradient method be used to solve shrinkage-thresholding problems? Successful algorithms for these types of problems include fast iterative shrinkage–thresholding algorithm (FISTA) method and forward–backward splitting method . The goal of this paper is to investigate to which extent extragradient method can be used to tackle similar problems. What is an extragradient method for optimization? One intuition behind extragradient method for optimization is the use of an additional iteration as a guide or a scout to provide an estimate of the gradient that better suits the geometry of the problem. This should eventually translate to taking larger steps leading to faster convergence. Does the extragradient method minimize the sum of two functions? We consider the extragradient method to minimize the sum of two functions , the first one being smooth and the second being convex. Under the Kurdyka–Łojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has finite length. How do we extend the classical extragradient method? The classical extragradient method relies on orthogonal projections. We extend it by considering more general nonsmooth functions and using proximal gradient steps at each iteration . An important challenge in this context is to balance the magnitude of the two associated parameters to maintain desirable convergence properties. May 30, 2025 · Title: Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Rates Abstract: This paper presents a comprehensive analysis of the well-known extragradient (EG) method for solving both equations and inclusions. First, we unify and generalize EG for [non]linear equations to a wider class of algorithms , encompassing various existing schemes and potentially new ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10957-017-1200-6", "content": "We let \\(A \\in \\mathbb {R}^{p \\times n}\\) be a real matrix, \\(b \\in \\mathbb {R}^n\\) be a real vector and \\(\\lambda > 0\\) be a scalar, all of them given and fixed. Following the notations of the previous section, we define \\(f :x \\mapsto \\frac{1}{2} \\Vert Ax - b\\Vert _2^2\\) and \\(g :x \\mapsto \\lambda \\Vert x\\Vert _1\\) (the sum of absolute values of ... See full list on link.springer.com One intuition behind extragradient method for optimization is the use of an additional iteration as a guide or a scout to provide an estimate of the gradient that better suits the geometry of the problem. This should eventually translate to taking larger steps leading to faster convergence . In order to evaluate extragradient method, we need a mecha... See full list on link.springer.com We generate a matrix A and vector busing the following process. 1. Set \\(n = 600\\) and \\(p = 300\\). 2. Set \\(A = D X\\) where X has standard Gaussian independent entries and D is a diagonal matrix which ith diagonal entry is \\(\\frac{1}{i^\\delta }\\) where \\(\\delta \\) is a positive parameter controlling the good conditioning of the matrix A (the small... See full list on link.springer.com Jan 1, 2018 · One of the most-studied algorithms is the so-called Extragradient algorithm , which was first studied and proposed by Korpelevich [27] in Euclidean spaces and it becomes a classical method for ... Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ... We consider the extragradient method to minimize the sum of two functions, the rst one being smooth and the second being convex. Under the Kurdyka-Lojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has nite length. The analysis is extended to the case when both functions are convex. We provide, in this case ... Can extragradient method be used to solve similar problems? The goal of this paper is to investigate to which extent extragradient method can be used to tackle similar problems. The extragradient method was initially proposed by Korpelevich , and it has become a classical method for solving variational inequality problems. Is the extragradient method asymptotic linear convergence of convex constrained optimization? In the context of convex constrained optimization, considered the performances of the extragradient method under error bounds assumptions. In this setting, Luo and Tseng have described asymptotic linear convergence of the extragradient method applied to constrained problems. Can extragradient method be used to solve shrinkage-thresholding problems? Successful algorithms for these types of problems include fast iterative shrinkage–thresholding algorithm (FISTA) method and forward–backward splitting method . The goal of this paper is to investigate to which extent extragradient method can be used to tackle similar problems. What is an extragradient method for optimization? One intuition behind extragradient method for optimization is the use of an additional iteration as a guide or a scout to provide an estimate of the gradient that better suits the geometry of the problem. This should eventually translate to taking larger steps leading to faster convergence. Does the extragradient method minimize the sum of two functions? We consider the extragradient method to minimize the sum of two functions , the first one being smooth and the second being convex. Under the Kurdyka–Łojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has finite length. How do we extend the classical extragradient method? The classical extragradient method relies on orthogonal projections. We extend it by considering more general nonsmooth functions and using proximal gradient steps at each iteration . An important challenge in this context is to balance the magnitude of the two associated parameters to maintain desirable convergence properties. May 30, 2025 · Title: Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Rates Abstract: This paper presents a comprehensive analysis of the well-known extragradient (EG) method for solving both equations and inclusions. First, we unify and generalize EG for [non]linear equations to a wider class of algorithms , encompassing various existing schemes and potentially new ..."}
+{"idx": 3, "title": "(PDF) Extragradient Method in Optimization: Convergence and ...", "date": "", "ddg_snippet": "Jan 1, 2018 · One of the most-studied algorithms is the so-called Extragradient algorithm , which was first studied and proposed by Korpelevich [27] in Euclidean spaces and it becomes a classical method for ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/308692475_Extragradient_Method_in_Optimization_Convergence_and_Complexity", "content": "Jan 1, 2018 · One of the most-studied algorithms is the so-called Extragradient algorithm , which was first studied and proposed by Korpelevich [27] in Euclidean spaces and it becomes a classical method for ..."}
+{"idx": 4, "title": "Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
+{"idx": 5, "title": "Extragradient Method in Optimization: Convergence and Complexity", "date": "", "ddg_snippet": "We consider the extragradient method to minimize the sum of two functions, the rst one being smooth and the second being convex. Under the Kurdyka-Lojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has nite length. The analysis is extended to the case when both functions are convex. We provide, in this case ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1609.08177", "content": "We consider the extragradient method to minimize the sum of two functions, the rst one being smooth and the second being convex. Under the Kurdyka-Lojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has nite length. The analysis is extended to the case when both functions are convex. We provide, in this case ..."}
+{"idx": 6, "title": "The Extragradient Method: A Key to Complex Problem Solving", "date": "", "ddg_snippet": "May 30, 2025 · Title: Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Rates Abstract: This paper presents a comprehensive analysis of the well-known extragradient (EG) method for solving both equations and inclusions. First, we unify and generalize EG for [non]linear equations to a wider class of algorithms , encompassing various existing schemes and potentially new ...", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-05-30-the-extragradient-method-a-key-to-complex-problem-solving--a9rdnrq", "content": "May 30, 2025 · Title: Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Rates Abstract: This paper presents a comprehensive analysis of the well-known extragradient (EG) method for solving both equations and inclusions. First, we unify and generalize EG for [non]linear equations to a wider class of algorithms , encompassing various existing schemes and potentially new ..."}
+{"idx": 7, "title": "Convergence in Games", "date": "", "ddg_snippet": "We now consider the Algorithm 5 Extragradient RM+ ( ExRM+ ).Figure 2: Empirical performances of RM+ , PRM+, ExRM+ , Stable PRM+ and Smooth PRM+on our 3 × 3 matrix game (left plot) and on random instances for different step sizes. Extensive-form games.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/c209cd57e13f3344a4cad4ce84d0ee1b-Paper-Conference.pdf", "content": "We now consider the Algorithm 5 Extragradient RM+ ( ExRM+ ).Figure 2: Empirical performances of RM+ , PRM+, ExRM+ , Stable PRM+ and Smooth PRM+on our 3 × 3 matrix game (left plot) and on random instances for different step sizes. Extensive-form games."}
+{"idx": 8, "title": "On generalized extragradient implicit method for systems of variational...", "date": "", "ddg_snippet": "In this article, we introduce a generalized extragradient implicit method for solving a general system of variational inequalities (GSVI) with the VI and CFPP constraints.", "subpage_snippet": "", "source": "www.degruyterbrill.com", "link": "https://www.degruyterbrill.com/document/doi/10.1515/math-2022-0536/html", "content": "In this article, we introduce a generalized extragradient implicit method for solving a general system of variational inequalities (GSVI) with the VI and CFPP constraints."}
+{"idx": 9, "title": "Extragradient Type Methods for Riemannian Variational Inequality...", "date": "", "ddg_snippet": "Introduces a novel stochastic optimization algorithm called the Stochastic Newton Proximal Extragradient (SNPE) method. Provides theoretical guarantees for SNPE's convergence and convergence rates under various settings.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/extragradient-type-methods-riemannian-variational-inequality-problems", "content": "Introduces a novel stochastic optimization algorithm called the Stochastic Newton Proximal Extragradient (SNPE) method. Provides theoretical guarantees for SNPE's convergence and convergence rates under various settings."}
diff --git a/data/sampled_jsons/FAIR1M_dataset_image_size_resolution_remote_sensing_benchmark.jsonl b/data/sampled_jsons/FAIR1M_dataset_image_size_resolution_remote_sensing_benchmark.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..036160b842b7876c06a1d609cc24013df230cfc7
--- /dev/null
+++ b/data/sampled_jsons/FAIR1M_dataset_image_size_resolution_remote_sensing_benchmark.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FAIR 1 M Dataset Case Study: Enhancing Satellite Image Object...", "date": "", "ddg_snippet": "October 8, 2024. TL;DR. The FAIR 1 M dataset , part of the International Society for Photogrammetry and Remote Sensing (ISPRS) Benchmark on Object Detection in High- Resolution Satellite Images , was analyzed using Hirundo's proprietary Data Influence Engine.", "subpage_snippet": "", "source": "www.hirundo.io", "link": "https://www.hirundo.io/blog/fair1m-remote-sensing", "content": "October 8, 2024. TL;DR. The FAIR 1 M dataset , part of the International Society for Photogrammetry and Remote Sensing (ISPRS) Benchmark on Object Detection in High- Resolution Satellite Images , was analyzed using Hirundo's proprietary Data Influence Engine."}
+{"idx": 1, "title": "FAIR1M: A benchmark dataset for fine-grained object ...", "date": "", "ddg_snippet": "Feb 1, 2022 · In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 40,000 images for Fine-grAined object recognItion in high- Resolution remote sensing imagery which is named as FAIR1M .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0924271621003269", "content": "Feb 1, 2022 · In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 40,000 images for Fine-grAined object recognItion in high- Resolution remote sensing imagery which is named as FAIR1M ."}
+{"idx": 2, "title": "[2103.05569] FAIR1M: A Benchmark Dataset for Fine-grained ... blanchon/FAIR1M · Datasets at Hugging Face ISPRS多模态遥感智能大赛 - gaofen-challenge.com FAIR1M FAIR1M Dataset Case Study: Enhancing Satellite Image Object ... [2103.05569] FAIR1M : A Benchmark Dataset for Fine-grained Object FAIR1M : A benchmark dataset for fine-grained object ... - ScienceDirect FAIR1M : A benchmark dataset for fine-grained object ... - ScienceDirect FAIR1M : A benchmark dataset for fine-grained object ... - ScienceDirect FAIR1M FAIR1M : A benchmark dataset for fine-grained object ... - ScienceDirect FAIR1M: A Benchmark Dataset For Fine-Grained Object ...", "date": "", "ddg_snippet": "Mar 9, 2021 · In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15,000 images for Fine-grAined object recognItion in high- Resolution remote sensing imagery which is named as FAIR1M . All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes. FAIR1M is a part of the ISPRS Benchmark on Object Detection in High- Resolution Satellite Images . Please note that, as of now, only a portion of the training dataset (1,732/15,000 images ) has been released for the challenge. Mar 20, 2021 · This benchmark provides a standard dataset for applying advanced deep learning technology to remote sensing . FAIR1M is a large-scale dataset for Fine-grAined object detection and recognItion in Remote sensing images . To meet the needs of practical applications, images in the FAIR1M dataset are collected from different sensors and platforms, with a spatial resolution ranging from 0.3m to 0.8m ... FAIR1M is a large-scale dataset for fine-grained object detection and recognition in remote sensing images . To meet the needs of practical applications, images in the FAIR1M dataset are collected from different sensors and platforms, with a spatial resolution ranging from 0.3m to 0.8m. There are more than 1 million instances and more than 40,000 images in this dataset . All objects in the ... The FAIR1M dataset , part of the International Society for Photogrammetry and Remote Sensing (ISPRS) Benchmark on Object Detection in High- Resolution Satellite Images , was analyzed using Hirundo's proprietary Data Influence Engine. What is fair1m benchmark dataset? In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15,000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery which is named as FAIR1M. All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes. How large are the image sizes in the fair1m dataset? The image sizes in the FAIR1M dataset are so large that the images were not directly input into the object detection networks. In this paper, we crop the images into 1024 × 1024 patches with a stride of 256. Baseline models. We investigate the state-of-the-art object detection algorithms in the field of object detection for remote sensing imagery. What is fair1m oriented object detection? All FAIR1M images are annotated with oriented bounding boxes (OBBs) and with respect to 5 categories and 37 subcategories under the guidance of many remote sensing experts. To the best of our knowledge, FAIR1M is the largest fine-grained oriented object detection dataset suitable for remote sensing scenes . Fig. 2. How many remote sensing images are in the fair1m dataset? In the FAIR1M dataset, we collect more than 40,000 remote sensing images with 1 million instances from Gaofen satellites and Google Earth platform. The scenes in these remote sensing images are spread across many continents, such as Asia, America, and Europe. How is the fair1m dataset annotated? All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes . Each image is of the size in the range from 600 to 10,000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. What is the largest fine-grained object recognition dataset in remote sensing images? A large-scale public dataset is proposed for object recognition in remote sensing images. To the best of our knowledge, the proposed FAIR1M benchmark dataset is the largest fine-grained object recognition dataset in remote sensing with more than 1 million instances. FAIR1M : A Benchmark Dataset For Fine-Grained Object Recognition In High- Resolution Remote Sensing Imagery Go back! The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2103.05569", "content": "Mar 9, 2021 · In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15,000 images for Fine-grAined object recognItion in high- Resolution remote sensing imagery which is named as FAIR1M . All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes. FAIR1M is a part of the ISPRS Benchmark on Object Detection in High- Resolution Satellite Images . Please note that, as of now, only a portion of the training dataset (1,732/15,000 images ) has been released for the challenge. Mar 20, 2021 · This benchmark provides a standard dataset for applying advanced deep learning technology to remote sensing . FAIR1M is a large-scale dataset for Fine-grAined object detection and recognItion in Remote sensing images . To meet the needs of practical applications, images in the FAIR1M dataset are collected from different sensors and platforms, with a spatial resolution ranging from 0.3m to 0.8m ... FAIR1M is a large-scale dataset for fine-grained object detection and recognition in remote sensing images . To meet the needs of practical applications, images in the FAIR1M dataset are collected from different sensors and platforms, with a spatial resolution ranging from 0.3m to 0.8m. There are more than 1 million instances and more than 40,000 images in this dataset . All objects in the ... The FAIR1M dataset , part of the International Society for Photogrammetry and Remote Sensing (ISPRS) Benchmark on Object Detection in High- Resolution Satellite Images , was analyzed using Hirundo's proprietary Data Influence Engine. What is fair1m benchmark dataset? In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15,000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery which is named as FAIR1M. All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes. How large are the image sizes in the fair1m dataset? The image sizes in the FAIR1M dataset are so large that the images were not directly input into the object detection networks. In this paper, we crop the images into 1024 × 1024 patches with a stride of 256. Baseline models. We investigate the state-of-the-art object detection algorithms in the field of object detection for remote sensing imagery. What is fair1m oriented object detection? All FAIR1M images are annotated with oriented bounding boxes (OBBs) and with respect to 5 categories and 37 subcategories under the guidance of many remote sensing experts. To the best of our knowledge, FAIR1M is the largest fine-grained oriented object detection dataset suitable for remote sensing scenes . Fig. 2. How many remote sensing images are in the fair1m dataset? In the FAIR1M dataset, we collect more than 40,000 remote sensing images with 1 million instances from Gaofen satellites and Google Earth platform. The scenes in these remote sensing images are spread across many continents, such as Asia, America, and Europe. How is the fair1m dataset annotated? All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes . Each image is of the size in the range from 600 to 10,000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. What is the largest fine-grained object recognition dataset in remote sensing images? A large-scale public dataset is proposed for object recognition in remote sensing images. To the best of our knowledge, the proposed FAIR1M benchmark dataset is the largest fine-grained object recognition dataset in remote sensing with more than 1 million instances. FAIR1M : A Benchmark Dataset For Fine-Grained Object Recognition In High- Resolution Remote Sensing Imagery Go back! The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications."}
+{"idx": 3, "title": "blanchon/FAIR1M · Datasets at Hugging Face", "date": "", "ddg_snippet": "FAIR1M is a part of the ISPRS Benchmark on Object Detection in High- Resolution Satellite Images . Please note that, as of now, only a portion of the training dataset (1,732/15,000 images ) has been released for the challenge.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/blanchon/FAIR1M", "content": "FAIR1M is a part of the ISPRS Benchmark on Object Detection in High- Resolution Satellite Images . Please note that, as of now, only a portion of the training dataset (1,732/15,000 images ) has been released for the challenge."}
+{"idx": 4, "title": "FAIR1M", "date": "", "ddg_snippet": "FAIR1M is a large-scale dataset for fine-grained object detection and recognition in remote sensing images . To meet the needs of practical applications, images in the FAIR1M dataset are collected from different sensors and platforms, with a spatial resolution ranging from 0.3m to 0.8m. There are more than 1 million instances and more than 40,000 images in this dataset . All objects in the ...", "subpage_snippet": "", "source": "eod-grss-ieee.com", "link": "https://eod-grss-ieee.com/dataset-detail/N0tpVnd4eTM2QUY4RkJMU0pweWdRQT09", "content": "FAIR1M is a large-scale dataset for fine-grained object detection and recognition in remote sensing images . To meet the needs of practical applications, images in the FAIR1M dataset are collected from different sensors and platforms, with a spatial resolution ranging from 0.3m to 0.8m. There are more than 1 million instances and more than 40,000 images in this dataset . All objects in the ..."}
+{"idx": 5, "title": "FAIR1M: A Benchmark Dataset For Fine-Grained Object ...", "date": "", "ddg_snippet": "FAIR1M : A Benchmark Dataset For Fine-Grained Object Recognition In High- Resolution Remote Sensing Imagery Go back! The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications.", "subpage_snippet": "", "source": "www.isprs.org", "link": "https://www.isprs.org/medialib/presentation.aspx?ID=779&Path=/medialib/nice2022/", "content": "FAIR1M : A Benchmark Dataset For Fine-Grained Object Recognition In High- Resolution Remote Sensing Imagery Go back! The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications."}
+{"idx": 6, "title": "OpenEarthSensing: Large-Scale Fine-Grained Benchmark for...", "date": "", "ddg_snippet": "Remote sensing image scene classification: Benchmark and state of the art. Fair 1 m : A benchmark dataset for fine-grained object recognition in high- resolution remote sensing imagery .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20668v1", "content": "Remote sensing image scene classification: Benchmark and state of the art. Fair 1 m : A benchmark dataset for fine-grained object recognition in high- resolution remote sensing imagery ."}
+{"idx": 7, "title": "GitHub - qiangsun89/UAV- datasets : Datasets for deep learning with...", "date": "", "ddg_snippet": "RSICC -> the Remote Sensing Image Change Captioning dataset contains 10077 pairs of bi-temporal remote sensing images and 50385 sentences describing the differences between images . Uses LEVIR-CD imagery . Weather Datasets .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/qiangsun89/UAV-datasets", "content": "RSICC -> the Remote Sensing Image Change Captioning dataset contains 10077 pairs of bi-temporal remote sensing images and 50385 sentences describing the differences between images . Uses LEVIR-CD imagery . Weather Datasets ."}
+{"idx": 8, "title": "Efficient Image Segmentation with Minimal Annotations in Remote ...", "date": "", "ddg_snippet": "The Potsdam dataset is part of the ISPRS 2D Semantic Labeling Contest and comprises 38 high- resolution aerial image tiles, each with an impressive resolution of 6000 × 6000 pixels.", "subpage_snippet": "", "source": "glcnd.io", "link": "https://glcnd.io/efficient-image-segmentation-with-minimal-annotations-in-remote-sensing/", "content": "The Potsdam dataset is part of the ISPRS 2D Semantic Labeling Contest and comprises 38 high- resolution aerial image tiles, each with an impressive resolution of 6000 × 6000 pixels."}
+{"idx": 9, "title": "MDAS: a new multimodal benchmark dataset for remote sensing", "date": "", "ddg_snippet": "Table 1 Remote sensing data sets comparison.These images are small in spatial size , which is not enough to test whether the developed method is capable of processing large dataset with low error and reasonable time cost.", "subpage_snippet": "", "source": "essd.copernicus.org", "link": "https://essd.copernicus.org/articles/15/113/2023/", "content": "Table 1 Remote sensing data sets comparison.These images are small in spatial size , which is not enough to test whether the developed method is capable of processing large dataset with low error and reasonable time cost."}
diff --git a/data/sampled_jsons/FBox_score_box_embeddings_equation.jsonl b/data/sampled_jsons/FBox_score_box_embeddings_equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a19d6c80853b88d0c324fa8e8b015ff5b83b7a1d
--- /dev/null
+++ b/data/sampled_jsons/FBox_score_box_embeddings_equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "THEORETIC CONSTRAINTS USING BOX EMBEDDINGS", "date": "", "ddg_snippet": "The overall containment score is the multiplication of FBox ... ceding equation ... Our empirical results confirm that box embeddings are ideally suited to the task ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0HWAbWgI3T", "content": "The overall containment score is the multiplication of FBox ... ceding equation ... Our empirical results confirm that box embeddings are ideally suited to the task ..."}
+{"idx": 1, "title": "Probabilistic Box Embeddings for Uncertain Knowledge ...", "date": "", "ddg_snippet": "by X Chen · 2021 · Cited by 60 — cen(f(Box(X);τ,∆)) = cen(Box(X)) + τ , off(f(Box(X);τ,∆)) = off(Box(X)) ◦ ∆, where ◦ is the Hadamard product. We use separate actions for the head and ... 12 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.naacl-main.68.pdf", "content": "by X Chen · 2021 · Cited by 60 — cen(f(Box(X);τ,∆)) = cen(Box(X)) + τ , off(f(Box(X);τ,∆)) = off(Box(X)) ◦ ∆, where ◦ is the Hadamard product. We use separate actions for the head and ... 12 pages"}
+{"idx": 2, "title": "RegD: Hierarchical Embeddings via Distances over ...", "date": "", "ddg_snippet": "by H Yang · 2025 · Cited by 1 — box (c, o) = {x ∈ Rn | c − o ≤ x ≤ c + o}. Given two such boxes , box1 and box2, by setting g(x) = x and f(box ) = h( box ) · kok, we obtain the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.17518", "content": "by H Yang · 2025 · Cited by 1 — box (c, o) = {x ∈ Rn | c − o ≤ x ≤ c + o}. Given two such boxes , box1 and box2, by setting g(x) = x and f(box ) = h( box ) · kok, we obtain the ..."}
+{"idx": 3, "title": "A Geometric Approach to Personalized Recommendation ...", "date": "", "ddg_snippet": "by S Dasgupta · 2025 — We conduct an extensive empirical study comparing various vector and box embedding models for the task of set-theoretic query recommendation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10875", "content": "by S Dasgupta · 2025 — We conduct an extensive empirical study comparing various vector and box embedding models for the task of set-theoretic query recommendation."}
+{"idx": 4, "title": "Learning Representations for Hierarchies with Minimal ...", "date": "", "ddg_snippet": "9 Dec 2024 — [2018], box embeddings represent entities using a box or hyperrectangle in RD, i.e ., a Cartesian product of intervals. image. where x⌞d < x ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95845", "content": "9 Dec 2024 — [2018], box embeddings represent entities using a box or hyperrectangle in RD, i.e ., a Cartesian product of intervals. image. where x⌞d < x ..."}
+{"idx": 5, "title": "Learning Representations for Hierarchies with Minimal ...", "date": "", "ddg_snippet": "by B Rozonoyer — [2018], box embeddings represent entities using a box or hyperrectangle in RD, i.e., a Cartesian product of intervals. D. Y d=1. [x⌞ d,x⌝ d]=[x⌞. 1,x⌝. 1 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=HFS800reZK", "content": "by B Rozonoyer — [2018], box embeddings represent entities using a box or hyperrectangle in RD, i.e., a Cartesian product of intervals. D. Y d=1. [x⌞ d,x⌝ d]=[x⌞. 1,x⌝. 1 ..."}
+{"idx": 6, "title": "adavis.pdf", "date": "", "ddg_snippet": "It leverages a box embedding -based knowledge graph to well model the possible one-to-many mapping relations among different entities (i.e., data features, ... 14 pages", "subpage_snippet": "", "source": "haotian-li.com", "link": "https://haotian-li.com/paper/adavis.pdf", "content": "It leverages a box embedding -based knowledge graph to well model the possible one-to-many mapping relations among different entities (i.e., data features, ... 14 pages"}
+{"idx": 7, "title": "Shape Composition with Recursive Substructure Priors", "date": "", "ddg_snippet": "14 Sept 2018 — We introduce SCORES , a neural network which learns structure fusion for 3D shape composition. SCORES takes box abstractions of two sets of parts.", "subpage_snippet": "", "source": "graphics.stanford.edu", "link": "https://graphics.stanford.edu/courses/cs348n-22-winter/PapersReferenced/RichardZhang_1809.05398.pdf", "content": "14 Sept 2018 — We introduce SCORES , a neural network which learns structure fusion for 3D shape composition. SCORES takes box abstractions of two sets of parts."}
+{"idx": 8, "title": "Modeling Transitivity and Cyclicity in Directed Graphs via ...", "date": "", "ddg_snippet": "by D Zhang · 2022 · Cited by 9 — In this paper, we introduced binary code box embeddings , a generalized box embedding method to model directed graphs. We provide both theoretical and ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10392230", "content": "by D Zhang · 2022 · Cited by 9 — In this paper, we introduced binary code box embeddings , a generalized box embedding method to model directed graphs. We provide both theoretical and ..."}
+{"idx": 9, "title": "PerturbNet predicts single-cell responses to unseen ...", "date": "", "ddg_snippet": "by H Yu · 2025 · Cited by 36 — ( F) Box plots of evaluation metrics in one test split of sci-Plex dataset (n = 38, plots for additional splits are shown in the supplement).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12322087/", "content": "by H Yu · 2025 · Cited by 36 — ( F) Box plots of evaluation metrics in one test split of sci-Plex dataset (n = 38, plots for additional splits are shown in the supplement)."}
diff --git a/data/sampled_jsons/FD2_synthetic_dataset_x_=_A_z_z_~_N_Likelihood_Based_Approach_to_Distribution_Regression.jsonl b/data/sampled_jsons/FD2_synthetic_dataset_x_=_A_z_z_~_N_Likelihood_Based_Approach_to_Distribution_Regression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..72857a895b2420545e088b715d43bb414e48baae
--- /dev/null
+++ b/data/sampled_jsons/FD2_synthetic_dataset_x_=_A_z_z_~_N_Likelihood_Based_Approach_to_Distribution_Regression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-."}
+{"idx": 2, "title": "A likelihood based approach to distribution regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — To the best of our knowledge, our study is the first attempt to explore the likelihood - based approach for distributional regression using a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — To the best of our knowledge, our study is the first attempt to explore the likelihood - based approach for distributional regression using a ..."}
+{"idx": 3, "title": "Multi-Scale Distribution Deep Variational Autoencoder for ...", "date": "", "ddg_snippet": "by Z Cai · 2022 · Cited by 5 — Extensive experiments show that our approach yields state-of-the-art results on three real- world datasets , demonstrating its effectiveness in ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.findings-acl.7.pdf", "content": "by Z Cai · 2022 · Cited by 5 — Extensive experiments show that our approach yields state-of-the-art results on three real- world datasets , demonstrating its effectiveness in ..."}
+{"idx": 4, "title": "TIVE MODELS", "date": "", "ddg_snippet": "To the best of our knowledge, our study is the first attempt to explore the likelihood - based ap- proach for distributional regression using a conditional deep ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=A21ktT4kXD&name=pdf", "content": "To the best of our knowledge, our study is the first attempt to explore the likelihood - based ap- proach for distributional regression using a conditional deep ..."}
+{"idx": 5, "title": "PrivBayes: Private Data Release via Bayesian Networks", "date": "", "ddg_snippet": "by JUN ZHANG · 2017 · Cited by 910 — (Data synthesis ) We show how to generate synthetic data from the differentially private Bayesian network, without explicitly materializing the full-dimensional ...", "subpage_snippet": "", "source": "dimacs.rutgers.edu", "link": "http://dimacs.rutgers.edu/~graham/pubs/papers/privbayes-tods.pdf", "content": "by JUN ZHANG · 2017 · Cited by 910 — (Data synthesis ) We show how to generate synthetic data from the differentially private Bayesian network, without explicitly materializing the full-dimensional ..."}
+{"idx": 6, "title": "Pure Differential Privacy for Functional Summaries with a ...", "date": "", "ddg_snippet": "by H Lin · 2024 · Cited by 1 — Numerical experiments on synthetic and real datasets demon- strate the effectiveness of the proposed mechanism. Keywords: Differential Privacy, Functional Data ...", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume25/22-1384/22-1384.pdf", "content": "by H Lin · 2024 · Cited by 1 — Numerical experiments on synthetic and real datasets demon- strate the effectiveness of the proposed mechanism. Keywords: Differential Privacy, Functional Data ..."}
+{"idx": 7, "title": "Towards Hardware-Aware Tractable Learning of ...", "date": "", "ddg_snippet": "by LIG Olascoaga · Cited by 11 — ACs represent a joint probability distribution over a set of random variables X : the leaf nodes are either binary indicator variables λX= x , where X ∈ X , or.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "http://papers.neurips.cc/paper/9525-towards-hardware-aware-tractable-learning-of-probabilistic-models.pdf", "content": "by LIG Olascoaga · Cited by 11 — ACs represent a joint probability distribution over a set of random variables X : the leaf nodes are either binary indicator variables λX= x , where X ∈ X , or."}
+{"idx": 8, "title": "Biomarker discovery for early breast cancer diagnosis ...", "date": "", "ddg_snippet": "by K Mayoral-Peña · 2025 — This study introduces a novel bioinformatics pipeline that uses machine learning algorithms (MLAs) to identify genetic biomarkers for classifying breast cancer.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0010482525009357", "content": "by K Mayoral-Peña · 2025 — This study introduces a novel bioinformatics pipeline that uses machine learning algorithms (MLAs) to identify genetic biomarkers for classifying breast cancer."}
+{"idx": 9, "title": "Prediction of digestible energy requirement in growing ...", "date": "", "ddg_snippet": "by NC Deb · 2025 — Therefore, this study sought to predict the digestible energy requirement (DER) in the growing-finishing phase of pigs, where four machine learning (ML) models: ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666154325000717", "content": "by NC Deb · 2025 — Therefore, this study sought to predict the digestible energy requirement (DER) in the growing-finishing phase of pigs, where four machine learning (ML) models: ..."}
diff --git a/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl b/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7ee17f300ff8a0e348447b4a36ea184e9841d305
--- /dev/null
+++ b/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Great Prices on Screen Sieves - eBay Money Back Guarantee Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "No matter what you love, you'll find it here. Search Screen Sieves and more. Looking for Screen Sieves? We have almost everything on eBay.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=ebay.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=wZ18fMRIAOmNfZf3W09x6PhcZcf9ZUhFcYIijnM3xAia430pHDoh61tkiPd7tEe9SdHKnkq2cn4i329cz61FkzVDU37aAdyergIrYojo8Afgc2TYAIMDedaIf584qANh.7dGavskRcJW5vSLyrQZixA&rut=9779beeb19e25706ee3f51b464f2fdb7d5fe5f5af91a53c9de7f459b430341de&u3=https://www.bing.com/aclick?ld=e8du5J4eYoe_x322YpAKOZtzVUCUwa1nv3o2NNMFqMeytasyVWZrDo6J0P2RupY8o-tqa3oPFRj3R78soUmMihuK31-veWthVLeLhhAAvtQ6T9mMxpWqqn13ADCfnxGUHqpEg5dpoIhXbr3UaHcvAeujSHkbVzZ_0gFg3-u5BeitT_FFoN123g_IOSM9MPM2I1OINBcg&u=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&rlid=e5a348cfe2a71cf75b9f201ecd181691&vqd=4-60065174650753198866726592808644712401&iurl={1}IG=FB50AE6BFFB14C03B84F598777FCD668&CID=368134DAE3B66E8E1ADB22B5E24B6FEA&ID=DevEx,5028.1", "content": "No matter what you love, you'll find it here. Search Screen Sieves and more. Looking for Screen Sieves? We have almost everything on eBay."}
+{"idx": 1, "title": "NIST reactor : summary activities July 1990 through June ...", "date": "", "ddg_snippet": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ...", "subpage_snippet": "", "source": "www.govinfo.gov", "link": "https://www.govinfo.gov/content/pkg/GOVPUB-C13-a64a50f25606b8ae2db871a896264bdc/pdf/GOVPUB-C13-a64a50f25606b8ae2db871a896264bdc.pdf", "content": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ..."}
+{"idx": 2, "title": "1310 CLEGG STREET (2) - IIS Windows Server", "date": "", "ddg_snippet": "25 Nov 2020 — ... FD3 WILLIAMS 4' 0\"L X 2' D\"W X 4 1/2\"D , FLUORESCENTTROFFER ... Nodal Displacements Node Load Case DX ft DY ft RZ deg N1 DL 0.0000 0.0000 ...", "subpage_snippet": "", "source": "petalumadocs.cityofpetaluma.net", "link": "https://petalumadocs.cityofpetaluma.net/WebLink/DocView.aspx?id=373223&dbid=0&repo=Petaluma", "content": "25 Nov 2020 — ... FD3 WILLIAMS 4' 0\"L X 2' D\"W X 4 1/2\"D , FLUORESCENTTROFFER ... Nodal Displacements Node Load Case DX ft DY ft RZ deg N1 DL 0.0000 0.0000 ..."}
+{"idx": 3, "title": "NIST reactor : summary activities July 1990 through June 1991", "date": "", "ddg_snippet": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ...", "subpage_snippet": "", "source": "nvlpubs.nist.gov", "link": "https://nvlpubs.nist.gov/nistpubs/Legacy/TN/nbstechnicalnote1292.pdf", "content": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 5, "title": "Wheat streak mosaic virus alters the transcriptome of its vector ...", "date": "", "ddg_snippet": "by AK Gupta · Cited by 18 — Abstract. Wheat streak mosaic virus (WSMV; genus Tritimovirus; family Potyviridae) is an economically important wheat virus that is.", "subpage_snippet": "", "source": "digitalcommons.unl.edu", "link": "https://digitalcommons.unl.edu/context/plantpathpapers/article/1630/viewcontent/Gupta_JGV_2019_Wheat_streak_mosaic.pdf", "content": "by AK Gupta · Cited by 18 — Abstract. Wheat streak mosaic virus (WSMV; genus Tritimovirus; family Potyviridae) is an economically important wheat virus that is."}
+{"idx": 6, "title": "lake champlain: partnersrips and research in the new ...", "date": "", "ddg_snippet": "Lake Champlain: partnerships and research in the new millenniurnledited by Thomas 0. Manley, PaUicia L. Manley, and Timothy B. Mihuc.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-1-4757-4080-6.pdf", "content": "Lake Champlain: partnerships and research in the new millenniurnledited by Thomas 0. Manley, PaUicia L. Manley, and Timothy B. Mihuc."}
+{"idx": 7, "title": "Final Report Volume I of II Testing of a 4-Stroke Diesel Cycle ...", "date": "", "ddg_snippet": "Testing of a 4-stroke diesel cycle oil-fired reciprocating internal combustion engine to determine the effectiveness of an oxidation reduction catalyst system.", "subpage_snippet": "", "source": "nepis.epa.gov", "link": "https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P100VGMK.TXT", "content": "Testing of a 4-stroke diesel cycle oil-fired reciprocating internal combustion engine to determine the effectiveness of an oxidation reduction catalyst system."}
+{"idx": 8, "title": "EHY223 HYSYS Dynamics Introduction To ...", "date": "", "ddg_snippet": "At the end of this course you will be able to: ll Develop the skills and techniques required for creating and running dynamic simulations.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/607789019/EHY223-HYSYS-Dynamics-Introduction-to-Dynamic-Modeling", "content": "At the end of this course you will be able to: ll Develop the skills and techniques required for creating and running dynamic simulations."}
+{"idx": 9, "title": "Chitosan and Chitosan Derivatives as Chelating Agents", "date": "", "ddg_snippet": "This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission and sources are indicated. A ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/23222791/Chitosan_and_Chitosan_Derivatives_as_Chelating_Agents", "content": "This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission and sources are indicated. A ..."}
diff --git a/data/sampled_jsons/FD3_dataset_Sieve_MLE_standard_deviation_estimation_results.jsonl b/data/sampled_jsons/FD3_dataset_Sieve_MLE_standard_deviation_estimation_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1aa53c48cbbd3be24e20c4ec9822f0083c96f261
--- /dev/null
+++ b/data/sampled_jsons/FD3_dataset_Sieve_MLE_standard_deviation_estimation_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF 10-315 Notes Maximum Likelihood Estimation", "date": "", "ddg_snippet": "Maximum likelihood estimation ( MLE ) is trying to find the best parameters for a specific dataset , D. Specifically, we want to find the parameters ˆθ MLE that maximize the likelihood for D.", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~10315/notes/10315_S24_Notes_MLE.pdf", "content": "Maximum likelihood estimation ( MLE ) is trying to find the best parameters for a specific dataset , D. Specifically, we want to find the parameters ˆθ MLE that maximize the likelihood for D."}
+{"idx": 1, "title": "Standard errors for maximum likelihood estimation - The DO Loop - SAS Blogs", "date": "", "ddg_snippet": "In several previous articles, I've shown how to use SAS to fit models to data by using maximum likelihood estimation ( MLE ).", "subpage_snippet": "", "source": "blogs.sas.com", "link": "https://blogs.sas.com/content/iml/2023/11/06/stderr-mle.html", "content": "In several previous articles, I've shown how to use SAS to fit models to data by using maximum likelihood estimation ( MLE )."}
+{"idx": 2, "title": "GitHub - justdoty/Sieve_MLE: Monte Carlo EM algorithm implementation of ...", "date": "", "ddg_snippet": "The estimator is a sieve maximum likelihood estimator, whose densities are conditioned on the unobserved covariates. I make a slight departure from their original implementation by using a Monte Carlo EM algorithm to integrate out the unobserved variable from the objective function.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/justdoty/Sieve_MLE", "content": "The estimator is a sieve maximum likelihood estimator, whose densities are conditioned on the unobserved covariates. I make a slight departure from their original implementation by using a Monte Carlo EM algorithm to integrate out the unobserved variable from the objective function."}
+{"idx": 3, "title": "Finding the standard deviation of the Maximum Likelihood Estimate", "date": "", "ddg_snippet": "@NX37B I would suggest to look at the book, \"Mathematical Statistics and Data Analysis\", I believe it is also available for the public for free. In Section 8.5 of the book you will see the MLE equation written out for the mean and standard deviation parameter.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/600210/finding-the-standard-deviation-of-the-maximum-likelihood-estimate", "content": "@NX37B I would suggest to look at the book, \"Mathematical Statistics and Data Analysis\", I believe it is also available for the public for free. In Section 8.5 of the book you will see the MLE equation written out for the mean and standard deviation parameter."}
+{"idx": 4, "title": "ML Series: Day 40 — Simple Problem with MLE - Medium", "date": "", "ddg_snippet": "We generated sample data, calculated the MLE estimates for the mean and standard deviation , and visualized the results . This simple example illustrates the power and utility of MLE in statistical ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ebimsv/ml-series-day-40-simple-problem-with-mle-1459c0947cb2", "content": "We generated sample data, calculated the MLE estimates for the mean and standard deviation , and visualized the results . This simple example illustrates the power and utility of MLE in statistical ..."}
+{"idx": 5, "title": "Fit a statistical model using MLE with NumPy and SciPy", "date": "", "ddg_snippet": "Write a Numpy program to generate a dataset from a known distribution and use SciPy's optimize.minimize to perform maximum likelihood estimation of its parameters. Write a Numpy program to fit a statistical model to data using SciPy's optimize module and compare the MLE results with method-of-moments estimates.", "subpage_snippet": "", "source": "www.w3resource.com", "link": "https://www.w3resource.com/python-exercises/numpy/fit-a-statistical-model-using-mle-with-numpy-and-scipy.php", "content": "Write a Numpy program to generate a dataset from a known distribution and use SciPy's optimize.minimize to perform maximum likelihood estimation of its parameters. Write a Numpy program to fit a statistical model to data using SciPy's optimize module and compare the MLE results with method-of-moments estimates."}
+{"idx": 6, "title": "PDF 20_mle_annotated - Stanford University", "date": "", "ddg_snippet": "equations 1 % = D MLE of the Poisson parameter, % , is the unbiased estimate of the mean, J (sample mean)", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/archive/cs/cs109/cs109.1234/lectures/20_mle_annotated.pdf", "content": "equations 1 % = D MLE of the Poisson parameter, % , is the unbiased estimate of the mean, J (sample mean)"}
+{"idx": 7, "title": "Chapter 3 Session II - Maximum Likelihood Estimation (MLE ...", "date": "", "ddg_snippet": "Here we just provide a brief definition and the intuition to the method application. What is Maximum likelihood estimation ( MLE )? MLE is an estimation method in which we obtain the parameters of our model under an assumed statistical model and the available data, such that our sample is the most probable.", "subpage_snippet": "", "source": "jaimono.github.io", "link": "https://jaimono.github.io/Microeconometrics_2019/session-ii-maximum-likelihood-estimation-mle.html", "content": "Here we just provide a brief definition and the intuition to the method application. What is Maximum likelihood estimation ( MLE )? MLE is an estimation method in which we obtain the parameters of our model under an assumed statistical model and the available data, such that our sample is the most probable."}
+{"idx": 8, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "th the mean and the standard deviation . We use Monte Carlo approximation to compute the mean and standard deviation for the sieve MLE , and nume ical integration for CKDE and Flexcode. This evaluation strategy resembles", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "th the mean and the standard deviation . We use Monte Carlo approximation to compute the mean and standard deviation for the sieve MLE , and nume ical integration for CKDE and Flexcode. This evaluation strategy resembles"}
+{"idx": 9, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Our results lead to the conver-gence rate of a sieve maximum likelihood estima-tor ( MLE ) for estimating the conditional distribu-tion (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend solely on the intrin-sic dimension and smoothness of the true con-ditional distribution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "Our results lead to the conver-gence rate of a sieve maximum likelihood estima-tor ( MLE ) for estimating the conditional distribu-tion (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend solely on the intrin-sic dimension and smoothness of the true con-ditional distribution."}
diff --git a/data/sampled_jsons/Federated_Learning_non-IID_data_FedAvg_FedProx_SCAFFOLD_algorithms.jsonl b/data/sampled_jsons/Federated_Learning_non-IID_data_FedAvg_FedProx_SCAFFOLD_algorithms.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ee9d0e7ebf7960458cab85918f2d937fe905219
--- /dev/null
+++ b/data/sampled_jsons/Federated_Learning_non-IID_data_FedAvg_FedProx_SCAFFOLD_algorithms.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Federated Learning for Non - IID Data via Client", "date": "", "ddg_snippet": "Feddc: Federated learning with non - iid data via local drift decoupling and correction. Federated learning on non - iid data silos: An experimental study. In IEEE International Conference on Data Engineering, ICDE 2022, 2022.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.08391", "content": "Feddc: Federated learning with non - iid data via local drift decoupling and correction. Federated learning on non - iid data silos: An experimental study. In IEEE International Conference on Data Engineering, ICDE 2022, 2022."}
+{"idx": 1, "title": "Federated Learning on Non - IID Data Silos... - Flower Baselines 1.21.0", "date": "", "ddg_snippet": "FedBN: Federated Learning on Non - IID Features via Local Batch Normalization.You can run four algorithms FedAvg , SCAFFOLD , FedProx , and FedNova.", "subpage_snippet": "", "source": "flower.ai", "link": "https://flower.ai/docs/baselines/niid_bench.html", "content": "FedBN: Federated Learning on Non - IID Features via Local Batch Normalization.You can run four algorithms FedAvg , SCAFFOLD , FedProx , and FedNova."}
+{"idx": 2, "title": "(PDF) Comparative analysis of federated learning algorithms under...", "date": "", "ddg_snippet": "Keywords: Federated Learning , Non - IID Data , Computer Vision, Experimental Study, Model.3. Federate Learning Algorithms . Here are the summaries of the FL algorithms relevant to the paper including FedAvg , FedProx , and.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382760575_Comparative_analysis_of_federated_learning_algorithms_under_extreme_non-IID_conditions_for_computer_vision", "content": "Keywords: Federated Learning , Non - IID Data , Computer Vision, Experimental Study, Model.3. Federate Learning Algorithms . Here are the summaries of the FL algorithms relevant to the paper including FedAvg , FedProx , and."}
+{"idx": 3, "title": "GitHub - Xtra-Computing/NIID-Bench: Federated Learning Benchmark...", "date": "", "ddg_snippet": "Federated Learning Benchmark - Federated Learning on Non - IID Data Silos: An Experimental Study (ICDE 2022).This code runs a benchmark for federated learning algorithms under non - IID data distribution scenarios.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xtra-Computing/NIID-Bench", "content": "Federated Learning Benchmark - Federated Learning on Non - IID Data Silos: An Experimental Study (ICDE 2022).This code runs a benchmark for federated learning algorithms under non - IID data distribution scenarios."}
+{"idx": 4, "title": "Practical Federated Learning on non - IID", "date": "", "ddg_snippet": "Federated Deep Learning on Non - IID Data . • FedProx . • Regularization based on model-parameters.", "subpage_snippet": "", "source": "ids.nus.edu.sg", "link": "https://ids.nus.edu.sg/docs/Qinbin_NUS_IDS_talk_slides.pdf", "content": "Federated Deep Learning on Non - IID Data . • FedProx . • Regularization based on model-parameters."}
+{"idx": 5, "title": "Understanding Aggregation Algorithms in Federated Learning .", "date": "", "ddg_snippet": "These algorithms , such as Federated Averaging ( FedAvg ), FedProx , SCAFFOLD and FedPer are designed to efficiently combine model updates from diverse sources while addressing the unique challenges of non - IID data and privacy concerns.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@praburam_93885/understanding-aggregation-algorithms-in-federated-learning-834ed2bbe860", "content": "These algorithms , such as Federated Averaging ( FedAvg ), FedProx , SCAFFOLD and FedPer are designed to efficiently combine model updates from diverse sources while addressing the unique challenges of non - IID data and privacy concerns."}
+{"idx": 6, "title": "FedDC: Federated Learning With Non - IID Data via Local Drift...", "date": "", "ddg_snippet": "non - IID data in FL, the performance of FedAvg is signif-. icantly reduced [17, 23], which indicates that the FedAvg . method that ignores local drift leads to the deviation of the. FedAvg FedProx Scaffold FedDyn FedDC.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_FedDC_Federated_Learning_With_Non-IID_Data_via_Local_Drift_Decoupling_CVPR_2022_paper.pdf", "content": "non - IID data in FL, the performance of FedAvg is signif-. icantly reduced [17, 23], which indicates that the FedAvg . method that ignores local drift leads to the deviation of the. FedAvg FedProx Scaffold FedDyn FedDC."}
+{"idx": 7, "title": "Techniques for Handling Non - IID Data in FL", "date": "", "ddg_snippet": "Explore methods like data sharing, modified aggregation, and local adaptation to cope with non - IID data .", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/federated-learning/chapter-4-addressing-heterogeneity-personalization/handling-non-iid-data", "content": "Explore methods like data sharing, modified aggregation, and local adaptation to cope with non - IID data ."}
+{"idx": 8, "title": "FedSC: A federated learning algorithm based on client-side clustering", "date": "", "ddg_snippet": "FedSC FedAvg FedNova Scaffold FedProx .When the data exhibits non - IID characteristics, the perfor-mance of federated learning deteriorates. This paper introduces a novel federated learning algorithm called FedSC, which outperforms other algorithms in non - IID data scenarios.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/aimspress-data/era/2023/9/PDF/era-31-09-266.pdf", "content": "FedSC FedAvg FedNova Scaffold FedProx .When the data exhibits non - IID characteristics, the perfor-mance of federated learning deteriorates. This paper introduces a novel federated learning algorithm called FedSC, which outperforms other algorithms in non - IID data scenarios."}
+{"idx": 9, "title": "Federated Learning Optimization Methods | Restackio", "date": "", "ddg_snippet": "Federated Averaging ( FedAvg ) in Federated Learning Addressing Non - IID Data Challenges with FedProx and Scaffold Federated Averaging ( FedAvg ) is a foundational algorithm in the realm of federated learning ...", "subpage_snippet": "", "source": "www.restack.io", "link": "https://www.restack.io/p/federated-learning-answer-optimization-methods-cat-ai", "content": "Federated Averaging ( FedAvg ) in Federated Learning Addressing Non - IID Data Challenges with FedProx and Scaffold Federated Averaging ( FedAvg ) is a foundational algorithm in the realm of federated learning ..."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_2403.07932_implementation_scheduler_weights_initial_values_0.5_0.8.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_2403.07932_implementation_scheduler_weights_initial_values_0.5_0.8.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3f689e0d2e11263130aa0019da351805173e6f53
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_2403.07932_implementation_scheduler_weights_initial_values_0.5_0.8.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "However, existing literature does not provide a comprehensive ( and /or concrete) formalization for Feint behaviors , and their implications on game strategies . In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932", "content": "However, existing literature does not provide a comprehensive ( and /or concrete) formalization for Feint behaviors , and their implications on game strategies . In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation ..."}
+{"idx": 1, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Background and Motivation Action-Level Formalization Existing works: Animate of Feint behaviors as proof-of-concept of character animation techniques [1]. Learn control strategies from motion clips which incorporate nuanced behaviors like Feint behaviors [2]. Uniqueness of Feint behaviors : (Semi-)palindrome Incorporation with other behaviors", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96274.pdf", "content": "Background and Motivation Action-Level Formalization Existing works: Animate of Feint behaviors as proof-of-concept of character animation techniques [1]. Learn control strategies from motion clips which incorporate nuanced behaviors like Feint behaviors [2]. Uniqueness of Feint behaviors : (Semi-)palindrome Incorporation with other behaviors"}
+{"idx": 2, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "13unified implementation scheme of Feint behaviors in existing MARL frameworks. 14The experimental results show that our design of Feint behaviors can (1) greatly im- 15prove the game reward gains; (2) significantly improve the diversity of Multi-Player 16Games; and (3) only incur negligible overheads in terms of time consumption.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "13unified implementation scheme of Feint behaviors in existing MARL frameworks. 14The experimental results show that our design of Feint behaviors can (1) greatly im- 15prove the game reward gains; (2) significantly improve the diversity of Multi-Player 16Games; and (3) only incur negligible overheads in terms of time consumption."}
+{"idx": 3, "title": "PDF F M -P G FEINT IN MULTI-PLA - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932.pdf", "content": "ABSTRACT This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of ..."}
+{"idx": 4, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "This paper is important because it provides the first comprehensive formalization of feint behaviors in game AI, significantly improving game rewards and diversity. It offers a unified implementation scheme usable across various MARL frameworks, opening new avenues for research in deception and strategy in multi-agent systems. The findings directly address current limitations in modeling ...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "This paper is important because it provides the first comprehensive formalization of feint behaviors in game AI, significantly improving game rewards and diversity. It offers a unified implementation scheme usable across various MARL frameworks, opening new avenues for research in deception and strategy in multi-agent systems. The findings directly address current limitations in modeling ..."}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Poster Feint Behaviors and Strategies : Formalization, Implementation and Evaluation Junyu Liu · Xiangjun Peng", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96274", "content": "Poster Feint Behaviors and Strategies : Formalization, Implementation and Evaluation Junyu Liu · Xiangjun Peng"}
+{"idx": 6, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Overview Research explores deceptive strategies ( feints ) in multi-player games Introduces novel framework for modeling feint behaviors in AI agents Focuses on strategic misdirection and opponent modeling Evaluates implementation across various game environments Demonstrates improved performance compared to traditional approaches", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/feint-behaviors-strategies-formalization-implementation-evaluation", "content": "Overview Research explores deceptive strategies ( feints ) in multi-player games Introduces novel framework for modeling feint behaviors in AI agents Focuses on strategic misdirection and opponent modeling Evaluates implementation across various game environments Demonstrates improved performance compared to traditional approaches"}
+{"idx": 7, "title": "Feint Behaviors and Strategies : Formalization, Implementation and...", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/ACIDDnTbSJ@OpenReview", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games."}
+{"idx": 8, "title": "8 Ways to Initialize Vector in C++ - GeeksforGeeks", "date": "", "ddg_snippet": "Initializing a vector means assigning some initial values to the std::vector elements. In this article, we will learn 8 different ways to initialize a vector in C++.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/cpp/initialize-a-vector-in-cpp-different-ways/", "content": "Initializing a vector means assigning some initial values to the std::vector elements. In this article, we will learn 8 different ways to initialize a vector in C++."}
+{"idx": 9, "title": "Junyu Liu - Projects", "date": "", "ddg_snippet": "However, existing literature does not provide a comprehensive ( and /or concrete) formalization for Feint behaviors , and their implications on game strategies . In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation ...", "subpage_snippet": "", "source": "junyu-liu-nate.github.io", "link": "https://junyu-liu-nate.github.io/projects/FeintFinal.html", "content": "However, existing literature does not provide a comprehensive ( and /or concrete) formalization for Feint behaviors , and their implications on game strategies . In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation ..."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_Equation_(1).jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_Equation_(1).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2c76a2955814b53943d876e142795421219a807d
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_Equation_(1).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v2", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Feint behaviors and strategies | Proceedings of the 38th ...", "date": "", "ddg_snippet": "The experimental results show that our design of Feint behaviors can (1) greatly improve the game reward gains; (2) significantly improve the diversity of Multi-Player Games; and (3) only incur negligible overheads in terms of time consumption.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738032", "content": "The experimental results show that our design of Feint behaviors can (1) greatly improve the game reward gains; (2) significantly improve the diversity of Multi-Player Games; and (3) only incur negligible overheads in terms of time consumption."}
+{"idx": 2, "title": "feint_video_slides", "date": "", "ddg_snippet": "Most real-world attack behaviors can be divided into 3 stages. Feint templates can be automatically generated from them. Standalone Feint behavior is meaningless. Need to be combined with other behaviors. Lead to physically plausible follow-up behaviors. Dual-Behavior Model. Enable temporal and spatial advantages.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96274.pdf", "content": "Most real-world attack behaviors can be divided into 3 stages. Feint templates can be automatically generated from them. Standalone Feint behavior is meaningless. Need to be combined with other behaviors. Lead to physically plausible follow-up behaviors. Dual-Behavior Model. Enable temporal and spatial advantages."}
+{"idx": 3, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "DiscoverModelsSearch About Feint Behaviors and Strategies : Formalization , Implementation and Evaluation in 3 weeks · NeurIPS", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96274/paper", "content": "DiscoverModelsSearch About Feint Behaviors and Strategies : Formalization , Implementation and Evaluation in 3 weeks · NeurIPS"}
+{"idx": 4, "title": "FEINT IN MULTI-PLAYER GAMES - OpenReview", "date": "", "ddg_snippet": "Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WbyWDWoXD3", "content": "Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts."}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "25 Sept 2024 — Feint Behaviors and Strategies: Formalization, Implementation and Evaluation ... In Equation ( 1 ) and (2), should α t and β t be inside ∑ t ?", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ&referrer=[the+profile+of+Xiangjun+Peng](/profile?id=~Xiangjun_Peng1)", "content": "25 Sept 2024 — Feint Behaviors and Strategies: Formalization, Implementation and Evaluation ... In Equation ( 1 ) and (2), should α t and β t be inside ∑ t ?"}
+{"idx": 6, "title": "Feint Behaviors and Strategies: Formalization, ...", "date": "", "ddg_snippet": "by J Liu · 2024 — In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation ... 29 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/064ae24cdbb3eaacc801ee7f4fe0e4f2-Paper-Conference.pdf", "content": "by J Liu · 2024 — In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation ... 29 pages"}
+{"idx": 7, "title": "Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "7 Jun 2025 — In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v2", "content": "7 Jun 2025 — In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide ..."}
+{"idx": 8, "title": "Feint Behaviors and Strategies: Formalization, ...", "date": "", "ddg_snippet": "Figure 1 : An example of Palindrome-directed Generation Templates of Feint behaviors. The first row shows an action sequence of a cross-punch behavior. 29 pages", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "Figure 1 : An example of Palindrome-directed Generation Templates of Feint behaviors. The first row shows an action sequence of a cross-punch behavior. 29 pages"}
+{"idx": 9, "title": "Xiangjun Peng's Homepage", "date": "", "ddg_snippet": "Finalized Papers. Some papers are rewritten and implications are added. \" Feint Behaviors and Strategies : Formalization , Implementation and Evaluation \" Preprint Version Only; and gonna be revised later. The conference version at NeurIPS 2024 is here. Related preprints are here and here. \"SLITS: Sparsity-Lightened Intelligent Thread Scheduling\"", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/", "content": "Finalized Papers. Some papers are rewritten and implications are added. \" Feint Behaviors and Strategies : Formalization , Implementation and Evaluation \" Preprint Version Only; and gonna be revised later. The conference version at NeurIPS 2024 is here. Related preprints are here and here. \"SLITS: Sparsity-Lightened Intelligent Thread Scheduling\""}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_filetypepdf.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..20f9268718a94656c7665fb996e319c10174b40a
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF feint_video_slides", "date": "", "ddg_snippet": "Feint Behaviors and Strategies: Formalization, Implementation and Evaluation The 38th Conference on Neural Information Processing Systems (NeurIPS 2024)", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96274.pdf", "content": "Feint Behaviors and Strategies: Formalization, Implementation and Evaluation The 38th Conference on Neural Information Processing Systems (NeurIPS 2024)"}
+{"idx": 1, "title": "Feint Behaviors and Strategies : Formalization , Implementation ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/064ae24cdbb3eaacc801ee7f4fe0e4f2-Abstract-Conference.html", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 2, "title": "Feint Behaviors and Strategies : Formalization , Implementation ...", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/ACIDDnTbSJ@OpenReview", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games."}
+{"idx": 3, "title": "Computer Science and Game Theory Mar 2024", "date": "", "ddg_snippet": "[2] arXiv:2403.00397 [ pdf , html, other].[3] arXiv:2403.00399 [ pdf , html, other].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.GT/2024-03", "content": "[2] arXiv:2403.00397 [ pdf , html, other].[3] arXiv:2403.00399 [ pdf , html, other]."}
+{"idx": 4, "title": "Feint Behaviors and Strategies: Formalization, ...", "date": "", "ddg_snippet": "by J Liu · 2024 — Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in ... 29 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/064ae24cdbb3eaacc801ee7f4fe0e4f2-Paper-Conference.pdf", "content": "by J Liu · 2024 — Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in ... 29 pages"}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, ...", "date": "", "ddg_snippet": "Figure 1: An example of Palindrome-directed Generation Templates of Feint behaviors. The first row shows an action sequence of a cross-punch behavior. 29 pages", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "Figure 1: An example of Palindrome-directed Generation Templates of Feint behaviors. The first row shows an action sequence of a cross-punch behavior. 29 pages"}
+{"idx": 6, "title": "Junyu LIU - Google Scholar", "date": "", "ddg_snippet": "Feint behaviors and strategies : formalization , implementation and evaluation .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=EiSrD1UAAAAJ&hl=en", "content": "Feint behaviors and strategies : formalization , implementation and evaluation ."}
+{"idx": 7, "title": "dblp: List of computer science publications by Xiangjun Peng", "date": "", "ddg_snippet": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization , Implementation and Evaluation . NeurIPS 2024.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/123/5256.html", "content": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization , Implementation and Evaluation . NeurIPS 2024."}
+{"idx": 8, "title": "Xiangjun Peng's Homepage", "date": "", "ddg_snippet": "\" Feint Behaviors and Strategies : Formalization , Implementation and Evaluation \" Preprint Version Only. The conference version at NeurIPS 2024 is TBA. Related preprints are here and here. Clearly, those RejectGPTs can be unsafe, and they need to be regulated.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/index.html", "content": "\" Feint Behaviors and Strategies : Formalization , Implementation and Evaluation \" Preprint Version Only. The conference version at NeurIPS 2024 is TBA. Related preprints are here and here. Clearly, those RejectGPTs can be unsafe, and they need to be regulated."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_section_4.2.2.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_section_4.2.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f5e9970c3ca18b147e5ab530c2ee04a211c2113d
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_section_4.2.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "zine.shamesoiree.com/homomorphic-encryption-and-predictive-architectures", "date": "", "ddg_snippet": "... of this Layer 2 , its chilling effects on privacy and behavior , the interplay between capitalist data machines and entropy , and the strategic ...", "subpage_snippet": "", "source": "zine.shamesoiree.com", "link": "https://zine.shamesoiree.com/homomorphic-encryption-and-predictive-architectures", "content": "... of this Layer 2 , its chilling effects on privacy and behavior , the interplay between capitalist data machines and entropy , and the strategic ..."}
+{"idx": 1, "title": "Designing for Perceptual Crossing: Applying and Evaluating", "date": "", "ddg_snippet": "... at the Compiègne University of Technology, France (2001), and gained a PhD with honours in Kansei Science at the University of Tsukuba, Japan (2006 ...", "subpage_snippet": "", "source": "www.ijdesign.org", "link": "http://www.ijdesign.org/index.php/IJDesign/article/view/1062/537", "content": "... at the Compiègne University of Technology, France (2001), and gained a PhD with honours in Kansei Science at the University of Tsukuba, Japan (2006 ..."}
+{"idx": 2, "title": "Designing for Perceptual Crossing: Applying and Evaluating", "date": "", "ddg_snippet": "... at the Compiègne University of Technology, France (2001), and gained a PhD with honours in Kansei Science at the University of Tsukuba, Japan (2006 ...", "subpage_snippet": "", "source": "www.ijdesign.org", "link": "https://www.ijdesign.org/index.php/IJDesign/article/view/1062/537", "content": "... at the Compiègne University of Technology, France (2001), and gained a PhD with honours in Kansei Science at the University of Tsukuba, Japan (2006 ..."}
+{"idx": 3, "title": "What is another word for way? | Way Synonyms - WordHippo", "date": "", "ddg_snippet": "... moral principles governing behavior in a particular sphere ... The ability to solve difficult problems, often in original, clever, and inventive ways", "subpage_snippet": "", "source": "www.wordhippo.com", "link": "https://www.wordhippo.com/what-is/another-word-for/way.html", "content": "... moral principles governing behavior in a particular sphere ... The ability to solve difficult problems, often in original, clever, and inventive ways"}
+{"idx": 4, "title": "What is another word for practice? | Practice Synonyms -", "date": "", "ddg_snippet": "A set of conventions or moral principles governing behavior ... Competitive activities such as sports and games requiring stamina, fitness, and skill", "subpage_snippet": "", "source": "www.wordhippo.com", "link": "https://www.wordhippo.com/what-is/another-word-for/practice.html", "content": "A set of conventions or moral principles governing behavior ... Competitive activities such as sports and games requiring stamina, fitness, and skill"}
+{"idx": 5, "title": "Artificial Generals Intelligence: Mastering Generals.io with", "date": "", "ddg_snippet": "... al., 2017 ; Brown & Sandholm, 2018 ) and Stratego (Perolat et al., 2022 ) , which require reasoning under uncertainty and strategic deception.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.06825v2", "content": "... al., 2017 ; Brown & Sandholm, 2018 ) and Stratego (Perolat et al., 2022 ) , which require reasoning under uncertainty and strategic deception."}
+{"idx": 6, "title": "Unleashing GHOST: An LLM-Powered Framework for Automated", "date": "", "ddg_snippet": "We evaluate three state-of-the-art LLMs (GPT- 4 , Gemini-1.5-pro, and LLaMA3) in generating and inserting HTs across different hardware designs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.02816v1", "content": "We evaluate three state-of-the-art LLMs (GPT- 4 , Gemini-1.5-pro, and LLaMA3) in generating and inserting HTs across different hardware designs ..."}
+{"idx": 7, "title": "Frontiers | Reflection on traditional sporting games: the", "date": "", "ddg_snippet": "... s Social Games ( von Neumann and Morgenstern, 2004 ), Bernoulli s (1713) Palm Game and Mathematical Expectation, Borel s Poker Game ( Ferguson, 2003 ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1123519/full", "content": "... s Social Games ( von Neumann and Morgenstern, 2004 ), Bernoulli s (1713) Palm Game and Mathematical Expectation, Borel s Poker Game ( Ferguson, 2003 ..."}
+{"idx": 8, "title": "China’s Military Infratsructure | Red Dragon 1949 /", "date": "", "ddg_snippet": "... to the greatest extent possible, comprehensively perceive, reason, and judge the strength, time and space position of the physical domain of the two ...", "subpage_snippet": "", "source": "reddragon1949.com", "link": "https://reddragon1949.com/category/chinas-military-infratsructure/page/4/", "content": "... to the greatest extent possible, comprehensively perceive, reason, and judge the strength, time and space position of the physical domain of the two ..."}
+{"idx": 9, "title": "Research — Neil Samuels Developmental Therapy, LLC", "date": "", "ddg_snippet": "The published preliminary and follow up data on the 2011 MEHRI Foundation study ( cited above) as well as separately other previous and current ...", "subpage_snippet": "", "source": "www.neilsamuelsdevtherapy.com", "link": "https://www.neilsamuelsdevtherapy.com/research", "content": "The published preliminary and follow up data on the 2011 MEHRI Foundation study ( cited above) as well as separately other previous and current ..."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..63a2331b20df1c605c2c7cc0def583a8aadb7ef5
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/064ae24cdbb3eaacc801ee7f4fe0e4f2-Abstract-Conference.html", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Feint behaviors and strategies: - ACM Digital Library", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738032", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 2, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Feint Behaviors and Strategies : Formalization , Implementation and Evaluation The 38th Conference on Neural Information Processing Systems (NeurIPS 2024)", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96274.pdf", "content": "Feint Behaviors and Strategies : Formalization , Implementation and Evaluation The 38th Conference on Neural Information Processing Systems (NeurIPS 2024)"}
+{"idx": 3, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "This research introduces a new way to understand and implement \" Feint behaviors ,\" which are tactics used in games to trick opponents and gain advantages. The authors created methods to automatically generate these deceptive actions and combine them with follow-up moves in multiplayer games. Their findings show that using Feints can significantly increase the chances of winning and make games ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96274/paper", "content": "This research introduces a new way to understand and implement \" Feint behaviors ,\" which are tactics used in games to trick opponents and gain advantages. The authors created methods to automatically generate these deceptive actions and combine them with follow-up moves in multiplayer games. Their findings show that using Feints can significantly increase the chances of winning and make games ..."}
+{"idx": 4, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "This paper introduces a novel formalization of feint behaviors in multi-player games, improving AI performance and game diversity via a unified MARL implementation .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "This paper introduces a novel formalization of feint behaviors in multi-player games, improving AI performance and game diversity via a unified MARL implementation ."}
+{"idx": 5, "title": "Feint in Multi-Player Games - arXiv.org", "date": "", "ddg_snippet": "This paper introduces the first formalization , implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v1", "content": "This paper introduces the first formalization , implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts."}
+{"idx": 6, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Behavioral analysis showed agents developing sophisticated deceptive strategies through self-play and multi-agent training. The system demonstrated strong performance across different game environments while maintaining interpretability.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/feint-behaviors-strategies-formalization-implementation-evaluation", "content": "Behavioral analysis showed agents developing sophisticated deceptive strategies through self-play and multi-agent training. The system demonstrated strong performance across different game environments while maintaining interpretability."}
+{"idx": 7, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Poster Feint Behaviors and Strategies : Formalization , Implementation and Evaluation Junyu Liu · Xiangjun Peng", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96274", "content": "Poster Feint Behaviors and Strategies : Formalization , Implementation and Evaluation Junyu Liu · Xiangjun Peng"}
+{"idx": 8, "title": "Formalizing Feint Actions, and Example Studies in Two-Player Games", "date": "", "ddg_snippet": "This work provides the first comprehensive and concrete formalization of Feint actions, which allows automatic generation of Feint actions, via the proposed Palindrome-directed Generation of Feint actions, and provides concrete principles to properly combine Feint and attack actions. Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Formalizing-Feint-Actions,-and-Example-Studies-in-Liu-Jin/1630f39b57450d1498bb1f9adf4a9344a8474be1", "content": "This work provides the first comprehensive and concrete formalization of Feint actions, which allows automatic generation of Feint actions, via the proposed Palindrome-directed Generation of Feint actions, and provides concrete principles to properly combine Feint and attack actions. Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from ..."}
+{"idx": 9, "title": "Xiangjun Peng's Homepage", "date": "", "ddg_snippet": "Finalized Papers. Some papers are rewritten and implications are added. \" Feint Behaviors and Strategies : Formalization , Implementation and Evaluation \" Preprint Version Only; and gonna be revised later. The conference version at NeurIPS 2024 is here. Related preprints are here and here. \"SLITS: Sparsity-Lightened Intelligent Thread Scheduling\" The conference version at SIGMETRICS 2023 is here ...", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/", "content": "Finalized Papers. Some papers are rewritten and implications are added. \" Feint Behaviors and Strategies : Formalization , Implementation and Evaluation \" Preprint Version Only; and gonna be revised later. The conference version at NeurIPS 2024 is here. Related preprints are here and here. \"SLITS: Sparsity-Lightened Intelligent Thread Scheduling\" The conference version at SIGMETRICS 2023 is here ..."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_scheduler_weights_lambda_shor_year_2023.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_scheduler_weights_lambda_shor_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ebfdb8fea61e05b664a2d9093b0ae5ccb0ee405
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_scheduler_weights_lambda_shor_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v2", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Feint behaviors and strategies: - ACM Digital Library", "date": "", "ddg_snippet": "However, existing literature does not provide a comprehensive ( and /or concrete) formalization for Feint behaviors , and their implications on game strategies . In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738032", "content": "However, existing literature does not provide a comprehensive ( and /or concrete) formalization for Feint behaviors , and their implications on game strategies . In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation ..."}
+{"idx": 2, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "7formalization of Feint behaviors at action-level and strategy-level, and provide 8concrete implementation and quantitative evaluation in Multi-Player games. The 9key idea of our work is to (1) allow automatic generation of Feint behaviors via 10Palindrome-directed templates, and combine them with intended high-reward ac-", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "7formalization of Feint behaviors at action-level and strategy-level, and provide 8concrete implementation and quantitative evaluation in Multi-Player games. The 9key idea of our work is to (1) allow automatic generation of Feint behaviors via 10Palindrome-directed templates, and combine them with intended high-reward ac-"}
+{"idx": 3, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Poster Feint Behaviors and Strategies : Formalization , Implementation and Evaluation Junyu Liu · Xiangjun Peng", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96274", "content": "Poster Feint Behaviors and Strategies : Formalization , Implementation and Evaluation Junyu Liu · Xiangjun Peng"}
+{"idx": 4, "title": "Formalizing Feint Actions, and Example Studies in Two-Player Games", "date": "", "ddg_snippet": "This work provides the first comprehensive and concrete formalization of Feint actions, which allows automatic generation of Feint actions, via the proposed Palindrome-directed Generation of Feint actions, and provides concrete principles to properly combine Feint and attack actions. Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Formalizing-Feint-Actions,-and-Example-Studies-in-Liu-Jin/1630f39b57450d1498bb1f9adf4a9344a8474be1", "content": "This work provides the first comprehensive and concrete formalization of Feint actions, which allows automatic generation of Feint actions, via the proposed Palindrome-directed Generation of Feint actions, and provides concrete principles to properly combine Feint and attack actions. Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from ..."}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "This paper introduces a novel formalization of feint behaviors in multi-player games, improving AI performance and game diversity via a unified MARL implementation .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "This paper introduces a novel formalization of feint behaviors in multi-player games, improving AI performance and game diversity via a unified MARL implementation ."}
+{"idx": 6, "title": "Feint in Multi-Player Games - OpenReview", "date": "", "ddg_snippet": "Abstract: This paper introduces the first formalization , implementation and quantitative evaluation of \\ feint in Multi-Player Games. Our work first formalizes \\ feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=WbyWDWoXD3", "content": "Abstract: This paper introduces the first formalization , implementation and quantitative evaluation of \\ feint in Multi-Player Games. Our work first formalizes \\ feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts."}
+{"idx": 7, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "This research introduces a new way to understand and implement \" Feint behaviors ,\" which are tactics used in games to trick opponents and gain advantages. The authors created methods to automatically generate these deceptive actions and combine them with follow-up moves in multiplayer games. Their findings show that using Feints can significantly increase the chances of winning and make games ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96274/paper", "content": "This research introduces a new way to understand and implement \" Feint behaviors ,\" which are tactics used in games to trick opponents and gain advantages. The authors created methods to automatically generate these deceptive actions and combine them with follow-up moves in multiplayer games. Their findings show that using Feints can significantly increase the chances of winning and make games ..."}
+{"idx": 8, "title": "Junyu Liu - Projects", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "junyu-liu-nate.github.io", "link": "https://junyu-liu-nate.github.io/projects/FeintFinal.html", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 9, "title": "Xiangjun Peng's Homepage", "date": "", "ddg_snippet": "Finalized Papers. Some papers are rewritten and implications are added. \" Feint Behaviors and Strategies : Formalization , Implementation and Evaluation \" Preprint Version Only; and gonna be revised later. The conference version at NeurIPS 2024 is here. Related preprints are here and here. \"SLITS: Sparsity-Lightened Intelligent Thread Scheduling\" The conference version at SIGMETRICS 2023 is here ...", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/", "content": "Finalized Papers. Some papers are rewritten and implications are added. \" Feint Behaviors and Strategies : Formalization , Implementation and Evaluation \" Preprint Version Only; and gonna be revised later. The conference version at NeurIPS 2024 is here. Related preprints are here and here. \"SLITS: Sparsity-Lightened Intelligent Thread Scheduling\" The conference version at SIGMETRICS 2023 is here ..."}
diff --git a/data/sampled_jsons/Fernando_Gama_Santiago_Segarra_Alejandro_Ribeiro_hierarchical_overlapping_clustering_cut_metrics_201.jsonl b/data/sampled_jsons/Fernando_Gama_Santiago_Segarra_Alejandro_Ribeiro_hierarchical_overlapping_clustering_cut_metrics_201.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aa2c4649f79bceb1ff1212582a213c33d0ac65c7
--- /dev/null
+++ b/data/sampled_jsons/Fernando_Gama_Santiago_Segarra_Alejandro_Ribeiro_hierarchical_overlapping_clustering_cut_metrics_201.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering of Network Data Using Cut ...", "date": "", "ddg_snippet": "Fernando Gama , Santiago Segarra , and Alejandro Ribeiro thod to obtain hierarchical and over - lapping clusters from network data – i.e., a set of nodes endowed with pairwise dissimilarities – is presented. The introduced method is hierarchical in the sense that it outputs a nested collection of groupings o", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1611.01393v1", "content": "Fernando Gama , Santiago Segarra , and Alejandro Ribeiro thod to obtain hierarchical and over - lapping clusters from network data – i.e., a set of nodes endowed with pairwise dissimilarities – is presented. The introduced method is hierarchical in the sense that it outputs a nested collection of groupings o"}
+{"idx": 1, "title": "Hierarchical Overlapping Clustering of Network Data Using Cut ...", "date": "", "ddg_snippet": "We then proceed to apply a hierarchical particularly useful when modeling uneven levels of influence (non- overlapping ) clustering method to each of the dithered among nodes.", "subpage_snippet": "", "source": "docslib.org", "link": "https://docslib.org/doc/1363438/hierarchical-overlapping-clustering-of-network-data-using-cut-metrics-fernando-gama-santiago-segarra-and-alejandro-ribeiro", "content": "We then proceed to apply a hierarchical particularly useful when modeling uneven levels of influence (non- overlapping ) clustering method to each of the dithered among nodes."}
+{"idx": 2, "title": "Overlapping clustering of network data using cut metrics", "date": "", "ddg_snippet": "Mar 1, 2016 · We present a novel method to hierarchically cluster networked data allowing nodes to simultaneously belong to multiple clusters. Given a network, our method outputs a cut metric on the underlying node set, which can be related to data coverings at different resolutions.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/ICASSP.2016.7472912", "content": "Mar 1, 2016 · We present a novel method to hierarchically cluster networked data allowing nodes to simultaneously belong to multiple clusters. Given a network, our method outputs a cut metric on the underlying node set, which can be related to data coverings at different resolutions."}
+{"idx": 3, "title": "Hierarchical Overlapping Clustering of Network Data Using Cut ...", "date": "", "ddg_snippet": "May 24, 2017 · Abstract: A novel method to obtain hierarchical and overlapping clusters from network data—i.e., a set of nodes endowed with pairwise dissimilarities—is presented.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/7933249", "content": "May 24, 2017 · Abstract: A novel method to obtain hierarchical and overlapping clusters from network data—i.e., a set of nodes endowed with pairwise dissimilarities—is presented."}
+{"idx": 4, "title": "Overlapping Clustering of Network Data Using Cut Metrics", "date": "", "ddg_snippet": "The cut metric is obtained by averaging a set of ultrametrics, which are themselves the output of (non- overlapping ) hierarchically clustering noisy versions of the original network of interest.", "subpage_snippet": "", "source": "sigport.org", "link": "https://sigport.org/documents/overlapping-clustering-network-data-using-cut-metrics-0", "content": "The cut metric is obtained by averaging a set of ultrametrics, which are themselves the output of (non- overlapping ) hierarchically clustering noisy versions of the original network of interest."}
+{"idx": 5, "title": "Hierarchical Overlapping Clustering of Network Data Using Cut ...", "date": "", "ddg_snippet": "This paper presents an algorithm for clustering asymmetric affinity data, and demonstrates the performance of this algorithm at recovering the higher levels of the Structural Classification of Proteins (SCOP) on a data base of highly conserved subsequences.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Hierarchical-Overlapping-Clustering-of-Network-Data-Gama-Segarra/17719a305b7de083243db5c79ea14761fe095d39/figure/6", "content": "This paper presents an algorithm for clustering asymmetric affinity data, and demonstrates the performance of this algorithm at recovering the higher levels of the Structural Classification of Proteins (SCOP) on a data base of highly conserved subsequences."}
+{"idx": 6, "title": "Hierarchical Overlapping Clustering of Network Data Using ...", "date": "", "ddg_snippet": "by F Gama · 2017 · Cited by 11 — Hierarchical Overlapping Clustering of Network Data Using Cut Metrics ... Fernando Gama ; Santiago Segarra ; Alejandro Ribeiro. All Authors.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/7933249/", "content": "by F Gama · 2017 · Cited by 11 — Hierarchical Overlapping Clustering of Network Data Using Cut Metrics ... Fernando Gama ; Santiago Segarra ; Alejandro Ribeiro. All Authors."}
+{"idx": 7, "title": "Hierarchical Overlapping Clustering of Network Data Using ...", "date": "", "ddg_snippet": "by F Gama · 2016 · Cited by 11 — Hierarchical Overlapping Clustering of Network. Data Using Cut Metrics . Fernando Gama , Santiago Segarra , and Alejandro Ribeiro . Abstract—A novel method to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1611.01393", "content": "by F Gama · 2016 · Cited by 11 — Hierarchical Overlapping Clustering of Network. Data Using Cut Metrics . Fernando Gama , Santiago Segarra , and Alejandro Ribeiro . Abstract—A novel method to ..."}
+{"idx": 8, "title": "Hierarchical Overlapping Clustering of Network Data Using ...", "date": "", "ddg_snippet": "A novel method to obtain hierarchical and overlapping clusters from network data—i.e., a set of nodes endowed with pairwise dissimilarities—is presented and ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Hierarchical-Overlapping-Clustering-of-Network-Data-Gama-Segarra/227d8d8c99882c93626f7051f199405f60b5fa9d", "content": "A novel method to obtain hierarchical and overlapping clusters from network data—i.e., a set of nodes endowed with pairwise dissimilarities—is presented and ..."}
+{"idx": 9, "title": "HIERARCHICAL OVERLAPPING CLUSTERING: COST", "date": "", "ddg_snippet": "by Y Pan — Fernando Gama, Santiago Segarra, and Alejandro Ribeiro. Hierarchical overlapping clustering of network data using cut metrics . IEEE Trans. Signal Inf ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "by Y Pan — Fernando Gama, Santiago Segarra, and Alejandro Ribeiro. Hierarchical overlapping clustering of network data using cut metrics . IEEE Trans. Signal Inf ..."}
diff --git a/data/sampled_jsons/Figure_2_alpha_noise_variance_distribution_regression_conditional_deep_generative_models.jsonl b/data/sampled_jsons/Figure_2_alpha_noise_variance_distribution_regression_conditional_deep_generative_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..206e0f0b21b7fba4951355fced9c7165328cdf32
--- /dev/null
+++ b/data/sampled_jsons/Figure_2_alpha_noise_variance_distribution_regression_conditional_deep_generative_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diffusion Models Chapter 4: Conditional Generation I", "date": "", "ddg_snippet": "Diffusion Models Chapter 4: Conditional Generation I Generative AI and Foundation Models Spring 2024 Department of Mathematical Sciences Ernest K. Ryu Seoul National University", "subpage_snippet": "", "source": "ernestryu.com", "link": "https://ernestryu.com/courses/FM/diffusion4.pdf", "content": "Diffusion Models Chapter 4: Conditional Generation I Generative AI and Foundation Models Spring 2024 Department of Mathematical Sciences Ernest K. Ryu Seoul National University"}
+{"idx": 1, "title": "deep_generative_models.ipynb - Colab - Google Colab", "date": "", "ddg_snippet": "A deep generative model is a generative model in which either pθ(x) or pθ(x|y) are represented by ( deep ) neural networks (with parameters θ)! To be more concrete, a deep generative model is a neural network that allows us to sample x from the distribution pθ(x) (or pθ(x|y)).", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/deep-learning-indaba/indaba-pracs-2022/blob/main/practicals/deep_generative_models.ipynb", "content": "A deep generative model is a generative model in which either pθ(x) or pθ(x|y) are represented by ( deep ) neural networks (with parameters θ)! To be more concrete, a deep generative model is a neural network that allows us to sample x from the distribution pθ(x) (or pθ(x|y))."}
+{"idx": 2, "title": "A Deep Generative Approach to Conditional Sampling", "date": "", "ddg_snippet": "Dec 10, 2021 · Abstract We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise -outsourcing lemma.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/01621459.2021.2016424", "content": "Dec 10, 2021 · Abstract We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise -outsourcing lemma."}
+{"idx": 3, "title": "A LIKELIHOOD BASED APPROACH TO DISTRIBUTION REGRESSION USING ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold."}
+{"idx": 5, "title": "Deep distribution regression - ScienceDirect", "date": "", "ddg_snippet": "Jul 1, 2021 · In this paper, we leverage the success of machine learning classification models such as neural network to build conditional distribution estimates. Our proposed two-stage framework transforms the conditional distribution estimation problem into a multi-class classification problem, which allows us to use flexible and robust machine learning tools.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167947321000372", "content": "Jul 1, 2021 · In this paper, we leverage the success of machine learning classification models such as neural network to build conditional distribution estimates. Our proposed two-stage framework transforms the conditional distribution estimation problem into a multi-class classification problem, which allows us to use flexible and robust machine learning tools."}
+{"idx": 6, "title": "(PDF) A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold Structure; Sieve MLE; Wasserstein Convergence.Our deep generative model focuses on the conditional distribution V|X.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold Structure; Sieve MLE; Wasserstein Convergence.Our deep generative model focuses on the conditional distribution V|X."}
+{"idx": 7, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "The text explores conditional deep generative models for distribution regression , focusing on high-dimensional data concentrated around a lower-dimensional manifold. It analyzes large-sample properties...", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "The text explores conditional deep generative models for distribution regression , focusing on high-dimensional data concentrated around a lower-dimensional manifold. It analyzes large-sample properties..."}
+{"idx": 8, "title": "Learning Structured Output Representation using Deep Conditional ...", "date": "", "ddg_snippet": "Figure 1: Illustration of the conditional graphical models (CGMs). (a) the predictive process of output Y for the baseline CNN; (b) the generative process of CGMs; (c) an approximate inference of Z (also known as recognition process [16]); (d) the generative process with recurrent connection.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2015/file/8d55a249e6baa5c06772297520da2051-Paper.pdf", "content": "Figure 1: Illustration of the conditional graphical models (CGMs). (a) the predictive process of output Y for the baseline CNN; (b) the generative process of CGMs; (c) an approximate inference of Z (also known as recognition process [16]); (d) the generative process with recurrent connection."}
+{"idx": 9, "title": "A Deep Learning Framework for the", "date": "", "ddg_snippet": "The deep generative models may be seen as producing representations of probability distributions over multiple variables [45].", "subpage_snippet": "", "source": "ccc.inaoep.mx", "link": "https://ccc.inaoep.mx/archivos/2021/CCC-23-005.pdf", "content": "The deep generative models may be seen as producing representations of probability distributions over multiple variables [45]."}
diff --git a/data/sampled_jsons/Fisher_information_I(p)_Remark_4.2_sitearxiv.org_gQlxd3Mtru_Learning_stochastic_dynamics_from_snapsh.jsonl b/data/sampled_jsons/Fisher_information_I(p)_Remark_4.2_sitearxiv.org_gQlxd3Mtru_Learning_stochastic_dynamics_from_snapsh.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..daf8bdea77885f0b73a900b81e1cf5a6ac9922b4
--- /dev/null
+++ b/data/sampled_jsons/Fisher_information_I(p)_Remark_4.2_sitearxiv.org_gQlxd3Mtru_Learning_stochastic_dynamics_from_snapsh.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "Learning stochastic dynamics from snapshots through regularized...", "date": "", "ddg_snippet": "Here we develop a new deep learning method (DeepRUOT) for learning general RUOT and inferring continuous unbalanced stochastic dynamics from samples based on the derived Fisher regularization form without requiring prior knowledge.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v4", "content": "Here we develop a new deep learning method (DeepRUOT) for learning general RUOT and inferring continuous unbalanced stochastic dynamics from samples based on the derived Fisher regularization form without requiring prior knowledge."}
+{"idx": 1, "title": "Learning stochastic dynamics from snapshots through regularized...", "date": "", "ddg_snippet": "Here we develop a new deep learning method (DeepRUOT) for learning general RUOT and inferring continuous unbalanced stochastic dynamics from samples based on the derived Fisher regularization form without requiring prior knowledge.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v2", "content": "Here we develop a new deep learning method (DeepRUOT) for learning general RUOT and inferring continuous unbalanced stochastic dynamics from samples based on the derived Fisher regularization form without requiring prior knowledge."}
+{"idx": 2, "title": "Learning stochastic dynamics from snapshots through regularized...", "date": "", "ddg_snippet": "Here we develop a new deep learning method (DeepRUOT) for learning general RUOT and inferring continuous unbalanced stochastic dynamics from samples based on the derived Fisher regularization form without requiring prior knowledge.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v3", "content": "Here we develop a new deep learning method (DeepRUOT) for learning general RUOT and inferring continuous unbalanced stochastic dynamics from samples based on the derived Fisher regularization form without requiring prior knowledge."}
+{"idx": 3, "title": "Learning stochastic dynamics from snapshots through regularized...", "date": "", "ddg_snippet": "Here we develop a new deep learning method (DeepRUOT) for learning general RUOT and inferring continuous unbalanced stochastic dynamics from samples based on the derived Fisher regularization form without requiring prior knowledge.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v5", "content": "Here we develop a new deep learning method (DeepRUOT) for learning general RUOT and inferring continuous unbalanced stochastic dynamics from samples based on the derived Fisher regularization form without requiring prior knowledge."}
+{"idx": 4, "title": "partially observed trajectory inference us", "date": "", "ddg_snippet": "by A Gu · 2024 · Cited by 8 — Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport. In The Thirteenth International Conference on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.07475?", "content": "by A Gu · 2024 · Cited by 8 — Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport. In The Thirteenth International Conference on ..."}
+{"idx": 5, "title": "Meta-Posterior Consistency for the Bayesian Inference of Metastable...", "date": "", "ddg_snippet": "The vast majority of the literature on learning dynamical systems or stochastic processes from time series has focused on stable or ergodic systems, for both Bayesian and frequentist inference procedures.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.01868v2", "content": "The vast majority of the literature on learning dynamical systems or stochastic processes from time series has focused on stable or ergodic systems, for both Bayesian and frequentist inference procedures."}
+{"idx": 6, "title": "The Limit Imbalanced Logistic Regression by Binary", "date": "", "ddg_snippet": "Remark 2.1. These equations are usually presented with a logistic function but we chose another expression to highlight the link with existence and uniqueness conditions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1703.08995", "content": "Remark 2.1. These equations are usually presented with a logistic function but we chose another expression to highlight the link with existence and uniqueness conditions."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Fisher_information_I(p)_gradient_log_probability_density_optimal_transport_regularization_mathematic.jsonl b/data/sampled_jsons/Fisher_information_I(p)_gradient_log_probability_density_optimal_transport_regularization_mathematic.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e41bab178f59aedb0fa7ed1c30381a65bd3c23f5
--- /dev/null
+++ b/data/sampled_jsons/Fisher_information_I(p)_gradient_log_probability_density_optimal_transport_regularization_mathematic.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fisher information regularization schemes for Wasserstein ...", "date": "", "ddg_snippet": "by W Li · 2020 · Cited by 55 — We propose a variational scheme for computing Wasserstein gradient flows. The scheme builds upon the Jordan–Kinderlehrer–Otto framework with ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0021999120302230", "content": "by W Li · 2020 · Cited by 55 — We propose a variational scheme for computing Wasserstein gradient flows. The scheme builds upon the Jordan–Kinderlehrer–Otto framework with ..."}
+{"idx": 1, "title": "computations of optimal transport distance with fisher ...", "date": "", "ddg_snippet": "by W LI · Cited by 52 — We propose a fast algorithm to approximate the optimal transport distance. The main idea is to add a Fisher information regularization into the dynamical ... 16 pages", "subpage_snippet": "", "source": "people.math.sc.edu", "link": "https://people.math.sc.edu/wuchen/papers/CSBP.pdf", "content": "by W LI · Cited by 52 — We propose a fast algorithm to approximate the optimal transport distance. The main idea is to add a Fisher information regularization into the dynamical ... 16 pages"}
+{"idx": 2, "title": "Relative fisher information and natural gradient for learning ...", "date": "", "ddg_snippet": "Abstract. Fisher information and natural gradient provided deep insights and powerful tools to artificial neu- ral networks.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/relative-fisher-information-and-natural-gradient-for-58tccfdw3m.pdf", "content": "Abstract. Fisher information and natural gradient provided deep insights and powerful tools to artificial neu- ral networks."}
+{"idx": 3, "title": "Regularized Regression and Density Estimation based on ...", "date": "", "ddg_snippet": "by M Burger · 2012 · Cited by 66 — Abstract. The aim of this paper is to investigate a novel nonparametric approach for estimating and smoothing density functions as well as probability dens.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/amrx/article/2012/2/209/101595", "content": "by M Burger · 2012 · Cited by 66 — Abstract. The aim of this paper is to investigate a novel nonparametric approach for estimating and smoothing density functions as well as probability dens."}
+{"idx": 4, "title": "Randomized methods for computing optimal transport ...", "date": "", "ddg_snippet": "by Y Xie · Cited by 3 — Computations of optimal transport distance with Fisher information regulariza- tion. ... ( p ≥η log n, ¯K≤K). ⇐= 2 ¯K−3. 2 ¯K−2. + log . ¯K. 2 ηn ≥ 2n + 4.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=UO1SFJ5GiK", "content": "by Y Xie · Cited by 3 — Computations of optimal transport distance with Fisher information regulariza- tion. ... ( p ≥η log n, ¯K≤K). ⇐= 2 ¯K−3. 2 ¯K−2. + log . ¯K. 2 ηn ≥ 2n + 4."}
+{"idx": 5, "title": "Optimal transport inference - Dan MacKinlay", "date": "", "ddg_snippet": "16 Mar 2021 — Optimal transport distances are powerful tools to compare probability distributions and have found many applications in machine learning.", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/optimal_transport_inference.html", "content": "16 Mar 2021 — Optimal transport distances are powerful tools to compare probability distributions and have found many applications in machine learning."}
+{"idx": 6, "title": "Convergence Rates of the Regularized Optimal Transport", "date": "", "ddg_snippet": "The aim of this paper is to provide tight rates of convergence on the Wasserstein distance W 2 ( γ ε , γ 0 ) , the cost term ( c , γ ε ) , and the entropy term ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/23M1591554", "content": "The aim of this paper is to provide tight rates of convergence on the Wasserstein distance W 2 ( γ ε , γ 0 ) , the cost term ( c , γ ε ) , and the entropy term ..."}
+{"idx": 7, "title": "arXiv:1907.02152v2 [math.NA] 19 Jul 2019", "date": "", "ddg_snippet": "by W Li · 2019 · Cited by 55 — It is also important to mention that the Fisher information regularization is closely related to the entropic regularization that has been ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1907.02152", "content": "by W Li · 2019 · Cited by 55 — It is also important to mention that the Fisher information regularization is closely related to the entropic regularization that has been ..."}
+{"idx": 8, "title": "arXiv:2003.10526v4 [math.DG] 29 Jul 2021", "date": "", "ddg_snippet": "by W Li · 2020 · Cited by 27 — For example, the transport Hessian gradient flow of Fisher information satisfies the heat equation . Our result can be summarized by a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2003.10526", "content": "by W Li · 2020 · Cited by 27 — For example, the transport Hessian gradient flow of Fisher information satisfies the heat equation . Our result can be summarized by a ..."}
+{"idx": 9, "title": "Affine Natural Proximal Learning", "date": "", "ddg_snippet": "by W Li · Cited by 21 — We consider two impor- tant types of statistical metrics, namely the Wasserstein and Fisher -Rao metrics, and introduce numerical methods for high dimensional ...", "subpage_snippet": "", "source": "ww3.math.ucla.edu", "link": "https://ww3.math.ucla.edu/camreport/cam19-19.pdf", "content": "by W Li · Cited by 21 — We consider two impor- tant types of statistical metrics, namely the Wasserstein and Fisher -Rao metrics, and introduce numerical methods for high dimensional ..."}
diff --git "a/data/sampled_jsons/Fisher_information_continuous_case_I(p)_=_integral_\342\210\207log_p^2_p_dx.jsonl" "b/data/sampled_jsons/Fisher_information_continuous_case_I(p)_=_integral_\342\210\207log_p^2_p_dx.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..9c5aadffc20899b408f623ad7eab01b3ecb632bd
--- /dev/null
+++ "b/data/sampled_jsons/Fisher_information_continuous_case_I(p)_=_integral_\342\210\207log_p^2_p_dx.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bernoulli distribution - Wikipedia", "date": "", "ddg_snippet": "Fisher information is calculated as the negative expected value of the second derivative of the log -likelihood Continuous univariate. supported on a bounded interval.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Bernoulli_distribution", "content": "Fisher information is calculated as the negative expected value of the second derivative of the log -likelihood Continuous univariate. supported on a bounded interval."}
+{"idx": 1, "title": "PowerPoint Presentation", "date": "", "ddg_snippet": "More on entropy (3) continuous case . • Information • Discrete case , independent.• Continuous case , (towards non-independent) • x_ i take value in R like the intensity value of a pixel of an image • Differential entropy H_diff ( p ) = Exp(- log p (x)) = - \\int p (x) log p (x) dx .", "subpage_snippet": "", "source": "cse.hkust.edu.hk", "link": "https://cse.hkust.edu.hk/~quan/comp5421/notes/generative.pdf", "content": "More on entropy (3) continuous case . • Information • Discrete case , independent.• Continuous case , (towards non-independent) • x_ i take value in R like the intensity value of a pixel of an image • Differential entropy H_diff ( p ) = Exp(- log p (x)) = - \\int p (x) log p (x) dx ."}
+{"idx": 2, "title": "Интегралы. Пошаговый калькулятор", "date": "", "ddg_snippet": "integral icon Интегралы.int f(x) dx = F(x). Значения.", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/int/ru/", "content": "integral icon Интегралы.int f(x) dx = F(x). Значения."}
+{"idx": 3, "title": "Variational convergence of the Scharfetter-Gummel scheme to", "date": "", "ddg_snippet": "Continuous case 6. From the Upwind Scheme to the Aggregation Equation Appendix A. Properties of the Tilted Dual Dissipation Potential References.", "subpage_snippet": "", "source": "pure.tue.nl", "link": "https://pure.tue.nl/ws/portalfiles/portal/313364617/2306.02226v1.pdf", "content": "Continuous case 6. From the Upwind Scheme to the Aggregation Equation Appendix A. Properties of the Tilted Dual Dissipation Potential References."}
+{"idx": 4, "title": "Dimension-free mixing times of Gibbs samplers for Bayesian...", "date": "", "ddg_snippet": "non-singular and continuous Fisher Information I (ψ). Let the prior measure be absolutely. continuous in a neighborhood of ψ∗ ∈ X with a continuous positive density at ψ∗. Finally, let Ψ be a compact neighborhood of ψ∗ for which there exists a sequence of tests un such.", "subpage_snippet": "", "source": "www.carloalberto.org", "link": "https://www.carloalberto.org/wp-content/uploads/2024/05/716.pdf", "content": "non-singular and continuous Fisher Information I (ψ). Let the prior measure be absolutely. continuous in a neighborhood of ψ∗ ∈ X with a continuous positive density at ψ∗. Finally, let Ψ be a compact neighborhood of ψ∗ for which there exists a sequence of tests un such."}
+{"idx": 5, "title": "Improved intermediate asymptotics for the heat equation", "date": "", "ddg_snippet": "Consider rst the case p = 1. An alternative approach is suggested by the method of [7, 6], which applies to. the fast diusion equation.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-00409935/document", "content": "Consider rst the case p = 1. An alternative approach is suggested by the method of [7, 6], which applies to. the fast diusion equation."}
+{"idx": 6, "title": "Tinbergen Institute", "date": "", "ddg_snippet": "b(θ) = log h(x)eθt(x) dx , R. as the integral is positive.(f) Find, informally, the asymptotic distribution of θˆnmom for n → ∞. (g) Also write down the equation that the Maximum likelihood esti-mator, call it θˆnml, has to satisfy. (h) Compute the Fisher information Iθ in one observation.", "subpage_snippet": "", "source": "staff.fnwi.uva.nl", "link": "https://staff.fnwi.uva.nl/p.j.c.spreij/onderwijs/TI/MTASall.pdf", "content": "b(θ) = log h(x)eθt(x) dx , R. as the integral is positive.(f) Find, informally, the asymptotic distribution of θˆnmom for n → ∞. (g) Also write down the equation that the Maximum likelihood esti-mator, call it θˆnml, has to satisfy. (h) Compute the Fisher information Iθ in one observation."}
+{"idx": 7, "title": "Learning from Data&Prior", "date": "", "ddg_snippet": "of Fisher information , which for discrete distributions Moreover, α > 0 represents the number of virtual (not. necessarily integer ) draws. The noninformative priors in this paper provide a rigorous justifica-. tion of the above as maximizers of an invariant notion of ignorance and.", "subpage_snippet": "", "source": "omega0.xyz", "link": "https://omega0.xyz/omega8008/dataprior/dp4arxiv1.pdf", "content": "of Fisher information , which for discrete distributions Moreover, α > 0 represents the number of virtual (not. necessarily integer ) draws. The noninformative priors in this paper provide a rigorous justifica-. tion of the above as maximizers of an invariant notion of ignorance and."}
+{"idx": 8, "title": "Gravity and the Quantum", "date": "", "ddg_snippet": "Fisher Information Revisited 3. 2 . The KG Equation 4. Scale Relativity and KG 5. Quantum Measurement and Geometry 5.1.", "subpage_snippet": "", "source": "dl.libcats.org", "link": "https://dl.libcats.org/genesis/16000/7990b26d4c05363c9562dd468413ec73/_as/[Volker_Dietrich,_Klaus_Habetha,_Gerhard_Jank]_Flu(libcats.org).pdf", "content": "Fisher Information Revisited 3. 2 . The KG Equation 4. Scale Relativity and KG 5. Quantum Measurement and Geometry 5.1."}
+{"idx": 9, "title": "shannon1948.dvi", "date": "", "ddg_snippet": "This integral , understood in the above sense, includes both the continuous and discrete cases and of course many others which cannot be represented in either form. It is trivial in this formulation that if x and u are in one-to-one correspondence, the rate from u to y is equal to that from x to y. If...", "subpage_snippet": "", "source": "www.ece.mcmaster.ca", "link": "https://www.ece.mcmaster.ca/~hranilovic/teaching/ece723/shannon1948.pdf", "content": "This integral , understood in the above sense, includes both the continuous and discrete cases and of course many others which cannot be represented in either form. It is trivial in this formulation that if x and u are in one-to-one correspondence, the rate from u to y is equal to that from x to y. If..."}
diff --git a/data/sampled_jsons/FlowDec-75m_vs_DAC-75_SIGMOS_Table_8_4.50_kbits.jsonl b/data/sampled_jsons/FlowDec-75m_vs_DAC-75_SIGMOS_Table_8_4.50_kbits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c17060220cbde34f74023f0022fe927724db2eb
--- /dev/null
+++ b/data/sampled_jsons/FlowDec-75m_vs_DAC-75_SIGMOS_Table_8_4.50_kbits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "FlowDec - 75 s: 75 Hz, single-bitrate.Figure 13: Objective metrics for FlowDec - 75 m at 7 . 5 kbit / s , comparing the Euler and Midpoint solver for different NFE. A. 8 Qualitative spectrogram comparisons.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "FlowDec - 75 s: 75 Hz, single-bitrate.Figure 13: Objective metrics for FlowDec - 75 m at 7 . 5 kbit / s , comparing the Euler and Midpoint solver for different NFE. A. 8 Qualitative spectrogram comparisons."}
+{"idx": 1, "title": "GISMETEO: Погода в Старом Осколе сегодня, прогноз погоды...", "date": "", "ddg_snippet": "Подробный прогноз погоды в Старом Осколе на сегодня.", "subpage_snippet": "", "source": "www.gismeteo.ru", "link": "https://www.gismeteo.ru/weather-stary-oskol-5024/", "content": "Подробный прогноз погоды в Старом Осколе на сегодня."}
+{"idx": 2, "title": "BESTIA vs . 2GAME at ESL Challenger League Season 50 South ...", "date": "", "ddg_snippet": "Your download will start shortly. Info: ECL S 50 SA Cup 2. BESTIA vs 2GAME. 2 maps.", "subpage_snippet": "", "source": "www.hltv.org", "link": "https://www.hltv.org/matches/2385809/bestia-vs-2game-esl-challenger-league-season-50-south-america-cup-2", "content": "Your download will start shortly. Info: ECL S 50 SA Cup 2. BESTIA vs 2GAME. 2 maps."}
+{"idx": 3, "title": "Gamma | Best AI Presentation Maker & Website Builder", "date": "", "ddg_snippet": "Gamma is your free-to-use AI design partner for creating effortless presentations, websites, and more. No coding or design skills required.", "subpage_snippet": "", "source": "gamma.app", "link": "https://gamma.app/", "content": "Gamma is your free-to-use AI design partner for creating effortless presentations, websites, and more. No coding or design skills required."}
+{"idx": 4, "title": "Не заходит в клэш рояль | Ответы Mail", "date": "", "ddg_snippet": "Хочу зайти в рояль, а выдает 75 % и бесконечное обновление , впн не помогает, в play market нет обновлений.Меняй днс сервер на xbox-dns.ru первого раза не зайдет надо будет зайти дойти до загрузки 75 % выйти и зайти заново.", "subpage_snippet": "", "source": "otvet.mail.ru", "link": "https://otvet.mail.ru/question/242100697", "content": "Хочу зайти в рояль, а выдает 75 % и бесконечное обновление , впн не помогает, в play market нет обновлений.Меняй днс сервер на xbox-dns.ru первого раза не зайдет надо будет зайти дойти до загрузки 75 % выйти и зайти заново."}
+{"idx": 5, "title": "Мир сегодня с \"Юрий Подоляка\" – Telegram", "date": "", "ddg_snippet": "\"Гераней-2\" и 4 \"Искандера\" поразили предприятие \"Днепр-4\", задействованное в работах по ОТРК \"Сапсан\". г. Павлоград: 50 \"Гераней-2\" и 8 \"Искандеров\" нанесли удар по Павлоградскому механичес...", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/yurasumy", "content": "\"Гераней-2\" и 4 \"Искандера\" поразили предприятие \"Днепр-4\", задействованное в работах по ОТРК \"Сапсан\". г. Павлоград: 50 \"Гераней-2\" и 8 \"Искандеров\" нанесли удар по Павлоградскому механичес..."}
+{"idx": 6, "title": "Как решить. Сколько орехов получила каждая девочка?", "date": "", "ddg_snippet": "81 + 34 + 17 + 23 + 75 . Для удобства подсчёта числа можно скомпоновать.81 + 34 + 17 + 23 + 75 = 230 орехов собрали все девочки. А следующим действием найдём, сколько же орехов получила каждая девочка", "subpage_snippet": "", "source": "www.bolshoyvopros.ru", "link": "https://www.bolshoyvopros.ru/questions/4587216-kak-reshit-skolko-orehov-poluchila-kazhdaja-devochka.html", "content": "81 + 34 + 17 + 23 + 75 . Для удобства подсчёта числа можно скомпоновать.81 + 34 + 17 + 23 + 75 = 230 орехов собрали все девочки. А следующим действием найдём, сколько же орехов получила каждая девочка"}
+{"idx": 7, "title": "Гостиницы Москвы — Лучшие цены на Отели 2025", "date": "", "ddg_snippet": "До центра 4. 8 км. Митьково МЦД-3 85 м 2 мин. Оплата при заселении.", "subpage_snippet": "", "source": "101Hotels.com", "link": "https://101Hotels.com/main/cities/moskva", "content": "До центра 4. 8 км. Митьково МЦД-3 85 м 2 мин. Оплата при заселении."}
+{"idx": 8, "title": "Wifi роутеры – купить вай фай роутер (роутеры и модемы) на...", "date": "", "ddg_snippet": "− 75 %. 145 шт осталось. Держатель для роутера на стену Xiaomi AX3000T.TP-Link Archer C50 (RU) Wi-Fi Роутер.", "subpage_snippet": "", "source": "www.OZON.ru", "link": "https://www.OZON.ru/category/marshrutizatory-15855/", "content": "− 75 %. 145 шт осталось. Держатель для роутера на стену Xiaomi AX3000T.TP-Link Archer C50 (RU) Wi-Fi Роутер."}
+{"idx": 9, "title": "Купить автомобили с пробегом по цене от 27 000 рублей в Москве...", "date": "", "ddg_snippet": "Maserati. 75 . Maxus.до 50 000 000 ₽.", "subpage_snippet": "", "source": "auto.ru", "link": "https://auto.ru/moskva/cars/used/", "content": "Maserati. 75 . Maxus.до 50 000 000 ₽."}
diff --git a/data/sampled_jsons/FlowDec-_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality.jsonl b/data/sampled_jsons/FlowDec-_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e70127131b18fe51d97ead49dd486b3c2715c225
--- /dev/null
+++ b/data/sampled_jsons/FlowDec-_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec | A flow-based full-band general audio codec with high", "date": "", "ddg_snippet": "inproceedings{ welker2025flowdec, title={ FlowDec : A flow - based full - band general audio codec with high perceptual quality }, author={Simon Welker and ...", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "inproceedings{ welker2025flowdec, title={ FlowDec : A flow - based full - band general audio codec with high perceptual quality }, author={Simon Welker and ..."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with high", "date": "", "ddg_snippet": "FlowDec : A flow - based full - band general audio codec with high perceptual quality ... FlowDec , a neural full - band audio codec for general audio sampled ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "FlowDec : A flow - based full - band general audio codec with high perceptual quality ... FlowDec , a neural full - band audio codec for general audio sampled ..."}
+{"idx": 2, "title": "SpectroStream: A Versatile Neural Codec for General Audio", "date": "", "ddg_snippet": "In this paper we propose SpectroStream , a neural codec that can encode full - band 48 kHz general audio with joint modeling of multiple audio channels.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05207v1", "content": "In this paper we propose SpectroStream , a neural codec that can encode full - band 48 kHz general audio with joint modeling of multiple audio channels."}
+{"idx": 3, "title": "Papers by Simon Welker", "date": "", "ddg_snippet": "FlowDec : A flow - based full - band general audio codec with high perceptual quality ... FlowDec is a new neural audio codec that achieves highquality ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/author-profile/simon-welker-793321d5-c2ba-4d7f-933a-86268b882b18", "content": "FlowDec : A flow - based full - band general audio codec with high perceptual quality ... FlowDec is a new neural audio codec that achieves highquality ..."}
+{"idx": 4, "title": "Alexander Richard", "date": "", "ddg_snippet": "FlowDec : A flow - based full - band general audio codec with high perceptual quality ... Abstract: We propose FlowDec , a neural full - band audio codec for ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Alexander+Richard", "content": "FlowDec : A flow - based full - band general audio codec with high perceptual quality ... Abstract: We propose FlowDec , a neural full - band audio codec for ..."}
+{"idx": 5, "title": "Timo GERKMANN | Professor, Head of Signal Processing Group |", "date": "", "ddg_snippet": "FlowDec : A flow - based full - band general audio codec with high perceptual quality ... FlowDec , a neural full - band audio codec for general audio sampled ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Timo-Gerkmann", "content": "FlowDec : A flow - based full - band general audio codec with high perceptual quality ... FlowDec , a neural full - band audio codec for general audio sampled ..."}
+{"idx": 6, "title": "Simon WELKER | PhD Student | Master of Science | Hamburg", "date": "", "ddg_snippet": "FlowDec : A flow - based full - band general audio codec with high perceptual quality ... FlowDec , a neural full - band audio codec for general audio sampled ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Simon-Welker", "content": "FlowDec : A flow - based full - band general audio codec with high perceptual quality ... FlowDec , a neural full - band audio codec for general audio sampled ..."}
+{"idx": 7, "title": "OpenAI Next Gen AI 🤖, Sutton & Barto win Turing Award", "date": "", "ddg_snippet": "FlowDec is a full - band audio codec leveraging conditional flow matching and non-adversarial training for high -fidelity 48 kHz audio compression.", "subpage_snippet": "", "source": "tldr.tech", "link": "https://tldr.tech/ai/2025-03-06", "content": "FlowDec is a full - band audio codec leveraging conditional flow matching and non-adversarial training for high -fidelity 48 kHz audio compression."}
+{"idx": 8, "title": "ICLR 2025 Schedule", "date": "", "ddg_snippet": "Accelerating Auto-regressive Text-to-Image ... SaRA: High -Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/calendar", "content": "Accelerating Auto-regressive Text-to-Image ... SaRA: High -Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation"}
+{"idx": 9, "title": "Sound Mar 2025", "date": "", "ddg_snippet": "We gratefully acknowledge support from the Simons ... Title: FlowDec : A flow - based full - band general audio codec with high perceptual quality", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.SD/2025-03", "content": "We gratefully acknowledge support from the Simons ... Title: FlowDec : A flow - based full - band general audio codec with high perceptual quality"}
diff --git a/data/sampled_jsons/FlowDec_NDAC-75_NDAC-25_nc_demb_Table_2_year_2024.jsonl b/data/sampled_jsons/FlowDec_NDAC-75_NDAC-25_nc_demb_Table_2_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e1a5da5819cd9fc19234d2f06b074b3a872f5fb1
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_NDAC-75_NDAC-25_nc_demb_Table_2_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - facebookresearch/ FlowDec : An neural full-band audio codec...", "date": "", "ddg_snippet": "You can for instance use our pre-trained NDAC variants - see the \"Inference\" section for how to run them. The expected input format for FlowDec datasets is a file containing a comma-separated list of paths, e.g.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "You can for instance use our pre-trained NDAC variants - see the \"Inference\" section for how to run them. The expected input format for FlowDec datasets is a file containing a comma-separated list of paths, e.g."}
+{"idx": 1, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "NDAC - 75 is targeted at 48 kHz audio with a whole-number feature rate (75 Hz) and whole-number bitrates. FlowDec -25s: 25 Hz, single-bitrate. Trained based on NDAC - 25 with a bitrate of 4.0 kbit/s. We do not train for multiple bitrates here as the bitrate and feature rate is already very low.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "NDAC - 75 is targeted at 48 kHz audio with a whole-number feature rate (75 Hz) and whole-number bitrates. FlowDec -25s: 25 Hz, single-bitrate. Trained based on NDAC - 25 with a bitrate of 4.0 kbit/s. We do not train for multiple bitrates here as the bitrate and feature rate is already very low."}
+{"idx": 2, "title": "flowdec · PyPI", "date": "", "ddg_snippet": "Flowdec /TensorFlow with full GPU acceleration: ~1 second. Signal Dimensions - Flowdec can support 1, 2 , or 3 dimensional images/signals.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/flowdec/", "content": "Flowdec /TensorFlow with full GPU acceleration: ~1 second. Signal Dimensions - Flowdec can support 1, 2 , or 3 dimensional images/signals."}
+{"idx": 3, "title": "Mazda MX-5 2016 Red AUTOMATIC 2 L | JM1 NDAC 75 G0113557", "date": "", "ddg_snippet": "JM1 NDAC 75 G0113557. Year: 2016. Condition: Run & Drive Verified.The car was up for sale with damage which can check in the photos. In order for the information to be open we post the VIN Code number of the car JM1 NDAC 75 G0113557.", "subpage_snippet": "", "source": "bidinfo.app", "link": "https://bidinfo.app/mazda/mx-5/copart-49607155-mazda-mx-5-2016-red-vin-jm1ndac75g0113557.html", "content": "JM1 NDAC 75 G0113557. Year: 2016. Condition: Run & Drive Verified.The car was up for sale with damage which can check in the photos. In order for the information to be open we post the VIN Code number of the car JM1 NDAC 75 G0113557."}
+{"idx": 4, "title": "2016 Mazda MX-5, Club | JM1 NDAC 75 G0103739 | Bid History | BidCars", "date": "", "ddg_snippet": "2016 Mazda MX-5 JM1 NDAC 75 G0103739. Ключ в наличии Автоматический Бензиновый Задний привод 2 .0L 4 cyl.", "subpage_snippet": "", "source": "bid.cars", "link": "https://bid.cars/ru/lot/1-85727464/2016-Mazda-MX-5-JM1NDAC75G0103739", "content": "2016 Mazda MX-5 JM1 NDAC 75 G0103739. Ключ в наличии Автоматический Бензиновый Задний привод 2 .0L 4 cyl."}
+{"idx": 5, "title": "September 11-12, 2023 Nonprescription Drugs Advisory Committee...", "date": "", "ddg_snippet": "NDAC Briefing Document: Oral Phenylephrine in the CCABA Monograph. Table of Contents.", "subpage_snippet": "", "source": "www.fda.gov", "link": "https://www.fda.gov/media/171915/download", "content": "NDAC Briefing Document: Oral Phenylephrine in the CCABA Monograph. Table of Contents."}
+{"idx": 6, "title": "Маркировка и расшифровка аккумуляторов 18650", "date": "", "ddg_snippet": "2 ,5–4, 25 В или 2 , 75 –4, 25 В.производитель Samsung указывает емкость 2 -мя последними цифрами в маркировке – модели INR18650-30Q имеют емкость 3000 мАч, а INR18650- 25 R – 2500 мАч. Принцип маркировки у разных компаний отличается, поэтому далее мы...", "subpage_snippet": "", "source": "VirtusTec.ru", "link": "https://VirtusTec.ru/news/18650/markirovka-i-rasshifrovka-akkumulyatorov-18650/", "content": "2 ,5–4, 25 В или 2 , 75 –4, 25 В.производитель Samsung указывает емкость 2 -мя последними цифрами в маркировке – модели INR18650-30Q имеют емкость 3000 мАч, а INR18650- 25 R – 2500 мАч. Принцип маркировки у разных компаний отличается, поэтому далее мы..."}
+{"idx": 7, "title": "Дмитрий Никотин – Telegram", "date": "", "ddg_snippet": "Про политику, простым языком о сложном. Контрпропаганда и интерпретация новостей.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/dmitrynikotin", "content": "Про политику, простым языком о сложном. Контрпропаганда и интерпретация новостей."}
+{"idx": 8, "title": "Масла для Nissan Serena C 25 (3 Gen), 2005-2010 года", "date": "", "ddg_snippet": "Поколение: 3 (C 25 ) Годы выпуска: 2005 - 2010 Кузов: C 25 , NC 25 , CC 25 , CNC 25 Рынок сбыта: Япония. Подберите масло для Вашего автомобиля бесплатно.Спецификация масла. API GL-5 для SAE 75 W-90 Периодичность замены: 60 тыс. км. или 4 года.", "subpage_snippet": "", "source": "podbormasla.ru", "link": "https://podbormasla.ru/nissan/serena/3gen/", "content": "Поколение: 3 (C 25 ) Годы выпуска: 2005 - 2010 Кузов: C 25 , NC 25 , CC 25 , CNC 25 Рынок сбыта: Япония. Подберите масло для Вашего автомобиля бесплатно.Спецификация масла. API GL-5 для SAE 75 W-90 Периодичность замены: 60 тыс. км. или 4 года."}
+{"idx": 9, "title": "Погода в Анталье на 14 дней (Турция) - подробный... - Погода Mail", "date": "", "ddg_snippet": "Подробный прогноз погоды на 14 дней в Анталье (Турция).", "subpage_snippet": "", "source": "pogoda.mail.ru", "link": "https://pogoda.mail.ru/prognoz/antalya/14dney/", "content": "Подробный прогноз погоды на 14 дней в Анталье (Турция)."}
diff --git a/data/sampled_jsons/FlowDec_ScoreDec_NFE_performance_explanation_paper_Section_5.1_year_2024.jsonl b/data/sampled_jsons/FlowDec_ScoreDec_NFE_performance_explanation_paper_Section_5.1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..88e538403359d38203532a0dc804905f7c133141
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_ScoreDec_NFE_performance_explanation_paper_Section_5.1_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2b2t - The History of NFE - YouTube", "date": "", "ddg_snippet": "The History of the NFE or also known as Negative Four hundred Eight hundred.75 likes and everyone gets AN END PORTAL FRAME ... ( ° ʖ °) OTHER COOL STUFF...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=YOzwYEds7i0", "content": "The History of the NFE or also known as Negative Four hundred Eight hundred.75 likes and everyone gets AN END PORTAL FRAME ... ( ° ʖ °) OTHER COOL STUFF..."}
+{"idx": 1, "title": "GitHub - facebookresearch/ FlowDec : An neural full-band audio codec...", "date": "", "ddg_snippet": "To actually train FlowDec , you should pre-enhance your own dataset(s) with a pre-trained underlying codec, save the results as .wav files, and store the paired paths in a text file. You can for instance use our pre-trained NDAC variants - see the \"Inference\" section for how to run them.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "To actually train FlowDec , you should pre-enhance your own dataset(s) with a pre-trained underlying codec, save the results as .wav files, and store the paired paths in a text file. You can for instance use our pre-trained NDAC variants - see the \"Inference\" section for how to run them."}
+{"idx": 2, "title": "[2503.01485] FlowDec : A flow -based full-band general audio codec...", "date": "", "ddg_snippet": "View a PDF of the paper titled FlowDec : A flow -based full-band general audio codec with high perceptual quality, by Simon Welker and 6 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "View a PDF of the paper titled FlowDec : A flow -based full-band general audio codec with high perceptual quality, by Simon Welker and 6 other authors."}
+{"idx": 3, "title": "FlowDec : A flow -based full-band general audio codec... | OpenReview", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 4, "title": "FlowDec | A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "FlowDec achieves the following metrics at 7.5 kbps with 6 NFE (neural network evaluations), compared to ScoreDec and other baselines. There is almost no drop in performance for FlowDec at 6 NFE compared to 50 NFE , while ScoreDec shows a significant drop.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "FlowDec achieves the following metrics at 7.5 kbps with 6 NFE (neural network evaluations), compared to ScoreDec and other baselines. There is almost no drop in performance for FlowDec at 6 NFE compared to 50 NFE , while ScoreDec shows a significant drop."}
+{"idx": 5, "title": "flowdec · PyPI", "date": "", "ddg_snippet": "Signal Dimensions - Flowdec can support 1, 2, or 3 dimensional images/signals. Multi-GPU Usage - This has yet to be tested, but theoretically this is possible since TF can do it (and this Multi-GPU Example is a start).", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/flowdec/", "content": "Signal Dimensions - Flowdec can support 1, 2, or 3 dimensional images/signals. Multi-GPU Usage - This has yet to be tested, but theoretically this is possible since TF can do it (and this Multi-GPU Example is a start)."}
+{"idx": 6, "title": "ICLR 2025 Thursday 04/24", "date": "", "ddg_snippet": "FlowDec : A flow -based full-band general audio codec with high perceptual quality.AuroraCap shows superior performance on various video and image captioning benchmarks, for example, obtaining a CIDEr of 88.9 on Flickr30k, beating GPT-4 V (55.3) and Gemini-1.5 Pro (82.2).", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/day/4/24", "content": "FlowDec : A flow -based full-band general audio codec with high perceptual quality.AuroraCap shows superior performance on various video and image captioning benchmarks, for example, obtaining a CIDEr of 88.9 on Flickr30k, beating GPT-4 V (55.3) and Gemini-1.5 Pro (82.2)."}
+{"idx": 7, "title": "Perchance AI Art", "date": "", "ddg_snippet": "Generated qmLk3 NFE 0uMyfeIL. Generated VJRe89Tv1IOKEMxj. Generated 6whQd7TPsnKoYplt. Generated Bp5SrGKYP82S9xdr. Generated FoOPme5uhIkD1Wfx.", "subpage_snippet": "", "source": "perchanceai.art", "link": "https://perchanceai.art/", "content": "Generated qmLk3 NFE 0uMyfeIL. Generated VJRe89Tv1IOKEMxj. Generated 6whQd7TPsnKoYplt. Generated Bp5SrGKYP82S9xdr. Generated FoOPme5uhIkD1Wfx."}
+{"idx": 8, "title": "Find Cheap Flights Worldwide & Book Your Ticket - Google Flights", "date": "", "ddg_snippet": "Find the best flights fast, track prices, and book with confidence.", "subpage_snippet": "", "source": "www.google.com", "link": "https://www.google.com/travel/flights", "content": "Find the best flights fast, track prices, and book with confidence."}
+{"idx": 9, "title": "Godhuman | Blox Fruits Wiki | Fandom", "date": "", "ddg_snippet": "Superhuman Godhuman. Article Combos. Godhuman is a fighting style that can be obtained in the Third Sea . It serves as a superior upgrade to Superhuman , offering enhanced stats and abilities. Godhuman is one of the best fighting styles in PvP due to...", "subpage_snippet": "", "source": "blox-fruits.fandom.com", "link": "https://blox-fruits.fandom.com/wiki/Godhuman", "content": "Superhuman Godhuman. Article Combos. Godhuman is a fighting style that can be obtained in the Third Sea . It serves as a superior upgrade to Superhuman , offering enhanced stats and abilities. Godhuman is one of the best fighting styles in PvP due to..."}
diff --git a/data/sampled_jsons/FlowDec_Table_8_FAD_scores_FlowDec-75m_7.5_kbits.jsonl b/data/sampled_jsons/FlowDec_Table_8_FAD_scores_FlowDec-75m_7.5_kbits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..31e53567f4d70ef5e7dcf0b3ade33563758b134f
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_Table_8_FAD_scores_FlowDec-75m_7.5_kbits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Comparison of audio coding formats - Wikipedia", "date": "", "ddg_snippet": "Comparison of audio coding formats The following tables compare general and technical information for a variety of audio coding formats. For listening tests comparing the perceived audio quality of audio formats and codecs, see the article Codec listening test.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Comparison_of_audio_coding_formats", "content": "Comparison of audio coding formats The following tables compare general and technical information for a variety of audio coding formats. For listening tests comparing the perceived audio quality of audio formats and codecs, see the article Codec listening test."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We use each method at a bitrate of 7.5 kbit/s for FlowDec-75m and 4.0 kbit/s for FlowDec -25s, and run inference at different NFE. At a low NFE, we see that the frequency-dependent σ y achieves on par or better logSpecMSE and FAD scores , particularly for the 25 Hz models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "We use each method at a bitrate of 7.5 kbit/s for FlowDec-75m and 4.0 kbit/s for FlowDec -25s, and run inference at different NFE. At a low NFE, we see that the frequency-dependent σ y achieves on par or better logSpecMSE and FAD scores , particularly for the 25 Hz models."}
+{"idx": 2, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio codec for ...", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - facebookresearch/ FlowDec"}
+{"idx": 3, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 4, "title": "FlowDec/README.md at main · facebookresearch/FlowDec · GitHub", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - FlowDec /README.md at main · facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec/blob/main/README.md", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - FlowDec /README.md at main · facebookresearch/ FlowDec"}
+{"idx": 5, "title": "GitHub - splinter21/FlowDec: An neural full-band audio codec for ...", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - splinter21/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/splinter21/FlowDec", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - splinter21/ FlowDec"}
+{"idx": 6, "title": "FlowDec/index.html at main · sp-uhh/FlowDec · GitHub", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sp-uhh/FlowDec/blob/main/index.html", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 7, "title": "FlowDec/enhance.py at main · facebookresearch/FlowDec · GitHub", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - FlowDec /enhance.py at main · facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec/blob/main/enhance.py", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - FlowDec /enhance.py at main · facebookresearch/ FlowDec"}
+{"idx": 8, "title": "PDF Audio Codec With High Perceptual Quality", "date": "", "ddg_snippet": "In Fig. 4, we show the objective metric results of FlowDec-75m and FlowDec - 75s compared to EnCodec (48kHz), DAC- 75 , 2xDAC- 75 and the official DAC 44.1kHz checkpoint, and also include 8", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/3d937d5e40a883d5da9fd4aeeeb372b8-Paper-Conference.pdf", "content": "In Fig. 4, we show the objective metric results of FlowDec-75m and FlowDec - 75s compared to EnCodec (48kHz), DAC- 75 , 2xDAC- 75 and the official DAC 44.1kHz checkpoint, and also include 8"}
+{"idx": 9, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "In Fig. 4, we show the objective metric results of FlowDec-75m and FlowDec - 75s compared to EnCodec (48kHz), DAC- 75 , 2xDAC- 75 and the official DAC 44.1kHz checkpoint, and also include 8", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "In Fig. 4, we show the objective metric results of FlowDec-75m and FlowDec - 75s compared to EnCodec (48kHz), DAC- 75 , 2xDAC- 75 and the official DAC 44.1kHz checkpoint, and also include 8"}
diff --git a/data/sampled_jsons/FlowDec_Table_8_SIGMOS_4.5_kbps_values.jsonl b/data/sampled_jsons/FlowDec_Table_8_SIGMOS_4.5_kbps_values.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cbc24cc71e06f65ad08ca6f65e1eae913be6867f
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_Table_8_SIGMOS_4.5_kbps_values.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio codec for ...", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps . - facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps . - facebookresearch/ FlowDec"}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "For FlowDec , DAC, and DAC-75, we show both examples at a higher bitrate (7.0 - 7.75 kbps ) as well as at a lower bitrate (4.3 - 4.5 kbps ). You can choose between Speech, Music and Audio Files.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "For FlowDec , DAC, and DAC-75, we show both examples at a higher bitrate (7.0 - 7.75 kbps ) as well as at a lower bitrate (4.3 - 4.5 kbps ). You can choose between Speech, Music and Audio Files."}
+{"idx": 2, "title": "FlowDec/README.md at main · facebookresearch/FlowDec · GitHub", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps . - FlowDec /README.md at main · facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec/blob/main/README.md", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps . - FlowDec /README.md at main · facebookresearch/ FlowDec"}
+{"idx": 3, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "At NFE=50, the metric values are now acceptable but still clearly worse than those of FlowDec in FAD, fwSSNR, SIGMOS , and logSpecMSE. In SI-SDR and SIGMOS , ScoreDec, and FlowDec achieve similar values at NFE=50.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "At NFE=50, the metric values are now acceptable but still clearly worse than those of FlowDec in FAD, fwSSNR, SIGMOS , and logSpecMSE. In SI-SDR and SIGMOS , ScoreDec, and FlowDec achieve similar values at NFE=50."}
+{"idx": 4, "title": "FlowDec/index.html at main · sp-uhh/FlowDec · GitHub", "date": "", "ddg_snippet": "Contribute to sp-uhh/ FlowDec development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sp-uhh/FlowDec/blob/main/index.html", "content": "Contribute to sp-uhh/ FlowDec development by creating an account on GitHub."}
+{"idx": 5, "title": "GitHub - splinter21/FlowDec: An neural full-band audio codec for ...", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps . - splinter21/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/splinter21/FlowDec", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps . - splinter21/ FlowDec"}
+{"idx": 6, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ..."}
+{"idx": 7, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "FlowDec -75m: 75Hz, multi-bitrate. Trained based on NDAC-75 with bitrates {7.5, 6.0, 4.5 , 3.0}kbit/s, by setting the number of codebooks at inference to {10, 8 , 6, 4}. We include only this set of bitrates for ease and speed of training, and because we found that the codec does not provide good results below 3.0kbit/s. 2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "FlowDec -75m: 75Hz, multi-bitrate. Trained based on NDAC-75 with bitrates {7.5, 6.0, 4.5 , 3.0}kbit/s, by setting the number of codebooks at inference to {10, 8 , 6, 4}. We include only this set of bitrates for ease and speed of training, and because we found that the codec does not provide good results below 3.0kbit/s. 2."}
+{"idx": 8, "title": "CodecBench: A Comprehensive Benchmark for Acoustic and Semantic Evaluation", "date": "", "ddg_snippet": "CodecBench provides a rich evaluation dataset comprising numerous open-source and self-collected datasets, covering a wide range of audio scenarios, including multilingual speech, noisy environments, and emotionally expressive audio, satisfying the requirements of speech language models;", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.20660", "content": "CodecBench provides a rich evaluation dataset comprising numerous open-source and self-collected datasets, covering a wide range of audio scenarios, including multilingual speech, noisy environments, and emotionally expressive audio, satisfying the requirements of speech language models;"}
+{"idx": 9, "title": "PDF Common VoIP Service Quality Thresholds Reference Chart", "date": "", "ddg_snippet": "Overview Mean Opinion Score (MOS) is commonly used to rate phone service speech quality, expressed on a scale from 1 to 5. A score of 4 is normally considered \"Toll Quality\" for calls placed over PSTN/TDM networks. MOS is a function of many factors, including the type of network and codecs used, wiring and premises equipment, and even the handset used to place the call. MOS was originally ...", "subpage_snippet": "", "source": "download.tek.com", "link": "https://download.tek.com/document/VoIPServiceQualityMetricsandThresholdsPoster.pdf", "content": "Overview Mean Opinion Score (MOS) is commonly used to rate phone service speech quality, expressed on a scale from 1 to 5. A score of 4 is normally considered \"Toll Quality\" for calls placed over PSTN/TDM networks. MOS is a function of many factors, including the type of network and codecs used, wiring and premises equipment, and even the handset used to place the call. MOS was originally ..."}
diff --git a/data/sampled_jsons/FlowDec_flow-based_full-band_general_audio_codec_Equation_6_probability_path_mean.jsonl b/data/sampled_jsons/FlowDec_flow-based_full-band_general_audio_codec_Equation_6_probability_path_mean.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9df196ee2b222d00853447ee0ba3ba2fa403ae1b
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_flow-based_full-band_general_audio_codec_Equation_6_probability_path_mean.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "3 Mar 2025 — We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "3 Mar 2025 — We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a ..."}
+{"idx": 1, "title": "BinauralFlow: A Causal and Streamable Approach for High-Quality", "date": "", "ddg_snippet": "We design a flow matching- based streaming binaural audio synthesis framework to render high-fidelity and continuous audio based on the mono input.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22865v1", "content": "We design a flow matching- based streaming binaural audio synthesis framework to render high-fidelity and continuous audio based on the mono input."}
+{"idx": 2, "title": "FLOWDEC: A FLOW-BASED FULL-BAND GENERAL", "date": "", "ddg_snippet": "We propose Flow Dec , a neural full-band audio codec for general audio sampled at. 48 kHz that combines non-adversarial codec training with a stochastic ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/46581252e0bf80bf4efd8fcbc4002f8627f498bb.pdf", "content": "We propose Flow Dec , a neural full-band audio codec for general audio sampled at. 48 kHz that combines non-adversarial codec training with a stochastic ..."}
+{"idx": 3, "title": "BinauralFlow: A Causal and Streamable Approach for High ...", "date": "", "ddg_snippet": "Flowdec : A flow - based fullband general audio codec with high perceptual quality. arXiv preprint arXiv:2503.01485, 2025. Wu, Y. and He, K. Group ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46249", "content": "Flowdec : A flow - based fullband general audio codec with high perceptual quality. arXiv preprint arXiv:2503.01485, 2025. Wu, Y. and He, K. Group ..."}
+{"idx": 4, "title": "ICLR 2025 Schedule", "date": "", "ddg_snippet": "24 Apr 2025 — Scaling In-the-Wild Training for Diffusion- based Illumination Harmonization and Editing by Imposing Consistent Light Transport.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/calendar", "content": "24 Apr 2025 — Scaling In-the-Wild Training for Diffusion- based Illumination Harmonization and Editing by Imposing Consistent Light Transport."}
+{"idx": 5, "title": "Daily Papers", "date": "", "ddg_snippet": "4 days ago — We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=feed-forward+digital+compressor", "content": "4 days ago — We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a ..."}
+{"idx": 6, "title": "ICLR 2025 Thursday 04/24", "date": "", "ddg_snippet": "Flow - based models allow for learning these dynamics at the population level - they model the evolution of the entire distribution of samples. However ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/day/4/24", "content": "Flow - based models allow for learning these dynamics at the population level - they model the evolution of the entire distribution of samples. However ..."}
+{"idx": 7, "title": "Search Results for author: Ricky TQ Chen", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Ricky+T.+Q.+Chen&order=asc", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter ..."}
+{"idx": 8, "title": "Signal Processing Mar 2025", "date": "", "ddg_snippet": "30 Mar 2025 — Title: FlowDec : A flow - based full - band general audio codec with high perceptual quality. Simon Welker, Matthew Le, Ricky T. Q. Chen, Wei-Ning ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/eess.SP/2025-03?skip=225&show=2000", "content": "30 Mar 2025 — Title: FlowDec : A flow - based full - band general audio codec with high perceptual quality. Simon Welker, Matthew Le, Ricky T. Q. Chen, Wei-Ning ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "FlowDec : A flow-based full-band general audio codec with high perceptual quality · We propose FlowDec, a neural full-band audio codec for general audio ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=unconditional+audio+generation", "content": "FlowDec : A flow-based full-band general audio codec with high perceptual quality · We propose FlowDec, a neural full-band audio codec for general audio ..."}
diff --git a/data/sampled_jsons/FlowDec_neural_audio_codec_architecture_causal_noncausal_DNNs.jsonl b/data/sampled_jsons/FlowDec_neural_audio_codec_architecture_causal_noncausal_DNNs.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9e0a55ce6ae0fcd4c34cbd3836141fdafbc8ce25
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_neural_audio_codec_architecture_causal_noncausal_DNNs.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "While FlowDec , like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs , our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a), which would pave the way for real-time communication and audio streaming applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "While FlowDec , like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs , our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a), which would pave the way for real-time communication and audio streaming applications."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "by S Welker · Cited by 8 — FlowDec is a flow-based postfilter codec for general audio without adversarial training, and a competitive alternative to current GAN-based SOTA codecs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "by S Welker · Cited by 8 — FlowDec is a flow-based postfilter codec for general audio without adversarial training, and a competitive alternative to current GAN-based SOTA codecs."}
+{"idx": 2, "title": "FLOWDEC: A FLOW-BASED FULL-BAND GENERAL", "date": "", "ddg_snippet": "While FlowDec, like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/46581252e0bf80bf4efd8fcbc4002f8627f498bb.pdf", "content": "While FlowDec, like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for a ..."}
+{"idx": 3, "title": "BinauralFlow: A Causal and Streamable Approach for High ...", "date": "", "ddg_snippet": "28 May 2025 — FlowDec (Liu et al., 2024) introduces a conditional flow matching-based audio codec to noticeably reduce the postfiler DNN evaluations from 60 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22865v1", "content": "28 May 2025 — FlowDec (Liu et al., 2024) introduces a conditional flow matching-based audio codec to noticeably reduce the postfiler DNN evaluations from 60 ..."}
+{"idx": 4, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "Mar 3, 2025 · An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - facebookresearch/ FlowDec"}
+{"idx": 5, "title": "facebookresearch/FlowDec | DeepWiki", "date": "", "ddg_snippet": "Purpose and Scope FlowDec is a full-band audio codec that combines traditional neural audio compression with flow-based enhancement to achieve high perceptual quality at low bitrates. This system processes general audio sampled at 48 kHz using a dual-model architecture consisting of a Deep Audio Codec (DAC) for compression and a flow-based postfilter for quality enhancement.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/facebookresearch/FlowDec", "content": "Purpose and Scope FlowDec is a full-band audio codec that combines traditional neural audio compression with flow-based enhancement to achieve high perceptual quality at low bitrates. This system processes general audio sampled at 48 kHz using a dual-model architecture consisting of a Deep Audio Codec (DAC) for compression and a flow-based postfilter for quality enhancement."}
+{"idx": 6, "title": "FlowDec:An neural full-band audio codec for general audio ...", "date": "", "ddg_snippet": "FlowDec FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "gitcode.com", "link": "https://gitcode.com/gh_mirrors/fl/FlowDec/overview", "content": "FlowDec FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 7, "title": "FlowDec | A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs , achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs , achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 8, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "Dec 31, 2024 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Gyh4Zgv03p", "content": "Dec 31, 2024 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "FlowDec : A flow-based full-band general audio codec with high perceptual quality · We propose FlowDec, a neural full-band audio codec for general audio ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Neural+audio+autoencoders", "content": "FlowDec : A flow-based full-band general audio codec with high perceptual quality · We propose FlowDec, a neural full-band audio codec for general audio ..."}
diff --git a/data/sampled_jsons/FlowDec_paper_Table_8_SIGMOS.jsonl b/data/sampled_jsons/FlowDec_paper_Table_8_SIGMOS.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4201d2ff8419bdb7f40531e5934ea0aebe37f175
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_paper_Table_8_SIGMOS.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "In Table 7, we show metric results of FlowDec -75s, compared to two ablation model variants: one trained with the original NCSN++ architecture (Song et al., 2021; Richter et al., 2023) , and one trained with the original choice of the feature representation parameter.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "In Table 7, we show metric results of FlowDec -75s, compared to two ablation model variants: one trained with the original NCSN++ architecture (Song et al., 2021; Richter et al., 2023) , and one trained with the original choice of the feature representation parameter."}
+{"idx": 1, "title": "[2503.01485] FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 2, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 3, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "Jan 22, 2025 · This paper proposes FlowDec , a 48 kHz general audio codec with a flow-matching diffusion post-filter. FlowDec modifies the DAC audio codec with different loss functions, the stochastic post-filter, and frequency-dependent noise.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "Jan 22, 2025 · This paper proposes FlowDec , a 48 kHz general audio codec with a flow-matching diffusion post-filter. FlowDec modifies the DAC audio codec with different loss functions, the stochastic post-filter, and frequency-dependent noise."}
+{"idx": 4, "title": "Paper page - FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 5, "title": "FlowDec | A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "Below we present audio examples from a low-feature-rate variant of FlowDec ( FlowDec -25s) and a retrained DAC (DAC-25). These operate at a feature rate of 25 Hz instead of around 75 Hz, and are aimed at generative audio tasks where low feature rates are desirable for long-range sequence modeling.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "Below we present audio examples from a low-feature-rate variant of FlowDec ( FlowDec -25s) and a retrained DAC (DAC-25). These operate at a feature rate of 25 Hz instead of around 75 Hz, and are aimed at generative audio tasks where low feature rates are desirable for long-range sequence modeling."}
+{"idx": 6, "title": "FlowDec/README.md at main · facebookresearch/FlowDec · GitHub", "date": "", "ddg_snippet": "Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec/blob/main/README.md", "content": "Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 7, "title": "flowdec · PyPI", "date": "", "ddg_snippet": "Flowdec is designed to construct and execute TF graphs in python as well as use frozen, exported graphs from other languages (e.g. Java). Here are a few other features, advantages, and disadvantages of the project currently", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/flowdec/", "content": "Flowdec is designed to construct and execute TF graphs in python as well as use frozen, exported graphs from other languages (e.g. Java). Here are a few other features, advantages, and disadvantages of the project currently"}
+{"idx": 8, "title": "ikea.com/us/en/cat/ table -lamps-10732", "date": "", "ddg_snippet": "ÅSKMULLER Table lamp with LED bulb, 9 \" $29.99 ( 8 ) More options.", "subpage_snippet": "", "source": "www.ikea.com", "link": "https://www.ikea.com/us/en/cat/table-lamps-10732/", "content": "ÅSKMULLER Table lamp with LED bulb, 9 \" $29.99 ( 8 ) More options."}
+{"idx": 9, "title": "Assassin's Creed Shadows - All Legendary Weapons In Claws Of Awaji", "date": "", "ddg_snippet": "Received as the end reward for completing the Turning the Tables main mission.", "subpage_snippet": "", "source": "thegameslayer.com", "link": "https://thegameslayer.com/guides/assassins-creed-shadows-how-to-get-all-legendary-weapons-in-claws-of-awaji-dlc/", "content": "Received as the end reward for completing the Turning the Tables main mission."}
diff --git a/data/sampled_jsons/FlowDec_paper_specifications_table_NDAC-75_NDAC-25_nc=8_demb=256.jsonl b/data/sampled_jsons/FlowDec_paper_specifications_table_NDAC-75_NDAC-25_nc=8_demb=256.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..190f37157831ba35170391263b7053c942bad3f1
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_paper_specifications_table_NDAC-75_NDAC-25_nc=8_demb=256.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - facebookresearch/ FlowDec : An neural full-band audio codec...", "date": "", "ddg_snippet": "You can for instance use our pre-trained NDAC variants - see the \"Inference\" section for how to run them. The expected input format for FlowDec datasets is a file containing a comma-separated list of paths, e.g.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "You can for instance use our pre-trained NDAC variants - see the \"Inference\" section for how to run them. The expected input format for FlowDec datasets is a file containing a comma-separated list of paths, e.g."}
+{"idx": 1, "title": "[2503.01485] FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "NDAC-75 is targeted at 48 kHz audio with a whole-number feature rate ( 75 Hz) and whole-number bitrates. NDAC-25 is a variant tailored for downstream generative audio tasks, with a lower feature rate ( 25 Hz) and feature dimension which are advantageous for audio generation due to more efficient memory usage and decreased modeling difficulties.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2503.01485", "content": "NDAC-75 is targeted at 48 kHz audio with a whole-number feature rate ( 75 Hz) and whole-number bitrates. NDAC-25 is a variant tailored for downstream generative audio tasks, with a lower feature rate ( 25 Hz) and feature dimension which are advantageous for audio generation due to more efficient memory usage and decreased modeling difficulties."}
+{"idx": 2, "title": "FlowDec | A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 3, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ..."}
+{"idx": 4, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "Jan 22, 2025 · The paper introduces FlowDec , a neural audio codec that employs a two-stage approach: (1) an autoencoder with residual vector quantization, trained without adversarial loss; and (2) a postfilter that mitigates coding artifacts and enhances perceptual quality. FlowDec leverages conditional flow matching for signal enhancement, achieving notable improvements over previous score-based and flow ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "Jan 22, 2025 · The paper introduces FlowDec , a neural audio codec that employs a two-stage approach: (1) an autoencoder with residual vector quantization, trained without adversarial loss; and (2) a postfilter that mitigates coding artifacts and enhances perceptual quality. FlowDec leverages conditional flow matching for signal enhancement, achieving notable improvements over previous score-based and flow ..."}
+{"idx": 5, "title": "Releases · facebookresearch/FlowDec - GitHub", "date": "", "ddg_snippet": "The release includes both NDAC and FlowDec -postfilter checkpoints. The folder structure is as follows. checkpoints flowdec flowdec _25s flowdec _75m ndac ndac-25 ndac-75", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec/releases", "content": "The release includes both NDAC and FlowDec -postfilter checkpoints. The folder structure is as follows. checkpoints flowdec flowdec _25s flowdec _75m ndac ndac-25 ndac-75"}
+{"idx": 6, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "FlowDec -25s: 25 Hz, single-bitrate. Trained based on NDAC - 25 with a bitrate of 4.0 kbit/s. We do not train for multiple bitrates here as the bitrate and feature rate is already very low.In Table 4, we compare FAD, SI-SDR and fwSSNR of FlowDec - 75 s at.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "FlowDec -25s: 25 Hz, single-bitrate. Trained based on NDAC - 25 with a bitrate of 4.0 kbit/s. We do not train for multiple bitrates here as the bitrate and feature rate is already very low.In Table 4, we compare FAD, SI-SDR and fwSSNR of FlowDec - 75 s at."}
+{"idx": 7, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "As baselines, we train DAC- 75 and DAC- 25 , equivalent versions of NDAC-75 and NDAC-25 with the original adversarial losses. To show that the differences between FlowDec and DAC are not just caused by the extra parameters from the postfilter, we also train baselines2xDAC- 75 and 2xDAC- 25 for which we double the channels of all decoder convolution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "As baselines, we train DAC- 75 and DAC- 25 , equivalent versions of NDAC-75 and NDAC-25 with the original adversarial losses. To show that the differences between FlowDec and DAC are not just caused by the extra parameters from the postfilter, we also train baselines2xDAC- 75 and 2xDAC- 25 for which we double the channels of all decoder convolution ..."}
+{"idx": 8, "title": "ГДЗ по информатике 8 класс Босова - учебник Просвещение 2023", "date": "", "ddg_snippet": "Вопросы в тексте параграфа 242 Вопросы и задания 251. Тестовые задания для самоконтроля 256 .", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/8-klass/informatika/bosova-uchebnik", "content": "Вопросы в тексте параграфа 242 Вопросы и задания 251. Тестовые задания для самоконтроля 256 ."}
+{"idx": 9, "title": "ESET NOD32 | Свежие Ключи 2025 | Бесплатно | СТЕНА – Telegram", "date": "", "ddg_snippet": "Личные ключи в боте @eset_free_nod32_bot (Напишите для начала /start). ESET HOME Security Premium | До 15-10-2025 1 | X7PP-XEFB- NC 49-DJUE-V654 2 | WCFV-XP37-BVVV-W3GA-6AAC 3 | G97T-X2X4-69XK-684K-V7DJ 4 | C64R-X5E9-FM5H-7DBS-U924 5 |.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/eset_nod32_free", "content": "Личные ключи в боте @eset_free_nod32_bot (Напишите для начала /start). ESET HOME Security Premium | До 15-10-2025 1 | X7PP-XEFB- NC 49-DJUE-V654 2 | WCFV-XP37-BVVV-W3GA-6AAC 3 | G97T-X2X4-69XK-684K-V7DJ 4 | C64R-X5E9-FM5H-7DBS-U924 5 |."}
diff --git a/data/sampled_jsons/FourCastNet_Pathak_evaluation_metrics_NLL_Negative_Log-Likelihood_experiments.jsonl b/data/sampled_jsons/FourCastNet_Pathak_evaluation_metrics_NLL_Negative_Log-Likelihood_experiments.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..45a7ca855304e5d0a14791565968a8709cc93c8b
--- /dev/null
+++ b/data/sampled_jsons/FourCastNet_Pathak_evaluation_metrics_NLL_Negative_Log-Likelihood_experiments.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluation of Predictive Uncertainty — Lightning-UQ-Box", "date": "", "ddg_snippet": "Negative Log Likelihood ( NLL ) # Negative log likelihood for a gaussian probability distribution p θ (y | x) which is also used as a loss functions in some methods. Note that the probability distribution have values between 0 and 1. If given a certain x, p θ (y | x) ≈ 1, we could almost be certain that our network predicts the label y.", "subpage_snippet": "", "source": "lightning-uq-box.readthedocs.io", "link": "https://lightning-uq-box.readthedocs.io/en/latest/tutorials/regression/evaluation_uncertainty.html", "content": "Negative Log Likelihood ( NLL ) # Negative log likelihood for a gaussian probability distribution p θ (y | x) which is also used as a loss functions in some methods. Note that the probability distribution have values between 0 and 1. If given a certain x, p θ (y | x) ≈ 1, we could almost be certain that our network predicts the label y."}
+{"idx": 1, "title": "Negative Log Likelihood Explained | Ji Kim", "date": "", "ddg_snippet": "Jan 19, 2025 · This post will provide a solid understanding of the fundamental concepts: probability, likelihood , log likelihood , maximum likelihood estimation, and negative log likelihood .", "subpage_snippet": "", "source": "jiselectric.github.io", "link": "https://jiselectric.github.io/posts/NLL/", "content": "Jan 19, 2025 · This post will provide a solid understanding of the fundamental concepts: probability, likelihood , log likelihood , maximum likelihood estimation, and negative log likelihood ."}
+{"idx": 2, "title": "Uncertainty quantification metrics for deep regression", "date": "", "ddg_snippet": "Oct 1, 2024 · While not as common as ECE, NLL ( negative log likelihood ) is also used, for example in , has also found new use in this area (for example in , ). Finally, the most recent method we examine is AUSE , used in for example , . While other uncertainty assessment methods exist, we have chosen to focus on these four as they are most commonly used.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167865524002733", "content": "Oct 1, 2024 · While not as common as ECE, NLL ( negative log likelihood ) is also used, for example in , has also found new use in this area (for example in , ). Finally, the most recent method we examine is AUSE , used in for example , . While other uncertainty assessment methods exist, we have chosen to focus on these four as they are most commonly used."}
+{"idx": 3, "title": "Mastering Negative Log Likelihood in ML - numberanalytics.com", "date": "", "ddg_snippet": "Jun 12, 2025 · In conclusion, Negative Log Likelihood is a crucial concept in Machine Learning that plays a vital role in model evaluation and optimization. By understanding NLL and its implementation in popular ML frameworks, you can harness its power to improve your models' performance and accuracy.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/mastering-negative-log-likelihood-in-ml", "content": "Jun 12, 2025 · In conclusion, Negative Log Likelihood is a crucial concept in Machine Learning that plays a vital role in model evaluation and optimization. By understanding NLL and its implementation in popular ML frameworks, you can harness its power to improve your models' performance and accuracy."}
+{"idx": 4, "title": "[2202.11214] FourCastNet: A Global Data-driven High ...", "date": "", "ddg_snippet": "Feb 22, 2022 · FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.25^{\\\\circ}$ resolution. FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor. It has important implications for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.11214", "content": "Feb 22, 2022 · FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.25^{\\\\circ}$ resolution. FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor. It has important implications for ..."}
+{"idx": 5, "title": "Metrics for uncertainty evaluation in regression problems", "date": "", "ddg_snippet": "Aug 12, 2022 · Metrics for uncertainty evaluation in regression problems How to evaluate uncertainty with Validity, Sharpness, Negative Log-Likelihood , and Continuous Ranked Probability Score (CRPS) metrics", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/towards-data-science/metrics-for-uncertainty-evaluation-in-regression-problems-210821761aa", "content": "Aug 12, 2022 · Metrics for uncertainty evaluation in regression problems How to evaluate uncertainty with Validity, Sharpness, Negative Log-Likelihood , and Continuous Ranked Probability Score (CRPS) metrics"}
+{"idx": 6, "title": "Evaluating and Calibrating Uncertainty Prediction in ...", "date": "", "ddg_snippet": "Negative log likelihood ( NLL ) is a standard measure of a model’s fit to the data [12] but combines both the accuracy of the model and its uncertainty estimation in one measure. Based on these measures, several calibration methods were proposed, which transformed the network’s confidence output to one that will produce a calibrated prediction.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9330317/", "content": "Negative log likelihood ( NLL ) is a standard measure of a model’s fit to the data [12] but combines both the accuracy of the model and its uncertainty estimation in one measure. Based on these measures, several calibration methods were proposed, which transformed the network’s confidence output to one that will produce a calibrated prediction."}
+{"idx": 7, "title": "𝛼-VI DeepONet: A prior-robust variational Bayesian approach", "date": "", "ddg_snippet": "... the variational objective function yields superior results in terms of minimising the mean squared error and improving the negative log - likelihood on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.00681v1", "content": "... the variational objective function yields superior results in terms of minimising the mean squared error and improving the negative log - likelihood on ..."}
+{"idx": 8, "title": "FNP: Fourier Neural Processes for Arbitrary-Resolution Data", "date": "", "ddg_snippet": "... of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art (SOTA) results in data assimilation experiments ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01645v1", "content": "... of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art (SOTA) results in data assimilation experiments ..."}
+{"idx": 9, "title": "A Practical Probabilistic Benchmark for AI Weather Models", "date": "", "ddg_snippet": "The purpose of this paper is to present a consistent and easy-to-use method for evaluating deterministic weather forecasts from the lens of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.15305v2", "content": "The purpose of this paper is to present a consistent and easy-to-use method for evaluating deterministic weather forecasts from the lens of ..."}
diff --git a/data/sampled_jsons/Fourier_Neural_Operator_architecture.jsonl b/data/sampled_jsons/Fourier_Neural_Operator_architecture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f9bdb642c52e64cba3c17c0b2c17e1683fd35a2
--- /dev/null
+++ b/data/sampled_jsons/Fourier_Neural_Operator_architecture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fourier Neural Operators — neuraloperator 1.0.2 documentation", "date": "", "ddg_snippet": "We propose a neural operator based on Fourier Transformation. It is the first work that learns the resolution-invariant solution operator for the family of Navier-Stokes equation in the turbulent regime, where previous graph-based neural operators do not converge.", "subpage_snippet": "", "source": "neuraloperator.github.io", "link": "https://neuraloperator.github.io/dev/theory_guide/fno.html", "content": "We propose a neural operator based on Fourier Transformation. It is the first work that learns the resolution-invariant solution operator for the family of Navier-Stokes equation in the turbulent regime, where previous graph-based neural operators do not converge."}
+{"idx": 1, "title": "Toward a Better Understanding of Fourier Neural Operators:", "date": "", "ddg_snippet": "Apr 10, 2024 · Among the neural operators , FNO (Li et al., 2021) incorporates the Fast Fourier Transform (FFT) (Cochran et al., 1967) in its network architecture , achieving both advantageous efficiency and prediction accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.07200v1", "content": "Apr 10, 2024 · Among the neural operators , FNO (Li et al., 2021) incorporates the Fast Fourier Transform (FFT) (Cochran et al., 1967) in its network architecture , achieving both advantageous efficiency and prediction accuracy."}
+{"idx": 2, "title": "Enhancing Fourier Neural Operators with CNNs architectures ...", "date": "", "ddg_snippet": "Jun 14, 2025 · In this work, we hypothesize that effective architectural configurations from Convolutional Neural Networks (CNNs) can significantly enhance the performance of the Fourier Neural Operator (FNO).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231225005776", "content": "Jun 14, 2025 · In this work, we hypothesize that effective architectural configurations from Convolutional Neural Networks (CNNs) can significantly enhance the performance of the Fourier Neural Operator (FNO)."}
+{"idx": 3, "title": "Fourier Neural Operators | neuraloperator/physics_informed ...", "date": "", "ddg_snippet": "May 26, 2025 · This document covers the implementation of Fourier Neural Operators (FNO), a neural network architecture that performs learning in the frequency domain to solve partial differential equations. FNOs serve as the foundation for the data-driven neural operator approach in this codebase.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/neuraloperator/physics_informed/3.1-fourier-neural-operators", "content": "May 26, 2025 · This document covers the implementation of Fourier Neural Operators (FNO), a neural network architecture that performs learning in the frequency domain to solve partial differential equations. FNOs serve as the foundation for the data-driven neural operator approach in this codebase."}
+{"idx": 4, "title": "FOURIER NEURAL OPERATORS - OpenReview", "date": "", "ddg_snippet": "Data61, CSIRO ABSTRACT We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based approach for simulating partial diffe. ential equations (PDEs). Starting from a recently proposed Fourier representation of flow fields, the F-FNO bridges the performance gap between pure machine learning approaches to that of the best num.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tmIiMPl4IPa", "content": "Data61, CSIRO ABSTRACT We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based approach for simulating partial diffe. ential equations (PDEs). Starting from a recently proposed Fourier representation of flow fields, the F-FNO bridges the performance gap between pure machine learning approaches to that of the best num."}
+{"idx": 5, "title": "GitHub - raj-brown/fourier_neural_operator: Use Fourier ...", "date": "", "ddg_snippet": "In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture . We perform experiments on Burgers' equation, Darcy flow, and the Navier-Stokes equation (including the turbulent regime).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/raj-brown/fourier_neural_operator", "content": "In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture . We perform experiments on Burgers' equation, Darcy flow, and the Navier-Stokes equation (including the turbulent regime)."}
+{"idx": 6, "title": "Fourier Neural Operator - Siml.ai Documentation", "date": "", "ddg_snippet": "The Fourier Neural Operator (FNO) is a unique, data-driven deep learning architecture . It can learn mappings between infinite-dimensional spaces of functions. Its key feature, known as spectral convolutions, involves operations that function in the Fourier space with an integral kernel. Operator learning can be taken as an image-to-image problem.", "subpage_snippet": "", "source": "docs.siml.ai", "link": "https://docs.siml.ai/creating-a-simulator/neural-network-node/fourier-neural-operator", "content": "The Fourier Neural Operator (FNO) is a unique, data-driven deep learning architecture . It can learn mappings between infinite-dimensional spaces of functions. Its key feature, known as spectral convolutions, involves operations that function in the Fourier space with an integral kernel. Operator learning can be taken as an image-to-image problem."}
+{"idx": 7, "title": "[2409.03302] Fourier Neural Operators for Learning Dynamics in", "date": "", "ddg_snippet": "Abstract: Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.03302", "content": "Abstract: Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations."}
+{"idx": 8, "title": "Fourier Neural Operators for Learning Dynamics in Quantum Spin", "date": "", "ddg_snippet": "Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.03302v1", "content": "Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations."}
+{"idx": 9, "title": "[2010.08895] Fourier Neural Operator for Parametric Partial", "date": "", "ddg_snippet": "... a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.08895", "content": "... a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture ."}
diff --git a/data/sampled_jsons/FridaysForFuture_ExtinctionRebellion_EarthStrike_Reddit_subreddit_users_activated.jsonl b/data/sampled_jsons/FridaysForFuture_ExtinctionRebellion_EarthStrike_Reddit_subreddit_users_activated.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..69f2a957df5b41313c29be6119c53c979e364bc3
--- /dev/null
+++ b/data/sampled_jsons/FridaysForFuture_ExtinctionRebellion_EarthStrike_Reddit_subreddit_users_activated.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Earth Strike - Wikipedia", "date": "", "ddg_snippet": "Earth Strike was founded on 10 November 2018 after a user on the subreddit r/Chomsky called for a \"General Strike to Save The Planet\". [citation needed] The post quickly gathered attention within Reddit , and the r/ EarthStrike subreddit was formed to organise a general strike. [citation needed] The initial protests were held on 15 January 2019, with 27 September being announced as the date for ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Earth_Strike", "content": "Earth Strike was founded on 10 November 2018 after a user on the subreddit r/Chomsky called for a \"General Strike to Save The Planet\". [citation needed] The post quickly gathered attention within Reddit , and the r/ EarthStrike subreddit was formed to organise a general strike. [citation needed] The initial protests were held on 15 January 2019, with 27 September being announced as the date for ..."}
+{"idx": 1, "title": "Reddit's economists demand a carbon tax, proceeds distributed", "date": "", "ddg_snippet": "Rendered by PID 138693 on reddit -service-r2-whoalane-6cbc896455-pmn76 at 2023-08-23 08:18:05.579691+00:00 running bd5478f country code: GB.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/duplicates/ghphsb/reddits_economists_demand_a_carbon_tax_proceeds/", "content": "Rendered by PID 138693 on reddit -service-r2-whoalane-6cbc896455-pmn76 at 2023-08-23 08:18:05.579691+00:00 running bd5478f country code: GB."}
+{"idx": 2, "title": "Posts from ClimateOffensive, ContraPoints, EarthStrike ...", "date": "", "ddg_snippet": "Western countries are not lifeboats for collapse, despite what people in this subreddit believe. Why you think a society built on hyper-consumption is the place to live and raise children during collapse is beyond me. If you don't produce more resources than you have to steal from the Global South, you're fucked.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ClimateOffensive+ContraPoints+EarthStrike+ExtinctionRebellion+FridaysForFuture+GardenWild+climate+collapse+environment+invasivespecies", "content": "Western countries are not lifeboats for collapse, despite what people in this subreddit believe. Why you think a society built on hyper-consumption is the place to live and raise children during collapse is beyond me. If you don't produce more resources than you have to steal from the Global South, you're fucked."}
+{"idx": 3, "title": "FridaysForFuture/Extinction Rebellion/Student Strike group ...", "date": "", "ddg_snippet": "237 votes, 66 comments. 233K subscribers in the Edmonton community. Edmonton's Local Subreddit ! For Edmontonians by Edmontonians!", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/Edmonton/comments/b1je2v/fridaysforfutureextinction_rebellionstudent/", "content": "237 votes, 66 comments. 233K subscribers in the Edmonton community. Edmonton's Local Subreddit ! For Edmontonians by Edmontonians!"}
+{"idx": 4, "title": "Fridays For Future Announces Global Climate Strike ... - Reddit", "date": "", "ddg_snippet": "26K subscribers in the ExtinctionRebellion community. This is our darkest hour. Humanity finds itself embroiled in an event unprecedented in its…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/sczmvf/fridays_for_future_announces_global_climate/", "content": "26K subscribers in the ExtinctionRebellion community. This is our darkest hour. Humanity finds itself embroiled in an event unprecedented in its…"}
+{"idx": 5, "title": "Leaked Audio Shows Oil Lobbyist Bragging About Success in", "date": "", "ddg_snippet": "In addition to enforcing reddit s content policy, we will also remove comments and content that is abusive in nature.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/duplicates/ct0tn3/leaked_audio_shows_oil_lobbyist_bragging_about/", "content": "In addition to enforcing reddit s content policy, we will also remove comments and content that is abusive in nature."}
+{"idx": 6, "title": "A conference in London on 12th July which will explore the", "date": "", "ddg_snippet": "Rendered by PID 75 on reddit -service-r2-whoalane-654686468c-hr52n at 2023-09-17 06:01:11.541315+00:00 running 68b80b2 country code: GB.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/13wjkbi/a_conference_in_london_on_12th_july_which_will/", "content": "Rendered by PID 75 on reddit -service-r2-whoalane-654686468c-hr52n at 2023-09-17 06:01:11.541315+00:00 running 68b80b2 country code: GB."}
+{"idx": 7, "title": "p2p Free Library: Help build humanity's free library on IPFS", "date": "", "ddg_snippet": "In addition to enforcing reddit s content policy, we will also remove comments and content that is abusive in nature.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/jb4pb2/p2p_free_library_help_build_humanitys_free/", "content": "In addition to enforcing reddit s content policy, we will also remove comments and content that is abusive in nature."}
+{"idx": 8, "title": "Leaked Audio Shows Oil Lobbyist Bragging About Success in", "date": "", "ddg_snippet": "Rendered by PID 57519 on reddit -service-r2-whoalane-846c7db66c-bxk2m at 2023-10-02 03:12:05.357273+00:00 running 5e79288 country code: GB.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/ct0tn3/leaked_audio_shows_oil_lobbyist_bragging_about/", "content": "Rendered by PID 57519 on reddit -service-r2-whoalane-846c7db66c-bxk2m at 2023-10-02 03:12:05.357273+00:00 running 5e79288 country code: GB."}
+{"idx": 9, "title": "Billionaire No More: Patagonia Founder Gives Away the Company,", "date": "", "ddg_snippet": "Rendered by PID 20 on reddit -service-r2-whoalane-c85b7d764-2fmk2 at 2023-04-25 19:47:01.920490+00:00 running 9961dd1 country code: GB.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/xf6rrj/billionaire_no_more_patagonia_founder_gives_away/", "content": "Rendered by PID 20 on reddit -service-r2-whoalane-c85b7d764-2fmk2 at 2023-04-25 19:47:01.920490+00:00 running 9961dd1 country code: GB."}
diff --git a/data/sampled_jsons/GPT-3.5-Turbo_Claude-3-Sonnet_Gemini-Pro_MedQA_accuracy_alpha_0.68_0.71_0.75.jsonl b/data/sampled_jsons/GPT-3.5-Turbo_Claude-3-Sonnet_Gemini-Pro_MedQA_accuracy_alpha_0.68_0.71_0.75.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f82f86a998ec5b6b369a5ba8cb4f739b62bb13d
--- /dev/null
+++ b/data/sampled_jsons/GPT-3.5-Turbo_Claude-3-Sonnet_Gemini-Pro_MedQA_accuracy_alpha_0.68_0.71_0.75.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MedQA", "date": "", "ddg_snippet": "Claude Sonnet 4 (Thinking).For particularly cost-sensitive use cases, consider a smaller completion-based model like GPT 4.1 or 4o mini from OpenAI or Mistral Small 3 .1 (03/2025) (the cheapest model we tested!) MedQA . Accuracy .", "subpage_snippet": "", "source": "www.vals.ai", "link": "https://www.vals.ai/benchmarks/medqa-05-30-2025", "content": "Claude Sonnet 4 (Thinking).For particularly cost-sensitive use cases, consider a smaller completion-based model like GPT 4.1 or 4o mini from OpenAI or Mistral Small 3 .1 (03/2025) (the cheapest model we tested!) MedQA . Accuracy ."}
+{"idx": 1, "title": "Битва сильнейших: ChatGPT o1 pro / DeepSeek r1 / Claude ... / Хабр", "date": "", "ddg_snippet": "Claude 3 .7 Sonnet , как и Gemini 2.5 Pro далее, предоставил уже действительно полноценный рассказ. Он дал несколько меньше информации, чем ChatGPT o1 Pro и DeepSeek R1, однако изложил её языком, наиболее подходящим для интересного объяснения ребёнку.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/bothub/articles/919366/", "content": "Claude 3 .7 Sonnet , как и Gemini 2.5 Pro далее, предоставил уже действительно полноценный рассказ. Он дал несколько меньше информации, чем ChatGPT o1 Pro и DeepSeek R1, однако изложил её языком, наиболее подходящим для интересного объяснения ребёнку."}
+{"idx": 2, "title": "Нейросеть онлайн Gemini PRO от Google AI | Бесплатно без...", "date": "", "ddg_snippet": "Gemini Pro представляет собой продвинутую и многофункциональную нейросеть, которая трансформирует наш подход к коммуникациям в интерактивные средства. Генерация текстов, совместная речь и перевод с помощью нейросетей. Бесплатно.", "subpage_snippet": "", "source": "smartbuddy.ru", "link": "https://smartbuddy.ru/models/gemini-pro", "content": "Gemini Pro представляет собой продвинутую и многофункциональную нейросеть, которая трансформирует наш подход к коммуникациям в интерактивные средства. Генерация текстов, совместная речь и перевод с помощью нейросетей. Бесплатно."}
+{"idx": 3, "title": "Google Gemini", "date": "", "ddg_snippet": "Google'ın yapay zeka asistanı Gemini ile tanışın. Yazı yazma, planlama, beyin fırtınası yapma ve daha pek çok konuda yardım alabilirsiniz.", "subpage_snippet": "", "source": "gemini.google.com", "link": "https://gemini.google.com/?hl=tr", "content": "Google'ın yapay zeka asistanı Gemini ile tanışın. Yazı yazma, planlama, beyin fırtınası yapma ve daha pek çok konuda yardım alabilirsiniz."}
+{"idx": 4, "title": "ТОП-15 ботов ChatGPT в Телеграм", "date": "", "ddg_snippet": "Какие модели поддерживает: Текст – GPT - 3 . 5 Turbo /-4o mini. Картинки – Midjourney, Dall-E 3 , Stable Diffusion, Face Swap. Аудио – Whisper.", "subpage_snippet": "", "source": "otzyvmarketing.ru", "link": "https://otzyvmarketing.ru/articles/boty-chatgpt-v-telegram/", "content": "Какие модели поддерживает: Текст – GPT - 3 . 5 Turbo /-4o mini. Картинки – Midjourney, Dall-E 3 , Stable Diffusion, Face Swap. Аудио – Whisper."}
+{"idx": 5, "title": "Monica - ChatGPT AI Assistant | GPT -4o, Claude 3 .7, Gemini 1.5", "date": "", "ddg_snippet": "Gemini 2.5 Pro . Claude 3 .7 Sonnet . Kling AI.Personalized, fast and free. Monica leverages cutting-edge AI models, including OpenAI o3-mini, DeepSeek R1, GPT -4o, Claude 3 .7, and Gemini 2.0, to enhance your chat, search, writing, and coding experiences.", "subpage_snippet": "", "source": "monica.im", "link": "https://monica.im/", "content": "Gemini 2.5 Pro . Claude 3 .7 Sonnet . Kling AI.Personalized, fast and free. Monica leverages cutting-edge AI models, including OpenAI o3-mini, DeepSeek R1, GPT -4o, Claude 3 .7, and Gemini 2.0, to enhance your chat, search, writing, and coding experiences."}
+{"idx": 6, "title": "Overview Leaderboard | LMArena", "date": "", "ddg_snippet": "gemini -2.0-flash-preview-image-generation.stablelm-tuned- alpha -7b.", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/leaderboard", "content": "gemini -2.0-flash-preview-image-generation.stablelm-tuned- alpha -7b."}
+{"idx": 7, "title": "We announce our new Grok-2 and Grok-2 mini models. | xAI", "date": "", "ddg_snippet": "Claude 3 Opus and Claude 3 . 5 Sonnet scores are from the June 2024 release. ‡ Grok-2 MMLU, MMLU- Pro , MMMU and MathVista were evaluated using 0-shot CoT. § For MATH, we present maj@1 results. ¶ For HumanEval, we report pass@1 benchmark scores.", "subpage_snippet": "", "source": "x.ai", "link": "https://x.ai/news/grok-2", "content": "Claude 3 Opus and Claude 3 . 5 Sonnet scores are from the June 2024 release. ‡ Grok-2 MMLU, MMLU- Pro , MMMU and MathVista were evaluated using 0-shot CoT. § For MATH, we present maj@1 results. ¶ For HumanEval, we report pass@1 benchmark scores."}
+{"idx": 8, "title": "LLM Mental Math", "date": "", "ddg_snippet": "openai/ gpt -4- turbo .anthropic/ claude - 3 .7- sonnet :thinking.", "subpage_snippet": "", "source": "sanand0.github.io", "link": "https://sanand0.github.io/llmmath/", "content": "openai/ gpt -4- turbo .anthropic/ claude - 3 .7- sonnet :thinking."}
+{"idx": 9, "title": "Neurosphere — AI для текста и изображений без VPN и загранкарт", "date": "", "ddg_snippet": "Neurosphere. pro — Создавайте тексты и изображения с помощью готовых AI-шаблонов. Ускорьте маркетинг, оформление бренда и генерацию контента в несколько кликов. Без VPN.", "subpage_snippet": "", "source": "neurosphere.pro", "link": "https://neurosphere.pro/", "content": "Neurosphere. pro — Создавайте тексты и изображения с помощью готовых AI-шаблонов. Ускорьте маркетинг, оформление бренда и генерацию контента в несколько кликов. Без VPN."}
diff --git a/data/sampled_jsons/GPT-4_accuracy_0.28_real-world_57%_unanswered_MedQA_87.2%.jsonl b/data/sampled_jsons/GPT-4_accuracy_0.28_real-world_57%_unanswered_MedQA_87.2%.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d7cf03167b4f83f8382f5a76941e12740adf6a27
--- /dev/null
+++ b/data/sampled_jsons/GPT-4_accuracy_0.28_real-world_57%_unanswered_MedQA_87.2%.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diagnostic accuracy of GPT‐4 on common clinical scenarios and ...", "date": "", "ddg_snippet": "By contrast, compared to the physicians, GPT‐4 achieved a significantly better top 3 diagnostic accuracy for the common clinical scenarios (100%) and a better top 6 diagnostic accuracy for the most challenging cases (61.1%).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11257049/", "content": "By contrast, compared to the physicians, GPT‐4 achieved a significantly better top 3 diagnostic accuracy for the common clinical scenarios (100%) and a better top 6 diagnostic accuracy for the most challenging cases (61.1%)."}
+{"idx": 1, "title": "Accuracy of a Generative Artificial Intelligence Model in a Complex ...", "date": "", "ddg_snippet": "This study assesses the diagnostic accuracy of the Generative Pre-trained Transformer 4 ( GPT-4 ) artificial intelligence (AI) model in a series of challenging cases.", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jama/fullarticle/2806457", "content": "This study assesses the diagnostic accuracy of the Generative Pre-trained Transformer 4 ( GPT-4 ) artificial intelligence (AI) model in a series of challenging cases."}
+{"idx": 2, "title": "Use of GPT-4 to Diagnose Complex Clinical Cases - NEJM AI", "date": "", "ddg_snippet": "We assessed the performance of the newly released AI GPT-4 in diagnosing complex medical case challenges and compared the success rate to that of medical-journal readers. GPT-4 correctly diagnosed 57% of cases, outperforming 99.98% of simulated human readers generated from online answers. We highlight the potential for AI to be a powerful supportive tool for diagnosis; however, further ...", "subpage_snippet": "", "source": "ai.nejm.org", "link": "https://ai.nejm.org/doi/full/10.1056/AIp2300031", "content": "We assessed the performance of the newly released AI GPT-4 in diagnosing complex medical case challenges and compared the success rate to that of medical-journal readers. GPT-4 correctly diagnosed 57% of cases, outperforming 99.98% of simulated human readers generated from online answers. We highlight the potential for AI to be a powerful supportive tool for diagnosis; however, further ..."}
+{"idx": 3, "title": "Assessing ChatGPT 4.0's test performance and clinical ... - Nature", "date": "", "ddg_snippet": "While there is data assessing the test performance of artificial intelligence (AI) chatbots, including the Generative Pre-trained Transformer 4.0 ( GPT 4 ) chatbot (ChatGPT 4.0), there is scarce ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-58760-x", "content": "While there is data assessing the test performance of artificial intelligence (AI) chatbots, including the Generative Pre-trained Transformer 4.0 ( GPT 4 ) chatbot (ChatGPT 4.0), there is scarce ..."}
+{"idx": 4, "title": "GPT-4 | OpenAI", "date": "", "ddg_snippet": "Continuous improvement from real-world use We've applied lessons from real-world use of our previous models into GPT‑4's safety research and monitoring system. Like ChatGPT, we'll be updating and improving GPT‑4 at a regular cadence as more people use it.", "subpage_snippet": "", "source": "openai.com", "link": "https://openai.com/index/gpt-4/", "content": "Continuous improvement from real-world use We've applied lessons from real-world use of our previous models into GPT‑4's safety research and monitoring system. Like ChatGPT, we'll be updating and improving GPT‑4 at a regular cadence as more people use it."}
+{"idx": 5, "title": "PDF Comparative analysis of GPT-4-based ChatGPTâ s diagnostic ... - Springer", "date": "", "ddg_snippet": "This approach facilitates a more robust and practical evaluation of GPT-4's accuracy , keeping in mind its potential applications in real-world clinical settings.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s00330-024-11032-8.pdf", "content": "This approach facilitates a more robust and practical evaluation of GPT-4's accuracy , keeping in mind its potential applications in real-world clinical settings."}
+{"idx": 6, "title": "Optimizing GPT-4 Turbo Diagnostic Accuracy in Neuroradiology ... - MDPI", "date": "", "ddg_snippet": "Background and Objectives: Integrating large language models (LLMs) such as GPT-4 Turbo into diagnostic imaging faces a significant challenge, with current misdiagnosis rates ranging from 30-50%. This study evaluates how prompt engineering and confidence thresholds can improve diagnostic accuracy in neuroradiology. Methods: We analyze 751 neuroradiology cases from the American Journal of ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2075-4418/14/14/1541", "content": "Background and Objectives: Integrating large language models (LLMs) such as GPT-4 Turbo into diagnostic imaging faces a significant challenge, with current misdiagnosis rates ranging from 30-50%. This study evaluates how prompt engineering and confidence thresholds can improve diagnostic accuracy in neuroradiology. Methods: We analyze 751 neuroradiology cases from the American Journal of ..."}
+{"idx": 7, "title": "Comparative analysis of GPT-4-based ChatGPT's diagnostic ... - PubMed", "date": "", "ddg_snippet": "We investigated the diagnostic accuracy of GPT-4 using real-world clinical MRI reports of brain tumors. GPT-4 achieved final and differential diagnostic accuracy that is comparable with neuroradiologists. GPT-4 has the potential to improve the diagnostic process in clinical radiology.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/39198333/", "content": "We investigated the diagnostic accuracy of GPT-4 using real-world clinical MRI reports of brain tumors. GPT-4 achieved final and differential diagnostic accuracy that is comparable with neuroradiologists. GPT-4 has the potential to improve the diagnostic process in clinical radiology."}
+{"idx": 8, "title": "Use of GPT-4 to Diagnose Complex Clinical Cases", "date": "", "ddg_snippet": "For example, it remains unclear to what extent GPT-4 can aid in clinical cases that contain long, complicated, and varied patient descriptions and how it performs on these complex real-world cases compared with humans. We assessed the performance of GPT-4 in real -life medical cases by comparing its perfor-mance with that of medical-journal readers.", "subpage_snippet": "", "source": "ai.nejm.org", "link": "https://ai.nejm.org/doi/pdf/10.1056/AIp2300031", "content": "For example, it remains unclear to what extent GPT-4 can aid in clinical cases that contain long, complicated, and varied patient descriptions and how it performs on these complex real-world cases compared with humans. We assessed the performance of GPT-4 in real -life medical cases by comparing its perfor-mance with that of medical-journal readers."}
+{"idx": 9, "title": "PDF Capabilities of GPT-4 on Medical Challenge Problems", "date": "", "ddg_snippet": "The leap in performance on medical challenge problems with the move from GPT 3.5 to GPT-4 suggests that we can achieve impressive gains on intensive real-world challenges with scale-and that we will likely continue to see advances with larger models for handling complex, real-world problems.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2023/03/GPT-4_medical_benchmarks.pdf", "content": "The leap in performance on medical challenge problems with the move from GPT 3.5 to GPT-4 suggests that we can achieve impressive gains on intensive real-world challenges with scale-and that we will likely continue to see advances with larger models for handling complex, real-world problems."}
diff --git a/data/sampled_jsons/GTA_all_n_workers_start_working_B_tasks_completed_inefficiency_total_worker_time.jsonl b/data/sampled_jsons/GTA_all_n_workers_start_working_B_tasks_completed_inefficiency_total_worker_time.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d9c8e4a5a54c80e1f91578dfccc977307021439b
--- /dev/null
+++ b/data/sampled_jsons/GTA_all_n_workers_start_working_B_tasks_completed_inefficiency_total_worker_time.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "... B tasks have been completed . In GTA , we initially ask all n workers to start working on a task, and as soon as some worker is done with a task, we ask it to ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "... B tasks have been completed . In GTA , we initially ask all n workers to start working on a task, and as soon as some worker is done with a task, we ask it to ..."}
+{"idx": 1, "title": "GTA V All Missions 100% Completed || Full Map Open 2025 New ... GTA 5 — Save (Game completed 100%) V2 / Savegames / Cheats GTA 5 Enhanced All Missions 100% Completed SaveGames Cluckin' Bell Career progress Completed - GTA V Online Gta Online: The Project Breakaway Priority File (All Tasks ... Gta Online The Brute Force File (All Tasks Completed) GTA San Andreas All Missions 100% Completed SaveGames Files", "date": "", "ddg_snippet": "GTA V All Missions 100% Completed || Full Map Open 2025 New ModMOD LINK:- https://www.gta5-mods.com/misc/ultimate-100-perfect-game-save-flava0ne📂 Included: ... Apr 27, 2023 · Download Save (Game completed 100%) V2 for GTA 5. This save will include the following: Completed all story and side missions (for gold). Completed all missions of eccentrics and strangers (for gold). All races completed . Completed all parachute jumps, all unique jumps, etc. All collectibles hav... Apr 5, 2025 · Download and install GTA V enhanced all missions 100% completed save games to unlock all stages, collectibles, and features in GTA 5 enhanced story mode. Mar 10, 2024 · Completing the Cluckin' Bell Raid and collecting the Tier 4 Overalls.Hope you enjoy ️As some of you may know I run on caffeine and now you can support me by... Jan 14, 2025 · Gta Online: The Project Breakaway Priority File (All Tasks Completed ) Bighippi 257 subscribers Subscribed Jan 5, 2025 · Gta Online The Brute Force File (All Tasks Completed ) Bighippi 257 subscribers Subscribed Feb 12, 2025 · Download all Grand Theft Auto San Andreas 100% completed mission skip stages, GTA San Andreas save games backup files of GTA SA for all Windows PC, PlayStation, Xbox, Android/IOS.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=jF07TxJwwl0", "content": "GTA V All Missions 100% Completed || Full Map Open 2025 New ModMOD LINK:- https://www.gta5-mods.com/misc/ultimate-100-perfect-game-save-flava0ne📂 Included: ... Apr 27, 2023 · Download Save (Game completed 100%) V2 for GTA 5. This save will include the following: Completed all story and side missions (for gold). Completed all missions of eccentrics and strangers (for gold). All races completed . Completed all parachute jumps, all unique jumps, etc. All collectibles hav... Apr 5, 2025 · Download and install GTA V enhanced all missions 100% completed save games to unlock all stages, collectibles, and features in GTA 5 enhanced story mode. Mar 10, 2024 · Completing the Cluckin' Bell Raid and collecting the Tier 4 Overalls.Hope you enjoy ️As some of you may know I run on caffeine and now you can support me by... Jan 14, 2025 · Gta Online: The Project Breakaway Priority File (All Tasks Completed ) Bighippi 257 subscribers Subscribed Jan 5, 2025 · Gta Online The Brute Force File (All Tasks Completed ) Bighippi 257 subscribers Subscribed Feb 12, 2025 · Download all Grand Theft Auto San Andreas 100% completed mission skip stages, GTA San Andreas save games backup files of GTA SA for all Windows PC, PlayStation, Xbox, Android/IOS."}
+{"idx": 2, "title": "GTA 5 — Save (Game completed 100%) V2 / Savegames / Cheats", "date": "", "ddg_snippet": "Apr 27, 2023 · Download Save (Game completed 100%) V2 for GTA 5. This save will include the following: Completed all story and side missions (for gold). Completed all missions of eccentrics and strangers (for gold). All races completed . Completed all parachute jumps, all unique jumps, etc. All collectibles hav...", "subpage_snippet": "", "source": "vgtimes.com", "link": "https://vgtimes.com/games/gta-5/files/60684-save-game-completed-100-v2.html", "content": "Apr 27, 2023 · Download Save (Game completed 100%) V2 for GTA 5. This save will include the following: Completed all story and side missions (for gold). Completed all missions of eccentrics and strangers (for gold). All races completed . Completed all parachute jumps, all unique jumps, etc. All collectibles hav..."}
+{"idx": 3, "title": "GTA 5 Enhanced All Missions 100% Completed SaveGames", "date": "", "ddg_snippet": "Apr 5, 2025 · Download and install GTA V enhanced all missions 100% completed save games to unlock all stages, collectibles, and features in GTA 5 enhanced story mode.", "subpage_snippet": "", "source": "gtaspot.com", "link": "https://gtaspot.com/gta-v-enhanced-savegames/", "content": "Apr 5, 2025 · Download and install GTA V enhanced all missions 100% completed save games to unlock all stages, collectibles, and features in GTA 5 enhanced story mode."}
+{"idx": 4, "title": "Cluckin' Bell Career progress Completed - GTA V Online", "date": "", "ddg_snippet": "Mar 10, 2024 · Completing the Cluckin' Bell Raid and collecting the Tier 4 Overalls.Hope you enjoy ️As some of you may know I run on caffeine and now you can support me by...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=4b-e_JyYVIM", "content": "Mar 10, 2024 · Completing the Cluckin' Bell Raid and collecting the Tier 4 Overalls.Hope you enjoy ️As some of you may know I run on caffeine and now you can support me by..."}
+{"idx": 5, "title": "Gta Online: The Project Breakaway Priority File (All Tasks ...", "date": "", "ddg_snippet": "Jan 14, 2025 · Gta Online: The Project Breakaway Priority File (All Tasks Completed ) Bighippi 257 subscribers Subscribed", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=ON22AL6q6Sw", "content": "Jan 14, 2025 · Gta Online: The Project Breakaway Priority File (All Tasks Completed ) Bighippi 257 subscribers Subscribed"}
+{"idx": 6, "title": "Gta Online The Brute Force File (All Tasks Completed)", "date": "", "ddg_snippet": "Jan 5, 2025 · Gta Online The Brute Force File (All Tasks Completed ) Bighippi 257 subscribers Subscribed", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=wSvg0w5OAYk", "content": "Jan 5, 2025 · Gta Online The Brute Force File (All Tasks Completed ) Bighippi 257 subscribers Subscribed"}
+{"idx": 7, "title": "GTA San Andreas All Missions 100% Completed SaveGames Files", "date": "", "ddg_snippet": "Feb 12, 2025 · Download all Grand Theft Auto San Andreas 100% completed mission skip stages, GTA San Andreas save games backup files of GTA SA for all Windows PC, PlayStation, Xbox, Android/IOS.", "subpage_snippet": "", "source": "gtaspot.com", "link": "https://gtaspot.com/gta-san-andreas-all-mission-saves/", "content": "Feb 12, 2025 · Download all Grand Theft Auto San Andreas 100% completed mission skip stages, GTA San Andreas save games backup files of GTA SA for all Windows PC, PlayStation, Xbox, Android/IOS."}
+{"idx": 8, "title": "Plot in GTA San Andreas - Grand Theft Wiki, the GTA wiki", "date": "", "ddg_snippet": "... brother Brian died, and Carl, upon being blamed by Sweet for negligence in trying to save Brian, was forced to leave his hometown Los Santos and move ...", "subpage_snippet": "", "source": "www.grandtheftwiki.com", "link": "https://www.grandtheftwiki.com/Plot_in_GTA_San_Andreas", "content": "... brother Brian died, and Carl, upon being blamed by Sweet for negligence in trying to save Brian, was forced to leave his hometown Los Santos and move ..."}
+{"idx": 9, "title": "cherryATA", "date": "", "ddg_snippet": "In GTA , we initially ask all n workers ... That is, we ensure that no more than B tasks are completed ... divide the total worker time of GTA by the corresponding.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "In GTA , we initially ask all n workers ... That is, we ensure that no more than B tasks are completed ... divide the total worker time of GTA by the corresponding."}
diff --git a/data/sampled_jsons/Gaetz_Gao_2024_citation.jsonl b/data/sampled_jsons/Gaetz_Gao_2024_citation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ac94a9af70df1ab0e7d5e04b9a56d7e0ca33abf9
--- /dev/null
+++ b/data/sampled_jsons/Gaetz_Gao_2024_citation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ethics report finds ‘substantial evidence’ Gaetz paid... - POLITICO", "date": "", "ddg_snippet": "Matt Gaetz found “substantial evidence” that the Florida Republican committed statutory rape, solicited prostitutes and used illegal drugs, according to a copy of the report obtained by POLITICO.", "subpage_snippet": "", "source": "www.politico.com", "link": "https://www.politico.com/news/2024/12/23/matt-gaetz-ethics-report-findings-00195875", "content": "Matt Gaetz found “substantial evidence” that the Florida Republican committed statutory rape, solicited prostitutes and used illegal drugs, according to a copy of the report obtained by POLITICO."}
+{"idx": 1, "title": "Who is Matt Gaetz , Trump’s pick for US attorney general ? | Al Jazeera", "date": "", "ddg_snippet": "Matt Gaetz (@RepMattGaetz) November 12, 2024 . What are the implications of his new role? In a twist, Gaetz would be set to head up the Department of Justice, the same institution that has come after both him and Trump.", "subpage_snippet": "", "source": "www.aljazeera.com", "link": "https://www.aljazeera.com/news/2024/11/14/who-is-matt-gaetz-trumps-pick-for-us-attorney-general", "content": "Matt Gaetz (@RepMattGaetz) November 12, 2024 . What are the implications of his new role? In a twist, Gaetz would be set to head up the Department of Justice, the same institution that has come after both him and Trump."}
+{"idx": 2, "title": "The child sex allegations against Matt Gaetz as... | Daily Mail Online", "date": "", "ddg_snippet": "America's next Attorney General described himself as 'the most investigated man in the United States Congress' following a probe into allegations he was involved in sex trafficking a minor. Matt Gaetz , 42, never faced criminal charges and has long denied any wrongdoing relating to the claims.", "subpage_snippet": "", "source": "www.dailymail.co.uk", "link": "https://www.dailymail.co.uk/news/article-14079603/child-sex-allegations-Matt-Gaetz-Trump-AG-nominated.html", "content": "America's next Attorney General described himself as 'the most investigated man in the United States Congress' following a probe into allegations he was involved in sex trafficking a minor. Matt Gaetz , 42, never faced criminal charges and has long denied any wrongdoing relating to the claims."}
+{"idx": 3, "title": "Yibo Gao 's research works | Peking University and other places", "date": "", "ddg_snippet": "Gaetz and Gao ( Gaetz & Gao , 2023) which resolved a long-standing conjecture of Billey and Postnikov (Billey & Postnikov, 2005) about the coefficients on Kazhdan-Lusztig polynomials (Section 4.4)...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Yibo-Gao-2084490604", "content": "Gaetz and Gao ( Gaetz & Gao , 2023) which resolved a long-standing conjecture of Billey and Postnikov (Billey & Postnikov, 2005) about the coefficients on Kazhdan-Lusztig polynomials (Section 4.4)..."}
+{"idx": 4, "title": "House Ethics Committee says Matt Gaetz may have violated state laws...", "date": "", "ddg_snippet": "23, 2024 , 10:45 AM EST / Updated Dec.Matt Gaetz , R-Fla., found that he engaged in a long list of conduct that violates House Rules and some actions that could be criminal offenses at the state level. The committee released its 42-page report Monday morning after a lengthy investigation.", "subpage_snippet": "", "source": "www.nbcnews.com", "link": "https://www.nbcnews.com/politics/congress/matt-gaetz-report-house-ethics-committee-sexual-allegations-rcna184702", "content": "23, 2024 , 10:45 AM EST / Updated Dec.Matt Gaetz , R-Fla., found that he engaged in a long list of conduct that violates House Rules and some actions that could be criminal offenses at the state level. The committee released its 42-page report Monday morning after a lengthy investigation."}
+{"idx": 5, "title": "Matt Gaetz Is Winning - The Atlantic", "date": "", "ddg_snippet": "When Matt Gaetz spotted me outside his office door one afternoon early last November, he popped in his AirPods and started speed walking down the hall.", "subpage_snippet": "", "source": "www.theatlantic.com", "link": "https://www.theatlantic.com/politics/archive/2024/04/matt-gaetz-house-republican-congress-profile/677915/", "content": "When Matt Gaetz spotted me outside his office door one afternoon early last November, he popped in his AirPods and started speed walking down the hall."}
+{"idx": 6, "title": "Matt Gaetz Controversies : From sex-trafficking... - Times of India", "date": "", "ddg_snippet": "Matt Gaetz withdrew from consideration as Donald Trump's attorney general , citing ongoing federal sex trafficking investigations as a distraction.", "subpage_snippet": "", "source": "timesofindia.indiatimes.com", "link": "https://timesofindia.indiatimes.com/world/us/top-controversies-of-matt-gaetz-from-inviting-an-internet-troll-to-trumps-address-to-personal-attack-on-michael-cohen/articleshow/115540581.cms", "content": "Matt Gaetz withdrew from consideration as Donald Trump's attorney general , citing ongoing federal sex trafficking investigations as a distraction."}
+{"idx": 7, "title": "Trump to nominate Matt Gaetz to be attorney general : NPR", "date": "", "ddg_snippet": "Gaetz said he would resign from the U.S. House of Representatives, effective immediately.Matt Gaetz arrives to speak during the third day of the 2024 Republican National Convention in Milwaukee, Wisc., on July 17.", "subpage_snippet": "", "source": "www.npr.org", "link": "https://www.npr.org/2024/11/13/g-s1-34072/trump-matt-gaetz-attorney-general-doj-oversight", "content": "Gaetz said he would resign from the U.S. House of Representatives, effective immediately.Matt Gaetz arrives to speak during the third day of the 2024 Republican National Convention in Milwaukee, Wisc., on July 17."}
+{"idx": 8, "title": "President-elect Trump picks Rep. Matt Gaetz for attorney general ...", "date": "", "ddg_snippet": "Gaetz has been a staunch ally of Trump and could lead the department that the president-elect would use to seek retribution for his legal troubles. To become U.S. attorney general , Gatez will have to undergo Senate confirmation.", "subpage_snippet": "", "source": "www.wbng.com", "link": "https://www.wbng.com/2024/11/14/president-elect-trump-picks-rep-matt-gaetz-attorney-general-gatez-resigns-congress/", "content": "Gaetz has been a staunch ally of Trump and could lead the department that the president-elect would use to seek retribution for his legal troubles. To become U.S. attorney general , Gatez will have to undergo Senate confirmation."}
+{"idx": 9, "title": "Matt Gaetz says he 'does not intend' to return to Congress... - ABC Ne...", "date": "", "ddg_snippet": "Gaetz said he'll still be \"in the fight but it's going to be from a new perch.\"Matt Gaetz departs after speaking during the Conservative Political Action Conference, CPAC 2024 , at the National Harbor, in Oxon Hill, Md., Feb.", "subpage_snippet": "", "source": "abcnews.go.com", "link": "https://abcnews.go.com/Politics/matt-gaetz-returning-congress-after-dropping-bid-attorney/story?id=116138261", "content": "Gaetz said he'll still be \"in the fight but it's going to be from a new perch.\"Matt Gaetz departs after speaking during the Conservative Political Action Conference, CPAC 2024 , at the National Harbor, in Oxon Hill, Md., Feb."}
diff --git a/data/sampled_jsons/Gaetz_Gao_mHeight_permutation_3412.jsonl b/data/sampled_jsons/Gaetz_Gao_mHeight_permutation_3412.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..54ad448a45500e6e0a4387b9a841ff742c683f55
--- /dev/null
+++ b/data/sampled_jsons/Gaetz_Gao_mHeight_permutation_3412.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Matt Gaetz - Wikipedia", "date": "", "ddg_snippet": "Gaetz on Ileana Ros-Lehtinen 's retirement. Matthew Louis Gaetz II (/ ɡeɪts / GAYTS; born May 7, 1982) is an American politician and lawyer who served as the U.S. representative for Florida's 1st congressional district from 2017 until his resignation in 2024.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Matt_Gaetz", "content": "Gaetz on Ileana Ros-Lehtinen 's retirement. Matthew Louis Gaetz II (/ ɡeɪts / GAYTS; born May 7, 1982) is an American politician and lawyer who served as the U.S. representative for Florida's 1st congressional district from 2017 until his resignation in 2024."}
+{"idx": 1, "title": "The rise and fall of Matt Gaetz in eight wild days - BBC", "date": "", "ddg_snippet": "Nov 22, 2024 · Gaetz , a lawyer, has been one of Trump’s most strident defenders on Capitol Hill. He helped prepare the Republican nominee for his televised debate against Biden that effectively knocked the...", "subpage_snippet": "", "source": "www.bbc.com", "link": "https://www.bbc.com/news/articles/c99r2m4y2zro", "content": "Nov 22, 2024 · Gaetz , a lawyer, has been one of Trump’s most strident defenders on Capitol Hill. He helped prepare the Republican nominee for his televised debate against Biden that effectively knocked the..."}
+{"idx": 2, "title": "Matt Gaetz among Floridians spotted at Charlie Kirk's funeral", "date": "", "ddg_snippet": "1 day ago · Matt Gaetz and his wife Ginger Luckey were among a handful of Florida Republicans who honored Charlie Kirk at his funeral on Sunday.", "subpage_snippet": "", "source": "www.usatoday.com", "link": "https://www.usatoday.com/story/news/2025/09/22/matt-gaetz-among-floridians-spotted-at-charlie-kirks-funeral/86290503007/", "content": "1 day ago · Matt Gaetz and his wife Ginger Luckey were among a handful of Florida Republicans who honored Charlie Kirk at his funeral on Sunday."}
+{"idx": 3, "title": "Gaetz withdraws from attorney general consideration after Trump...", "date": "", "ddg_snippet": "Nov 21, 2024 · Early Thursday afternoon, Gaetz — who quit his seat in the House the day Trump announced he’d selected him to helm the Justice Department — wrote on social media that he was withdrawing and that...", "subpage_snippet": "", "source": "www.cnn.com", "link": "https://www.cnn.com/2024/11/21/politics/matt-gaetz-withdrawing-attorney-general", "content": "Nov 21, 2024 · Early Thursday afternoon, Gaetz — who quit his seat in the House the day Trump announced he’d selected him to helm the Justice Department — wrote on social media that he was withdrawing and that..."}
+{"idx": 4, "title": "Matt Gaetz withdraws as Trump's pick for attorney general : NPR", "date": "", "ddg_snippet": "Nov 21, 2024 · Former Rep. Matt Gaetz , R-Fla., is withdrawing his candidacy to be President-elect Trump's attorney general, after sex trafficking and drug use allegations threatened to imperil his confirmation.", "subpage_snippet": "", "source": "www.npr.org", "link": "https://www.npr.org/2024/11/21/g-s1-35211/gaetz-out-attorney-general-trump", "content": "Nov 21, 2024 · Former Rep. Matt Gaetz , R-Fla., is withdrawing his candidacy to be President-elect Trump's attorney general, after sex trafficking and drug use allegations threatened to imperil his confirmation."}
+{"idx": 5, "title": "Key moments in the Trump transition: Matt Gaetz withdraws as...", "date": "", "ddg_snippet": "Matt Gaetz has withdrawn as President-elect Donald Trump’s pick for attorney general following scrutiny over a federal sex trafficking investigation that cast doubt on his ability to be confirmed as the nation’s chief federal law enforcement officer.", "subpage_snippet": "", "source": "apnews.com", "link": "https://apnews.com/live/matt-gaetz-trump-transition-updates", "content": "Matt Gaetz has withdrawn as President-elect Donald Trump’s pick for attorney general following scrutiny over a federal sex trafficking investigation that cast doubt on his ability to be confirmed as the nation’s chief federal law enforcement officer."}
+{"idx": 6, "title": "Matt Gaetz resigns from Congress after being picked for attorney...", "date": "", "ddg_snippet": "Nov 13, 2024 · President-elect Donald Trump announced on Wednesday that he has chosen Rep. Matt Gaetz as his pick for attorney general, a move that, if he's confirmed by the Senate, would place a firebrand and one of Trump's most loyal allies at the head of the Justice Department.", "subpage_snippet": "", "source": "abcnews.go.com", "link": "https://abcnews.go.com/Politics/trump-picks-rep-matt-gaetz-attorney-general/story?id=115835796", "content": "Nov 13, 2024 · President-elect Donald Trump announced on Wednesday that he has chosen Rep. Matt Gaetz as his pick for attorney general, a move that, if he's confirmed by the Senate, would place a firebrand and one of Trump's most loyal allies at the head of the Justice Department."}
+{"idx": 7, "title": "Matt Gaetz Allegations and Investigations: What to Know | TIME", "date": "", "ddg_snippet": "Nov 18, 2024 · Gaetz , a fierce defender of Trump and critic of the Justice Department, now finds himself poised to lead the very agency that investigated him for allegedly sex trafficking a minor—an...", "subpage_snippet": "", "source": "time.com", "link": "https://time.com/7177301/matt-gaetz-allegations-justice-house-ethics/", "content": "Nov 18, 2024 · Gaetz , a fierce defender of Trump and critic of the Justice Department, now finds himself poised to lead the very agency that investigated him for allegedly sex trafficking a minor—an..."}
+{"idx": 8, "title": "Trump AG pick Matt Gaetz says he's withdrawing - CNBC", "date": "", "ddg_snippet": "Nov 21, 2024 · Matt Gaetz said Thursday he is withdrawing as President-elect Donald Trump 's pick for U.S. attorney general, ending the controversial bid that put prior allegations of sexual misconduct in the...", "subpage_snippet": "", "source": "www.cnbc.com", "link": "https://www.cnbc.com/2024/11/21/trump-ag-pick-matt-gaetz-says-hes-withdrawing.html", "content": "Nov 21, 2024 · Matt Gaetz said Thursday he is withdrawing as President-elect Donald Trump 's pick for U.S. attorney general, ending the controversial bid that put prior allegations of sexual misconduct in the..."}
+{"idx": 9, "title": "What's next for Matt Gaetz after withdrawing from AG nomination?...", "date": "", "ddg_snippet": "Nov 21, 2024 · Gaetz resigned his seat for the current 118th Congress, but his resignation letter left some wiggle room to return for the 119th Congress that starts Jan. 3, 2025, since he was reelected to the...", "subpage_snippet": "", "source": "thehill.com", "link": "https://thehill.com/homenews/house/5003258-gaetz-future-career-options/", "content": "Nov 21, 2024 · Gaetz resigned his seat for the current 118th Congress, but his resignation letter left some wiggle room to return for the 119th Congress that starts Jan. 3, 2025, since he was reelected to the..."}
diff --git a/data/sampled_jsons/Gao_et_al._2024_'2.4_Preventing_dead_latents'_section_modifications.jsonl b/data/sampled_jsons/Gao_et_al._2024_'2.4_Preventing_dead_latents'_section_modifications.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b4a494d64f31da5e5b1369249c04b4d3103ac170
--- /dev/null
+++ b/data/sampled_jsons/Gao_et_al._2024_'2.4_Preventing_dead_latents'_section_modifications.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2406.04093v1 [cs.LG] 6 Jun 2024", "date": "", "ddg_snippet": "2.4 Preventing dead latents Dead latents pose another significant dificulty in autoencoder training. In larger autoencoders, an increasingly large proportion of latents stop activating entirely at some point in training. For example, Templeton et al. [ 2024 ] train a 34 million latent autoencoder with only 12 million alive latents , and in our ablations we find up to 90% dead latents7 when no ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04093", "content": "2.4 Preventing dead latents Dead latents pose another significant dificulty in autoencoder training. In larger autoencoders, an increasingly large proportion of latents stop activating entirely at some point in training. For example, Templeton et al. [ 2024 ] train a 34 million latent autoencoder with only 12 million alive latents , and in our ablations we find up to 90% dead latents7 when no ..."}
+{"idx": 1, "title": "Methods that reduce the number of dead latents (gpt2sm 2M, k=32). With ...", "date": "", "ddg_snippet": "Methods that reduce the number of dead latents (gpt2sm 2M, k=32). With AuxK and/or tied initialization, number of dead latents generally decreases over the course of training, after an early spike.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Methods-that-reduce-the-number-of-dead-latents-gpt2sm-2M-k32-With-AuxK-and-or-tied_fig5_381227195", "content": "Methods that reduce the number of dead latents (gpt2sm 2M, k=32). With AuxK and/or tied initialization, number of dead latents generally decreases over the course of training, after an early spike."}
+{"idx": 2, "title": "Decomposing GPT-4's internal representations into 16 million oft ...", "date": "", "ddg_snippet": "iii) Preventing dead latents In larger autoencoders, an increasingly large proportion of latents stop activating entirely at some point in training, resulting in worse MSE and makes training ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@techsachin/decomposing-gpt-4s-internal-representations-into-16-million-oft-interpretable-patterns-with-e16fb02e5189", "content": "iii) Preventing dead latents In larger autoencoders, an increasingly large proportion of latents stop activating entirely at some point in training, resulting in worse MSE and makes training ..."}
+{"idx": 3, "title": "Scaling and evaluating sparse autoencoders | OpenReview", "date": "", "ddg_snippet": "Scaling SAEs to a model the size of GPT-4 is a challenge due to the increasing concentration of dead features, and the early works in this field (Cunningham et al , 2023; Bricken et al , 2023) focused on relatively small language models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tcsZt9ZNKD", "content": "Scaling SAEs to a model the size of GPT-4 is a challenge due to the increasing concentration of dead features, and the early works in this field (Cunningham et al , 2023; Bricken et al , 2023) focused on relatively small language models."}
+{"idx": 4, "title": "(PDF) Scaling and evaluating sparse autoencoders - ResearchGate", "date": "", "ddg_snippet": "Methods that reduce the number of dead latents (gpt2sm 2M, k=32). With AuxK and/or tied initialization, number of dead latents generally decreases over the course of training, after an early spike.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381227195_Scaling_and_evaluating_sparse_autoencoders", "content": "Methods that reduce the number of dead latents (gpt2sm 2M, k=32). With AuxK and/or tied initialization, number of dead latents generally decreases over the course of training, after an early spike."}
+{"idx": 5, "title": "Scaling Sparse Autoencoders: Insights on Feature Recovery and - Course Hero", "date": "", "ddg_snippet": "We releasecode and autoencoders for open-source models, as well as avisualizer. 3 1 Introduction Sparse autoencoders (SAEs) have shown great promise for finding features [Cunningham et al.,2023, Bricken et al.,2023,Templeton et al.,2024,Goh,2016] and circuits [Marks et al.,2024 ] in language models.", "subpage_snippet": "", "source": "www.coursehero.com", "link": "https://www.coursehero.com/file/243997197/sparse-autoencoderspdf/", "content": "We releasecode and autoencoders for open-source models, as well as avisualizer. 3 1 Introduction Sparse autoencoders (SAEs) have shown great promise for finding features [Cunningham et al.,2023, Bricken et al.,2023,Templeton et al.,2024,Goh,2016] and circuits [Marks et al.,2024 ] in language models."}
+{"idx": 6, "title": "PDF Scaling and evaluating sparse autoencoders - OpenAI", "date": "", "ddg_snippet": "2.4 Preventing dead latents Dead latents pose another significant dificulty in autoencoder training. In larger autoencoders, an increasingly large proportion of latents stop activating entirely at some point in training. For example, Templeton et al. [ 2024 ] train a 34 million latent autoencoder with only 12 million alive latents , and in our ablations we find up to 90% dead latents7 when no ...", "subpage_snippet": "", "source": "cdn.openai.com", "link": "https://cdn.openai.com/papers/sparse-autoencoders.pdf", "content": "2.4 Preventing dead latents Dead latents pose another significant dificulty in autoencoder training. In larger autoencoders, an increasingly large proportion of latents stop activating entirely at some point in training. For example, Templeton et al. [ 2024 ] train a 34 million latent autoencoder with only 12 million alive latents , and in our ablations we find up to 90% dead latents7 when no ..."}
+{"idx": 7, "title": "[2406.04093] Scaling and evaluating sparse autoencoders", "date": "", "ddg_snippet": "However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents . We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04093", "content": "However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents . We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier."}
+{"idx": 8, "title": "Scaling and evaluating sparse autoencoders - arXiv.org", "date": "", "ddg_snippet": "Differences include: replacing the resampling of dead latents with a L 1 coefficient warm-up over 5% of training [Conerly et al ., 2024 ]; removing the decoder unit-norm constraint and adding the decoder norm in the L 1 penalty [Conerly et al ., 2024 ].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04093v1", "content": "Differences include: replacing the resampling of dead latents with a L 1 coefficient warm-up over 5% of training [Conerly et al ., 2024 ]; removing the decoder unit-norm constraint and adding the decoder norm in the L 1 penalty [Conerly et al ., 2024 ]."}
+{"idx": 9, "title": "Llama 3 Interpretability with Sparse Autoencoders - GitHub", "date": "", "ddg_snippet": "A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible. - PaulPauls/llama3_interpretability_sae", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/PaulPauls/llama3_interpretability_sae", "content": "A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible. - PaulPauls/llama3_interpretability_sae"}
diff --git a/data/sampled_jsons/Geirhos_Meding_Wichmann_2020_Beyond_accuracy_quantifying_trial-by-trial_behaviour_CNNs_humans_error_.jsonl b/data/sampled_jsons/Geirhos_Meding_Wichmann_2020_Beyond_accuracy_quantifying_trial-by-trial_behaviour_CNNs_humans_error_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..17d3d4cea05b8162dcce8ca660f734a272fa683c
--- /dev/null
+++ b/data/sampled_jsons/Geirhos_Meding_Wichmann_2020_Beyond_accuracy_quantifying_trial-by-trial_behaviour_CNNs_humans_error_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "quantifying trial-by-trial behaviour of CNNs and humans ...", "date": "", "ddg_snippet": "by R Geirhos · 2020 · Cited by 160 — Here we introduce trial-by-trial error consistency , a quantitative analysis for measuring whether two decision making systems systematically make errors on the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.16736", "content": "by R Geirhos · 2020 · Cited by 160 — Here we introduce trial-by-trial error consistency , a quantitative analysis for measuring whether two decision making systems systematically make errors on the ..."}
+{"idx": 1, "title": "quantifying trial-by-trial behaviour of CNNs and humans by ...", "date": "", "ddg_snippet": "by R Geirhos · 2020 · Cited by 160 — Making consistent errors on a trial -by- trial basis is a necessary condition if we want to ascertain similar processing strategies between decision makers. Our ... 13 pages", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2020/file/9f6992966d4c363ea0162a056cb45fe5-Paper.pdf", "content": "by R Geirhos · 2020 · Cited by 160 — Making consistent errors on a trial -by- trial basis is a necessary condition if we want to ascertain similar processing strategies between decision makers. Our ... 13 pages"}
+{"idx": 2, "title": "quantifying trial-by-trial behaviour of CNNs and humans ...", "date": "", "ddg_snippet": "6 Dec 2020 — Here we introduce trial-by-trial error consistency , a quantitative analysis for measuring whether two decision making systems systematically make errors on the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3495724.3496889", "content": "6 Dec 2020 — Here we introduce trial-by-trial error consistency , a quantitative analysis for measuring whether two decision making systems systematically make errors on the ..."}
+{"idx": 3, "title": "Publications | Robert Geirhos", "date": "", "ddg_snippet": "Geirhos , R., Meding , K., & Wichmann , F. A. ( 2020 ). Beyond accuracy : quantifying trial -by- trial behaviour of CNNs and humans by measuring error consistency .", "subpage_snippet": "", "source": "robertgeirhos.com", "link": "https://robertgeirhos.com/publications/", "content": "Geirhos , R., Meding , K., & Wichmann , F. A. ( 2020 ). Beyond accuracy : quantifying trial -by- trial behaviour of CNNs and humans by measuring error consistency ."}
+{"idx": 4, "title": "A comparison between humans and AI at recognizing ...", "date": "", "ddg_snippet": "Robert Geirhos, Kristof Meding, and Felix A. Wichmann. Beyond accuracy: quantifying trial-by-trial behaviour of cnns and humans by measuring error consistency.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/ff3970fa5adb91f089f85b2edb97353c0083c420.pdf", "content": "Robert Geirhos, Kristof Meding, and Felix A. Wichmann. Beyond accuracy: quantifying trial-by-trial behaviour of cnns and humans by measuring error consistency."}
+{"idx": 5, "title": "The developmental trajectory of object recognition ...", "date": "", "ddg_snippet": "by LS Huber · 2023 · Cited by 32 — Geirhos, R., Meding, K., & Wichmann, F. A. (2020). Beyond accuracy: Quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10337805/", "content": "by LS Huber · 2023 · Cited by 32 — Geirhos, R., Meding, K., & Wichmann, F. A. (2020). Beyond accuracy: Quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency."}
+{"idx": 6, "title": "Are Deep Neural Networks Adequate Behavioral Models of ...", "date": "", "ddg_snippet": "by FA Wichmann · 2023 · Cited by 76 — Geirhos R , Meding K, Wichmann FA. 2020b. Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency ...", "subpage_snippet": "", "source": "www.annualreviews.org", "link": "https://www.annualreviews.org/content/journals/10.1146/annurev-vision-120522-031739", "content": "by FA Wichmann · 2023 · Cited by 76 — Geirhos R , Meding K, Wichmann FA. 2020b. Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency ..."}
+{"idx": 7, "title": "UC Merced", "date": "", "ddg_snippet": "by S Tuli · 2021 · Cited by 295 — Geirhos , R., Meding , K., & Wichmann , F. A. ( 2020 ). Beyond accuracy : Quantifying trial -by- trial behaviour of CNNs and humans by measuring error consistency . 8 pages", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt2pm5q7k6/qt2pm5q7k6.pdf", "content": "by S Tuli · 2021 · Cited by 295 — Geirhos , R., Meding , K., & Wichmann , F. A. ( 2020 ). Beyond accuracy : Quantifying trial -by- trial behaviour of CNNs and humans by measuring error consistency . 8 pages"}
+{"idx": 8, "title": "9.58 – Projects – Poggio Lab - MIT", "date": "", "ddg_snippet": "by P RT — Geirhos, Meding, and Wichmann . Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency https://arxiv.org/abs/ ...", "subpage_snippet": "", "source": "poggio-lab.mit.edu", "link": "https://poggio-lab.mit.edu/f20-9-58/projects/", "content": "by P RT — Geirhos, Meding, and Wichmann . Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency https://arxiv.org/abs/ ..."}
+{"idx": 9, "title": "HUMAN ALIGNMENT OF NEURAL NETWORK REPRE", "date": "", "ddg_snippet": "by L Muttenthaler · Cited by 114 — Robert Geirhos, Kristof Meding, and Felix A. Wichmann. Beyond accuracy: quantifying trial-by- trial behaviour of cnns and humans by measuring error consistency.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ReDQ1OUQR0X", "content": "by L Muttenthaler · Cited by 114 — Robert Geirhos, Kristof Meding, and Felix A. Wichmann. Beyond accuracy: quantifying trial-by- trial behaviour of cnns and humans by measuring error consistency."}
diff --git a/data/sampled_jsons/GenAI_Arena_Playground_V2.5_SDXL_training_data_internal_dataset.jsonl b/data/sampled_jsons/GenAI_Arena_Playground_V2.5_SDXL_training_data_internal_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44116d251404926ba1e2d05f0a1e3f1d57402028
--- /dev/null
+++ b/data/sampled_jsons/GenAI_Arena_Playground_V2.5_SDXL_training_data_internal_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenAI Arena : An Open Evaluation Platform for", "date": "", "ddg_snippet": "GENAI - ARENA has been operating for seven months, amassing over 9000 votes from the community. Playground V 2 and Playground V 2 . 5 are based on SDXL architecture, but trained by Playground .ai from scratch with an internal dataset .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/92249f9233286e437f808fa535d88b26-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "GENAI - ARENA has been operating for seven months, amassing over 9000 votes from the community. Playground V 2 and Playground V 2 . 5 are based on SDXL architecture, but trained by Playground .ai from scratch with an internal dataset ."}
+{"idx": 1, "title": "playgroundai/ playground - v 2 . 5 -1024px-aesthetic · Hugging Face", "date": "", "ddg_snippet": "Playground v 2 . 5 is the state-of-the-art open-source model in aesthetic quality. Our user studies demonstrate that our model outperforms SDXL , Playground v 2 , PixArt-α, DALL-E 3, and Midjourney 5.2. For details on the development and training of our model...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic", "content": "Playground v 2 . 5 is the state-of-the-art open-source model in aesthetic quality. Our user studies demonstrate that our model outperforms SDXL , Playground v 2 , PixArt-α, DALL-E 3, and Midjourney 5.2. For details on the development and training of our model..."}
+{"idx": 2, "title": "GenAI - Arena : An Open Platform for... - MarkTechPost", "date": "", "ddg_snippet": "GenAI - Arena supports text-to-image generation, image editing, and text-to-video generation tasks with features like anonymous side-by-side voting, battle playground , direct generation tab, and leaderboards.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/06/12/genai-arena-an-open-platform-for-community-based-evaluation-of-generative-ai-models/", "content": "GenAI - Arena supports text-to-image generation, image editing, and text-to-video generation tasks with features like anonymous side-by-side voting, battle playground , direct generation tab, and leaderboards."}
+{"idx": 3, "title": "Rip sdxl lora! ultimate free NSFW flux lora training !", "date": "", "ddg_snippet": "The AI training revolution is HERE, and it’s FREE, UNCENSORED, and STUPID EASY with FLUXGYM!", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=LILai5jIW1w", "content": "The AI training revolution is HERE, and it’s FREE, UNCENSORED, and STUPID EASY with FLUXGYM!"}
+{"idx": 4, "title": "GitHub - TIGER-AI-Lab/ GenAI - Arena : Interface for GenAI - Arena", "date": "", "ddg_snippet": "Contribute to TIGER-AI-Lab/ GenAI - Arena development by creating an account on GitHub.update leaderboard data . cd arena _elo export LOGDIR=\"/home/tianle/ arena _vote\" bash update_elo.sh.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TIGER-AI-Lab/GenAI-Arena", "content": "Contribute to TIGER-AI-Lab/ GenAI - Arena development by creating an account on GitHub.update leaderboard data . cd arena _elo export LOGDIR=\"/home/tianle/ arena _vote\" bash update_elo.sh."}
+{"idx": 5, "title": "googleapis.github.io/python- genai / genai .html", "date": "", "ddg_snippet": "genai .tunings module.", "subpage_snippet": "", "source": "googleapis.github.io", "link": "https://googleapis.github.io/python-genai/genai.html", "content": "genai .tunings module."}
+{"idx": 6, "title": "【 GenAI - Arena ...", "date": "", "ddg_snippet": "GenAI Arena : An Open Evaluation Platform for Generative Models written by Dongfu Jiang, Max Ku, Tianle Li, Yuansheng Ni, Shizhuo Sun, Rongqi Fan, Wenhu Chen (Submitted on 6 Jun 2024) Comments: 9 pages,7 figures Subjects: Artificial Intelligence (cs.AI); Computer Vision and...", "subpage_snippet": "", "source": "ai-scholar.tech", "link": "https://ai-scholar.tech/articles/large-language-models/genai-arena", "content": "GenAI Arena : An Open Evaluation Platform for Generative Models written by Dongfu Jiang, Max Ku, Tianle Li, Yuansheng Ni, Shizhuo Sun, Rongqi Fan, Wenhu Chen (Submitted on 6 Jun 2024) Comments: 9 pages,7 figures Subjects: Artificial Intelligence (cs.AI); Computer Vision and..."}
+{"idx": 7, "title": "playground - v 2 -512px-base-anime-finetune - v0.0Base... | Tensor.Art", "date": "", "ddg_snippet": "playground - v 2 -512px-base-anime-finetune. This is an experimental fine tuning. Fine-tuning is performed on a 100,000-image dataset that mainly contains anime images, but also some realistic and AI images. The training resolution is 512px.", "subpage_snippet": "", "source": "tensor.art", "link": "https://tensor.art/models/746023372770609397/playground-v2-512px-base-anime-finetune-v0.0Base-P", "content": "playground - v 2 -512px-base-anime-finetune. This is an experimental fine tuning. Fine-tuning is performed on a 100,000-image dataset that mainly contains anime images, but also some realistic and AI images. The training resolution is 512px."}
+{"idx": 8, "title": "NeurIPS Poster GenAI Arena : An Open Evaluation Platform for...", "date": "", "ddg_snippet": "GenAI - Arena has been operating for seven months, amassing over 9000 votes from the community.To further promote the research in building model-based evaluation metrics, we release a cleaned version of our preference data for the three tasks, namely GenAI -Bench.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/97878", "content": "GenAI - Arena has been operating for seven months, amassing over 9000 votes from the community.To further promote the research in building model-based evaluation metrics, we release a cleaned version of our preference data for the three tasks, namely GenAI -Bench."}
+{"idx": 9, "title": "Нейросеть для работы с файлами (pdf, word, excel). Анализ.", "date": "", "ddg_snippet": "Midjourney 6.0, Stable Diffusion XL, Dall-E 3, Playground v 2 . 5 , Flux.1 Schnell, Flux.1 Dev, Flux.1 Pro, Flux.1.1 Pro, Kolors, Recraft v3, GPT Image 1 (low), GPT Image 1 (medium), GPT Image 1 (high). Создание музыки. Нейросеть Suno создает музыку на основе вашего текста.", "subpage_snippet": "", "source": "smartbuddy.ru", "link": "https://smartbuddy.ru/ai/files", "content": "Midjourney 6.0, Stable Diffusion XL, Dall-E 3, Playground v 2 . 5 , Flux.1 Schnell, Flux.1 Dev, Flux.1 Pro, Flux.1.1 Pro, Kolors, Recraft v3, GPT Image 1 (low), GPT Image 1 (medium), GPT Image 1 (high). Создание музыки. Нейросеть Suno создает музыку на основе вашего текста."}
diff --git a/data/sampled_jsons/GenAI_Arena_paper_equation_1_Elo_probability_formula_arxiv2406.04485_year_2024.jsonl b/data/sampled_jsons/GenAI_Arena_paper_equation_1_Elo_probability_formula_arxiv2406.04485_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae80f07242624ee2e08b23f2dfbbc62d9cf56d57
--- /dev/null
+++ b/data/sampled_jsons/GenAI_Arena_paper_equation_1_Elo_probability_formula_arxiv2406.04485_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04485", "content": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance."}
+{"idx": 1, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models - arXiv.org", "date": "", "ddg_snippet": "Figure 1 : GenAI Arena contains three components: ( 1 ) text-to-image, text-to-video and image editing arena , which accept community voting to obtain the preference pairs. (2) The leaderboard utilizes the preference pairs to calculate elo ranking for all the evaluated models. (3) We further release GenAI -Bench to judge different multimodal LLM judges. 1 Introduction", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v1", "content": "Figure 1 : GenAI Arena contains three components: ( 1 ) text-to-image, text-to-video and image editing arena , which accept community voting to obtain the preference pairs. (2) The leaderboard utilizes the preference pairs to calculate elo ranking for all the evaluated models. (3) We further release GenAI -Bench to judge different multimodal LLM judges. 1 Introduction"}
+{"idx": 2, "title": "arXiv:2406.04485v4 [cs.AI] 11 Nov 2024", "date": "", "ddg_snippet": "GenAI-Arena , the first open platform to rank multi-modal generative AI based on user preferences. Discussion and case studies of collected user votes, showing the reliability of GenAI-Arena . GenAI -Bench, a public benchmark for judging MLLM's evaluation ability for generative tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04485", "content": "GenAI-Arena , the first open platform to rank multi-modal generative AI based on user preferences. Discussion and case studies of collected user votes, showing the reliability of GenAI-Arena . GenAI -Bench, a public benchmark for judging MLLM's evaluation ability for generative tasks."}
+{"idx": 3, "title": "GitHub - TIGER-AI-Lab/GenAI-Bench: Code and Data for \"GenAI Arena: An ...", "date": "", "ddg_snippet": "Citation @article {jiang2024genai, title = { GenAI Arena : An Open Evaluation Platform for Generative Models}, author = {Jiang, Dongfu and Ku, Max and Li, Tianle and Ni, Yuansheng and Sun, Shizhuo and Fan, Rongqi and Chen, Wenhu}, journal = { arXiv preprint arXiv:2406.04485 }, year = {2024} }", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TIGER-AI-Lab/GenAI-Bench", "content": "Citation @article {jiang2024genai, title = { GenAI Arena : An Open Evaluation Platform for Generative Models}, author = {Jiang, Dongfu and Ku, Max and Li, Tianle and Ni, Yuansheng and Sun, Shizhuo and Fan, Rongqi and Chen, Wenhu}, journal = { arXiv preprint arXiv:2406.04485 }, year = {2024} }"}
+{"idx": 4, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294137_GenAI_Arena_An_Open_Evaluation_Platform_for_Generative_Models", "content": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models."}
+{"idx": 5, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "Overview The paper introduces GenAI-Arena , an open evaluation platform for generative models, which integrates community votes to provide a more accurate measure of model performance. A unique methodology using side-by-side model comparisons and the Elo rating system is employed to evaluate user preferences and update rankings dynamically. GenAI-Arena's results and the release of GenAI -Bench ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.04485", "content": "Overview The paper introduces GenAI-Arena , an open evaluation platform for generative models, which integrates community votes to provide a more accurate measure of model performance. A unique methodology using side-by-side model comparisons and the Elo rating system is employed to evaluate user preferences and update rankings dynamically. GenAI-Arena's results and the release of GenAI -Bench ..."}
+{"idx": 6, "title": "GenAI arena | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "This paper proposes an open platform GENAI-ARENA to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GENAI-ARENA aims to provide a more democratic and accurate measure of model performance.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740454", "content": "This paper proposes an open platform GENAI-ARENA to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GENAI-ARENA aims to provide a more democratic and accurate measure of model performance."}
+{"idx": 7, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "The paper presents GenAI arena , platform for collecting user votes to evaluate different generative models on 3 types of tasks: text-to-image, image editing and text-to-video. 27 open-source generative models are already implemented in the platform and results using ELO voting based on 6000 votes from the community are reported.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0Gmi8TkUC7", "content": "The paper presents GenAI arena , platform for collecting user votes to evaluate different generative models on 3 types of tasks: text-to-image, image editing and text-to-video. 27 open-source generative models are already implemented in the platform and results using ELO voting based on 6000 votes from the community are reported."}
+{"idx": 8, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "The paper primarily addresses the evaluation challenges in the field of Generative AI by proposing a new open evaluation platform—GenAI- Arena . This platform aims to more accurately assess the performance of different generative models (such as text-to-image, text-guided image editing, and text-to-video generation tasks) by collecting user ...", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2406.04485", "content": "The paper primarily addresses the evaluation challenges in the field of Generative AI by proposing a new open evaluation platform—GenAI- Arena . This platform aims to more accurately assess the performance of different generative models (such as text-to-image, text-guided image editing, and text-to-video generation tasks) by collecting user ..."}
+{"idx": 9, "title": "#1 GenAI Arena: An Open Evaluation Platform for ... - papers.cool", "date": "", "ddg_snippet": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2406.04485", "content": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance."}
diff --git a/data/sampled_jsons/GenAI_Arena_text-to-image_leaderboard_Table_5_Section_4.1_analysis.jsonl b/data/sampled_jsons/GenAI_Arena_text-to-image_leaderboard_Table_5_Section_4.1_analysis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dedc0e8faf1d17f45f6b0f69a54466f34e5ecea0
--- /dev/null
+++ b/data/sampled_jsons/GenAI_Arena_text-to-image_leaderboard_Table_5_Section_4.1_analysis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Text to Image Models and Providers Leaderboard | Artificial Analysis", "date": "", "ddg_snippet": "Analysis of Text to Image AI models and providers across quality, generation time and price. Analysis to help you choose the best Text to Image model and provider for your use-case.", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/text-to-image", "content": "Analysis of Text to Image AI models and providers across quality, generation time and price. Analysis to help you choose the best Text to Image model and provider for your use-case."}
+{"idx": 1, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "Figure 1: GenAI Arena contains three components: (1) text-to-image , text - to -video and image editing arena , which accept community voting to obtain the preference pairs. (2) The leaderboard utilizes the preference pairs to calculate elo ranking for all the evaluated models. (3) We further release GenAI -Bench to judge different multimodal LLM judges.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v4", "content": "Figure 1: GenAI Arena contains three components: (1) text-to-image , text - to -video and image editing arena , which accept community voting to obtain the preference pairs. (2) The leaderboard utilizes the preference pairs to calculate elo ranking for all the evaluated models. (3) We further release GenAI -Bench to judge different multimodal LLM judges."}
+{"idx": 2, "title": "Leaderboard Overview - LMArena", "date": "", "ddg_snippet": "Leaderboard Overview See how leading models stack up across text , image , vision, and beyond. This page gives you a snapshot of each Arena , you can explore deeper insights in their dedicated tabs. Learn more about it here.", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/leaderboard", "content": "Leaderboard Overview See how leading models stack up across text , image , vision, and beyond. This page gives you a snapshot of each Arena , you can explore deeper insights in their dedicated tabs. Learn more about it here."}
+{"idx": 3, "title": "AI Model & API Providers Analysis | Artificial Analysis", "date": "", "ddg_snippet": "Comparison and analysis of AI models and API hosting providers. Independent benchmarks across key performance metrics including quality, price, output speed & latency.", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/", "content": "Comparison and analysis of AI models and API hosting providers. Independent benchmarks across key performance metrics including quality, price, output speed & latency."}
+{"idx": 4, "title": "GenAI-Arena arXiv:2406.04485v2 [cs.AI] 5 Aug 2024", "date": "", "ddg_snippet": "cratic and accurate measure of model performance. It covers three arenas for text-to-image generation, text-to video generation, and image editing respectively. Currently, we over a total of 27 open-source generative models. GENAI-ARENA hasbeen operating for four mo", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04485v2", "content": "cratic and accurate measure of model performance. It covers three arenas for text-to-image generation, text-to video generation, and image editing respectively. Currently, we over a total of 27 open-source generative models. GENAI-ARENA hasbeen operating for four mo"}
+{"idx": 5, "title": "Image generation - labelbox.com", "date": "", "ddg_snippet": "Our image generation leaderboard evaluates AI models on their ability to generate high-quality images from textual descriptions", "subpage_snippet": "", "source": "labelbox.com", "link": "https://labelbox.com/leaderboards/image-generation/", "content": "Our image generation leaderboard evaluates AI models on their ability to generate high-quality images from textual descriptions"}
+{"idx": 6, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "It covers three arenas for text-to-image generation, text - to -video generation, and image editing respectively. Currently, we cover a total of 27 open-source generative models. GenAI-Arena has been operating for four months, amassing over 6000 votes from the community.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v1", "content": "It covers three arenas for text-to-image generation, text - to -video generation, and image editing respectively. Currently, we cover a total of 27 open-source generative models. GenAI-Arena has been operating for four months, amassing over 6000 votes from the community."}
+{"idx": 7, "title": "2.4 Using and Choosing GenAI Tools - Technical Writing Essentials", "date": "", "ddg_snippet": "This open textbook has been partially adapted from the original text by Suzan Last. This text is designed to introduce readers to the basics of technical communication: audience and task analysis in workplace contexts, clear and concise communication style, effective document design, teamwork and collaboration, fundamental research and documentation skills. Interactive elements are contributed ...", "subpage_snippet": "", "source": "pressbooks.senecapolytechnic.ca", "link": "https://pressbooks.senecapolytechnic.ca/technicalwriting/chapter/using-and-choosing-genai-tools/", "content": "This open textbook has been partially adapted from the original text by Suzan Last. This text is designed to introduce readers to the basics of technical communication: audience and task analysis in workplace contexts, clear and concise communication style, effective document design, teamwork and collaboration, fundamental research and documentation skills. Interactive elements are contributed ..."}
+{"idx": 8, "title": "GenAI-Arena arXiv:2406.04485v1 [cs.AI] 6 Jun 2024", "date": "", "ddg_snippet": "a more holistic evaluation of model capabilities. To our knowledge, GenAI-Arena is the first evaluation platform with comprehensive e aluation capabilities across multiple properties. Unlike other platforms, it supports a wide range of tasks across text-to-image generation, text -guided image editing, and text - to -video generation, along with a publ", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04485v1", "content": "a more holistic evaluation of model capabilities. To our knowledge, GenAI-Arena is the first evaluation platform with comprehensive e aluation capabilities across multiple properties. Unlike other platforms, it supports a wide range of tasks across text-to-image generation, text -guided image editing, and text - to -video generation, along with a publ"}
+{"idx": 9, "title": "The use of Generative Artificial Intelligence (GenAI) in operations ...", "date": "", "ddg_snippet": "ABSTRACT The emergence of Generative Artificial Intelligence ( GenAI ) represents a significant advance-ment in computational capabilities, offering transformative potential for the field of Operations Research (OR). This study explores the role of GenAI in OR by conducting a sys-tematic literature review. Following a careful analysis of the collected works, the reviewed papers are classified ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/pdf/10.1080/01605682.2025.2561762", "content": "ABSTRACT The emergence of Generative Artificial Intelligence ( GenAI ) represents a significant advance-ment in computational capabilities, offering transformative potential for the field of Operations Research (OR). This study explores the role of GenAI in OR by conducting a sys-tematic literature review. Following a careful analysis of the collected works, the reviewed papers are classified ..."}
diff --git a/data/sampled_jsons/Generalizable_Origin_Identification_transformation_complexity_overfitting_Section_5.5.jsonl b/data/sampled_jsons/Generalizable_Origin_Identification_transformation_complexity_overfitting_Section_5.5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0645ec139c320ce9fec856c0e590648a8f9cccbe
--- /dev/null
+++ b/data/sampled_jsons/Generalizable_Origin_Identification_transformation_complexity_overfitting_Section_5.5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "3D tooth identification for forensic dentistry using deep", "date": "", "ddg_snippet": "Results Section presents the experimental outcomes of our approach, comparing its performance against existing methods, followed by a discussion of ...", "subpage_snippet": "", "source": "bmcoralhealth.biomedcentral.com", "link": "https://bmcoralhealth.biomedcentral.com/articles/10.1186/s12903-025-06017-y", "content": "Results Section presents the experimental outcomes of our approach, comparing its performance against existing methods, followed by a discussion of ..."}
+{"idx": 1, "title": "4.4. Model Selection, Underfitting, and Overfitting —", "date": "", "ddg_snippet": "Dog Breed Identification (ImageNet Dogs) on Kaggle ... Bidirectional Encoder Representations from Transformers (BERT)", "subpage_snippet": "", "source": "classic.d2l.ai", "link": "https://classic.d2l.ai/chapter_multilayer-perceptrons/underfit-overfit.html", "content": "Dog Breed Identification (ImageNet Dogs) on Kaggle ... Bidirectional Encoder Representations from Transformers (BERT)"}
+{"idx": 2, "title": "Identification of robust and generalizable biomarkers for", "date": "", "ddg_snippet": "Identifying subjects that will not respond to generalized lifestyle therapeutic interventions targeting the restructuring of gut microbiota is ...", "subpage_snippet": "", "source": "microbiomejournal.biomedcentral.com", "link": "https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01604-z", "content": "Identifying subjects that will not respond to generalized lifestyle therapeutic interventions targeting the restructuring of gut microbiota is ..."}
+{"idx": 3, "title": "Metabolomics and nutrient intake reveal metabolite–nutrient", "date": "", "ddg_snippet": "In view of the complexity of MetS as a multifactorial condition, integrating insights from multiple biological domains through advanced omics ...", "subpage_snippet": "", "source": "nutritionj.biomedcentral.com", "link": "https://nutritionj.biomedcentral.com/articles/10.1186/s12937-025-01189-3", "content": "In view of the complexity of MetS as a multifactorial condition, integrating insights from multiple biological domains through advanced omics ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 5, "title": "Target image detection algorithm of complex road scene based on", "date": "", "ddg_snippet": "Current object detection algorithms are not very good at handling complex road scenes, and public datasets do not always adequately represent city ...", "subpage_snippet": "", "source": "www.ijsmdo.org", "link": "https://www.ijsmdo.org/articles/smdo/full_html/2025/01/smdo240174/smdo240174.html", "content": "Current object detection algorithms are not very good at handling complex road scenes, and public datasets do not always adequately represent city ..."}
+{"idx": 6, "title": "TransformEEG: Towards Improving Model Generalizability in Deep", "date": "", "ddg_snippet": "To identify differences between healthy and PD individuals in EEG recordings, several automatic classification approaches have been investigated [ 9 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07622v1", "content": "To identify differences between healthy and PD individuals in EEG recordings, several automatic classification approaches have been investigated [ 9 ..."}
+{"idx": 7, "title": "LoLA-SpecViT: Local Attention SwiGLU Vision Transformer with", "date": "", "ddg_snippet": "To systematically address these identified bottlenecks while leveraging the proven strengths of transformer architectures, we introduce LoLA-SpecViT ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.17759v1", "content": "To systematically address these identified bottlenecks while leveraging the proven strengths of transformer architectures, we introduce LoLA-SpecViT ..."}
+{"idx": 8, "title": "Machine Learning Approaches in Software Vulnerability", "date": "", "ddg_snippet": "To identify relevant studies for this systematic review, an automated search was conducted across four academic digital repositories: the ACM Digital ...", "subpage_snippet": "", "source": "www.researchsquare.com", "link": "https://www.researchsquare.com/article/rs-5975490/v1", "content": "To identify relevant studies for this systematic review, an automated search was conducted across four academic digital repositories: the ACM Digital ..."}
+{"idx": 9, "title": "Connectome-wide brain signature during fast-food advertisement", "date": "", "ddg_snippet": "Here, 121 adolescents [14.1 ± 1.0 years; 50.4% female; body mass index (BMI): 23.4 ± 4.8; 71.9% White] completed both a baseline fMRI ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/scan/article/20/1/nsaf018/8063749", "content": "Here, 121 adolescents [14.1 ± 1.0 years; 50.4% female; body mass index (BMI): 23.4 ± 4.8; 71.9% White] completed both a baseline fMRI ..."}
diff --git a/data/sampled_jsons/Generalization_Spectrum_Gap_BOX-GEOMETRIC_recommendation_year_2023.jsonl b/data/sampled_jsons/Generalization_Spectrum_Gap_BOX-GEOMETRIC_recommendation_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..561af4b1c79c45c5a692d23feb280ff6fbe20b0d
--- /dev/null
+++ b/data/sampled_jsons/Generalization_Spectrum_Gap_BOX-GEOMETRIC_recommendation_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gaps Archives - Tech-Clarity", "date": "", "ddg_snippet": "... Product Value Gap (1) ... Geometric Search (1)", "subpage_snippet": "", "source": "tech-clarity.com", "link": "https://tech-clarity.com/tag/gaps", "content": "... Product Value Gap (1) ... Geometric Search (1)"}
+{"idx": 1, "title": "Fill IoT Gaps with Low-Code (guest post) - Tech-Clarity", "date": "", "ddg_snippet": "Jim Brown's guest post on Siemen's blog shows how companies can use low-code applications to help fill digital transformation gaps .", "subpage_snippet": "", "source": "tech-clarity.com", "link": "https://tech-clarity.com/fill-iot-gaps-with-low-code-guest-post/10360", "content": "Jim Brown's guest post on Siemen's blog shows how companies can use low-code applications to help fill digital transformation gaps ."}
+{"idx": 2, "title": "Product Information Gap Archives - Tech-Clarity", "date": "", "ddg_snippet": "... Product Value Gap (1) ... Geometric Search (1)", "subpage_snippet": "", "source": "tech-clarity.com", "link": "https://tech-clarity.com/tag/product-information-gap", "content": "... Product Value Gap (1) ... Geometric Search (1)"}
+{"idx": 3, "title": "Product Value Gap Archives - Tech-Clarity", "date": "", "ddg_snippet": "... IoT Gaps (1) ... Product Information Gap (1) ... STEM Gap (1)", "subpage_snippet": "", "source": "tech-clarity.com", "link": "https://tech-clarity.com/tag/product-value-gap", "content": "... IoT Gaps (1) ... Product Information Gap (1) ... STEM Gap (1)"}
+{"idx": 4, "title": "Engineering Skills Gap in the Age of Digitalization -", "date": "", "ddg_snippet": "How Academia Can Close the Engineering Skills Gap shares recommendations to help prepare today's engineering students for the real world.", "subpage_snippet": "", "source": "tech-clarity.com", "link": "https://tech-clarity.com/engineering-skills-gap/9581", "content": "How Academia Can Close the Engineering Skills Gap shares recommendations to help prepare today's engineering students for the real world."}
+{"idx": 5, "title": "General and Geometric Design", "date": "", "ddg_snippet": "This report draws on literature and crash data analysis to identify and quantify geometric road design elements which contribute to casualty crash ...", "subpage_snippet": "", "source": "www.lpcb.org", "link": "http://www.lpcb.org/index.php/document-library/road-pavement-design-and-performance/general-and-geometric-design", "content": "This report draws on literature and crash data analysis to identify and quantify geometric road design elements which contribute to casualty crash ..."}
+{"idx": 6, "title": "The Gaseous Prototype (GaP): a GanESS demonstrator.", "date": "", "ddg_snippet": "The Gaseous Prototype ( GaP ) has been built to characterize the technique at the few-keV energy regime and to understand various aspects related to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11324v1", "content": "The Gaseous Prototype ( GaP ) has been built to characterize the technique at the few-keV energy regime and to understand various aspects related to ..."}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "A NON-PARAMETRIC REGRESSION VIEWPOINT : GENERALIZATION OF OVERPARAMETRIZED DEEP RELU NETWORK UNDER NOISY OBSERVATIONS", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "A NON-PARAMETRIC REGRESSION VIEWPOINT : GENERALIZATION OF OVERPARAMETRIZED DEEP RELU NETWORK UNDER NOISY OBSERVATIONS"}
+{"idx": 8, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Improving generalization by controlling ... Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Improving generalization by controlling ... Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms"}
+{"idx": 9, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Improving generalization by controlling ... Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Improving generalization by controlling ... Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms"}
diff --git a/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Rosenbrock_function.jsonl b/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Rosenbrock_function.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..58cbb3f51914ed639a952bbd684b3eb7e220ba91
--- /dev/null
+++ b/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Rosenbrock_function.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Global Optimization with A Power-Transformed Objective and ...", "date": "", "ddg_snippet": "Abstract We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f to obtain f N, and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on f, for any δ> 0, we prove that with a sufficiently large power N δ, this ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.05204v1", "content": "Abstract We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f to obtain f N, and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on f, for any δ> 0, we prove that with a sufficiently large power N δ, this ..."}
+{"idx": 1, "title": "Global Optimization with a Power-Transformed Objective and ...", "date": "", "ddg_snippet": "Abstract We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponen-tial) power -N transformation to the not necessar-Rd ! ily differentiable objective f : and get R fN, and (2) optimize the Gaussian -smoothed fN with stochastic approximations. Under mild con-ditions on f ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6ojzpDczIY", "content": "Abstract We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponen-tial) power -N transformation to the not necessar-Rd ! ily differentiable objective f : and get R fN, and (2) optimize the Gaussian -smoothed fN with stochastic approximations. Under mild con-ditions on f ..."}
+{"idx": 2, "title": "dblp: Global Optimization with A Power-Transformed Objective ...", "date": "", "ddg_snippet": "Jan 26, 2025 · Bibliographic details on Global Optimization with A Power - Transformed Objective and Gaussian Smoothing .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2412-05204", "content": "Jan 26, 2025 · Bibliographic details on Global Optimization with A Power - Transformed Objective and Gaussian Smoothing ."}
+{"idx": 3, "title": "Global Optimization with a Power-Transformed Objective and ...", "date": "", "ddg_snippet": "Abstract: We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not necessarily differentiable objective f : R d → R and get f N , and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chen-research/GS-PowerTransform", "content": "Abstract: We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not necessarily differentiable objective f : R d → R and get f N , and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on ..."}
+{"idx": 4, "title": "Global Optimization with A Power-Transformed Objective and ... Global Optimization with a Power-Transformed Objective and ... Global Optimization with A Power-Transformed Objective and ...", "date": "", "ddg_snippet": "Dec 6, 2024 · We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f and get f_N, and (2) optimize the Gaussian -smoothed f_N with stochastic approximations. Under mild conditions on f, for any \\delta>0, we prove that with a sufficiently large power N_\\delta, this method ... We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves the following continuous optimization problems max f (x), x∈S⊂Rd (1) where S is a compact set and f : S → R is a continuous and possibly non-concave function with a unique global maximum point x∗ = arg maxx∈S f (x). We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power -$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian -smoothed $f_N$ with stochastic approximations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.05204", "content": "Dec 6, 2024 · We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f and get f_N, and (2) optimize the Gaussian -smoothed f_N with stochastic approximations. Under mild conditions on f, for any \\delta>0, we prove that with a sufficiently large power N_\\delta, this method ... We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves the following continuous optimization problems max f (x), x∈S⊂Rd (1) where S is a compact set and f : S → R is a continuous and possibly non-concave function with a unique global maximum point x∗ = arg maxx∈S f (x). We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power -$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian -smoothed $f_N$ with stochastic approximations."}
+{"idx": 5, "title": "Global Optimization with a Power-Transformed Objective and ...", "date": "", "ddg_snippet": "We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves the following continuous optimization problems max f (x), x∈S⊂Rd (1) where S is a compact set and f : S → R is a continuous and possibly non-concave function with a unique global maximum point x∗ = arg maxx∈S f (x).", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46360_JdTUNKo.pdf", "content": "We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves the following continuous optimization problems max f (x), x∈S⊂Rd (1) where S is a compact set and f : S → R is a continuous and possibly non-concave function with a unique global maximum point x∗ = arg maxx∈S f (x)."}
+{"idx": 6, "title": "Global Optimization with A Power-Transformed Objective and ...", "date": "", "ddg_snippet": "We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power -$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian -smoothed $f_N$ with stochastic approximations.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv241205204X/abstract", "content": "We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power -$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian -smoothed $f_N$ with stochastic approximations."}
+{"idx": 7, "title": "Function definitions – COmparing Continuous Optimizers", "date": "", "ddg_snippet": "... objective is truly convex-quadratic with a condition number of 1, the second objective is only globally quadratic with smooth local irregularities and ...", "subpage_snippet": "", "source": "coco-platform.org", "link": "https://coco-platform.org/testsuites/bbob-biobj/def.html", "content": "... objective is truly convex-quadratic with a condition number of 1, the second objective is only globally quadratic with smooth local irregularities and ..."}
+{"idx": 8, "title": "Visualizations of problem landscapes – COmparing Continuous", "date": "", "ddg_snippet": "... objective is truly convex-quadratic with a condition number of 1, the second objective is only globally quadratic with smooth local irregularities and ...", "subpage_snippet": "", "source": "coco-platform.org", "link": "https://coco-platform.org/testsuites/bbob-biobj/viz.html", "content": "... objective is truly convex-quadratic with a condition number of 1, the second objective is only globally quadratic with smooth local irregularities and ..."}
+{"idx": 9, "title": "Sampling from Gaussian Processes: A Tutorial and Applications", "date": "", "ddg_snippet": "However, their high cost often limits their applications in two critical tasks: global sensitivity analysis (GSA) and optimization .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14746v1", "content": "However, their high cost often limits their applications in two critical tasks: global sensitivity analysis (GSA) and optimization ."}
diff --git a/data/sampled_jsons/Going_Deeper_Locally_Differentially_Private_Graph_Neural_Networks_Section_5_Related_work_multi-bit_m_year_2024.jsonl b/data/sampled_jsons/Going_Deeper_Locally_Differentially_Private_Graph_Neural_Networks_Section_5_Related_work_multi-bit_m_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e108f465596634459614169c889129018079e4fd
--- /dev/null
+++ b/data/sampled_jsons/Going_Deeper_Locally_Differentially_Private_Graph_Neural_Networks_Section_5_Related_work_multi-bit_m_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "In this section , we begin with a theoretical analysis of prior work on locally differentially private graph neural networks (LDPGNN) in Sec. 3.1, identifying the key factors limit-ing their utility.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2aKHuXdr7Q", "content": "In this section , we begin with a theoretical analysis of prior work on locally differentially private graph neural networks (LDPGNN) in Sec. 3.1, identifying the key factors limit-ing their utility."}
+{"idx": 1, "title": "Local Differential Privacy in Graph Neural Networks: a Reconstruction ...", "date": "", "ddg_snippet": "They propose an LDP protocol called multi-bit mechanism to perturb node features by extending the 1-bit mechanism [8] to multidimen-sional features. The multi-bit mechanism randomly selects a subset of features for each user, transforms each selected feature value to either 1 or -1, and indiscriminately reports the value 0 for the remaining ones.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/pdf/10.1137/1.9781611978032.1", "content": "They propose an LDP protocol called multi-bit mechanism to perturb node features by extending the 1-bit mechanism [8] to multidimen-sional features. The multi-bit mechanism randomly selects a subset of features for each user, transforms each selected feature value to either 1 or -1, and indiscriminately reports the value 0 for the remaining ones."}
+{"idx": 2, "title": "GitHub - tamaramueller/DP-GNNs: Differentially private graph neural ...", "date": "", "ddg_snippet": "However, the differentially private training of GNNs has so far remained under-explored due to the challenges presented by the intrinsic structural connectivity of graphs . In this work , we introduce a framework for differential private graph -level classification.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tamaramueller/DP-GNNs", "content": "However, the differentially private training of GNNs has so far remained under-explored due to the challenges presented by the intrinsic structural connectivity of graphs . In this work , we introduce a framework for differential private graph -level classification."}
+{"idx": 3, "title": "Differentially private graph neural networks for graph classification ...", "date": "", "ddg_snippet": "Abstract Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular structure elucidation, have demonstrated superior performance in processing graph data.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0957417424026654", "content": "Abstract Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular structure elucidation, have demonstrated superior performance in processing graph data."}
+{"idx": 4, "title": "Node-Level Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data and computing node-level representations via aggregation of information from the neighborhood of each node. However, this aggregation implies an increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy-preserving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.15521", "content": "Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data and computing node-level representations via aggregation of information from the neighborhood of each node. However, this aggregation implies an increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy-preserving ..."}
+{"idx": 5, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks ...", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal information or variables related to individuals, learning from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these concerns, they ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/2aKHuXdr7Q@OpenReview", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal information or variables related to individuals, learning from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these concerns, they ..."}
+{"idx": 6, "title": "Locally Private Graph Neural Networks - ACM Digital Library", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work , we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism , called the multi-bit mechanism , for high-dimensional feature perturbation. We also ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3460120.3484565", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work , we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism , called the multi-bit mechanism , for high-dimensional feature perturbation. We also ..."}
+{"idx": 7, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Poster Going Deeper into Locally Differentially Private Graph Neural Networks Longzhu He · Chaozhuo Li · Peng Tang · Sen Su East Exhibition Hall A-B #E-905", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46579", "content": "Poster Going Deeper into Locally Differentially Private Graph Neural Networks Longzhu He · Chaozhuo Li · Peng Tang · Sen Su East Exhibition Hall A-B #E-905"}
+{"idx": 8, "title": "[2006.05535] Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human- related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human- related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ..."}
+{"idx": 9, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "The paper introduces UPGNET, a utility-enhanced framework for locally differentially private (LDP) graph learning. It addresses privacy challenges in Graph Neural Networks (GNNs) by proposing a three-stage pipeline to generalize LDP protocols for node feature perturbation. Key contributions include identifying two critical factors influencing estimation error: feature dimension and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2aKHuXdr7Q", "content": "The paper introduces UPGNET, a utility-enhanced framework for locally differentially private (LDP) graph learning. It addresses privacy challenges in Graph Neural Networks (GNNs) by proposing a three-stage pipeline to generalize LDP protocols for node feature perturbation. Key contributions include identifying two critical factors influencing estimation error: feature dimension and ..."}
diff --git a/data/sampled_jsons/Great_Models_Think_Alike_Section_4_mechanism_negative_correlation_similarity_weak-to-strong.jsonl b/data/sampled_jsons/Great_Models_Think_Alike_Section_4_mechanism_negative_correlation_similarity_weak-to-strong.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..87b148bdf12d7f76b5a649864ab04321e97cda64
--- /dev/null
+++ b/data/sampled_jsons/Great_Models_Think_Alike_Section_4_mechanism_negative_correlation_similarity_weak-to-strong.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "\" Great Models Think Alike and this Undermines AI Oversight\"", "date": "", "ddg_snippet": "→ Weak - to - strong generalization, training a strong student model on annotations from a weaker supervisor, yields greater performance gains when the supervisor and student models are less similar .", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/great-models-think-alike-and-this", "content": "→ Weak - to - strong generalization, training a strong student model on annotations from a weaker supervisor, yields greater performance gains when the supervisor and student models are less similar ."}
+{"idx": 1, "title": "Great Models Think Alike and this Undermines AI... | OpenReview", "date": "", "ddg_snippet": "Keywords : Model Similarity , AI Oversight, LLM as a Judge, Weak to Strong Generalization, Model Differences. TL;DR : We find model similarity has negative effects on using LMs to judge or train other models ; unfortunately, LMs are getting similar with increasing capabilities.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=sYB0Y0hOxi", "content": "Keywords : Model Similarity , AI Oversight, LLM as a Judge, Weak to Strong Generalization, Model Differences. TL;DR : We find model similarity has negative effects on using LMs to judge or train other models ; unfortunately, LMs are getting similar with increasing capabilities."}
+{"idx": 2, "title": "The AI oversight trap: When smarter models make the same mistakes", "date": "", "ddg_snippet": "Weak - to - strong generalization: When AI learns from AI.If AI models continue to be trained on similar datasets, use the same architectures, and rely on AI oversight, they risk developing systemic blind spots.", "subpage_snippet": "", "source": "www.devdiscourse.com", "link": "https://www.devdiscourse.com/article/technology/3256561-the-ai-oversight-trap-when-smarter-models-make-the-same-mistakes", "content": "Weak - to - strong generalization: When AI learns from AI.If AI models continue to be trained on similar datasets, use the same architectures, and rely on AI oversight, they risk developing systemic blind spots."}
+{"idx": 3, "title": "similarities - Correlation and Similarity - Cross Validated", "date": "", "ddg_snippet": "Both correlation and cosine similarity /distance are measures of similarity /distance.Thanks! But how should I interpret when two probability vectors have high cosine similarity (e.g., >0.9) but negative Pearson correlation (around -.2)?", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/296393/correlation-and-similarity", "content": "Both correlation and cosine similarity /distance are measures of similarity /distance.Thanks! But how should I interpret when two probability vectors have high cosine similarity (e.g., >0.9) but negative Pearson correlation (around -.2)?"}
+{"idx": 4, "title": "Great Models Think Alike and this Undermines AI Oversight - AI for...", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results.", "subpage_snippet": "", "source": "ai-search.io", "link": "https://ai-search.io/papers/great-models-think-alike-and-this-undermines-ai-oversight", "content": "We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results."}
+{"idx": 5, "title": "Language Models Tend To Favor Other LLMs That... - Pro AI Tools", "date": "", "ddg_snippet": "Great models think alike – according to the researchers, this makes effective external control by other AI systems more difficult. |The team also explored what happens when stronger models learn from content generated by weaker ones.", "subpage_snippet": "", "source": "proaitools.tech", "link": "https://proaitools.tech/language-models-tend-to-favor-other-llms-that-make-mistakes-similar-to-their-own/", "content": "Great models think alike – according to the researchers, this makes effective external control by other AI systems more difficult. |The team also explored what happens when stronger models learn from content generated by weaker ones."}
+{"idx": 6, "title": "[Literature Review] Great Models Think Alike and this Undermines AI...", "date": "", "ddg_snippet": "Model Similarity . Weak - to - Strong Generalization. Affinity Bias.This similarity raises significant concerns regarding safety and the effectiveness of model oversight mechanisms .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/great-models-think-alike-and-this-undermines-ai-oversight", "content": "Model Similarity . Weak - to - Strong Generalization. Affinity Bias.This similarity raises significant concerns regarding safety and the effectiveness of model oversight mechanisms ."}
+{"idx": 7, "title": "Similarity of Neural Networks: A Survey of Functional and...", "date": "", "ddg_snippet": "To model the similarity of neurons that is needed to construct the graphs, they proposed four different variants of RSMs that either consider pure neuron activations or also gradients with respect to neuron activations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.06329", "content": "To model the similarity of neurons that is needed to construct the graphs, they proposed four different variants of RSMs that either consider pure neuron activations or also gradients with respect to neuron activations."}
+{"idx": 8, "title": "What is Correlational Research? Types and... | Analytics Steps", "date": "", "ddg_snippet": "Correlational research is a great way to quickly collect data from natural situations. This allows you to apply your results to real-life problems in a way that is externally legitimate.", "subpage_snippet": "", "source": "www.analyticssteps.com", "link": "https://www.analyticssteps.com/blogs/what-correlational-research-types-and-characteristics", "content": "Correlational research is a great way to quickly collect data from natural situations. This allows you to apply your results to real-life problems in a way that is externally legitimate."}
+{"idx": 9, "title": "Thread By @TheAITimeline - This week's top AI/ML research...", "date": "", "ddg_snippet": "Weak - to - Strong Diffusion with Reflection. Great Models Think Alike and this Undermines AI Oversight.", "subpage_snippet": "", "source": "unrollnow.com", "link": "https://unrollnow.com/status/1888720075793793121", "content": "Weak - to - Strong Diffusion with Reflection. Great Models Think Alike and this Undermines AI Oversight."}
diff --git a/data/sampled_jsons/Great_Models_Think_Alike_Undermines_AI_Oversight_Equation_2_Observed_Agreement_c_obs_year_2024.jsonl b/data/sampled_jsons/Great_Models_Think_Alike_Undermines_AI_Oversight_Equation_2_Observed_Agreement_c_obs_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..50682e82e34678cca2993ad3a04a33bc7b71ab91
--- /dev/null
+++ b/data/sampled_jsons/Great_Models_Think_Alike_Undermines_AI_Oversight_Equation_2_Observed_Agreement_c_obs_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM ..."}
+{"idx": 1, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "AI oversight is gaining popularity as capabilities increase. The above results show the benefits of diverse models for AI oversight – less similarity between models reduces bias in LLM-as-a-judge, and also leads to greater gains when training on LM annotations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v1", "content": "AI oversight is gaining popularity as capabilities increase. The above results show the benefits of diverse models for AI oversight – less similarity between models reduces bias in LLM-as-a-judge, and also leads to greater gains when training on LM annotations."}
+{"idx": 2, "title": "[Literature Review] Great Models Think Alike and this Undermines AI ...", "date": "", "ddg_snippet": "Observed Error Overlap ( cobs ): The fraction of samples on which both models either agree or disagree. Expected Error Overlap (cexp): The chance agreement expected from independent models given their accuracies. The CAPA metric is presented through the equation", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/great-models-think-alike-and-this-undermines-ai-oversight", "content": "Observed Error Overlap ( cobs ): The fraction of samples on which both models either agree or disagree. Expected Error Overlap (cexp): The chance agreement expected from independent models given their accuracies. The CAPA metric is presented through the equation"}
+{"idx": 3, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on overlap in model mistakes.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Great-Models-Think-Alike-and-this-Undermines-AI-Oversight-1f8d8bac-95d4-4413-8064-c3afceca72ae", "content": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on overlap in model mistakes."}
+{"idx": 4, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "AI oversight , where one AI system monitors or evaluates another. If all models think alike , they may share the same blindspots and biases, making them poor choices for checking each other's work.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/great-models-think-alike-this-undermines-ai", "content": "AI oversight , where one AI system monitors or evaluates another. If all models think alike , they may share the same blindspots and biases, making them poor choices for checking each other's work."}
+{"idx": 5, "title": "The AI oversight trap: When smarter models make the same mistakes", "date": "", "ddg_snippet": "Recent research suggests that AI models tend to think alike , which could lead to systemic risks in AI oversight .Submitted in arXiv (2025), the study introduces a novel metric, Chance Adjusted Probabilistic Agreement (CAPA), to measure functional similarity between LMs.", "subpage_snippet": "", "source": "www.devdiscourse.com", "link": "https://www.devdiscourse.com/article/technology/3256561-the-ai-oversight-trap-when-smarter-models-make-the-same-mistakes", "content": "Recent research suggests that AI models tend to think alike , which could lead to systemic risks in AI oversight .Submitted in arXiv (2025), the study introduces a novel metric, Chance Adjusted Probabilistic Agreement (CAPA), to measure functional similarity between LMs."}
+{"idx": 6, "title": "Similarity affects Oversight", "date": "", "ddg_snippet": "Great Models Think Alike and this Undermines AI Oversight .We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on overlap in model mistakes.", "subpage_snippet": "", "source": "model-similarity.github.io", "link": "https://model-similarity.github.io/", "content": "Great Models Think Alike and this Undermines AI Oversight .We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on overlap in model mistakes."}
+{"idx": 7, "title": "Great Models Think Alike and this Undermines AI Oversight - AI for...", "date": "", "ddg_snippet": "This paper talks about how the increasing similarity between advanced AI language models can create problems for using AI to oversee other AI systems, a concept known as ' AI Oversight '.", "subpage_snippet": "", "source": "ai-search.io", "link": "https://ai-search.io/papers/great-models-think-alike-and-this-undermines-ai-oversight", "content": "This paper talks about how the increasing similarity between advanced AI language models can create problems for using AI to oversee other AI systems, a concept known as ' AI Oversight '."}
+{"idx": 8, "title": "If you are interested in using AI oversight , this recent preprint by Goel", "date": "", "ddg_snippet": "In the paper titled \" Great Models Think Alike and this Undermines AI Oversight \" the authors introduce a new large language model similarity metric: \"Chance Adjusted Probabilistic Agreement \". With the new similarity metric, the authors perform an analysis over a large range of open...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/lcrosenbaum_if-you-are-interested-in-using-ai-oversight-activity-7298619671321149441-6Oef", "content": "In the paper titled \" Great Models Think Alike and this Undermines AI Oversight \" the authors introduce a new large language model similarity metric: \"Chance Adjusted Probabilistic Agreement \". With the new similarity metric, the authors perform an analysis over a large range of open..."}
+{"idx": 9, "title": "Great Models Think Alike and this Undermines AI Oversight ...", "date": "", "ddg_snippet": "The site owner hides the web page description.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/1ill18f/great_models_think_alike_and_this_undermines_ai/", "content": "The site owner hides the web page description."}
diff --git a/data/sampled_jsons/Gumiho_A_Hybrid_Architecture_MT-Bench_temperature_speedup_Eagle-2_year_2024.jsonl b/data/sampled_jsons/Gumiho_A_Hybrid_Architecture_MT-Bench_temperature_speedup_Eagle-2_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cb1acb010af6ccd8523c77f95958ca804f4ec3ab
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_A_Hybrid_Architecture_MT-Bench_temperature_speedup_Eagle-2_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "At temperature 0, Gumiho outperforms EAGLE - 2 by 11.7% on LLaMA2 70B and 15.8% on LLaMA3 70B. This substantial improvement is primarily attributed to enhancements in τ and a reduction in draft time. MT - Bench Speedup τ.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135", "content": "At temperature 0, Gumiho outperforms EAGLE - 2 by 11.7% on LLaMA2 70B and 15.8% on LLaMA3 70B. This substantial improvement is primarily attributed to enhancements in τ and a reduction in draft time. MT - Bench Speedup τ."}
+{"idx": 1, "title": "GitHub - AMD-AIG-AIMA/ Gumiho : Official Implementation of...", "date": "", "ddg_snippet": "Model. Method. MT - Bench Speedup .Acknowledgements. This project is built upon Eagle , with additional modifications and extensions. About. Official Implementation of \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding\" (ICML'25).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AIG-AIMA/Gumiho", "content": "Model. Method. MT - Bench Speedup .Acknowledgements. This project is built upon Eagle , with additional modifications and extensions. About. Official Implementation of \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding\" (ICML'25)."}
+{"idx": 2, "title": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens... | alphaXiv", "date": "", "ddg_snippet": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.10135v1", "content": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads."}
+{"idx": 3, "title": "Gumiho - a amd Collection", "date": "", "ddg_snippet": "Official Model Parameters for \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding\".", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/collections/amd/gumiho-684a3b7cbbe86ab23b393e9f", "content": "Official Model Parameters for \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding\"."}
+{"idx": 4, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in", "date": "", "ddg_snippet": "... we propose Gumiho , a hybrid model that prioritizes the initial positions in the draft sequence, combining both sequential and parallel architectures ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10135v2", "content": "... we propose Gumiho , a hybrid model that prioritizes the initial positions in the draft sequence, combining both sequential and parallel architectures ..."}
+{"idx": 5, "title": "Org profile for AMD on Hugging Face, the AI community building the...", "date": "", "ddg_snippet": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding. Paper • 2503.10135 • Published Mar 13.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/amd/collections", "content": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding. Paper • 2503.10135 • Published Mar 13."}
+{"idx": 6, "title": "Figure 4. Ablation Study on serial head depth. The serial head is a...", "date": "", "ddg_snippet": "Speculation, Hybrid and Architecture | ResearchGate, the professional network for scientists.The serial head is a Transformer model, whose depth represents the number of layers within the Transformer architecture .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Ablation-Study-on-serial-head-depth-The-serial-head-is-a-Transformer-model-whose-depth_fig1_389821466", "content": "Speculation, Hybrid and Architecture | ResearchGate, the professional network for scientists.The serial head is a Transformer model, whose depth represents the number of layers within the Transformer architecture ."}
+{"idx": 7, "title": "A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation...", "date": "", "ddg_snippet": "Related articles. [1] Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/a-token-is-worth-over-1-000-tokens-efficient-knowledge-distillation-through-low-rank-clone/1131559626442014785-108592", "content": "Related articles. [1] Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding."}
+{"idx": 8, "title": "openreview.net/profile?id=~Haiduo_Huang1", "date": "", "ddg_snippet": "LLM distillation and fine-tune. 2023 – 2025. Model architecture design. Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Haiduo_Huang1", "content": "LLM distillation and fine-tune. 2023 – 2025. Model architecture design. Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding."}
+{"idx": 9, "title": "LogitSpec: Accelerating Retrieval-based Speculative Decoding", "date": "", "ddg_snippet": "Extensive experiments on a wide range of text generation benchmarks demonstrate that LogitSpec can achieve up to 2 .61 × \\times × speedup and 3.28 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01449v1", "content": "Extensive experiments on a wide range of text generation benchmarks demonstrate that LogitSpec can achieve up to 2 .61 × \\times × speedup and 3.28 ..."}
diff --git a/data/sampled_jsons/Gumiho_Hybrid_Architecture_to_Prioritize_Early_Tokens_Speculative_Decoding_PDF_Table_3_Figure_4_abla_year_2024.jsonl b/data/sampled_jsons/Gumiho_Hybrid_Architecture_to_Prioritize_Early_Tokens_Speculative_Decoding_PDF_Table_3_Figure_4_abla_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d7515056a6d3375efc76ceb5013c43870b8db532
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_Hybrid_Architecture_to_Prioritize_Early_Tokens_Speculative_Decoding_PDF_Table_3_Figure_4_abla_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. For later to-kens , we utilize multiple lightweight MLP heads operating in parallel to enhance eficiency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135v1", "content": "Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. For later to-kens , we utilize multiple lightweight MLP heads operating in parallel to enhance eficiency."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ..."}
+{"idx": 2, "title": "Gumiho - a amd Collection - Hugging Face", "date": "", "ddg_snippet": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Paper • 2503.10135 •Published Mar 13 Upvote - Share collection View history Collection guide Browse collections", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/amd/gumiho-684a3b7cbbe86ab23b393e9f", "content": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Paper • 2503.10135 •Published Mar 13 Upvote - Share collection View history Collection guide Browse collections"}
+{"idx": 3, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0ObGn4e1IS", "content": "This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ..."}
+{"idx": 4, "title": "(PDF) Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389821466_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding", "content": "Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 5, "title": "dblp: Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Bibliographic details on Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2503-10135", "content": "Bibliographic details on Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding ."}
+{"idx": 6, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10135", "content": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 7, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "To address this, Gumiho proposes an innovative hybrid model that integrates both serial and parallel heads. 🔍 Key Features: - Early Tokens : Utilizes an advanced Transformer architecture in a ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/abdullah-kasri_gumiho-a-hybrid-architecture-to-prioritize-activity-7306519551876161536-HoDU", "content": "To address this, Gumiho proposes an innovative hybrid model that integrates both serial and parallel heads. 🔍 Key Features: - Early Tokens : Utilizes an advanced Transformer architecture in a ..."}
+{"idx": 8, "title": "GitHub - hemingkx/SpeculativeDecodingPapers: Must-read papers and ...", "date": "", "ddg_snippet": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Jinze Li, Yixing Xu, Haiduo Huang, Xuanwu Yin, Dong Li, Edith C.H. Ngai, Emad Barsoum. [ pdf ], 2025.03.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hemingkx/SpeculativeDecodingPapers", "content": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Jinze Li, Yixing Xu, Haiduo Huang, Xuanwu Yin, Dong Li, Edith C.H. Ngai, Emad Barsoum. [ pdf ], 2025.03."}
+{"idx": 9, "title": "Accelerating LLMs with Smarter Token Prioritization", "date": "", "ddg_snippet": "Gumiho : A hybrid approach to optimize speculative decoding Gumiho introduces a novel hybrid architecture that intelligently prioritizes early tokens in speculative decoding to significantly accelerate LLM inference.", "subpage_snippet": "", "source": "www.zerna.io", "link": "https://www.zerna.io/page/engineering/presentation_set/engineering-llm-research/presentation/engineering-model-optimization/slide/engineering-paper-2503_10135", "content": "Gumiho : A hybrid approach to optimize speculative decoding Gumiho introduces a novel hybrid architecture that intelligently prioritizes early tokens in speculative decoding to significantly accelerate LLM inference."}
diff --git a/data/sampled_jsons/Gumiho_Table_3_Ablation_study_on_the_wall_time_1st_serial_head_2.80ms.jsonl b/data/sampled_jsons/Gumiho_Table_3_Ablation_study_on_the_wall_time_1st_serial_head_2.80ms.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..17ae6fc3a908594e1aa56f2599cbc01ba7efd6d7
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_Table_3_Ablation_study_on_the_wall_time_1st_serial_head_2.80ms.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "Kumiho - Wikipedia", "date": "", "ddg_snippet": "A kumiho or gumiho (Korean: 구미호; Hanja: 九尾狐; lit. nine-tailed fox ) is a creature that appears in the folktales of East Asia and legends of Korea. It is similar to the Chinese jiuweihu, the Japanese kitsune and the Vietnamese hồ ly tinh.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Kumiho", "content": "A kumiho or gumiho (Korean: 구미호; Hanja: 九尾狐; lit. nine-tailed fox ) is a creature that appears in the folktales of East Asia and legends of Korea. It is similar to the Chinese jiuweihu, the Japanese kitsune and the Vietnamese hồ ly tinh."}
+{"idx": 1, "title": "Top 7 Gumiho K-Dramas - tvshowpilot.com", "date": "", "ddg_snippet": "Dec 27, 2023 · Gumihos (구미호) are mythical fox-like creatures from South Korean folklore. K-dramas often portray gumihos as nine-tailed fox spirits with unearthly beauty, intelligence, and supernatural abilities, such as shape-shifting into human form. Notable elements often include the gumiho ’s ability to manipulate illusions, their mystical powers, and the moral dilemmas surrounding their pursuit of ...", "subpage_snippet": "", "source": "tvshowpilot.com", "link": "https://tvshowpilot.com/fun-posts/best-korean-gumiho-dramas/", "content": "Dec 27, 2023 · Gumihos (구미호) are mythical fox-like creatures from South Korean folklore. K-dramas often portray gumihos as nine-tailed fox spirits with unearthly beauty, intelligence, and supernatural abilities, such as shape-shifting into human form. Notable elements often include the gumiho ’s ability to manipulate illusions, their mystical powers, and the moral dilemmas surrounding their pursuit of ..."}
+{"idx": 2, "title": "Dramas with Gumiho | Kumiho (12 shows) - MyDramaList", "date": "", "ddg_snippet": "Feb 26, 2023 · Dramas with Gumiho | Kumiho medical_julie Feb 26, 2023 12 Titles 2 Loves 0% Watched Report Sort By: Author's Order", "subpage_snippet": "", "source": "mydramalist.com", "link": "https://mydramalist.com/list/1Nk5R2lL", "content": "Feb 26, 2023 · Dramas with Gumiho | Kumiho medical_julie Feb 26, 2023 12 Titles 2 Loves 0% Watched Report Sort By: Author's Order"}
+{"idx": 3, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Build-ing on this insight, we propose Gumiho , a hy-brid model combining serial and parallel heads . Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135", "content": "Build-ing on this insight, we propose Gumiho , a hy-brid model combining serial and parallel heads . Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 4, "title": "Tale of the Nine Tailed (TV Series 2020– ) - IMDb Kumiho - Wikipedia Nine-Tailed Fox: A Legend of Korean Mythology What is Gumiho? 구미호는 뭐예요? - My Korean Scribbles", "date": "", "ddg_snippet": "Tale of the Nine Tailed: With Lee Dong-wook, Kim Bum, Kim Yong-ji, Hwang Hee. An urban dark fantasy drama about a gumiho who settles into the city and a producer who is after it. A kumiho or gumiho (Korean: 구미호; Hanja: 九尾狐; lit. nine-tailed fox ) is a creature that appears in the folktales of East Asia and legends of Korea. It is similar to the Chinese jiuweihu, the Japanese kitsune and the Vietnamese hồ ly tinh. Jul 12, 2024 · The gumiho is more than just a mythical creature; it’s a symbol of the dark side of human nature and the constant struggle between good and evil. The Nine-Tailed Fox ’s Origins: From Beast to Legend The origins of the nine-tailed fox can be traced back to ancient Korean beliefs about the natural world. Legend of Gumiho Have you ever heard of the folklore or the legend of the Korean gumiho (kumiho)? The word '구미호 ( gumiho )' is literally translated as ' nine-tailed fox '.", "subpage_snippet": "", "source": "www.imdb.com", "link": "https://www.imdb.com/title/tt12879418/", "content": "Tale of the Nine Tailed: With Lee Dong-wook, Kim Bum, Kim Yong-ji, Hwang Hee. An urban dark fantasy drama about a gumiho who settles into the city and a producer who is after it. A kumiho or gumiho (Korean: 구미호; Hanja: 九尾狐; lit. nine-tailed fox ) is a creature that appears in the folktales of East Asia and legends of Korea. It is similar to the Chinese jiuweihu, the Japanese kitsune and the Vietnamese hồ ly tinh. Jul 12, 2024 · The gumiho is more than just a mythical creature; it’s a symbol of the dark side of human nature and the constant struggle between good and evil. The Nine-Tailed Fox ’s Origins: From Beast to Legend The origins of the nine-tailed fox can be traced back to ancient Korean beliefs about the natural world. Legend of Gumiho Have you ever heard of the folklore or the legend of the Korean gumiho (kumiho)? The word '구미호 ( gumiho )' is literally translated as ' nine-tailed fox '."}
+{"idx": 5, "title": "What is Gumiho? 구미호는 뭐예요? - My Korean Scribbles", "date": "", "ddg_snippet": "Legend of Gumiho Have you ever heard of the folklore or the legend of the Korean gumiho (kumiho)? The word '구미호 ( gumiho )' is literally translated as ' nine-tailed fox '.", "subpage_snippet": "", "source": "www.mykoreanscribbles.com", "link": "https://www.mykoreanscribbles.com/2021/12/gumiho.html", "content": "Legend of Gumiho Have you ever heard of the folklore or the legend of the Korean gumiho (kumiho)? The word '구미호 ( gumiho )' is literally translated as ' nine-tailed fox '."}
+{"idx": 6, "title": "Nine-Tailed Fox: A Legend of Korean Mythology", "date": "", "ddg_snippet": "Jul 12, 2024 · The gumiho is more than just a mythical creature; it’s a symbol of the dark side of human nature and the constant struggle between good and evil. The Nine-Tailed Fox ’s Origins: From Beast to Legend The origins of the nine-tailed fox can be traced back to ancient Korean beliefs about the natural world.", "subpage_snippet": "", "source": "mythologyworldwide.com", "link": "https://mythologyworldwide.com/nine-tailed-fox-a-legend-of-korean-mythology/", "content": "Jul 12, 2024 · The gumiho is more than just a mythical creature; it’s a symbol of the dark side of human nature and the constant struggle between good and evil. The Nine-Tailed Fox ’s Origins: From Beast to Legend The origins of the nine-tailed fox can be traced back to ancient Korean beliefs about the natural world."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Gumiho_hybrid_architecture_speculative_decoding_experimental_setup_section_4.1.jsonl b/data/sampled_jsons/Gumiho_hybrid_architecture_speculative_decoding_experimental_setup_section_4.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..748565a496db7f46dd3edfd13f7466b8e852c011
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_hybrid_architecture_speculative_decoding_experimental_setup_section_4.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reducing AI Inference Latency With Speculative Decoding", "date": "", "ddg_snippet": "Explore how speculative decoding techniques, including EAGLE-3, reduce latency and enhance efficiency in AI inference, optimizing large language model performance on NVIDIA GPUs.", "subpage_snippet": "", "source": "digiitallife.com", "link": "https://digiitallife.com/reducing-ai-inference-latency-with-speculative-decoding/", "content": "Explore how speculative decoding techniques, including EAGLE-3, reduce latency and enhance efficiency in AI inference, optimizing large language model performance on NVIDIA GPUs."}
+{"idx": 1, "title": "An Introduction to Speculative Decoding for Reducing Latency in AI...", "date": "", "ddg_snippet": "Speculative decoding offers an alternative by offloading part of the work to a less resource-intensive model. Speculative decoding works much like a chief scientist in a laboratory, relying on a less experienced but efficient assistant to handle routine experiments .", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/an-introduction-to-speculative-decoding-for-reducing-latency-in-ai-inference/", "content": "Speculative decoding offers an alternative by offloading part of the work to a less resource-intensive model. Speculative decoding works much like a chief scientist in a laboratory, relying on a less experienced but efficient assistant to handle routine experiments ."}
+{"idx": 2, "title": "Boosting Efficiency in Language Models with Speculative Decoding", "date": "", "ddg_snippet": "Speeding Up LanguageSpeeding Up LanguageModelsmodel efficiency and performance. Speculative decoding improves language. Experimental Setup and Results. Experiments with Different Models. Latency Observations.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-09-12-boosting-efficiency-in-language-models-with-speculative-decoding--ak600p0", "content": "Speeding Up LanguageSpeeding Up LanguageModelsmodel efficiency and performance. Speculative decoding improves language. Experimental Setup and Results. Experiments with Different Models. Latency Observations."}
+{"idx": 3, "title": "Speculative Decoding : Exact Speedups with Draft Models", "date": "", "ddg_snippet": "Speed up LLM sampling using a small draft model and a batched verifier while preserving the exact target distribution. Speculative Decoding : Exact Speedups with Draft Models.", "subpage_snippet": "", "source": "rd.me", "link": "https://rd.me/speculative-decoding", "content": "Speed up LLM sampling using a small draft model and a batched verifier while preserving the exact target distribution. Speculative Decoding : Exact Speedups with Draft Models."}
+{"idx": 4, "title": "Done Is Better than Perfect: Unlocking Efficient Reasoning by...", "date": "", "ddg_snippet": "Section 4 . 1 describes the experimental setup . Section 4 .2 presents the main results, focusing on token reduction, accuracy, and latency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.19788v2", "content": "Section 4 . 1 describes the experimental setup . Section 4 .2 presents the main results, focusing on token reduction, accuracy, and latency."}
+{"idx": 5, "title": "Can You Unlock Qwen Next's Full Potential with API Integration?", "date": "", "ddg_snippet": "Qwen Next’s hybrid architecture combines gated mechanisms with advanced normalization, optimizing it for API-driven tasks.", "subpage_snippet": "", "source": "apidog.com", "link": "https://apidog.com/blog/how-to-use-qwen-next-via-api/", "content": "Qwen Next’s hybrid architecture combines gated mechanisms with advanced normalization, optimizing it for API-driven tasks."}
+{"idx": 6, "title": "GLM-4.5 by Zhipu AI: Model for Coding, Reasoning, and Vision", "date": "", "ddg_snippet": "-- speculative -num-steps, -- speculative -eagle-topk, and -- speculative -num-draft-tokens control decoding strategy parameters. Adjust --tp-size based on your available GPUs and model size.", "subpage_snippet": "", "source": "www.labellerr.com", "link": "https://www.labellerr.com/blog/glm-4-5/", "content": "-- speculative -num-steps, -- speculative -eagle-topk, and -- speculative -num-draft-tokens control decoding strategy parameters. Adjust --tp-size based on your available GPUs and model size."}
+{"idx": 7, "title": "Mixture-of-Recursions: Learning Dynamic Recursive Depths for...", "date": "", "ddg_snippet": "Experimental Setup . We experiment with four scales—135M, 360M, 730M, and 1.7B parameters—fixing the number of recursions to three for both Recursive and MoR configurations, resulting in roughly one-third the number of unique parameters.", "subpage_snippet": "", "source": "gptrack.ai", "link": "https://gptrack.ai/paper/MoR.pdf", "content": "Experimental Setup . We experiment with four scales—135M, 360M, 730M, and 1.7B parameters—fixing the number of recursions to three for both Recursive and MoR configurations, resulting in roughly one-third the number of unique parameters."}
+{"idx": 8, "title": "transcribethis.io", "date": "", "ddg_snippet": "Speculative decoding can achieve up to a 2x speedup in Whisper inference without any degradation in transcription accuracy.Speeding Up Whisper Transcriptions Techniques for Faster Audio Processing - Optimized Pipeline Architectures - WhisperS2T and Efficient Backends.", "subpage_snippet": "", "source": "transcribethis.io", "link": "https://transcribethis.io/blog/speeding_up_whisper_transcriptions_techniques_for_faster_aud.php", "content": "Speculative decoding can achieve up to a 2x speedup in Whisper inference without any degradation in transcription accuracy.Speeding Up Whisper Transcriptions Techniques for Faster Audio Processing - Optimized Pipeline Architectures - WhisperS2T and Efficient Backends."}
+{"idx": 9, "title": "Intrusion Detection for Edge-IoT Using LSTM-Autoencoder", "date": "", "ddg_snippet": "Section 5 describes the experimental setup , Section 6 analyzes results, and Section 7 concludes with future directions. Decoder (⋅): Reconstructs the original input from the compressed representa", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/pdf/ojapps_2313340.pdf", "content": "Section 5 describes the experimental setup , Section 6 analyzes results, and Section 7 concludes with future directions. Decoder (⋅): Reconstructs the original input from the compressed representa"}
diff --git a/data/sampled_jsons/Gumiho_paper_MT-Bench_Llama2_70B_Eagle-2_speedup_comparison_year_2024.jsonl b/data/sampled_jsons/Gumiho_paper_MT-Bench_Llama2_70B_Eagle-2_speedup_comparison_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a2125326c06e14586df252ea63a70dd61e0a58eb
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_paper_MT-Bench_Llama2_70B_Eagle-2_speedup_comparison_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees", "date": "", "ddg_snippet": "Subsequent work has largely focused on reduc-Table 2 : Speedup ratios and average acceptance lengths τ with LLaMA2 -Chat 70B , LLaMA3-Instruct 70B , and LLaMA3-Instruct 8B as the original LLMs, with the tem-perature set to 0, on the MT-bench dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.16858", "content": "Subsequent work has largely focused on reduc-Table 2 : Speedup ratios and average acceptance lengths τ with LLaMA2 -Chat 70B , LLaMA3-Instruct 70B , and LLaMA3-Instruct 8B as the original LLMs, with the tem-perature set to 0, on the MT-bench dataset."}
+{"idx": 1, "title": "GitHub - AMD-AGI/Gumiho: Official Implementation of \"Gumiho: A Hybrid ...", "date": "", "ddg_snippet": "This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding. Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding. Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ..."}
+{"idx": 2, "title": "meta-llama/Llama-2-70b-chat-hf · Hugging Face", "date": "", "ddg_snippet": "Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/meta-llama/Llama-2-70b-chat-hf", "content": "Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom."}
+{"idx": 3, "title": "Speedup comparison of various methods on Spec-Bench at different model ...", "date": "", "ddg_snippet": "Experimental results show that ParallelSpec accelerates baseline methods in latency up to 62% on text generation benchmarks from different domains, and it achieves 2.84X overall speedup on the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Speedup-comparison-of-various-methods-on-Spec-Bench-at-different-model-scales_fig5_384216103", "content": "Experimental results show that ParallelSpec accelerates baseline methods in latency up to 62% on text generation benchmarks from different domains, and it achieves 2.84X overall speedup on the ..."}
+{"idx": 4, "title": "Best Practices to Increase Speed of Llama-2 70B? : r/LocalLLaMA - Reddit", "date": "", "ddg_snippet": "Thanks to everyone in this community for all of the helpful posts! I'm looping over many prompts with the following specs: Instruct v2 version of Llama-2 70B (see here) 8 bit quantization Two A100s 4k Tokens of input text Minimal output text (just a JSON response) Each prompt takes about one minute to complete. I would like to cut down on this time, substantially if possible, since I have ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/15zdzuw/best_practices_to_increase_speed_of_llama2_70b/", "content": "Thanks to everyone in this community for all of the helpful posts! I'm looping over many prompts with the following specs: Instruct v2 version of Llama-2 70B (see here) 8 bit quantization Two A100s 4k Tokens of input text Minimal output text (just a JSON response) Each prompt takes about one minute to complete. I would like to cut down on this time, substantially if possible, since I have ..."}
+{"idx": 5, "title": "eagle-llm · PyPI", "date": "", "ddg_snippet": "GPT-Fast primarily accelerates generation through quantization and compilation, which we have integrated into EAGLE . Here is the result of an experiment conducted on MT-bench with a single RTX3090, using LLaMA2 -chat 7B. ... Inference is conducted on a single RTX3090 GPU at int4 precision using the LLaMA2 -chat 7B model. No additional training ...", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/eagle-llm/", "content": "GPT-Fast primarily accelerates generation through quantization and compilation, which we have integrated into EAGLE . Here is the result of an experiment conducted on MT-bench with a single RTX3090, using LLaMA2 -chat 7B. ... Inference is conducted on a single RTX3090 GPU at int4 precision using the LLaMA2 -chat 7B model. No additional training ..."}
+{"idx": 6, "title": "EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty", "date": "", "ddg_snippet": "Figure 1: Speedup ratio of Vicuna and LLaMA2 -Chat inference latency on the MT-bench for greedy (temperature=0) settings. Speedup ratio of Medusa and Lookahead are copied from their original technical reports. With speculative sampling, there is a lack of suitable draft models to accelerate the 7B model. Employing a 7B model as the draft model for a 13B model results in slow speeds due to the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.15077v1", "content": "Figure 1: Speedup ratio of Vicuna and LLaMA2 -Chat inference latency on the MT-bench for greedy (temperature=0) settings. Speedup ratio of Medusa and Lookahead are copied from their original technical reports. With speculative sampling, there is a lack of suitable draft models to accelerate the 7B model. Employing a 7B model as the draft model for a 13B model results in slow speeds due to the ..."}
+{"idx": 7, "title": "GitHub - SafeAILab/EAGLE: Official Implementation of EAGLE-1 (ICML'24 ...", "date": "", "ddg_snippet": "EAGLE | Paper ( EAGLE ) | Paper ( EAGLE-2 ) | Paper (EAGLE-3) | Blog | EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) is a new baseline for fast decoding of Large Language Models (LLMs) with provable performance maintenance.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SafeAILab/EAGLE", "content": "EAGLE | Paper ( EAGLE ) | Paper ( EAGLE-2 ) | Paper (EAGLE-3) | Blog | EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) is a new baseline for fast decoding of Large Language Models (LLMs) with provable performance maintenance."}
+{"idx": 8, "title": "PDF EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees", "date": "", "ddg_snippet": "Figure 1: Speedup ratios of different methods at tem- perature=1. For speculative sampling, the Vicuna series uses Vicuna-68M as the draft model. LLaMA2 -Chat lacks a suitable draft model, and is marked as N/A. Methods like Medusa relax acceptance conditions under non-greedy settings, which do not guarantee lossless acceleration. In this paper , we only compare with specu- lative sampling based ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.422.pdf", "content": "Figure 1: Speedup ratios of different methods at tem- perature=1. For speculative sampling, the Vicuna series uses Vicuna-68M as the draft model. LLaMA2 -Chat lacks a suitable draft model, and is marked as N/A. Methods like Medusa relax acceptance conditions under non-greedy settings, which do not guarantee lossless acceleration. In this paper , we only compare with specu- lative sampling based ..."}
+{"idx": 9, "title": "Llama 2 70B: An MLPerf Inference Benchmark for Large Language Models", "date": "", "ddg_snippet": "The MLPerf Inference v4.0 round adds Llama 2 70B model as the flagship \"larger\" LLM for its latest benchmark round.", "subpage_snippet": "", "source": "mlcommons.org", "link": "https://mlcommons.org/2024/03/mlperf-llama2-70b/", "content": "The MLPerf Inference v4.0 round adds Llama 2 70B model as the flagship \"larger\" LLM for its latest benchmark round."}
diff --git a/data/sampled_jsons/HDR-IPPO_objective_function_formula.jsonl b/data/sampled_jsons/HDR-IPPO_objective_function_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b554a61eb6802ff6b9127ee62ff67f32fe2d543e
--- /dev/null
+++ b/data/sampled_jsons/HDR-IPPO_objective_function_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Table of Content | HDR Image Reconstruction and Tone Mapping", "date": "", "ddg_snippet": "The first term in the objective function ensures the solution satisfies the aforementioned equations, and the second term ensures the estimated function g is smooth. Minimizing the objective function is a straightforward linear least square problem and can be easily solved in Matlab.", "subpage_snippet": "", "source": "zhangboyu.github.io", "link": "https://zhangboyu.github.io/", "content": "The first term in the objective function ensures the solution satisfies the aforementioned equations, and the second term ensures the estimated function g is smooth. Minimizing the objective function is a straightforward linear least square problem and can be easily solved in Matlab."}
+{"idx": 1, "title": "CG1 - HDR Imaging", "date": "", "ddg_snippet": "Varying aperture: f/2.0 – f/22.0 1:100 (appro.) Change only exposure time, not aperture due to diff. depth-of-field !! Both steps ... What difference to pick between exposures levels? How many input images are necessary to get good results? Why Tone Mapping? Mapping HDR radiance values to LDR pixel values?", "subpage_snippet": "", "source": "graphics.cg.uni-saarland.de", "link": "https://graphics.cg.uni-saarland.de/courses/cg1-2018/slides/CG15-HDRImaging.pdf", "content": "Varying aperture: f/2.0 – f/22.0 1:100 (appro.) Change only exposure time, not aperture due to diff. depth-of-field !! Both steps ... What difference to pick between exposures levels? How many input images are necessary to get good results? Why Tone Mapping? Mapping HDR radiance values to LDR pixel values?"}
+{"idx": 2, "title": "Objective Function - What Is Objective Function in LPP?", "date": "", "ddg_snippet": "Objective Function is a function represented as a linear equation Z = ax + by, and is used to represent optimization problems in linear programming. Here x and y are the decision variables of the objective function .", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/algebra/objective-function/", "content": "Objective Function is a function represented as a linear equation Z = ax + by, and is used to represent optimization problems in linear programming. Here x and y are the decision variables of the objective function ."}
+{"idx": 3, "title": "Mac How to: Basic Formulas & Functions in Numbers - YouTube", "date": "", "ddg_snippet": "Learn how to add formulas and functions to Tables in Numbers for Mac.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=O2iHLlw1shU", "content": "Learn how to add formulas and functions to Tables in Numbers for Mac."}
+{"idx": 4, "title": "SVD optimization formula - Stack Overflow", "date": "", "ddg_snippet": "Does anyone know what convex optimization formula ( Objective function ) results in the singular value decomposition or Principle component analysis of matrix X (n by p)?", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/12677879/svd-optimization-formula", "content": "Does anyone know what convex optimization formula ( Objective function ) results in the singular value decomposition or Principle component analysis of matrix X (n by p)?"}
+{"idx": 5, "title": "Get Today's Date in Power Query ( Formulas and Functions )", "date": "", "ddg_snippet": "Note: Use the formula DateTime.Date( DateTime.LocalNow() ) to get today’s date using power query.In this formula , we have used two functions : The first function (DateTime.LocalNow) returns the current date and time according to the date and time of your system.", "subpage_snippet": "", "source": "excelchamps.com", "link": "https://excelchamps.com/power-query/todays-date/", "content": "Note: Use the formula DateTime.Date( DateTime.LocalNow() ) to get today’s date using power query.In this formula , we have used two functions : The first function (DateTime.LocalNow) returns the current date and time according to the date and time of your system."}
+{"idx": 6, "title": "How to Find Maximum and Minimum Values of a Function ?", "date": "", "ddg_snippet": "Steps to find the maximum and minimum value of the function are added below: Step 1: Find the first derivative of the function .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/maths/how-to-find-maximum-and-minimum-values-of-a-function/", "content": "Steps to find the maximum and minimum value of the function are added below: Step 1: Find the first derivative of the function ."}
+{"idx": 7, "title": "Proximal Policy Optimization (PPO) - Hugging Face", "date": "", "ddg_snippet": "Aug 5, 2022 · Here with PPO, the idea is to constrain our policy update with a new objective function called the Clipped surrogate objective function that will constrain the policy change in a small range using a clip.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/deep-rl-ppo", "content": "Aug 5, 2022 · Here with PPO, the idea is to constrain our policy update with a new objective function called the Clipped surrogate objective function that will constrain the policy change in a small range using a clip."}
+{"idx": 8, "title": "Proximal Policy Optimization Family — MARLlib v1.0.0 ...", "date": "", "ddg_snippet": "The objective function of PPO takes the minimum value between the original value and the clipped value, where the clipped value is obtained by multiplying the policy ratio by the advantage estimate and then clipping it to the interval [1 ϵ, 1 + ϵ].", "subpage_snippet": "", "source": "marllib.readthedocs.io", "link": "https://marllib.readthedocs.io/en/latest/algorithm/ppo_family.html", "content": "The objective function of PPO takes the minimum value between the original value and the clipped value, where the clipped value is obtained by multiplying the policy ratio by the advantage estimate and then clipping it to the interval [1 ϵ, 1 + ϵ]."}
+{"idx": 9, "title": "Proximal Policy Optimization (PPO) Agent - MATLAB & Simulink", "date": "", "ddg_snippet": "Proximal policy optimization (PPO) is an on-policy, policy gradient reinforcement learning method for environments with a discrete or continuous action space. It directly estimates a stochastic policy and uses a value function critic to estimate the value of the policy.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/help/reinforcement-learning/ug/proximal-policy-optimization-agents.html", "content": "Proximal policy optimization (PPO) is an on-policy, policy gradient reinforcement learning method for environments with a discrete or continuous action space. It directly estimates a stochastic policy and uses a value function critic to estimate the value of the policy."}
diff --git a/data/sampled_jsons/HTML_Content_Cleaning_section_HtmlRAG_arxiv.org_year_2024.jsonl b/data/sampled_jsons/HTML_Content_Cleaning_section_HtmlRAG_arxiv.org_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..484f02305c4c84f0bea77022167245e4b147c318
--- /dev/null
+++ b/data/sampled_jsons/HTML_Content_Cleaning_section_HtmlRAG_arxiv.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system."}
+{"idx": 1, "title": "GitHub - plageon/HtmlRAG: HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "📝 Introduction We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "📝 Introduction We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 2, "title": "Implementing HtmlRAG: Enhancing Retrieval-Augmented ... - Dev Genius", "date": "", "ddg_snippet": "6. Conclusion Implementing HtmlRAG enhances RAG systems by utilizing the rich structural and semantic information present in HTML documents. By carefully cleaning , structuring, and pruning HTML content , we can provide LLMs with more informative inputs, leading to better understanding and generation of responses. Key Takeaways:", "subpage_snippet": "", "source": "blog.devgenius.io", "link": "https://blog.devgenius.io/implementing-htmlrag-enhancing-retrieval-augmented-generation-with-html-knowledge-91cdd6278e23", "content": "6. Conclusion Implementing HtmlRAG enhances RAG systems by utilizing the rich structural and semantic information present in HTML documents. By carefully cleaning , structuring, and pruning HTML content , we can provide LLMs with more informative inputs, leading to better understanding and generation of responses. Key Takeaways:"}
+{"idx": 3, "title": "Unlocking Hidden Context: How HTML Structure Revolutionizes AI ...", "date": "", "ddg_snippet": "HTML incorporates a variety of tags that structures content , offering valuable context that simple plain text cannot — these tags give information meaning and hierarchy.", "subpage_snippet": "", "source": "timothy-urista.medium.com", "link": "https://timothy-urista.medium.com/unlocking-hidden-context-how-html-structure-revolutionizes-ai-knowledge-retrieval-37d0b428647d", "content": "HTML incorporates a variety of tags that structures content , offering valuable context that simple plain text cannot — these tags give information meaning and hierarchy."}
+{"idx": 4, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "To tackle the additional tokens brought 918 by HTML , we design HTML cleaning and HTML pruning to shorten 919 HTML while retaining key information. Experiments show that 920 HtmlRAG outperforms existing post-retrieval processes based on 921 plain text, and validates the priority of HTML as the format of 922 retrieved knowledge.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "To tackle the additional tokens brought 918 by HTML , we design HTML cleaning and HTML pruning to shorten 919 HTML while retaining key information. Experiments show that 920 HtmlRAG outperforms existing post-retrieval processes based on 921 plain text, and validates the priority of HTML as the format of 922 retrieved knowledge."}
+{"idx": 5, "title": "[2411.02959] HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system."}
+{"idx": 6, "title": "HtmlRAG/toolkit/README.md at main · plageon/HtmlRAG · GitHub", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG /toolkit/README.md at main · plageon/ HtmlRAG", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG/blob/main/toolkit/README.md", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG /toolkit/README.md at main · plageon/ HtmlRAG"}
+{"idx": 7, "title": "arXiv.org", "date": "", "ddg_snippet": "arXiv.org", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959", "content": "arXiv.org"}
+{"idx": 8, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new chal-lenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v2", "content": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new chal-lenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system."}
+{"idx": 9, "title": "Clean up HTML Content for Retrieval-Augmented Generation with ...", "date": "", "ddg_snippet": "Scraping web pages is one way to fetch content for your retrieval-augmented generation (RAG)... Tagged with node, rag, javascript, ai.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/datastax/clean-up-html-content-for-retrieval-augmented-generation-with-readabilityjs-5gmg", "content": "Scraping web pages is one way to fetch content for your retrieval-augmented generation (RAG)... Tagged with node, rag, javascript, ai."}
diff --git a/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Lossless_Structural_C.jsonl b/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Lossless_Structural_C.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..68263eb4ffb293a89055d825f8c30a3befac0bdc
--- /dev/null
+++ b/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Lossless_Structural_C.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieval Results ...", "date": "", "ddg_snippet": "We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. Lossless HTML Cleaning: This cleaning process just removes totally irrelevant contents and compress redundant structures, retaining all semantic information in the ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. Lossless HTML Cleaning: This cleaning process just removes totally irrelevant contents and compress redundant structures, retaining all semantic information in the ..."}
+{"idx": 2, "title": "Implementing HtmlRAG: Enhancing Retrieval-Augmented ... - Dev Genius", "date": "", "ddg_snippet": "Retrieval-Augmented Generation ( RAG ) systems have gained significant traction in enhancing the knowledge capabilities of Large Language Models (LLMs) and mitigating issues like hallucinations. Traditional RAG systems typically use plain text extracted from HTML documents as the format for retrieved knowledge . However, this approach often leads to the loss of structural and semantic information ...", "subpage_snippet": "", "source": "blog.devgenius.io", "link": "https://blog.devgenius.io/implementing-htmlrag-enhancing-retrieval-augmented-generation-with-html-knowledge-91cdd6278e23", "content": "Retrieval-Augmented Generation ( RAG ) systems have gained significant traction in enhancing the knowledge capabilities of Large Language Models (LLMs) and mitigating issues like hallucinations. Traditional RAG systems typically use plain text extracted from HTML documents as the format for retrieved knowledge . However, this approach often leads to the loss of structural and semantic information ..."}
+{"idx": 3, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=leCEFyMyxg", "content": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen"}
+{"idx": 4, "title": "Understanding HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy.", "subpage_snippet": "", "source": "techchilli.com", "link": "https://techchilli.com/artificial-intelligence/understanding-htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledge-in-rag-systems/", "content": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy."}
+{"idx": 5, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3696410.3714546", "content": "To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML ."}
+{"idx": 6, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "HTML contains a lot of intrinsic information, so we propose taking HTML as the format of retrieved knowledge in RAG systems , and design HTML cleaning and block-tree-based HTML pruning to shorten the length and preserve information.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "HTML contains a lot of intrinsic information, so we propose taking HTML as the format of retrieved knowledge in RAG systems , and design HTML cleaning and block-tree-based HTML pruning to shorten the length and preserve information."}
+{"idx": 7, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "The best and second best results are in bold and underlined. The symbol † signifies that our model achieves superior results among baselines in a statistically significant manner (t-test, 𝑝-value < 0.05). - \"HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/HtmlRAG:-HTML-is-Better-Than-Plain-Text-for-in-RAG-Tan-Dou/7cfd2426ca908c8c5a81bd7c7ca01f914a972de4/figure/1", "content": "The best and second best results are in bold and underlined. The symbol † signifies that our model achieves superior results among baselines in a statistically significant manner (t-test, 𝑝-value < 0.05). - \"HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems \""}
+{"idx": 8, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges."}
+{"idx": 9, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain - text -based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain - text -based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG ."}
diff --git a/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function,_Algorithm_and_Scalability_authors_time_.jsonl b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function,_Algorithm_and_Scalability_authors_time_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..49563b2616a2a4cb3baa4e2d39eb0c8486eee9d6
--- /dev/null
+++ b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function,_Algorithm_and_Scalability_authors_time_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability ... Time complexity . The runtime of Algorithm 2 consists of three parts: ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "by Y Pan — Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability ... Time complexity . The runtime of Algorithm 2 consists of three parts: ..."}
+{"idx": 1, "title": "WO2018222064A1 - Systems and methods of hierarchical community", "date": "", "ddg_snippet": "Hierarchical CD is usually more computationally complex than flat CD, but yields layered community topology and may provide better precision.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2018222064A1/en", "content": "Hierarchical CD is usually more computationally complex than flat CD, but yields layered community topology and may provide better precision."}
+{"idx": 2, "title": "US20070239694A1 - Graph querying, graph motif mining and the", "date": "", "ddg_snippet": "Legal status (The legal status is an assumption and is not a legal conclusion. ... Priority date (The priority date is an assumption and is not a ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20070239694A1/en", "content": "Legal status (The legal status is an assumption and is not a legal conclusion. ... Priority date (The priority date is an assumption and is not a ..."}
+{"idx": 3, "title": "Algorithmic Aspects of Vertex Elimination on Graphs | SIAM", "date": "", "ddg_snippet": "One algorithm , based on breadth-first search, finds a perfect elimination ordering, if any exists, in $O(n + e)$ time , if the problem graph has n ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/0205021", "content": "One algorithm , based on breadth-first search, finds a perfect elimination ordering, if any exists, in $O(n + e)$ time , if the problem graph has n ..."}
+{"idx": 4, "title": "(PDF) Density-based semi-supervised clustering", "date": "", "ddg_snippet": "Density-based algorithms are traditionally used in applications, where the anticipated groups are expected to assume non-spherical shapes and /or ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/220451675_Density-based_semi-supervised_clustering", "content": "Density-based algorithms are traditionally used in applications, where the anticipated groups are expected to assume non-spherical shapes and /or ..."}
+{"idx": 5, "title": "Learn Clustering in Python – A Machine Learning Engineering", "date": "", "ddg_snippet": "... clustering algorithms rely heavily on well-structured data, you need to know how to handle missing values, normalize or standardize numerical ...", "subpage_snippet": "", "source": "www.freecodecamp.org", "link": "https://www.freecodecamp.org/news/clustering-in-python-a-machine-learning-handbook/", "content": "... clustering algorithms rely heavily on well-structured data, you need to know how to handle missing values, normalize or standardize numerical ..."}
+{"idx": 6, "title": "Understanding HNSW - Hierarchical Navigable Small World | Keyur", "date": "", "ddg_snippet": "Early proximity graph methods achieved impressive results on certain datasets but suffered from fundamental scalability limitations.", "subpage_snippet": "", "source": "keyurramoliya.com", "link": "https://keyurramoliya.com/posts/Understading-HNSW-Hierarchical-Navigable-Small-World/", "content": "Early proximity graph methods achieved impressive results on certain datasets but suffered from fundamental scalability limitations."}
+{"idx": 7, "title": "Track: Poster Session 5 East", "date": "", "ddg_snippet": "17 Jul 2025 — Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability ... time complexity . Fortunately, attention ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50267", "content": "17 Jul 2025 — Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability ... time complexity . Fortunately, attention ..."}
+{"idx": 8, "title": "ICML 2025 Thursday 07/17", "date": "", "ddg_snippet": "Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability ... time complexity . Fortunately, attention commonly exhibits ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/17", "content": "Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability ... time complexity . Fortunately, attention commonly exhibits ..."}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability .PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability .PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities."}
diff --git a/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_Lossless_Structural_Compression_Section_3.2.2.jsonl b/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_Lossless_Structural_Compression_Section_3.2.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ad713dae32aafafbd123f835d36daa27578c8c4
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_Lossless_Structural_Compression_Section_3.2.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain Text for...", "date": "", "ddg_snippet": "The compressed HTML of lossless HTML cleaning is suitable for RAG systems that have long-context LLMs and are not willing to loss any information before generation. Two-Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps, both of...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "The compressed HTML of lossless HTML cleaning is suitable for RAG systems that have long-context LLMs and are not willing to loss any information before generation. Two-Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps, both of..."}
+{"idx": 1, "title": "[2411.02959] HtmlRAG : HTML is Better Than Plain Text for Modeling...", "date": "", "ddg_snippet": "Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures , is lost during this plain - text -based RAG process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures , is lost during this plain - text -based RAG process."}
+{"idx": 2, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling...", "date": "", "ddg_snippet": "Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures , is lost during this plain - text -based RAG process.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures , is lost during this plain - text -based RAG process."}
+{"idx": 3, "title": "Paper tables with annotated results for HtmlRAG : HTML is Better ...", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems. Retrieval-Augmented Generation ( RAG ) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/htmlrag-html-is-better-than-plain-text-for/review/", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems. Retrieval-Augmented Generation ( RAG ) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs."}
+{"idx": 4, "title": "Implementing HtmlRAG : Enhancing Retrieval-Augmented... | Dev Genius", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems. BeautifulSoup Documentation. Cosine Similarity in Text Embeddings. Artificial Intelligence.", "subpage_snippet": "", "source": "blog.devgenius.io", "link": "https://blog.devgenius.io/implementing-htmlrag-enhancing-retrieval-augmented-generation-with-html-knowledge-91cdd6278e23", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems. BeautifulSoup Documentation. Cosine Similarity in Text Embeddings. Artificial Intelligence."}
+{"idx": 5, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling... | alphaXiv", "date": "", "ddg_snippet": "View 1 comments: The comparison in Table 1 is not fair. HtmlRAG has a pruner, while other methods compared do not.Select any part of the paper to ask specific questions about that section . Add Context. Type @ to reference other papers and expand the discussion.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2411.02959v1", "content": "View 1 comments: The comparison in Table 1 is not fair. HtmlRAG has a pruner, while other methods compared do not.Select any part of the paper to ask specific questions about that section . Add Context. Type @ to reference other papers and expand the discussion."}
+{"idx": 6, "title": "ℹ 1 0 1 **What is HtmlRAG , Multimodal RAG and Agentic...", "date": "", "ddg_snippet": "How does HtmlRAG work? HtmlRAG works directly with HTML to keep more of the structure and meaning of the original content, missing the step of converting the data into the plain text . The main parts of HtmlRAG ’s working process are HTML Cleaning and Pruning techniques", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/blog/Kseniase/html-multimodal-agentic-rag", "content": "How does HtmlRAG work? HtmlRAG works directly with HTML to keep more of the structure and meaning of the original content, missing the step of converting the data into the plain text . The main parts of HtmlRAG ’s working process are HTML Cleaning and Pruning techniques"}
+{"idx": 7, "title": "plageon/ HtmlRAG | DeepWiki", "date": "", "ddg_snippet": "HtmlRAG is a system that leverages HTML structure rather than plain text for Retrieval-Augmented Generation ( RAG ) systems. This document provides a comprehensive overview of the HtmlRAG repository, in.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/plageon/HtmlRAG", "content": "HtmlRAG is a system that leverages HTML structure rather than plain text for Retrieval-Augmented Generation ( RAG ) systems. This document provides a comprehensive overview of the HtmlRAG repository, in."}
+{"idx": 8, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling...", "date": "", "ddg_snippet": "Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures , is lost during this plain - text -based RAG process.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/htmlrag-html-is-better-than-plain-text-for-modeling-37eto4bp5nih", "content": "Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures , is lost during this plain - text -based RAG process."}
+{"idx": 9, "title": "HTML > Plain Text for RAG - by Pascal Biese - LLM Watch", "date": "", "ddg_snippet": "3. HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems. Watching: HtmlRAG (paper). What problem does it solve?", "subpage_snippet": "", "source": "www.llmwatch.com", "link": "https://www.llmwatch.com/p/html-plain-text-for-rag", "content": "3. HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems. Watching: HtmlRAG (paper). What problem does it solve?"}
diff --git a/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..531e92eee2586e69cdeb26e155fefee0f228aff7
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "For a fair comparison, all end-to-end QA results are experimented with the latest open-source LLM, Llama-3.1-70B-Instruct and Llama -3.1-8B- Instruct (Dubey et al., 2024) under a 4K context window.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "For a fair comparison, all end-to-end QA results are experimented with the latest open-source LLM, Llama-3.1-70B-Instruct and Llama -3.1-8B- Instruct (Dubey et al., 2024) under a 4K context window."}
+{"idx": 1, "title": "GitHub - plageon/HtmlRAG: HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - plageon/ HtmlRAG", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - plageon/ HtmlRAG"}
+{"idx": 2, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieval Results ...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "libraries.io", "link": "https://libraries.io/pypi/htmlrag", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 3, "title": "HtmlRAG - a zstanjj Collection - Hugging Face", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/zstanjj/htmlrag-671f03af5c3da2e7b5371aa4", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71"}
+{"idx": 4, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=E91gjsccP1", "content": "To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML ."}
+{"idx": 5, "title": "Understanding HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation (RAG) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy.", "subpage_snippet": "", "source": "techchilli.com", "link": "https://techchilli.com/artificial-intelligence/understanding-htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledge-in-rag-systems/", "content": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation (RAG) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy."}
+{"idx": 6, "title": "Table 1 from HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Table 1: Results of HtmlRAG and baselines under the short-context setting. Hit@1 is the proportion of instances where at least one short answer matches. The best and second best results are in bold and underlined. The symbol † signifies that our model achieves superior results among baselines in a statistically significant manner (t-test, 𝑝-value < 0.05). - \" HtmlRAG : HTML is Better Than ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/HtmlRAG:-HTML-is-Better-Than-Plain-Text-for-in-RAG-Tan-Dou/7cfd2426ca908c8c5a81bd7c7ca01f914a972de4/figure/1", "content": "Table 1: Results of HtmlRAG and baselines under the short-context setting. Hit@1 is the proportion of instances where at least one short answer matches. The best and second best results are in bold and underlined. The symbol † signifies that our model achieves superior results among baselines in a statistically significant manner (t-test, 𝑝-value < 0.05). - \" HtmlRAG : HTML is Better Than ..."}
+{"idx": 7, "title": "Paper page - HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We check some cases in which Llama-3.1-8B wins Llama-3.1-70B , and it seems Llama-3.1-70B gets distracted by those noise. We think this also demonstrates the necessity to do HTML pruning.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "We check some cases in which Llama-3.1-8B wins Llama-3.1-70B , and it seems Llama-3.1-70B gets distracted by those noise. We think this also demonstrates the necessity to do HTML pruning."}
+{"idx": 8, "title": "HtmlRAG/toolkit/README.md at main · plageon/HtmlRAG", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG /toolkit/README.md at main · plageon/ HtmlRAG", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG/blob/main/toolkit/README.md", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG /toolkit/README.md at main · plageon/ HtmlRAG"}
+{"idx": 9, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "For a fair comparison, all end-to-end QA results are experimented with the latest open-source LLM, Llama-3.1-70B-Instruct and Llama -3.1-8B- Instruct [12] under a 4K context window.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v1", "content": "For a fair comparison, all end-to-end QA results are experimented with the latest open-source LLM, Llama-3.1-70B-Instruct and Llama -3.1-8B- Instruct [12] under a 4K context window."}
diff --git a/data/sampled_jsons/HtmlRAG_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_MarkTechPost_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_MarkTechPost_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b7fe2a480d5839a66a6b6f9a32aa7570b093e94
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_MarkTechPost_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Опенсорс на арене: правда ли так хороша Llama 3 . 1 405B? / Хабр", "date": "", "ddg_snippet": "Llama 3 . 1 405B - это самая большая модель от Meta*, обученная на колоссальном объеме данных – более 15 триллионов токенов.Помимо версии с 405 млрд параметров, доступны также модели поменьше - с 8 млрд и 70 млрд параметров.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/bothub/articles/835100/", "content": "Llama 3 . 1 405B - это самая большая модель от Meta*, обученная на колоссальном объеме данных – более 15 триллионов токенов.Помимо версии с 405 млрд параметров, доступны также модели поменьше - с 8 млрд и 70 млрд параметров."}
+{"idx": 1, "title": "GitHub - plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems.Instantiaize a LLM inference model with VLLM. We recommend using Llama - 3 . 1 -8 B - Instruct or Llama - 3 . 1 - 70 B - Instruct .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems.Instantiaize a LLM inference model with VLLM. We recommend using Llama - 3 . 1 -8 B - Instruct or Llama - 3 . 1 - 70 B - Instruct ."}
+{"idx": 2, "title": "meta-llama/ Llama - 3 . 1 - 70 B - Instruct · Hugging Face", "date": "", "ddg_snippet": "huggingface-cli download meta-llama/Meta- Llama - 3 . 1 - 70 B - Instruct --include \"original/*\" --local-dir Meta- Llama - 3 . 1 - 70 B - Instruct . Hardware and Software. Training Factors We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining.", "subpage_snippet": "", "source": "huggingface.1319lm.top", "link": "https://huggingface.1319lm.top/meta-llama/Llama-3.1-70B-Instruct", "content": "huggingface-cli download meta-llama/Meta- Llama - 3 . 1 - 70 B - Instruct --include \"original/*\" --local-dir Meta- Llama - 3 . 1 - 70 B - Instruct . Hardware and Software. Training Factors We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining."}
+{"idx": 3, "title": "NIM Llama 3 . 1 70 B Instruct API | Together AI", "date": "", "ddg_snippet": "NVIDIA NIM for GPU accelerated Llama 3 . 1 70 B Instruct inference through OpenAI compatible APIs. Deploy this NIM model.", "subpage_snippet": "", "source": "www.together.ai", "link": "https://www.together.ai/models/nim-llama-3-1-70b-instruct", "content": "NVIDIA NIM for GPU accelerated Llama 3 . 1 70 B Instruct inference through OpenAI compatible APIs. Deploy this NIM model."}
+{"idx": 4, "title": "How to deploy Llama 3 . 1 70 B Instruct on Modal | Modal Blog", "date": "", "ddg_snippet": "To run Llama 3 . 1 70 B Instruct with a LLM serving framework like vLLM for better latency and throughput, refer to this more detailed example here. (You can modify the code in that example to run the 70 B version instead of the 8B version.) import modal.", "subpage_snippet": "", "source": "modal.com", "link": "https://modal.com/blog/how-to-run-llama-3-1-70b-instruct-on-modal", "content": "To run Llama 3 . 1 70 B Instruct with a LLM serving framework like vLLM for better latency and throughput, refer to this more detailed example here. (You can modify the code in that example to run the 70 B version instead of the 8B version.) import modal."}
+{"idx": 5, "title": "o4-mini (high) vs Llama 3 . 1 Instruct 70 B : Model Comparison", "date": "", "ddg_snippet": "Comparison between o4-mini (high) and Llama 3 . 1 Instruct 70 B across intelligence, price, speed, context window and more.", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/models/comparisons/o4-mini-vs-llama-3-1-instruct-70b", "content": "Comparison between o4-mini (high) and Llama 3 . 1 Instruct 70 B across intelligence, price, speed, context window and more."}
+{"idx": 6, "title": "togetherai/meta- llama - 3 . 1 - 70 b - instruct -turbo", "date": "", "ddg_snippet": "togetherai. meta- llama - 3 . 1 - 70 b - instruct -turbo.Preview. YAML. together Meta Llama 3 . 1 70 B Instruct Turbo model icon.", "subpage_snippet": "", "source": "hub.continue.dev", "link": "https://hub.continue.dev/togetherai/meta-llama-3.1-70b-instruct-turbo", "content": "togetherai. meta- llama - 3 . 1 - 70 b - instruct -turbo.Preview. YAML. together Meta Llama 3 . 1 70 B Instruct Turbo model icon."}
+{"idx": 7, "title": "bartowski/ Llama - 3 . 1 -Nemotron- 70 B - Instruct -HF-GGUF · Hugging Face", "date": "", "ddg_snippet": "Llamacpp imatrix Quantizations of Llama - 3 . 1 -Nemotron- 70 B - Instruct -HF. Using llama.cpp release b3901 for quantization.Decent quality, smaller than Q 4 _ K _S with similar performance, recommended. Llama - 3 . 1 -Nemotron- 70 B - Instruct -HF-Q3_K_L.gguf.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF", "content": "Llamacpp imatrix Quantizations of Llama - 3 . 1 -Nemotron- 70 B - Instruct -HF. Using llama.cpp release b3901 for quantization.Decent quality, smaller than Q 4 _ K _S with similar performance, recommended. Llama - 3 . 1 -Nemotron- 70 B - Instruct -HF-Q3_K_L.gguf."}
+{"idx": 8, "title": "llama - 3 . 1 -nemotron- 70 b - instruct | Model Details", "date": "", "ddg_snippet": "NVIDIA's Llama 3 . 1 Nemotron 70 B is a language model designed for generating precise and useful responses.", "subpage_snippet": "", "source": "langdb.ai", "link": "https://langdb.ai/app/providers/openrouter/llama-3.1-nemotron-70b-instruct", "content": "NVIDIA's Llama 3 . 1 Nemotron 70 B is a language model designed for generating precise and useful responses."}
+{"idx": 9, "title": "Meta- Llama - 3 . 1 - 70 B - Instruct - 4 bit huggingface.co api... - Toolify", "date": "", "ddg_snippet": "Meta- Llama - 3 . 1 - 70 B - Instruct - 4 bit is an open source model from GitHub that offers a free installation service, and any user can find Meta- Llama - 3 . 1 - 70 B - Instruct - 4 bit on GitHub to install.", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-model/cmarkea-meta-llama-3-1-70b-instruct-4bit", "content": "Meta- Llama - 3 . 1 - 70 B - Instruct - 4 bit is an open source model from GitHub that offers a free installation service, and any user can find Meta- Llama - 3 . 1 - 70 B - Instruct - 4 bit on GitHub to install."}
diff --git a/data/sampled_jsons/HtmlRAG_Prune-Embed_embedding-based_pruning_HTML_blocks_two-step_block-tree_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_Prune-Embed_embedding-based_pruning_HTML_blocks_two-step_block-tree_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0e7ba0ec80a61570efcae9040f74cef5f43a424e
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_Prune-Embed_embedding-based_pruning_HTML_blocks_two-step_block-tree_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain...", "date": "", "ddg_snippet": "Two - Step Block - Tree - Based HTML Pruning : The block - tree - based HTML pruning consists of two steps , both of which are conducted on the block tree structure.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "Two - Step Block - Tree - Based HTML Pruning : The block - tree - based HTML pruning consists of two steps , both of which are conducted on the block tree structure."}
+{"idx": 1, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "The block - tree -based HTML pruning consists of two steps , both of which are conducted on the block tree structure.The embedding - based HTML pruning algorithm is lightweight but effective. It adapts to the HTML format better compared to plain-text-based refiners.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "The block - tree -based HTML pruning consists of two steps , both of which are conducted on the block tree structure.The embedding - based HTML pruning algorithm is lightweight but effective. It adapts to the HTML format better compared to plain-text-based refiners."}
+{"idx": 2, "title": "zstanjj/ HTML - Pruner -Phi-3.8B · Hugging Face", "date": "", "ddg_snippet": "Two - Step Block - Tree - Based HTML Pruning : The block - tree - based HTML pruning consists of two steps , both of which are conducted on the block tree structure.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/zstanjj/HTML-Pruner-Phi-3.8B", "content": "Two - Step Block - Tree - Based HTML Pruning : The block - tree - based HTML pruning consists of two steps , both of which are conducted on the block tree structure."}
+{"idx": 3, "title": "HTMLRAG , Multimodal RAG , and Agentic RAG : A Technical Deep Dive", "date": "", "ddg_snippet": "Relevance Pruning – Embedding Stage: In the first pruning pass, each HTML block is scored for relevance to the user’s query using an embedding model.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/htmlrag-multimodal-rag-agentic-technical-deep-dive-nagesh-nama-pv9be", "content": "Relevance Pruning – Embedding Stage: In the first pruning pass, each HTML block is scored for relevance to the user’s query using an embedding model."}
+{"idx": 4, "title": "HTML Pruner Phi 3.8B · Models · Dataloop", "date": "", "ddg_snippet": "HtmlRAG uses a two - step block - tree - based HTML pruning approach. The first step uses an embedding model to calculate scores for blocks , while the second step uses a path generative model.", "subpage_snippet": "", "source": "dataloop.ai", "link": "https://dataloop.ai/library/model/zstanjj_html-pruner-phi-38b/", "content": "HtmlRAG uses a two - step block - tree - based HTML pruning approach. The first step uses an embedding model to calculate scores for blocks , while the second step uses a path generative model."}
+{"idx": 5, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling...", "date": "", "ddg_snippet": "step block - tree - based pruning method that prunes useless HTML . blocks and keeps only the relevant part of the HTML .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "step block - tree - based pruning method that prunes useless HTML . blocks and keeps only the relevant part of the HTML ."}
+{"idx": 6, "title": "A smart toolkit for HTML cleaning and pruning for RAG systems.", "date": "", "ddg_snippet": "Prune HTML Blocks with Embedding Model. from htmlrag import EmbedHTMLPruner. question, simplified_ html , block _ tree ) print( block _rankings) #. [ 2 , 0, 1] #. . alternatively you can use bm25 to rank the blocks from htmlrag import BM25HTMLPruner.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/htmlrag/", "content": "Prune HTML Blocks with Embedding Model. from htmlrag import EmbedHTMLPruner. question, simplified_ html , block _ tree ) print( block _rankings) #. [ 2 , 0, 1] #. . alternatively you can use bm25 to rank the blocks from htmlrag import BM25HTMLPruner."}
+{"idx": 7, "title": "HtmlRAG by Plageon - A undefined Template | Built At Lightspeed", "date": "", "ddg_snippet": "The system introduces Lossless HTML Cleaning and Two - Step Block - Tree - Based HTML Pruning to manage the complexities of HTML formats.", "subpage_snippet": "", "source": "www.builtatlightspeed.com", "link": "https://www.builtatlightspeed.com/theme/plageon-htmlrag", "content": "The system introduces Lossless HTML Cleaning and Two - Step Block - Tree - Based HTML Pruning to manage the complexities of HTML formats."}
+{"idx": 8, "title": "Unleashing the Power of HtmlRAG : Transforming RAG with HTML ...", "date": "", "ddg_snippet": "After cleaning, the HTML document is still relatively long for LLMs. So, HtmlRAG further refines the document using a two - step block - tree - based pruning strategy. The first step involves pruning based on text embedding .", "subpage_snippet": "", "source": "undatas.io", "link": "https://undatas.io/blog/posts/unleashing-the-power-of-htmlrag-transforming-rag-with-html-enhanced-table-data-from-undatas-io/", "content": "After cleaning, the HTML document is still relatively long for LLMs. So, HtmlRAG further refines the document using a two - step block - tree - based pruning strategy. The first step involves pruning based on text embedding ."}
+{"idx": 9, "title": "HtmlRAG : Enhancing RAG Systems with Richer... - MarkTechPost", "date": "", "ddg_snippet": "HtmlRAG implements a two - step pruning mechanism to process retrieved HTML documents efficiently. Initially, the system concatenates all retrieved HTML documents and parses them into a single DOM tree using Beautiful Soup.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/11/10/htmlrag-enhancing-rag-systems-with-richer-semantic-and-structural-information-through-html/", "content": "HtmlRAG implements a two - step pruning mechanism to process retrieved HTML documents efficiently. Initially, the system concatenates all retrieved HTML documents and parses them into a single DOM tree using Beautiful Soup."}
diff --git "a/data/sampled_jsons/I(p)_=_4\342\210\253\342\210\207\342\210\232p\302\262_OR_I(p)_=_\342\210\253\342\210\207log_p\302\262_p_OR_I(p)_=_\342\210\253(\342\210\207log_p)\302\262_p_Fisher_information.jsonl" "b/data/sampled_jsons/I(p)_=_4\342\210\253\342\210\207\342\210\232p\302\262_OR_I(p)_=_\342\210\253\342\210\207log_p\302\262_p_OR_I(p)_=_\342\210\253(\342\210\207log_p)\302\262_p_Fisher_information.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ "b/data/sampled_jsons/I(p)_=_4\342\210\253\342\210\207\342\210\232p\302\262_OR_I(p)_=_\342\210\253\342\210\207log_p\302\262_p_OR_I(p)_=_\342\210\253(\342\210\207log_p)\302\262_p_Fisher_information.jsonl"
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/ICFL_matching_pursuit_algorithm_J_iterations_sparsity_K_parameter_year_2024.jsonl b/data/sampled_jsons/ICFL_matching_pursuit_algorithm_J_iterations_sparsity_K_parameter_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eaf57ef9f9fe49794ccc266b5fd0873adb124d34
--- /dev/null
+++ b/data/sampled_jsons/ICFL_matching_pursuit_algorithm_J_iterations_sparsity_K_parameter_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Matching Pursuit Algorithm — sparse-plex v2019.02", "date": "", "ddg_snippet": "The matching pursuit algorithm is a very simple iterative approach to solve the sparse recovery problem. We are given the signal \\ (y\\) and the dictionary \\ (\\Phi\\) and we are to recover the sparse representation \\ (x\\) satisfying \\ (y = \\Phi x\\). In each iteration of matching pursuit : A current estimate of the representation vector \\ (x\\) is maintained in the variable \\ (z\\). Current residual ...", "subpage_snippet": "", "source": "sparse-plex.readthedocs.io", "link": "https://sparse-plex.readthedocs.io/en/latest/book/pursuit/mp/algorithm.html", "content": "The matching pursuit algorithm is a very simple iterative approach to solve the sparse recovery problem. We are given the signal \\ (y\\) and the dictionary \\ (\\Phi\\) and we are to recover the sparse representation \\ (x\\) satisfying \\ (y = \\Phi x\\). In each iteration of matching pursuit : A current estimate of the representation vector \\ (x\\) is maintained in the variable \\ (z\\). Current residual ..."}
+{"idx": 1, "title": "PDF Generalized Matching Pursuits for the Sparse Optimization of Separable ...", "date": "", "ddg_snippet": "Matching pursuit algorithms are a popular family of algo-rithms for compressed sensing and feature selection. Origi-nally, Matching Pursuit (MP) was proposed as an algorithm for the least-squares objective, but has recently been general-ized to arbitrary convex objectives.", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/gomes/pdf/2022_ament_icassp_mp.pdf", "content": "Matching pursuit algorithms are a popular family of algo-rithms for compressed sensing and feature selection. Origi-nally, Matching Pursuit (MP) was proposed as an algorithm for the least-squares objective, but has recently been general-ized to arbitrary convex objectives."}
+{"idx": 2, "title": "matchingPursuit - Recover sparse signal using matching pursuit ...", "date": "", "ddg_snippet": "The pursuit algorithm stops if the number of iterations reaches maxIterations. Note that the number of iterations matchingPursuit performs to recover the sparse signal is equal to the length of the index vector I.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/help/wavelet/ref/sensingdictionary.matchingpursuit.html", "content": "The pursuit algorithm stops if the number of iterations reaches maxIterations. Note that the number of iterations matchingPursuit performs to recover the sparse signal is equal to the length of the index vector I."}
+{"idx": 3, "title": "Improved Generalized Sparsity Adaptive Matching Pursuit Algorithm Based ...", "date": "", "ddg_snippet": "The modified adaptive orthogonal matching pursuit algorithm has a lower convergence speed. To overcome this problem, an improved method with faster convergence speed is proposed. In respect of atomic selection, the proposed method computes the correlation between the measurement matrix and residual and then selects the atoms most related to residual to construct the candidate atomic set. The ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1155/2020/2782149", "content": "The modified adaptive orthogonal matching pursuit algorithm has a lower convergence speed. To overcome this problem, an improved method with faster convergence speed is proposed. In respect of atomic selection, the proposed method computes the correlation between the measurement matrix and residual and then selects the atoms most related to residual to construct the candidate atomic set. The ..."}
+{"idx": 4, "title": "Matching Pursuit - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "Matching Pursuit The matching pursuit algorithm in Section 12.3.1 is extended by searching for dictionary elements that maximize the norm of the multichannel inner product vector. We set the relaxation parameter α = 1. Let = {φ p} p ∈ Γ be a dictionary of unit vectors. The matching pursuit algorithm is initialized with R 0 f → = f →.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/computer-science/matching-pursuit", "content": "Matching Pursuit The matching pursuit algorithm in Section 12.3.1 is extended by searching for dictionary elements that maximize the norm of the multichannel inner product vector. We set the relaxation parameter α = 1. Let = {φ p} p ∈ Γ be a dictionary of unit vectors. The matching pursuit algorithm is initialized with R 0 f → = f →."}
+{"idx": 5, "title": "PDF On the Number of Iterations for the Matching Pursuit Algorithm", "date": "", "ddg_snippet": "The matching pursuit algorithm (MPA) is well-known in the signal processing community mainly due to [1]; it is also applied in statistics and in approximation theory where it is known as L2-boosting [2] and as the pure greedy algorithm [3], respectively.", "subpage_snippet": "", "source": "eurasip.org", "link": "https://eurasip.org/Proceedings/Eusipco/Eusipco2017/papers/1570346793.pdf", "content": "The matching pursuit algorithm (MPA) is well-known in the signal processing community mainly due to [1]; it is also applied in statistics and in approximation theory where it is known as L2-boosting [2] and as the pure greedy algorithm [3], respectively."}
+{"idx": 6, "title": "PDF Sequential Sparse Matching Pursuit", "date": "", "ddg_snippet": "Abstract—We propose a new algorithm , called Sequential Sparse Matching Pursuit (SSMP), for solving sparse recovery problems. The algorithm provably recovers a k-sparse approx-imation to an arbitrary n-dimensional signal vector x from only O(k log(n=k)) linear measurements of x. The recovery process takes time that is only near-linear in n.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/58832/Berinde-2009-Sequential+sparse+matching+pursuit.pdf", "content": "Abstract—We propose a new algorithm , called Sequential Sparse Matching Pursuit (SSMP), for solving sparse recovery problems. The algorithm provably recovers a k-sparse approx-imation to an arbitrary n-dimensional signal vector x from only O(k log(n=k)) linear measurements of x. The recovery process takes time that is only near-linear in n."}
+{"idx": 7, "title": "PDF A Unified Optimization View on Generalized Matching Pursuit and Frank-Wol", "date": "", "ddg_snippet": "Two of the most fundamental prototypes of greedy optimization are the matching pursuit and Frank-Wolfe algorithms . In this paper, we take a unified view on both classes of methods, leading to the first explicit convergence rates of matching pur-suit methods in an optimization sense, for general sets of atoms. We derive sublinear (1=t) conver-gence for both classes on general smooth objec-tives ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v54/locatello17a/locatello17a.pdf", "content": "Two of the most fundamental prototypes of greedy optimization are the matching pursuit and Frank-Wolfe algorithms . In this paper, we take a unified view on both classes of methods, leading to the first explicit convergence rates of matching pur-suit methods in an optimization sense, for general sets of atoms. We derive sublinear (1=t) conver-gence for both classes on general smooth objec-tives ..."}
+{"idx": 8, "title": "PDF Matching Pursuit (MP)", "date": "", "ddg_snippet": "At each iteration m, the vector d`m selected by the Matching Pursuit algorithm is a priori not orthogonal to the previously selected vectors d`k, k 2 m The Orthogonal Matching Pursuit (OMP) algorithm improves the MP algorithm by orthogonalizing the directions of projections.", "subpage_snippet": "", "source": "machinelearning.tf.fau.de", "link": "https://machinelearning.tf.fau.de/pdfs/mmmlisp19/2-omp.pdf", "content": "At each iteration m, the vector d`m selected by the Matching Pursuit algorithm is a priori not orthogonal to the previously selected vectors d`k, k 2 m The Orthogonal Matching Pursuit (OMP) algorithm improves the MP algorithm by orthogonalizing the directions of projections."}
+{"idx": 9, "title": "Multidimensional orthogonal matching pursuit: theory and application to ...", "date": "", "ddg_snippet": "Every matching pursuit algorithm consists of two-stage iterations , namely match-ing projection and residual update. The classical orthogonal matching pursuit algorithm [26] starts by initializing the residual observation to O.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.11600", "content": "Every matching pursuit algorithm consists of two-stage iterations , namely match-ing projection and residual update. The classical orthogonal matching pursuit algorithm [26] starts by initializing the residual observation to O."}
diff --git a/data/sampled_jsons/ICML_2025_Medium_paper_3_initialization_year_2025.jsonl b/data/sampled_jsons/ICML_2025_Medium_paper_3_initialization_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a6dbba1c3027714449f5d3b42f455a792993ceb0
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_Medium_paper_3_initialization_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International Conference on Learning Representations - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. The International Conference on Learning Representations is a machine learning conference typically held in late April or early May each year. Along with NeurIPS and ICML ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/International_Conference_on_Learning_Representations", "content": "Machine learningand data mining. v. t. e. The International Conference on Learning Representations is a machine learning conference typically held in late April or early May each year. Along with NeurIPS and ICML ..."}
+{"idx": 1, "title": "A quote from ICML 2025 | Simon Willison’s Weblog", "date": "", "ddg_snippet": "Although ICML 2025 reviewers are forbidden from using LLMs to produce their reviews of paper submissions, this fact does not excuse the attempted subversion.— ICML 2025 , Statement about subversive hidden LLM prompts.", "subpage_snippet": "", "source": "simonwillison.net", "link": "https://simonwillison.net/2025/jul/23/icml-2025/", "content": "Although ICML 2025 reviewers are forbidden from using LLMs to produce their reviews of paper submissions, this fact does not excuse the attempted subversion.— ICML 2025 , Statement about subversive hidden LLM prompts."}
+{"idx": 2, "title": "2025 Conference", "date": "", "ddg_snippet": "Forty-Second International Conference on Machine Learning . Vancouver Convention Center. ICML 2025 virtual content is now free. An account is required. Beware of emails that claim they can provide a list of ICML attendees.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/", "content": "Forty-Second International Conference on Machine Learning . Vancouver Convention Center. ICML 2025 virtual content is now free. An account is required. Beware of emails that claim they can provide a list of ICML attendees."}
+{"idx": 3, "title": "Explore our scientific papers on fundamental problems in machine ...", "date": "", "ddg_snippet": "ICML , 2025 . Abstract. This paper addresses the problem of quantifying diversity for a set of objects. First, we conduct a systematic review of existing diversity measures and explore their undesirable behavior in certain cases.", "subpage_snippet": "", "source": "research.yandex.com", "link": "https://research.yandex.com/publications", "content": "ICML , 2025 . Abstract. This paper addresses the problem of quantifying diversity for a set of objects. First, we conduct a systematic review of existing diversity measures and explore their undesirable behavior in certain cases."}
+{"idx": 4, "title": "Lakera's paper on AI security accepted to ICML 2025 | LinkedIn", "date": "", "ddg_snippet": "Excited to share that our paper \"Gandalf the Red: Adaptive Security for LLMs\" has been accepted to ICML 2025 ! This marks a big milestone for us at Lakera, and a testament to the incredible work behind Gandalf—our AI security game that has g...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/mateor_icml2025-aisecurity-llms-activity-7328690131379363840-CZlO", "content": "Excited to share that our paper \"Gandalf the Red: Adaptive Security for LLMs\" has been accepted to ICML 2025 ! This marks a big milestone for us at Lakera, and a testament to the incredible work behind Gandalf—our AI security game that has g..."}
+{"idx": 5, "title": "Position: Democratic AI is Possible. The Democracy... | OpenReview", "date": "", "ddg_snippet": "This position paper argues that effectively \"democratizing AI\" requires democratic governance and alignment of AI, and that this is particularly valuable for decisions with systemic societal impacts.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=yYJo8czj4f", "content": "This position paper argues that effectively \"democratizing AI\" requires democratic governance and alignment of AI, and that this is particularly valuable for decisions with systemic societal impacts."}
+{"idx": 6, "title": "An open platform for evaluating AI through human preference", "date": "", "ddg_snippet": "Learn more about our open datasets and research papers . LMArena has open-sourced the largest repository of organic human preferences on generative models in the world.", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/how-it-works", "content": "Learn more about our open datasets and research papers . LMArena has open-sourced the largest repository of organic human preferences on generative models in the world."}
+{"idx": 7, "title": "Discrete flow matching framework for graph generation - ΑΙhub", "date": "", "ddg_snippet": "International Conference on Machine Learning ( 2025 ) [5] Xu, Zhe, et al. Discrete-state continuous-time diffusion for graph generation.Onno writes about work presented at ICML 2025 , introducing an alternative memory framework.", "subpage_snippet": "", "source": "aihub.org", "link": "https://aihub.org/2025/09/17/discrete-flow-matching-framework-for-graph-generation/", "content": "International Conference on Machine Learning ( 2025 ) [5] Xu, Zhe, et al. Discrete-state continuous-time diffusion for graph generation.Onno writes about work presented at ICML 2025 , introducing an alternative memory framework."}
+{"idx": 8, "title": "Can AI Create a Profitable Python Trading Bot for Stocks? An In-Depth...", "date": "", "ddg_snippet": "Paper Trading: Simulated Real-World Trading Before Deployment. After successful backtesting, the next step is paper trading (or simulated trading). This involves running your bot in a simulated environment using real-time market data but without risking actual money.", "subpage_snippet": "", "source": "trading-strategies.academy", "link": "https://trading-strategies.academy/archives/1648", "content": "Paper Trading: Simulated Real-World Trading Before Deployment. After successful backtesting, the next step is paper trading (or simulated trading). This involves running your bot in a simulated environment using real-time market data but without risking actual money."}
+{"idx": 9, "title": "MEME INSTANTS | Meme Buttons", "date": "", "ddg_snippet": "© localhost limited 2025 Company Logo.", "subpage_snippet": "", "source": "memeinstants.com", "link": "https://memeinstants.com/", "content": "© localhost limited 2025 Company Logo."}
diff --git a/data/sampled_jsons/ICML_2025_MultiPDENet_PDE-embedded_Learning.jsonl b/data/sampled_jsons/ICML_2025_MultiPDENet_PDE-embedded_Learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f8bcce0ecb154eb7db5cf3902fe5d6e32ed37d91
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_MultiPDENet_PDE-embedded_Learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster MultiPDENet: PDE-embedded Learning with Multi-time-stepping ...", "date": "", "ddg_snippet": "MultiPDENet : PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation Qi Wang · Yuan Mi · Wang Haoyun · Yi Zhang · Ruizhi Chengze · Hongsheng Liu · Ji-Rong Wen · Hao Sun", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46029", "content": "MultiPDENet : PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation Qi Wang · Yuan Mi · Wang Haoyun · Yi Zhang · Ruizhi Chengze · Hongsheng Liu · Ji-Rong Wen · Hao Sun"}
+{"idx": 1, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.15987v1", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows."}
+{"idx": 2, "title": "PDE-constrained Learning with Multi-time-stepping for Accelerated...", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of fluid flows.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of fluid flows."}
+{"idx": 3, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations ( PDEs ) by numerical methods meet ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/MultiPDENet:-PDE-embedded-Learning-with-for-Flow-Wang-Mi/6aee4adf8e7489f251995859a5f0432a2c60bb82", "content": "A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations ( PDEs ) by numerical methods meet ..."}
+{"idx": 4, "title": "PDE-EMBEDDED LEARNING WITH MULTI TIME STEPPING FOR ... - OpenReview", "date": "", "ddg_snippet": "Machine learning can accelerate this process, but struggle with weak generalizability, interpretability, and data dependency, as well as suf- fer in long-term prediction.To this end, we propose a PDE -embeddednetwork with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numer- ical methods and machine learning , for accelerated ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=stcN89QGfL", "content": "Machine learning can accelerate this process, but struggle with weak generalizability, interpretability, and data dependency, as well as suf- fer in long-term prediction.To this end, we propose a PDE -embeddednetwork with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numer- ical methods and machine learning , for accelerated ..."}
+{"idx": 5, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows."}
+{"idx": 6, "title": "Track: Poster Session 2 West - icml.cc", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50258", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows."}
+{"idx": 7, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Enable Javascript in your browser to see the papers page.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html?search=agent", "content": "Enable Javascript in your browser to see the papers page."}
+{"idx": 8, "title": "Yuan Mi - catalyzex.com", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Yuan+Mi", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows."}
+{"idx": 9, "title": "Yi Zhang - OpenReview", "date": "", "ddg_snippet": "Publications MultiPDENet : PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation Qi Wang, Yuan Mi, Wang Haoyun, Yi Zhang, Ruizhi Chengze, Hongsheng Liu, Ji-Rong Wen, Hao Sun Published: 01 May 2025 , Last Modified: 23 Jul 2025 ICML 2025 poster PDE -constrained Learning with Multi-time-stepping for Accelerated Fluid Simulation", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Yi_Zhang92", "content": "Publications MultiPDENet : PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation Qi Wang, Yuan Mi, Wang Haoyun, Yi Zhang, Ruizhi Chengze, Hongsheng Liu, Ji-Rong Wen, Hao Sun Published: 01 May 2025 , Last Modified: 23 Jul 2025 ICML 2025 poster PDE -constrained Learning with Multi-time-stepping for Accelerated Fluid Simulation"}
diff --git a/data/sampled_jsons/ICML_2025_Socialized_Coevolution_Dynamic_Hierarchical_Collaboration_equation_scaling.jsonl b/data/sampled_jsons/ICML_2025_Socialized_Coevolution_Dynamic_Hierarchical_Collaboration_equation_scaling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6fe07908a2f753b188645681d05ddc3e7e3c9fec
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_Socialized_Coevolution_Dynamic_Hierarchical_Collaboration_equation_scaling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Socialized Coevolution : Advancing a Better World...", "date": "", "ddg_snippet": "Login. Select Year: ( 2025 ).Specifically, we introduce the dynamic hierarchical collaboration and dynamic selective collaboration modules to enable dynamic and effective interactions among models, allowing them to acquire knowledge from these interactions.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46680", "content": "Login. Select Year: ( 2025 ).Specifically, we introduce the dynamic hierarchical collaboration and dynamic selective collaboration modules to enable dynamic and effective interactions among models, allowing them to acquire knowledge from these interactions."}
+{"idx": 1, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "SPEX: Scaling Feature Interaction Explanations for LLMs · Statistical Test for Feature Selection Pipelines by Selective Inference · Position: AI Safety should ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "SPEX: Scaling Feature Interaction Explanations for LLMs · Statistical Test for Feature Selection Pipelines by Selective Inference · Position: AI Safety should ..."}
+{"idx": 2, "title": "(PDF) Dynamical Scenarios for the Coevolution of Multiple AGIs", "date": "", "ddg_snippet": "Coevolution of Multiple AGIs. Frederic Victor Donzé. *Corresponding author e‑mail: frederic.donze@univ-grenoble-alpes.fr.model based on coupled differential equations describing the state variables of each AGI (e.g., capability level, resource control, influence).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395014463_Dynamical_Scenarios_for_the_Coevolution_of_Multiple_AGIs", "content": "Coevolution of Multiple AGIs. Frederic Victor Donzé. *Corresponding author e‑mail: frederic.donze@univ-grenoble-alpes.fr.model based on coupled differential equations describing the state variables of each AGI (e.g., capability level, resource control, influence)."}
+{"idx": 3, "title": "两篇论文被 ICML 2025 录用 – VISDRONE", "date": "", "ddg_snippet": "论文《CKD: Contrastive Knowledge Distillation from A Sample-Wise Perspective》被TIP 2025 录用.论文题目： Socialized Coevolution : Advancing a Better World through Cross-Task Collaboration .", "subpage_snippet": "", "source": "aiskyeye.com", "link": "https://aiskyeye.com/论文《socialized-coevolution-advancing-a-better-world-through-cross-task-collaboration》和《task-gated-multi-expert-collaboration-network-for-degraded-multi/", "content": "论文《CKD: Contrastive Knowledge Distillation from A Sample-Wise Perspective》被TIP 2025 录用.论文题目： Socialized Coevolution : Advancing a Better World through Cross-Task Collaboration ."}
+{"idx": 4, "title": "Trust then talk or talk then trust? The coevolution of communication...", "date": "", "ddg_snippet": "A dynamic social network experiment with multi-team systems. In 2014 IEEE fourth international conference on big data and cloud computing (BdCloud) (pp. 587–593).The coevolution of communication networks and inter-organizational trust.", "subpage_snippet": "", "source": "cognitiveresearchjournal.springeropen.com", "link": "https://cognitiveresearchjournal.springeropen.com/articles/10.1186/s41235-025-00662-1", "content": "A dynamic social network experiment with multi-team systems. In 2014 IEEE fourth international conference on big data and cloud computing (BdCloud) (pp. 587–593).The coevolution of communication networks and inter-organizational trust."}
+{"idx": 5, "title": "ACT Token Price Prediction 2025 -2030: Market Analysis... | LeveX", "date": "", "ddg_snippet": "FeaturedAug 28, 2025 . ACT Token Price Prediction 2025 -2030: Market Analysis & Investment Outlook. Act I: The AI Prophecy has emerged as one of the most compelling AI-cryptocurrency projects since its October 2024 launch, with analysts projecting significant price potential through 2030.", "subpage_snippet": "", "source": "levex.com", "link": "https://levex.com/en/blog/act-price-prediction", "content": "FeaturedAug 28, 2025 . ACT Token Price Prediction 2025 -2030: Market Analysis & Investment Outlook. Act I: The AI Prophecy has emerged as one of the most compelling AI-cryptocurrency projects since its October 2024 launch, with analysts projecting significant price potential through 2030."}
+{"idx": 6, "title": "Modern Artificial Intelligence Based on Soft Computing ...", "date": "", "ddg_snippet": "scaling formula computes the model size.M as .M = αd · βw · γr ,. (8) ... cooperative coevolutionary paradigm for dynamic multiobjective optimization ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-88279-1.pdf", "content": "scaling formula computes the model size.M as .M = αd · βw · γr ,. (8) ... cooperative coevolutionary paradigm for dynamic multiobjective optimization ..."}
+{"idx": 7, "title": "Computer Science Apr 2024", "date": "", "ddg_snippet": "8 Apr 2024 — Title: A New Hybrid Automaton Framework with Partial Differential Equation Dynamics ... Hierarchical Cooperative Attention. Xinxin Jiao, Liejun ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2024-04?skip=5210&show=1000", "content": "8 Apr 2024 — Title: A New Hybrid Automaton Framework with Partial Differential Equation Dynamics ... Hierarchical Cooperative Attention. Xinxin Jiao, Liejun ..."}
+{"idx": 8, "title": "Innovations for Community Services", "date": "", "ddg_snippet": "11 Jun 2025 — In 2025 , the International Conference on Innovations for Community Services (I4CS) celebrates its 25th edition, visiting Munich for the second ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-94263-1.pdf", "content": "11 Jun 2025 — In 2025 , the International Conference on Innovations for Community Services (I4CS) celebrates its 25th edition, visiting Munich for the second ..."}
+{"idx": 9, "title": "SIGACCESS Annual Report July 1, 2023", "date": "", "ddg_snippet": "SIGAPP has also reorganized the Steering Committee of ACM. SAC 2025 , its flagship conference, to comply with ACM policies on diversity, equality, and inclusion, ... 189 pages", "subpage_snippet": "", "source": "www.acm.org", "link": "https://www.acm.org/binaries/content/assets/about/annual-reports/appendix-d-fy24-sig-annual-reports.pdf", "content": "SIGAPP has also reorganized the Steering Committee of ACM. SAC 2025 , its flagship conference, to comply with ACM policies on diversity, equality, and inclusion, ... 189 pages"}
diff --git a/data/sampled_jsons/ICML_2025_collective_action_papers_year_2025.jsonl b/data/sampled_jsons/ICML_2025_collective_action_papers_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c76e88b65ee8a0a9c882439be991a9e693bc12da
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_collective_action_papers_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2025 Conference", "date": "", "ddg_snippet": "Forty-Second International Conference on Machine Learning . Vancouver Convention Center. ICML 2025 virtual content is now free. An account is required. Beware of emails that claim they can provide a list of ICML attendees.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/", "content": "Forty-Second International Conference on Machine Learning . Vancouver Convention Center. ICML 2025 virtual content is now free. An account is required. Beware of emails that claim they can provide a list of ICML attendees."}
+{"idx": 1, "title": "Meet Seed at ICML 2025 : 25 Papers Accepted", "date": "", "ddg_snippet": "The International Conference on Machine Learning ( ICML ) 2025 is scheduled from July 13 to July 19 in Vancouver, Canada.Click for more details on Seed's ICML 2025 accepted papers and other publicly available research!", "subpage_snippet": "", "source": "research.doubao.com", "link": "https://research.doubao.com/en/blog/meet-seed-at-icml-2025-25-papers-accepted", "content": "The International Conference on Machine Learning ( ICML ) 2025 is scheduled from July 13 to July 19 in Vancouver, Canada.Click for more details on Seed's ICML 2025 accepted papers and other publicly available research!"}
+{"idx": 2, "title": "A quote from ICML 2025 | Simon Willison’s Weblog", "date": "", "ddg_snippet": "Although ICML 2025 reviewers are forbidden from using LLMs to produce their reviews of paper submissions, this fact does not excuse the attempted subversion.— ICML 2025 , Statement about subversive hidden LLM prompts.", "subpage_snippet": "", "source": "simonwillison.net", "link": "https://simonwillison.net/2025/jul/23/icml-2025/", "content": "Although ICML 2025 reviewers are forbidden from using LLMs to produce their reviews of paper submissions, this fact does not excuse the attempted subversion.— ICML 2025 , Statement about subversive hidden LLM prompts."}
+{"idx": 3, "title": "ICML 2025 Template - Overleaf, Online-LaTeX-Editor", "date": "", "ddg_snippet": "Conference Paper . International Conference on Machine Learning ( ICML 2025 )} %. It is OKAY to include author information, even for blind % submissions: the style file will automatically remove it for you % unless you've provided the [accepted] option to the icml 2025 % package. %", "subpage_snippet": "", "source": "de.overleaf.com", "link": "https://de.overleaf.com/latex/templates/icml2025-template/dhxrkcgkvnkt", "content": "Conference Paper . International Conference on Machine Learning ( ICML 2025 )} %. It is OKAY to include author information, even for blind % submissions: the style file will automatically remove it for you % unless you've provided the [accepted] option to the icml 2025 % package. %"}
+{"idx": 4, "title": "ICML 2027", "date": "", "ddg_snippet": "International Conference on Minority Languages. 22–26 June 2027, Bozen/Bolzano/Bulsan, Eurac Research. About ICML XXI.", "subpage_snippet": "", "source": "icml2027.eurac.edu", "link": "https://icml2027.eurac.edu/en", "content": "International Conference on Minority Languages. 22–26 June 2027, Bozen/Bolzano/Bulsan, Eurac Research. About ICML XXI."}
+{"idx": 5, "title": "\" International Conference on Machine Learning ( ICML ) 2025 in...\"", "date": "", "ddg_snippet": "Since September 17, 2025 . Share.in this collection in this repository across all repositories.", "subpage_snippet": "", "source": "digitalcommons.imsa.edu", "link": "https://digitalcommons.imsa.edu/homepage/79/", "content": "Since September 17, 2025 . Share.in this collection in this repository across all repositories."}
+{"idx": 6, "title": "18th International Conference on Malignant Lymphoma ( ICML )...", "date": "", "ddg_snippet": "Stay up to date with the latest developments in chronic lymphocytic leukemia and small lymphocytic lymphoma with our social media coverage from the 18th International Conference on Malignant Lymphoma ( ICML ), June 17–21, 2025 , Lugano, CH.", "subpage_snippet": "", "source": "lymphomahub.com", "link": "https://lymphomahub.com/medical-information/18th-international-conference-on-malignant-lymphoma-icml-congress-coverage-chronic-lymphocytic-leukemia-and-small-lymphocytic-lymphoma", "content": "Stay up to date with the latest developments in chronic lymphocytic leukemia and small lymphocytic lymphoma with our social media coverage from the 18th International Conference on Malignant Lymphoma ( ICML ), June 17–21, 2025 , Lugano, CH."}
+{"idx": 7, "title": "Sail to Gaza - Collective Action for Gaza", "date": "", "ddg_snippet": "Yes, this action is legal under international law. The Israeli occupation's blockade of Gaza constitutes collective punishment, a violation of the Geneva Conventions.", "subpage_snippet": "", "source": "globalsumudflotilla.org", "link": "https://globalsumudflotilla.org/", "content": "Yes, this action is legal under international law. The Israeli occupation's blockade of Gaza constitutes collective punishment, a violation of the Geneva Conventions."}
+{"idx": 8, "title": "Icml 2025 Workshop List - Amelie S. Mountford", "date": "", "ddg_snippet": "Icml 2025 Paper List Lorne Rebecka. Icml 2025 List Jill Romona, This section sets the theme of the workshop and also reviews climsim, a paper and dataset accepted in the neurips.", "subpage_snippet": "", "source": "ameliemountfords.pages.dev", "link": "https://ameliemountfords.pages.dev/vhsjf-icml-2025-workshop-list-fmskh/", "content": "Icml 2025 Paper List Lorne Rebecka. Icml 2025 List Jill Romona, This section sets the theme of the workshop and also reviews climsim, a paper and dataset accepted in the neurips."}
+{"idx": 9, "title": "Muslims in ML Workshop | ICML '25", "date": "", "ddg_snippet": "Call for Papers : Muslims in ML Workshop co-located with ICML 2025 . We are pleased to announce the 4th Muslims in Machine Learning (MusIML) Workshop, which will take place at ICML 2025 .", "subpage_snippet": "", "source": "www.musiml.org", "link": "https://www.musiml.org/events/2025-ICML/cfp.html", "content": "Call for Papers : Muslims in ML Workshop co-located with ICML 2025 . We are pleased to announce the 4th Muslims in Machine Learning (MusIML) Workshop, which will take place at ICML 2025 ."}
diff --git a/data/sampled_jsons/ICML_2025_language_models_papers_medium_tier_5.jsonl b/data/sampled_jsons/ICML_2025_language_models_papers_medium_tier_5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ddf35001fb61cf49a0434645515e03bcf2aaf61a
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_language_models_papers_medium_tier_5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Select Year: ( 2025 ) Getting Started Schedule Tutorials Main Conference Workshops Community Exhibitors Organizers Help Browse Visualization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html?search=position:", "content": "Select Year: ( 2025 ) Getting Started Schedule Tutorials Main Conference Workshops Community Exhibitors Organizers Help Browse Visualization"}
+{"idx": 1, "title": "ICML.2025 | Cool Papers - Immersive Paper Discovery", "date": "", "ddg_snippet": "The list of accepted papers for ICML.2025 , including titles, authors, and abstracts, with support for paper interpretation based on Kimi AI.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/ICML.2025", "content": "The list of accepted papers for ICML.2025 , including titles, authors, and abstracts, with support for paper interpretation based on Kimi AI."}
+{"idx": 2, "title": "ICML 2025 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "How to use the paper list below:- Overview: This table presents papers from the ICML conference, year 2025 .- Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/icml-paper-list/icml-2025-paper-list/", "content": "How to use the paper list below:- Overview: This table presents papers from the ICML conference, year 2025 .- Filtering: By default, the table loads the first 100 records."}
+{"idx": 3, "title": "Paper Digest: ICML 2025 Papers & Highlights", "date": "", "ddg_snippet": "Interested users can choose to read All 3,300 ICML-2025 papers in a separate page. To search for papers presented at ICML-2025 on a specific topic, please make use of the search by venue ( ICML-2025 ) service. To summarize the latest research published at ICML-2025 on a specific topic, you can utilize the review by venue ( ICML-2025 ) service.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/06/icml-2025-papers-highlights/", "content": "Interested users can choose to read All 3,300 ICML-2025 papers in a separate page. To search for papers presented at ICML-2025 on a specific topic, please make use of the search by venue ( ICML-2025 ) service. To summarize the latest research published at ICML-2025 on a specific topic, you can utilize the review by venue ( ICML-2025 ) service."}
+{"idx": 4, "title": "Stanford AI Lab Papers and Talks at ICML 2025 | SAIL Blog", "date": "", "ddg_snippet": "The Forty-Second International Conference on Machine Learning ( ICML ) 2025 is being hosted in Vancouver from July 13 to July 19. We're excited to share all the work from SAIL that's being presented, and you'll find links to papers , videos and blogs below.", "subpage_snippet": "", "source": "ai.stanford.edu", "link": "https://ai.stanford.edu/blog/icml-2025/", "content": "The Forty-Second International Conference on Machine Learning ( ICML ) 2025 is being hosted in Vancouver from July 13 to July 19. We're excited to share all the work from SAIL that's being presented, and you'll find links to papers , videos and blogs below."}
+{"idx": 5, "title": "ICML 2025 top 50 Papers That Might Shape the Future of ML — According ...", "date": "", "ddg_snippet": "ICML 2025 top 50 Papers That Might Shape the Future of ML — According to Gemini Created on July 12, 2025 2025 · ICML 2025 Research Papers Machine Learning Foundation Models Interpretability Generative Models · Machine Learning ICML Research So I tried something different.", "subpage_snippet": "", "source": "blog.faraji.info", "link": "https://blog.faraji.info/machine-learning/icml-2025-top-papers.html", "content": "ICML 2025 top 50 Papers That Might Shape the Future of ML — According to Gemini Created on July 12, 2025 2025 · ICML 2025 Research Papers Machine Learning Foundation Models Interpretability Generative Models · Machine Learning ICML Research So I tried something different."}
+{"idx": 6, "title": "ICML 2025 Oral Papers - Yan Lin's Blog", "date": "", "ddg_snippet": "Editor's note: This is not the complete list of ICML 2025 oral papers , but rather a selection of papers that I found particularly interesting or insightful.", "subpage_snippet": "", "source": "blog.yanlincs.com", "link": "https://blog.yanlincs.com/conf-paper/icml-2025-oral", "content": "Editor's note: This is not the complete list of ICML 2025 oral papers , but rather a selection of papers that I found particularly interesting or insightful."}
+{"idx": 7, "title": "ICML2025 - a Hugging Face Space by ai-conferences", "date": "", "ddg_snippet": "ICML 2025 Advanced Search Options Presentation Type (ALL) Oral Spotlight Poster", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/ai-conferences/ICML2025", "content": "ICML 2025 Advanced Search Options Presentation Type (ALL) Oral Spotlight Poster"}
+{"idx": 8, "title": "ICML 2025 Workshop on Multi-modal Foundation Models and Large Language ...", "date": "", "ddg_snippet": "About Foundation models and large language models (LLMs) represent a transformative advancement in life sciences, offering a new paradigm for understanding complex biological systems. These large-scale models are pretrained on extensive datasets encompassing various biological data types, enabling them to perform a wide range of tasks such as predicting protein structures, analyzing genomic ...", "subpage_snippet": "", "source": "fm4ls.github.io", "link": "https://fm4ls.github.io/", "content": "About Foundation models and large language models (LLMs) represent a transformative advancement in life sciences, offering a new paradigm for understanding complex biological systems. These large-scale models are pretrained on extensive datasets encompassing various biological data types, enabling them to perform a wide range of tasks such as predicting protein structures, analyzing genomic ..."}
+{"idx": 9, "title": "ICML 2025 Sneak Peek: The New Architectures Powering Tomorrow ... - Medium", "date": "", "ddg_snippet": "ICML 2025 Sneak Peek: The New Architectures Powering Tomorrow's AI From diffusion mosaics to LoRA leaps, discover the blueprints powering the next wave of agentic AI.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/foundation-models-deep-dive/icml-2025-sneak-peak-the-new-architectures-powering-tomorrows-ai-2462bd829988", "content": "ICML 2025 Sneak Peek: The New Architectures Powering Tomorrow's AI From diffusion mosaics to LoRA leaps, discover the blueprints powering the next wave of agentic AI."}
diff --git a/data/sampled_jsons/ISPRS_Potsdam_dataset_tile_size_resolution_year_2024.jsonl b/data/sampled_jsons/ISPRS_Potsdam_dataset_tile_size_resolution_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aac641afca82fba39e280c1db88b2ded59dc4950
--- /dev/null
+++ b/data/sampled_jsons/ISPRS_Potsdam_dataset_tile_size_resolution_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2D Semantic Labeling Contest - Potsdam - isprs.org", "date": "", "ddg_snippet": "The data set contains 38 patches (of the same size ), each consisting of a true orthophoto (TOP) extracted from a larger TOP mosaic, see Figure below and a DSM. Outlines of all patches given in Tab.1 overlaid with the true orthophoto mosaic.", "subpage_snippet": "", "source": "www.isprs.org", "link": "https://www.isprs.org/resources/datasets/benchmarks/UrbanSemLab/2d-sem-label-potsdam.aspx", "content": "The data set contains 38 patches (of the same size ), each consisting of a true orthophoto (TOP) extracted from a larger TOP mosaic, see Figure below and a DSM. Outlines of all patches given in Tab.1 overlaid with the true orthophoto mosaic."}
+{"idx": 1, "title": "Prepare the ISPRS Potsdam dataset - GitHub", "date": "", "ddg_snippet": "Geo- Tiles for Semantic Segmentation of Earth Observation Imagery - SBCV/EarthObservationTiles", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SBCV/EarthObservationTiles/blob/master/doc/examples_potsdam_preparation.md", "content": "Geo- Tiles for Semantic Segmentation of Earth Observation Imagery - SBCV/EarthObservationTiles"}
+{"idx": 2, "title": "Datasets | sstary/SSRS | DeepWiki", "date": "", "ddg_snippet": "The ISPRS datasets are standard benchmarks for remote sensing semantic segmentation, consisting of high- resolution aerial imagery with labeled ground truth. Potsdam Dataset The ISPRS Potsdam dataset consists of very high- resolution aerial images over an urban area. Resolution : 9cm ground sampling distance Bands: RGB + Infrared (4 channels)", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/sstary/SSRS/6.1-datasets", "content": "The ISPRS datasets are standard benchmarks for remote sensing semantic segmentation, consisting of high- resolution aerial imagery with labeled ground truth. Potsdam Dataset The ISPRS Potsdam dataset consists of very high- resolution aerial images over an urban area. Resolution : 9cm ground sampling distance Bands: RGB + Infrared (4 channels)"}
+{"idx": 3, "title": "Accurate semantic segmentation of very high-resolution remote sensing ...", "date": "", "ddg_snippet": "(3) ISPRS Potsdam : The ISPRS Potsdam dataset consists of 38 TOP image tiles with a spatial resolution (5 cm), averaging at 6000 × 6000 pixels. The classes of this dataset are identical to those of the Vaihingen dataset , but the distribution of categories differs due to the geographical location of data collection.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0924271625000176", "content": "(3) ISPRS Potsdam : The ISPRS Potsdam dataset consists of 38 TOP image tiles with a spatial resolution (5 cm), averaging at 6000 × 6000 pixels. The classes of this dataset are identical to those of the Vaihingen dataset , but the distribution of categories differs due to the geographical location of data collection."}
+{"idx": 4, "title": "ISPRS Potsdam - Dataset - LDM", "date": "", "ddg_snippet": "The ISPRS Potsdam dataset consists of remotely sensed imagery with a spatial resolution of 5 centimeters, and includes 6 semantic classes.", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/isprs-potsdam", "content": "The ISPRS Potsdam dataset consists of remotely sensed imagery with a spatial resolution of 5 centimeters, and includes 6 semantic classes."}
+{"idx": 5, "title": "The remote sensing image classsification of Potsdam dataset using ...", "date": "", "ddg_snippet": "The data set contains 38 patches (of the same size ), each consisting of a true orthophoto (TOP) extracted from a larger TOP mosaic, see Figure below and a DSM. Outlines of all patches given in Tab.1 overlaid with the true orthophoto mosaic. Numbers refer to the individual patch numbers, encoded in ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yuke-xie/potsdam-tensorflow-practice", "content": "The data set contains 38 patches (of the same size ), each consisting of a true orthophoto (TOP) extracted from a larger TOP mosaic, see Figure below and a DSM. Outlines of all patches given in Tab.1 overlaid with the true orthophoto mosaic. Numbers refer to the individual patch numbers, encoded in ..."}
+{"idx": 6, "title": "Overview of the ISPRS Potsdam dataset. The aerial images shown are ...", "date": "", "ddg_snippet": "Download scientific diagram | Overview of the ISPRS Potsdam dataset . The aerial images shown are those provided by the ISPRS benchmark [54]. from publication: Learning Aerial Image Segmentation ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Overview-of-the-ISPRS-Potsdam-dataset-The-aerial-images-shown-are-those-provided-by-the_fig3_318615305", "content": "Download scientific diagram | Overview of the ISPRS Potsdam dataset . The aerial images shown are those provided by the ISPRS benchmark [54]. from publication: Learning Aerial Image Segmentation ..."}
+{"idx": 7, "title": "ISPRS Vaihingen and ISPRS Potsdam datasets - Dataset - LDM", "date": "", "ddg_snippet": "ISPRS Vaihingen and ISPRS Potsdam datasets High- resolution remote sensing images", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/isprs-vaihingen-and-isprs-potsdam-datasets", "content": "ISPRS Vaihingen and ISPRS Potsdam datasets High- resolution remote sensing images"}
+{"idx": 8, "title": "ISPRS Working Group III/4 - Test result for 2D labeling (Potsdam ...", "date": "", "ddg_snippet": "Overall accuracy 0.872 ============================================================= Tile top_potsdam_2_13_class.tif, reference set: full_reference", "subpage_snippet": "", "source": "www.isprs.org", "link": "https://www.isprs.org/resources/datasets/benchmarks/UrbanSemLab/results/2D_labeling_potsdam/2D_labeling_Potsdam_details_UFMG_2/index.html", "content": "Overall accuracy 0.872 ============================================================= Tile top_potsdam_2_13_class.tif, reference set: full_reference"}
+{"idx": 9, "title": "EarthObservationTiles/data_preparation/prepare_isprs_potsdam.py at ...", "date": "", "ddg_snippet": "Geo- Tiles for Semantic Segmentation of Earth Observation Imagery - EarthObservationTiles/data_preparation/prepare_isprs_potsdam.py at master · SBCV/EarthObservationTiles", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SBCV/EarthObservationTiles/blob/master/data_preparation/prepare_isprs_potsdam.py", "content": "Geo- Tiles for Semantic Segmentation of Earth Observation Imagery - EarthObservationTiles/data_preparation/prepare_isprs_potsdam.py at master · SBCV/EarthObservationTiles"}
diff --git a/data/sampled_jsons/ITBench_Evaluating_AI_Agents_gpt-4o-mini_gpt-4o_Table_5_OA_pass@1_score_year_2024.jsonl b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_gpt-4o-mini_gpt-4o_Table_5_OA_pass@1_score_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5e108fc4d15b1ff2a5960fe30602aa12be5265cb
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_gpt-4o-mini_gpt-4o_Table_5_OA_pass@1_score_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "For instance, GPT-4o ’s pass@1 in diagnosis falls from 13.81% with traces to 9.52% without them, and mitigation plummets to 2.86%. This highlights the critical role of system observability in SRE, which ITBench can evaluate under varying conditions.", "subpage_snippet": "", "source": "yinfangchen.github.io", "link": "https://yinfangchen.github.io/assets/pdf/it_bench_paper.pdf", "content": "For instance, GPT-4o ’s pass@1 in diagnosis falls from 13.81% with traces to 9.52% without them, and mitigation plummets to 2.86%. This highlights the critical role of system observability in SRE, which ITBench can evaluate under varying conditions."}
+{"idx": 1, "title": "GPT-5 vs GPT-4.1: choosing the right model for your use case", "date": "", "ddg_snippet": "Sep 16, 2025 · Understanding the latency differences between GPT - 5 and GPT -4. 1 is key to selecting the right model for your needs. GPT - 5 delivers powerful reasoning and deeper analysis, but this comes with slightly longer wait times before you see your first response, especially for shorter prompts.", "subpage_snippet": "", "source": "learn.microsoft.com", "link": "https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/how-to/model-choice-guide", "content": "Sep 16, 2025 · Understanding the latency differences between GPT - 5 and GPT -4. 1 is key to selecting the right model for your needs. GPT - 5 delivers powerful reasoning and deeper analysis, but this comes with slightly longer wait times before you see your first response, especially for shorter prompts."}
+{"idx": 2, "title": "GPT-4o Mini vs GPT-4o - Detailed Performance & Feature Comparison", "date": "", "ddg_snippet": "Discover how OpenAI's GPT-4o Mini and OpenAI's GPT-4o stack up in performance, features, and applications. Read our detailed comparison to find out which AI model best suits your needs.", "subpage_snippet": "", "source": "docsbot.ai", "link": "https://docsbot.ai/models/compare/gpt-4o-mini/gpt-4o", "content": "Discover how OpenAI's GPT-4o Mini and OpenAI's GPT-4o stack up in performance, features, and applications. Read our detailed comparison to find out which AI model best suits your needs."}
+{"idx": 3, "title": "Evaluating Azure OpenAI GPT-4o and GPT-4o Mini | by Harsh ...", "date": "", "ddg_snippet": "Aug 24, 2024 · Azure OpenAI GPT-4o Mini , alternatively, is a scaled-down model of GPT-4o . It’s designed to be extra environment friendly and accessible, working easily on a broader vary of hardware whereas nonetheless delivering sturdy efficiency.", "subpage_snippet": "", "source": "bakshiharsh55.medium.com", "link": "https://bakshiharsh55.medium.com/evaluating-azure-openai-gpt-4o-and-gpt-4o-mini-de621de0e73e", "content": "Aug 24, 2024 · Azure OpenAI GPT-4o Mini , alternatively, is a scaled-down model of GPT-4o . It’s designed to be extra environment friendly and accessible, working easily on a broader vary of hardware whereas nonetheless delivering sturdy efficiency."}
+{"idx": 4, "title": "GPT-4o mini: advancing cost-efficient intelligence - OpenAI", "date": "", "ddg_snippet": "Jul 18, 2024 · Today, we're announcing GPT‑4o mini , our most cost-efficient small model. We expect GPT‑4o mini will significantly expand the range of applications built with AI by making intelligence much more affordable. GPT‑4o mini scores 82% on MMLU and currently outperforms GPT ‑4 1 on chat preferences in LMSYS leaderboard .", "subpage_snippet": "", "source": "openai.com", "link": "https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/", "content": "Jul 18, 2024 · Today, we're announcing GPT‑4o mini , our most cost-efficient small model. We expect GPT‑4o mini will significantly expand the range of applications built with AI by making intelligence much more affordable. GPT‑4o mini scores 82% on MMLU and currently outperforms GPT ‑4 1 on chat preferences in LMSYS leaderboard ."}
+{"idx": 5, "title": "GPT-4.1 Mini and Nano: The Best AI Models for Building ...", "date": "", "ddg_snippet": "Apr 15, 2025 · Discover why GPT -4. 1 Mini and Nano are game-changers for AI agent developers, offering 83% cost reduction, half the latency, and superior instruction following. Find out which model suits your voice assistants, MCP agents , and automation tools.", "subpage_snippet": "", "source": "topaitech.com", "link": "https://topaitech.com/gpt-41-mini-nano-best-models-building-intelligent-ai-agents/", "content": "Apr 15, 2025 · Discover why GPT -4. 1 Mini and Nano are game-changers for AI agent developers, offering 83% cost reduction, half the latency, and superior instruction following. Find out which model suits your voice assistants, MCP agents , and automation tools."}
+{"idx": 6, "title": "Paper tables with annotated results for ITBench : Evaluating AI ...", "date": "", "ddg_snippet": "gpt - 4 o - mini . Table 19: Evaluation of SRE- agent only on scenarios with tracing enabled.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/itbench-evaluating-ai-agents-across-diverse/review/", "content": "gpt - 4 o - mini . Table 19: Evaluation of SRE- agent only on scenarios with tracing enabled."}
+{"idx": 7, "title": "ITBench : Evaluating AI Agents across", "date": "", "ddg_snippet": "Table 4 , Table 5 and Table 6 show the performance of SRE- agent , CISO- agent , and FinOps- agent respectively. ITBench Table 5 : Evaluation of CISO Compliance Assessment Agent on CISO scenarios. Models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "Table 4 , Table 5 and Table 6 show the performance of SRE- agent , CISO- agent , and FinOps- agent respectively. ITBench Table 5 : Evaluation of CISO Compliance Assessment Agent on CISO scenarios. Models."}
+{"idx": 8, "title": "(PDF) ITBench : Evaluating AI Agents across Diverse Real-World IT...", "date": "", "ddg_snippet": "Table 4 , Table 5 and Table 6show the performance of SREWe present evaluation results for four LM-based agents . across 42 SRE scenarios in the ITBench framework. Overall agent results. gpt - 4 o shows the strongest per-. formance, achieving a 13.81% pass @ 1 in diagnosis and.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388882803_ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks", "content": "Table 4 , Table 5 and Table 6show the performance of SREWe present evaluation results for four LM-based agents . across 42 SRE scenarios in the ITBench framework. Overall agent results. gpt - 4 o shows the strongest per-. formance, achieving a 13.81% pass @ 1 in diagnosis and."}
+{"idx": 9, "title": "GPT - 5 - API, Providers, Stats | OpenRouter", "date": "", "ddg_snippet": "GPT - 5 Mini is a compact version of GPT - 5 , designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT - 5 , but with reduced latency and cost. GPT - 5 Mini is the successor to OpenAI's o 4- mini model.", "subpage_snippet": "", "source": "openrouter.ai", "link": "https://openrouter.ai/openai/gpt-5", "content": "GPT - 5 Mini is a compact version of GPT - 5 , designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT - 5 , but with reduced latency and cost. GPT - 5 Mini is the successor to OpenAI's o 4- mini model."}
diff --git a/data/sampled_jsons/ITBench_MemoryLeak_scenario_parameters_fault_propagation_resolution_technology.jsonl b/data/sampled_jsons/ITBench_MemoryLeak_scenario_parameters_fault_propagation_resolution_technology.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9a2793d4146d6a5f261257d23fd12dd2299d6452
--- /dev/null
+++ b/data/sampled_jsons/ITBench_MemoryLeak_scenario_parameters_fault_propagation_resolution_technology.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - itbench-hub/ITBench: Code repository for ITBench", "date": "", "ddg_snippet": "📢 Announcements Latest Updates [June 13, 2025] Identified 25+ additional scenarios to be developed over the summer. [May 2, 2025] 🚀 ITBench now provides fully-managed scenario environments for everyone! Our platform handles the complete workflow—from scenario deployment to agent evaluation and leaderboard updates.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench", "content": "📢 Announcements Latest Updates [June 13, 2025] Identified 25+ additional scenarios to be developed over the summer. [May 2, 2025] 🚀 ITBench now provides fully-managed scenario environments for everyone! Our platform handles the complete workflow—from scenario deployment to agent evaluation and leaderboard updates."}
+{"idx": 1, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks", "date": "", "ddg_snippet": "ITBench includes an initial set of 94 real-world scenarios , which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios , 25.2% of CISO scenarios , and 0% of FinOps scenarios .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "ITBench includes an initial set of 94 real-world scenarios , which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios , 25.2% of CISO scenarios , and 0% of FinOps scenarios ."}
+{"idx": 2, "title": "ITBench: Next-Gen Benchmarking for IT Automation Evaluation", "date": "", "ddg_snippet": "Learn more about ITBench , an open framework to benchmark AI agents for IT automation, focusing on reliability, efficiency, and real-world IT scenarios .", "subpage_snippet": "", "source": "dzone.com", "link": "https://dzone.com/articles/itbench-next-gen-benchmarking-it-automation", "content": "Learn more about ITBench , an open framework to benchmark AI agents for IT automation, focusing on reliability, efficiency, and real-world IT scenarios ."}
+{"idx": 3, "title": "Faster Fixes: How ITBench transforms system reliability with AI", "date": "", "ddg_snippet": "As the demand for scalable, resilient, and cost-effective IT infrastructure continues to grow, tools like ITBench represent a critical step toward a future where AI not only supports but also actively strengthens the backbone of modern technology systems.", "subpage_snippet": "", "source": "discoveryacceleratorinstitute.grainger.illinois.edu", "link": "https://discoveryacceleratorinstitute.grainger.illinois.edu/news/76016", "content": "As the demand for scalable, resilient, and cost-effective IT infrastructure continues to grow, tools like ITBench represent a critical step toward a future where AI not only supports but also actively strengthens the backbone of modern technology systems."}
+{"idx": 4, "title": "GitHub - itbench-hub/ITBench-Scenarios: Code repository for scenarios ...", "date": "", "ddg_snippet": "ITBench - Scenarios This repository contains infrastructure automation scripts for both deploying the environments and configuring the scenarios required to run ITBench .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench-Scenarios", "content": "ITBench - Scenarios This repository contains infrastructure automation scripts for both deploying the environments and configuring the scenarios required to run ITBench ."}
+{"idx": 5, "title": "Scenario 1: Diagnosing a memory leak - IBM", "date": "", "ddg_snippet": "He requires more detailed information to diagnose the cause of the problem. He clicks the Diagnostic Memory Leak link in the Garbage Collection Analysis workspace to open the Memory Leak Confirmation report page in MSVE. From examining the Memory Leak Confirmation report page in MSVE, Jim suspects that there is a memory leak.", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/docs/en/itcam-app-mgr/7.2.x?topic=scenarios-scenario-1-diagnosing-memory-leak", "content": "He requires more detailed information to diagnose the cause of the problem. He clicks the Diagnostic Memory Leak link in the Garbage Collection Analysis workspace to open the Memory Leak Confirmation report page in MSVE. From examining the Memory Leak Confirmation report page in MSVE, Jim suspects that there is a memory leak."}
+{"idx": 6, "title": "Yinfang CHEN", "date": "", "ddg_snippet": "ITBench includes an initial set of 94 realworld scenarios , which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios , 25.2% of CISO scenarios , and 0% of FinOps scenarios .", "subpage_snippet": "", "source": "yinfangchen.github.io", "link": "https://yinfangchen.github.io/", "content": "ITBench includes an initial set of 94 realworld scenarios , which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios , 25.2% of CISO scenarios , and 0% of FinOps scenarios ."}
+{"idx": 7, "title": "IT-Bench: A First-of-a-Kind Extensible Open-Source Framework for ...", "date": "", "ddg_snippet": "In this session, we shall introduce ITBench —an open-source, cloud-native, and extensible benchmarking framework purpose-built to evaluate AI-driven ITOps solutions. ITBench supports diverse, real-world incident simulations on standardized applications and provides a systematic approach to assessing AI agents across domains such as SRE, CISO, and FinOps. We will share our development journey ...", "subpage_snippet": "", "source": "hasgeek.com", "link": "https://hasgeek.com/rootconf/rootconf-2025-cfp/sub/it-bench-a-first-of-a-kind-extensible-open-source-T5G2u6JzBKPtFgQJjgMqNi", "content": "In this session, we shall introduce ITBench —an open-source, cloud-native, and extensible benchmarking framework purpose-built to evaluate AI-driven ITOps solutions. ITBench supports diverse, real-world incident simulations on standardized applications and provides a systematic approach to assessing AI agents across domains such as SRE, CISO, and FinOps. We will share our development journey ..."}
+{"idx": 8, "title": "Find a Memory Leak - Windows drivers | Microsoft Learn", "date": "", "ddg_snippet": "A memory leak occurs when a process allocates memory from the paged or nonpaged pools, but doesn't free the memory. As a result, these limited pools of memory are depleted over time, causing Windows to slow down.", "subpage_snippet": "", "source": "learn.microsoft.com", "link": "https://learn.microsoft.com/en-us/windows-hardware/drivers/debugger/finding-a-memory-leak", "content": "A memory leak occurs when a process allocates memory from the paged or nonpaged pools, but doesn't free the memory. As a result, these limited pools of memory are depleted over time, causing Windows to slow down."}
+{"idx": 9, "title": "Memory Leak: Causes, Detection, and How to Fix It - DataCamp", "date": "", "ddg_snippet": "What is a memory leak? Learn its causes, examples, and detection techniques to prevent performance issues in software and optimize memory management.", "subpage_snippet": "", "source": "www.datacamp.com", "link": "https://www.datacamp.com/blog/memory-leak", "content": "What is a memory leak? Learn its causes, examples, and detection techniques to prevent performance issues in software and optimize memory management."}
diff --git a/data/sampled_jsons/ITBench_complexity_formula_Equation_6_POMDP_geometric_mean.jsonl b/data/sampled_jsons/ITBench_complexity_formula_Equation_6_POMDP_geometric_mean.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c823b842e3805ab69bb5095125dd43b748346de5
--- /dev/null
+++ b/data/sampled_jsons/ITBench_complexity_formula_Equation_6_POMDP_geometric_mean.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Geometric mean - Wikipedia", "date": "", "ddg_snippet": "Example of the geometric mean : is the geometric mean of. and. , is an example in which the line segment. is given as a perpendicular to. . is the diameter of a circle and. . In mathematics, the geometric mean is a mean or average which indicates a ce...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Geometric_mean", "content": "Example of the geometric mean : is the geometric mean of. and. , is an example in which the line segment. is given as a perpendicular to. . is the diameter of a circle and. . In mathematics, the geometric mean is a mean or average which indicates a ce..."}
+{"idx": 1, "title": "What Is a Geometric Mean ? How to Calculate and Example", "date": "", "ddg_snippet": "Technically, a geometric mean is defined as \"the nth root product of n numbers.\" The geometric mean must be used when working with percentages, which are derived from values, while the standard arithmetic mean works with the values themselves.", "subpage_snippet": "", "source": "www.investopedia.com", "link": "https://www.investopedia.com/terms/g/geometricmean.asp", "content": "Technically, a geometric mean is defined as \"the nth root product of n numbers.\" The geometric mean must be used when working with percentages, which are derived from values, while the standard arithmetic mean works with the values themselves."}
+{"idx": 2, "title": "Geometric Mean - Definition, Examples, Applications", "date": "", "ddg_snippet": "In Mathematics, the Geometric Mean (GM) is the average value or mean which signifies the central tendency of the set of numbers by finding the product of their values.The formula to calculate the geometric mean is given below", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/data/geometric-mean/", "content": "In Mathematics, the Geometric Mean (GM) is the average value or mean which signifies the central tendency of the set of numbers by finding the product of their values.The formula to calculate the geometric mean is given below"}
+{"idx": 3, "title": "ITBench : Evaluating AI Agents across", "date": "", "ddg_snippet": "Figure 4: Characterization of ITBench scenarios. the formulation can be thought of as a POMDP : ot = h(st).Figure 9 presents the distribution of task complexity values across our incident dataset using the above geometric mean formulation .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "Figure 4: Characterization of ITBench scenarios. the formulation can be thought of as a POMDP : ot = h(st).Figure 9 presents the distribution of task complexity values across our incident dataset using the above geometric mean formulation ."}
+{"idx": 4, "title": "(PDF) ITBench : Evaluating AI Agents across Diverse Real-World IT...", "date": "", "ddg_snippet": "geometric mean . Equation ( 6 ) captures this relationship: 24. ITBench .This formulation offers a balanced complexity measure, where the geometric mean ensures that all three factors con-. tribute proportionally, rather than allowing one dominant.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388882803_ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks", "content": "geometric mean . Equation ( 6 ) captures this relationship: 24. ITBench .This formulation offers a balanced complexity measure, where the geometric mean ensures that all three factors con-. tribute proportionally, rather than allowing one dominant."}
+{"idx": 5, "title": "Geometric Mean - GeeksforGeeks", "date": "", "ddg_snippet": "In geometric mean , we first multiply the given number altogether and then take the nth root of the given product. In this article, we will learn about Geometric Mean Definition, Geometric Mean Formula , Examples, and others in detail.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/maths/geometric-mean/", "content": "In geometric mean , we first multiply the given number altogether and then take the nth root of the given product. In this article, we will learn about Geometric Mean Definition, Geometric Mean Formula , Examples, and others in detail."}
+{"idx": 6, "title": "Geometric Mean", "date": "", "ddg_snippet": "The Geometric Mean is a special type of average where we multiply the numbers together and then take a square root (for two numbers), cube root (for three numbers) etc.", "subpage_snippet": "", "source": "www.mathsisfun.com", "link": "https://www.mathsisfun.com/numbers/geometric-mean.html", "content": "The Geometric Mean is a special type of average where we multiply the numbers together and then take a square root (for two numbers), cube root (for three numbers) etc."}
+{"idx": 7, "title": "Mean (Average) Calculator", "date": "", "ddg_snippet": "The mean calculator determines the mean (average), median, mode, range, and many other values of a given set of numbers.", "subpage_snippet": "", "source": "mean-calculator.net", "link": "https://mean-calculator.net/", "content": "The mean calculator determines the mean (average), median, mode, range, and many other values of a given set of numbers."}
+{"idx": 8, "title": "Robot navigation through crowds | ANU School of Computing", "date": "", "ddg_snippet": "This one-year project will investigate the application of our recent approximate solver for Partially Observable Markov Decision Processes ( POMDPs ) to one of the physical robots in our lab, namely Hello Robot Stretch 3 for navigation in crowd. The ideal candiate would", "subpage_snippet": "", "source": "comp.anu.edu.au", "link": "https://comp.anu.edu.au/study/projects/robotnavigationincrowds/", "content": "This one-year project will investigate the application of our recent approximate solver for Partially Observable Markov Decision Processes ( POMDPs ) to one of the physical robots in our lab, namely Hello Robot Stretch 3 for navigation in crowd. The ideal candiate would"}
+{"idx": 9, "title": "Statistics Calculator", "date": "", "ddg_snippet": "geometric mean equation .The geometric mean has applications within proportional growth, the social sciences, aspect ratios, geometry , and finance among others, and like most other statistical values, can provide highly useful information when used in the proper contexts.", "subpage_snippet": "", "source": "www.calculator.net", "link": "https://www.calculator.net/statistics-calculator.html", "content": "geometric mean equation .The geometric mean has applications within proportional growth, the social sciences, aspect ratios, geometry , and finance among others, and like most other statistical values, can provide highly useful information when used in the proper contexts."}
diff --git a/data/sampled_jsons/Identity_Preference_Optimization_Azar_et_al._abstract_text.jsonl b/data/sampled_jsons/Identity_Preference_Optimization_Azar_et_al._abstract_text.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..667c59e135f5df134cdf9aa992fcc2fe0c79e45a
--- /dev/null
+++ b/data/sampled_jsons/Identity_Preference_Optimization_Azar_et_al._abstract_text.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Preference Ranking Optimization for Human Alignment | Request PDF", "date": "", "ddg_snippet": "Show abstract . ... (Nguyen et al ., 2022) introduced a method for interactive text summarization with preference feedback, integrating preference learning to simulate human evaluation. Additionally, (Song, 2024) proposed Preference Ranking Optimization (PRO) as a technique to...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/379282811_Preference_Ranking_Optimization_for_Human_Alignment", "content": "Show abstract . ... (Nguyen et al ., 2022) introduced a method for interactive text summarization with preference feedback, integrating preference learning to simulate human evaluation. Additionally, (Song, 2024) proposed Preference Ranking Optimization (PRO) as a technique to..."}
+{"idx": 1, "title": "FIPO: Free-form Instruction-oriented Prompt Optimization with...", "date": "", "ddg_snippet": "(DPO) (Rafailov et al ., 2023) and Identity Prefer -ence Optimization (IPO) ( Azar et al ., 2023). Pref - erence Optimization takes pairwise rejected label xo− and chosen label xo+ as supervision. One of the core differences from Preference Optimization and SFT is the former one not only...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.coling-main.731.pdf", "content": "(DPO) (Rafailov et al ., 2023) and Identity Prefer -ence Optimization (IPO) ( Azar et al ., 2023). Pref - erence Optimization takes pairwise rejected label xo− and chosen label xo+ as supervision. One of the core differences from Preference Optimization and SFT is the former one not only..."}
+{"idx": 2, "title": "Distributional Preference Alignment of LLMs", "date": "", "ddg_snippet": "When paired preference data is not available, and the preference data instead takes the form of distinct marginals of chosen prompt/response pairs and rejected prompt/response pairs, we refer to this setup as the unpaired data setting. Ethayarajh et al .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/bce96750dcb64f3257ceabadf0b9c9bf-Paper-Conference.pdf", "content": "When paired preference data is not available, and the preference data instead takes the form of distinct marginals of chosen prompt/response pairs and rejected prompt/response pairs, we refer to this setup as the unpaired data setting. Ethayarajh et al ."}
+{"idx": 3, "title": "Beyond Preferences in AI Alignment | Philosophical Studies", "date": "", "ddg_snippet": "This includes Direct Preference Optimization (Rafailov et al ., 2024), Contrastive Preference Learning (Hejna et al ., 2024), and Distributional Preference Learning (Siththaranjan et al ., 2024). The distinction between “narrow” and “ambitious” value learning is due to Christiano (2015a)...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11098-024-02249-w", "content": "This includes Direct Preference Optimization (Rafailov et al ., 2024), Contrastive Preference Learning (Hejna et al ., 2024), and Distributional Preference Learning (Siththaranjan et al ., 2024). The distinction between “narrow” and “ambitious” value learning is due to Christiano (2015a)..."}
+{"idx": 4, "title": "Anchored Preference Optimization and Contrastive Revisions", "date": "", "ddg_snippet": "Direct Preference Optimization (DPO; Rafailov et al ., 2024b) is a widely used and empirically suc-cessful alignment objective. The core stipulation of DPO is that the likelihood change of winning outputs during training needs to be greater than the likelihood change of losing outputs.", "subpage_snippet": "", "source": "users.atlantis.ugent.be", "link": "http://users.atlantis.ugent.be/cdvelder/papers/2025/doosterlinck2025.pdf", "content": "Direct Preference Optimization (DPO; Rafailov et al ., 2024b) is a widely used and empirically suc-cessful alignment objective. The core stipulation of DPO is that the likelihood change of winning outputs during training needs to be greater than the likelihood change of losing outputs."}
+{"idx": 5, "title": "Exploring Text -to-Motion Generation with Human Preference", "date": "", "ddg_snippet": "Abstract . This paper presents an exploration of preference learn-ing in text -to-motion generation.We formulate the objective function for learning from hu-man preference data as in Azar et al .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024W/HuMoGen/papers/Sheng_Exploring_Text-to-Motion_Generation_with_Human_Preference_CVPRW_2024_paper.pdf", "content": "Abstract . This paper presents an exploration of preference learn-ing in text -to-motion generation.We formulate the objective function for learning from hu-man preference data as in Azar et al ."}
+{"idx": 6, "title": "Understanding Preference Fine-Tuning", "date": "", "ddg_snippet": "The first is Direct Preference Optimization (DPO) (Rafailov et al ., 2024b), where the objective is πdpo ∈ argmaxπ ℓdpo(π) with.", "subpage_snippet": "", "source": "rlbrew-workshop.github.io", "link": "https://rlbrew-workshop.github.io/papers/32_understanding_preference_fine_.pdf", "content": "The first is Direct Preference Optimization (DPO) (Rafailov et al ., 2024b), where the objective is πdpo ∈ argmaxπ ℓdpo(π) with."}
+{"idx": 7, "title": "Human Alignment of Large Language Models through Online...", "date": "", "ddg_snippet": "Identity preference optimisation ( Azar et al ., 2023, IPO) is an algorithm that aims to optimise preference probabilities against a fixed data distribution, and does so with an offline contrastive loss, as with DPO.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.08635", "content": "Identity preference optimisation ( Azar et al ., 2023, IPO) is an algorithm that aims to optimise preference probabilities against a fixed data distribution, and does so with an offline contrastive loss, as with DPO."}
+{"idx": 8, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "The Identity Preference Optimization (IPO) ( Azar et al ., 2024) also avoids a reward learning process and potentially unstable RL training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=jfwe9qNqRi", "content": "The Identity Preference Optimization (IPO) ( Azar et al ., 2024) also avoids a reward learning process and potentially unstable RL training."}
+{"idx": 9, "title": "InfAlign: Inference-aware language model... | Read Paper on Bytez", "date": "", "ddg_snippet": "The formulation of optimizing standard win-rate vs KL tradeoff has been previous studied for RLHF in Gui et al .Optimal reward transformations for standard win rate. The identity mapping Φ(x) = x proposed by Azar et al .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44424/paper", "content": "The formulation of optimizing standard win-rate vs KL tradeoff has been previous studied for RLHF in Gui et al .Optimal reward transformations for standard win rate. The identity mapping Φ(x) = x proposed by Azar et al ."}
diff --git a/data/sampled_jsons/ImagineFSL_CVPR_2025_paper_references_bibliography.jsonl b/data/sampled_jsons/ImagineFSL_CVPR_2025_paper_references_bibliography.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d987470e001ac45e441447251d097de4a4ab686a
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_CVPR_2025_paper_references_bibliography.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore."}
+{"idx": 1, "title": "\"ImagineFSL: Self-Supervised Pretraining Matters on Imagined ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/cvpr/YangLLCWL25", "content": "Bibliographic details on ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning."}
+{"idx": 2, "title": "ImagineFSL: Self-Supervised Pretraining Matters on ... - IEEE Xplore", "date": "", "ddg_snippet": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Adapting CLIP models for few-shot recognition has recently attracted significant attention. Despite considerable progress, these adaptations remain hindered by the pervasive challenge of data scarcity.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094502", "content": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Adapting CLIP models for few-shot recognition has recently attracted significant attention. Despite considerable progress, these adaptations remain hindered by the pervasive challenge of data scarcity."}
+{"idx": 3, "title": "GitHub - HaoyuanYang-2023/ImagineFSL: Official implementation of ...", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper : We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper : We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 4, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set", "date": "", "ddg_snippet": "Citation @InProceedings {ImagineFSL_CVPR25, author = {Haoyuan, Yang and Xiaoou, Li and Jiaming, Lv and Xianjun, Cheng and Qilong, Wang and Peihua, Li}, title = { ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning},", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/ImagineFSL/index.html", "content": "Citation @InProceedings {ImagineFSL_CVPR25, author = {Haoyuan, Yang and Xiaoou, Li and Jiaming, Lv and Xianjun, Cheng and Qilong, Wang and Peihua, Li}, title = { ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning},"}
+{"idx": 5, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.html", "content": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
+{"idx": 6, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2025/436400af020/299a9wM2Rck", "content": "Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
+{"idx": 7, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023/ImagineFSL · GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper : We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper : We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 8, "title": "ImagineFSL: Self-Supervised Pretraining Matters on ... - papers.cool", "date": "", "ddg_snippet": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based@CVPR2025@CVF", "content": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
+{"idx": 9, "title": "Welcome to Vision by Statistical Learning lab - Peihua Li", "date": "", "ddg_snippet": "Representative publications Selected publications by area My google scholar citations", "subpage_snippet": "", "source": "www.peihuali.org", "link": "https://www.peihuali.org/Publications.html", "content": "Representative publications Selected publications by area My google scholar citations"}
diff --git a/data/sampled_jsons/ImagineFSL_Section_3.3_caption_synthesis_GPT-4_LLaMA_in-context_year_2024.jsonl b/data/sampled_jsons/ImagineFSL_Section_3.3_caption_synthesis_GPT-4_LLaMA_in-context_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2062f762a9bfa1f9d00d8261ae3830b06c2a1df0
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_Section_3.3_caption_synthesis_GPT-4_LLaMA_in-context_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Llama (language model) - Wikipedia", "date": "", "ddg_snippet": "Llama is a family of large language models released by Meta AI starting in February 2023. The latest version is Llama 4 , released in April 2025. Llama models come in different sizes, ranging from 1 billion to 2 trillion parameters.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Llama_(language_model)", "content": "Llama is a family of large language models released by Meta AI starting in February 2023. The latest version is Llama 4 , released in April 2025. Llama models come in different sizes, ranging from 1 billion to 2 trillion parameters."}
+{"idx": 1, "title": "Supplementary Material for “ ImagineFSL : Self-Supervised Pretraining...", "date": "", "ddg_snippet": "Generate Exemplary Captions via GPT - 4 . Extensive Image Caption Generation via Llama . Summary of Synthetic Images.To this end, we design the template prompts for Llama , where exemplary captions generated by GPT - 4 are provided to facilitate in - context learning.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "Generate Exemplary Captions via GPT - 4 . Extensive Image Caption Generation via Llama . Summary of Synthetic Images.To this end, we design the template prompts for Llama , where exemplary captions generated by GPT - 4 are provided to facilitate in - context learning."}
+{"idx": 2, "title": "Опенсорс на арене: правда ли так хороша Llama 3.1 405B? / Хабр", "date": "", "ddg_snippet": "Llama 3.1 продемонстрировала превосходство над GPT - 4 o в ряде тестов, таких как GSM8K, Hellaswag, boolq, MMLU-humanities, MMLU-other, MMLU-stem и winograd.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/bothub/articles/835100/", "content": "Llama 3.1 продемонстрировала превосходство над GPT - 4 o в ряде тестов, таких как GSM8K, Hellaswag, boolq, MMLU-humanities, MMLU-other, MMLU-stem и winograd."}
+{"idx": 3, "title": "GPT чат онлайн бесплатно – ChatGPT на русском без ограничений", "date": "", "ddg_snippet": "Чат GPT (ChatGPT 4) бесплатно и без ограничений.Ознакомьтесь с ответом и, при необходимости, задайте уточняющий или новый вопрос. GPT - 4 нейросеть онлайн доступна для диалога и гибко реагирует на дополнительные запросы.", "subpage_snippet": "", "source": "ai.mitup.ru", "link": "https://ai.mitup.ru/chatgpt-free", "content": "Чат GPT (ChatGPT 4) бесплатно и без ограничений.Ознакомьтесь с ответом и, при необходимости, задайте уточняющий или новый вопрос. GPT - 4 нейросеть онлайн доступна для диалога и гибко реагирует на дополнительные запросы."}
+{"idx": 4, "title": "The Llama 3 Herd of Models", "date": "", "ddg_snippet": "In Llama 3 405B pre-training, we increased context length gradually in six stages, starting from the original 8K context window and ending in the final 128K context window. This long- context pre-training stage was performed using approximately 800B training tokens.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.21783", "content": "In Llama 3 405B pre-training, we increased context length gradually in six stages, starting from the original 8K context window and ending in the final 128K context window. This long- context pre-training stage was performed using approximately 800B training tokens."}
+{"idx": 5, "title": "GPT - 4 .1 от OpenAI: чем отличаются модели... — ChatGPT на vc.ru", "date": "", "ddg_snippet": "СодержаниеЧем отличаются модели семейства GPT - 4 .1Как соотносятся цена и способности GPT - 4 .1 и конкурентов", "subpage_snippet": "", "source": "vc.ru", "link": "https://vc.ru/chatgpt/1927093-gpt-4-1-ot-openai-otlichiya-modeley-i-sovety-po-zaprosam", "content": "СодержаниеЧем отличаются модели семейства GPT - 4 .1Как соотносятся цена и способности GPT - 4 .1 и конкурентов"}
+{"idx": 6, "title": "Llama Coder – AI Code Generator", "date": "", "ddg_snippet": "Qwen 3 Coder GLM 4 .5 Air Llama 3 . 3 70B Kimi K2.1 DeepSeek V3. High quality [slower].Built with Llama and Together AI.", "subpage_snippet": "", "source": "llamacoder.together.ai", "link": "https://llamacoder.together.ai/", "content": "Qwen 3 Coder GLM 4 .5 Air Llama 3 . 3 70B Kimi K2.1 DeepSeek V3. High quality [slower].Built with Llama and Together AI."}
+{"idx": 7, "title": "Как скачать, установить и запустить Llama 4", "date": "", "ddg_snippet": "Как скачать и запустить LLaMA 4 Scout или Maverick. Пошаговая инструкция для macOS и Windows: установка, запуск на CPU, тестирование.", "subpage_snippet": "", "source": "aidive.org", "link": "https://aidive.org/blog/setup-llama4", "content": "Как скачать и запустить LLaMA 4 Scout или Maverick. Пошаговая инструкция для macOS и Windows: установка, запуск на CPU, тестирование."}
+{"idx": 8, "title": "LLM Leaderboard - Comparison of over 100 AI... | Artificial Analysis", "date": "", "ddg_snippet": "Comparison and ranking the performance of over 100 AI models (LLMs) across key metrics including intelligence, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others.", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/leaderboards/models", "content": "Comparison and ranking the performance of over 100 AI models (LLMs) across key metrics including intelligence, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others."}
+{"idx": 9, "title": "Как работают LLM в 2025 году: глубокий разбор языковых моделей...", "date": "", "ddg_snippet": "Мы разберём GPT - 4 , LLaMA 3, DeepSeek-R1 и другие модели LLM, узнаем, что такое RLHF и SFT, почему модели иногда ошибаются, как они обучаются, и что означают токены в их мире.", "subpage_snippet": "", "source": "hostkey.ru", "link": "https://hostkey.ru/blog/122-kak-rabotayut-llm-v-2025-godu-glubokij-razbor-yazykovykh-modelej-ot-gpt-4-do-deepseek-r1/", "content": "Мы разберём GPT - 4 , LLaMA 3, DeepSeek-R1 и другие модели LLM, узнаем, что такое RLHF и SFT, почему модели иногда ошибаются, как они обучаются, и что означают токены в их мире."}
diff --git a/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Section_3.3_caption_synthesis_pipeline_GPT-4_Llama.jsonl b/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Section_3.3_caption_synthesis_pipeline_GPT-4_Llama.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..77d8112767b9c67af179238d02b5fd1b6f68fd60
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Section_3.3_caption_synthesis_pipeline_GPT-4_Llama.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Supplementary Material for “ ImagineFSL : Self - Supervised ...", "date": "", "ddg_snippet": "Implementation Details on Synthesizing both Captions and Images. Analyzing Factors via GPT - 4 .Limitations and Future Research. Supplementary Material for “ ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning”.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "Implementation Details on Synthesizing both Captions and Images. Analyzing Factors via GPT - 4 .Limitations and Future Research. Supplementary Material for “ ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning”."}
+{"idx": 1, "title": "Опенсорс на арене: правда ли так хороша Llama 3.1 405B? / Хабр", "date": "", "ddg_snippet": "Llama 3.1 продемонстрировала превосходство над GPT - 4 o в ряде тестов, таких как GSM8K, Hellaswag, boolq, MMLU-humanities, MMLU-other, MMLU-stem и winograd.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/bothub/articles/835100/", "content": "Llama 3.1 продемонстрировала превосходство над GPT - 4 o в ряде тестов, таких как GSM8K, Hellaswag, boolq, MMLU-humanities, MMLU-other, MMLU-stem и winograd."}
+{"idx": 2, "title": "Как скачать, установить и запустить Llama 4", "date": "", "ddg_snippet": "Как скачать и запустить LLaMA 4 Scout или Maverick. Пошаговая инструкция для macOS и Windows: установка, запуск на CPU, тестирование.", "subpage_snippet": "", "source": "aidive.org", "link": "https://aidive.org/blog/setup-llama4", "content": "Как скачать и запустить LLaMA 4 Scout или Maverick. Пошаговая инструкция для macOS и Windows: установка, запуск на CPU, тестирование."}
+{"idx": 3, "title": "Comparison Analysis: Claude 3.5 Sonnet vs GPT - 4 o", "date": "", "ddg_snippet": "Learn how Claude 3.5 Sonnet compares to GPT 4 o on data extraction, classification and verbal reasoning tasks.", "subpage_snippet": "", "source": "www.vellum.ai", "link": "https://www.vellum.ai/blog/claude-3-5-sonnet-vs-gpt4o", "content": "Learn how Claude 3.5 Sonnet compares to GPT 4 o on data extraction, classification and verbal reasoning tasks."}
+{"idx": 4, "title": "Llama Coder – AI Code Generator", "date": "", "ddg_snippet": "Generate your next app with Llama 3.1 405B.Built with Llama and Together AI.", "subpage_snippet": "", "source": "llamacoder.together.ai", "link": "https://llamacoder.together.ai/", "content": "Generate your next app with Llama 3.1 405B.Built with Llama and Together AI."}
+{"idx": 5, "title": "GPT - 4 .1 от OpenAI: чем отличаются модели... — ChatGPT на vc.ru", "date": "", "ddg_snippet": "СодержаниеЧем отличаются модели семейства GPT - 4 .1Как соотносятся цена и способности GPT - 4 .1 и конкурентов", "subpage_snippet": "", "source": "vc.ru", "link": "https://vc.ru/chatgpt/1927093-gpt-4-1-ot-openai-otlichiya-modeley-i-sovety-po-zaprosam", "content": "СодержаниеЧем отличаются модели семейства GPT - 4 .1Как соотносятся цена и способности GPT - 4 .1 и конкурентов"}
+{"idx": 6, "title": "Problems with a response image from a GPT Action - Plugins / Actions...", "date": "", "ddg_snippet": "Hi, I created a Custom GPT that takes some input data from the user, and then sends that data to my API via an action as a post request, and then the API returns an Image.", "subpage_snippet": "", "source": "community.openai.com", "link": "https://community.openai.com/t/problems-with-a-response-image-from-a-gpt-action/599038", "content": "Hi, I created a Custom GPT that takes some input data from the user, and then sends that data to my API via an action as a post request, and then the API returns an Image."}
+{"idx": 7, "title": "Telegram: Launch @ GPT 4 Telegrambot", "date": "", "ddg_snippet": "@ GPT 4 Telegrambot. 3 577 994 monthly users.Join us: @perplexity Support: @i_abramov_gpt. Start Bot.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/GPT4Telegrambot", "content": "@ GPT 4 Telegrambot. 3 577 994 monthly users.Join us: @perplexity Support: @i_abramov_gpt. Start Bot."}
+{"idx": 8, "title": "Как работают LLM в 2025 году: глубокий разбор языковых моделей...", "date": "", "ddg_snippet": "Мы разберём GPT - 4 , LLaMA 3, DeepSeek-R1 и другие модели LLM, узнаем, что такое RLHF и SFT, почему модели иногда ошибаются, как они обучаются, и что означают токены в их мире.", "subpage_snippet": "", "source": "hostkey.ru", "link": "https://hostkey.ru/blog/122-kak-rabotayut-llm-v-2025-godu-glubokij-razbor-yazykovykh-modelej-ot-gpt-4-do-deepseek-r1/", "content": "Мы разберём GPT - 4 , LLaMA 3, DeepSeek-R1 и другие модели LLM, узнаем, что такое RLHF и SFT, почему модели иногда ошибаются, как они обучаются, и что означают токены в их мире."}
+{"idx": 9, "title": "LLM Leaderboard - Comparison of over 100 AI... | Artificial Analysis", "date": "", "ddg_snippet": "Time period: Metrics are 'live' and are based on the past 72 hours of measurements, measurements are taken 8 times a day for single requests and 2 times per day for parallel requests. Models compared: OpenAI: GPT 4 o Audio, GPT 4 o Realtime, GPT 4 o Speech Pipeline , GPT Realtime...", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/leaderboards/models", "content": "Time period: Metrics are 'live' and are based on the past 72 hours of measurements, measurements are taken 8 times a day for single requests and 2 times per day for parallel requests. Models compared: OpenAI: GPT 4 o Audio, GPT 4 o Realtime, GPT 4 o Speech Pipeline , GPT Realtime..."}
diff --git a/data/sampled_jsons/ImagineFSL_caption_generation_methodology_GPT-4_Llama_chain-of-thought_in-context_learning.jsonl b/data/sampled_jsons/ImagineFSL_caption_generation_methodology_GPT-4_Llama_chain-of-thought_in-context_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..be058c8dc16317dd0c67e3495a146f01b714b4ec
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_caption_generation_methodology_GPT-4_Llama_chain-of-thought_in-context_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - HaoyuanYang-2023/ImagineFSL: Official implementation of ...", "date": "", "ddg_snippet": "We develop a systematic and scalable pipeline for synthesizing both captions and images, enabling generation of large-scale base sets for pretraining and task-specific datasets. Distinct from existing arts, we leverage chain - of -though and in -conetext learning techniques for diverse, realistic image generation .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "We develop a systematic and scalable pipeline for synthesizing both captions and images, enabling generation of large-scale base sets for pretraining and task-specific datasets. Distinct from existing arts, we leverage chain - of -though and in -conetext learning techniques for diverse, realistic image generation ."}
+{"idx": 1, "title": "PDF ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "Our method employs chain-of-thought (CoT) [29] and in-context learning (ICL) tech-niques [30] for caption generation . Specifically, we instruct GPT-4 [31] to analyze key factors that define image descrip-tions, including attribute, background, viewpoint, lighting condition, and cause of image degradation.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "Our method employs chain-of-thought (CoT) [29] and in-context learning (ICL) tech-niques [30] for caption generation . Specifically, we instruct GPT-4 [31] to analyze key factors that define image descrip-tions, including attribute, background, viewpoint, lighting condition, and cause of image degradation."}
+{"idx": 2, "title": "GPT4-V Experiments with General, Specific questions and Chain Of ...", "date": "", "ddg_snippet": "Let's start analyzing these images by following these steps for our questions: General Question: Simply ask, \"Analyze the image.\" Specific Inquiry: Question the performance of a certain category or model in detail. Chain of Thought Prompting: Use a step-by-step reasoning method to walk through the analysis. These guidelines aim to test how different questioning techniques might improve the ...", "subpage_snippet": "", "source": "docs.llamaindex.ai", "link": "https://docs.llamaindex.ai/en/stable/examples/multi_modal/gpt4v_experiments_cot/", "content": "Let's start analyzing these images by following these steps for our questions: General Question: Simply ask, \"Analyze the image.\" Specific Inquiry: Question the performance of a certain category or model in detail. Chain of Thought Prompting: Use a step-by-step reasoning method to walk through the analysis. These guidelines aim to test how different questioning techniques might improve the ..."}
+{"idx": 3, "title": "[2311.09241] Chain of Images for Intuitively Reasoning - arXiv.org", "date": "", "ddg_snippet": "The human brain is naturally equipped to comprehend and interpret visual information rapidly. When confronted with complex problems or concepts, we use flowcharts, sketches, and diagrams to aid our thought process. Leveraging this inherent ability can significantly enhance logical reasoning. However, current Large Language Models (LLMs) do not utilize such visual intuition to help their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.09241", "content": "The human brain is naturally equipped to comprehend and interpret visual information rapidly. When confronted with complex problems or concepts, we use flowcharts, sketches, and diagrams to aid our thought process. Leveraging this inherent ability can significantly enhance logical reasoning. However, current Large Language Models (LLMs) do not utilize such visual intuition to help their ..."}
+{"idx": 4, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set", "date": "", "ddg_snippet": "Additionally, we present a systematic and scalable pipeline that employs chain-of-thought and in-context learning techniques, harnessing foundation models to automatically generate diverse, realistic images. Validated across eleven datasets, our methods consistently outperform state- of -the-art approaches by substantial margins. Highlights", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/ImagineFSL/index.html", "content": "Additionally, we present a systematic and scalable pipeline that employs chain-of-thought and in-context learning techniques, harnessing foundation models to automatically generate diverse, realistic images. Validated across eleven datasets, our methods consistently outperform state- of -the-art approaches by substantial margins. Highlights"}
+{"idx": 5, "title": "Chain-Of-Thought ( CoT ) in Large Language Models prompting ... - Medium", "date": "", "ddg_snippet": "Chain-of-Thought prompting, introduced in recent advancements, seeks to address this limitation by transforming the way these models process and respond to tasks that require multi-step reasoning.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@devmallyakarar/chain-of-thought-cot-in-large-language-models-prompting-and-concise-cot-with-code-82821f9a832d", "content": "Chain-of-Thought prompting, introduced in recent advancements, seeks to address this limitation by transforming the way these models process and respond to tasks that require multi-step reasoning."}
+{"idx": 6, "title": "Chain-of-Thought Hub: Measuring LLMs' Reasoning Performance", "date": "", "ddg_snippet": "The credibility of chain-of-thought hub comes from the very carefully mediculously picked datasets and models that can clearly help the development of LLMs. The resutls and scripts from Chain-of-thought Hub is being used and referred by leading industrial and academic organizations in the space of large language models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FranxYao/chain-of-thought-hub", "content": "The credibility of chain-of-thought hub comes from the very carefully mediculously picked datasets and models that can clearly help the development of LLMs. The resutls and scripts from Chain-of-thought Hub is being used and referred by leading industrial and academic organizations in the space of large language models."}
+{"idx": 7, "title": "[2502.13942] A Chain-of-Thought Subspace Meta-Learning for Few-shot ...", "date": "", "ddg_snippet": "We evaluated our method on three commonly used image captioning datasets, i.e., MSCOCO, Flickr8k, and Flickr30k, under few-shot settings. The results of our experiments indicate that our chain-of-thought subspace meta- learning strategy is superior to the baselines in terms of performance across different datasets measured by different metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.13942", "content": "We evaluated our method on three commonly used image captioning datasets, i.e., MSCOCO, Flickr8k, and Flickr30k, under few-shot settings. The results of our experiments indicate that our chain-of-thought subspace meta- learning strategy is superior to the baselines in terms of performance across different datasets measured by different metrics."}
+{"idx": 8, "title": "PDF Supplementary Material for \"ImagineFSL: Self-Supervised Pretraining ...", "date": "", "ddg_snippet": "To this end, we design the template prompts for Llama , where exemplary captions generated by GPT-4 are provided to facilitate in-context learning . For a given con-cept, a pattern is first selected, and then the specific forms of factors associated with this pattern, alongside three con-textually relevant examples, are also chosen randomly.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "To this end, we design the template prompts for Llama , where exemplary captions generated by GPT-4 are provided to facilitate in-context learning . For a given con-cept, a pattern is first selected, and then the specific forms of factors associated with this pattern, alongside three con-textually relevant examples, are also chosen randomly."}
+{"idx": 9, "title": "BradyFU/Awesome-Multimodal-Large-Language-Models - GitHub", "date": "", "ddg_snippet": "multi-modality instruction-following in-context-learning large-language-models chain-of-thought instruction-tuning visual-instruction-tuning large-vision-language-model multimodal-instruction-tuning large-vision-language-models multimodal-large-language-models multimodal- in - context - learning multimodal- chain - of - thought Readme Activity 16.3k stars", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models", "content": "multi-modality instruction-following in-context-learning large-language-models chain-of-thought instruction-tuning visual-instruction-tuning large-vision-language-model multimodal-instruction-tuning large-vision-language-models multimodal-large-language-models multimodal- in - context - learning multimodal- chain - of - thought Readme Activity 16.3k stars"}
diff --git a/data/sampled_jsons/Implicit_Language_Models_are_RNNs-_Balancing_Parallelization_and_Expressivity_ICML_2025.jsonl b/data/sampled_jsons/Implicit_Language_Models_are_RNNs-_Balancing_Parallelization_and_Expressivity_ICML_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5a7fcea1d66e112ef4c3e3e58b7697e7df520cda
--- /dev/null
+++ b/data/sampled_jsons/Implicit_Language_Models_are_RNNs-_Balancing_Parallelization_and_Expressivity_ICML_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study"}
+{"idx": 1, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study"}
+{"idx": 2, "title": "Most Influential ICML Papers (2025-03 Version) –", "date": "", "ddg_snippet": "BLIP-2: Bootstrapping Language -Image Pre-training with Frozen Image Encoders and Large Language Models IF:8 Related Papers Related Patents Related ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/03/most-influential-icml-papers-2025-03-version/", "content": "BLIP-2: Bootstrapping Language -Image Pre-training with Frozen Image Encoders and Large Language Models IF:8 Related Papers Related Patents Related ..."}
+{"idx": 3, "title": "Most Influential ICML Papers (2024-05 Version) –", "date": "", "ddg_snippet": "BLIP-2: Bootstrapping Language -Image Pre-training with Frozen Image Encoders and Large Language Models IF:8 Related Papers Related Patents Related ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2024/05/most-influential-icml-papers-2024-05/", "content": "BLIP-2: Bootstrapping Language -Image Pre-training with Frozen Image Encoders and Large Language Models IF:8 Related Papers Related Patents Related ..."}
+{"idx": 4, "title": "Overcoming Long-Context Limitations of State-Space Models via", "date": "", "ddg_snippet": "... language models (LLMs) have broadened the landscape of long-context modeling , enabling new capabilities such as autonomous agents, retrieval-augmented ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.00449v1", "content": "... language models (LLMs) have broadened the landscape of long-context modeling , enabling new capabilities such as autonomous agents, retrieval-augmented ..."}
+{"idx": 5, "title": "CodeSSM: Towards State Space Models for Code Understanding", "date": "", "ddg_snippet": "Our objective in training the CodeSSM model is not to achieve state-of-the-art performance on coding benchmarks, but to explore and document the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.01475v2", "content": "Our objective in training the CodeSSM model is not to achieve state-of-the-art performance on coding benchmarks, but to explore and document the ..."}
+{"idx": 6, "title": "GitHub - multimodal-art-projection/LatentCoT-Horizon: 📖 This", "date": "", "ddg_snippet": "We welcome feedback, suggestions, and contributions that can help improve this survey and repository and make them valuable resources for the entire ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/multimodal-art-projection/LatentCoT-Horizon/", "content": "We welcome feedback, suggestions, and contributions that can help improve this survey and repository and make them valuable resources for the entire ..."}
+{"idx": 7, "title": "Neural nets learn to program neural nets with with fast weights", "date": "", "ddg_snippet": "GPT3] ChatGPT and similar large language models (LLMs) are based on a family of artificial neural networks (NNs) called Transformers .", "subpage_snippet": "", "source": "people.idsia.ch", "link": "https://people.idsia.ch/~juergen/fast-weight-programmer-1991-transformer.html", "content": "GPT3] ChatGPT and similar large language models (LLMs) are based on a family of artificial neural networks (NNs) called Transformers ."}
+{"idx": 8, "title": "Recurrent neural networks — The Dan MacKinlay stable of", "date": "", "ddg_snippet": "Learning such models by classical gradient descent can be painful, but the tools to mitigate that problem are well-understood even if they are not ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/nn_recurrent", "content": "Learning such models by classical gradient descent can be painful, but the tools to mitigate that problem are well-understood even if they are not ..."}
+{"idx": 9, "title": "Practical machine learning | PPT", "date": "", "ddg_snippet": "... implicit signals from user actions often provide better predictions than explicit feedback; combining implicit and explicit signals can improve ...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/faizanj/practical-machine-learning-93864919", "content": "... implicit signals from user actions often provide better predictions than explicit feedback; combining implicit and explicit signals can improve ..."}
diff --git a/data/sampled_jsons/Improved_algorithms_for_linear_stochastic_bandits_Abbasi-Yadkori_2011_year_2011.jsonl b/data/sampled_jsons/Improved_algorithms_for_linear_stochastic_bandits_Abbasi-Yadkori_2011_year_2011.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..96de18bd5a85b9e97b54af80efaabed887ef86e3
--- /dev/null
+++ b/data/sampled_jsons/Improved_algorithms_for_linear_stochastic_bandits_Abbasi-Yadkori_2011_year_2011.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi-armed bandit - Wikipedia", "date": "", "ddg_snippet": "... bandits under worst-case assumptions, obtaining algorithms to minimize regret in both finite and infinite ( asymptotic ) time horizons for both ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Multi-armed_bandit", "content": "... bandits under worst-case assumptions, obtaining algorithms to minimize regret in both finite and infinite ( asymptotic ) time horizons for both ..."}
+{"idx": 1, "title": "(Private) Kernelized Bandits with Distributed Biased Feedback |", "date": "", "ddg_snippet": "Improved algorithms for linear stochastic bandits . ... A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3579318", "content": "Improved algorithms for linear stochastic bandits . ... A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits ."}
+{"idx": 2, "title": "Yasin Abbasi-Yadkori | DeepAI", "date": "", "ddg_snippet": "We study the non-stationary stochastic multi-armed bandit problem ... We study two model selection settings in stochastic linear bandits (LB)....", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/yasin-abbasi-yadkori", "content": "We study the non-stationary stochastic multi-armed bandit problem ... We study two model selection settings in stochastic linear bandits (LB)...."}
+{"idx": 3, "title": "Regret minimization in Linear Bandits with offline data via", "date": "", "ddg_snippet": "Current work : We introduce the Offline-Online Phased Elimination ( OOPE ) algorithm for regret minimization in linear bandits in the online phase ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08420v1", "content": "Current work : We introduce the Offline-Online Phased Elimination ( OOPE ) algorithm for regret minimization in linear bandits in the online phase ..."}
+{"idx": 4, "title": "COBRA: Contextual Bandit Algorithm for Ensuring Truthful", "date": "", "ddg_snippet": "... the mean reward for each arm is typically modeled as an unknown function, which may be linear (Li et al., 2010 ; Chu et al., 2011 ; Abbasi - Yadkori ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23720v1", "content": "... the mean reward for each arm is typically modeled as an unknown function, which may be linear (Li et al., 2010 ; Chu et al., 2011 ; Abbasi - Yadkori ..."}
+{"idx": 5, "title": "Bridging Learning and Decision Making", "date": "", "ddg_snippet": "Improved algorithms for linear stochastic bandits . ... Improved rates for the stochastic continuum-armed bandit problem .", "subpage_snippet": "", "source": "dylanfoster.net", "link": "https://dylanfoster.net/bldm.html", "content": "Improved algorithms for linear stochastic bandits . ... Improved rates for the stochastic continuum-armed bandit problem ."}
+{"idx": 6, "title": "Most Influential NIPS Papers (2024-09 Version) –", "date": "", "ddg_snippet": "... refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2024/09/most-influential-nips-papers-2024-09/", "content": "... refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement."}
+{"idx": 7, "title": "Most Influential NIPS Papers (2024-09) – Paper Digest", "date": "", "ddg_snippet": "... refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2024/09/most-influential-nips-papers-2024-09/", "content": "... refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement."}
+{"idx": 8, "title": "David Pal", "date": "", "ddg_snippet": "Using adaptive Kelly betting based on Krichevsky-Trofimov estimator as the base betting algorithm , we derive new algorithms for online linear ...", "subpage_snippet": "", "source": "www.david.palenica.com", "link": "https://www.david.palenica.com/", "content": "Using adaptive Kelly betting based on Krichevsky-Trofimov estimator as the base betting algorithm , we derive new algorithms for online linear ..."}
+{"idx": 9, "title": "Tor Lattimore – DeepLearn 2022 Summer", "date": "", "ddg_snippet": "... idea of Bellman to use value functions to organize the search for good policies is at the heart of all successful reinforcement learning algorithms ...", "subpage_snippet": "", "source": "deeplearn.irdta.eu", "link": "https://deeplearn.irdta.eu/2022su/blog/speakers/csaba-szepesvari/", "content": "... idea of Bellman to use value functions to organize the search for good policies is at the heart of all successful reinforcement learning algorithms ..."}
diff --git a/data/sampled_jsons/Indyk_Motwani_1998_Locality-Sensitive_Hashing_approximate_nearest_neighbors_principle.jsonl b/data/sampled_jsons/Indyk_Motwani_1998_Locality-Sensitive_Hashing_approximate_nearest_neighbors_principle.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e9c91359ca34c90abafcda381558f08196a3e1fe
--- /dev/null
+++ b/data/sampled_jsons/Indyk_Motwani_1998_Locality-Sensitive_Hashing_approximate_nearest_neighbors_principle.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1708.07586] Fast Locality - Sensitive Hashing Frameworks for...", "date": "", "ddg_snippet": "The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution $\\mathcal{H}$ over locality - sensitive hash functions that partition space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1708.07586", "content": "The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution $\\mathcal{H}$ over locality - sensitive hash functions that partition space."}
+{"idx": 1, "title": "Lecture 13: Nearest Neighbor Search and Locality Sensitive", "date": "", "ddg_snippet": "Approximate Near Neighbor . PLEB via Locality Sensitive Hashing . Other examples of locality sensitive hash functions.Theorem 4 ( Indyk , Motwani , 1998 ). Given a (r1, r2, p1, p2)- locality sensitive hash family, (r1, r2)-PLEB can be solved with constant probability using", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/~hy2/teaching/fall23-cos521/notes/NNS+&+LSH.pdf", "content": "Approximate Near Neighbor . PLEB via Locality Sensitive Hashing . Other examples of locality sensitive hash functions.Theorem 4 ( Indyk , Motwani , 1998 ). Given a (r1, r2, p1, p2)- locality sensitive hash family, (r1, r2)-PLEB can be solved with constant probability using"}
+{"idx": 2, "title": "Fast Locality - Sensitive Hashing Frameworks for Approximate Near ...", "date": "", "ddg_snippet": "The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-32047-8_1", "content": "The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution..."}
+{"idx": 3, "title": "Local Sensitivity Hashing (L.S.H.): A Comprehensive... - Zilliz Learn", "date": "", "ddg_snippet": "Locality - Sensitive Hashing (LSH), introduced by Indyk - Motwani in 1998 , is a technique used in approximate nearest neighbor (ANN) searches that underpins and accelerates the efficiency of similarity searches.", "subpage_snippet": "", "source": "zilliz.com", "link": "https://zilliz.com/learn/Local-Sensitivity-Hashing-A-Comprehensive-Guide", "content": "Locality - Sensitive Hashing (LSH), introduced by Indyk - Motwani in 1998 , is a technique used in approximate nearest neighbor (ANN) searches that underpins and accelerates the efficiency of similarity searches."}
+{"idx": 4, "title": "CS-GY 9223 D: Lecture 4 Near neighbor search + locality sensitive ...", "date": "", "ddg_snippet": "locality sensitive hash functions. LSH for s(q, y) equal to Jaccard similarity: • Let c : {0, 1}d → [0, 1] be a single instantiation of MinHash. • approximate nearest neighbor search.", "subpage_snippet": "", "source": "www.chrismusco.com", "link": "https://www.chrismusco.com/amlds2020/lectures/lec4.pdf", "content": "locality sensitive hash functions. LSH for s(q, y) equal to Jaccard similarity: • Let c : {0, 1}d → [0, 1] be a single instantiation of MinHash. • approximate nearest neighbor search."}
+{"idx": 5, "title": "Fast Locality - sensitive Hashing Frameworks For Approximate Near ...", "date": "", "ddg_snippet": "We state this result as a general framework and provide a simpler analysis showing that the number of lookups and distance computations closely match the Indyk - Motwani framework, making it a viable replacement in practice.", "subpage_snippet": "", "source": "learning2hash.github.io", "link": "https://learning2hash.github.io/publications/christiani2017fast/", "content": "We state this result as a general framework and provide a simpler analysis showing that the number of lookups and distance computations closely match the Indyk - Motwani framework, making it a viable replacement in practice."}
+{"idx": 6, "title": "Optimal Las Vegas Locality Sensitive Data Structures", "date": "", "ddg_snippet": "Near -optimal hashing algorithms for approximate nearest neighbor in high dimensions.[ Indyk and Motwani , 1998 ] Indyk , P. and Motwani , R. ( 1998 ). Approximate nearest neighbors : towards removing the curse of dimensionality.", "subpage_snippet": "", "source": "thomasahle.com", "link": "https://thomasahle.com/papers/lasvegas.pdf", "content": "Near -optimal hashing algorithms for approximate nearest neighbor in high dimensions.[ Indyk and Motwani , 1998 ] Indyk , P. and Motwani , R. ( 1998 ). Approximate nearest neighbors : towards removing the curse of dimensionality."}
+{"idx": 7, "title": "(PDF) A locality - sensitive hash for real vectors", "date": "", "ddg_snippet": "Locality - sensitive hashing (LSH) is a basic primitive in several large-scale data processing applications, including nearest - neighbor search, de-duplication, clustering, etc.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/1444489/A_locality_sensitive_hash_for_real_vectors", "content": "Locality - sensitive hashing (LSH) is a basic primitive in several large-scale data processing applications, including nearest - neighbor search, de-duplication, clustering, etc."}
+{"idx": 8, "title": "PPT - Approximate Nearest Neighbors : Towards Removing the...", "date": "", "ddg_snippet": "Approximate Nearest Neighbor ( Locality Sensitive Hashing ) - Theory. Given by: Erez Eyal Uri Klein. Overview.", "subpage_snippet": "", "source": "www.slideserve.com", "link": "https://www.slideserve.com/carlo/approximate-nearest-neighbors-towards-removing-the-curse-of-dimensionality", "content": "Approximate Nearest Neighbor ( Locality Sensitive Hashing ) - Theory. Given by: Erez Eyal Uri Klein. Overview."}
+{"idx": 9, "title": "algorithm - Nearest neighbors in high-dimensional... - Stack Overflow", "date": "", "ddg_snippet": "[1] Datar, Indyk , Immorlica, Mirrokni, \" Locality - Sensitive Hashing Scheme Based on p-Stable Distributions,\" 2004. [2] Weber, Schek, Blott, \"A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces,\" 1998 .", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/5751114/nearest-neighbors-in-high-dimensional-data", "content": "[1] Datar, Indyk , Immorlica, Mirrokni, \" Locality - Sensitive Hashing Scheme Based on p-Stable Distributions,\" 2004. [2] Weber, Schek, Blott, \"A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces,\" 1998 ."}
diff --git a/data/sampled_jsons/Indyk_Motwani_1998_Locality_Sensitive_Hashing_fundamental_principle_year_1998.jsonl b/data/sampled_jsons/Indyk_Motwani_1998_Locality_Sensitive_Hashing_fundamental_principle_year_1998.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae51ca2071fa54ff723f003633c3bff9457991e8
--- /dev/null
+++ b/data/sampled_jsons/Indyk_Motwani_1998_Locality_Sensitive_Hashing_fundamental_principle_year_1998.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Locality Sensitive Hashing & ANN - GitHub Pages", "date": "", "ddg_snippet": "Locality Sensitive Hashing (LSH) [ Indyk - Motwani , 1998 ] Family of hash functions Close points to same buckets Faraway points to different buckets Idea: Only examine those items where the buckets are shared (Pro) Designed correctly, only a small fraction of pairs are examined", "subpage_snippet": "", "source": "whytin.github.io", "link": "https://whytin.github.io/cs584_s16/slides/lsh-11.pdf", "content": "Locality Sensitive Hashing (LSH) [ Indyk - Motwani , 1998 ] Family of hash functions Close points to same buckets Faraway points to different buckets Idea: Only examine those items where the buckets are shared (Pro) Designed correctly, only a small fraction of pairs are examined"}
+{"idx": 1, "title": "[1708.07586] Fast Locality-Sensitive Hashing Frameworks for ... Lower bounds on Locality Sensitive Hashing Locality Sensitive Hashing & ANN - GitHub Pages Lower bounds on Locality Sensitive Hashing Fast Locality-Sensitive Hashing Frameworks for Approximate ... [cs/0510088] Lower bounds on Locality Sensitive Hashing (PDF) Lower Bounds on Locality Sensitive Hashing - ResearchGate", "date": "", "ddg_snippet": "Aug 25, 2017 · The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution H over locality - sensitive hash functions that partition space. In this note we study the complexity of finding the nearest neighbor of a query point in certain high di-mensional spaces using Locality Sensitive Hashing (LSH). Locality Sensitive Hashing (LSH) [ Indyk - Motwani , 1998 ] Family of hash functions Close points to same buckets Faraway points to different buckets Idea: Only examine those items where the buckets are shared (Pro) Designed correctly, only a small fraction of pairs are examined Locality - sensitive hashing scheme based on p-stable distributions.In SCG ’04: Proceedings of the Twentieth Annual Symposium on Computational Geometry (Brooklyn, NY, 2004), pages 253–262.ACM Press, New York, NY, 2004. The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distri-bution over locality - sensitive hash functions that partition space. Oct 29, 2005 · This notion was introduced by Indyk and Motwani in 1998 as the basis for an efficient approximate nearest neighbor search algorithm, and has since been used extensively for this purpose. Nov 29, 2005 · This notion was introduced by Indyk and Motwani in 1998 as the basis for an efficient approximate nearest neighbor search algorithm, and has since been used extensively for this purpose.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1708.07586", "content": "Aug 25, 2017 · The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution H over locality - sensitive hash functions that partition space. In this note we study the complexity of finding the nearest neighbor of a query point in certain high di-mensional spaces using Locality Sensitive Hashing (LSH). Locality Sensitive Hashing (LSH) [ Indyk - Motwani , 1998 ] Family of hash functions Close points to same buckets Faraway points to different buckets Idea: Only examine those items where the buckets are shared (Pro) Designed correctly, only a small fraction of pairs are examined Locality - sensitive hashing scheme based on p-stable distributions.In SCG ’04: Proceedings of the Twentieth Annual Symposium on Computational Geometry (Brooklyn, NY, 2004), pages 253–262.ACM Press, New York, NY, 2004. The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distri-bution over locality - sensitive hash functions that partition space. Oct 29, 2005 · This notion was introduced by Indyk and Motwani in 1998 as the basis for an efficient approximate nearest neighbor search algorithm, and has since been used extensively for this purpose. Nov 29, 2005 · This notion was introduced by Indyk and Motwani in 1998 as the basis for an efficient approximate nearest neighbor search algorithm, and has since been used extensively for this purpose."}
+{"idx": 2, "title": "Lower bounds on Locality Sensitive Hashing", "date": "", "ddg_snippet": "In this note we study the complexity of finding the nearest neighbor of a query point in certain high di-mensional spaces using Locality Sensitive Hashing (LSH).", "subpage_snippet": "", "source": "web.math.princeton.edu", "link": "https://web.math.princeton.edu/~naor/homepage+files/lsh-new.pdf", "content": "In this note we study the complexity of finding the nearest neighbor of a query point in certain high di-mensional spaces using Locality Sensitive Hashing (LSH)."}
+{"idx": 3, "title": "Lower bounds on Locality Sensitive Hashing", "date": "", "ddg_snippet": "Locality - sensitive hashing scheme based on p-stable distributions.In SCG ’04: Proceedings of the Twentieth Annual Symposium on Computational Geometry (Brooklyn, NY, 2004), pages 253–262.ACM Press, New York, NY, 2004.", "subpage_snippet": "", "source": "theory.stanford.edu", "link": "https://theory.stanford.edu/~rinap/papers/lshlb.pdf", "content": "Locality - sensitive hashing scheme based on p-stable distributions.In SCG ’04: Proceedings of the Twentieth Annual Symposium on Computational Geometry (Brooklyn, NY, 2004), pages 253–262.ACM Press, New York, NY, 2004."}
+{"idx": 4, "title": "Fast Locality-Sensitive Hashing Frameworks for Approximate ...", "date": "", "ddg_snippet": "The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distri-bution over locality - sensitive hash functions that partition space.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-32047-8_1.pdf", "content": "The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distri-bution over locality - sensitive hash functions that partition space."}
+{"idx": 5, "title": "[cs/0510088] Lower bounds on Locality Sensitive Hashing", "date": "", "ddg_snippet": "Oct 29, 2005 · This notion was introduced by Indyk and Motwani in 1998 as the basis for an efficient approximate nearest neighbor search algorithm, and has since been used extensively for this purpose.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/cs/0510088", "content": "Oct 29, 2005 · This notion was introduced by Indyk and Motwani in 1998 as the basis for an efficient approximate nearest neighbor search algorithm, and has since been used extensively for this purpose."}
+{"idx": 6, "title": "(PDF) Lower Bounds on Locality Sensitive Hashing - ResearchGate", "date": "", "ddg_snippet": "Nov 29, 2005 · This notion was introduced by Indyk and Motwani in 1998 as the basis for an efficient approximate nearest neighbor search algorithm, and has since been used extensively for this purpose.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/1958659_Lower_Bounds_on_Locality_Sensitive_Hashing", "content": "Nov 29, 2005 · This notion was introduced by Indyk and Motwani in 1998 as the basis for an efficient approximate nearest neighbor search algorithm, and has since been used extensively for this purpose."}
+{"idx": 7, "title": "Lecture 10: Nearest Neighbor Search and Locality Sensitive", "date": "", "ddg_snippet": "PLEB via Locality Sensitive Hashing . Other examples of locality sensitive hash functions.Theorem 4 ( Indyk , Motwani , 1998 ). Given a (r1, r2, p1, p2)- locality sensitive hash family, (r1, r2)-PLEB can be solved with constant probability using", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/~smattw/Teaching/Fa19Lectures/lec10/lec10.pdf", "content": "PLEB via Locality Sensitive Hashing . Other examples of locality sensitive hash functions.Theorem 4 ( Indyk , Motwani , 1998 ). Given a (r1, r2, p1, p2)- locality sensitive hash family, (r1, r2)-PLEB can be solved with constant probability using"}
+{"idx": 8, "title": "On the Problem of $p_1^{-1}$ in Locality - Sensitive Hashing", "date": "", "ddg_snippet": "These functions form the basis of the successful Indyk - Motwani algorithm (STOC 1998 ) for nearest neighbour problems.View a PDF of the paper titled On the Problem of $p_1^{-1}$ in Locality - Sensitive Hashing , by Thomas Dybdahl Ahle.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2005.12065", "content": "These functions form the basis of the successful Indyk - Motwani algorithm (STOC 1998 ) for nearest neighbour problems.View a PDF of the paper titled On the Problem of $p_1^{-1}$ in Locality - Sensitive Hashing , by Thomas Dybdahl Ahle."}
+{"idx": 9, "title": "Local Sensitivity Hashing (L.S.H.): A Comprehensive... - Zilliz Learn", "date": "", "ddg_snippet": "Locality - Sensitive Hashing (LSH), introduced by Indyk - Motwani in 1998 , is a technique used in approximate nearest neighbor (ANN) searches that underpins and accelerates the efficiency of similarity searches.", "subpage_snippet": "", "source": "zilliz.com", "link": "https://zilliz.com/learn/Local-Sensitivity-Hashing-A-Comprehensive-Guide", "content": "Locality - Sensitive Hashing (LSH), introduced by Indyk - Motwani in 1998 , is a technique used in approximate nearest neighbor (ANN) searches that underpins and accelerates the efficiency of similarity searches."}
diff --git a/data/sampled_jsons/Information_avoidance_testing_COVID-19_Kerner_2024_full_title_year_2024.jsonl b/data/sampled_jsons/Information_avoidance_testing_COVID-19_Kerner_2024_full_title_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48ea744b0511bc94eb26f9658899a4af73bfd6b7
--- /dev/null
+++ b/data/sampled_jsons/Information_avoidance_testing_COVID-19_Kerner_2024_full_title_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "COVID - 19 - Wikipedia", "date": "", "ddg_snippet": "Further information : COVID - 19 testing . COVID ‑ 19 can provisionally be diagnosed on the basis of symptoms and confirmed using reverse transcription polymerase chain reaction (RT-PCR) or other nucleic acid testing of infected secretions.[20][174] Along with laboratory testing , chest...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/COVID-19", "content": "Further information : COVID - 19 testing . COVID ‑ 19 can provisionally be diagnosed on the basis of symptoms and confirmed using reverse transcription polymerase chain reaction (RT-PCR) or other nucleic acid testing of infected secretions.[20][174] Along with laboratory testing , chest..."}
+{"idx": 1, "title": "Information avoidance and testing for COVID ‐ 19 - Kerner - 2024 ...", "date": "", "ddg_snippet": "Abstract To limit the spread of the COVID - 19 virus, many employers and institutions developed procedures for people who tested positive.Lipsey, N. P., & Shepperd, J. A. (2019a). Powerful audiences are linked to health information avoidance : Results from two surveys.", "subpage_snippet": "", "source": "compass.onlinelibrary.wiley.com", "link": "https://compass.onlinelibrary.wiley.com/doi/10.1111/spc3.12920", "content": "Abstract To limit the spread of the COVID - 19 virus, many employers and institutions developed procedures for people who tested positive.Lipsey, N. P., & Shepperd, J. A. (2019a). Powerful audiences are linked to health information avoidance : Results from two surveys."}
+{"idx": 2, "title": "New paper detailing evidence of DNA contamination in covid vaccines...", "date": "", "ddg_snippet": "The paper, titled ‘Quantification of residual plasmid DNA and SV40 promoter-enhancer sequences in Pfizer/BioNTech and Moderna modRNA covid - 19 vaccines from Ontario, Canada’, was authored by David J. Speicher, Jessica Rose and Kevin McKernan.", "subpage_snippet": "", "source": "expose-news.com", "link": "https://expose-news.com/2025/09/19/paper-detailing-evidence-of-dna-contamination/", "content": "The paper, titled ‘Quantification of residual plasmid DNA and SV40 promoter-enhancer sequences in Pfizer/BioNTech and Moderna modRNA covid - 19 vaccines from Ontario, Canada’, was authored by David J. Speicher, Jessica Rose and Kevin McKernan."}
+{"idx": 3, "title": "Coronavirus Disease ( COVID - 19 ) Situation Reports", "date": "", "ddg_snippet": "The Weekly Epidemiological Update provides an overview of the global, regional and country-level COVID - 19 cases and deaths, highlighting key data and trends; as well as other pertinent epidemiological information concerning the COVID - 19 pandemic.", "subpage_snippet": "", "source": "www.who.int", "link": "https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports", "content": "The Weekly Epidemiological Update provides an overview of the global, regional and country-level COVID - 19 cases and deaths, highlighting key data and trends; as well as other pertinent epidemiological information concerning the COVID - 19 pandemic."}
+{"idx": 4, "title": "Unmasking the Many Truths about the COVID 19 Plandemic - The...", "date": "", "ddg_snippet": "Was the ultimate goa these past few monthsl to conceal the COVID 19 Plandemic from the public?But that hasn’t stopped the COVID 19 vaccine train from leaving the station. Pharma still has many problems to overcome beyond a book and a movie by a former government scientist.", "subpage_snippet": "", "source": "vaxxter.com", "link": "https://vaxxter.com/unmasking-the-many-truths-about-the-covid19-plandemic/", "content": "Was the ultimate goa these past few monthsl to conceal the COVID 19 Plandemic from the public?But that hasn’t stopped the COVID 19 vaccine train from leaving the station. Pharma still has many problems to overcome beyond a book and a movie by a former government scientist."}
+{"idx": 5, "title": "How have some people managed to avoid Covid - 19 for so long?", "date": "", "ddg_snippet": "If you have managed to avoid Covid - 19 to date, maybe you do have a natural immunity to SARS-CoV-2 infection, or perhaps you have just been lucky. Either way, it is sensible to continue to take precautions against this virus that we still know so little about.", "subpage_snippet": "", "source": "scroll.in", "link": "https://scroll.in/article/1024281/how-have-some-people-managed-to-avoid-covid-19-for-so-long-it-could-be-more-than-just-luck", "content": "If you have managed to avoid Covid - 19 to date, maybe you do have a natural immunity to SARS-CoV-2 infection, or perhaps you have just been lucky. Either way, it is sensible to continue to take precautions against this virus that we still know so little about."}
+{"idx": 6, "title": "\" COVID - 19 Naik Lagi di Pertengahan Tahun 2025, Fakta atau Cuma...", "date": "", "ddg_snippet": "Testing kan nggak murah dan bukan jaman seperti COVID - 19 yang tesnya bisa gratis,\" jelas Pandu kepada detikcom, Senin (2/6/2025). Faktanya, meski status PHEIC atau 'pandemi' dalam istilah awam, dicabut, seluruh dunia belum benar-benar 'terbebas' dari virus COVID - 19 .", "subpage_snippet": "", "source": "sarolangunkab.go.id", "link": "https://sarolangunkab.go.id/artikel/baca/covid-19-naik-lagi-di-pertengahan-tahun-2025-fakta-atau-cuma-propaganda", "content": "Testing kan nggak murah dan bukan jaman seperti COVID - 19 yang tesnya bisa gratis,\" jelas Pandu kepada detikcom, Senin (2/6/2025). Faktanya, meski status PHEIC atau 'pandemi' dalam istilah awam, dicabut, seluruh dunia belum benar-benar 'terbebas' dari virus COVID - 19 ."}
+{"idx": 7, "title": "Visiting Croatia During Covid - 19 Pandemic: What You Need to Know", "date": "", "ddg_snippet": "If you suspect a Covid - 19 infection, inform the staff of the accommodation you are staying at. They will know what doctor to call, and the doctor will assess if you need a Covid - 19 test or no. Meanwhile, keep self-isolating until you get a negative test .", "subpage_snippet": "", "source": "www.frankaboutcroatia.com", "link": "https://www.frankaboutcroatia.com/visiting-croatia-during-covid-19-pandemic/", "content": "If you suspect a Covid - 19 infection, inform the staff of the accommodation you are staying at. They will know what doctor to call, and the doctor will assess if you need a Covid - 19 test or no. Meanwhile, keep self-isolating until you get a negative test ."}
+{"idx": 8, "title": "COVID Cases Are Rising: Is a Winter Wave on the Way?", "date": "", "ddg_snippet": "Visitation Information .Will the 2024 /25 COVID - 19 vaccine protect against XEC – the dominant variant circulating now? As with past iterations of the COVID vaccine, the current shot protects against severe illness and hospitalizations.", "subpage_snippet": "", "source": "www.southshorehealth.org", "link": "https://www.southshorehealth.org/wellness/blog/covid-cases-are-rising-winter-wave-way", "content": "Visitation Information .Will the 2024 /25 COVID - 19 vaccine protect against XEC – the dominant variant circulating now? As with past iterations of the COVID vaccine, the current shot protects against severe illness and hospitalizations."}
+{"idx": 9, "title": "It’s Not Fear Driving Insane COVID - 19 Panic. It’s Disgust", "date": "", "ddg_snippet": "Throughout the COVID - 19 outbreak, incurable skeptics have argued that many COVID - 19 measures are based not on science and reason, but on fearmongering by an increasingly autocratic ruling class imposing new norms on an increasingly intolerant and conformist population.", "subpage_snippet": "", "source": "thefederalist.com", "link": "https://thefederalist.com/2021/04/23/its-not-fear-driving-covid-19-panic-its-disgust/", "content": "Throughout the COVID - 19 outbreak, incurable skeptics have argued that many COVID - 19 measures are based not on science and reason, but on fearmongering by an increasingly autocratic ruling class imposing new norms on an increasingly intolerant and conformist population."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_AGM-Net_implementation_details_section_4_training_dataset_year_2024.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_AGM-Net_implementation_details_section_4_training_dataset_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f253f7d2ebc15579cdb5aecf36ea203232245675
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_AGM-Net_implementation_details_section_4_training_dataset_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Train AGM-Net Datasets Preparation Our Training Dataset Download our processed data from 4 sequences of N3DV, which can be directly used for training . It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Train AGM-Net Datasets Preparation Our Training Dataset Download our processed data from 4 sequences of N3DV, which can be directly used for training . It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows:"}
+{"idx": 1, "title": "(PDF) Instant Gaussian Stream : Fast and Generalizable Streaming ...", "date": "", "ddg_snippet": "tical, we introduce Instant Gaussian Stream (IGS), a. streaming approach for dynamic scene reconstruction that. 4 . Implementation details . In this Section , we first introduce the datasets we used, along with the partitioning and preprocessing of training .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "tical, we introduce Instant Gaussian Stream (IGS), a. streaming approach for dynamic scene reconstruction that. 4 . Implementation details . In this Section , we first introduce the datasets we used, along with the partitioning and preprocessing of training ."}
+{"idx": 2, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "4 . Implementation details . In this Section , we first introduce the datasets we used, along with the partitioning and preprocessing of training data , in Sec. 4 .1. Next, we provide a detailed explanation of the configuration of the AGM network and the training hy-perparameters in Sec.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "4 . Implementation details . In this Section , we first introduce the datasets we used, along with the partitioning and preprocessing of training data , in Sec. 4 .1. Next, we provide a detailed explanation of the configuration of the AGM network and the training hy-perparameters in Sec."}
+{"idx": 3, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ... (PDF) Instant Gaussian Stream: Fast and Generalizable ... Instant Gaussian Stream: Fast and Generalizable Streaming of ... AGM-Net Model & Training · Issue #4 · yjb6/IGS - GitHub arXiv:2503.16979v1 [cs.CV] 21 Mar 2025 Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic arXiv:2503.16979v1 [cs.CV] 21 Mar 2025 Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic (PDF) Instant Gaussian Stream : Fast and Generalizable Streaming of Dy… arXiv:2503.16979v1 [cs.CV] 21 Mar 2025 IGS/README.md at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream-ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality. Jul 8, 2025 · Which dataset did you train the AGM-Net on, while generating the quantitative results on the N3DV dataset ? We would like to add your paper to our benchmark for an upcoming submission. With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ... Is instant Gaussian stream a fast and generalizable streaming framework? However, current streaming approaches face challenges of high per-frame reconstruction time (10s+) and error accumulation, limiting their broader application. In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework , to address these issues. What is instant Gaussian stream (IGS)? In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework , to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. How can a generalized network drive the motion of all Gaussians? to drive the mo-tion of all Gaussians. This generalized Network gener-ates the motion of Gaussians for each target frame in th time required for a single inference. Second, we propose a Key-frame-guided Streaming Strategy that refines each key frame, enabling accurate reconstruction of temporally complex scen What is a generalized anchor-driven Gaussian motion network? First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space , using anchor points to drive the motion of all Gaussians. This generalized Network generates the motion of Gaussians for each target frame in the time required for a single inference. How many input views can be used in AGM model? Our AGM model accepts an arbitrary number of input views. To bal- 128 channels and a resolution of 128 x 128. We sample ficiently captures dynamic details. The Transformer block ing a 3D motion feature with C= 128 channels. For ren- and geometry. use 8 views for supervision. Training is conducted on four of 15 epochs with a batch size of 16. What is anchor-driven Gaussian motion Network (AGM-net)? n overview of the pipeline in Sec.3.1. Then, in Sec. 3.3, we introduce the Anchor-driven Gaussian Motion Network (AGM-Net), a general-ized model that drives Gaussian motion from the previous frame using anchor points, whi h serve as key points in the 3D scene. Following this, we present our Key-fram Train AGM-Net Datasets Preparation Our Training Dataset Download our processed data from 4 sequences of N3DV, which can be directly used for training . It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.16979", "content": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream-ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality. Jul 8, 2025 · Which dataset did you train the AGM-Net on, while generating the quantitative results on the N3DV dataset ? We would like to add your paper to our benchmark for an upcoming submission. With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ... Is instant Gaussian stream a fast and generalizable streaming framework? However, current streaming approaches face challenges of high per-frame reconstruction time (10s+) and error accumulation, limiting their broader application. In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework , to address these issues. What is instant Gaussian stream (IGS)? In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework , to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. How can a generalized network drive the motion of all Gaussians? to drive the mo-tion of all Gaussians. This generalized Network gener-ates the motion of Gaussians for each target frame in th time required for a single inference. Second, we propose a Key-frame-guided Streaming Strategy that refines each key frame, enabling accurate reconstruction of temporally complex scen What is a generalized anchor-driven Gaussian motion network? First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space , using anchor points to drive the motion of all Gaussians. This generalized Network generates the motion of Gaussians for each target frame in the time required for a single inference. How many input views can be used in AGM model? Our AGM model accepts an arbitrary number of input views. To bal- 128 channels and a resolution of 128 x 128. We sample ficiently captures dynamic details. The Transformer block ing a 3D motion feature with C= 128 channels. For ren- and geometry. use 8 views for supervision. Training is conducted on four of 15 epochs with a batch size of 16. What is anchor-driven Gaussian motion Network (AGM-net)? n overview of the pipeline in Sec.3.1. Then, in Sec. 3.3, we introduce the Anchor-driven Gaussian Motion Network (AGM-Net), a general-ized model that drives Gaussian motion from the previous frame using anchor points, whi h serve as key points in the 3D scene. Following this, we present our Key-fram Train AGM-Net Datasets Preparation Our Training Dataset Download our processed data from 4 sequences of N3DV, which can be directly used for training . It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows:"}
+{"idx": 4, "title": "AGM-Net Model & Training · Issue #4 · yjb6/IGS - GitHub", "date": "", "ddg_snippet": "Jul 8, 2025 · Which dataset did you train the AGM-Net on, while generating the quantitative results on the N3DV dataset ? We would like to add your paper to our benchmark for an upcoming submission.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/issues/4", "content": "Jul 8, 2025 · Which dataset did you train the AGM-Net on, while generating the quantitative results on the N3DV dataset ? We would like to add your paper to our benchmark for an upcoming submission."}
+{"idx": 5, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "4 Implementation details . 4 .1 Datasets . 4 .2 AGM Network .In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "4 Implementation details . 4 .1 Datasets . 4 .2 AGM Network .In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 6, "title": "Instant Gaussian Stream : Fast and Generalizable... | alphaXiv", "date": "", "ddg_snippet": "The AGM - Net is trained on a diverse dataset of dynamic scenes, enabling it to generalize to new scenes without scene-specific optimization. This represents a significant departure from previous approaches that required per-scene training .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.16979", "content": "The AGM - Net is trained on a diverse dataset of dynamic scenes, enabling it to generalize to new scenes without scene-specific optimization. This represents a significant departure from previous approaches that required per-scene training ."}
+{"idx": 7, "title": "Model Training with Ultralytics YOLO - Ultralytics YOLO Docs", "date": "", "ddg_snippet": "Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization.", "subpage_snippet": "", "source": "docs.ultralytics.com", "link": "https://docs.ultralytics.com/modes/train/", "content": "Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization."}
+{"idx": 8, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ..."}
+{"idx": 9, "title": "IGS/README.md at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "Train AGM-Net Datasets Preparation Our Training Dataset Download our processed data from 4 sequences of N3DV, which can be directly used for training . It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/blob/master/README.md", "content": "Train AGM-Net Datasets Preparation Our Training Dataset Download our processed data from 4 sequences of N3DV, which can be directly used for training . It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows:"}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_CVPR_2025_Table_2_storage_efficiency_Meeting_Room.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_CVPR_2025_Table_2_storage_efficiency_Meeting_Room.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..274cfd5d9e64f3a9ce33e60ec55b16d50119cfdd
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_CVPR_2025_Table_2_storage_efficiency_Meeting_Room.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream-ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream-ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality."}
+{"idx": 1, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "n, limiting their broader application. In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and generalizable stream ng framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion fea-tures into 3D space, using anchor points", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "n, limiting their broader application. In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and generalizable stream ng framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion fea-tures into 3D space, using anchor points"}
+{"idx": 2, "title": "(PDF) Instant Gaussian Stream: Fast and Generalizable ...", "date": "", "ddg_snippet": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 3, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Step 2 : Train the Gaussian of Frame 0 In this step, we recommend first optimizing the Gaussian model for several thousand iterations using RaDe-GS, followed by compressing the Gaussian points with LightGaussian. This process ensures an efficient and high-quality reconstruction of the 3D scene.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Step 2 : Train the Gaussian of Frame 0 In this step, we recommend first optimizing the Gaussian model for several thousand iterations using RaDe-GS, followed by compressing the Gaussian points with LightGaussian. This process ensures an efficient and high-quality reconstruction of the 3D scene."}
+{"idx": 4, "title": "CVPR 2024 Papers", "date": "", "ddg_snippet": "3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos ... Scenes for Efficient Storage and ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/papers.html", "content": "3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos ... Scenes for Efficient Storage and ..."}
+{"idx": 5, "title": "CVPR 2024 Papers", "date": "", "ddg_snippet": "3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos ... Scenes for Efficient Storage and ...", "subpage_snippet": "", "source": "cvpr2023.thecvf.com", "link": "https://cvpr2023.thecvf.com/virtual/2024/papers.html", "content": "3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos ... Scenes for Efficient Storage and ..."}
+{"idx": 6, "title": "VR-Pipe: Streamlining Hardware Graphics Pipeline for Volume", "date": "", "ddg_snippet": "Based on our analysis, we present VR-Pipe, which seamlessly integrates two innovations into graphics hardware to streamline the hardware pipeline for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.17078v1", "content": "Based on our analysis, we present VR-Pipe, which seamlessly integrates two innovations into graphics hardware to streamline the hardware pipeline for ..."}
+{"idx": 7, "title": "CVPR 2024 Schedule", "date": "", "ddg_snippet": "1st Workshop on Urban Scene Modeling: Where Vision Meets ... Rhobin 2024: The second Rhobin challenge on Reconstruction of Human-Object Interaction", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/calendar", "content": "1st Workshop on Urban Scene Modeling: Where Vision Meets ... Rhobin 2024: The second Rhobin challenge on Reconstruction of Human-Object Interaction"}
+{"idx": 8, "title": "CVPR 2024 Schedule", "date": "", "ddg_snippet": "1st Workshop on Urban Scene Modeling: Where Vision Meets ... Rhobin 2024: The second Rhobin challenge on Reconstruction of Human-Object Interaction", "subpage_snippet": "", "source": "cvpr2023.thecvf.com", "link": "https://cvpr2023.thecvf.com/virtual/2024/calendar", "content": "1st Workshop on Urban Scene Modeling: Where Vision Meets ... Rhobin 2024: The second Rhobin challenge on Reconstruction of Human-Object Interaction"}
+{"idx": 9, "title": "IGS/README.md at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "CVPR 25 This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/blob/master/README.md", "content": "CVPR 25 This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting"}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_Eq._(6)_Gaussian_anchors_motion_interpolation_year_2024.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_Eq._(6)_Gaussian_anchors_motion_interpolation_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8ba7d40b451a7978958463aaed948377559bfbf3
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_Eq._(6)_Gaussian_anchors_motion_interpolation_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "First, to tackle the issue of high per-frame reconstruction time, we developed a generalized Anchor -driven Gaussian Motion Network (AGM-Net). This network utilizes a set of key points, called anchor points, to carry motion features that guide Gaussian transformations.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "First, to tackle the issue of high per-frame reconstruction time, we developed a generalized Anchor -driven Gaussian Motion Network (AGM-Net). This network utilizes a set of key points, called anchor points, to carry motion features that guide Gaussian transformations."}
+{"idx": 1, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor -driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor -driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians."}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Step 2: Train the Gaussian of Frame 0 In this step, we recommend first optimizing the Gaussian model for several thousand iterations using RaDe-GS, followed by compressing the Gaussian points with LightGaussian. This process ensures an efficient and high-quality reconstruction of the 3D scene.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Step 2: Train the Gaussian of Frame 0 In this step, we recommend first optimizing the Gaussian model for several thousand iterations using RaDe-GS, followed by compressing the Gaussian points with LightGaussian. This process ensures an efficient and high-quality reconstruction of the 3D scene."}
+{"idx": 3, "title": "CVPR Poster Instant Gaussian Stream", "date": "", "ddg_snippet": "First, we introduce a generalized Anchor -driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33775", "content": "First, we introduce a generalized Anchor -driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to ..."}
+{"idx": 4, "title": "Scale-GS: Efficient Scalable Gaussian Splatting via ...", "date": "", "ddg_snippet": "29 Aug 2025 — Instant Gaussian Stream (IGS) [62 ] proposes enables single-pass motion computation guided by keyframes, reducing error accumulation and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21444v1", "content": "29 Aug 2025 — Instant Gaussian Stream (IGS) [62 ] proposes enables single-pass motion computation guided by keyframes, reducing error accumulation and ..."}
+{"idx": 5, "title": "Awesome 3D Gaussian Splatting Resources", "date": "", "ddg_snippet": "Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Saddy21/gaussian_resources", "content": "Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months."}
+{"idx": 6, "title": "Anisotropic View-Dependent Appearance for 3D Gaussian ...", "date": "", "ddg_snippet": "Thanks to the guidance of the anchor Gaussian , the anchor feature fv can be used directly to compress N ASGs, further reducing storage pressure. To make the ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/93509", "content": "Thanks to the guidance of the anchor Gaussian , the anchor feature fv can be used directly to compress N ASGs, further reducing storage pressure. To make the ..."}
+{"idx": 7, "title": "A Survey on 3D Gaussian Splatting", "date": "", "ddg_snippet": "Abstract—3D Gaussian splatting (GS) has recently emerged as a transformative technique in the realm of explicit radiance field and computer graphics.", "subpage_snippet": "", "source": "www.cs.jhu.edu", "link": "https://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Chen24.pdf", "content": "Abstract—3D Gaussian splatting (GS) has recently emerged as a transformative technique in the realm of explicit radiance field and computer graphics."}
+{"idx": 8, "title": "MrNeRF/awesome-3D-gaussian-splatting", "date": "", "ddg_snippet": "Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MrNeRF/awesome-3D-gaussian-splatting", "content": "Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months."}
+{"idx": 9, "title": "MrNeRF's Awesome-3D-Gaussian-Splatting-Paper-List", "date": "", "ddg_snippet": "... 6 DoF transformation bases, which can be locally interpolated through learned interpolation weights to yield the motion field of 3D Gaussians. We employ a ...", "subpage_snippet": "", "source": "mrnerf.github.io", "link": "https://mrnerf.github.io/awesome-3D-gaussian-splatting/", "content": "... 6 DoF transformation bases, which can be locally interpolated through learned interpolation weights to yield the motion field of 3D Gaussians. We employ a ..."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_Equation_6_interpolation_motion_feature_anchor.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_Equation_6_interpolation_motion_feature_anchor.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7a2330c06ab49fa1b7cf631b269c94a713aaa29b
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_Equation_6_interpolation_motion_feature_anchor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Adaptive 3D Gaussian Splatting Video Streaming", "date": "", "ddg_snippet": "by H Gong · 2025 — Specifically, we propose an adaptive 3DGS tiling technique guided by saliency analysis, which integrates both spatial and temporal features .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.14454", "content": "by H Gong · 2025 — Specifically, we propose an adaptive 3DGS tiling technique guided by saliency analysis, which integrates both spatial and temporal features ."}
+{"idx": 1, "title": "Awesome 3D Gaussian Splatting Resources", "date": "", "ddg_snippet": "Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Saddy21/gaussian_resources", "content": "Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months."}
+{"idx": 2, "title": "3D Gaussian Splatting as a New Era: A Survey", "date": "", "ddg_snippet": "by B Fei · 2024 · Cited by 134 — It utilizes initialized points from Structure- from- motion (SfM) [43] to construct a sparse grid of anchor points, to each of which a set of learnable Gaussians ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2402.07181", "content": "by B Fei · 2024 · Cited by 134 — It utilizes initialized points from Structure- from- motion (SfM) [43] to construct a sparse grid of anchor points, to each of which a set of learnable Gaussians ..."}
+{"idx": 3, "title": "Anisotropic View-Dependent Appearance for 3D Gaussian ...", "date": "", "ddg_snippet": "Thanks to the guidance of the anchor Gaussian , the anchor feature fv can be used directly to compress N ASGs, further reducing storage pressure. To make the ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/93509", "content": "Thanks to the guidance of the anchor Gaussian , the anchor feature fv can be used directly to compress N ASGs, further reducing storage pressure. To make the ..."}
+{"idx": 4, "title": "Multigranularity Feature Aggregation and Cross-level ...", "date": "", "ddg_snippet": "7 Mar 2025 — This article presents a Temporal Action Detection (TAD) method with Multigranularity (MG) feature aggregation and Cross-level Boundary Modeling (CBM).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3712598", "content": "7 Mar 2025 — This article presents a Temporal Action Detection (TAD) method with Multigranularity (MG) feature aggregation and Cross-level Boundary Modeling (CBM)."}
+{"idx": 5, "title": "Lee-JaeWon/2025-Arxiv-Paper-List-Gaussian-Splatting", "date": "", "ddg_snippet": "This is crawled to find out about the 2025 Gaussian Splatting papers in arxiv. There may be errors, so please leave a Pull Request or Issue and I will ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Lee-JaeWon/2025-Arxiv-Paper-List-Gaussian-Splatting", "content": "This is crawled to find out about the 2025 Gaussian Splatting papers in arxiv. There may be errors, so please leave a Pull Request or Issue and I will ..."}
+{"idx": 6, "title": "Data‐Driven Crowd Motion Control With Multi‐Touch ...", "date": "", "ddg_snippet": "by Y Shen · 2018 · Cited by 15 — We interpolate the K crowd motion and generate the resultant run-time crowd motion features (Figure 1g), which is finally converted into crowd ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1111/cgf.13333", "content": "by Y Shen · 2018 · Cited by 15 — We interpolate the K crowd motion and generate the resultant run-time crowd motion features (Figure 1g), which is finally converted into crowd ..."}
+{"idx": 7, "title": "Gaussian Splatting: 3D Reconstruction and Novel View ...", "date": "", "ddg_snippet": "by A Dalal · 2024 · Cited by 47 — This technique involves iterative refinement of multiple Gaussians to generate 3D objects from 2D images, allowing for the rendering of novel ... 24 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10380310/10545567.pdf", "content": "by A Dalal · 2024 · Cited by 47 — This technique involves iterative refinement of multiple Gaussians to generate 3D objects from 2D images, allowing for the rendering of novel ... 24 pages"}
+{"idx": 8, "title": "Relightable Gaussian Codec Avatars - CVF Open Access", "date": "", "ddg_snippet": "by S Saito · 2024 · Cited by 129 — The tracked mesh, head pose, unwrapped averaged texture, and gaze are interpolated to adjacent partially lit frames for the following avatar training. 3.2. 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Saito_Relightable_Gaussian_Codec_Avatars_CVPR_2024_paper.pdf", "content": "by S Saito · 2024 · Cited by 129 — The tracked mesh, head pose, unwrapped averaged texture, and gaze are interpolated to adjacent partially lit frames for the following avatar training. 3.2. 12 pages"}
+{"idx": 9, "title": "Track: Poster Session 4 - CVPR", "date": "", "ddg_snippet": "14 Jun 2025 — First, we introduce a generalized Anchor -driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/session/35268", "content": "14 Jun 2025 — First, we introduce a generalized Anchor -driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using ..."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_Section_4_implementation_details_test_sequences_coffee_room_plant_scene.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_Section_4_implementation_details_test_sequences_coffee_room_plant_scene.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f527de31ac1b92b0dc5b551d6b8ac028ce515b8
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_Section_4_implementation_details_test_sequences_coffee_room_plant_scene.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - yjb6/IGS: [CVPR25 Highlight] Instant Gaussian Stream : Fast...", "date": "", "ddg_snippet": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting."}
+{"idx": 1, "title": "(PDF) Instant Gaussian Stream : Fast and Generalizable Streaming ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. 4 . Implementation details . In this Section , we first introduce the datasets we used, along with the partitioning and preprocessing of training.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. 4 . Implementation details . In this Section , we first introduce the datasets we used, along with the partitioning and preprocessing of training."}
+{"idx": 2, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "4 Implementation details .In this Section , we first introduce the datasets we used, along with the partitioning and preprocessing of training data, in Sec. 4 .1. Next, we provide a detailed explanation of the configuration of the AGM network and the training hyperparameters in Sec.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "4 Implementation details .In this Section , we first introduce the datasets we used, along with the partitioning and preprocessing of training data, in Sec. 4 .1. Next, we provide a detailed explanation of the configuration of the AGM network and the training hyperparameters in Sec."}
+{"idx": 3, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene@CVPR2025@CVF", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 4, "title": "Instant Gaussian Splats in Unreal Engine 5 with Luma AI", "date": "", "ddg_snippet": "Luma AI plugin for Unreal Engine 5. This post explores how Luma AI transforms gaussian splat and Nerf captures, offering a free, user-friendly workflow for artists and developers.", "subpage_snippet": "", "source": "www.onsetfacilities.com", "link": "https://www.onsetfacilities.com/post/instant-gaussian-splats-in-unreal-engine-5-with-luma-ai", "content": "Luma AI plugin for Unreal Engine 5. This post explores how Luma AI transforms gaussian splat and Nerf captures, offering a free, user-friendly workflow for artists and developers."}
+{"idx": 5, "title": "Instant Gaussian SLAM: Transforming 3D Mapping - Simple Science", "date": "", "ddg_snippet": "IG-SLAM stands for Instant Gaussian SLAM. It’s a new method that improves how we create 3D maps using only color images. Traditional ways of mapping can be slow or inaccurate, often lacking the detail we want.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-03-instant-gaussian-slam-transforming-3d-mapping--akxyjqx", "content": "IG-SLAM stands for Instant Gaussian SLAM. It’s a new method that improves how we create 3D maps using only color images. Traditional ways of mapping can be slow or inaccurate, often lacking the detail we want."}
+{"idx": 6, "title": "[Optuna v 4 .5] Gaussian Process-Based Sampler (GPSampler) Can...", "date": "", "ddg_snippet": "Implementation Details . We run each optimization for 300 trials and repeat it five times with different random seeds to evaluate the standard error.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/optuna/optuna-v4-5-81e78d8e077a", "content": "Implementation Details . We run each optimization for 300 trials and repeat it five times with different random seeds to evaluate the standard error."}
+{"idx": 7, "title": "blog.ivank.net/fastest- gaussian -blur.html", "date": "", "ddg_snippet": "detail di sini.", "subpage_snippet": "", "source": "blog.ivank.net", "link": "https://blog.ivank.net/fastest-gaussian-blur.html", "content": "detail di sini."}
+{"idx": 8, "title": "50+ Prompts Nano Banana AI (Google Gemini) Photo or Image Editing...", "date": "", "ddg_snippet": "Add specific details to enhance realism and creativity. Mention lighting, perspective, or pose for more control. Combine multiple prompts for unique and creative outcomes. 4 . Set AI Parameters. Choose resolution settings for high-quality images.", "subpage_snippet": "", "source": "unplix.com", "link": "https://unplix.com/nano-banana-ai-prompts/", "content": "Add specific details to enhance realism and creativity. Mention lighting, perspective, or pose for more control. Combine multiple prompts for unique and creative outcomes. 4 . Set AI Parameters. Choose resolution settings for high-quality images."}
+{"idx": 9, "title": "Unblur Image - Free Online AI Photo Blur Remover", "date": "", "ddg_snippet": "Unblur images online in 1 click with Artguru AI's blur remover. Turn blurry photos into clear, sharp pictures instantly . Try for free!", "subpage_snippet": "", "source": "www.artguru.ai", "link": "https://www.artguru.ai/unblur-image/", "content": "Unblur images online in 1 click with Artguru AI's blur remover. Turn blurry photos into clear, sharp pictures instantly . Try for free!"}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_interpolation_formula_anchor_points_motion_features_weighted_year_2024.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_interpolation_formula_anchor_points_motion_features_weighted_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bb4e8f1fe65a48cfd0b1c5de8ee0a4dd312ca4d1
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_interpolation_formula_anchor_points_motion_features_weighted_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "Anchor -driven Gaussian Motion Network.per, we propose Instant Gaussian Stream (IGS), a fast and view images is a valuable area of research, with appli-. generalizable streaming framework, to address these issues. cations spanning immersive media such as VR, AR, and.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "Anchor -driven Gaussian Motion Network.per, we propose Instant Gaussian Stream (IGS), a fast and view images is a valuable area of research, with appli-. generalizable streaming framework, to address these issues. cations spanning immersive media such as VR, AR, and."}
+{"idx": 1, "title": "GitHub - yjb6/IGS: [CVPR25 Highlight] Instant Gaussian Stream : Fast...", "date": "", "ddg_snippet": "title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting}It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting}It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows"}
+{"idx": 2, "title": "How to Move the Anchor Point in After Effects", "date": "", "ddg_snippet": "In a practical sense the anchor point is the point in which your layer will scale and rotate around. While it may seem silly to have an anchor point and a position transform property both of these parameters do very different things.", "subpage_snippet": "", "source": "www.schoolofmotion.com", "link": "https://www.schoolofmotion.com/blog/anchor-point-in-after-effects", "content": "In a practical sense the anchor point is the point in which your layer will scale and rotate around. While it may seem silly to have an anchor point and a position transform property both of these parameters do very different things."}
+{"idx": 3, "title": "Makima Piecewise Cubic Interpolation » Cleve’s Corner: Cleve Moler...", "date": "", "ddg_snippet": "vq = AKIMA(x,v,xq) interpolates the data (x,v) at the query points xq % using Akima's cubic Hermite interpolation formula . % %", "subpage_snippet": "", "source": "blogs.mathworks.com", "link": "https://blogs.mathworks.com/cleve/2019/04/29/makima-piecewise-cubic-interpolation/", "content": "vq = AKIMA(x,v,xq) interpolates the data (x,v) at the query points xq % using Akima's cubic Hermite interpolation formula . % %"}
+{"idx": 4, "title": "Interpolate In Excel - Example, Formula , How To Use?", "date": "", "ddg_snippet": "Guide to Interpolate In Excel. Here, we find linear interpolation using FORECAST(), FORECAST.LINEAR() with examples & excel template.", "subpage_snippet": "", "source": "www.excelmojo.com", "link": "https://www.excelmojo.com/interpolate-in-excel/", "content": "Guide to Interpolate In Excel. Here, we find linear interpolation using FORECAST(), FORECAST.LINEAR() with examples & excel template."}
+{"idx": 5, "title": "weizmann.ac.il/math/yonina", "date": "", "ddg_snippet": "High Rate Interpolation of Random Signals from Nonideal Samples\",", "subpage_snippet": "", "source": "www.weizmann.ac.il", "link": "https://www.weizmann.ac.il/math/yonina/", "content": "High Rate Interpolation of Random Signals from Nonideal Samples\","}
+{"idx": 6, "title": "lagrange — SciPy v1.16.2 Manual", "date": "", "ddg_snippet": "Given two 1-D arrays x and w, returns the Lagrange interpolating polynomial through the points (x, w).w represents the y-coordinates of a set of datapoints, i.e., f(x). Returns: lagrangenumpy.poly1d instance . The Lagrange interpolating polynomial. Notes.", "subpage_snippet": "", "source": "docs.scipy.org", "link": "https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.lagrange.html", "content": "Given two 1-D arrays x and w, returns the Lagrange interpolating polynomial through the points (x, w).w represents the y-coordinates of a set of datapoints, i.e., f(x). Returns: lagrangenumpy.poly1d instance . The Lagrange interpolating polynomial. Notes."}
+{"idx": 7, "title": "How to Zoom in Adobe Premiere Pro? (w/ Screenshots)", "date": "", "ddg_snippet": "Temporal Interpolation applies the interpolation method to changes in motion and time, while Spatial Interpolation applies the method in shape. Types of Interpolation : Linear Interpolation : Creates an evenly-paced change between keyframes, changing at a constant rate.", "subpage_snippet": "", "source": "www.designbuckle.com", "link": "https://www.designbuckle.com/how-to-create-a-zoom-in-effect-in-adobe-premiere-pro/", "content": "Temporal Interpolation applies the interpolation method to changes in motion and time, while Spatial Interpolation applies the method in shape. Types of Interpolation : Linear Interpolation : Creates an evenly-paced change between keyframes, changing at a constant rate."}
+{"idx": 8, "title": "But what is the Central Limit Theorem?", "date": "", "ddg_snippet": "Introduction to Universal Variable Formulation video Thumbnail Source: https...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=zeJD6dqJ5lo", "content": "Introduction to Universal Variable Formulation video Thumbnail Source: https..."}
+{"idx": 9, "title": "Video Generation with Wan2.1 - Chutes Documentation", "date": "", "ddg_snippet": "Advanced Features . Batch Video Generation. Process multiple prompts efficientlyclassInterpolationInput(BaseModel): start_prompt: str end_prompt: str interpolation _steps: int = Field(5, ge=3, le=10) resolution: Resolution = Resolution.WIDESCREEN @.", "subpage_snippet": "", "source": "chutes.ai", "link": "https://chutes.ai/docs/examples/video-generation", "content": "Advanced Features . Batch Video Generation. Process multiple prompts efficientlyclassInterpolationInput(BaseModel): start_prompt: str end_prompt: str interpolation _steps: int = Field(5, ge=3, le=10) resolution: Resolution = Resolution.WIDESCREEN @."}
diff --git a/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Definition_5.1_probability_of_necessity.jsonl b/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Definition_5.1_probability_of_necessity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8575c4926f25d7653694ee43154b407d61cb6e6a
--- /dev/null
+++ b/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Definition_5.1_probability_of_necessity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.14728", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities ."}
+{"idx": 1, "title": "(PDF) Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380847642_Intervention_and_Conditioning_in_Causal_Bayesian_Networks", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities ."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Even though causalmodels are extremely popular, conditional probability calculation offormulas involving interventions pose significant challenges.In case of Causal Bayesian Networks (CBNs)...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/hash/a2118322165fffb648d1e341ff5a5b05-Abstract-Conference.html", "content": "Even though causalmodels are extremely popular, conditional probability calculation offormulas involving interventions pose significant challenges.In case of Causal Bayesian Networks (CBNs)..."}
+{"idx": 3, "title": "Causality", "date": "", "ddg_snippet": "1.3 causal bayesian networks . 1.3.1 Causal Networks as Oracles for Interventions . 1.3.2 Causal Relationships and Their Stability. 1.4 functional causal models. 1.4.1 Structural Equations.", "subpage_snippet": "", "source": "projects.illc.uva.nl", "link": "https://projects.illc.uva.nl/cil/uploaded_files/inlineitem/Pearl_2009_Causality.pdf", "content": "1.3 causal bayesian networks . 1.3.1 Causal Networks as Oracles for Interventions . 1.3.2 Causal Relationships and Their Stability. 1.4 functional causal models. 1.4.1 Structural Equations."}
+{"idx": 4, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/intervention-conditioning-causal-bayesian-networks", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities ."}
+{"idx": 5, "title": "2405.14728 - Intervention and Conditioning in Causal Bayesian ...", "date": "", "ddg_snippet": "This paper presents a method to accurately compute interventional probabilities in Causal Bayesian Networks using observational data and independence assumptions.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2405.14728", "content": "This paper presents a method to accurately compute interventional probabilities in Causal Bayesian Networks using observational data and independence assumptions."}
+{"idx": 6, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "# Estimating probabilities involving interventions in causal models, especially Causal Bayesian Networks (CBNs), is challenging. Existing methods struggle with accurate calculations, particularly for formulas involving interventions and conditioning .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/dc28fpk76s/", "content": "# Estimating probabilities involving interventions in causal models, especially Causal Bayesian Networks (CBNs), is challenging. Existing methods struggle with accurate calculations, particularly for formulas involving interventions and conditioning ."}
+{"idx": 7, "title": "Towards causal inference", "date": "", "ddg_snippet": "Probabilistic inference. Reminder: Naive Bayesian network . • Definition : conditional independence of „effects” Xi given „ cause ” Y. • Properties", "subpage_snippet": "", "source": "www.mit.bme.hu", "link": "https://www.mit.bme.hu/system/files/oktatas/targyak/9892/IDA_FromAssociationsToCausations_v2020Presented.pdf", "content": "Probabilistic inference. Reminder: Naive Bayesian network . • Definition : conditional independence of „effects” Xi given „ cause ” Y. • Properties"}
+{"idx": 8, "title": "Differential Semantics of Intervention in Bayesian Networks", "date": "", "ddg_snippet": "Causal Bayesian networks are another kind of directed acyclic graph, which conveys causal information as well as the traditional conditional independencies, and permits one to infer the causal effects.the intervention in causal Bayesian networks .", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/Proceedings/15/Papers/106.pdf", "content": "Causal Bayesian networks are another kind of directed acyclic graph, which conveys causal information as well as the traditional conditional independencies, and permits one to infer the causal effects.the intervention in causal Bayesian networks ."}
+{"idx": 9, "title": "On Pearl’s Hierarchy and", "date": "", "ddg_snippet": "Causal Inference via L2-constraints – Semi-Markovian Causal Bayes Networks . Summary – Background Context and Recent Developments. Reflections and Conclusions. TECHNICAL REPORT R-60. First Version: July, 2020 Last Revision: Mar, 2021.", "subpage_snippet": "", "source": "causalai.net", "link": "https://causalai.net/r60.pdf", "content": "Causal Inference via L2-constraints – Semi-Markovian Causal Bayes Networks . Summary – Background Context and Recent Developments. Reflections and Conclusions. TECHNICAL REPORT R-60. First Version: July, 2020 Last Revision: Mar, 2021."}
diff --git a/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Section_2_a_causal_model_M_is_a_pair.jsonl b/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Section_2_a_causal_model_M_is_a_pair.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a7de984fcd0f552b114937c61f75c42475dc250
--- /dev/null
+++ b/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Section_2_a_causal_model_M_is_a_pair.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Re-examining Granger Causality from Causal Bayesian Networks", "date": "", "ddg_snippet": "Section 2 overviews CSL in observational datasets and formalises Granger’s causality and Reichenbach’s common cause principles (RCCPs) and causal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02672v1", "content": "Section 2 overviews CSL in observational datasets and formalises Granger’s causality and Reichenbach’s common cause principles (RCCPs) and causal ..."}
+{"idx": 1, "title": "Attribution Projection Calculus: A Novel Framework for Causal", "date": "", "ddg_snippet": "... as causal Bayesian networks [ Pearl(2009) ] , structural equation models [ Bollen(1989) ] , and potential outcomes frameworks [ 1 ] , have been ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.12094v1", "content": "... as causal Bayesian networks [ Pearl(2009) ] , structural equation models [ Bollen(1989) ] , and potential outcomes frameworks [ 1 ] , have been ..."}
+{"idx": 2, "title": "Characterization and Learning of Causal Graphs from Hard", "date": "", "ddg_snippet": "... interventional equivalence class of causal graphs with latent variables and introduce a graphical representation that can be used to determine whether ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.01037v1", "content": "... interventional equivalence class of causal graphs with latent variables and introduce a graphical representation that can be used to determine whether ..."}
+{"idx": 3, "title": "Causal Discovery with fewer independence tests", "date": "", "ddg_snippet": "These findings highlight the contrast between causal structure and minimal Bayesian network learning, suggesting that causal structure learning is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01823v1", "content": "These findings highlight the contrast between causal structure and minimal Bayesian network learning, suggesting that causal structure learning is ..."}
+{"idx": 4, "title": "Causal Models (Stanford Encyclopedia of Philosophy)", "date": "", "ddg_snippet": "This section introduces some of the basic formal tools used in causal modeling, as well as terminology and notational conventions.", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/causal-models/", "content": "This section introduces some of the basic formal tools used in causal modeling, as well as terminology and notational conventions."}
+{"idx": 5, "title": "Causal mediation | Statistical Modeling, Causal Inference, and", "date": "", "ddg_snippet": "When I was a statistics student I was somehow given the impression that causal inference could, and even should, be done from a sort of black-box ...", "subpage_snippet": "", "source": "statmodeling.stat.columbia.edu", "link": "https://statmodeling.stat.columbia.edu/2016/07/07/30608/", "content": "When I was a statistics student I was somehow given the impression that causal inference could, and even should, be done from a sort of black-box ..."}
+{"idx": 6, "title": "Causal Modeling - Bibliography - PhilPapers", "date": "", "ddg_snippet": "The key, somewhat surprisingly, is to integrate two approaches to causal modeling: the Rubin causal model, more familiar in health and social ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/causal-modeling", "content": "The key, somewhat surprisingly, is to integrate two approaches to causal modeling: the Rubin causal model, more familiar in health and social ..."}
+{"idx": 7, "title": "An introduction to causal discovery | Swiss Journal of", "date": "", "ddg_snippet": "Comprehensive surveys on causal inference are, for instance, provided in Imbens ( 2004 ); Imbens and Wooldridge ( 2009 ), and Abadie and Cattaneo ...", "subpage_snippet": "", "source": "sjes.springeropen.com", "link": "https://sjes.springeropen.com/articles/10.1186/s41937-024-00131-4", "content": "Comprehensive surveys on causal inference are, for instance, provided in Imbens ( 2004 ); Imbens and Wooldridge ( 2009 ), and Abadie and Cattaneo ..."}
+{"idx": 8, "title": "Actual Causality", "date": "", "ddg_snippet": "My work on causality has been funded in part by the NSF, AFOSR, and ARO; an NSF grant for work on Causal Databases was particularly helpful.", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/home/halpern/papers/causalitybook-ch1-3.html", "content": "My work on causality has been funded in part by the NSF, AFOSR, and ARO; an NSF grant for work on Causal Databases was particularly helpful."}
+{"idx": 9, "title": "1 Introduction", "date": "", "ddg_snippet": "Comprehensive surveys on causal inference are for instance provided in Imbens ( 2004 ) , Imbens and Wooldridge ( 2009 ) , and Abadie and Cattaneo ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.08602v1", "content": "Comprehensive surveys on causal inference are for instance provided in Imbens ( 2004 ) , Imbens and Wooldridge ( 2009 ) , and Abadie and Cattaneo ..."}
diff --git "a/data/sampled_jsons/It_then_tallies_the_frequencies_of_these_top_emotions_across_all_N_\303\227_L_instances.jsonl" "b/data/sampled_jsons/It_then_tallies_the_frequencies_of_these_top_emotions_across_all_N_\303\227_L_instances.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..678b41cbc9090718fa34b857eaff54bdab2f00da
--- /dev/null
+++ "b/data/sampled_jsons/It_then_tallies_the_frequencies_of_these_top_emotions_across_all_N_\303\227_L_instances.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unveiling the dynamics of emotions in society through an ...", "date": "", "ddg_snippet": "by B Sener · 2023 · Cited by 5 — This paper investigates the frequency , duration, and transition of 24 distinct emotions over a 2-year period, analyzing more than 5 million tweets.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10495421/", "content": "by B Sener · 2023 · Cited by 5 — This paper investigates the frequency , duration, and transition of 24 distinct emotions over a 2-year period, analyzing more than 5 million tweets."}
+{"idx": 1, "title": "Emotion on the edge: An evaluation of feature ...", "date": "", "ddg_snippet": "by JT Black · 2025 · Cited by 2 — This paper presents a comprehensive analysis of textual emotion classification, employing a tweet-based dataset to classify emotions such as surprise, love, ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2949719125000032", "content": "by JT Black · 2025 · Cited by 2 — This paper presents a comprehensive analysis of textual emotion classification, employing a tweet-based dataset to classify emotions such as surprise, love, ..."}
+{"idx": 2, "title": "Automatically Classifying Emotions based on Text", "date": "", "ddg_snippet": "by A Koufakou · 2023 · Cited by 9 — Abstract— Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2302.14727", "content": "by A Koufakou · 2023 · Cited by 9 — Abstract— Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This ..."}
+{"idx": 3, "title": "An Adversarial Behavior Model for Contextual Ethical ...", "date": "", "ddg_snippet": "May 8, 2024 · We introduce $\\DIKE$, a framework that enhances LLMs ' ability to internalize and reflect human values, adapting to cultural contexts to promote transparency and trust. The methodology involves...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_An_Adversarial_Behavior_Model_for_Contextual_Ethical_Alignment_in_Large_Language_Models", "content": "May 8, 2024 · We introduce $\\DIKE$, a framework that enhances LLMs ' ability to internalize and reflect human values, adapting to cultural contexts to promote transparency and trust. The methodology involves..."}
+{"idx": 4, "title": "Do Large Language Models Have “Emotion Neurons”? ...", "date": "", "ddg_snippet": "by J Lee · 2025 · Cited by 1 — This study comprehensively explores whether there actually exist “ emotion neurons” within large language models (LLMs) that selectively. 23 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.806.pdf", "content": "by J Lee · 2025 · Cited by 1 — This study comprehensively explores whether there actually exist “ emotion neurons” within large language models (LLMs) that selectively. 23 pages"}
+{"idx": 5, "title": "A repeated-measures study on emotional responses after ...", "date": "", "ddg_snippet": "by M Mozes · 2021 · Cited by 18 — This paper examines the emotional responses to the pandemic in a repeated-measures design. Data ( n = 1698) were collected in April 2020 (during strict lockdown ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-021-02414-9", "content": "by M Mozes · 2021 · Cited by 18 — This paper examines the emotional responses to the pandemic in a repeated-measures design. Data ( n = 1698) were collected in April 2020 (during strict lockdown ..."}
+{"idx": 6, "title": "Measures of emotion: A review - PMC", "date": "", "ddg_snippet": "by IB Mauss · 2009 · Cited by 3127 — A consensual, componential model of emotions conceptualises them as experiential, physiological, and behavioural responses to personally meaningful stimuli.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC2756702/", "content": "by IB Mauss · 2009 · Cited by 3127 — A consensual, componential model of emotions conceptualises them as experiential, physiological, and behavioural responses to personally meaningful stimuli."}
+{"idx": 7, "title": "Evaluation of Chinese-English Machine Translation of ...", "date": "", "ddg_snippet": "by S Qian · 2023 · Cited by 9 — These studies reveal the challenges and im- portance of translating emotions or emotional lexica in cross-cultural communication. But very few studies focused ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.eamt-1.13.pdf", "content": "by S Qian · 2023 · Cited by 9 — These studies reveal the challenges and im- portance of translating emotions or emotional lexica in cross-cultural communication. But very few studies focused ..."}
+{"idx": 8, "title": "How emotional communication happens in social media", "date": "", "ddg_snippet": "by M Xu · 2022 · Cited by 13 — This study proposed the “Arousal-Homophily-Echo” model with multi-dimensional features as a frame of fine-grained research practice.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772503022000172", "content": "by M Xu · 2022 · Cited by 13 — This study proposed the “Arousal-Homophily-Echo” model with multi-dimensional features as a frame of fine-grained research practice."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/JUICE_Llama2_World_Capital_dataset_Type_3_coherent_conflict_accuracy_improvement.jsonl b/data/sampled_jsons/JUICE_Llama2_World_Capital_dataset_Type_3_coherent_conflict_accuracy_improvement.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07a4a6e330a13657a778c33f03f34927bfa70f66
--- /dev/null
+++ b/data/sampled_jsons/JUICE_Llama2_World_Capital_dataset_Type_3_coherent_conflict_accuracy_improvement.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Paraphrased Prompts World Capital - Conflict Datasets .The dataset includes multiple forms of knowledge conflict , ensuring robustness against clean inputs, substitution-based conflicts , and coherent conflicts , rather than focusing on a single type .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10996", "content": "Paraphrased Prompts World Capital - Conflict Datasets .The dataset includes multiple forms of knowledge conflict , ensuring robustness against clean inputs, substitution-based conflicts , and coherent conflicts , rather than focusing on a single type ."}
+{"idx": 1, "title": "(PDF) Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "(ap) on the World Capital dataset . Three different scenarios are considered: clean inputs, substitution conflict inputs, and coherent . Coherent Conflict ( Type 3 ). For presentation clarity, we use.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389894493_Taming_Knowledge_Conflicts_in_Language_Models", "content": "(ap) on the World Capital dataset . Three different scenarios are considered: clean inputs, substitution conflict inputs, and coherent . Coherent Conflict ( Type 3 ). For presentation clarity, we use."}
+{"idx": 2, "title": "Building an LLM fine-tuning Dataset", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=pCX_3p40Efc", "content": ""}
+{"idx": 3, "title": "Llama 4: How to Run & Fine-tune | Unsloth Documentation", "date": "", "ddg_snippet": "How to run Llama 4 locally using our dynamic GGUFs which recovers accuracy compared to standard quantization.Plus multiple improvements to tool calling. Scout 1.78-bit fits in a 24GB VRAM GPU for fast inference at ~20 tokens/sec.", "subpage_snippet": "", "source": "docs.unsloth.ai", "link": "https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/llama-4-how-to-run-and-fine-tune", "content": "How to run Llama 4 locally using our dynamic GGUFs which recovers accuracy compared to standard quantization.Plus multiple improvements to tool calling. Scout 1.78-bit fits in a 24GB VRAM GPU for fast inference at ~20 tokens/sec."}
+{"idx": 4, "title": "GitHub - ollama/ollama: Get up and running with OpenAI gpt-oss...", "date": "", "ddg_snippet": "FROM llama 3 .2 #. set the temperature to 1 [higher is more creative, lower is more coherent ] PARAMETER temperature 1 #.\" I'm a basic program that prints the famous \"Hello, world !\" message to the console. Multimodal models. ollama run llava \"What's in this image? /", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ollama/ollama", "content": "FROM llama 3 .2 #. set the temperature to 1 [higher is more creative, lower is more coherent ] PARAMETER temperature 1 #.\" I'm a basic program that prints the famous \"Hello, world !\" message to the console. Multimodal models. ollama run llava \"What's in this image? /"}
+{"idx": 5, "title": "9 Best Uncensored Local LLM (7 To 20B) - Sci Fi Logic", "date": "", "ddg_snippet": "In this article, I’m spilling the tea on the best uncensored LLMs out there, the ones with like 7 to 20 billion parameters (think super-powered brains). We’re gonna dive into what makes each one special, what they can do, and how you can actually use them in the real world .", "subpage_snippet": "", "source": "scifilogic.com", "link": "https://scifilogic.com/open-uncensored-llm-model/", "content": "In this article, I’m spilling the tea on the best uncensored LLMs out there, the ones with like 7 to 20 billion parameters (think super-powered brains). We’re gonna dive into what makes each one special, what they can do, and how you can actually use them in the real world ."}
+{"idx": 6, "title": "Hugging Face – The AI community building the future.", "date": "", "ddg_snippet": "Edit Datasets filters. Main. Tasks.nvidia/Llama-Nemotron-Post-Training- Dataset . Viewer • Updated May 8 •.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets", "content": "Edit Datasets filters. Main. Tasks.nvidia/Llama-Nemotron-Post-Training- Dataset . Viewer • Updated May 8 •."}
+{"idx": 7, "title": "Llama AI: Meta Llama 3 .1 [Open Source AI Model]", "date": "", "ddg_snippet": "Llama 3 .1, by Meta, boasts massive scale and high accuracy , excelling in complex tasks. Meanwhile, Gemini stands out with superior contextual understanding, creativity, and faster responses, making it ideal for interactive use.", "subpage_snippet": "", "source": "llama3-1.com", "link": "https://llama3-1.com/", "content": "Llama 3 .1, by Meta, boasts massive scale and high accuracy , excelling in complex tasks. Meanwhile, Gemini stands out with superior contextual understanding, creativity, and faster responses, making it ideal for interactive use."}
+{"idx": 8, "title": "Llama - AI Chat Online", "date": "", "ddg_snippet": "Chat with Llama AI online for free. Powered by Meta, Llama is a cutting-edge AI model crafted for intelligent, real-time interactions across diverse topics.", "subpage_snippet": "", "source": "llama.online", "link": "https://llama.online/", "content": "Chat with Llama AI online for free. Powered by Meta, Llama is a cutting-edge AI model crafted for intelligent, real-time interactions across diverse topics."}
+{"idx": 9, "title": "Llama 2 Online - Next-Gen Large Language Model by Meta", "date": "", "ddg_snippet": "Experience the power of Llama 2 , the second-generation Large Language Model by Meta. Choose from three model sizes, pre-trained on 2 trillion tokens, and fine-tuned with over a million human-annotated examples. Try it now online!", "subpage_snippet": "", "source": "www.llama2.space", "link": "https://www.llama2.space/", "content": "Experience the power of Llama 2 , the second-generation Large Language Model by Meta. Choose from three model sizes, pre-trained on 2 trillion tokens, and fine-tuned with over a million human-annotated examples. Try it now online!"}
diff --git a/data/sampled_jsons/JUICE_Stage_1_Stage_2_stable_steering_signals_first_run_year_2024.jsonl b/data/sampled_jsons/JUICE_Stage_1_Stage_2_stable_steering_signals_first_run_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..555768ca539d4833545691e953c641c1106b04d4
--- /dev/null
+++ b/data/sampled_jsons/JUICE_Stage_1_Stage_2_stable_steering_signals_first_run_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "First-Time Buyer Tips: Avoid Mistakes in Mossel Bay -", "date": "", "ddg_snippet": "Additionally, first -time buyers may qualify for specific government initiatives designed to facilitate home ownership, such as the FLISP (Finance ...", "subpage_snippet": "", "source": "click2check.net", "link": "https://click2check.net/first-time-buyer-tips-avoid-mistakes-in-mossel-bay/", "content": "Additionally, first -time buyers may qualify for specific government initiatives designed to facilitate home ownership, such as the FLISP (Finance ..."}
+{"idx": 1, "title": "The JUICE Media Podcast", "date": "", "ddg_snippet": "The other reason I'm dividing it into two sections is that I have two amazing guests, and rather than rush them and try and squeeze them into one ...", "subpage_snippet": "", "source": "thejuicemedia.simplecast.com", "link": "https://thejuicemedia.simplecast.com/episodes/the-fires-part-1-feat-simon-holmes-a-court-teMKl_9T", "content": "The other reason I'm dividing it into two sections is that I have two amazing guests, and rather than rush them and try and squeeze them into one ..."}
+{"idx": 2, "title": "From the Moon to the Stars: How Lasers Are Rewriting the", "date": "", "ddg_snippet": "From lunar probes to Mars rovers, RF signals in the X-band (8-12 GHz) and Ka-band (26.5-40 GHz) have been like \" space postmen ...", "subpage_snippet": "", "source": "www.laserfair.com", "link": "https://www.laserfair.com/English/202509/17/929.html", "content": "From lunar probes to Mars rovers, RF signals in the X-band (8-12 GHz) and Ka-band (26.5-40 GHz) have been like \" space postmen ..."}
+{"idx": 3, "title": "Xl professional air 🥇 | Posot Class", "date": "", "ddg_snippet": "... xl capacity to create creamy, frozen drinks and smoothies for the whole family, ( 2 ) 16 oz * nutri ninja cups with to-go lids for nutrient-rich juices ...", "subpage_snippet": "", "source": "ca.class.posot.com", "link": "https://ca.class.posot.com/xl-professional-air/", "content": "... xl capacity to create creamy, frozen drinks and smoothies for the whole family, ( 2 ) 16 oz * nutri ninja cups with to-go lids for nutrient-rich juices ..."}
+{"idx": 4, "title": "Trade Larumba™ | The Official & Updated", "date": "", "ddg_snippet": "Setting these two options opposite each other gives Trade Larumba a chance to emit signals that serve the participant in aligning his or her approach ...", "subpage_snippet": "", "source": "ekronaapp.de", "link": "https://ekronaapp.de/", "content": "Setting these two options opposite each other gives Trade Larumba a chance to emit signals that serve the participant in aligning his or her approach ..."}
+{"idx": 5, "title": "The Yale Review | Marta Figlerowicz: \"It Me\"", "date": "", "ddg_snippet": "... of cultural transmission, or a unit of imitation,” is how Richard Dawkins defined the term meme on his first use of it in The Selfish Gene (1976).", "subpage_snippet": "", "source": "yalereview.org", "link": "https://yalereview.org/article/it-me", "content": "... of cultural transmission, or a unit of imitation,” is how Richard Dawkins defined the term meme on his first use of it in The Selfish Gene (1976)."}
+{"idx": 6, "title": "Barbecue Archives — Being John Curtas", "date": "", "ddg_snippet": "... and even Martha Stewart — the octogenarian queen of brand-whoring — pretty much signal that the celebrity chef restaurant has run its ...", "subpage_snippet": "", "source": "www.eatinglv.com", "link": "http://www.eatinglv.com/tag/barbecue/", "content": "... and even Martha Stewart — the octogenarian queen of brand-whoring — pretty much signal that the celebrity chef restaurant has run its ..."}
+{"idx": 7, "title": "Robots of the Not Too Distant Future – Random Thoughts", "date": "", "ddg_snippet": "CHARLI, who won the 2011 Robo Cup soccer challenge was the USA’s first full size, fully autonomous, bipedal robot. All its power systems and ...", "subpage_snippet": "", "source": "pineymountainfoster.org", "link": "https://pineymountainfoster.org/thoughts/robots-of-the-near-future/", "content": "CHARLI, who won the 2011 Robo Cup soccer challenge was the USA’s first full size, fully autonomous, bipedal robot. All its power systems and ..."}
+{"idx": 8, "title": "▷ Portable Power Station - available here!", "date": "", "ddg_snippet": "The hood modules for Defenders Td5 and Td4, New Defender, Jimny 2 , Mercedes G-Class and Sprinter have an output of between 75 and 90 W at 12 V ...", "subpage_snippet": "", "source": "www.nakatanenga.de", "link": "https://www.nakatanenga.de/en/portable-powerstation-with-lifepo-or-agm-battery-with-multiple-out-outlets/AKKUBOX-BLK-BU40WM", "content": "The hood modules for Defenders Td5 and Td4, New Defender, Jimny 2 , Mercedes G-Class and Sprinter have an output of between 75 and 90 W at 12 V ..."}
+{"idx": 9, "title": "Idiom Connection - S Idioms - Quizzes", "date": "", "ddg_snippet": "Quiz 1 - Choose the correct idiom to replace the expression in the brackets. ... Quiz 2 - Choose the correct idiom to replace the expression in the ...", "subpage_snippet": "", "source": "www.idiomconnection.com", "link": "https://www.idiomconnection.com/squiz.html", "content": "Quiz 1 - Choose the correct idiom to replace the expression in the brackets. ... Quiz 2 - Choose the correct idiom to replace the expression in the ..."}
diff --git a/data/sampled_jsons/JUICE_accuracy_improvement_percentage_points_Llama2_World_Capital_Type_3.jsonl b/data/sampled_jsons/JUICE_accuracy_improvement_percentage_points_Llama2_World_Capital_Type_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3acd592fb63286e90f231de7471281e342fc0fff
--- /dev/null
+++ b/data/sampled_jsons/JUICE_accuracy_improvement_percentage_points_Llama2_World_Capital_Type_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Llama 3.1: Same model, different results. The impact of a percentage point.", "date": "", "ddg_snippet": "The impact of a percentage point As shown above, when tested in challenging benchmarks like AlpacaEval 2.0 quality metrics can vary significantly across providers for the same model.", "subpage_snippet": "", "source": "www.together.ai", "link": "https://www.together.ai/blog/llama-31-quality", "content": "The impact of a percentage point As shown above, when tested in challenging benchmarks like AlpacaEval 2.0 quality metrics can vary significantly across providers for the same model."}
+{"idx": 1, "title": "Question about llama2 Accuracy in paper with the measured", "date": "", "ddg_snippet": "Hi, We find the evaluation accuracy (BoolQ, PIQA, HellaSwag, WinoGrande, ARC-e/c) results are different between the data from llama2 paper and here or the ones we measured with this tool For example, llama2 paper shows HellaSwag acc 77.2 for llama2 7b but here shows 78.6; Paper shows ARC-c acc is 45.9, while we measured with this tool it is 43.43.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/meta-llama/llama/issues/591", "content": "Hi, We find the evaluation accuracy (BoolQ, PIQA, HellaSwag, WinoGrande, ARC-e/c) results are different between the data from llama2 paper and here or the ones we measured with this tool For example, llama2 paper shows HellaSwag acc 77.2 for llama2 7b but here shows 78.6; Paper shows ARC-c acc is 45.9, while we measured with this tool it is 43.43."}
+{"idx": 2, "title": "Characterizing the Accuracy-Efficiency Trade-off of Low-rank ...", "date": "", "ddg_snippet": "We formalize the low-rank decomposition design space and show that the decomposition design space is enormous (e.g., O(239) for Llama2 -7B). To navigate such a vast design space, we formulate it and perform thorough case studies of accuracy -eficiency trade-ofs using six widely used LLM benchmarks on BERT and Llama 2 models. Our results show that we can achieve a 9% model size reduction with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.06626", "content": "We formalize the low-rank decomposition design space and show that the decomposition design space is enormous (e.g., O(239) for Llama2 -7B). To navigate such a vast design space, we formulate it and perform thorough case studies of accuracy -eficiency trade-ofs using six widely used LLM benchmarks on BERT and Llama 2 models. Our results show that we can achieve a 9% model size reduction with ..."}
+{"idx": 3, "title": "Llama 2 vs. GPT-4: Nearly As Accurate and 30X Cheaper - Anyscale", "date": "", "ddg_snippet": "Using Anyscale Endpoints, we compared Llama 2 7b, 13b and 70b vs. OpenAI's GPT-3.5-turbo and GPT-4 for accuracy and cost.", "subpage_snippet": "", "source": "www.anyscale.com", "link": "https://www.anyscale.com/blog/llama-2-is-about-as-factually-accurate-as-gpt-4-for-summaries-and-is-30x-cheaper", "content": "Using Anyscale Endpoints, we compared Llama 2 7b, 13b and 70b vs. OpenAI's GPT-3.5-turbo and GPT-4 for accuracy and cost."}
+{"idx": 4, "title": "Llama 3's Performance Benchmark Values Explained - Medium", "date": "", "ddg_snippet": "The LLM is scored based on Pass@1, which is the percentage of problems for which the highest-ranked completion (the model's initial choice of solution) successfully passes the tests.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ingridwickstevens/more-llm-acronyms-an-explainer-on-llama-3s-performance-benchmark-values-36722c6dcabb", "content": "The LLM is scored based on Pass@1, which is the percentage of problems for which the highest-ranked completion (the model's initial choice of solution) successfully passes the tests."}
+{"idx": 5, "title": "A Comprehensive Technical Analysis of Llama 3 & Comparison ... - LinkedIn", "date": "", "ddg_snippet": "With substantial improvements in efficiency, accuracy , natural language processing capabilities, scalability, and deployment features, Llama 3 is poised to offer enhanced performance and utility ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/comprehensive-technical-analysis-llama-3-comparison-2-ibad-rehman-kw8pe", "content": "With substantial improvements in efficiency, accuracy , natural language processing capabilities, scalability, and deployment features, Llama 3 is poised to offer enhanced performance and utility ..."}
+{"idx": 6, "title": "Validation | How-to guides - Meta Llama", "date": "", "ddg_snippet": "In this section, we are going to cover different ways to measure and ultimately validate Llama so it's possible to determine the improvements provided by different fine tuning techniques.", "subpage_snippet": "", "source": "www.llama.com", "link": "https://www.llama.com/docs/how-to-guides/validation/", "content": "In this section, we are going to cover different ways to measure and ultimately validate Llama so it's possible to determine the improvements provided by different fine tuning techniques."}
+{"idx": 7, "title": "How to Effectively Fine-Tune Llama 3 for Optimal Results?", "date": "", "ddg_snippet": "Does fine-tuning improve accuracy ? Fine-tuning can improve accuracy for specific tasks or domains. How many epochs to fine-tune a Llama? Typically, 3-5 epochs are sufficient, depending on the dataset. What is the difference between fine-tuning and RAG?", "subpage_snippet": "", "source": "blogs.novita.ai", "link": "https://blogs.novita.ai/how-to-effectively-fine-tune-llama3-for-optimal-results/", "content": "Does fine-tuning improve accuracy ? Fine-tuning can improve accuracy for specific tasks or domains. How many epochs to fine-tune a Llama? Typically, 3-5 epochs are sufficient, depending on the dataset. What is the difference between fine-tuning and RAG?"}
+{"idx": 8, "title": "Llama-2-13B Benchmarking: Performance Analysis - TrueFoundry", "date": "", "ddg_snippet": "We benchmark the performance of LLama2 -13B in this article from latency, cost, and requests per second perspective. This will help us evaluate if it can be a good choice based on the business requirements. Please note that we don't cover the qualitative performance in this article - there are different methods to compare LLMs which can be found here. Model: Llama2 -13B In this blog, we have ...", "subpage_snippet": "", "source": "www.truefoundry.com", "link": "https://www.truefoundry.com/blog/benchmarking-llama-2-13b", "content": "We benchmark the performance of LLama2 -13B in this article from latency, cost, and requests per second perspective. This will help us evaluate if it can be a good choice based on the business requirements. Please note that we don't cover the qualitative performance in this article - there are different methods to compare LLMs which can be found here. Model: Llama2 -13B In this blog, we have ..."}
+{"idx": 9, "title": "meta-llama/Llama-3.2-1B-Instruct · MMLU-Pro benchmark", "date": "", "ddg_snippet": "Llama-3.2-1B-Instruct like 1.05k Meta Llama 59.6k Text Generation Transformers Safetensors PyTorch 8 languages llama facebook meta llama-3 conversational text-generation-inference arxiv:2204.05149 arxiv:2405.16406 License:llama3.2 Model card FilesFiles and versions xet Community 119 Train Deploy Use this model", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct/discussions/16", "content": "Llama-3.2-1B-Instruct like 1.05k Meta Llama 59.6k Text Generation Transformers Safetensors PyTorch 8 languages llama facebook meta llama-3 conversational text-generation-inference arxiv:2204.05149 arxiv:2405.16406 License:llama3.2 Model card FilesFiles and versions xet Community 119 Train Deploy Use this model"}
diff --git a/data/sampled_jsons/Jiang_Meng_Yu_Lan_Shan_Hauptmann_self-paced_learning_AAAI_2015_abstract_year_2015.jsonl b/data/sampled_jsons/Jiang_Meng_Yu_Lan_Shan_Hauptmann_self-paced_learning_AAAI_2015_abstract_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cfc75cc503bf9fc1cd190bd0fd3e555ee410381f
--- /dev/null
+++ b/data/sampled_jsons/Jiang_Meng_Yu_Lan_Shan_Hauptmann_self-paced_learning_AAAI_2015_abstract_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self-Paced Curriculum Learning | Proceedings of the AAAI Conference on ...", "date": "", "ddg_snippet": "Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/9608", "content": "Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes."}
+{"idx": 1, "title": "Self-Paced Learning with Diversity - proceedings.neurips.cc", "date": "", "ddg_snippet": "Authors Lu Jiang , Deyu Meng , Shoou-I Yu , Zhenzhong Lan , Shiguang Shan , Alexander G. Hauptmann Abstract Self-paced learning (SPL) is a recently proposed learning regime inspired by the learning process of humans and animals that gradually incorporates easy to more complex samples into training. Existing methods are limited in that they ignore an important aspect in learning : diversity. To ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2014/hash/8786a6172486a9582ff0b94492e8f06a-Abstract.html", "content": "Authors Lu Jiang , Deyu Meng , Shoou-I Yu , Zhenzhong Lan , Shiguang Shan , Alexander G. Hauptmann Abstract Self-paced learning (SPL) is a recently proposed learning regime inspired by the learning process of humans and animals that gradually incorporates easy to more complex samples into training. Existing methods are limited in that they ignore an important aspect in learning : diversity. To ..."}
+{"idx": 2, "title": "(PDF) Self-paced Curriculum Learning - ResearchGate", "date": "", "ddg_snippet": "Self-paced Curriculum Learning Lu Jiang 1, Deyu Meng1,2, Qian Zhao1,2, Shiguang Shan1,3, Alexander G. Hauptmann1 1 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/279853657_Self-paced_Curriculum_Learning", "content": "Self-paced Curriculum Learning Lu Jiang 1, Deyu Meng1,2, Qian Zhao1,2, Shiguang Shan1,3, Alexander G. Hauptmann1 1 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA ..."}
+{"idx": 3, "title": "Deyu Meng's Publications - CMU School of Computer Science", "date": "", "ddg_snippet": "Self-paced Learning with Diversity Lu Jiang , Deyu Meng , Shoou-I Yu , Zhen-Zhong Lan , Shiguang Shan , Alexander Hauptmann . Neural Information Processing Systems (NIPS), 2014. [pdf] [Supplementary material ]", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~deyum/Publications.htm", "content": "Self-paced Learning with Diversity Lu Jiang , Deyu Meng , Shoou-I Yu , Zhen-Zhong Lan , Shiguang Shan , Alexander Hauptmann . Neural Information Processing Systems (NIPS), 2014. [pdf] [Supplementary material ]"}
+{"idx": 4, "title": "\"Self-Paced Curriculum Learning.\" - dblp", "date": "", "ddg_snippet": "Lu Jiang , Deyu Meng , Qian Zhao, Shiguang Shan , Alexander G. Hauptmann : Self-Paced Curriculum Learning . AAAI 2015 : 2694-2700", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/aaai/JiangMZSH15", "content": "Lu Jiang , Deyu Meng , Qian Zhao, Shiguang Shan , Alexander G. Hauptmann : Self-Paced Curriculum Learning . AAAI 2015 : 2694-2700"}
+{"idx": 5, "title": "Self-paced curriculum learning | Proceedings of the Twenty-Ninth AAAI ...", "date": "", "ddg_snippet": "Abstract Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2886521.2886696", "content": "Abstract Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes."}
+{"idx": 6, "title": "Self-Paced Curriculum Learning - AAAI", "date": "", "ddg_snippet": "Self-Paced Curriculum Learning Authors Lu Jiang Carnegie Mellon University Deyu Meng Xi'an Jiaotong University Qian Zhao Xi'an Jiaotong University Shiguang Shan Chinese Academy of Sciences Alexander Hauptmann Carnegie Mellon University Proceedings:", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/papers/9608-self-paced-curriculum-learning/", "content": "Self-Paced Curriculum Learning Authors Lu Jiang Carnegie Mellon University Deyu Meng Xi'an Jiaotong University Qian Zhao Xi'an Jiaotong University Shiguang Shan Chinese Academy of Sciences Alexander Hauptmann Carnegie Mellon University Proceedings:"}
+{"idx": 7, "title": "Self-paced learning with identification refinement for SPOC student ...", "date": "", "ddg_snippet": "Lu Jiang , Deyu Meng , Shoou I Yu , Zhenzhong Lan , Shiguang Shan , and Alexander Hauptmann . 2014. Self-paced Learning with Diversity. Advances in Neural Information Processing Systems (2014), 2078--2086.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3210713.3210732", "content": "Lu Jiang , Deyu Meng , Shoou I Yu , Zhenzhong Lan , Shiguang Shan , and Alexander Hauptmann . 2014. Self-paced Learning with Diversity. Advances in Neural Information Processing Systems (2014), 2078--2086."}
+{"idx": 8, "title": "Self-Paced Curriculum Learning", "date": "", "ddg_snippet": "Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex sam- ples in training. The two methods share a similar con- ceptual learning paradigm, but differ in specific learn - ing schemes.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/9608/9467", "content": "Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex sam- ples in training. The two methods share a similar con- ceptual learning paradigm, but differ in specific learn - ing schemes."}
+{"idx": 9, "title": "Cognitive Driven Multilayer Self-Paced Learning with Misclassified ...", "date": "", "ddg_snippet": "Q. Zhao, D. Meng , L. Jiang , Q. Xie, Z. Xu, and A. G. Hauptmann , \" Self-paced learning for Matrix factorization,\" in Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 , pp. 3196-3202, 2015 .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1155/2019/8127869", "content": "Q. Zhao, D. Meng , L. Jiang , Q. Xie, Z. Xu, and A. G. Hauptmann , \" Self-paced learning for Matrix factorization,\" in Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 , pp. 3196-3202, 2015 ."}
diff --git a/data/sampled_jsons/KAIR_models_dncnn.py_m_head_m_body.jsonl b/data/sampled_jsons/KAIR_models_dncnn.py_m_head_m_body.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..244c7957be308f1da69427a9ef4b0a75a3546e4d
--- /dev/null
+++ b/data/sampled_jsons/KAIR_models_dncnn.py_m_head_m_body.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - cszn/KAIR: Image Restoration Toolbox (PyTorch ...", "date": "", "ddg_snippet": "Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN , FFDNet, SRMD, DPSR, BSRGAN, SwinIR - cszn/KAIR", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/KAIR", "content": "Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN , FFDNet, SRMD, DPSR, BSRGAN, SwinIR - cszn/KAIR"}
+{"idx": 1, "title": "DnCNN and SRMD | cszn/KAIR | DeepWiki", "date": "", "ddg_snippet": "Sources: main_test_ dncnn.py 43-51 main_test_fdncnn. py 44-48 2. DnCNN Architecture and Implementation 2.1 DnCNN Model Architecture DnCNN utilizes a residual learning approach where the network predicts the noise component rather than the clean image directly.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/KAIR/2.2-dncnn-and-srmd", "content": "Sources: main_test_ dncnn.py 43-51 main_test_fdncnn. py 44-48 2. DnCNN Architecture and Implementation 2.1 DnCNN Model Architecture DnCNN utilizes a residual learning approach where the network predicts the noise component rather than the clean image directly."}
+{"idx": 2, "title": "KAIR: https://github.com/cszn/KAIR", "date": "", "ddg_snippet": "Explore and code with more than 13.5 million developers，Free private repositories ！：） Back Clone or Download New submodule Upload file Branches 1 Tags 0 contribute c93a6ee data figs kernels model _zoo models options results retinaface testsets trainsets utils LICENSE README.md main_challenge_sr. py main_test_ dncnn.py main_test_dncnn3_deblocking. py main_test_dpsr. py main_test_face ...", "subpage_snippet": "", "source": "gitee.com", "link": "https://gitee.com/yuelaiyue2/kair", "content": "Explore and code with more than 13.5 million developers，Free private repositories ！：） Back Clone or Download New submodule Upload file Branches 1 Tags 0 contribute c93a6ee data figs kernels model _zoo models options results retinaface testsets trainsets utils LICENSE README.md main_challenge_sr. py main_test_ dncnn.py main_test_dncnn3_deblocking. py main_test_dpsr. py main_test_face ..."}
+{"idx": 3, "title": "KAIR/README.md at master · cszn/KAIR · GitHub", "date": "", "ddg_snippet": "Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN , FFDNet, SRMD, DPSR, BSRGAN, SwinIR - KAIR/README.md at master · cszn/KAIR", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/KAIR/blob/master/README.md", "content": "Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN , FFDNet, SRMD, DPSR, BSRGAN, SwinIR - KAIR/README.md at master · cszn/KAIR"}
+{"idx": 4, "title": "PyTorch Implementation | cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · PyTorch Implementation Relevant source files This document describes the PyTorch implementation of DnCNN (Denoising Convolutional Neural Network), detailing the model structure, data handling, training process, and testing procedures.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN/3-pytorch-implementation", "content": "May 12, 2025 · PyTorch Implementation Relevant source files This document describes the PyTorch implementation of DnCNN (Denoising Convolutional Neural Network), detailing the model structure, data handling, training process, and testing procedures."}
+{"idx": 5, "title": "main_test_dncnn.py · master · Yuchi Meng / KAIR · GitLab", "date": "", "ddg_snippet": "KAIR main_test_ dncnn.py Find file Blame History Permalink Support argparse Kai Zhang authored Sep 07, 2021 3717b588", "subpage_snippet": "", "source": "gitlab.com", "link": "https://gitlab.com/mengyuchi/KAIR/-/blob/master/main_test_dncnn.py?ref_type=heads", "content": "KAIR main_test_ dncnn.py Find file Blame History Permalink Support argparse Kai Zhang authored Sep 07, 2021 3717b588"}
+{"idx": 6, "title": "DNCNN Eval Notebook", "date": "", "ddg_snippet": "Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/code/mingyuouyang/dncnn-eval-notebook", "content": "Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources."}
+{"idx": 7, "title": "KAIR/models/network_dncnn.py · lambdalabs/LambdaSuperRes at main", "date": "", "ddg_snippet": "We’re on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/lambdalabs/LambdaSuperRes/blob/main/KAIR/models/network_dncnn.py", "content": "We’re on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 8, "title": "https://huggingface.co/api/resolve-cache/spaces/la...", "date": "", "ddg_snippet": "... DnCNN (20 conv layers) # FDnCNN (20 conv layers) # IRCNN (7 conv layers) ... models /network_dncnn. py .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/api/resolve-cache/spaces/lambdalabs/LambdaSuperRes/5168985779ffa83dfc0aaee6349735972f96a552/KAIR/models/network_dncnn.py?download=true&etag=\"7a3f20f65d5e76c5bc187563e21cb34d04a426e8\"", "content": "... DnCNN (20 conv layers) # FDnCNN (20 conv layers) # IRCNN (7 conv layers) ... models /network_dncnn. py ."}
+{"idx": 9, "title": "Advances in Artificial Intelligence and Security", "date": "", "ddg_snippet": "15 Jul 2022 — The 8th International Conference on Artificial Intelligence and Security (ICAIS. 2022), formerly called the International Conference on ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-06767-9.pdf", "content": "15 Jul 2022 — The 8th International Conference on Artificial Intelligence and Security (ICAIS. 2022), formerly called the International Conference on ..."}
diff --git a/data/sampled_jsons/KAIR_repository_DnCNN_PyTorch_implementation_m_head_m_body.jsonl b/data/sampled_jsons/KAIR_repository_DnCNN_PyTorch_implementation_m_head_m_body.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9de15ace29bb3ae3ba31907c0b584fbd8d264861
--- /dev/null
+++ b/data/sampled_jsons/KAIR_repository_DnCNN_PyTorch_implementation_m_head_m_body.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - cszn/KAIR: Image Restoration Toolbox (PyTorch ...", "date": "", "ddg_snippet": "Image Restoration Toolbox ( PyTorch ). Training and testing codes for DPIR, USRNet, DnCNN , FFDNet, SRMD, DPSR, BSRGAN, SwinIR - cszn/ KAIR", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/KAIR", "content": "Image Restoration Toolbox ( PyTorch ). Training and testing codes for DPIR, USRNet, DnCNN , FFDNet, SRMD, DPSR, BSRGAN, SwinIR - cszn/ KAIR"}
+{"idx": 1, "title": "PyTorch Implementation | cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · PyTorch Implementation Relevant source files This document describes the PyTorch implementation of DnCNN (Denoising Convolutional Neural Network), detailing the model structure, data handling, training process, and testing procedures.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN/3-pytorch-implementation", "content": "May 12, 2025 · PyTorch Implementation Relevant source files This document describes the PyTorch implementation of DnCNN (Denoising Convolutional Neural Network), detailing the model structure, data handling, training process, and testing procedures."}
+{"idx": 2, "title": "【图像去噪】论文复现：新手入门必看！DnCNN的Pytorch源码训练测试全...", "date": "", "ddg_snippet": "Aug 12, 2024 · 【图像去噪】论文复现：新手入门必看！ DnCNN 的 Pytorch 源码训练测试全流程解析！为源码做详细注释！补充 DnCNN -B和 DnCNN -3的模型训练和测试！附各种情况下训练好的模型权重文件！ 阅读量1.3w 174 分类专栏：文章标签： Pytorch 深度学习图像去噪算法100例（论文精读+复现）专栏收录该内容 303 篇文章¥299.90¥ ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_36584673/article/details/139743314", "content": "Aug 12, 2024 · 【图像去噪】论文复现：新手入门必看！ DnCNN 的 Pytorch 源码训练测试全流程解析！为源码做详细注释！补充 DnCNN -B和 DnCNN -3的模型训练和测试！附各种情况下训练好的模型权重文件！ 阅读量1.3w 174 分类专栏：文章标签： Pytorch 深度学习图像去噪算法100例（论文精读+复现）专栏收录该内容 303 篇文章¥299.90¥ ..."}
+{"idx": 3, "title": "Image Restoration Toolbox (PyTorch). Training and testing ...", "date": "", "ddg_snippet": "Dec 31, 2022 · cszn/ KAIR , Image Restoration Toolbox ( PyTorch ). Training and testing codes for DPIR, USRNet, DnCNN , FFDNet, SRMD, DPSR, BSRGAN, SwinIR", "subpage_snippet": "", "source": "pythonrepo.com", "link": "https://pythonrepo.com/repo/cszn-KAIR-python-deep-learning", "content": "Dec 31, 2022 · cszn/ KAIR , Image Restoration Toolbox ( PyTorch ). Training and testing codes for DPIR, USRNet, DnCNN , FFDNet, SRMD, DPSR, BSRGAN, SwinIR"}
+{"idx": 4, "title": "KAIR: Image Restoration Toolbox (PyTorch). Training and ...", "date": "", "ddg_snippet": "Image Restoration Toolbox ( PyTorch ). Training and testing codes for DnCNN , FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, IMDN", "subpage_snippet": "", "source": "gitee.com", "link": "https://gitee.com/wonderfulhugo/KAIR", "content": "Image Restoration Toolbox ( PyTorch ). Training and testing codes for DnCNN , FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, IMDN"}
+{"idx": 5, "title": "GitHub - SaoYan/DnCNN-PyTorch: PyTorch implementation of the ...", "date": "", "ddg_snippet": "DnCNN-PyTorch This is a PyTorch implementation of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. The author's MATLAB implementation is here. This code was written with PyTorch =0.4 today. Migrating the code is easy. Please refer to PyTorch 0.4.0 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SaoYan/DnCNN-PyTorch", "content": "DnCNN-PyTorch This is a PyTorch implementation of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. The author's MATLAB implementation is here. This code was written with PyTorch =0.4 today. Migrating the code is easy. Please refer to PyTorch 0.4.0 ..."}
+{"idx": 6, "title": "GitHub - Liyong8490/ DnCNN - pytorch : DnCNN implement based on...", "date": "", "ddg_snippet": "DnCNN implement based on pytorch . Contribute to Liyong8490/ DnCNN - pytorch development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Liyong8490/DnCNN-pytorch", "content": "DnCNN implement based on pytorch . Contribute to Liyong8490/ DnCNN - pytorch development by creating an account on GitHub."}
+{"idx": 7, "title": "cszn/ DnCNN | DeepWiki", "date": "", "ddg_snippet": "KAIR repository which contains the latest PyTorch code for training and testing DnCNN models. Sources: README.md17-29.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN", "content": "KAIR repository which contains the latest PyTorch code for training and testing DnCNN models. Sources: README.md17-29."}
+{"idx": 8, "title": "model.architectures. pytorch . dncnn — OpenDenoising...", "date": "", "ddg_snippet": "Pytorch implementation of DnCNN . Implementation followed the original paper [1]_. Authors original code can be. found on `their Github Page >>.", "subpage_snippet": "", "source": "opendenoising-docs.readthedocs.io", "link": "https://opendenoising-docs.readthedocs.io/en/latest/_modules/model/architectures/pytorch/dncnn.html", "content": "Pytorch implementation of DnCNN . Implementation followed the original paper [1]_. Authors original code can be. found on `their Github Page >>."}
+{"idx": 9, "title": "Review: DnCNN — Residual Learning of Deep CNN for... | Medium", "date": "", "ddg_snippet": "DnCNN can have a relatively high speed on CPU and it is faster than two discriminative models, MLP and CSF. Though it is slower than BM3D and TNRD, by taking the image quality improvement into consideration, DnCNN is still very competitive in CPU implementation .", "subpage_snippet": "", "source": "sh-tsang.medium.com", "link": "https://sh-tsang.medium.com/review-dncnn-residual-learning-of-deep-cnn-image-denoising-super-resolution-jpeg-deblocking-cbf464b03130", "content": "DnCNN can have a relatively high speed on CPU and it is faster than two discriminative models, MLP and CSF. Though it is slower than BM3D and TNRD, by taking the image quality improvement into consideration, DnCNN is still very competitive in CPU implementation ."}
diff --git a/data/sampled_jsons/Kaplan_2020_arxiv_2001.08361_scaling_law_exponent_dataset_size.jsonl b/data/sampled_jsons/Kaplan_2020_arxiv_2001.08361_scaling_law_exponent_dataset_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a3eed2c79c20a5fd8fa7ba1ab420d70c8bb5e444
--- /dev/null
+++ b/data/sampled_jsons/Kaplan_2020_arxiv_2001.08361_scaling_law_exponent_dataset_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2001.08361] Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "by J Kaplan · 2020 · Cited by 4597 — We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size , dataset size , and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2001.08361", "content": "by J Kaplan · 2020 · Cited by 4597 — We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size , dataset size , and the ..."}
+{"idx": 1, "title": "Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "23 Jan 2020 — Throughout we will observe precise power - law scalings for performance as a function of training time, con- text length , dataset size , model size ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2001.08361", "content": "23 Jan 2020 — Throughout we will observe precise power - law scalings for performance as a function of training time, con- text length , dataset size , model size ..."}
+{"idx": 2, "title": "Paper page - Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "22 Jan 2020 — Empirical scaling laws reveal power - law relationships between language model performance, model size , dataset size , and computational resources, ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2001.08361", "content": "22 Jan 2020 — Empirical scaling laws reveal power - law relationships between language model performance, model size , dataset size , and computational resources, ..."}
+{"idx": 3, "title": "Explaining neural scaling laws - PMC", "date": "", "ddg_snippet": "by Y Bahri · 2024 · Cited by 384 — For a large variety of models and datasets , neural network performance has been empirically observed to scale as a power law with model size and dataset size (1 ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11228526/", "content": "by Y Bahri · 2024 · Cited by 384 — For a large variety of models and datasets , neural network performance has been empirically observed to scale as a power law with model size and dataset size (1 ..."}
+{"idx": 4, "title": "The Evolution of Scaling Laws for Large Language Models", "date": "", "ddg_snippet": "This article traces the evolution of scaling laws for Large Language Models (LLMs), from the Transformer's inception in 2017 to the ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@lmpo/the-evolution-of-scaling-laws-for-large-language-models-3b73a8570809", "content": "This article traces the evolution of scaling laws for Large Language Models (LLMs), from the Transformer's inception in 2017 to the ..."}
+{"idx": 5, "title": "Scaling Laws for LLMs: From GPT-3 to o3", "date": "", "ddg_snippet": "Scaling laws define a relationship, based upon a power law , between training compute (or model / dataset size ) and the test loss of an LLM.", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/llm-scaling-laws", "content": "Scaling laws define a relationship, based upon a power law , between training compute (or model / dataset size ) and the test loss of an LLM."}
+{"idx": 6, "title": "From Scaling Law to Sub-Scaling Law: Understanding the ...", "date": "", "ddg_snippet": "by Z Chen — Traditional scaling laws suggest that performance metrics of language models improve predictably with increases in model or dataset size .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LJ1zlaGdPm", "content": "by Z Chen — Traditional scaling laws suggest that performance metrics of language models improve predictably with increases in model or dataset size ."}
+{"idx": 7, "title": "Scaling Laws - LM Notes - Mohit Agarwal", "date": "", "ddg_snippet": "17 Jan 2025 — OpenAI researchers ( Kaplan et al., 2020 ) ... α and β are scaling exponents that determine the impact of model size and dataset size on model loss.", "subpage_snippet": "", "source": "meagmohit.github.io", "link": "http://meagmohit.github.io/llm-notes/docs/scaling-laws/", "content": "17 Jan 2025 — OpenAI researchers ( Kaplan et al., 2020 ) ... α and β are scaling exponents that determine the impact of model size and dataset size on model loss."}
+{"idx": 8, "title": "Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "Abstract: We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size , dataset size ...", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2001.08361v1/", "content": "Abstract: We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size , dataset size ..."}
+{"idx": 9, "title": "Data Scaling Laws in NMT: The Effect of Noise and Architecture", "date": "", "ddg_snippet": "by Y Bansal · 2022 · Cited by 51 — In all, we produce 20 different data scaling curves, each consisting of 10 different dataset sizes which take ≈ 25K. TPUv3-hours to produce. We find that, with ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/bansal22b/bansal22b.pdf", "content": "by Y Bansal · 2022 · Cited by 51 — In all, we produce 20 different data scaling curves, each consisting of 10 different dataset sizes which take ≈ 25K. TPUv3-hours to produce. We find that, with ..."}
diff --git a/data/sampled_jsons/Kaplan_2020_scaling_law_exponent_-0.5_dataset_size_D^-0.5_cross_entropy_loss.jsonl b/data/sampled_jsons/Kaplan_2020_scaling_law_exponent_-0.5_dataset_size_D^-0.5_cross_entropy_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e93de5204090c45edd5f33382f2ef989518680fb
--- /dev/null
+++ b/data/sampled_jsons/Kaplan_2020_scaling_law_exponent_-0.5_dataset_size_D^-0.5_cross_entropy_loss.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law - Wikipedia", "date": "", "ddg_snippet": "Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down."}
+{"idx": 1, "title": "Scaling Laws and Emergent Abilities in LLMs | by LM Po | Medium", "date": "", "ddg_snippet": "The Kaplan scaling law , introduced by OpenAI in 2020 , reveals the relationship between model size , dataset size , and performance in large language models (LLMs).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@lmpo/scaling-laws-and-emergent-abilities-in-llms-a02d6e98bb14", "content": "The Kaplan scaling law , introduced by OpenAI in 2020 , reveals the relationship between model size , dataset size , and performance in large language models (LLMs)."}
+{"idx": 2, "title": "machine learning - What is cross - entropy ? - Stack Overflow", "date": "", "ddg_snippet": "Cross entropy loss formula. Where p(x) is the true probability distribution (one-hot) and q(x) is the predicted probability distribution. The sum is over the three classes A, B, and C. In this case the loss is 0.479", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/41990250/what-is-cross-entropy", "content": "Cross entropy loss formula. Where p(x) is the true probability distribution (one-hot) and q(x) is the predicted probability distribution. The sum is over the three classes A, B, and C. In this case the loss is 0.479"}
+{"idx": 3, "title": "Binary Cross Entropy /Log Loss for Binary... - GeeksforGeeks", "date": "", "ddg_snippet": "Binary cross - entropy (log loss ) is a loss function used in binary classification problems.We will calculate the Binary Cross - Entropy loss for this set of observations step-by-step.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/deep-learning/binary-cross-entropy-log-loss-for-binary-classification/", "content": "Binary cross - entropy (log loss ) is a loss function used in binary classification problems.We will calculate the Binary Cross - Entropy loss for this set of observations step-by-step."}
+{"idx": 4, "title": "PyTorch Loss Functions: The Ultimate Guide", "date": "", "ddg_snippet": "Cross - Entropy Loss . Hinge Embedding Loss . Margin Ranking Loss .Softmax refers to an activation function that calculates the normalized exponential function of every unit in the layer.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/pytorch-loss-functions", "content": "Cross - Entropy Loss . Hinge Embedding Loss . Margin Ranking Loss .Softmax refers to an activation function that calculates the normalized exponential function of every unit in the layer."}
+{"idx": 5, "title": "Data Scaling Laws for Radiology Foundation Models", "date": "", "ddg_snippet": "We establish scaling laws by analyzing performance across varying pretraining dataset sizes ( Kaplan et al., 2020 ). For classification on INST-CXR-BENCH, as few as 30k samples of continual pretraining can surpass open-weight baselines.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.12818", "content": "We establish scaling laws by analyzing performance across varying pretraining dataset sizes ( Kaplan et al., 2020 ). For classification on INST-CXR-BENCH, as few as 30k samples of continual pretraining can surpass open-weight baselines."}
+{"idx": 6, "title": "Scaling Laws for Floating Point Quantization Training · HF Daily Paper...", "date": "", "ddg_snippet": "Key insights from the scaling law include discovering the optimal exponent -mantissa bit ratio for various precision levels and determining the critical training data size to prevent performance degradation.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2501.02423/", "content": "Key insights from the scaling law include discovering the optimal exponent -mantissa bit ratio for various precision levels and determining the critical training data size to prevent performance degradation."}
+{"idx": 7, "title": "CLIP или SigLIP. База по Computer vision собеседованиям. / Хабр", "date": "", "ddg_snippet": "Обучение CLIP основано на contrastive-подходе — модель сравнивает элементы обучающей выборки между собой. В отличие от классического supervised-обучения (например, классификатора «кошка/собака» с метками и Cross - entropy loss )...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/908168/", "content": "Обучение CLIP основано на contrastive-подходе — модель сравнивает элементы обучающей выборки между собой. В отличие от классического supervised-обучения (например, классификатора «кошка/собака» с метками и Cross - entropy loss )..."}
+{"idx": 8, "title": "AI - Paper Resources | PixelsTech", "date": "", "ddg_snippet": "Scaling Laws for Neural Language Models. The paper empirically uncovers robust power- law relationships between a language model’s performance (measured via cross - entropy loss ) and three core factors: model size , dataset size , and compute budget.", "subpage_snippet": "", "source": "www.pixelstech.net", "link": "https://www.pixelstech.net/resource/ai/paper", "content": "Scaling Laws for Neural Language Models. The paper empirically uncovers robust power- law relationships between a language model’s performance (measured via cross - entropy loss ) and three core factors: model size , dataset size , and compute budget."}
+{"idx": 9, "title": "Learning the natural history of human disease with generative... | Nature", "date": "", "ddg_snippet": "The dataset size was 400,000 trajectories, the same as for the original training set .Top, change in cross - entropy of disease token predictions relative to a baseline derived from age (in years) and sex incidence. Bottom, change in the exponential loss for the waiting time until the next token.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-025-09529-3?error=cookies_not_supported&code=4e95c3f3-df3d-477f-b3b0-b834ddc85dae", "content": "The dataset size was 400,000 trajectories, the same as for the original training set .Top, change in cross - entropy of disease token predictions relative to a baseline derived from age (in years) and sex incidence. Bottom, change in the exponential loss for the waiting time until the next token."}
diff --git a/data/sampled_jsons/Kaplan_et_al_scaling_law_exponent_language_model_loss.jsonl b/data/sampled_jsons/Kaplan_et_al_scaling_law_exponent_language_model_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b429c2925e9c981b680d9e41c04927af475fa53f
--- /dev/null
+++ b/data/sampled_jsons/Kaplan_et_al_scaling_law_exponent_language_model_loss.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2001.08361] Scaling Laws for Neural Language Models - arXiv.org", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power- law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2001.08361", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power- law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the ..."}
+{"idx": 1, "title": "PDF Scaling Laws for Neural Language Models - papers.baulab.info", "date": "", "ddg_snippet": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power- law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ...", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/Kaplan-2020.pdf", "content": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power- law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ..."}
+{"idx": 2, "title": "Scaling Laws for Neural Language Models - Semantic Scholar", "date": "", "ddg_snippet": "Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence. We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power- law with model size, dataset size, and the amount of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Scaling-Laws-for-Neural-Language-Models-Kaplan-McCandlish/e6c561d02500b2596a230b341a8eb8b921ca5bf2", "content": "Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence. We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power- law with model size, dataset size, and the amount of ..."}
+{"idx": 3, "title": "Explaining neural scaling laws - PNAS", "date": "", "ddg_snippet": "The population loss of trained deep neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both dataset and model size, for a total of four ...", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.2311878121", "content": "The population loss of trained deep neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both dataset and model size, for a total of four ..."}
+{"idx": 4, "title": "PDF Scaling Laws from the Data Manifold Dimension", "date": "", "ddg_snippet": "Compute scaling exponents Kaplan et al . (2020) are also not far from model -size exponents , but combine optimization and model scaling . It seems most natural to interpret them by modeling learning curves, but perhaps optimization can be re-interpreted as the identi cation and dissection of the data manifold.", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume23/20-1111/20-1111.pdf", "content": "Compute scaling exponents Kaplan et al . (2020) are also not far from model -size exponents , but combine optimization and model scaling . It seems most natural to interpret them by modeling learning curves, but perhaps optimization can be re-interpreted as the identi cation and dissection of the data manifold."}
+{"idx": 5, "title": "Scaling Laws of Neural Language Models - GitHub", "date": "", "ddg_snippet": "This repository contains an implementation of scaling laws as first found by Kaplan et al in Scaling Laws of Neural Language Models . We find how the test loss of a language model scales with parameter count, dataset size, total compute, and the number of training steps. We also find the optimal ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shehper/scaling_laws", "content": "This repository contains an implementation of scaling laws as first found by Kaplan et al in Scaling Laws of Neural Language Models . We find how the test loss of a language model scales with parameter count, dataset size, total compute, and the number of training steps. We also find the optimal ..."}
+{"idx": 6, "title": "Scaling Laws for Neural Language Models | Fan Pu Zeng", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power- law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.", "subpage_snippet": "", "source": "fanpu.io", "link": "https://fanpu.io/summaries/2024-03-23-scaling-laws-for-neural-language-models/", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power- law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude."}
+{"idx": 7, "title": "Demystifying Scaling Laws for Large Language Models: Derivation ...", "date": "", "ddg_snippet": "Scaling laws for neural language models were first formalized by Kaplan et al . [4] and Henighan et al . [5], who demonstrated power- law relationships between cross-entropy loss and factors such as model size, dataset size, and compute budget.", "subpage_snippet": "", "source": "bhaktavaschal.substack.com", "link": "https://bhaktavaschal.substack.com/p/demystifying-scaling-laws-for-large", "content": "Scaling laws for neural language models were first formalized by Kaplan et al . [4] and Henighan et al . [5], who demonstrated power- law relationships between cross-entropy loss and factors such as model size, dataset size, and compute budget."}
+{"idx": 8, "title": "Loss-to-Loss Prediction - Kempner Institute", "date": "", "ddg_snippet": "The recent surge in building ever-larger language models was, in part, due to the work of Kaplan et al ., who noticed that model performance scaled in a predictable way with N and D, spanning several orders of magnitude.", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/loss-to-loss-prediction/", "content": "The recent surge in building ever-larger language models was, in part, due to the work of Kaplan et al ., who noticed that model performance scaled in a predictable way with N and D, spanning several orders of magnitude."}
+{"idx": 9, "title": "[2001.08361] Scaling Laws for Neural Language Models - ar5iv", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power- law with model size, dataset size, and the amount of compute used for training, with some trends span…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2001.08361", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power- law with model size, dataset size, and the amount of compute used for training, with some trends span…"}
diff --git a/data/sampled_jsons/Kirilenko_Stepchenkova_Romsdahl_2015_Public_conversations_about_climate_change_on_Twitter_abstract.jsonl b/data/sampled_jsons/Kirilenko_Stepchenkova_Romsdahl_2015_Public_conversations_about_climate_change_on_Twitter_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e76e29a56c9320c5db5788735789a7fb6da6c27
--- /dev/null
+++ b/data/sampled_jsons/Kirilenko_Stepchenkova_Romsdahl_2015_Public_conversations_about_climate_change_on_Twitter_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) The influence of temperature on # ClimateChange and...", "date": "", "ddg_snippet": "change [ Kirilenko , Molodtsova and Stepchenkova , 2015 ], there has been no.in topics of Twitter conversation using the terms “ climate change ” and “global.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/324808493_The_influence_of_temperature_on_ClimateChange_and_GlobalWarming_discourses_on_Twitter", "content": "change [ Kirilenko , Molodtsova and Stepchenkova , 2015 ], there has been no.in topics of Twitter conversation using the terms “ climate change ” and “global."}
+{"idx": 1, "title": "Yeo | The influence of temperature on # ClimateChange and...", "date": "", "ddg_snippet": "While scholars have linked Twitter discourse to temperature changes and climate change [ Kirilenko , Molodtsova and Stepchenkova , 2015 ], there has been no investigation of the topics of discussion associated with the terms “global warming” and “ climate change .”", "subpage_snippet": "", "source": "jcom.sissa.it", "link": "https://jcom.sissa.it/article/pubid/JCOM_1605_2017_A01/", "content": "While scholars have linked Twitter discourse to temperature changes and climate change [ Kirilenko , Molodtsova and Stepchenkova , 2015 ], there has been no investigation of the topics of discussion associated with the terms “global warming” and “ climate change .”"}
+{"idx": 2, "title": "Platform Cultures and Emotional Communication About Climate ...", "date": "", "ddg_snippet": "Climate change sentiment on Twitter : An unsolicited public opinion poll. Kirilenko , A. P., & Stepchenkova , S. O. (2014). Public microblogging on climate change : One year of Twitter worldwide.", "subpage_snippet": "", "source": "edepot.wur.nl", "link": "https://edepot.wur.nl/687287", "content": "Climate change sentiment on Twitter : An unsolicited public opinion poll. Kirilenko , A. P., & Stepchenkova , S. O. (2014). Public microblogging on climate change : One year of Twitter worldwide."}
+{"idx": 3, "title": "Exploring climate change on Twitter using seven aspects: Stance...", "date": "", "ddg_snippet": "7. Kirilenko AP, Stepchenkova SO. Public microblogging on climate change : One year of Twitter worldwide. Global environmental change .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9491544/", "content": "7. Kirilenko AP, Stepchenkova SO. Public microblogging on climate change : One year of Twitter worldwide. Global environmental change ."}
+{"idx": 4, "title": "Climate change sentiment on Twitter : An unsolicited public opinion poll", "date": "", "ddg_snippet": "The diction used to describe climate change attitudes on Twitter may vary by user. For exam-ple, some users may consistently use “ climate change ” and others may use “global warming”.43. Kirilenko AP, Molodtsova T, Stepchenkova SO.", "subpage_snippet": "", "source": "scholarworks.uvm.edu", "link": "https://scholarworks.uvm.edu/cgi/viewcontent.cgi?article=1047&context=cemsfac", "content": "The diction used to describe climate change attitudes on Twitter may vary by user. For exam-ple, some users may consistently use “ climate change ” and others may use “global warming”.43. Kirilenko AP, Molodtsova T, Stepchenkova SO."}
+{"idx": 5, "title": "(PDF) Climate change and Twitter in Belgium", "date": "", "ddg_snippet": "Climate change on Twitter : topics, communities and conversations about the 2013 IPCC Working Group 1 report.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/143689396/Climate_change_and_Twitter_in_Belgium", "content": "Climate change on Twitter : topics, communities and conversations about the 2013 IPCC Working Group 1 report."}
+{"idx": 6, "title": "Detecting Climate Change Deniers on Twitter ... | Semantic Scholar", "date": "", "ddg_snippet": "Taking Twitter data as an example, this study analyzed public discussions about climate change and global warming in year 2016.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Detecting-Climate-Change-Deniers-on-Twitter-Using-a-Chen-Zou/69ad5b2af09d62f2664cfd24340ba9d0cfebbcc6", "content": "Taking Twitter data as an example, this study analyzed public discussions about climate change and global warming in year 2016."}
+{"idx": 7, "title": "Emotional framing in online environmental activism: Pairing a Twitter ...", "date": "", "ddg_snippet": "climate change , activism, Twitter , emotion, framing, mood, suppression, psychology. Kirilenko , A. P., Molodtsova, T., and Stepchenkova , S. O. ( 2015 ). People as sensors: mass media and local temperature inuence climate change discussion on Twitter .", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.1099331/pdf", "content": "climate change , activism, Twitter , emotion, framing, mood, suppression, psychology. Kirilenko , A. P., Molodtsova, T., and Stepchenkova , S. O. ( 2015 ). People as sensors: mass media and local temperature inuence climate change discussion on Twitter ."}
+{"idx": 8, "title": "Men deny more than they believe about climate change on Twitter (X)", "date": "", "ddg_snippet": "Climate change on Twitter : Topics, communities and conversations about the 2013 IPCC Working Group 1 report. PloS One, 13(4), e0196588.30. Kirilenko AP, Stepchenkova SO. Public microblogging on climate change : One year of Twitter worldwide.", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0303007", "content": "Climate change on Twitter : Topics, communities and conversations about the 2013 IPCC Working Group 1 report. PloS One, 13(4), e0196588.30. Kirilenko AP, Stepchenkova SO. Public microblogging on climate change : One year of Twitter worldwide."}
+{"idx": 9, "title": "Climate Engagement in a Digital Age: Exploring the Drivers of", "date": "", "ddg_snippet": "Accordingly, we expect that interpersonal conversations about climate change will motivate participation in climate change discourse online (H6). Kirilenko , A. P., & Stepchenkova , S. O. (2014). Public microblogging on climate change : One year of Twitter worldwide.", "subpage_snippet": "", "source": "climatematters.blogs.uni-hamburg.de", "link": "https://climatematters.blogs.uni-hamburg.de/wp-content/uploads/2018/09/Climate-Engagement-in-a-Digital-Age.pdf", "content": "Accordingly, we expect that interpersonal conversations about climate change will motivate participation in climate change discourse online (H6). Kirilenko , A. P., & Stepchenkova , S. O. (2014). Public microblogging on climate change : One year of Twitter worldwide."}
diff --git a/data/sampled_jsons/Kirilenko_et_al.,_2015_People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_d.jsonl b/data/sampled_jsons/Kirilenko_et_al.,_2015_People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_d.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..331db3ecee12fcb0ce3e4f1c87c4075f9eb1dcae
--- /dev/null
+++ b/data/sampled_jsons/Kirilenko_et_al.,_2015_People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_d.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Social media messaging by climate action NGOs: a case study of", "date": "", "ddg_snippet": "... tweets discussed risk factors beyond climate change that influence bushfire impacts, such as firefighting, emergency responses, hazard reduction, and ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/oocc/article/3/1/kgad011/7311736", "content": "... tweets discussed risk factors beyond climate change that influence bushfire impacts, such as firefighting, emergency responses, hazard reduction, and ..."}
+{"idx": 1, "title": "Climate Change Disinformation and How to Combat It | Annual", "date": "", "ddg_snippet": "Climate change also challenges many people & s worldview because any climate mitigation regime will have economic and political implications that are ...", "subpage_snippet": "", "source": "www.annualreviews.org", "link": "https://www.annualreviews.org/content/journals/10.1146/annurev-publhealth-090419-102409", "content": "Climate change also challenges many people & s worldview because any climate mitigation regime will have economic and political implications that are ..."}
+{"idx": 2, "title": "Yeo | The influence of temperature on #ClimateChange and", "date": "", "ddg_snippet": "... temperature changes and climate change [ Kirilenko , Molodtsova and Stepchenkova, 2015 ], there has been no investigation of the topics of discussion ...", "subpage_snippet": "", "source": "jcom.sissa.it", "link": "https://jcom.sissa.it/article/pubid/JCOM_1605_2017_A01/", "content": "... temperature changes and climate change [ Kirilenko , Molodtsova and Stepchenkova, 2015 ], there has been no investigation of the topics of discussion ..."}
+{"idx": 3, "title": "(PDF) Using Social Media to Detect and Locate Wildfires", "date": "", "ddg_snippet": "Distributions of mean and median distance to closest fire from each null model on each social media dataset are shown as semi-transparent histograms ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/311133785_Using_Social_Media_to_Detect_and_Locate_Wildfires", "content": "Distributions of mean and median distance to closest fire from each null model on each social media dataset are shown as semi-transparent histograms ..."}
+{"idx": 4, "title": "Trust in social media is associated with misperceptions about", "date": "", "ddg_snippet": "... media platforms such as Facebook, Twitter, and YouTube are not only consumed by a large population but have also become a ‘public square’ for ...", "subpage_snippet": "", "source": "www.hnmr.org", "link": "https://www.hnmr.org/journal/view.php?number=55", "content": "... media platforms such as Facebook, Twitter, and YouTube are not only consumed by a large population but have also become a ‘public square’ for ..."}
+{"idx": 5, "title": "People as sensors: Mass media and local temperature influence ...", "date": "", "ddg_snippet": "Jan 1, 2015 · This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0959378014001952", "content": "Jan 1, 2015 · This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process."}
+{"idx": 6, "title": "People as sensors: Mass media and local temperature influence ...", "date": "", "ddg_snippet": "Jul 25, 2014 · Specifically, the study addressed the role of temperature variables and mass media in influencing public perceptions of climate change and, in addition, investigated whether mass media mediates the relation-ship between temperature and tweeting about climate change .", "subpage_snippet": "", "source": "research.fit.edu", "link": "https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/climate-communications/youth-climate-amp-social-media/Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect-CC-Discussion-On-Twitter.pdf", "content": "Jul 25, 2014 · Specifically, the study addressed the role of temperature variables and mass media in influencing public perceptions of climate change and, in addition, investigated whether mass media mediates the relation-ship between temperature and tweeting about climate change ."}
+{"idx": 7, "title": "Sci-Hub | People as sensors: Mass media and local temperature ...", "date": "", "ddg_snippet": "Kirilenko , A. P., Molodtsova, T., & Stepchenkova, S. O. ( 2015 ). People as sensors : Mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "sci-hub.se", "link": "https://sci-hub.se/10.1016/j.gloenvcha.2014.11.003", "content": "Kirilenko , A. P., Molodtsova, T., & Stepchenkova, S. O. ( 2015 ). People as sensors : Mass media and local temperature influence climate change discussion on Twitter ."}
+{"idx": 8, "title": "People as sensors: mass media and local temperature influence ...", "date": "", "ddg_snippet": "We examined whether people living under significant temperature anomalies connect their sensory experiences to climate change and the role that media plays in this process.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2014AGUFMGC41B0548K/abstract", "content": "We examined whether people living under significant temperature anomalies connect their sensory experiences to climate change and the role that media plays in this process."}
+{"idx": 9, "title": "People as sensors: Mass media and local temperature influence ...", "date": "", "ddg_snippet": "Dec 1, 2015 · Kirilenko , Molodtsova and Stepchenkova ( 2015 ) showed US social media activity about climate change was correlated with local incidence of extreme temperatures.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/268221450_People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter", "content": "Dec 1, 2015 · Kirilenko , Molodtsova and Stepchenkova ( 2015 ) showed US social media activity about climate change was correlated with local incidence of extreme temperatures."}
diff --git a/data/sampled_jsons/Kirilenko_et_al.,_2015_climate_activism.jsonl b/data/sampled_jsons/Kirilenko_et_al.,_2015_climate_activism.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1dc4507f469a005fab104ea03707dfb68bec1d3f
--- /dev/null
+++ b/data/sampled_jsons/Kirilenko_et_al.,_2015_climate_activism.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Frontiers | Emotional framing in online environmental activism : Pairing...", "date": "", "ddg_snippet": "Keywords: climate change, activism , Twitter, emotion, framing, mood, suppression, psychology. Citation: Sanford M, Witkowska M, Gifford R and Formanowicz M (2023) Emotional framing in online environmental activism : Pairing a Twitter study with an offline experiment.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.1099331/full", "content": "Keywords: climate change, activism , Twitter, emotion, framing, mood, suppression, psychology. Citation: Sanford M, Witkowska M, Gifford R and Formanowicz M (2023) Emotional framing in online environmental activism : Pairing a Twitter study with an offline experiment."}
+{"idx": 1, "title": "Towards Fine-grained Classification of Climate Change related Social", "date": "", "ddg_snippet": "Moreover, Kirilenko et al . (2014) performed the analysis on tweets during 2012-2013 to conclude that users are establishing a relationship between temperature anomalies and climate change.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.acl-srw.35.pdf", "content": "Moreover, Kirilenko et al . (2014) performed the analysis on tweets during 2012-2013 to conclude that users are establishing a relationship between temperature anomalies and climate change."}
+{"idx": 2, "title": "When do extreme weather events generate attention to climate change?", "date": "", "ddg_snippet": "2011; Lang 2014; Kirilenko et al . 2015 ), as well as severe rains and associated flooding (Spence et al . 2011; Weber 2013), can increase people’s concern about climate change, at least temporarily.", "subpage_snippet": "", "source": "elke-u-weber.com", "link": "https://elke-u-weber.com/media/2017_climaticchange.sisco.pdf", "content": "2011; Lang 2014; Kirilenko et al . 2015 ), as well as severe rains and associated flooding (Spence et al . 2011; Weber 2013), can increase people’s concern about climate change, at least temporarily."}
+{"idx": 3, "title": "The social media life of climate change: Platforms, publics, and future...", "date": "", "ddg_snippet": "Kirilenko et al . ( 2015 ) found that, in the United States, the rate of tweeting both at local and national levels was controlled by experience of such anomalies, and that this effect was not mediated by mass media coverage of the same anomalies.", "subpage_snippet": "", "source": "eprints.whiterose.ac.uk", "link": "https://eprints.whiterose.ac.uk/id/eprint/140988/1/Pearce,+W+-+The+social+media+life+of+climate+change+-+AFC+2018-11-22.pdf", "content": "Kirilenko et al . ( 2015 ) found that, in the United States, the rate of tweeting both at local and national levels was controlled by experience of such anomalies, and that this effect was not mediated by mass media coverage of the same anomalies."}
+{"idx": 4, "title": "Raising Climate Change Awareness Across Twitter - Peeref", "date": "", "ddg_snippet": "( 2015 ) Andrei P. Kirilenko et al . Global environmental change-human and policy dimensions. Gender differences in the climate change communication on Twitter.", "subpage_snippet": "", "source": "www.peeref.com", "link": "https://www.peeref.com/works/29029694", "content": "( 2015 ) Andrei P. Kirilenko et al . Global environmental change-human and policy dimensions. Gender differences in the climate change communication on Twitter."}
+{"idx": 5, "title": "Extreme weather and climate change: social media results, 2008…2017", "date": "", "ddg_snippet": "Exceptions include Kirilenko et al . ( 2015 ), who examine to what extent extreme temperature anomalies inuence climate -change discussion on Twitter in dierent local regions in the USA.", "subpage_snippet": "", "source": "www.mobt3ath.com", "link": "https://www.mobt3ath.com/uplode/book/book-102500.pdf", "content": "Exceptions include Kirilenko et al . ( 2015 ), who examine to what extent extreme temperature anomalies inuence climate -change discussion on Twitter in dierent local regions in the USA."}
+{"idx": 6, "title": "(PDF) Content Analysis Methods for Assessing Climate Change...", "date": "", "ddg_snippet": "et al ., 2015 ), can be under investigation in climate change communication research. Content analyses of these documents are basically conducted in the same way mass media content is analyzed: a codebook is developed that fits the specifics of the documents under study.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/29774066/Content_Analysis_Methods_for_Assessing_Climate_Change_Communication_and_Media_Portrayals", "content": "et al ., 2015 ), can be under investigation in climate change communication research. Content analyses of these documents are basically conducted in the same way mass media content is analyzed: a codebook is developed that fits the specifics of the documents under study."}
+{"idx": 7, "title": "Do extreme weather events generate attention to climate change?", "date": "", "ddg_snippet": "2011; Lang 2014; Kirilenko et al . 2015 ) as well as severe rains and associated flooding (Spence et al . 2011; Weber 2013) can increase people’s concern about climate change, at least temporarily. Several studies have looked at the effects of local flooding.", "subpage_snippet": "", "source": "www.econstor.eu", "link": "https://www.econstor.eu/bitstream/10419/149526/1/NDL2016-053.pdf", "content": "2011; Lang 2014; Kirilenko et al . 2015 ) as well as severe rains and associated flooding (Spence et al . 2011; Weber 2013) can increase people’s concern about climate change, at least temporarily. Several studies have looked at the effects of local flooding."}
+{"idx": 8, "title": "Attention, sentiments and emotions towards emerging climate ...", "date": "", "ddg_snippet": "Overall, we contribute to the growing efforts to systematically collect and analyze data pertaining to public opinion about climate change on social media, and factors that influence it ( Kirilenko et al ., 2015 ; Moore et al ., 2019; Effrosynidis et al ., 2022).", "subpage_snippet": "", "source": "pure.iiasa.ac.at", "link": "https://pure.iiasa.ac.at/id/eprint/19384/1/1-s2.0-S0959378023001310-main+(1).pdf", "content": "Overall, we contribute to the growing efforts to systematically collect and analyze data pertaining to public opinion about climate change on social media, and factors that influence it ( Kirilenko et al ., 2015 ; Moore et al ., 2019; Effrosynidis et al ., 2022)."}
+{"idx": 9, "title": "People as sensors: Mass media and local temperature influence...", "date": "", "ddg_snippet": "A.P. Kirilenko et al . / Global Environmental Change 30 ( 2015 ) 92–100. 93. e.g., corresponds to air temperature anomaly exceeding the multi-year mean by at least one standard deviation) was suggested to be a perceptible climate change.", "subpage_snippet": "", "source": "research.fit.edu", "link": "https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/climate-communications/youth-climate-amp-social-media/Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect-CC-Discussion-On-Twitter.pdf", "content": "A.P. Kirilenko et al . / Global Environmental Change 30 ( 2015 ) 92–100. 93. e.g., corresponds to air temperature anomaly exceeding the multi-year mean by at least one standard deviation) was suggested to be a perceptible climate change."}
diff --git a/data/sampled_jsons/Kirkpatrick_et_al.,_2017_catastrophic_forgetting_abstract.jsonl b/data/sampled_jsons/Kirkpatrick_et_al.,_2017_catastrophic_forgetting_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..788d434fe57dfed3c641074eb593a78d540b79a9
--- /dev/null
+++ b/data/sampled_jsons/Kirkpatrick_et_al.,_2017_catastrophic_forgetting_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "by J Kirkpatrick · 2017 · Cited by 10393 — In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks.", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.1611835114", "content": "by J Kirkpatrick · 2017 · Cited by 10393 — In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks."}
+{"idx": 1, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "by J Kirkpatrick · 2016 · Cited by 10393 — View a PDF of the paper titled Overcoming catastrophic forgetting in neural networks , by James Kirkpatrick and 13 other authors. View PDF.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1612.00796", "content": "by J Kirkpatrick · 2016 · Cited by 10393 — View a PDF of the paper titled Overcoming catastrophic forgetting in neural networks , by James Kirkpatrick and 13 other authors. View PDF."}
+{"idx": 2, "title": "Measuring Catastrophic Forgetting in Neural Networks", "date": "", "ddg_snippet": "by R Kemker · 2018 · Cited by 999 — Elastic weight consolidation (EWC) ( Kirkpatrick et al . 2017 ) adds a constraint to the loss function that di- rects plasticity away from weights ...", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/11651/11651-13-15179-1-2-20201228.pdf", "content": "by R Kemker · 2018 · Cited by 999 — Elastic weight consolidation (EWC) ( Kirkpatrick et al . 2017 ) adds a constraint to the loss function that di- rects plasticity away from weights ..."}
+{"idx": 3, "title": "Complementary Learning for Overcoming Catastrophic ...", "date": "", "ddg_snippet": "by M Rostami · Cited by 102 — Past works have addressed catastrophic forgetting using two main approaches: model consolidation [ Kirkpatrick et al .,. 2017 ] and experience replay [Robins, 1995] ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2019/0463.pdf", "content": "by M Rostami · Cited by 102 — Past works have addressed catastrophic forgetting using two main approaches: model consolidation [ Kirkpatrick et al .,. 2017 ] and experience replay [Robins, 1995] ..."}
+{"idx": 4, "title": "Overcoming Catastrophic Forgetting by Bayesian Generative ...", "date": "", "ddg_snippet": "by PH Chen · 2021 · Cited by 33 — EWC ( Kirkpatrick et al ., 2017 ) aims to minimize the change of weights that are important to previous tasks through the estimation of diagonal empirical fisher ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v139/chen21v/chen21v.pdf", "content": "by PH Chen · 2021 · Cited by 33 — EWC ( Kirkpatrick et al ., 2017 ) aims to minimize the change of weights that are important to previous tasks through the estimation of diagonal empirical fisher ..."}
+{"idx": 5, "title": "Overcoming Catastrophic Forgetting by Incremental ...", "date": "", "ddg_snippet": "by SW Lee · Cited by 830 — Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task. Here, we propose a ... 11 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "http://papers.neurips.cc/paper/7051-overcoming-catastrophic-forgetting-by-incremental-moment-matching.pdf", "content": "by SW Lee · Cited by 830 — Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task. Here, we propose a ... 11 pages"}
+{"idx": 6, "title": "OVERCOMING CATASTROPHIC FORGETTING FOR ...", "date": "", "ddg_snippet": "by W Hu · Cited by 225 — EWC ( Kirkpatrick et al ., 2017 ) quantifies the importance of weights to previous tasks, and selectively alters the learning rates of weights. Following EWC, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ryGvcoA5YX", "content": "by W Hu · Cited by 225 — EWC ( Kirkpatrick et al ., 2017 ) quantifies the importance of weights to previous tasks, and selectively alters the learning rates of weights. Following EWC, ..."}
+{"idx": 7, "title": "Continual Learning and Catastrophic Forgetting", "date": "", "ddg_snippet": "8 Mar 2024 — This book chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a non-stationary stream of data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.05175v1", "content": "8 Mar 2024 — This book chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a non-stationary stream of data."}
+{"idx": 8, "title": "Overcoming catastrophic forgetting in tabular data ...", "date": "", "ddg_snippet": "by P García-Santaclara · 2025 · Cited by 1 — In this paper, we introduce a new methodology, coined the Tabular-data Rehearsal-based Incremental Lifelong Learning framework (TRIL3), designed to address the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S095219762500908X", "content": "by P García-Santaclara · 2025 · Cited by 1 — In this paper, we introduce a new methodology, coined the Tabular-data Rehearsal-based Incremental Lifelong Learning framework (TRIL3), designed to address the ..."}
+{"idx": 9, "title": "Balanced Training for Neural Machine Translation", "date": "", "ddg_snippet": "by C Shao · 2022 · Cited by 49 — There are also a number of efforts to address the catastrophic forgetting problem for the do- main adaptation of NMT. Kirkpatrick et al . ( 2017 );.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.acl-long.143.pdf", "content": "by C Shao · 2022 · Cited by 49 — There are also a number of efforts to address the catastrophic forgetting problem for the do- main adaptation of NMT. Kirkpatrick et al . ( 2017 );."}
diff --git a/data/sampled_jsons/Kirkpatrick_et_al.,_2017_catastrophic_forgetting_abstract_year_2017.jsonl b/data/sampled_jsons/Kirkpatrick_et_al.,_2017_catastrophic_forgetting_abstract_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c3bfa67b922a732fe9aeceb48f1eb8d888ca1878
--- /dev/null
+++ b/data/sampled_jsons/Kirkpatrick_et_al.,_2017_catastrophic_forgetting_abstract_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possib …", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/28292907/", "content": "The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possib …"}
+{"idx": 1, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "Overcoming catastrophic forgetting in neural networks James Kirkpatrick kirkpatrick@google.com, Razvan Pascanu, Neil Rabinowitz, +10, and Raia Hadsell Authors Info & Affiliations Edited by James L. McClelland, Stanford University, Stanford, CA, and approved February 13, 2017 (received for review July 19, 2016) March 14, 2017", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.1611835114", "content": "Overcoming catastrophic forgetting in neural networks James Kirkpatrick kirkpatrick@google.com, Razvan Pascanu, Neil Rabinowitz, +10, and Raia Hadsell Authors Info & Affiliations Edited by James L. McClelland, Stanford University, Stanford, CA, and approved February 13, 2017 (received for review July 19, 2016) March 14, 2017"}
+{"idx": 2, "title": "Kirkpatrick Et Al. - 2017 - Overcoming Catastrophic Forgetting in ...", "date": "", "ddg_snippet": "Kirkpatrick et al . - 2017 - Overcoming catastrophic forgetting in neural networks (2) - Free download as PDF File (.pdf), Text File (.txt) or read online for free.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/810260024/Kirkpatrick-et-al-2017-Overcoming-catastrophic-forgetting-in-neural-networks-2", "content": "Kirkpatrick et al . - 2017 - Overcoming catastrophic forgetting in neural networks (2) - Free download as PDF File (.pdf), Text File (.txt) or read online for free."}
+{"idx": 3, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "View a PDF of the paper titled Overcoming catastrophic forgetting in neural networks, by James Kirkpatrick and 13 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1612.00796", "content": "View a PDF of the paper titled Overcoming catastrophic forgetting in neural networks, by James Kirkpatrick and 13 other authors"}
+{"idx": 4, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "Abstract and Figures Significance Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/311411551_Overcoming_catastrophic_forgetting_in_neural_networks", "content": "Abstract and Figures Significance Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image ..."}
+{"idx": 5, "title": "Overcoming catastrophic forgetting in neural networks.", "date": "", "ddg_snippet": "Abstract The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks ...", "subpage_snippet": "", "source": "psycnet.apa.org", "link": "https://psycnet.apa.org/record/2017-15783-002", "content": "Abstract The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks ..."}
+{"idx": 6, "title": "Overcoming catastrophic forgetting in incremental few-shot learning by ...", "date": "", "ddg_snippet": "James Kirkpatrick , Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al . Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114 (13):3521-3526, 2017 .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3540261.3540778", "content": "James Kirkpatrick , Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al . Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114 (13):3521-3526, 2017 ."}
+{"idx": 7, "title": "Overcoming catastrophic forgetting in neural networks (2017)", "date": "", "ddg_snippet": "Paper Kirkpatrick , J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R. ( 2017 ). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences (PNAS), pages 201611835+ Significance Deep neural networks are currently the ...", "subpage_snippet": "", "source": "catniplab.github.io", "link": "https://catniplab.github.io/kirkpatrick/", "content": "Paper Kirkpatrick , J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R. ( 2017 ). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences (PNAS), pages 201611835+ Significance Deep neural networks are currently the ..."}
+{"idx": 8, "title": "Overcoming catastrophic forgetting in neural networks - PNAS", "date": "", "ddg_snippet": "Recent evidence suggests that the mammalian brain may avoid catastrophic forgetting by protecting previously acquired knowledge in neocortical circuits (11-14). When a mouse acquires a new skill, a proportion of excitatory synapses are strengthened; this manifests as an increase in the volume of individual dendritic spines of neurons (13).", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/pdf/10.1073/pnas.1611835114", "content": "Recent evidence suggests that the mammalian brain may avoid catastrophic forgetting by protecting previously acquired knowledge in neocortical circuits (11-14). When a mouse acquires a new skill, a proportion of excitatory synapses are strengthened; this manifests as an increase in the volume of individual dendritic spines of neurons (13)."}
+{"idx": 9, "title": "Overcoming catastrophic forgetting in neural networks - ADS", "date": "", "ddg_snippet": "Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2017PNAS..114.3521K/abstract", "content": "Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the ..."}
diff --git a/data/sampled_jsons/Kirkpatrick_et_al_2017_catastrophic_forgetting_neural_networks_year_2017.jsonl b/data/sampled_jsons/Kirkpatrick_et_al_2017_catastrophic_forgetting_neural_networks_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd49ece7f860b247d87ff62321fb29eac81cb9d8
--- /dev/null
+++ b/data/sampled_jsons/Kirkpatrick_et_al_2017_catastrophic_forgetting_neural_networks_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1612.00796", "content": "The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they ..."}
+{"idx": 1, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "We next addressed the problem of whether EWC could allow deep neural networks to learn a set of more complex tasks without catastrophic forgetting . In particular, we trained a fully connected multilayer neural network on several supervised learning tasks in sequence.", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.1611835114", "content": "We next addressed the problem of whether EWC could allow deep neural networks to learn a set of more complex tasks without catastrophic forgetting . In particular, we trained a fully connected multilayer neural network on several supervised learning tasks in sequence."}
+{"idx": 2, "title": "Kirkpatrick Et Al. - 2017 - Overcoming Catastrophic Forgetting in ...", "date": "", "ddg_snippet": "Kirkpatrick et al . - 2017 - Overcoming catastrophic forgetting in neural networks (2) - Free download as PDF File (.pdf), Text File (.txt) or read online for free.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/810260024/Kirkpatrick-et-al-2017-Overcoming-catastrophic-forgetting-in-neural-networks-2", "content": "Kirkpatrick et al . - 2017 - Overcoming catastrophic forgetting in neural networks (2) - Free download as PDF File (.pdf), Text File (.txt) or read online for free."}
+{"idx": 3, "title": "Overcoming catastrophic forgetting in neural networks (2017)", "date": "", "ddg_snippet": "Paper Kirkpatrick , J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R. ( 2017 ). Overcoming catastrophic forgetting in neural networks . Proceedings of the National Academy of Sciences (PNAS), pages 201611835+ Significance Deep neural networks are currently the ...", "subpage_snippet": "", "source": "catniplab.github.io", "link": "https://catniplab.github.io/kirkpatrick/", "content": "Paper Kirkpatrick , J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R. ( 2017 ). Overcoming catastrophic forgetting in neural networks . Proceedings of the National Academy of Sciences (PNAS), pages 201611835+ Significance Deep neural networks are currently the ..."}
+{"idx": 4, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possib …", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/28292907/", "content": "The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possib …"}
+{"idx": 5, "title": "Overcoming catastrophic forgetting in neural networks - ADS", "date": "", "ddg_snippet": "Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2017PNAS..114.3521K/abstract", "content": "Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the ..."}
+{"idx": 6, "title": "Overcoming catastrophic forgetting by incremental moment matching", "date": "", "ddg_snippet": "Springer, 2016. James Kirkpatrick , Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al . Overcoming catastrophic forgetting in neural networks . Proceedings of the National Academy of Sciences, 2017 . David JC MacKay.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3294996.3295218", "content": "Springer, 2016. James Kirkpatrick , Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al . Overcoming catastrophic forgetting in neural networks . Proceedings of the National Academy of Sciences, 2017 . David JC MacKay."}
+{"idx": 7, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "Overcoming catastrophic forgetting in neural networks December 2016 Proceedings of the National Academy of Sciences 114 (13) DOI: 10.1073/pnas.1611835114 Authors:", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/311411551_Overcoming_catastrophic_forgetting_in_neural_networks", "content": "Overcoming catastrophic forgetting in neural networks December 2016 Proceedings of the National Academy of Sciences 114 (13) DOI: 10.1073/pnas.1611835114 Authors:"}
+{"idx": 8, "title": "Avoiding Catastrophic Forgetting - ScienceDirect", "date": "", "ddg_snippet": "The continual learning of different tasks has proved to be a difficult challenge for computational models of neural network models, as addressed in the target article of this Spotlight, Kirkpatrick et al . [1]. This problem was recognized at an early stage of neural networks research and was described as the stability-plasticity dilemma [2]. The stability-plasticity dilemma concerns the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1364661317300736", "content": "The continual learning of different tasks has proved to be a difficult challenge for computational models of neural network models, as addressed in the target article of this Spotlight, Kirkpatrick et al . [1]. This problem was recognized at an early stage of neural networks research and was described as the stability-plasticity dilemma [2]. The stability-plasticity dilemma concerns the ..."}
+{"idx": 9, "title": "Overcoming catastrophic forgetting in neural networks - PNAS", "date": "", "ddg_snippet": "In marked contrast to artificial neural networks , humans and other animals appear to be able to learn in a continual fashion (11). Recent evidence suggests that the mammalian brain may avoid catastrophic forgetting by protecting previously acquired knowledge in neocortical circuits (11-14).", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/pdf/10.1073/pnas.1611835114", "content": "In marked contrast to artificial neural networks , humans and other animals appear to be able to learn in a continual fashion (11). Recent evidence suggests that the mammalian brain may avoid catastrophic forgetting by protecting previously acquired knowledge in neocortical circuits (11-14)."}
diff --git a/data/sampled_jsons/LAUREL-LR_implementation_github_BAW2501.jsonl b/data/sampled_jsons/LAUREL-LR_implementation_github_BAW2501.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..291bd851436d909cd53f8ccd9c11a2554c80e092
--- /dev/null
+++ b/data/sampled_jsons/LAUREL-LR_implementation_github_BAW2501.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - BAW 2501 /FSEI-M_TimeTabler: FSEI-M TimeTabler...", "date": "", "ddg_snippet": "The ReadME Project. GitHub community articles. BAW 2501 / FSEI-M_TimeTabler Public.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/FSEI-M_TimeTabler", "content": "The ReadME Project. GitHub community articles. BAW 2501 / FSEI-M_TimeTabler Public."}
+{"idx": 1, "title": "索引 | GitHub 中文社区", "date": "", "ddg_snippet": "Collections. 帮您快速探索 GitHub 上优秀开源项目.由 GitHub 社区贡献的精选集合，按功能用途归档.", "subpage_snippet": "", "source": "www.github-zh.com", "link": "https://www.github-zh.com/collections", "content": "Collections. 帮您快速探索 GitHub 上优秀开源项目.由 GitHub 社区贡献的精选集合，按功能用途归档."}
+{"idx": 2, "title": "Implement Github SignIn", "date": "", "ddg_snippet": "The video explains the detailed flow of how authentication works, starting with an anonymous user clicking \"sign in with GitHub \" on a Next.js page.", "subpage_snippet": "", "source": "jsmastery.com", "link": "https://jsmastery.com/course/the-ultimate-next-js-15-course/authentication/implement-github-signin", "content": "The video explains the detailed flow of how authentication works, starting with an anonymous user clicking \"sign in with GitHub \" on a Next.js page."}
+{"idx": 3, "title": "Github Baw 2501 Arabic Handwritten Characters Dataset The Arabic...", "date": "", "ddg_snippet": "The author further enhances the discussion by integrating suitable demonstrations and concrete applications that frame the theoretical constructs. Another element that distinguishes this content is the in-depth research of multiple angles related to Github Baw 2501 Arabic Handwritten...", "subpage_snippet": "", "source": "hubloaded.com", "link": "https://hubloaded.com/github-baw2501-arabic-handwritten-characters-dataset-the-arabic-handwritten-characters/", "content": "The author further enhances the discussion by integrating suitable demonstrations and concrete applications that frame the theoretical constructs. Another element that distinguishes this content is the in-depth research of multiple angles related to Github Baw 2501 Arabic Handwritten..."}
+{"idx": 4, "title": "StocasticGradientDescent implementation LR python | Kaggle", "date": "", "ddg_snippet": "Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources...", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/code/premvardhan/stocasticgradientdescent-implementation-lr-python", "content": "Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources..."}
+{"idx": 5, "title": "Aman's AI Journal • Primers • Skip Connections", "date": "", "ddg_snippet": "Implementation and Performance: ResNet-50 on ImageNet-1K: Baseline: 74.95% top-1 accuracy. LAUREL -RW+ LR +PA: 75.25% (+2.40% params), outperforming naive scaling with fewer parameters. 1B LLM Pretraining (LLM-1)", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/skip-connections/", "content": "Implementation and Performance: ResNet-50 on ImageNet-1K: Baseline: 74.95% top-1 accuracy. LAUREL -RW+ LR +PA: 75.25% (+2.40% params), outperforming naive scaling with fewer parameters. 1B LLM Pretraining (LLM-1)"}
+{"idx": 6, "title": "Запуск скачивания файла по клику на кнопку в HTML и JS", "date": "", "ddg_snippet": "GitHub – eligrey/FileSaver.js: An HTML5 saveAs() FileSaver implementation — Инструмент для сохранения файлов на клиентской стороне с использованием FileSaver.js.", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/wiki/html/zapusk-skachivaniya-fayla-po-kliku-na-knopku-v-html-i-js/", "content": "GitHub – eligrey/FileSaver.js: An HTML5 saveAs() FileSaver implementation — Инструмент для сохранения файлов на клиентской стороне с использованием FileSaver.js."}
+{"idx": 7, "title": "Google Apps Script: Как настроить автоматический запуск скриптов?", "date": "", "ddg_snippet": "TODO: Replace with actual implementation to fetch ad campaign data return { impressions: 1000, clicks: 100, conversions: 10 }", "subpage_snippet": "", "source": "external.software", "link": "https://external.software/archives/53783", "content": "TODO: Replace with actual implementation to fetch ad campaign data return { impressions: 1000, clicks: 100, conversions: 10 }"}
+{"idx": 8, "title": "Laur1200 слушать и скачать музыку, треки, mp3 – LightAudio", "date": "", "ddg_snippet": "Laura - 2020, LAUR - Лампочки, Laur - Explore, Longinus, Longinus, 本田TADAKATSU - Laurel , Lars Leonhard - Supererde, LouisAura - Lala, Laurel - Blue Blood, LR 24 - 1000, Laurion - Sorry и другие скачать в mp3 и слушать музыку онлайн бесплатно.", "subpage_snippet": "", "source": "lightaudio.ru", "link": "https://lightaudio.ru/mp3/laur1200", "content": "Laura - 2020, LAUR - Лампочки, Laur - Explore, Longinus, Longinus, 本田TADAKATSU - Laurel , Lars Leonhard - Supererde, LouisAura - Lala, Laurel - Blue Blood, LR 24 - 1000, Laurion - Sorry и другие скачать в mp3 и слушать музыку онлайн бесплатно."}
+{"idx": 9, "title": "Погружение в JNI: проблемы и решения при... / Хабр", "date": "", "ddg_snippet": "Dropbox Djinni ( GitHub ) — мощный кросс‑платформенный фреймворк (больше не поддерживается с 2020 года).", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/yandex/articles/948570/", "content": "Dropbox Djinni ( GitHub ) — мощный кросс‑платформенный фреймворк (больше не поддерживается с 2020 года)."}
diff --git a/data/sampled_jsons/LAUREL-LR_xi+1_=_formula_equation.jsonl b/data/sampled_jsons/LAUREL-LR_xi+1_=_formula_equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd90179ac512ebef65d16b2dc629434d12510a2b
--- /dev/null
+++ b/data/sampled_jsons/LAUREL-LR_xi+1_=_formula_equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Body mass index - Wikipedia", "date": "", "ddg_snippet": "Adolphe Quetelet , a Belgian astronomer , mathematician, statistician , and sociologist , devised the basis of the BMI between 1830 and 1850 as he ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Body_mass_index", "content": "Adolphe Quetelet , a Belgian astronomer , mathematician, statistician , and sociologist , devised the basis of the BMI between 1830 and 1850 as he ..."}
+{"idx": 1, "title": "LAUREL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "The LAUREL-LR variant has 2rD parameters per instantia-tion (rD for each of the two low-rank matrices, A and B), and uses Θ(rD) memory. The latency is upper bounded by O(rD2), since the input is a vector of dimension D (ignor-ing the batch and sequence dimensions), and there are two matrix multiplications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501", "content": "The LAUREL-LR variant has 2rD parameters per instantia-tion (rD for each of the two low-rank matrices, A and B), and uses Θ(rD) memory. The latency is upper bounded by O(rD2), since the input is a vector of dimension D (ignor-ing the batch and sequence dimensions), and there are two matrix multiplications."}
+{"idx": 2, "title": "LAUREL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "2.2. Low-Rank Version ( LAUREL-LR ) In this version, we fix α = 1, and g(xi) = Wxi in (2) to obtain xi+1 = f(xi) + Wxi, where W is learnable. Note that, as written, W is a D × D matrix, where D is the model dimension; hence this will add D2 new parameters (per LAUREL layer) to the model. Figure 2. An illustration of the LAUREL framework; see (2).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501v2", "content": "2.2. Low-Rank Version ( LAUREL-LR ) In this version, we fix α = 1, and g(xi) = Wxi in (2) to obtain xi+1 = f(xi) + Wxi, where W is learnable. Note that, as written, W is a D × D matrix, where D is the model dimension; hence this will add D2 new parameters (per LAUREL layer) to the model. Figure 2. An illustration of the LAUREL framework; see (2)."}
+{"idx": 3, "title": "LAUREL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "The LAUREL-LR variant has 2rD parameters per instan-tiation (rD for each of the two rank-r matrices, A and B), and uses Θ(rD) memory. The latency is upper bounded by O(rD2), since the input is a vector of dimension D (ignor-ing the batch and sequence dimensions), and there are two matrix multiplication steps.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501v4", "content": "The LAUREL-LR variant has 2rD parameters per instan-tiation (rD for each of the two rank-r matrices, A and B), and uses Θ(rD) memory. The latency is upper bounded by O(rD2), since the input is a vector of dimension D (ignor-ing the batch and sequence dimensions), and there are two matrix multiplication steps."}
+{"idx": 4, "title": "© 2024 Jacob Scott Laurel - Sasa Misailovic", "date": "", "ddg_snippet": "by JS LAUREL · 2024 · Cited by 1 — [ xi = 1 /xj]D(σ) ≜ [xi[∅]=1/(xj[∅])](σ) σ = [dxi[1] = −xi[∅] · xi[∅]](σ) ... analytical formula , we can use the analytical formula to solve for x∗ directly. 181 pages", "subpage_snippet": "", "source": "misailo.cs.illinois.edu", "link": "https://misailo.cs.illinois.edu/papers/jacob-laurel-phd-thesis.pdf", "content": "by JS LAUREL · 2024 · Cited by 1 — [ xi = 1 /xj]D(σ) ≜ [xi[∅]=1/(xj[∅])](σ) σ = [dxi[1] = −xi[∅] · xi[∅]](σ) ... analytical formula , we can use the analytical formula to solve for x∗ directly. 181 pages"}
+{"idx": 5, "title": "R66-34", "date": "", "ddg_snippet": "by EL Bourodimos · 1966 · Cited by 7 — ... formula . 2. Reflection and transmission coefficients were derived ... XI.1 . 1. H3. -- 13. >L3. AlP. b2P. A39. S TEF. SIEPO a I. CELTI DELT2 CEUT4.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/142986/04293357.pdf?sequence=1&isAllowed=y", "content": "by EL Bourodimos · 1966 · Cited by 7 — ... formula . 2. Reflection and transmission coefficients were derived ... XI.1 . 1. H3. -- 13. >L3. AlP. b2P. A39. S TEF. SIEPO a I. CELTI DELT2 CEUT4."}
+{"idx": 6, "title": "A Study of lumber used for bracing trenches in the United ...", "date": "", "ddg_snippet": "... laurel , northern red, pin, scarlet, southern red, water, willow); white oak ... XI 1 —. 01. T3. CO o. 4-> x;. 4-1. CO. J-l. X CO. 0) •r-l. 4J. 0). XI. D 0) x o -o.", "subpage_snippet": "", "source": "www.govinfo.gov", "link": "https://www.govinfo.gov/content/pkg/GOVPUB-C13-14a713beb9825ed6dcc7db8614336d0f/pdf/GOVPUB-C13-14a713beb9825ed6dcc7db8614336d0f.pdf", "content": "... laurel , northern red, pin, scarlet, southern red, water, willow); white oak ... XI 1 —. 01. T3. CO o. 4-> x;. 4-1. CO. J-l. X CO. 0) •r-l. 4J. 0). XI. D 0) x o -o."}
+{"idx": 7, "title": "Tips - Fundamentals of Algorithmics PDF", "date": "", "ddg_snippet": "This formula can also be proved using Equation 1.1. The formula gives ... xi < 1 for 1 < i < n. Here the conditions on vi andw1 are constraints on the ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/420641666/epdf-tips-fundamentals-of-algorithmics-pdf", "content": "This formula can also be proved using Equation 1.1. The formula gives ... xi < 1 for 1 < i < n. Here the conditions on vi andw1 are constraints on the ..."}
+{"idx": 8, "title": "A Study of lumber used for bracing trenches in the United ...", "date": "", "ddg_snippet": "... laurel , northern red, pin, scarlet, southern red, water, willow); white oak ... XI 1 —. 01. T3. CO o. 4-> x;. 4-1. CO. J-l. X CO. 0) •r-l. 4J. 0). XI. D 0) x o -o.", "subpage_snippet": "", "source": "nvlpubs.nist.gov", "link": "https://nvlpubs.nist.gov/nistpubs/Legacy/BSS/nbsbuildingscience122.pdf", "content": "... laurel , northern red, pin, scarlet, southern red, water, willow); white oak ... XI 1 —. 01. T3. CO o. 4-> x;. 4-1. CO. J-l. X CO. 0) •r-l. 4J. 0). XI. D 0) x o -o."}
+{"idx": 9, "title": "978-1-4757-6044-6.pdf", "date": "", "ddg_snippet": "3 Jul 2025 — ... XI = 1 if f.J.,s :;:: 3 and p.,' is even, zero otherwise. The following table gives the values of these parameters in many cases. In this ... 392 pages", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-1-4757-6044-6.pdf", "content": "3 Jul 2025 — ... XI = 1 if f.J.,s :;:: 3 and p.,' is even, zero otherwise. The following table gives the values of these parameters in many cases. In this ... 392 pages"}
diff --git a/data/sampled_jsons/LAUREL_Learned_Augmented_Residual_Layer_Equation_3.jsonl b/data/sampled_jsons/LAUREL_Learned_Augmented_Residual_Layer_Equation_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6c27cee84223a39de7246ffd3a70e2349f4ac9b5
--- /dev/null
+++ b/data/sampled_jsons/LAUREL_Learned_Augmented_Residual_Layer_Equation_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LAuReL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "2 Learned Augmented Residual Layer . 2.1 Residual Weights Version ( LAuReL -RW).In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v3", "content": "2 Learned Augmented Residual Layer . 2.1 Residual Weights Version ( LAuReL -RW).In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection."}
+{"idx": 1, "title": "LAuReL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "The paper introduces the LAUREL ( Learned Augmented Residual Layer ) framework, which represents a significant architectural innovation aimed at enhancing model quality while maintaining efficiency in terms of model size and latency.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-LAuReL-Learned-Augmented-cm3hsv976bjks016fio3mu37r", "content": "The paper introduces the LAUREL ( Learned Augmented Residual Layer ) framework, which represents a significant architectural innovation aimed at enhancing model quality while maintaining efficiency in terms of model size and latency."}
+{"idx": 2, "title": "Google AI Introduces LAuReL ( Learned Augmented Residual Layer )...", "date": "", "ddg_snippet": "LAUREL ’s implementation is tested in both vision and language domains, focusing on the ResNet-50 model for ImageNet-1K classification and a 3 B parameter decoder-only transformer for language tasks. The architecture seamlessly integrates with existing residual connections...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/11/16/google-ai-introduces-laurel-learned-augmented-residual-layer-revolutionizing-neural-networks-with-enhanced-residual-connections-for-efficient-model-performance/", "content": "LAUREL ’s implementation is tested in both vision and language domains, focusing on the ResNet-50 model for ImageNet-1K classification and a 3 B parameter decoder-only transformer for language tasks. The architecture seamlessly integrates with existing residual connections..."}
+{"idx": 3, "title": "\" LAuReL : Learned Augmented Residual Layer \" - by Rohan Paul", "date": "", "ddg_snippet": "Solution in this Paper: → LAUREL ( Learned Augmented Residual Layer ) introduces learnable parameters to the residual connection, making it dynamic and context-aware. → It comes in three versions: LAUREL -RW adds learnable weights to scale both function...", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/laurel-learned-augmented-residual", "content": "Solution in this Paper: → LAUREL ( Learned Augmented Residual Layer ) introduces learnable parameters to the residual connection, making it dynamic and context-aware. → It comes in three versions: LAUREL -RW adds learnable weights to scale both function..."}
+{"idx": 4, "title": "LAUREL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce \\emph{ Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385750324_LAUREL_Learned_Augmented_Residual_Layer", "content": "In this paper we introduce \\emph{ Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 5, "title": "Vidhyanand (Vick) Mahase PharmD, PhD. on LinkedIn: LAuReL ...", "date": "", "ddg_snippet": "Google researchers have introduced a groundbreaking method called Learned Augmented Residual Layer ( LAUREL ). This replaces conventional residual connections, enhancing both model quality and efficiency.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/vick-mahase-pharmd-phd_laurel-learned-augmented-residual-layer-activity-7264001619392970752-_BOd", "content": "Google researchers have introduced a groundbreaking method called Learned Augmented Residual Layer ( LAUREL ). This replaces conventional residual connections, enhancing both model quality and efficiency."}
+{"idx": 6, "title": "AI Innovations: From LAuReL ’s Neural Network Revolution to...", "date": "", "ddg_snippet": "Explore the latest in AI advancements with Google's LAuReL enhancing neural network efficiency and NVIDIA's LLaMA-Mesh unifying 3 D mesh generation with language models.", "subpage_snippet": "", "source": "simply-ai.podbean.com", "link": "https://simply-ai.podbean.com/e/ai-innovations-from-laurel-s-neural-network-revolution-to-llama-mesh-s-3d-breakthrough/", "content": "Explore the latest in AI advancements with Google's LAuReL enhancing neural network efficiency and NVIDIA's LLaMA-Mesh unifying 3 D mesh generation with language models."}
+{"idx": 7, "title": "Aman's AI Journal • Primers • Skip Connections", "date": "", "ddg_snippet": "LAUREL : Learned Augmented Residual Layer .Related Papers. Deep Residual Learning for Image Recognition. ResNet paper by He et al. from Facebook AI in CVPR 2016. Most cited in several AI fields.", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/skip-connections/", "content": "LAUREL : Learned Augmented Residual Layer .Related Papers. Deep Residual Learning for Image Recognition. ResNet paper by He et al. from Facebook AI in CVPR 2016. Most cited in several AI fields."}
+{"idx": 8, "title": "GitHub - antimatter15/reverse-engineering-gemma- 3 n: Reverse...", "date": "", "ddg_snippet": "LAuReL : Learned Augmented Residual Layers .The type of Laurel block that appears to be used in Gemma- 3 n seems to be the \" Laurel -LR (Low Rank)\" variety.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antimatter15/reverse-engineering-gemma-3n", "content": "LAuReL : Learned Augmented Residual Layers .The type of Laurel block that appears to be used in Gemma- 3 n seems to be the \" Laurel -LR (Low Rank)\" variety."}
+{"idx": 9, "title": "Gemma- 3 n Deep Dive: The Compact Genius of... | Medium", "date": "", "ddg_snippet": "a) LAuReL : Learned Augmented Residual Layer . You know how a normal Transformer works, right? You pass an input into a self-attention layer , then you add the result back to the original input.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@learnwithadvi/gemma-3n-deep-dive-the-compact-genius-of-googles-next-gen-ai-b4e8c02cecb2", "content": "a) LAuReL : Learned Augmented Residual Layer . You know how a normal Transformer works, right? You pass an input into a self-attention layer , then you add the result back to the original input."}
diff --git a/data/sampled_jsons/LAuReL_Learned_Augmented_Residual_Layer_ICML_2025.jsonl b/data/sampled_jsons/LAuReL_Learned_Augmented_Residual_Layer_ICML_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..680492887d11237ed44221b7f38c1ec4dd760055
--- /dev/null
+++ b/data/sampled_jsons/LAuReL_Learned_Augmented_Residual_Layer_ICML_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.07501", "content": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 1, "title": "ICML Poster LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43889", "content": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation."}
+{"idx": 2, "title": "ICML 2025 Conference Submissions - OpenReview", "date": "", "ddg_snippet": "LAuReL : Learned Augmented Residual Layer Gaurav Menghani, Ravi Kumar, Sanjiv Kumar Published: 01 May 2025 , Last Modified: 23 Jul 2025 ICML 2025 poster Readers: Everyone", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/submissions?page=33&venue=ICML.cc/2025/Conference", "content": "LAuReL : Learned Augmented Residual Layer Gaurav Menghani, Ravi Kumar, Sanjiv Kumar Published: 01 May 2025 , Last Modified: 23 Jul 2025 ICML 2025 poster Readers: Everyone"}
+{"idx": 3, "title": "BAW2501/LAuReL-Learned-Augmented-Residual-Layer - GitHub", "date": "", "ddg_snippet": "This repository contains my independent implementations of the three LAuReL variants described in the article titled \" LAuReL : Learned Augmented Residual Layer \". These implementations aim to explore the concepts presented in the paper and evaluate their effectiveness on image datasets. Unofficial ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/LAuReL-Learned-Augmented-Residual-Layer", "content": "This repository contains my independent implementations of the three LAuReL variants described in the article titled \" LAuReL : Learned Augmented Residual Layer \". These implementations aim to explore the concepts presented in the paper and evaluate their effectiveness on image datasets. Unofficial ..."}
+{"idx": 4, "title": "LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "Since then the residual connection has become ubiquitous in not just convolutional neural networks but also transformer-based architectures, the backbone of LLMs.In this paper we introduce \\emph { Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2411.07501", "content": "Since then the residual connection has become ubiquitous in not just convolutional neural networks but also transformer-based architectures, the backbone of LLMs.In this paper we introduce \\emph { Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ ..."}
+{"idx": 5, "title": "LAuReL: Learned Augmented Residual Layer | alphaXiv", "date": "", "ddg_snippet": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2411.07501v4", "content": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 6, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In this paper, we introduce the Learned Augmented Residual Layer (LAuReL)—a novel generalization of the canonical residual connection—designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v4", "content": "In this paper, we introduce the Learned Augmented Residual Layer (LAuReL)—a novel generalization of the canonical residual connection—designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics."}
+{"idx": 7, "title": "LAUREL: Learned Augmented Residual Layer | Cool Papers - Immersive ...", "date": "", "ddg_snippet": "In this paper we introduce \\emph { Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2411.07501", "content": "In this paper we introduce \\emph { Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 8, "title": "LAuReL: Learned Augmented Residual Layer - catalyzex.com", "date": "", "ddg_snippet": "LAuReL : Learned Augmented Residual Layer : Paper and Code. One of the core pillars of efficient deep learning methods is architectural improvements such as the residual /skip connection, which has led to significantly better model convergence and quality. Since then the residual connection has become ubiquitous in not just convolutional neural networks but also transformer-based architectures ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/laurel-learned-augmented-residual-layer", "content": "LAuReL : Learned Augmented Residual Layer : Paper and Code. One of the core pillars of efficient deep learning methods is architectural improvements such as the residual /skip connection, which has led to significantly better model convergence and quality. Since then the residual connection has become ubiquitous in not just convolutional neural networks but also transformer-based architectures ..."}
+{"idx": 9, "title": "ICML LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/39619", "content": "The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale."}
diff --git a/data/sampled_jsons/LHRS-Bench_dataset_annotations_scale_Muhtar_2024_year_2024.jsonl b/data/sampled_jsons/LHRS-Bench_dataset_annotations_scale_Muhtar_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a5b379db97910ae2155d79bcd9e169e3144d4f6a
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Bench_dataset_annotations_scale_Muhtar_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - NJU-LHRS/LHRS-Bot: VGI-Enhanced multimodal large ...", "date": "", "ddg_snippet": "author=\" Muhtar , Dilxat and Li, Zhenshi and Gu, Feng and Zhang, Xueliang and Xiao, Pengfeng\", title=\"LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model\",", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "author=\" Muhtar , Dilxat and Li, Zhenshi and Gu, Feng and Zhang, Xueliang and Xiao, Pengfeng\", title=\"LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model\","}
+{"idx": 1, "title": "LHRS-Bot-Nova: Improved Multimodal Large Language Model for ...", "date": "", "ddg_snippet": "Nov 14, 2024 · Beyond that, Muhtar et al. ( 2024 ) introduced LHRS-Bot, which leverages a newly proposed large- scale RS vision-language dataset and enhances visual features with a novel vision perceiver.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.09301v1", "content": "Nov 14, 2024 · Beyond that, Muhtar et al. ( 2024 ) introduced LHRS-Bot, which leverages a newly proposed large- scale RS vision-language dataset and enhances visual features with a novel vision perceiver."}
+{"idx": 2, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "Table 1: Overview of the dataset distri- bution. LHRS-Bench contains 108 RS im- ageswith690question-answerpairs,cover- ingfivemajorevaluationdimensionsand11 sub-dimensionsofquestions.Eachsampleis a human-annotated single-choice question thatmayinvolveoneormoreevaluationdi- mensionssimultaneously.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/09511.pdf", "content": "Table 1: Overview of the dataset distri- bution. LHRS-Bench contains 108 RS im- ageswith690question-answerpairs,cover- ingfivemajorevaluationdimensionsand11 sub-dimensionsofquestions.Eachsampleis a human-annotated single-choice question thatmayinvolveoneormoreevaluationdi- mensionssimultaneously."}
+{"idx": 3, "title": "LHRS-Bot-Nova: Improved multimodal large language model for ...", "date": "", "ddg_snippet": "Sep 1, 2025 · Beyond that, ( Muhtar et al., 2024 ) introduced LHRS-Bot, which leverages a newly proposed large- scale RS vision-language dataset and enhances visual features with a novel vision perceiver.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0924271625002230", "content": "Sep 1, 2025 · Beyond that, ( Muhtar et al., 2024 ) introduced LHRS-Bot, which leverages a newly proposed large- scale RS vision-language dataset and enhances visual features with a novel vision perceiver."}
+{"idx": 4, "title": "PumpkinCat/LHRS_Data at main - Hugging Face", "date": "", "ddg_snippet": "main LHRS_Data / LHRS-Bench 2 contributors History: 1 commit pUmpKin-Co commit LHRS-Bench", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data/tree/main/LHRS-Bench", "content": "main LHRS_Data / LHRS-Bench 2 contributors History: 1 commit pUmpKin-Co commit LHRS-Bench"}
+{"idx": 5, "title": "[2402.02544] LHRS-Bot: Empowering Remote Sensing with VGI ...", "date": "", "ddg_snippet": "Feb 4, 2024 · To bridge this gap, we construct a large- scale RS image-text dataset , LHRS-Align, and an informative RS-specific instruction dataset , LHRS-Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.02544", "content": "Feb 4, 2024 · To bridge this gap, we construct a large- scale RS image-text dataset , LHRS-Align, and an informative RS-specific instruction dataset , LHRS-Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images."}
+{"idx": 6, "title": "LHRS-Bot/README.md at main · NJU-LHRS/LHRS-Bot · GitHub", "date": "", "ddg_snippet": "LHRS-Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/README.md", "content": "LHRS-Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !"}
+{"idx": 7, "title": "ECCV Poster LHRS -Bot: Empowering Remote Sensing with...", "date": "", "ddg_snippet": "To bridge this gap, we construct a large- scale RS image-text dataset , LHRS -Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images.", "subpage_snippet": "", "source": "eccv.ecva.net", "link": "https://eccv.ecva.net/virtual/2024/poster/2604", "content": "To bridge this gap, we construct a large- scale RS image-text dataset , LHRS -Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images."}
+{"idx": 8, "title": "LHRS -Bot: Empowering Remote Sensing with VGI-Enhanced Large...", "date": "", "ddg_snippet": "2023) and LHRS - Bench ( Muhtar et al. 2024 ) adapt existing remote sensing datasets to create visual reasoning benchmarks for LMMs, while Geochat (Kuckreja et al. 2024 ) primarily assesses regional perception capabilities.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386016876_LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model", "content": "2023) and LHRS - Bench ( Muhtar et al. 2024 ) adapt existing remote sensing datasets to create visual reasoning benchmarks for LMMs, while Geochat (Kuckreja et al. 2024 ) primarily assesses regional perception capabilities."}
+{"idx": 9, "title": "LHRS -Bot Empowering Remote Sensing with VGI-Enhanced Large...", "date": "", "ddg_snippet": "Recent Advances in remote sensing vision language models and datasets .Posted Apr 15, 2024 . By 1 min read.", "subpage_snippet": "", "source": "irip-buaa.github.io", "link": "https://irip-buaa.github.io/posts/LHRS-Bot-Empowering-Remote-Sensing-with-VGI-Enhanced-Large-Multimodal-Language-Model/", "content": "Recent Advances in remote sensing vision language models and datasets .Posted Apr 15, 2024 . By 1 min read."}
diff --git a/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model.jsonl b/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..89c31144d5dd493ce3397d8c6a5739f3c1842f7b
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "When Large Vision-Language Model Meets Large Remote Sensing", "date": "", "ddg_snippet": "Efficient vision- language understanding of large Remote Sensing Images (RSIs) is meaningful but challenging. ... Large Language Models (LLMs) [ 2 , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.07588v3", "content": "Efficient vision- language understanding of large Remote Sensing Images (RSIs) is meaningful but challenging. ... Large Language Models (LLMs) [ 2 , ..."}
+{"idx": 1, "title": "Towards LLM-Centric Multimodal Fusion: A Survey on Integration", "date": "", "ddg_snippet": "The rapid progress of Multimodal Large Language Models (MLLMs) has transformed the AI landscape. ... Multimodality refers to combining different data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.04788v1", "content": "The rapid progress of Multimodal Large Language Models (MLLMs) has transformed the AI landscape. ... Multimodality refers to combining different data ..."}
+{"idx": 2, "title": "TAMMs: Temporal-Aware Multimodal Model for Satellite Image", "date": "", "ddg_snippet": "... efficiency while endowing foundation models with the capacity to perceive, reason about, and generate complex temporal dynamics in remote sensing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.18862v1", "content": "... efficiency while endowing foundation models with the capacity to perceive, reason about, and generate complex temporal dynamics in remote sensing ..."}
+{"idx": 3, "title": "UrBench: A Comprehensive Benchmark for Evaluating Large", "date": "", "ddg_snippet": "Recent evaluations of Large Multimodal Models (LMMs) have explored their capabilities in various domains, with only few benchmarks specifically ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17267v3", "content": "Recent evaluations of Large Multimodal Models (LMMs) have explored their capabilities in various domains, with only few benchmarks specifically ..."}
+{"idx": 4, "title": "Landsat30-AU: A Vision-Language Dataset for Australian Landsat", "date": "", "ddg_snippet": "Vision language models (VLMs) that enable natural language interaction with satellite imagery can democratize Earth observation by accelerating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03127v1", "content": "Vision language models (VLMs) that enable natural language interaction with satellite imagery can democratize Earth observation by accelerating ..."}
+{"idx": 5, "title": "Remote Sensing Vision Language - The Latest in AI", "date": "", "ddg_snippet": "... large vision- language models (VLMs) specifically tailored ... LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model", "subpage_snippet": "", "source": "thelatestinai.com", "link": "https://thelatestinai.com/topic/remote-sensing-vision-language", "content": "... large vision- language models (VLMs) specifically tailored ... LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model"}
+{"idx": 6, "title": "VHM: Versatile and Honest Vision Language Model for Remote", "date": "", "ddg_snippet": "... large -scale remote sensing image-text dataset with rich-content captions (VersaD), and an honest instruction dataset comprising both factual and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.20213v4", "content": "... large -scale remote sensing image-text dataset with rich-content captions (VersaD), and an honest instruction dataset comprising both factual and ..."}
+{"idx": 7, "title": "UrbanLLaVA: A Multi-modal Large Language Model for Urban", "date": "", "ddg_snippet": "... large language models (MLLMs) [ 49 ] have made notable advancements by leveraging large language models (LLMs) [ 38 ] with build-in common sense ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.23219v1", "content": "... large language models (MLLMs) [ 49 ] have made notable advancements by leveraging large language models (LLMs) [ 38 ] with build-in common sense ..."}
+{"idx": 8, "title": "GeoGrid-Bench: Can Foundation Models Understand Multimodal", "date": "", "ddg_snippet": "Comprehensive evaluation: Evaluation of foundation models with language , coding, multimodal , and reasoning capabilities across find-grained answer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10714v1", "content": "Comprehensive evaluation: Evaluation of foundation models with language , coding, multimodal , and reasoning capabilities across find-grained answer ..."}
+{"idx": 9, "title": "EarthGPT-X: Enabling MLLMs to Flexibly and Comprehensively", "date": "", "ddg_snippet": "... language area have developed natural multi-modal ... Multi-source, Spatial reasoning, Remote sensing (RS), Multi-modal large language models (MLLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.12795v1", "content": "... language area have developed natural multi-modal ... Multi-source, Spatial reasoning, Remote sensing (RS), Multi-modal large language models (MLLMs)."}
diff --git a/data/sampled_jsons/LUNO-LA_Sample-LA_NLL_Table_1_siteopenreview.netforumid=4Z04wVQ9FY_year_2024.jsonl b/data/sampled_jsons/LUNO-LA_Sample-LA_NLL_Table_1_siteopenreview.netforumid=4Z04wVQ9FY_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/LUNO-LA_Sample-LA_NLL_Table_1_siteopenreview.netforumid=4Z04wVQ9FY_year_2024.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/LUNO_Linearized_Uncertainty_Neural_Operators_GitHub.jsonl b/data/sampled_jsons/LUNO_Linearized_Uncertainty_Neural_Operators_GitHub.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f7849877694cffdca14cd779e59760bd19d1d0aa
--- /dev/null
+++ b/data/sampled_jsons/LUNO_Linearized_Uncertainty_Neural_Operators_GitHub.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LUNO: Linearized Predictive Uncertainty in Neural Operators - GitHub", "date": "", "ddg_snippet": "luno - Linearized Uncertainty for Neural Operators This repository contains the main algorithm of the paper \"Linearization Turns Neural Operators into Function-Valued Gaussian Processes\" by Magnani et al. (2025).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno", "content": "luno - Linearized Uncertainty for Neural Operators This repository contains the main algorithm of the paper \"Linearization Turns Neural Operators into Function-Valued Gaussian Processes\" by Magnani et al. (2025)."}
+{"idx": 1, "title": "luno/README.md at main · MethodsOfMachineLearning/luno · GitHub", "date": "", "ddg_snippet": "LUNO : Linearized Predictive Uncertainty in Neural Operators - MethodsOfMachineLearning/ luno", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno/blob/main/README.md", "content": "LUNO : Linearized Predictive Uncertainty in Neural Operators - MethodsOfMachineLearning/ luno"}
+{"idx": 2, "title": "GitHub - 2bys/luno-experiments: Experiments for the paper ...", "date": "", "ddg_snippet": "Experiments for the paper `Linearization Turns Neural Operators Into Function-Valued Gaussian Processes` - 2bys/ luno -experiments", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/2bys/luno-experiments", "content": "Experiments for the paper `Linearization Turns Neural Operators Into Function-Valued Gaussian Processes` - 2bys/ luno -experiments"}
+{"idx": 3, "title": "fourier-neural-operators · GitHub Topics · GitHub", "date": "", "ddg_snippet": "Improve this page Add a description, image, and links to the fourier- neural - operators topic page so that developers can more easily learn about it.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/fourier-neural-operators", "content": "Improve this page Add a description, image, and links to the fourier- neural - operators topic page so that developers can more easily learn about it."}
+{"idx": 4, "title": "Framework for probabilistic neural operators with linearized ... - GitHub", "date": "", "ddg_snippet": "Overview ProbNeural- Operator -Lab extends neural operators with principled uncertainty quantification through linearized Laplace approximations. This enables active learning, risk-aware decision making, and reliable extrapolation for scientific computing applications where understanding model confidence is crucial.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/danieleschmidt/probneural-operator-lab", "content": "Overview ProbNeural- Operator -Lab extends neural operators with principled uncertainty quantification through linearized Laplace approximations. This enables active learning, risk-aware decision making, and reliable extrapolation for scientific computing applications where understanding model confidence is crucial."}
+{"idx": 5, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "In this section, we show how to obtain linearized predic-tive uncertainty in neural operators ( LUNO ). We lever-age model linearization to propagate Gaussian weight-space uncertainty through the neural operator to its pre-dictions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.05072v2", "content": "In this section, we show how to obtain linearized predic-tive uncertainty in neural operators ( LUNO ). We lever-age model linearization to propagate Gaussian weight-space uncertainty through the neural operator to its pre-dictions."}
+{"idx": 6, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "We introduce LUNO , a novel framework for approximate Bayesian uncertainty quantification in trained neural operators . Our approach leverages model linearization to push (Gaussian) weight-space uncertainty forward to the neural operator's predictions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.05072", "content": "We introduce LUNO , a novel framework for approximate Bayesian uncertainty quantification in trained neural operators . Our approach leverages model linearization to push (Gaussian) weight-space uncertainty forward to the neural operator's predictions."}
+{"idx": 7, "title": "Issues: MethodsOfMachineLearning/luno - GitHub", "date": "", "ddg_snippet": "LUNO : Linearized Predictive Uncertainty in Neural Operators - Issues · MethodsOfMachineLearning/ luno", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno/issues", "content": "LUNO : Linearized Predictive Uncertainty in Neural Operators - Issues · MethodsOfMachineLearning/ luno"}
+{"idx": 8, "title": "neural-operators · GitHub Topics · GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/neural-operators", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 9, "title": "publications | Marvin Pförtner", "date": "", "ddg_snippet": "We introduce LUNO , a novel framework for approximate Bayesian uncertainty quantification in trained neural operators . Our approach leverages model linearization to push (Gaussian) weight-space uncertainty forward to the neural operator's predictions.", "subpage_snippet": "", "source": "marvinpfoertner.github.io", "link": "https://marvinpfoertner.github.io/publications/", "content": "We introduce LUNO , a novel framework for approximate Bayesian uncertainty quantification in trained neural operators . Our approach leverages model linearization to push (Gaussian) weight-space uncertainty forward to the neural operator's predictions."}
diff --git a/data/sampled_jsons/Langevin_unlearning_A_new_perspective_of_noisy_gradient_descent_for_machine_unlearning_abstract_Chie_year_2024.jsonl b/data/sampled_jsons/Langevin_unlearning_A_new_perspective_of_noisy_gradient_descent_for_machine_unlearning_abstract_Chie_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d12c28f2940a7c156bef58eb918c751b08c86701
--- /dev/null
+++ b/data/sampled_jsons/Langevin_unlearning_A_new_perspective_of_noisy_gradient_descent_for_machine_unlearning_abstract_Chie_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Paul Langevin - Wikipedia", "date": "", "ddg_snippet": "Paul Langevin (23 January 1872 – 19 December 1946) was a French physicist who developed Langevin dynamics and the Langevin equation. He was one of the founders of the Comité de vigilance des intellectuels antifascistes, an anti-fascist organization created after the 6 February 1934 far right riots.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Paul_Langevin", "content": "Paul Langevin (23 January 1872 – 19 December 1946) was a French physicist who developed Langevin dynamics and the Langevin equation. He was one of the founders of the Comité de vigilance des intellectuels antifascistes, an anti-fascist organization created after the 6 February 1934 far right riots."}
+{"idx": 1, "title": "Langevin equation - Wikipedia", "date": "", "ddg_snippet": "In physics, a Langevin equation (named after Paul Langevin ) is a stochastic differential equation describing how a system evolves when subjected to a combination of deterministic and fluctuating (\"random\") forces.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Langevin_equation", "content": "In physics, a Langevin equation (named after Paul Langevin ) is a stochastic differential equation describing how a system evolves when subjected to a combination of deterministic and fluctuating (\"random\") forces."}
+{"idx": 2, "title": "Langevin dynamics - Wikipedia", "date": "", "ddg_snippet": "In physics, Langevin dynamics is an approach to the mathematical modeling of the dynamics of molecular systems using the Langevin equation. It was originally developed by French physicist Paul Langevin .", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Langevin_dynamics", "content": "In physics, Langevin dynamics is an approach to the mathematical modeling of the dynamics of molecular systems using the Langevin equation. It was originally developed by French physicist Paul Langevin ."}
+{"idx": 3, "title": "Train the Trainer Courses, Workshops & Certifications | Langevin", "date": "", "ddg_snippet": "More than 20,000 trainers have achieved a Professional Certification with Langevin . You can gain the highest credentials available in the training industry too.", "subpage_snippet": "", "source": "langevin.com", "link": "https://langevin.com/", "content": "More than 20,000 trainers have achieved a Professional Certification with Langevin . You can gain the highest credentials available in the training industry too."}
+{"idx": 4, "title": "Statement on the Retirement of NCCIH Director Dr. Helene Langevin", "date": "", "ddg_snippet": "6 days ago · Helene M. Langevin , M.D., director of the National Center for Complementary and Integrative Health (NCCIH), is retiring from federal service on November 30, 2025.", "subpage_snippet": "", "source": "www.nih.gov", "link": "https://www.nih.gov/about-nih/nih-director/statements/statement-retirement-nccih-director-dr-helene-langevin", "content": "6 days ago · Helene M. Langevin , M.D., director of the National Center for Complementary and Integrative Health (NCCIH), is retiring from federal service on November 30, 2025."}
+{"idx": 5, "title": "Browse Workshops and Dates - Langevin .com", "date": "", "ddg_snippet": "Instructional Design for New Designers This fundamental workshop provides you with a proven step-by-step instructional design process. Learn how to confidently apply Langevin ’s 12-Step Design Cycle...", "subpage_snippet": "", "source": "langevin.com", "link": "https://langevin.com/browse-workshops/", "content": "Instructional Design for New Designers This fundamental workshop provides you with a proven step-by-step instructional design process. Learn how to confidently apply Langevin ’s 12-Step Design Cycle..."}
+{"idx": 6, "title": "About Langevin - About Us | World's Largest Train the Trainer...", "date": "", "ddg_snippet": "For more than 40 years, Langevin has been a provider of highly renowned workshops, professional certifications, and products for trainers and business professionals. Based on the success of our in-person programs, we also pioneered and expanded into the world of virtual classroom training.", "subpage_snippet": "", "source": "langevin.com", "link": "https://langevin.com/about-us/", "content": "For more than 40 years, Langevin has been a provider of highly renowned workshops, professional certifications, and products for trainers and business professionals. Based on the success of our in-person programs, we also pioneered and expanded into the world of virtual classroom training."}
+{"idx": 7, "title": "Helene M. Langevin Joins UVM’s Osher Center for Integrative...", "date": "", "ddg_snippet": "Sep 16, 2025 · The Osher Center for Integrative Health at University of Vermont (UVM) is proud to announce that Dr. Helene M. Langevin , MD, a national leader in integrative health, will join its team in early 2026, to shape the Center’s mission-driven research efforts.", "subpage_snippet": "", "source": "www.uvm.edu", "link": "https://www.uvm.edu/osher/news/helene-m-langevin-joins-uvms-osher-center-integrative-health", "content": "Sep 16, 2025 · The Osher Center for Integrative Health at University of Vermont (UVM) is proud to announce that Dr. Helene M. Langevin , MD, a national leader in integrative health, will join its team in early 2026, to shape the Center’s mission-driven research efforts."}
+{"idx": 8, "title": "Biography and publications | Paul Langevin - General and...", "date": "", "ddg_snippet": "Paul Langevin Biography and publications General and experimental physics Podcasts / RSS", "subpage_snippet": "", "source": "www.college-de-france.fr", "link": "https://www.college-de-france.fr/en/chair/paul-langevin-general-and-experimental-physics-statutory-chair/biography", "content": "Paul Langevin Biography and publications General and experimental physics Podcasts / RSS"}
+{"idx": 9, "title": "Auburn woman dies Tuesday in Turner crash - Lewiston Sun Journal", "date": "", "ddg_snippet": "Sep 9, 2025 · TURNER — A 28-year-old Auburn woman died Tuesday in a crash on Route 4. Police said Jacqueline Langevin was pronounced dead at the scene of the wreck near Scott’s Recreation at 2239 Auburn Road.", "subpage_snippet": "", "source": "www.sunjournal.com", "link": "https://www.sunjournal.com/2025/09/09/one-dead-in-route-4-crash-in-turner-2/", "content": "Sep 9, 2025 · TURNER — A 28-year-old Auburn woman died Tuesday in a crash on Route 4. Police said Jacqueline Langevin was pronounced dead at the scene of the wreck near Scott’s Recreation at 2239 Auburn Road."}
diff --git a/data/sampled_jsons/Langevin_unlearning_noisy_gradient_descent_machine_unlearning_Chien_2024_year_2024.jsonl b/data/sampled_jsons/Langevin_unlearning_noisy_gradient_descent_machine_unlearning_Chien_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..030bd923d86cfe7508698ccba0a92892f0ca6ed2
--- /dev/null
+++ b/data/sampled_jsons/Langevin_unlearning_noisy_gradient_descent_machine_unlearning_Chien_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2401.10371] Langevin Unlearning: A New Perspective of Noisy", "date": "", "ddg_snippet": "View a PDF of the paper titled Langevin Unlearning : A New Perspective of Noisy Gradient Descent for Machine Unlearning , by Eli Chien and 3 other ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.10371", "content": "View a PDF of the paper titled Langevin Unlearning : A New Perspective of Noisy Gradient Descent for Machine Unlearning , by Eli Chien and 3 other ..."}
+{"idx": 1, "title": "Towards Reliable Forgetting: A Survey on Machine Unlearning", "date": "", "ddg_snippet": "Existing machine unlearning techniques can be broadly classified into exact unlearning (Bourtoule et al . ... methods for machine unlearning offer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15115v1", "content": "Existing machine unlearning techniques can be broadly classified into exact unlearning (Bourtoule et al . ... methods for machine unlearning offer ..."}
+{"idx": 2, "title": "Certified Unlearning for Neural Networks", "date": "", "ddg_snippet": "To this end, we propose a novel method for certified machine unlearning , leveraging the connection between unlearning and privacy amplification by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06985v2", "content": "To this end, we propose a novel method for certified machine unlearning , leveraging the connection between unlearning and privacy amplification by ..."}
+{"idx": 3, "title": "[2505.18786] Leveraging Per-Instance Privacy for Machine", "date": "", "ddg_snippet": "We begin by sharpening an analysis of noisy gradient descent for unlearning ( Chien et al., 2024 ), obtaining a better utility- unlearning tradeoff by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "We begin by sharpening an analysis of noisy gradient descent for unlearning ( Chien et al., 2024 ), obtaining a better utility- unlearning tradeoff by ..."}
+{"idx": 4, "title": "Distributional Unlearning: Forgetting Distributions, Not Just", "date": "", "ddg_snippet": "Machine unlearning seeks to remove unwanted information from trained models, initially at the individual‐sample level, but increasingly at the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15112v1", "content": "Machine unlearning seeks to remove unwanted information from trained models, initially at the individual‐sample level, but increasingly at the ..."}
+{"idx": 5, "title": "NeurIPS 2024 – Research Impact & Leadership", "date": "", "ddg_snippet": "Trustworthy machine learning aims to create AI systems people can rely on, delivering fair, transparent, secure and accurate results aligned with ...", "subpage_snippet": "", "source": "sites.gatech.edu", "link": "https://sites.gatech.edu/research/neurips-2024/", "content": "Trustworthy machine learning aims to create AI systems people can rely on, delivering fair, transparent, secure and accurate results aligned with ..."}
+{"idx": 6, "title": "TPDP 2024 – Theory and Practice of Differential Privacy", "date": "", "ddg_snippet": "... types of batch sampling: (i) Shuffling, and (ii) Poisson subsampling; the typical analysis of Differentially Private Stochastic Gradient Descent (DP ...", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2024/", "content": "... types of batch sampling: (i) Shuffling, and (ii) Poisson subsampling; the typical analysis of Differentially Private Stochastic Gradient Descent (DP ..."}
+{"idx": 7, "title": "Distributional Unlearning: Forgetting Distributions, Not Just", "date": "", "ddg_snippet": "Machine unlearning seeks to remove unwanted information from trained models, initially at the individual‐sample level, but increasingly at the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15112v2", "content": "Machine unlearning seeks to remove unwanted information from trained models, initially at the individual‐sample level, but increasingly at the ..."}
+{"idx": 8, "title": "Adversarial Sample-Based Approach for Tighter Privacy Auditing", "date": "", "ddg_snippet": "Auditing Differentially Private Stochastic Gradient Descent (DP-SGD) in the final model setting is challenging and often results in empirical lower ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.01756v2", "content": "Auditing Differentially Private Stochastic Gradient Descent (DP-SGD) in the final model setting is challenging and often results in empirical lower ..."}
+{"idx": 9, "title": "Differentially Private Graph Diffusion with Applications in", "date": "", "ddg_snippet": "Besides, works that share a similar spirit in leveraging PABI have been conducted for other scenarios including machine unlearning [ 45 ] and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.00077v5", "content": "Besides, works that share a similar spirit in leveraging PABI have been conducted for other scenarios including machine unlearning [ 45 ] and ..."}
diff --git a/data/sampled_jsons/Larsen_2023_discrepancy_minimization_input_sparsity_year_2023.jsonl b/data/sampled_jsons/Larsen_2023_discrepancy_minimization_input_sparsity_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..be1b3285a5325b1c91002e9389024bf5a0511ba0
--- /dev/null
+++ b/data/sampled_jsons/Larsen_2023_discrepancy_minimization_input_sparsity_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Kasper Green Larsen", "date": "", "ddg_snippet": "We present a new lower bound for multi-color discrepancy , showing that there is a set system with n subsets over a set of elements in which any k ...", "subpage_snippet": "", "source": "cs.au.dk", "link": "https://cs.au.dk/~larsen/", "content": "We present a new lower bound for multi-color discrepancy , showing that there is a set system with n subsets over a set of elements in which any k ..."}
+{"idx": 1, "title": "COLT 2023 Abstracts", "date": "", "ddg_snippet": "Abstract: We show that convex-concave Lipschitz stochastic saddle point problems (also known as stochastic minimax optimization) can be solved under ...", "subpage_snippet": "", "source": "learningtheory.org", "link": "https://learningtheory.org/colt2023/abstracts.html", "content": "Abstract: We show that convex-concave Lipschitz stochastic saddle point problems (also known as stochastic minimax optimization) can be solved under ..."}
+{"idx": 2, "title": "ThermalGuardian: Temperature-Aware Testing of Automotive Deep", "date": "", "ddg_snippet": "... quality issues cannot be successfully detected by existing DL framework testing methods (Wang et al., 2020 ; Gu et al., 2022 ; Liu et al., 2023 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15815v1", "content": "... quality issues cannot be successfully detected by existing DL framework testing methods (Wang et al., 2020 ; Gu et al., 2022 ; Liu et al., 2023 ..."}
+{"idx": 3, "title": "Advancing Additive Manufacturing through Deep Learning: A", "date": "", "ddg_snippet": "This popularity can be easily realized from the rapidly expanding trend of the AM market size, from USD 20,670 million in 2023 to USD 98,310 million ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00669v2", "content": "This popularity can be easily realized from the rapidly expanding trend of the AM market size, from USD 20,670 million in 2023 to USD 98,310 million ..."}
+{"idx": 4, "title": "Cross-validation for training and testing co-occurrence network", "date": "", "ddg_snippet": "Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity ...", "subpage_snippet": "", "source": "bmcbioinformatics.biomedcentral.com", "link": "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-025-06083-7", "content": "Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity ..."}
+{"idx": 5, "title": "COLT 2025 Book of Abstracts", "date": "", "ddg_snippet": "... in the adversarial regime and $O(\\ln{T})$ in the stochastic regime while simultaneously being adaptive to data-dependent quantities such as sparsity ...", "subpage_snippet": "", "source": "learningtheory.org", "link": "https://learningtheory.org/colt2025/abstracts.html", "content": "... in the adversarial regime and $O(\\ln{T})$ in the stochastic regime while simultaneously being adaptive to data-dependent quantities such as sparsity ..."}
+{"idx": 6, "title": "GraphPCA: a fast and interpretable dimension reduction", "date": "", "ddg_snippet": "However, analyzing gene expression profiles obtained from various ST technologies faces enormous challenges due to the sparsity , ultra-high ...", "subpage_snippet": "", "source": "genomebiology.biomedcentral.com", "link": "https://genomebiology.biomedcentral.com/articles/10.1186/s13059-024-03429-x", "content": "However, analyzing gene expression profiles obtained from various ST technologies faces enormous challenges due to the sparsity , ultra-high ..."}
+{"idx": 7, "title": "Brain-Cognitive Gaps in relation to Dopamine and Health-related", "date": "", "ddg_snippet": "Recent research suggests that brain age offers minimal additional information on cognitive decline beyond what chronological age provides, prompting ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/104053v1", "content": "Recent research suggests that brain age offers minimal additional information on cognitive decline beyond what chronological age provides, prompting ..."}
+{"idx": 8, "title": "Brain-Cognitive Gaps in relation to Dopamine and Health-related", "date": "", "ddg_snippet": "Recent research suggests that brain age offers minimal additional information on cognitive decline beyond what chronological age provides, prompting ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/104053", "content": "Recent research suggests that brain age offers minimal additional information on cognitive decline beyond what chronological age provides, prompting ..."}
+{"idx": 9, "title": "Research Seminar - Universität Bremen", "date": "", "ddg_snippet": "Abstract: We study the 2-edge-connected spanning subgraph (2-ECSS) problem: Given a graph G, compute a connected subgraph H of G with the minimum ...", "subpage_snippet": "", "source": "www.uni-bremen.de", "link": "https://www.uni-bremen.de/en/cslog/seminar", "content": "Abstract: We study the 2-edge-connected spanning subgraph (2-ECSS) problem: Given a graph G, compute a connected subgraph H of G with the minimum ..."}
diff --git a/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_normalization_operator.jsonl b/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_normalization_operator.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c9b8d8de41e024b1086b9e9a71ed204c6e72d72a
--- /dev/null
+++ b/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_normalization_operator.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games", "date": "", "ddg_snippet": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching$^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last-iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.00676", "content": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching$^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last-iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ..."}
+{"idx": 1, "title": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games", "date": "", "ddg_snippet": "Our analysis builds on a new characterization of the geometric structure of the limit points of our algorithms , marking a significant departure from most of the literature on last-iterate convergence .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/7c20cba1f6aeeb219f9f9cbdde5c7382-Abstract-Conference.html", "content": "Our analysis builds on a new characterization of the geometric structure of the limit points of our algorithms , marking a significant departure from most of the literature on last-iterate convergence ."}
+{"idx": 2, "title": "Gabriele Farina - Last-Iterate Convergence Properties of Regret ...", "date": "", "ddg_snippet": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last-iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2025/iclr25_rm_lastiterate/", "content": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last-iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ..."}
+{"idx": 3, "title": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games", "date": "", "ddg_snippet": "Since last-iterate convergence is an attractive property both for numerical optimization reasons and because no- regret learning is viewed as a plausible method of real-world learning in games . In this paper, we study the last-iterate convergence properties of various popular variants of RM$^+$.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fWk5Qx0exc", "content": "Since last-iterate convergence is an attractive property both for numerical optimization reasons and because no- regret learning is viewed as a plausible method of real-world learning in games . In this paper, we study the last-iterate convergence properties of various popular variants of RM$^+$."}
+{"idx": 4, "title": "PDF Regret Matching+: - Instability, average- and last-iterate convergence ...", "date": "", "ddg_snippet": "instability. ) New algorithms for game solving: O(1=T) O(1=p average convergence T) best- iterate convergence , last-iterate convergence Why is this interesting?", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "instability. ) New algorithms for game solving: O(1=T) O(1=p average convergence T) best- iterate convergence , last-iterate convergence Why is this interesting?"}
+{"idx": 5, "title": "PDF Last-iterate Convergence in Extensive-Form Games", "date": "", "ddg_snippet": "Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games . However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence . Inspired by recent advances on last-iterate con - vergence of optimistic algorithms in zero-sum normal-form ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2021/file/77bb14f6132ea06dea456584b7d5581e-Paper.pdf", "content": "Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games . However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence . Inspired by recent advances on last-iterate con - vergence of optimistic algorithms in zero-sum normal-form ..."}
+{"idx": 6, "title": "Last-Iterate Convergence Properties of Regret Matching Algorithms in ...", "date": "", "ddg_snippet": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+(RM+). Despite their widespread use for solving real games , virtually nothing is known about their last-iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+(RM+). Despite their widespread use for solving real games , virtually nothing is known about their last-iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ..."}
+{"idx": 7, "title": "Last-i Convergence Properties of R M Algorithms in Games", "date": "", "ddg_snippet": "ABSTRACT We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last-iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LWeVVPuIx0", "content": "ABSTRACT We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last-iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by ..."}
+{"idx": 8, "title": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games", "date": "", "ddg_snippet": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng April 2025 arxiv", "subpage_snippet": "", "source": "www.cs.yale.edu", "link": "https://www.cs.yale.edu/homes/cai/publication/cai-regret-matching-2025/", "content": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng April 2025 arxiv"}
+{"idx": 9, "title": "PDF Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
diff --git a/data/sampled_jsons/Learned_Augmented_Residual_Layer_Figure_3_accuracy@1_rank_32_vs_128_year_2023.jsonl b/data/sampled_jsons/Learned_Augmented_Residual_Layer_Figure_3_accuracy@1_rank_32_vs_128_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7c94a544ff74e60f762a5fc15b7e0fce23047d0b
--- /dev/null
+++ b/data/sampled_jsons/Learned_Augmented_Residual_Layer_Figure_3_accuracy@1_rank_32_vs_128_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improved Inception-Residual Convolutional Neural ...", "date": "", "ddg_snippet": "by MZ Alom · 2017 · Cited by 159 — Validation accuracy on the Tiny-ImageNet dataset. We have evaluated our proposed approach for both Top- 1 % and Top-5% testing accuracy as shown in Figure 10.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1712.09888", "content": "by MZ Alom · 2017 · Cited by 159 — Validation accuracy on the Tiny-ImageNet dataset. We have evaluated our proposed approach for both Top- 1 % and Top-5% testing accuracy as shown in Figure 10."}
+{"idx": 1, "title": "An Attention Residual U-Net with Differential ...", "date": "", "ddg_snippet": "by N Mu · 2022 · Cited by 82 — The proposed ARU-Net model can automatically segment the IAs in 3DRA images with relatively high accuracy and potentially has significant value for clinical ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9830590/", "content": "by N Mu · 2022 · Cited by 82 — The proposed ARU-Net model can automatically segment the IAs in 3DRA images with relatively high accuracy and potentially has significant value for clinical ..."}
+{"idx": 2, "title": "Learned feature representations are biased by complexity, ...", "date": "", "ddg_snippet": "20 Sept 2024 — We find that their learned feature representations are systematically biased towards representing some features more strongly than others.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.05847v3", "content": "20 Sept 2024 — We find that their learned feature representations are systematically biased towards representing some features more strongly than others."}
+{"idx": 3, "title": "Exploiting Explainable Metrics for Augmented SGD", "date": "", "ddg_snippet": "by MS Hosseini · 2022 · Cited by 6 — We report ImageNet re- sults in Table 2, Table 3 , and Figure 4. The ∼ 7% top- 1 test accuracy performance gap highlights RMSGD's strength.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Hosseini_Exploiting_Explainable_Metrics_for_Augmented_SGD_CVPR_2022_paper.pdf", "content": "by MS Hosseini · 2022 · Cited by 6 — We report ImageNet re- sults in Table 2, Table 3 , and Figure 4. The ∼ 7% top- 1 test accuracy performance gap highlights RMSGD's strength."}
+{"idx": 4, "title": "Analyzing the effect of residual connections to ...", "date": "", "ddg_snippet": "by D Kelesis · 2025 — In this section we analyze the effect of residual connections and identity mapping. We first explore the architecture of APPNP, demonstrating ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-025-06822-0", "content": "by D Kelesis · 2025 — In this section we analyze the effect of residual connections and identity mapping. We first explore the architecture of APPNP, demonstrating ..."}
+{"idx": 5, "title": "What Variables Affect Out-of-Distribution Generalization in ...", "date": "", "ddg_snippet": "by MY Harun · 2024 · Cited by 7 — 1 ,. ID accuracy for 32 × 32 images reaches 95% of the final accuracy at layer 14, while OOD accuracy degrades starting at layer 9. We use normalized accuracy ... 47 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/6701e9c94bc7c7d6b5fc47c0fc13ab5b-Paper-Conference.pdf", "content": "by MY Harun · 2024 · Cited by 7 — 1 ,. ID accuracy for 32 × 32 images reaches 95% of the final accuracy at layer 14, while OOD accuracy degrades starting at layer 9. We use normalized accuracy ... 47 pages"}
+{"idx": 6, "title": "Deep learning in computer vision: A critical review of ...", "date": "", "ddg_snippet": "by J Chai · 2021 · Cited by 988 — Experiments on several image datasets showed that NIN achieved comparable or better classification accuracy with much fewer parameters. Szegedy ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666827021000670", "content": "by J Chai · 2021 · Cited by 988 — Experiments on several image datasets showed that NIN achieved comparable or better classification accuracy with much fewer parameters. Szegedy ..."}
+{"idx": 7, "title": "Early-Exit Deep Neural Network - A Comprehensive Survey", "date": "", "ddg_snippet": "The number of hidden layers determines the network's depth and complexity. Figure 3 illustrates the layer -wise architecture of a neural network. Input layers ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3698767", "content": "The number of hidden layers determines the network's depth and complexity. Figure 3 illustrates the layer -wise architecture of a neural network. Input layers ..."}
+{"idx": 8, "title": "Application of Dense Neural Networks for Detection ...", "date": "", "ddg_snippet": "by V Krasteva · 2021 · Cited by 38 — Based on hyperparameters' grid search of densely connected NN layers , we derive the optimal topology with three layers and 128 , 32 , 4 neurons per layer ( ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8538849/", "content": "by V Krasteva · 2021 · Cited by 38 — Based on hyperparameters' grid search of densely connected NN layers , we derive the optimal topology with three layers and 128 , 32 , 4 neurons per layer ( ..."}
+{"idx": 9, "title": "Loss of plasticity in deep continual learning", "date": "", "ddg_snippet": "by S Dohare · 2024 · Cited by 189 — Standard deep-learning methods gradually lose plasticity in continual-learning settings until they learn no better than a shallow network.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-024-07711-7", "content": "by S Dohare · 2024 · Cited by 189 — Standard deep-learning methods gradually lose plasticity in continual-learning settings until they learn no better than a shallow network."}
diff --git a/data/sampled_jsons/Learning_Linear_Causal_Representations_from_Unknown_Latent_Interventions_Buchholz_abstract_Gaussian.jsonl b/data/sampled_jsons/Learning_Linear_Causal_Representations_from_Unknown_Latent_Interventions_Buchholz_abstract_Gaussian.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c2e74156eecac88f0b2d6e0a480d1b17621e9461
--- /dev/null
+++ b/data/sampled_jsons/Learning_Linear_Causal_Representations_from_Unknown_Latent_Interventions_Buchholz_abstract_Gaussian.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Linear Causal Representations from Interventions under General ...", "date": "", "ddg_snippet": "We study the problem of learning causal representations from unknown , latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general. We prove strong identifiability results given unknown single-node interventions , i.e., without having access to the intervention targets. This generalizes prior works which have focused on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.02235", "content": "We study the problem of learning causal representations from unknown , latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general. We prove strong identifiability results given unknown single-node interventions , i.e., without having access to the intervention targets. This generalizes prior works which have focused on ..."}
+{"idx": 1, "title": "Learning Linear Causal Representations from Interventions ... - NeurIPS", "date": "", "ddg_snippet": "Bibtex Paper Supplemental Authors Simon Buchholz , Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep K. Ravikumar Abstract We study the problem of learning causal representations from unknown , latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/8e5de4cb639ef718f44060dc257cb04f-Abstract-Conference.html", "content": "Bibtex Paper Supplemental Authors Simon Buchholz , Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep K. Ravikumar Abstract We study the problem of learning causal representations from unknown , latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely ..."}
+{"idx": 2, "title": "PDF Learning nonparametric latent causal graphs with unknown interventions", "date": "", "ddg_snippet": "Abstract We establish conditions under which latent causal graphs are nonparametrically identifiable and can be reconstructed from unknown interventions in the latent space. Our primary focus is the identification of the latent structure in measurement models without parametric assumptions such as linearity or Gaussianity. Moreover, we do not assume the number of hidden variables is known, and ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2023/file/bdeab378efe6eb289714e2a5abc6ed42-Paper-Conference.pdf", "content": "Abstract We establish conditions under which latent causal graphs are nonparametrically identifiable and can be reconstructed from unknown interventions in the latent space. Our primary focus is the identification of the latent structure in measurement models without parametric assumptions such as linearity or Gaussianity. Moreover, we do not assume the number of hidden variables is known, and ..."}
+{"idx": 3, "title": "Learning Nonparametric Latent Causal Graphs with Unknown Interventions", "date": "", "ddg_snippet": "We establish conditions under which latent causal graphs are nonparametrically identifiable and can be reconstructed from unknown interventions in the latent space. Our primary focus is the identification of the latent structure in measurement models without parametric assumptions such as linearity or Gaussianity.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=S8DFqgmEbe", "content": "We establish conditions under which latent causal graphs are nonparametrically identifiable and can be reconstructed from unknown interventions in the latent space. Our primary focus is the identification of the latent structure in measurement models without parametric assumptions such as linearity or Gaussianity."}
+{"idx": 4, "title": "Linear Causal Representation Learning from Unknown Multi-node Interventions", "date": "", "ddg_snippet": "This paper focuses on interventional CRL under unknown multi-node (UMN) interventional environments and establishes the first identifiability results for general latent causal models (parametric or nonparametric) under stochastic interventions (soft or hard) and linear transformation from the latent to observed space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.05937", "content": "This paper focuses on interventional CRL under unknown multi-node (UMN) interventional environments and establishes the first identifiability results for general latent causal models (parametric or nonparametric) under stochastic interventions (soft or hard) and linear transformation from the latent to observed space."}
+{"idx": 5, "title": "PDF Learning Linear Causal Representations from Interventions ... - NeurIPS", "date": "", "ddg_snippet": "Abstract We study the problem of learning causal representations from unknown , latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general. We prove strong identifiability results given unknown single-node interventions , i.e., without having access to the intervention targets. This generalizes prior works which have focused ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/8e5de4cb639ef718f44060dc257cb04f-Paper-Conference.pdf", "content": "Abstract We study the problem of learning causal representations from unknown , latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general. We prove strong identifiability results given unknown single-node interventions , i.e., without having access to the intervention targets. This generalizes prior works which have focused ..."}
+{"idx": 6, "title": "Learning nonparametric latent causal graphs with unknown interventions ...", "date": "", "ddg_snippet": "Learning and scoring Gaussian latent variable causal models with unknown additive interventions With observational data alone, causal structure learning is a challenging problem.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3666122.3668763", "content": "Learning and scoring Gaussian latent variable causal models with unknown additive interventions With observational data alone, causal structure learning is a challenging problem."}
+{"idx": 7, "title": "Learning Linear Causal Representations from Interventions under General ...", "date": "", "ddg_snippet": "We study the problem of learning causal representations from unknown , latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371311138_Learning_Linear_Causal_Representations_from_Interventions_under_General_Nonlinear_Mixing", "content": "We study the problem of learning causal representations from unknown , latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely ..."}
+{"idx": 8, "title": "Linear Causal Representation Learning from Unknown Multi-node Interventions", "date": "", "ddg_snippet": "Abstract Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions . This assumption is not valid in a wide range of applications, and generally, the subset of nodes intervened in an interventional environment is fully unknown .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.05937", "content": "Abstract Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions . This assumption is not valid in a wide range of applications, and generally, the subset of nodes intervened in an interventional environment is fully unknown ."}
+{"idx": 9, "title": "Learning Linear Causal Representations from Interventions under General ...", "date": "", "ddg_snippet": "Abstract We study the problem of learning causal repre-sentations from unknown , latent interventions in a general setting, where the latent distribu-tion is Gaussian but the mixing function is com-pletely general. We prove strong identifiability results given unknown single-node interventions , i.e., without having access to the intervention tar-gets. This generalizes prior works which have ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kPINfLNUik", "content": "Abstract We study the problem of learning causal repre-sentations from unknown , latent interventions in a general setting, where the latent distribu-tion is Gaussian but the mixing function is com-pletely general. We prove strong identifiability results given unknown single-node interventions , i.e., without having access to the intervention tar-gets. This generalizes prior works which have ..."}
diff --git a/data/sampled_jsons/Learning_Place_Cell_Representations_and_Context-Dependent_Remapping_arXiv_year_2023.jsonl b/data/sampled_jsons/Learning_Place_Cell_Representations_and_Context-Dependent_Remapping_arXiv_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4d8f80bd458edb0a5f01cf7471c2ad0bb0e7cef4
--- /dev/null
+++ b/data/sampled_jsons/Learning_Place_Cell_Representations_and_Context-Dependent_Remapping_arXiv_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Place Cell Representations and Context-Dependent Remapping", "date": "", "ddg_snippet": "Such transformations of the original trained representation (e.g. rotations), therefore yield new representations distinct from the original, without explicit relearning, akin to remapping . Our findings shed new light on the formation and encoding properties of place cells , and also demonstrate an interesting case of representational reuse.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7ESHFpqjNO", "content": "Such transformations of the original trained representation (e.g. rotations), therefore yield new representations distinct from the original, without explicit relearning, akin to remapping . Our findings shed new light on the formation and encoding properties of place cells , and also demonstrate an interesting case of representational reuse."}
+{"idx": 1, "title": "Learning place cells and remapping by decoding the cognitive map", "date": "", "ddg_snippet": "This suggests a potential mechanism for the interaction between place cells , border cells , and grid cells . Our model provides a normative framework for learning spatial representations previously reserved for biological place cells , providing new insight into place cell field formation and remapping .", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/99302", "content": "This suggests a potential mechanism for the interaction between place cells , border cells , and grid cells . Our model provides a normative framework for learning spatial representations previously reserved for biological place cells , providing new insight into place cell field formation and remapping ."}
+{"idx": 2, "title": "Visual Place Cell Encoding: A Computational Model for Spatial ...", "date": "", "ddg_snippet": "These results suggest that structured visual input, even in the absence of motion cues or reward-driven learning , is sufficient to generate place - cell -like spatial representations and support biologically inspired cognitive mapping.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.15953", "content": "These results suggest that structured visual input, even in the absence of motion cues or reward-driven learning , is sufficient to generate place - cell -like spatial representations and support biologically inspired cognitive mapping."}
+{"idx": 3, "title": "Learning place cell representations and context-dependent remapping ...", "date": "", "ddg_snippet": "However, place cells also respond to contextual cues, such as smell. Furthermore, they have the ability to remap, wherein the firing fields and rates of cells change in response to changes in the environment. How place cell responses emerge, and how these representations remap is not fully understood.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3737924", "content": "However, place cells also respond to contextual cues, such as smell. Furthermore, they have the ability to remap, wherein the firing fields and rates of cells change in response to changes in the environment. How place cell responses emerge, and how these representations remap is not fully understood."}
+{"idx": 4, "title": "Trading Place for Space: Increasing Location Resolution Reduces ...", "date": "", "ddg_snippet": "Many animals learn cognitive maps of their environment - a simultaneous representation of context , experience, and position. Place cells in the hippocampus, named for their explicit encoding of position, are believed to be a neural substrate of these maps, with place cell \" remapping \" explaining how this system can represent different contexts .", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/39554091/", "content": "Many animals learn cognitive maps of their environment - a simultaneous representation of context , experience, and position. Place cells in the hippocampus, named for their explicit encoding of position, are believed to be a neural substrate of these maps, with place cell \" remapping \" explaining how this system can represent different contexts ."}
+{"idx": 5, "title": "Decoding the Cognitive map: Learning place cells and remapping", "date": "", "ddg_snippet": "This suggests a potential mechanism for the interaction between place cells , border cells , and grid cells . Our model provides a normative framework for learning spatial representations previously reserved for biological place cells , providing new insight into place cell field formation and remapping .", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2024.03.14.585049v3", "content": "This suggests a potential mechanism for the interaction between place cells , border cells , and grid cells . Our model provides a normative framework for learning spatial representations previously reserved for biological place cells , providing new insight into place cell field formation and remapping ."}
+{"idx": 6, "title": "Learning Place Cell Representations and Context-Dependent Remapping", "date": "", "ddg_snippet": "Neural networks learn place cell -like representations and context-dependent remapping using a novel similarity-based objective function, providing insights into hippocampal encoding.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/7eshfpqjno/", "content": "Neural networks learn place cell -like representations and context-dependent remapping using a novel similarity-based objective function, providing insights into hippocampal encoding."}
+{"idx": 7, "title": "Decoding the Cognitive map: Learning place cells and remapping", "date": "", "ddg_snippet": "We train our model in multiple environments and observe that the network learns global, rate and geometric remapping akin to biological place cells . We find that remapping in the place -like units of the network can be understood as a consequence of sparse input from near-independent sets of upstream, rate- remapping boundary-tuned units.", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/99302", "content": "We train our model in multiple environments and observe that the network learns global, rate and geometric remapping akin to biological place cells . We find that remapping in the place -like units of the network can be understood as a consequence of sparse input from near-independent sets of upstream, rate- remapping boundary-tuned units."}
+{"idx": 8, "title": "Enhanced Reactivation of Remapping Place Cells during Aversive Learning", "date": "", "ddg_snippet": "Task learning induced partial remapping in CA1 place cells , allowing us to identify both remapping and stable cell populations. Remapping cells were recruited into sharp-wave ripples and associated replay events to a greater extent than stable cells , despite having similar firing rates during navigation of the maze.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/36596695/", "content": "Task learning induced partial remapping in CA1 place cells , allowing us to identify both remapping and stable cell populations. Remapping cells were recruited into sharp-wave ripples and associated replay events to a greater extent than stable cells , despite having similar firing rates during navigation of the maze."}
+{"idx": 9, "title": "Learning Place Cell Representations and Context-Dependent Remapping", "date": "", "ddg_snippet": "Learning Place Cell Representations and Context-Dependent Remapping . In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, Cheng Zhang 0005, editors, Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC ...", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/PettersenRL24", "content": "Learning Place Cell Representations and Context-Dependent Remapping . In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, Cheng Zhang 0005, editors, Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC ..."}
diff --git a/data/sampled_jsons/Leutgeb_2004_hippocampus_global_remapping_CA3_CA1_year_2004.jsonl b/data/sampled_jsons/Leutgeb_2004_hippocampus_global_remapping_CA3_CA1_year_2004.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0b5dfaf3e2ee3827395d89bd401b54901f344cde
--- /dev/null
+++ b/data/sampled_jsons/Leutgeb_2004_hippocampus_global_remapping_CA3_CA1_year_2004.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hippocampal Global Remapping Can Occur without Input ...", "date": "", "ddg_snippet": "by MI Schlesiger · 2018 · Cited by 79 — Leutgeb S, Leutgeb JK, Treves A, Moser MB, Moser EI. Distinct ensemble codes in hippocampal areas CA3 and CA1 . Science. 2004 ;305:1295–1298. doi: 10.1126 ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5929481/", "content": "by MI Schlesiger · 2018 · Cited by 79 — Leutgeb S, Leutgeb JK, Treves A, Moser MB, Moser EI. Distinct ensemble codes in hippocampal areas CA3 and CA1 . Science. 2004 ;305:1295–1298. doi: 10.1126 ..."}
+{"idx": 1, "title": "Distinct Ensemble Codes in Hippocampal Areas CA3 and ...", "date": "", "ddg_snippet": "by S Leutgeb · 2004 · Cited by 998 — The hippocampus has differentiated into an extensively connected recurrent stage ( CA3 ) followed by a feed-forward stage ( CA1 ). We examined the function of ...", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/science.1100265", "content": "by S Leutgeb · 2004 · Cited by 998 — The hippocampus has differentiated into an extensively connected recurrent stage ( CA3 ) followed by a feed-forward stage ( CA1 ). We examined the function of ..."}
+{"idx": 2, "title": "Place Cell Rate Remapping by CA3 Recurrent Collaterals", "date": "", "ddg_snippet": "by T Solstad · 2014 · Cited by 32 — Such ' global remapping ' reveals that memory for different environments is encoded as separate spatial maps. Smaller changes to features within ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4046921/", "content": "by T Solstad · 2014 · Cited by 32 — Such ' global remapping ' reveals that memory for different environments is encoded as separate spatial maps. Smaller changes to features within ..."}
+{"idx": 3, "title": "Understanding memory through hippocampal remapping", "date": "", "ddg_snippet": "by LL Colgin · 2008 · Cited by 604 — Remapping refers to the formation of distinct representations in populations of place cells after minor changes in inputs to the hippocampus .", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/trends/neurosciences/fulltext/S0166-2236(08)00167-7", "content": "by LL Colgin · 2008 · Cited by 604 — Remapping refers to the formation of distinct representations in populations of place cells after minor changes in inputs to the hippocampus ."}
+{"idx": 4, "title": "Fast rate coding in hippocampal CA3 cell ensembles", "date": "", "ddg_snippet": "by S Leutgeb · 2006 · Cited by 85 — We have previously distinguished two types of remapping, global remapping ... Distinct ensemble codes in hippocampal areas CA3 and CA1.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/abs/10.1002/hipo.20201", "content": "by S Leutgeb · 2006 · Cited by 85 — We have previously distinguished two types of remapping, global remapping ... Distinct ensemble codes in hippocampal areas CA3 and CA1."}
+{"idx": 5, "title": "Hippocampal Remapping and Its Entorhinal Origin", "date": "", "ddg_snippet": "by P Latuske · 2018 · Cited by 99 — This possibility was tested by Leutgeb et al. ( 2004 ). They recorded the activity of pyramidal cells in the CA3 and CA1 regions in rats chasing ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2017.00253/full", "content": "by P Latuske · 2018 · Cited by 99 — This possibility was tested by Leutgeb et al. ( 2004 ). They recorded the activity of pyramidal cells in the CA3 and CA1 regions in rats chasing ..."}
+{"idx": 6, "title": "Ensemble Dynamics of Hippocampal Regions CA3 and CA1", "date": "", "ddg_snippet": "by JF Guzowski · 2004 · Cited by 479 — CA3 is uniquely positioned as an autoassociative memory network, capable of performing the competing functions of pattern completion and pattern separation.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0896627304007172", "content": "by JF Guzowski · 2004 · Cited by 479 — CA3 is uniquely positioned as an autoassociative memory network, capable of performing the competing functions of pattern completion and pattern separation."}
+{"idx": 7, "title": "Hippocampal Remapping after Partial Inactivation of the ...", "date": "", "ddg_snippet": "by C Miao · 2015 · Cited by 140 — Hippocampal place cells undergo remapping when the environment is changed. The mechanism of hippocampal remapping remains elusive but ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/neuron/fulltext/S0896-6273(15)00840-5", "content": "by C Miao · 2015 · Cited by 140 — Hippocampal place cells undergo remapping when the environment is changed. The mechanism of hippocampal remapping remains elusive but ..."}
+{"idx": 8, "title": "Memory-Guided Learning: CA1 and CA3 Neuronal ...", "date": "", "ddg_snippet": "by AS Bahar · 2011 · Cited by 54 — CA1 and CA3 ensembles represented the similarities and differences between new and familiar situations through concurrent rate and place remapping.", "subpage_snippet": "", "source": "www.jneurosci.org", "link": "https://www.jneurosci.org/content/31/34/12270", "content": "by AS Bahar · 2011 · Cited by 54 — CA1 and CA3 ensembles represented the similarities and differences between new and familiar situations through concurrent rate and place remapping."}
+{"idx": 9, "title": "Progressive Transformation of Hippocampal Neuronal ...", "date": "", "ddg_snippet": "by JK Leutgeb · 2005 · Cited by 463 — The latter type of remapping in CA1 is accompanied by strong but exclusive rate remapping in CA3 ( Leutgeb et al., 2005). These contrasting types ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0896627305007737", "content": "by JK Leutgeb · 2005 · Cited by 463 — The latter type of remapping in CA1 is accompanied by strong but exclusive rate remapping in CA3 ( Leutgeb et al., 2005). These contrasting types ..."}
diff --git a/data/sampled_jsons/Leveraging_Passage_Retrieval_with_Generative_Models_for_Open_Domain_Question_Answering_Izacard_Grave.jsonl b/data/sampled_jsons/Leveraging_Passage_Retrieval_with_Generative_Models_for_Open_Domain_Question_Answering_Izacard_Grave.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..19188ccb562010485795524687fd40c8fc123d0b
--- /dev/null
+++ b/data/sampled_jsons/Leveraging_Passage_Retrieval_with_Generative_Models_for_Open_Domain_Question_Answering_Izacard_Grave.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2007.01282] Leveraging Passage Retrieval with Generative ...", "date": "", "ddg_snippet": "Abstract : Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.01282", "content": "Abstract : Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we..."}
+{"idx": 1, "title": "Leveraging Passage Retrieval with Generative Models for Open ...", "date": "", "ddg_snippet": "Abstract . Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.eacl-main.74/", "content": "Abstract . Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query."}
+{"idx": 2, "title": "Leveraging passage retrieval with ... preview & related info | Mendeley", "date": "", "ddg_snippet": "Abstract . Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. Izacard , G., & Grave , E. ( 2021 ). Leveraging passage retrieval with generative models for open domain question answering .", "subpage_snippet": "", "source": "www.mendeley.com", "link": "https://www.mendeley.com/catalogue/6a85979a-8764-3883-8388-b8699883f5da/", "content": "Abstract . Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. Izacard , G., & Grave , E. ( 2021 ). Leveraging passage retrieval with generative models for open domain question answering ."}
+{"idx": 3, "title": "Leveraging Passage Retrieval with Generative Models for Open ...", "date": "", "ddg_snippet": "Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/355430399_Leveraging_Passage_Retrieval_with_Generative_Models_for_Open_Domain_Question_Answering", "content": "Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how..."}
+{"idx": 4, "title": "[PDF] Leveraging Passage Retrieval with Generative Models for ...", "date": "", "ddg_snippet": "Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Leveraging-Passage-Retrieval-with-Generative-Models-Izacard-Grave/ea8c46e193d5121e440daf96edfd15a47151c293", "content": "Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how..."}
+{"idx": 5, "title": "Papertalk - the platform for scientific paper presentations", "date": "", "ddg_snippet": "Leveraging passage retrieval with generative models for open domain question answering .", "subpage_snippet": "", "source": "papertalk.org", "link": "https://papertalk.org/papertalks?q=Gautier+Izacard&authors=on&exact=on", "content": "Leveraging passage retrieval with generative models for open domain question answering ."}
+{"idx": 6, "title": "Exploiting Abstract Meaning Representation for", "date": "", "ddg_snippet": "The Open - Domain Question Answering (ODQA) task involves retrieving and subse-quently generating answers from fine-grained relevant passages within a database. Current systems leverage Pretrained Language Models (PLMs) to model the relationship between...", "subpage_snippet": "", "source": "assets.amazon.science", "link": "https://assets.amazon.science/83/9e/8459125b4ff1bcda6344b57fe3c3/exploiting-abstract-meaning-representation-for-open-domain-question-answering.pdf", "content": "The Open - Domain Question Answering (ODQA) task involves retrieving and subse-quently generating answers from fine-grained relevant passages within a database. Current systems leverage Pretrained Language Models (PLMs) to model the relationship between..."}
+{"idx": 7, "title": "Retrieval -Augmented Generation (RAG) and LLM", "date": "", "ddg_snippet": "Izacard & Grave ( 2021 ): Leveraging Passage Retrieval with Generative Models for Open - Domain QA. Enhances RAG with multi-document input for better answer quality. Paper.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/retrieval-augmented-generation-rag-and-llm-2a20bb32350a", "content": "Izacard & Grave ( 2021 ): Leveraging Passage Retrieval with Generative Models for Open - Domain QA. Enhances RAG with multi-document input for better answer quality. Paper."}
+{"idx": 8, "title": "GitHub - xfactlab/emnlp2023-damaging- retrieval : This repository...", "date": "", "ddg_snippet": "title={ Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering }[4] G. Izacard , M. Caron, L. Hosseini, S. Riedel, P. Bojanowski, A. Joulin, E. Grave Unsupervised Dense Information Retrieval with Contrastive Learning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xfactlab/emnlp2023-damaging-retrieval", "content": "title={ Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering }[4] G. Izacard , M. Caron, L. Hosseini, S. Riedel, P. Bojanowski, A. Joulin, E. Grave Unsupervised Dense Information Retrieval with Contrastive Learning."}
+{"idx": 9, "title": "Qasa: Advanced Question Answering on Scientific Articles", "date": "", "ddg_snippet": "Izacard , G. and Grave , É. Leveraging passage retrieval with generative models for open domain question answering . In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 874–880, 2021 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5ud0h8OXwD", "content": "Izacard , G. and Grave , É. Leveraging passage retrieval with generative models for open domain question answering . In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 874–880, 2021 ."}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_Conditional_Deep_Generative_Models_Figure_2_alpha_.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_Conditional_Deep_Generative_Models_Figure_2_alpha_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cb8e8c8b80488f2a293cac8c3231f803db8baf78
--- /dev/null
+++ b/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_Conditional_Deep_Generative_Models_Figure_2_alpha_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generative model - Wikipedia", "date": "", "ddg_snippet": "In statistical classification, two main approaches are called the generative approach and the discriminative approach . These compute classifiers by different approaches , differing in the degree of statistical modelling .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Generative_model", "content": "In statistical classification, two main approaches are called the generative approach and the discriminative approach . These compute classifiers by different approaches , differing in the degree of statistical modelling ."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold Structure; Sieve MLE; Wasserstein Convergence. 1 Introduction. Conditional distribution estimation provides a principled framework for characterizing the dependence relationship between...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold Structure; Sieve MLE; Wasserstein Convergence. 1 Introduction. Conditional distribution estimation provides a principled framework for characterizing the dependence relationship between..."}
+{"idx": 2, "title": "(PDF) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold StructureThe second approach , based on adversarial learning, matches the empirical distribution of the data with a. distribution estimator using an adversarial loss.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold StructureThe second approach , based on adversarial learning, matches the empirical distribution of the data with a. distribution estimator using an adversarial loss."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "These findings provide an explanation of why conditional deep generative models can circumvent the curse of dimensionality from the perspective of statistical foundations and demonstrate that they can learn a broader class of nearly singular conditional distributions .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/A-Likelihood-Based-Approach-to-Distribution-Regression-Using-Conditional-Deep-Generative-Models-0a08e8b9-48c6-483c-8ab4-4ef338d3619c", "content": "These findings provide an explanation of why conditional deep generative models can circumvent the curse of dimensionality from the perspective of statistical foundations and demonstrate that they can learn a broader class of nearly singular conditional distributions ."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models ."}
+{"idx": 5, "title": "Learning Structured Output Representation using Deep Conditional ...", "date": "", "ddg_snippet": "4 Deep Conditional Generative Models for Structured Output Prediction. Figure 2 : Multi-scale prediction. train the network to predict outputs at different scales. By do-ing so, we can make a global-to-local, coarse-to-ne-grained prediction of pixel-level semantic labels.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2015/file/8d55a249e6baa5c06772297520da2051-Paper.pdf", "content": "4 Deep Conditional Generative Models for Structured Output Prediction. Figure 2 : Multi-scale prediction. train the network to predict outputs at different scales. By do-ing so, we can make a global-to-local, coarse-to-ne-grained prediction of pixel-level semantic labels."}
+{"idx": 6, "title": "Reliable likelihoods from conditional flow matching", "date": "", "ddg_snippet": "Noise flow: Noise modeling with conditional normalizing flows.Input complex-ity and out-of- distribution detection with likelihood - based generative models . International Conference on Learning Representations, 2020. Jiaming Song, Yang Song, and Stefano Ermon.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=cs5NwO1cYp", "content": "Noise flow: Noise modeling with conditional normalizing flows.Input complex-ity and out-of- distribution detection with likelihood - based generative models . International Conference on Learning Representations, 2020. Jiaming Song, Yang Song, and Stefano Ermon."}
+{"idx": 7, "title": "(PDF) Input Complexity and Out-of- distribution Detection with...", "date": "", "ddg_snippet": "Likelihood - based generative models are a promising resource to detect out-of- distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/77495017/Input_Complexity_and_Out_of_distribution_Detection_with_Likelihood_based_Generative_Models", "content": "Likelihood - based generative models are a promising resource to detect out-of- distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system."}
+{"idx": 8, "title": "Modeling bounded data with a new unit distribution : regression ...", "date": "", "ddg_snippet": "Based on this distribution , a new regression model is developed to link bounded response variables to linear predictors, increasing its practical applicability. The maximum likelihood approach is used to estimate the parameters of the regression model and the suggested distribution .", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/doi/10.3934/math.2025790", "content": "Based on this distribution , a new regression model is developed to link bounded response variables to linear predictors, increasing its practical applicability. The maximum likelihood approach is used to estimate the parameters of the regression model and the suggested distribution ."}
+{"idx": 9, "title": "Direct Discriminative Optimization: Your Likelihood - Based Visual...", "date": "", "ddg_snippet": "# Likelihood - based generative models , like diffusion and autoregressive models , have shown great image generation quality.To solve this, Direct Discriminative Optimization (DDO) bridges likelihood - based training and GAN objectives for enhanced generative models .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2503.01103/", "content": "# Likelihood - based generative models , like diffusion and autoregressive models , have shown great image generation quality.To solve this, Direct Discriminative Optimization (DDO) bridges likelihood - based training and GAN objectives for enhanced generative models ."}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_Table_1_MSE_values_FD3.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_Table_1_MSE_values_FD3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7e17f611cae8c8c9821582626947571450b44c38
--- /dev/null
+++ b/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_Table_1_MSE_values_FD3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "More specifically, we study the large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.02025", "content": "More specifically, we study the large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric."}
+{"idx": 2, "title": "PDF Lecture 6: The Method of Maximum Likelihood for Simple Linear Regression", "date": "", "ddg_snippet": "1 Recapitulation We introduced the method of maximum likelihood for simple linear regression in the notes for two lectures ago. Let's review.", "subpage_snippet": "", "source": "www.stat.cmu.edu", "link": "https://www.stat.cmu.edu/~cshalizi/mreg/15/lectures/06/lecture-06.pdf", "content": "1 Recapitulation We introduced the method of maximum likelihood for simple linear regression in the notes for two lectures ago. Let's review."}
+{"idx": 3, "title": "PDF Linear Regression via Maximization of the Likelihood", "date": "", "ddg_snippet": "We now revisit the linear regression problem with a maximum likelihood approach . As in the previous lecture, we assume our data are tuples of the form {xn, yn}N n=1, where xn RD and yn R.", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/courses/archive/fall18/cos324/files/mle-regression.pdf", "content": "We now revisit the linear regression problem with a maximum likelihood approach . As in the previous lecture, we assume our data are tuples of the form {xn, yn}N n=1, where xn RD and yn R."}
+{"idx": 4, "title": "3 Likelihood-based inference - MATH 60604A - Statistical Modelling", "date": "", "ddg_snippet": "To estimate the scale parameter λ and obtain suitable uncertainty measures, we need a modelling framework. We turn to likelihood-based inference. 3.1 Maximum likelihood estimation For any given value of θ, we can obtain the probability mass or density of the sample observations, and we use this to derive an objective function for the estimation.", "subpage_snippet": "", "source": "lbelzile.github.io", "link": "https://lbelzile.github.io/math60604a/likelihood.html", "content": "To estimate the scale parameter λ and obtain suitable uncertainty measures, we need a modelling framework. We turn to likelihood-based inference. 3.1 Maximum likelihood estimation For any given value of θ, we can obtain the probability mass or density of the sample observations, and we use this to derive an objective function for the estimation."}
+{"idx": 5, "title": "12.2 A maximum-likelihood approach | An Introduction to Data Analysis", "date": "", "ddg_snippet": "12.2 A maximum- likelihood approach In order to be able to extend regression modeling to predictor variables other than metric variables (so-called generalized linear regression models, see Chapter 15), the geometric approach needs to be abandoned in favor of a likelihood-based approach . The likelihood-based approach tries to find coefficients that explain the observed data most plausibly.", "subpage_snippet": "", "source": "michael-franke.github.io", "link": "https://michael-franke.github.io/intro-data-analysis/Chap-04-01-linear-regression-MLE.html", "content": "12.2 A maximum- likelihood approach In order to be able to extend regression modeling to predictor variables other than metric variables (so-called generalized linear regression models, see Chapter 15), the geometric approach needs to be abandoned in favor of a likelihood-based approach . The likelihood-based approach tries to find coefficients that explain the observed data most plausibly."}
+{"idx": 6, "title": "PDF 3.1 Parameters and Distributions 3.2 MLE: Maximum Likelihood Estimator", "date": "", "ddg_snippet": "3.2 MLE: Maximum Likelihood Estimator Assume that our random sample X1; ; Xn F, where F = F is a distribution depending on a parameter . For instance, if F is a Normal distribution , then = ( ; 2), the mean and the variance; if F is an Exponential distribution , then = , the rate; if F is a Bernoulli distribution , then = p, the probability of generating 1 .", "subpage_snippet": "", "source": "sites.stat.washington.edu", "link": "https://sites.stat.washington.edu/mmp/courses/403/sp25/handouts/Lec3_MLE_MSE.pdf", "content": "3.2 MLE: Maximum Likelihood Estimator Assume that our random sample X1; ; Xn F, where F = F is a distribution depending on a parameter . For instance, if F is a Normal distribution , then = ( ; 2), the mean and the variance; if F is an Exponential distribution , then = , the rate; if F is a Bernoulli distribution , then = p, the probability of generating 1 ."}
+{"idx": 7, "title": "Multiple Linear Regressions by Maximizing the Likelihood under ...", "date": "", "ddg_snippet": "Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution , the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for ...", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5174750/", "content": "Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution , the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for ..."}
+{"idx": 8, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "To the best of our knowledge, our study is the first at-tempt to explore the likelihood-based approach for distri-butional regression using a conditional deep generative model, considering full-dimensional noise and the poten-tial presence of singular underlying support.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "To the best of our knowledge, our study is the first at-tempt to explore the likelihood-based approach for distri-butional regression using a conditional deep generative model, considering full-dimensional noise and the poten-tial presence of singular underlying support."}
+{"idx": 9, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Equation_12_Table_3_Two_Moon_Wasserstein_distan.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Equation_12_Table_3_Two_Moon_Wasserstein_distan.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f8738fb86cf6764b9968885634b41693ebbd730
--- /dev/null
+++ b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Equation_12_Table_3_Two_Moon_Wasserstein_distan.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Preconditioned Regularized Wasserstein Proximal Sampling Methods", "date": "", "ddg_snippet": "... to Fokker–Planck equations [ 34 ] , which are ODEs in density space, which in turn correspond to score- based particle evolutions based on Liouville ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.01685v1", "content": "... to Fokker–Planck equations [ 34 ] , which are ODEs in density space, which in turn correspond to score- based particle evolutions based on Liouville ..."}
+{"idx": 1, "title": "A LIKELIHOOD BASED APPROACH TO DISTRIBUTION REGRESSION USING ...", "date": "", "ddg_snippet": "he large-sample properties of a likelihood - based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolv d counterpart) of the response given predictors in the Hellinger ( Wasserstein ) metric. Our rates depend", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "he large-sample properties of a likelihood - based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolv d counterpart) of the response given predictors in the Hellinger ( Wasserstein ) metric. Our rates depend"}
+{"idx": 2, "title": "20_mle_annotated - Stanford University A Likelihood Based Approach to Distribution Regression Using ... Lecture Notes 27 36-705 - Carnegie Mellon University 3 Likelihood-based inference – MATH 60604A - Statistical ... Empirical Likelihood Methods - uwaterloo.ca arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "... , , , are iid random variables, where drawn from distribution Var = . unbiased estimate of Sample variance: unbiased estimate of See full list on web.stanford.edu In the real world, we don’t know the true parameters. • But we do get to observe data: # times coin comes up heads, lifetimes of disk drives produced, # visitors to website per day, offer amount for a used bike def estimator 9 : a random variable estimating true parameter . In parameter estimation, We use the point estimate of parameter estimate (b... See full list on web.stanford.edu of iid random variables , , ... , . • was drawn from a distribution with density function | . (or mass) • Sample: , , ... , Likelihood question: How likely is the sample , , ... , given the parameter ? See full list on web.stanford.edu = : , , ... , | = A | % This is just a product, since are iid. See full list on web.stanford.edu variables , , ... , distribution | . iid random , drawn from a maximizes the likelihood of our sample, : also maximizes the log- likelihood See full list on web.stanford.edu More specifically, we study the large-sample properties of a likelihood - based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger ( Wasserstein ) metric. In particular, a completely reasonable estimate of the Wasserstein distance between two distributions, given samples from each of them is the Wasserstein distance between the corresponding empirical measures, i.e. we estimate: 3 Likelihood - based inference This chapter is dedicated to the basics of statistical modelling using likelihood - based inference, arguably the most popular estimation paradigm in statistics. The scale-load approach of Hartley and Rao (1968, 1969) based on a multinomial distri-bution has the same spirit as empirical likelihood proposed later by Owen (1988). d approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictor in the Hellinger ( Wasserstein ) metric. Our rates depend solely on the intrinsic dimension and smoothnes", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/archive/cs/cs109/cs109.1234/lectures/20_mle_annotated.pdf", "content": "... , , , are iid random variables, where drawn from distribution Var = . unbiased estimate of Sample variance: unbiased estimate of See full list on web.stanford.edu In the real world, we don’t know the true parameters. • But we do get to observe data: # times coin comes up heads, lifetimes of disk drives produced, # visitors to website per day, offer amount for a used bike def estimator 9 : a random variable estimating true parameter . In parameter estimation, We use the point estimate of parameter estimate (b... See full list on web.stanford.edu of iid random variables , , ... , . • was drawn from a distribution with density function | . (or mass) • Sample: , , ... , Likelihood question: How likely is the sample , , ... , given the parameter ? See full list on web.stanford.edu = : , , ... , | = A | % This is just a product, since are iid. See full list on web.stanford.edu variables , , ... , distribution | . iid random , drawn from a maximizes the likelihood of our sample, : also maximizes the log- likelihood See full list on web.stanford.edu More specifically, we study the large-sample properties of a likelihood - based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger ( Wasserstein ) metric. In particular, a completely reasonable estimate of the Wasserstein distance between two distributions, given samples from each of them is the Wasserstein distance between the corresponding empirical measures, i.e. we estimate: 3 Likelihood - based inference This chapter is dedicated to the basics of statistical modelling using likelihood - based inference, arguably the most popular estimation paradigm in statistics. The scale-load approach of Hartley and Rao (1968, 1969) based on a multinomial distri-bution has the same spirit as empirical likelihood proposed later by Owen (1988). d approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictor in the Hellinger ( Wasserstein ) metric. Our rates depend solely on the intrinsic dimension and smoothnes"}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "More specifically, we study the large-sample properties of a likelihood - based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger ( Wasserstein ) metric.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.02025", "content": "More specifically, we study the large-sample properties of a likelihood - based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger ( Wasserstein ) metric."}
+{"idx": 4, "title": "3 Likelihood-based inference – MATH 60604A - Statistical ...", "date": "", "ddg_snippet": "3 Likelihood - based inference This chapter is dedicated to the basics of statistical modelling using likelihood - based inference, arguably the most popular estimation paradigm in statistics.", "subpage_snippet": "", "source": "lbelzile.github.io", "link": "https://lbelzile.github.io/math60604a/likelihood.html", "content": "3 Likelihood - based inference This chapter is dedicated to the basics of statistical modelling using likelihood - based inference, arguably the most popular estimation paradigm in statistics."}
+{"idx": 5, "title": "Empirical Likelihood Methods - uwaterloo.ca", "date": "", "ddg_snippet": "The scale-load approach of Hartley and Rao (1968, 1969) based on a multinomial distri-bution has the same spirit as empirical likelihood proposed later by Owen (1988).", "subpage_snippet": "", "source": "sas.uwaterloo.ca", "link": "https://sas.uwaterloo.ca/~cbwu/paper/08handbook-1.pdf", "content": "The scale-load approach of Hartley and Rao (1968, 1969) based on a multinomial distri-bution has the same spirit as empirical likelihood proposed later by Owen (1988)."}
+{"idx": 6, "title": "ASCL.net - Browsing Codes", "date": "", "ddg_snippet": "The package integrates tidal evolution equations and computes the rotational and dynamical evolution of a planet under tidal and triaxial torques.", "subpage_snippet": "", "source": "ascl.net", "link": "http://ascl.net/code/all/page/4/limit/100/order/title/listmode/full/dir/desc", "content": "The package integrates tidal evolution equations and computes the rotational and dynamical evolution of a planet under tidal and triaxial torques."}
+{"idx": 7, "title": "Reverse engineering of a mechanistic model of gene expression", "date": "", "ddg_snippet": "It is based on a hybrid version of the well-established two -state model of gene expression [ 21, 38 ], where a gene is described by the state of a ...", "subpage_snippet": "", "source": "content.iospress.com", "link": "https://content.iospress.com/articles/in-silico-biology/isb210226", "content": "It is based on a hybrid version of the well-established two -state model of gene expression [ 21, 38 ], where a gene is described by the state of a ..."}
+{"idx": 8, "title": "1 INTRODUCTION", "date": "", "ddg_snippet": "... to the limitations of existing methods, we propose a new approach that (1) infers the distribution of trajectories from sample snapshots at multiple ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.06277v4", "content": "... to the limitations of existing methods, we propose a new approach that (1) infers the distribution of trajectories from sample snapshots at multiple ..."}
+{"idx": 9, "title": "Scaling Offline RL via Efficient and Expressive Shortcut Models", "date": "", "ddg_snippet": "Distillation- based approaches to offline RL with generative models (Ding and Jin, 2023 ; Chen et al., 2023 , 2024 ; Park et al., 2025 ) avoid ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22866v1", "content": "Distillation- based approaches to offline RL with generative models (Ding and Jin, 2023 ; Chen et al., 2023 , 2024 ; Park et al., 2025 ) avoid ..."}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Figure.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Figure.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dbea812de03511a01381f6c63e3d7af20498bf1f
--- /dev/null
+++ b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Figure.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Guided Unconditional and Conditional Generative Models for", "date": "", "ddg_snippet": "In this work, we apply four generative diffusion modeling approaches to super-resolution and inference of forced two -dimensional quasi-geostrophic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.00719v1", "content": "In this work, we apply four generative diffusion modeling approaches to super-resolution and inference of forced two -dimensional quasi-geostrophic ..."}
+{"idx": 1, "title": "Spatiodynamic Inference Using Vision-Based Generative Modeling", "date": "", "ddg_snippet": "To bridge this gap, we defined an integrative approach that combines a generative deep learning model with ABC to infer the underlying data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22256v1", "content": "To bridge this gap, we defined an integrative approach that combines a generative deep learning model with ABC to infer the underlying data ..."}
+{"idx": 2, "title": "Demystify Protein Generation with Hierarchical Conditional", "date": "", "ddg_snippet": "Denoising Diffusion Probabilistic Models (DDPMs) (Ho et al., 2020 ) are a class of generative models that learn to model data distributions .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.18603v1", "content": "Denoising Diffusion Probabilistic Models (DDPMs) (Ho et al., 2020 ) are a class of generative models that learn to model data distributions ."}
+{"idx": 3, "title": "Moment- and Power-Spectrum-Based Gaussianity Regularization for", "date": "", "ddg_snippet": "In particular, within generative modeling , the standard Gaussian is commonly used as the latent distribution mapped to complex data distributions .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07027v3", "content": "In particular, within generative modeling , the standard Gaussian is commonly used as the latent distribution mapped to complex data distributions ."}
+{"idx": 4, "title": "Deep learning joint extremes of metocean variables using the", "date": "", "ddg_snippet": "In this work we take a different approach , and adopt a deep learning scheme, where the radial distribution parameters are modelled using artificial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.15808v3", "content": "In this work we take a different approach , and adopt a deep learning scheme, where the radial distribution parameters are modelled using artificial ..."}
+{"idx": 5, "title": "(PDF) Generative AI for Bayesian Computation", "date": "", "ddg_snippet": "Generative Bayesian Computation (GBC) provides a simulation- based approach to Bayesian inference. ... To illustrate our methodology, we analyze the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/393106970_Generative_AI_for_Bayesian_Computation", "content": "Generative Bayesian Computation (GBC) provides a simulation- based approach to Bayesian inference. ... To illustrate our methodology, we analyze the ..."}
+{"idx": 6, "title": "(PDF) Generative Causal Inference", "date": "", "ddg_snippet": "They directrix simulate large samples of observables and unobservable (parameters, latent variables) and then using high-dimensional deep learner to ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371944060_Generative_Causal_Inference", "content": "They directrix simulate large samples of observables and unobservable (parameters, latent variables) and then using high-dimensional deep learner to ..."}
+{"idx": 7, "title": "Neural denoising diffusion models — The Dan MacKinlay stable", "date": "", "ddg_snippet": "With score- based generative models , we can generate samples from the unconditional distribution \\(p(x(0)) \\approx p(x)\\) .", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/nn_diffusion.html", "content": "With score- based generative models , we can generate samples from the unconditional distribution \\(p(x(0)) \\approx p(x)\\) ."}
+{"idx": 8, "title": "Udemy™ Official Site - Enroll Now & Start Learning Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Understand how to use various statistical distributions & how regression models work. Join millions of learners from around the world already learning on Udemy.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=li90q_GJcuAZoOoxvqGfms1ZnEfuRrJtmGyJfeJdUL4oUmJlOFiAb4MY-5DGZHyk2hvOLj8jX3Td5VERK3fLfgg8O9mTrLjMsYmMHxnuVRQRnWZy7VcT8NGeUXBk8o2d.45HChkzPpBlDp9TdRdeJkg&rut=3f327417a76117d9b60201ed84f8371aea6724bd34f0e8707841060b2957e159&u3=https://www.bing.com/aclick?ld=e8JGamou8bL_9t1uKFEvX2jTVUCUz0kcCZZLE1kzu29NKq_04C5_3L5C1DMbY2SNl86JNejYdwS5zq8Lcn0I6zLaTGd7sy-eZjGXFawbnS99qHaG7VK4y4wVkP8aHmdEaXXX-h3wQRHI_LYedM1ZQ50zRoP0yc7WTMutsMKgCpvdwf5Y_8WGTDCb6G8aZhFxLRIQSP7A&u=aHR0cHMlM2ElMmYlMmZ3d3cudWRlbXkuY29tJTJmY291cnNlJTJmcHJvYmFiaWxpdHktYW5kLXN0YXRpc3RpY3MtZm9yLWJ1c2luZXNzLWFuZC1kYXRhLXNjaWVuY2UlMmYlM2Z1dG1fc291cmNlJTNkYmluZyUyNnV0bV9tZWRpdW0lM2R1ZGVteWFkcyUyNnV0bV9jYW1wYWlnbiUzZEJHLVNlYXJjaF9LZXl3b3JkX0JldGFfUHJvZl9sYS5FTl9jYy5ST1ctRW5nbGlzaCUyNmNhbXBhaWdudHlwZSUzZFNlYXJjaCUyNnBvcnRmb2xpbyUzZEJpbmctUk9XLUVuZ2xpc2glMjZsYW5ndWFnZSUzZEVOJTI2cHJvZHVjdCUzZENvdXJzZSUyNnRlc3QlM2QlMjZhdWRpZW5jZSUzZEtleXdvcmQlMjZ0b3BpYyUzZFN0YXRpc3RpY3MlMjZwcmlvcml0eSUzZEJldGElMjZ1dG1fY29udGVudCUzZGRlYWw0NTg0JTI2dXRtX3Rlcm0lM2RfLl9hZ18xMzE4MzE2ODMwMjE4ODA1Xy5fYWRfXy5fa3dfU3RhdGlzdGljcyUyNTIwcHJvYmFiaWxpdHlfLl9kZV9jXy5fZG1fXy5fcGxfXy5fdGlfa3dkLTgyMzk1OTI0MDA0NzA0JTNhbG9jLTY2Xy5fbGlfMTI3MjY3Xy5fcGRfXy5fJTI2bWF0Y2h0eXBlJTNkcCUyNm1zY2xraWQlM2Q2YzEzZjczMzQzMWExMjVjZTUwYjlmZmRjNmQzMDFmMA&rlid=6c13f733431a125ce50b9ffdc6d301f0&vqd=4-249050014816035488489640717622336384233&iurl={1}IG=4490C3ADF40E484D9B96FDD8FBE9BDE7&CID=071EEFB3BAD86C6419CCF9DEBB146D0E&ID=DevEx,5034.1", "content": "Understand how to use various statistical distributions & how regression models work. Join millions of learners from around the world already learning on Udemy."}
+{"idx": 9, "title": "An introduction to Flow Matching · Cambridge MLG Blog", "date": "", "ddg_snippet": "... base distribution $q_0$ into another $p_1$ via a transformation $\\phi$ is interesting, yet its direct application in generative modelling is limited.", "subpage_snippet": "", "source": "mlg.eng.cam.ac.uk", "link": "https://mlg.eng.cam.ac.uk/blog/2024/01/20/flow-matching.html", "content": "... base distribution $q_0$ into another $p_1$ via a transformation $\\phi$ is interesting, yet its direct application in generative modelling is limited."}
diff --git "a/data/sampled_jsons/Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge.jsonl" "b/data/sampled_jsons/Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..36c0e86162f3fe0a3f1221963b8819820597b037
--- /dev/null
+++ "b/data/sampled_jsons/Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "16 Jul 2025 — Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge . Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46671", "content": "16 Jul 2025 — Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge . Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506"}
+{"idx": 1, "title": "Linear Convergence of Sinkhorn’s Algorithm for Generalized ...", "date": "", "ddg_snippet": "We establish Kantorovich du-ality and linear convergence of Sinkhorn ’ s algo - rithm for the generalized SSB problem under mild conditions. Our results provide a new rigorous foundation for understanding Sinkhorn-type iter-ative methods in the context of large-scale gener - alized Schrödinger bridges . 1. Introduction.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "We establish Kantorovich du-ality and linear convergence of Sinkhorn ’ s algo - rithm for the generalized SSB problem under mild conditions. Our results provide a new rigorous foundation for understanding Sinkhorn-type iter-ative methods in the context of large-scale gener - alized Schrödinger bridges . 1. Introduction."}
+{"idx": 2, "title": "On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "Our main result is the convergence of the algorithm in the sense of rel-ative entropy and its rate, under general conditions including unbounded costs. Methodologically, we proceed in two steps. First, we show that certain. (exponential) moment estimates for the iterates imply a convergence rate.", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/Sinkhorn_rate.pdf", "content": "Our main result is the convergence of the algorithm in the sense of rel-ative entropy and its rate, under general conditions including unbounded costs. Methodologically, we proceed in two steps. First, we show that certain. (exponential) moment estimates for the iterates imply a convergence rate."}
+{"idx": 3, "title": "[2212.06000] On the Convergence Rate of Sinkhorn's Algorithm ICML Poster Linear convergence of Sinkhorn's algorithm for ... On the Linear Convergence of the Multimarginal Sinkhorn ... [2212.06000] On the Convergence Rate of Sinkhorn ' s Algorithm - arXiv.… Generative modeling via Schrödinger bridge (basics on ... - vdb Generative modeling via Schrödinger bridge (basics on ... - vdb Generative modeling via Schrödinger bridge (basics on ... - vdb Generative modeling via Schrödinger bridge (basics on ... - vdb Generative modeling via Schrödinger bridge (basics on ... - vdb Generative modeling via Schrödinger bridge (basics on ... - vdb", "date": "", "ddg_snippet": "Dec 12, 2022 · We study Sinkhorn ' s algorithm for solving the entropically regularized optimal transport problem. Its iterate πt is shown to satisfy H(πt|π∗) + H(π∗|πt) = O(t−1) where H denotes relative entropy and π∗ the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with subgaussian marginals. It is a vector graphic and may be used at any scale. The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. How does Sinkhorn solve the entropically regularized optimal transport problem? We study Sinkhorn ' s algorithm for solving the entropically regularized optimal transport problem. Its iterate πt is shown to satisfy H(πt|π∗) + H(π∗|πt) = O(t−1) where H denotes relative entropy and π∗ the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with subgaussian marginals. Does Sinkhorn solve Schrödinger equations? These equations are called the Schrödinger equations. This a coupled system of equations. We will see that the Sinkhorn algorithm iteratively solves these equations . First proof of existence of such potentials by Fortet (see Léonard (2019) for a recent presentation and survey). 0 = f0, N = and define for any l ∈ {1, . . . What is Sinkhorn algorithm? Iteratively solve the system of equations looking for a fixed point . This is the Sinkhorn algorithm, also sometimes called Iterative Proportional Fitting (IPF). ( ). π2n+1 = arg min{KL(π|π2n) : π ∈ P((Rd)2), π1 = ν1} , π2n+2 = arg min{KL(π|π2n+1) : π ∈ P((Rd)2), π0 = ν0} . What is the solution of Schrödinger bridge? Denote π⋆, s ε the solution of the static Schrödinger bridge . Under mild assumptions we have limε→0 εKL(π⋆, s ε |π0,ε ) = 0,N W2 2(ν0, ν1) . Wasserstein distance of order 2. So far we have introduced the Schrödinger bridge in their static and dynamic formulations. We have seen a potential formulation and a link with entropic regularized OT. Who wrote the Schrödinger equation and I-projections? Note on the schrödinger equation and i-projections. , 17(5):369–375, 1993. Erwin Schrödinger . Sur la théorie relativiste de l’électron et l’interprétation de la mécanique quantique. In , volume 2, pages 269–310, 1932. Richard Sinkhorn and Paul Knopp. Concerning nonnegative matrices and doubly stochastic matrices. How do you solve Schrödinger equations? The Schrödinger equations become Rd f0( )π0( , )d )−1 . Rd x x y x n+1 ( )π0( , )d )−1 . Iteratively solve the system of equations looking for a fixed point. This is the Sinkhorn algorithm , also sometimes called Iterative Proportional Fitting (IPF). ( ). A dynamic and static Schrödinger bridges . Convergence of the Sinkhorn algorithm . Figure 2: A Schrödinger Bridge between two data distributions. Image extracted from De Bortoli et al. (2021).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.06000", "content": "Dec 12, 2022 · We study Sinkhorn ' s algorithm for solving the entropically regularized optimal transport problem. Its iterate πt is shown to satisfy H(πt|π∗) + H(π∗|πt) = O(t−1) where H denotes relative entropy and π∗ the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with subgaussian marginals. It is a vector graphic and may be used at any scale. The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. How does Sinkhorn solve the entropically regularized optimal transport problem? We study Sinkhorn ' s algorithm for solving the entropically regularized optimal transport problem. Its iterate πt is shown to satisfy H(πt|π∗) + H(π∗|πt) = O(t−1) where H denotes relative entropy and π∗ the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with subgaussian marginals. Does Sinkhorn solve Schrödinger equations? These equations are called the Schrödinger equations. This a coupled system of equations. We will see that the Sinkhorn algorithm iteratively solves these equations . First proof of existence of such potentials by Fortet (see Léonard (2019) for a recent presentation and survey). 0 = f0, N = and define for any l ∈ {1, . . . What is Sinkhorn algorithm? Iteratively solve the system of equations looking for a fixed point . This is the Sinkhorn algorithm, also sometimes called Iterative Proportional Fitting (IPF). ( ). π2n+1 = arg min{KL(π|π2n) : π ∈ P((Rd)2), π1 = ν1} , π2n+2 = arg min{KL(π|π2n+1) : π ∈ P((Rd)2), π0 = ν0} . What is the solution of Schrödinger bridge? Denote π⋆, s ε the solution of the static Schrödinger bridge . Under mild assumptions we have limε→0 εKL(π⋆, s ε |π0,ε ) = 0,N W2 2(ν0, ν1) . Wasserstein distance of order 2. So far we have introduced the Schrödinger bridge in their static and dynamic formulations. We have seen a potential formulation and a link with entropic regularized OT. Who wrote the Schrödinger equation and I-projections? Note on the schrödinger equation and i-projections. , 17(5):369–375, 1993. Erwin Schrödinger . Sur la théorie relativiste de l’électron et l’interprétation de la mécanique quantique. In , volume 2, pages 269–310, 1932. Richard Sinkhorn and Paul Knopp. Concerning nonnegative matrices and doubly stochastic matrices. How do you solve Schrödinger equations? The Schrödinger equations become Rd f0( )π0( , )d )−1 . Rd x x y x n+1 ( )π0( , )d )−1 . Iteratively solve the system of equations looking for a fixed point. This is the Sinkhorn algorithm , also sometimes called Iterative Proportional Fitting (IPF). ( ). A dynamic and static Schrödinger bridges . Convergence of the Sinkhorn algorithm . Figure 2: A Schrödinger Bridge between two data distributions. Image extracted from De Bortoli et al. (2021)."}
+{"idx": 4, "title": "On the Linear Convergence of the Multimarginal Sinkhorn ...", "date": "", "ddg_snippet": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/epdf/10.1137/21M1410634", "content": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces."}
+{"idx": 5, "title": "Generative modeling via Schrödinger bridge (basics on ... - vdb", "date": "", "ddg_snippet": "A dynamic and static Schrödinger bridges . Convergence of the Sinkhorn algorithm . Figure 2: A Schrödinger Bridge between two data distributions. Image extracted from De Bortoli et al. (2021).", "subpage_snippet": "", "source": "vdeborto.github.io", "link": "https://vdeborto.github.io/project/generative_modeling/session_5.pdf", "content": "A dynamic and static Schrödinger bridges . Convergence of the Sinkhorn algorithm . Figure 2: A Schrödinger Bridge between two data distributions. Image extracted from De Bortoli et al. (2021)."}
+{"idx": 6, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "1 May 2025 — Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge . Download PDF. Rahul Choudhary, Hanbaek Lyu. Published: 01 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0hrkN07DuO&noteId=O4YQkrxppf", "content": "1 May 2025 — Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge . Download PDF. Rahul Choudhary, Hanbaek Lyu. Published: 01 ..."}
+{"idx": 7, "title": "Linear Convergence of Sinkhorn's Algorithm for Generalized ...", "date": "", "ddg_snippet": "Abstract. The classical static Schrödinger Bridge (SSB) problem, which seeks the most likely stochastic evolution between two marginal probability mea-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/a644c39247d5073d97fae0bbd2c5b921800516ea.pdf", "content": "Abstract. The classical static Schrödinger Bridge (SSB) problem, which seeks the most likely stochastic evolution between two marginal probability mea-."}
+{"idx": 8, "title": "Soft-constrained Schrödinger Bridge: a Stochastic Control ...", "date": "", "ddg_snippet": "4 May 2024 — Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge · Finite-horizon covariance control of linear time-varying ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10515369-soft-constrained-schrodinger-bridge-stochastic-control-approach", "content": "4 May 2024 — Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge · Finite-horizon covariance control of linear time-varying ..."}
+{"idx": 9, "title": "Hanbaek Lyu", "date": "", "ddg_snippet": "Aug 6, 2025 · Hanbaek Lyu and Yuchen Li, “ Block majorization-minimization with diminishing radius for constrained nonconvex optimization.” SIAM Journal on Optimization, Vol. 35, Iss. 2 (2025) [Journal, Preprint, GitHub, FreePDF: BMM_SIOPT]", "subpage_snippet": "", "source": "hanbaeklyu.com", "link": "https://hanbaeklyu.com/publications/", "content": "Aug 6, 2025 · Hanbaek Lyu and Yuchen Li, “ Block majorization-minimization with diminishing radius for constrained nonconvex optimization.” SIAM Journal on Optimization, Vol. 35, Iss. 2 (2025) [Journal, Preprint, GitHub, FreePDF: BMM_SIOPT]"}
diff --git "a/data/sampled_jsons/Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge_Equation_6.jsonl" "b/data/sampled_jsons/Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge_Equation_6.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..9e2f5bdd47f6d40ed8314ac4c74a0c8e6dea4549
--- /dev/null
+++ "b/data/sampled_jsons/Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge_Equation_6.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear Convergence of Sinkhorn's Algorithm for Generalized Static ...", "date": "", "ddg_snippet": "In this paper, we present the generalized static Schrödinger bridge problem, establish its Kantorovich dual, and show that the associated generalized Sinkhorn algorithm con-verges linearly in a dimension independent manner under mild assumptions on the general divergence functional f , weight matrix W and margin (r,c).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "In this paper, we present the generalized static Schrödinger bridge problem, establish its Kantorovich dual, and show that the associated generalized Sinkhorn algorithm con-verges linearly in a dimension independent manner under mild assumptions on the general divergence functional f , weight matrix W and margin (r,c)."}
+{"idx": 1, "title": "PDF On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates.", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/Sinkhorn_rate.pdf", "content": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates."}
+{"idx": 2, "title": "Sharper Exponential Convergence Rates for Sinkhorn's Algorithm in ...", "date": "", "ddg_snippet": "Our results are fully non-asymptotic and explicit in all the parameters of the problem. Keywords — Sinkhorn's algorithm , entropic optimal transport, Schrödinger bridge problem, linear convergence . 2020 Mathematics Subject Classification — 90C08, 90-08, 65K10, 49M29.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.01202", "content": "Our results are fully non-asymptotic and explicit in all the parameters of the problem. Keywords — Sinkhorn's algorithm , entropic optimal transport, Schrödinger bridge problem, linear convergence . 2020 Mathematics Subject Classification — 90C08, 90-08, 65K10, 49M29."}
+{"idx": 3, "title": "Linear convergence of Sinkhorn's algorithm for generalized static ...", "date": "", "ddg_snippet": "We establish Kantorovich duality and linear convergence of Sinkhorn's algorithm for the generalized SSB problem under mild conditions. Our results provide a new rigorous foundation for understanding Sinkhorn -type iterative methods in the context of large-scale generalized Schrödinger bridges . more »", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10625991-linear-convergence-sinkhorn-algorithm-generalized-static-schrodinger-bridge", "content": "We establish Kantorovich duality and linear convergence of Sinkhorn's algorithm for the generalized SSB problem under mild conditions. Our results provide a new rigorous foundation for understanding Sinkhorn -type iterative methods in the context of large-scale generalized Schrödinger bridges . more »"}
+{"idx": 4, "title": "PDF Generative modeling via Schrödinger bridge (basics on ... - vdb", "date": "", "ddg_snippet": "Outline of the course A dynamic and static Schrödinger bridges . Convergence of the Sinkhorn algorithm . Figure 2: A Schrödinger Bridge between two data distributions. Image extracted from De Bortoli et al. (2021).", "subpage_snippet": "", "source": "vdeborto.github.io", "link": "https://vdeborto.github.io/project/generative_modeling/session_5.pdf", "content": "Outline of the course A dynamic and static Schrödinger bridges . Convergence of the Sinkhorn algorithm . Figure 2: A Schrödinger Bridge between two data distributions. Image extracted from De Bortoli et al. (2021)."}
+{"idx": 5, "title": "Linear convergence of Sinkhorn's algorithm for generalized static ...", "date": "", "ddg_snippet": "Poster Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46671", "content": "Poster Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506"}
+{"idx": 6, "title": "On the Convergence Rate of Sinkhorn's AlgorithmThe authors thank ...", "date": "", "ddg_snippet": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condition, [20]proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.06000v2", "content": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condition, [20]proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates."}
+{"idx": 7, "title": "PDF Onthelinearconvergenceofthe multi-marginalSinkhornalgorithm", "date": "", "ddg_snippet": "The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel) method of ...", "subpage_snippet": "", "source": "www.mathtube.org", "link": "https://www.mathtube.org/sites/default/files/lecture-extra-files/linear-sinkhorn.pdf", "content": "The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel) method of ..."}
+{"idx": 8, "title": "Hanbaek Lyu", "date": "", "ddg_snippet": "Contingency tables, Random Matrices, Optimal Transport Hanbaek Lyu and Sumit Muhkerjee, \"Large random matrices with given margins.\" Submitted. Prepint (2024) (Last updated ver: Aug 6 , 2025 CT_limit) Rahul Choudhary and Hanbaek Lyu, \" Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge \", To appear in ICML 2025 Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer ...", "subpage_snippet": "", "source": "hanbaeklyu.com", "link": "https://hanbaeklyu.com/publications/", "content": "Contingency tables, Random Matrices, Optimal Transport Hanbaek Lyu and Sumit Muhkerjee, \"Large random matrices with given margins.\" Submitted. Prepint (2024) (Last updated ver: Aug 6 , 2025 CT_limit) Rahul Choudhary and Hanbaek Lyu, \" Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge \", To appear in ICML 2025 Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer ..."}
+{"idx": 9, "title": "PDF Introduction to on Entropic Optimal Transport", "date": "", "ddg_snippet": "Section 5 studies the convergence of entropic optimal transport (1) to (standard) optimal transport as the regularization parameter ε → 0. Specifically, we are interested in the convergence of the optimal value (1), the optimal couplings and the Schrödinger potentials. We conclude with Section 6 on Sinkhorn's algorithm .", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/EOT_lecture_notes.pdf", "content": "Section 5 studies the convergence of entropic optimal transport (1) to (standard) optimal transport as the regularization parameter ε → 0. Specifically, we are interested in the convergence of the optimal value (1), the optimal couplings and the Schrödinger potentials. We conclude with Section 6 on Sinkhorn's algorithm ."}
diff --git a/data/sampled_jsons/Linearization_Turns_Neural_Operators_Function-Valued_Gaussian_Processes_Theorem_3.2.jsonl b/data/sampled_jsons/Linearization_Turns_Neural_Operators_Function-Valued_Gaussian_Processes_Theorem_3.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8255e766fab8a140581a47092212f9819aa61690
--- /dev/null
+++ b/data/sampled_jsons/Linearization_Turns_Neural_Operators_Function-Valued_Gaussian_Processes_Theorem_3.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Theorem 3.2 reveals an insight into the abstract concept of function-valued Gaussian processes : Function-valued Gaus-sian processes are equivalent to (multi-output) Gaussian processes with augmented input spaces.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.05072", "content": "Theorem 3.2 reveals an insight into the abstract concept of function-valued Gaussian processes : Function-valued Gaus-sian processes are equivalent to (multi-output) Gaussian processes with augmented input spaces."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "This turns the neural operator into a kind of function-valued Gaussian process — a well-established tool for modeling uncertainty — but extended to the setting of operators .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "This turns the neural operator into a kind of function-valued Gaussian process — a well-established tool for modeling uncertainty — but extended to the setting of operators ."}
+{"idx": 2, "title": "Neural Operator induced Gaussian Process framework for probabilistic ...", "date": "", "ddg_snippet": "In this work, we propose a novel Neural Operator -induced Gaussian Process (NOGaP), which exploits the probabilistic characteristics of Gaussian Processes (GPs) while leveraging the learning prowess of operator learning. The proposed framework leads to improved prediction accuracy and offers a quantifiable measure of uncertainty.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782524005218", "content": "In this work, we propose a novel Neural Operator -induced Gaussian Process (NOGaP), which exploits the probabilistic characteristics of Gaussian Processes (GPs) while leveraging the learning prowess of operator learning. The proposed framework leads to improved prediction accuracy and offers a quantifiable measure of uncertainty."}
+{"idx": 3, "title": "PDF Gaussian Processes with Linear Operator Inequality Constraints", "date": "", "ddg_snippet": "Gaussian Processes (GPs) are a exible tool for Bayesian nonparametric function estima-tion, and widely used for applications that require inference on functions such as regression and classi cation.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume20/19-065/19-065.pdf", "content": "Gaussian Processes (GPs) are a exible tool for Bayesian nonparametric function estima-tion, and widely used for applications that require inference on functions such as regression and classi cation."}
+{"idx": 4, "title": "PDF Fast Adaptation with Linearized Neural Networks", "date": "", "ddg_snippet": "2 we summarize related work using the Jacobian matrix of neural networks, in Section 3 we describe how we efficiently use the Jacobian matrix in either a Bayesian (generalized) linear model or a degenerate Gaussian process.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v130/maddox21a/maddox21a.pdf", "content": "2 we summarize related work using the Jacobian matrix of neural networks, in Section 3 we describe how we efficiently use the Jacobian matrix in either a Bayesian (generalized) linear model or a degenerate Gaussian process."}
+{"idx": 5, "title": "Operator learning with Gaussian processes - ScienceDirect", "date": "", "ddg_snippet": "To build more flexible neural operators that accommodate irregular geometries and sampling grids, [34] introduces a coordinate-based model that leverages implicit neural representations to encode functions into low-dimensional latent spaces and, in turn , infer mappings between function representations in the latent space.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782524008351", "content": "To build more flexible neural operators that accommodate irregular geometries and sampling grids, [34] introduces a coordinate-based model that leverages implicit neural representations to encode functions into low-dimensional latent spaces and, in turn , infer mappings between function representations in the latent space."}
+{"idx": 6, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Theorem 3.2 reveals an insight into the abstract concept of function-valued Gaussian processes : Function-valued Gaussian processes are equivalent to (multi-output) Gaussian processes with augmented input spaces.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46474/paper", "content": "Theorem 3.2 reveals an insight into the abstract concept of function-valued Gaussian processes : Function-valued Gaussian processes are equivalent to (multi-output) Gaussian processes with augmented input spaces."}
+{"idx": 7, "title": "PDF Deep Neural Networks as Gaussian Processes - GitHub Pages", "date": "", "ddg_snippet": "This equivalence means that the prior function computed by a NN after initialization of the weights is drawn from a Neural Network Gaussian Process (NNGP). The authors additionally show the analytical form of the covariance matrix, respectively the kernel of the corresponding GP, as well as how to efficiently compute it.", "subpage_snippet": "", "source": "mathieuchevalley.github.io", "link": "https://mathieuchevalley.github.io/reports/PaperReview.pdf", "content": "This equivalence means that the prior function computed by a NN after initialization of the weights is drawn from a Neural Network Gaussian Process (NNGP). The authors additionally show the analytical form of the covariance matrix, respectively the kernel of the corresponding GP, as well as how to efficiently compute it."}
+{"idx": 8, "title": "PDF Abstract - ResearchGate", "date": "", "ddg_snippet": "ocesses. We then use Gaussian currying to construct function-valued Gaussian processes from neural operators with Gaussian weight po teriors. We discuss related work in Section 4 and showcase the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes/fulltext/6666938685a4ee7261b375f0/Linearization-Turns-Neural-Operators-into-Function-Valued-Gaussian-Processes.pdf", "content": "ocesses. We then use Gaussian currying to construct function-valued Gaussian processes from neural operators with Gaussian weight po teriors. We discuss related work in Section 4 and showcase the ..."}
+{"idx": 9, "title": "PDF Technische Universit at Munc hen Department of Mathematics", "date": "", "ddg_snippet": "It is interesting that neural networks can be turned into Gaussian processes in the limit of in nite neurons in hidden layers, from which we convert neural networks into familiar probabilistic models that are already well studied. Gaussian processes are de ned by kernel functions , which are actually much related to linear operators . By the virtue of rich and sophisticated theories that have ...", "subpage_snippet": "", "source": "mediatum.ub.tum.de", "link": "https://mediatum.ub.tum.de/doc/1690738/1690738.pdf", "content": "It is interesting that neural networks can be turned into Gaussian processes in the limit of in nite neurons in hidden layers, from which we convert neural networks into familiar probabilistic models that are already well studied. Gaussian processes are de ned by kernel functions , which are actually much related to linear operators . By the virtue of rich and sophisticated theories that have ..."}
diff --git a/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_OpenReview.jsonl b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_OpenReview.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ee5db40dd68ebe856a7a1a3ff6c0ad57cd02e56
--- /dev/null
+++ b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_OpenReview.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — The paper is in the domain of neural operators and generally touches on uncertainty quantification and utilization of Gaussian processes .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — The paper is in the domain of neural operators and generally touches on uncertainty quantification and utilization of Gaussian processes ."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — We first develop Gaussian processes that take values in (infinite-dimensional) Banach spaces of functions , along with the notion of probabilistic currying,.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — We first develop Gaussian processes that take values in (infinite-dimensional) Banach spaces of functions , along with the notion of probabilistic currying,."}
+{"idx": 2, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be ..."}
+{"idx": 3, "title": "Probabilistic predictions with Fourier neural operators", "date": "", "ddg_snippet": "by C Bülte · Cited by 1 — Linearization Turns Neural Operators into . Function - Valued Gaussian Processes . Number arXiv:2406.05072. arXiv, June 2024. L. Pacchiardi, R. A. Adewoyin, P ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=orKA6gJwlB", "content": "by C Bülte · Cited by 1 — Linearization Turns Neural Operators into . Function - Valued Gaussian Processes . Number arXiv:2406.05072. arXiv, June 2024. L. Pacchiardi, R. A. Adewoyin, P ..."}
+{"idx": 4, "title": "Function-Space Priors for the Laplace Approximation in ...", "date": "", "ddg_snippet": "by T Cinquin · 2024 · Cited by 8 — We propose a method for applying the Laplace approximation to neural networks with interpretable. Gaussian process priors in function space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.13711", "content": "by T Cinquin · 2024 · Cited by 8 — We propose a method for applying the Laplace approximation to neural networks with interpretable. Gaussian process priors in function space."}
+{"idx": 5, "title": "Search", "date": "", "ddg_snippet": "Readers: Everyone. Linearization Turns Neural Operators into Function-Valued Gaussian Processes · pdf icon · Emilia Magnani, Marvin Pförtner, Tobias Weber ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search?term=~Philipp_Hennig1&content=authors&group=all&source=forum&sort=cdate:desc", "content": "Readers: Everyone. Linearization Turns Neural Operators into Function-Valued Gaussian Processes · pdf icon · Emilia Magnani, Marvin Pförtner, Tobias Weber ..."}
+{"idx": 6, "title": "publications | Marvin Pförtner - GitHub Pages", "date": "", "ddg_snippet": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be ...", "subpage_snippet": "", "source": "marvinpfoertner.github.io", "link": "https://marvinpfoertner.github.io/publications/", "content": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be ..."}
+{"idx": 7, "title": "Philipp Hennig - Full Professor, University of Tübingen", "date": "", "ddg_snippet": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes · Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig. Published: 01 May ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Philipp_Hennig1", "content": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes · Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig. Published: 01 May ..."}
+{"idx": 8, "title": "Tobias Weber", "date": "", "ddg_snippet": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes · Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig. Published: 01 May ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Tobias_Weber3", "content": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes · Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig. Published: 01 May ..."}
+{"idx": 9, "title": "Search", "date": "", "ddg_snippet": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes · pdf icon · Published: 01 May 2025, Last Modified: 23 Jul 2025 · ICML 2025 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search?content=authors&group=all&page=5&sort=cdate:desc&source=forum&term=~Philipp_Hennig1", "content": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes · pdf icon · Published: 01 May 2025, Last Modified: 23 Jul 2025 · ICML 2025 ..."}
diff --git a/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_Table_1_NLL_scores_LUNO_year_2024.jsonl b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_Table_1_NLL_scores_LUNO_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3902bf5905931d14aed990dbd9f61c3918e6d627
--- /dev/null
+++ b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_Table_1_NLL_scores_LUNO_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Identification of Waves in Plasma Using Convolutional Neural ...", "date": "", "ddg_snippet": "by SK Fordin · 2025 — Linearization is a process used to approximate a nonlinear function as a linear one . One approach (e.g., [179, 174]) is to determine an ... 211 pages", "subpage_snippet": "", "source": "www.physics.udel.edu", "link": "https://www.physics.udel.edu/~shay/papers/FordinS.2025.PhD-Thesis.pdf", "content": "by SK Fordin · 2025 — Linearization is a process used to approximate a nonlinear function as a linear one . One approach (e.g., [179, 174]) is to determine an ... 211 pages"}
+{"idx": 1, "title": "Complex Logical Query Answering Meets Graph Databases", "date": "", "ddg_snippet": "by H Ren · 2023 · Cited by 50 — Here, we discuss the foundational terms and preliminaries often seen in the database and graph learning literature before introducing neural graph databases. 65 pages", "subpage_snippet": "", "source": "cs.stanford.edu", "link": "https://cs.stanford.edu/~jure/pubs/neuralgr-arxiv23.pdf", "content": "by H Ren · 2023 · Cited by 50 — Here, we discuss the foundational terms and preliminaries often seen in the database and graph learning literature before introducing neural graph databases. 65 pages"}
+{"idx": 2, "title": "Algebraic Statistics vol. 11 (2020), no. 2", "date": "", "ddg_snippet": "Keywords: algebraic geometry, algebraic statistics, linear Gaussian covariance models, intersection theory, plane curves, maximum likelihood estimation, maximum ...", "subpage_snippet": "", "source": "msp.org", "link": "https://msp.org/astat/2020/11-2/astat-v11-n2-s.pdf", "content": "Keywords: algebraic geometry, algebraic statistics, linear Gaussian covariance models, intersection theory, plane curves, maximum likelihood estimation, maximum ..."}
+{"idx": 3, "title": "IJCAI 19 Program Schedule", "date": "", "ddg_snippet": "Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning ... value function transfer and NSR-based value function ...", "subpage_snippet": "", "source": "static.ijcai.org", "link": "https://static.ijcai.org/2019-Program.html", "content": "Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning ... value function transfer and NSR-based value function ..."}
+{"idx": 4, "title": "K-means and Alternative Clustering Methods in Modern ...", "date": "", "ddg_snippet": "by SM Miraftabzadeh · 2023 · Cited by 150 — The K-means algorithm aims to minimize the within-cluster variance, forcing the centroids to move to positions surrounded by samples closer to them than any ... 42 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/6287639/6514899/10295469.pdf", "content": "by SM Miraftabzadeh · 2023 · Cited by 150 — The K-means algorithm aims to minimize the within-cluster variance, forcing the centroids to move to positions surrounded by samples closer to them than any ... 42 pages"}
+{"idx": 5, "title": "https://archive.ics.uci.edu/ml/machine-learning-da...", "date": "", "ddg_snippet": "First, Gaussian processes model functions in terms of high-level properties ... Functions defining the model as well as intermediate calculations are valued in ...", "subpage_snippet": "", "source": "archive.ics.uci.edu", "link": "https://archive.ics.uci.edu/ml/machine-learning-databases/00307/%5bUCI%5d%20AAAI-14%20Accepted%20Papers%20-%20Papers.csv", "content": "First, Gaussian processes model functions in terms of high-level properties ... Functions defining the model as well as intermediate calculations are valued in ..."}
+{"idx": 6, "title": "ACL 2020 The 5th Workshop on Representation Learning ...", "date": "", "ddg_snippet": "9 Jul 2020 — The 5th Workshop on Representation Learning for NLP (RepL4NLP-2020) will be hosted at ACL 2020. The workshop is being organised by Spandana ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2020.repl4nlp-1.pdf", "content": "9 Jul 2020 — The 5th Workshop on Representation Learning for NLP (RepL4NLP-2020) will be hosted at ACL 2020. The workshop is being organised by Spandana ..."}
+{"idx": 7, "title": "Useful interpretability for real-world machine learning", "date": "", "ddg_snippet": "8 May 2022 — The recent surge in highly successful, but opaque, machine-learning models has given rise to a dire need for interpretability.", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-40.pdf", "content": "8 May 2022 — The recent surge in highly successful, but opaque, machine-learning models has given rise to a dire need for interpretability."}
+{"idx": 8, "title": "EMNLP 2021", "date": "", "ddg_snippet": "EMNLP 2021 is one of the first hybrid conferences in the field of natural language process - ing. It is also for us, the organizing team, uncharted domain.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.emnlp.handbook.pdf", "content": "EMNLP 2021 is one of the first hybrid conferences in the field of natural language process - ing. It is also for us, the organizing team, uncharted domain."}
+{"idx": 9, "title": "Advances in Soft Computing 41", "date": "", "ddg_snippet": "Soft. Computing (SC) consists of several computing paradigms, including fuzzy logic, neu- ral networks, and genetic algorithms, which can be used to produce ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-540-72432-2.pdf", "content": "Soft. Computing (SC) consists of several computing paradigms, including fuzzy logic, neu- ral networks, and genetic algorithms, which can be used to produce ..."}
diff --git a/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_Table_1_year_2024.jsonl b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_Table_1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..32997717460d9d6b47a905863403e08376001258
--- /dev/null
+++ b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_Table_1_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "In a case study on Fourier neural operators , we show that, even for a discretized input, our method yields a Gaussian closure--a structured Gaussian process posterior capturing the uncertainty in the output function of the neural operator , which can be evaluated at an arbitrary set of points.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.05072v1", "content": "In a case study on Fourier neural operators , we show that, even for a discretized input, our method yields a Gaussian closure--a structured Gaussian process posterior capturing the uncertainty in the output function of the neural operator , which can be evaluated at an arbitrary set of points."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "We want to use model linearization to extend the Gaussian belief over the parameters of a neural network f : Rd × Rp → Rd′ into a (multi-output) Gaussian process belief over the function learned by the neural network.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "We want to use model linearization to extend the Gaussian belief over the parameters of a neural network f : Rd × Rp → Rd′ into a (multi-output) Gaussian process belief over the function learned by the neural network."}
+{"idx": 2, "title": "LUNO: Linearized Predictive Uncertainty in Neural Operators", "date": "", "ddg_snippet": "This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes \" by Magnani et al. (2025).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno", "content": "This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes \" by Magnani et al. (2025)."}
+{"idx": 3, "title": "Linearizing Neural Operators as Function-Valued GPs", "date": "", "ddg_snippet": "The paper presents NOLA, a framework that linearizes neural operators into function-valued Gaussian processes for uncertainty quantification in PDE models.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.05072", "content": "The paper presents NOLA, a framework that linearizes neural operators into function-valued Gaussian processes for uncertainty quantification in PDE models."}
+{"idx": 4, "title": "Neural Operator induced Gaussian Process framework for probabilistic ...", "date": "", "ddg_snippet": "In this work, we propose a novel Neural Operator -induced Gaussian Process (NOGaP), which exploits the probabilistic characteristics of Gaussian Processes (GPs) while leveraging the learning prowess of operator learning. The proposed framework leads to improved prediction accuracy and offers a quantifiable measure of uncertainty.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0045782524005218", "content": "In this work, we propose a novel Neural Operator -induced Gaussian Process (NOGaP), which exploits the probabilistic characteristics of Gaussian Processes (GPs) while leveraging the learning prowess of operator learning. The proposed framework leads to improved prediction accuracy and offers a quantifiable measure of uncertainty."}
+{"idx": 5, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "As for all statistical models, the predictions of these models are imperfect and exhibit errors. Such errors are particularly difficult to spot in the complex nonlinear behaviour of dynamical systems. We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes .", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240605072M/abstract", "content": "As for all statistical models, the predictions of these models are imperfect and exhibit errors. Such errors are particularly difficult to spot in the complex nonlinear behaviour of dynamical systems. We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes ."}
+{"idx": 6, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Spotlight Poster Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani · Marvin Pförtner · Tobias Weber · Philipp Hennig East Exhibition Hall A-B #E-1207", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Spotlight Poster Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani · Marvin Pförtner · Tobias Weber · Philipp Hennig East Exhibition Hall A-B #E-1207"}
+{"idx": 7, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Finally, probabilistic currying transforms f into a function-valued Gaussian process posterior F over the operator learned by the neural operator F (bottom left).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.05072", "content": "Finally, probabilistic currying transforms f into a function-valued Gaussian process posterior F over the operator learned by the neural operator F (bottom left)."}
+{"idx": 8, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "最近在这个领域中的相关研究包括：1.《Deep Learning for Partial Differential Equations: A Review》；2.《Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations》；3.《Solving High-Dimensional Partial Differential Equations Using ...", "subpage_snippet": "", "source": "hub.baai.ac.cn", "link": "https://hub.baai.ac.cn/paper/a1594339-87b9-4e4a-88e1-b3645371a7f8", "content": "最近在这个领域中的相关研究包括：1.《Deep Learning for Partial Differential Equations: A Review》；2.《Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations》；3.《Solving High-Dimensional Partial Differential Equations Using ..."}
+{"idx": 9, "title": "PDF Abstract - ResearchGate", "date": "", "ddg_snippet": "Abstract Linearization Turns Neural Operators into Function-Valued Gaussian Processes", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes/fulltext/6666938685a4ee7261b375f0/Linearization-Turns-Neural-Operators-into-Function-Valued-Gaussian-Processes.pdf", "content": "Abstract Linearization Turns Neural Operators into Function-Valued Gaussian Processes"}
diff --git a/data/sampled_jsons/Llama-2-7b-chat_ASR_values_before_after_ablation_16x_improvement.jsonl b/data/sampled_jsons/Llama-2-7b-chat_ASR_values_before_after_ablation_16x_improvement.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f8421175252bc29426585a9e738fbb3916b7335
--- /dev/null
+++ b/data/sampled_jsons/Llama-2-7b-chat_ASR_values_before_after_ablation_16x_improvement.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How To Install UNCENSORED Llama 2 Locally... - YouTube", "date": "", "ddg_snippet": "Welcome to the ultimate guide on how to unlock the full potential of the language model in Llama 2 by installing the uncensored version!", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=RpGXhpeH668", "content": "Welcome to the ultimate guide on how to unlock the full potential of the language model in Llama 2 by installing the uncensored version!"}
+{"idx": 1, "title": "GitHub - AnshChoudhary/finetuning- Llama 2 - 7 b : Fine-tuning the...", "date": "", "ddg_snippet": "Fine-tuning the LLaMA 2 - 7 B model (7 billion parameters) using QLoRA technique on a T4 GPU with 16 GB of VRAM.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AnshChoudhary/finetuning-Llama2-7b", "content": "Fine-tuning the LLaMA 2 - 7 B model (7 billion parameters) using QLoRA technique on a T4 GPU with 16 GB of VRAM."}
+{"idx": 2, "title": "TheBloke/ Llama - 2 - 7 B -32K-Instruct-GGUF · Hugging Face", "date": "", "ddg_snippet": "Data Collection Details. Llama - 2 - 7 B -32K-Instruct is fine-tuned over a combination of two parts: 19K single- and multi-round conversations generated by human instructions and Llama - 2 -70 B - Chat outputs.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGUF", "content": "Data Collection Details. Llama - 2 - 7 B -32K-Instruct is fine-tuned over a combination of two parts: 19K single- and multi-round conversations generated by human instructions and Llama - 2 -70 B - Chat outputs."}
+{"idx": 3, "title": "Построение RAG c большой языковой моделью LLM ( Llama ...)", "date": "", "ddg_snippet": "Узнайте, как построить RAG (Retrieval-Augmented Generation) с использованием большой языковой модели LLM ( Llama 2 ) и FAISS. Пошаговое руководство для оптимизации генерации ответов на запросы.", "subpage_snippet": "", "source": "blogs.epsilonmetrics.ru", "link": "https://blogs.epsilonmetrics.ru/guide-building-rag-llama-2-faiss/", "content": "Узнайте, как построить RAG (Retrieval-Augmented Generation) с использованием большой языковой модели LLM ( Llama 2 ) и FAISS. Пошаговое руководство для оптимизации генерации ответов на запросы."}
+{"idx": 4, "title": "meta-llama/ Llama - 2 - 7 b -hf · Hugging Face", "date": "", "ddg_snippet": "of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof).Model Developers Meta. Variations Llama 2 comes in a range of parameter sizes — 7 B , 13B, and 70B — as well as pretrained and fine-tuned variations.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/meta-llama/Llama-2-7b-hf", "content": "of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof).Model Developers Meta. Variations Llama 2 comes in a range of parameter sizes — 7 B , 13B, and 70B — as well as pretrained and fine-tuned variations."}
+{"idx": 5, "title": "Суммаризация текста с помощью LLaMA - 2 13B для новичков", "date": "", "ddg_snippet": "Существуют 3 версии LLaMA - 2 по числу параметров 7 B , 13B, 70B.Что касается LLaMA по параметрам можно увидеть из этого сравнения, что 13B не сильно хуже по многим тестам, чем 70B. Суммаризация текста с помощью LLaMA - 2 13B для новичков.", "subpage_snippet": "", "source": "vc.ru", "link": "https://vc.ru/id224943/796760-summarizaciya-teksta-s-pomoshyu-llama-2-13b-dlya-novichkov", "content": "Существуют 3 версии LLaMA - 2 по числу параметров 7 B , 13B, 70B.Что касается LLaMA по параметрам можно увидеть из этого сравнения, что 13B не сильно хуже по многим тестам, чем 70B. Суммаризация текста с помощью LLaMA - 2 13B для новичков."}
+{"idx": 6, "title": "Локальные нейросети. Аналог ChatGPT-3.5 на домашнем ПК... / Хабр", "date": "", "ddg_snippet": "llama server - реализация сервера для Chatbot UI, реализация интерфейса как у chatGPT. open- chat -ui - запуск сервера и UI для OpenChat как на их сайте онлайн. Как запустить LLaVA.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/776314/", "content": "llama server - реализация сервера для Chatbot UI, реализация интерфейса как у chatGPT. open- chat -ui - запуск сервера и UI для OpenChat как на их сайте онлайн. Как запустить LLaVA."}
+{"idx": 7, "title": "Installing llama -cpp-python with GPU Support", "date": "", "ddg_snippet": "If llama -cpp-python cannot find the CUDA toolkit, it will default to a CPU-only installation. I got the installation to work with the commands below. If you have tried to install the package before , you will most likely need the --no-cache-dir option to get it to work.", "subpage_snippet": "", "source": "michaelriedl.com", "link": "https://michaelriedl.com/2023/09/10/llama2-install-gpu.html", "content": "If llama -cpp-python cannot find the CUDA toolkit, it will default to a CPU-only installation. I got the installation to work with the commands below. If you have tried to install the package before , you will most likely need the --no-cache-dir option to get it to work."}
+{"idx": 8, "title": "Llama 2 Online - Next-Gen Large Language Model by Meta", "date": "", "ddg_snippet": "Experience the power of Llama 2 , the second-generation Large Language Model by Meta. Choose from three model sizes, pre-trained on 2 trillion tokens, and fine-tuned with over a million human-annotated examples. Try it now online!", "subpage_snippet": "", "source": "www.llama2.space", "link": "https://www.llama2.space/", "content": "Experience the power of Llama 2 , the second-generation Large Language Model by Meta. Choose from three model sizes, pre-trained on 2 trillion tokens, and fine-tuned with over a million human-annotated examples. Try it now online!"}
+{"idx": 9, "title": "Llama-2-7b-chat-hf-awq-g128-int4-asym-fp16-onnx-hybrid", "date": "", "ddg_snippet": "meta- llama / Llama -2- 7b - chat -hf Introduction This model was prepared using the AMD Quark Quantization tool, followed by necessary post-processing. Quantization Strategy AWQ / Group 128 / Asymmetric / UINT4 Weights / FP16 activations Excluded Layers: None Quick Start For quickstart, refer to Ryzen AI doucmentation Evaluation scores The perplexity measurement is run on the wikitext-2-raw-v1 (raw ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/amd/Llama-2-7b-chat-hf-awq-g128-int4-asym-fp16-onnx-hybrid", "content": "meta- llama / Llama -2- 7b - chat -hf Introduction This model was prepared using the AMD Quark Quantization tool, followed by necessary post-processing. Quantization Strategy AWQ / Group 128 / Asymmetric / UINT4 Weights / FP16 activations Excluded Layers: None Quick Start For quickstart, refer to Ryzen AI doucmentation Evaluation scores The perplexity measurement is run on the wikitext-2-raw-v1 (raw ..."}
diff --git a/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_Sajadmanesh_abstract.jsonl b/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_Sajadmanesh_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ce0e24926629a4b0dff97545e8e8917dc5c6455
--- /dev/null
+++ b/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_Sajadmanesh_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2006.05535] Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica-Perez.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica-Perez."}
+{"idx": 1, "title": "Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "ABSTRACT . Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks.ACM Reference Format: Sina Sajadmanesh and Daniel Gatica-Perez.", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/downloads/papers/2021/Sajadmanesh_CCS2021_2021.pdf", "content": "ABSTRACT . Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks.ACM Reference Format: Sina Sajadmanesh and Daniel Gatica-Perez."}
+{"idx": 2, "title": "(Open Access) Locally Private Graph Neural Networks (2020)", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/locally-private-graph-neural-networks-1lusvir819", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 3, "title": "GitHub - sisaman/LPGNN: Locally Private Graph Neural Networks ...", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/LPGNN", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or..."}
+{"idx": 4, "title": "Locally Private Graph Neural Networks | Request PDF", "date": "", "ddg_snippet": "Differentially private GNNs ( Graph Neural Networks ) have been recently studied to provide high accuracy in various tasks on graph data while strongly protecting user privacy. In particular, a recent study proposes an algorithm to protect each user's feature vector in an attributed graph...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/356203924_Locally_Private_Graph_Neural_Networks", "content": "Differentially private GNNs ( Graph Neural Networks ) have been recently studied to provide high accuracy in various tasks on graph data while strongly protecting user privacy. In particular, a recent study proposes an algorithm to protect each user's feature vector in an attributed graph..."}
+{"idx": 5, "title": "Locally Private Graph Neural Networks | Proceedings of the 2021...", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460120.3484565?cookieSet=1", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private."}
+{"idx": 6, "title": "Sina Sajadmanesh - Google Scholar", "date": "", "ddg_snippet": "327. 2020. Locally Private Graph Neural Networks . S Sajadmanesh , D Gatica-Perez. ACM CCS, 2021.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Gtw3NoAAAAAJ&hl=en", "content": "327. 2020. Locally Private Graph Neural Networks . S Sajadmanesh , D Gatica-Perez. ACM CCS, 2021."}
+{"idx": 7, "title": "Locally Private Graph Neural Networks - Paper Detail", "date": "", "ddg_snippet": "Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/179057/locally-private-graph-neural-networks", "content": "Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks."}
+{"idx": 8, "title": "Locally Private Graph Neural Networks -Bohrium", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/locally-private-graph-neural-networks/867757841736269842-108614", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 9, "title": "GAP: Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Locally private graph neural networks . In Proceedings of the.Graphmi: Extracting private graph data from graph neural networks . In Zhi-Hua Zhou, editor, Proceed-ings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 3749–3755.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/sec23fall-prepub-196-sajadmanesh.pdf", "content": "Locally private graph neural networks . In Proceedings of the.Graphmi: Extracting private graph data from graph neural networks . In Zhi-Hua Zhou, editor, Proceed-ings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 3749–3755."}
diff --git a/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_Sina_Sajadmanesh_Daniel_Gatica-Perez_abstract.jsonl b/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_Sina_Sajadmanesh_Daniel_Gatica-Perez_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b30ec78a70db9877141048f8b5c802785a3808ec
--- /dev/null
+++ b/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_Sina_Sajadmanesh_Daniel_Gatica-Perez_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "'Locally' Convex Function - Mathematics Stack Exchange", "date": "", "ddg_snippet": "Jun 2, 2020 · My intuition suggests that a continuously differentiable function on a convex set which is locally convex everywhere should be globally convex, but I have trouble constructing the argument.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/3702990/locally-convex-function", "content": "Jun 2, 2020 · My intuition suggests that a continuously differentiable function on a convex set which is locally convex everywhere should be globally convex, but I have trouble constructing the argument."}
+{"idx": 1, "title": "The definition of locally Lipschitz - Mathematics Stack Exchange", "date": "", "ddg_snippet": "Actually, a continuously differentiable function is locally Lipschitz, but since the derivative isn't assumed continuous in the theorem, one has only the weaker property that might be dubbed \"pointwise Lipschitz\".", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/518323/the-definition-of-locally-lipschitz", "content": "Actually, a continuously differentiable function is locally Lipschitz, but since the derivative isn't assumed continuous in the theorem, one has only the weaker property that might be dubbed \"pointwise Lipschitz\"."}
+{"idx": 2, "title": "A locally injective but non globally injective function?", "date": "", "ddg_snippet": "A condition on the function might be missing. Otherwise, in dimension 1, the function \"fractional part\" is locally injective but not injective on R.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/58484/a-locally-injective-but-non-globally-injective-function", "content": "A condition on the function might be missing. Otherwise, in dimension 1, the function \"fractional part\" is locally injective but not injective on R."}
+{"idx": 3, "title": "Locally Closed Immersion - Mathematics Stack Exchange", "date": "", "ddg_snippet": "Apr 4, 2019 · But this work in exactly the opposite direction then the problem we have here. Does anybody see how the auther here conclude that $\\Delta_X$ is locally closed immersion?", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/3174516/locally-closed-immersion", "content": "Apr 4, 2019 · But this work in exactly the opposite direction then the problem we have here. Does anybody see how the auther here conclude that $\\Delta_X$ is locally closed immersion?"}
+{"idx": 4, "title": "general topology - Is every compact space locally compact ...", "date": "", "ddg_snippet": "4 This is true trivially. A space is locally compact if every point has a compact neighborhood. If the space itself is compact, then it is a compact neighborhood of every point.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1632637/is-every-compact-space-locally-compact", "content": "4 This is true trivially. A space is locally compact if every point has a compact neighborhood. If the space itself is compact, then it is a compact neighborhood of every point."}
+{"idx": 5, "title": "the equivalency of two definitions of locally closed sets", "date": "", "ddg_snippet": "Apr 2, 2014 · the equivalency of two definitions of locally closed sets Ask Question Asked 11 years, 5 months ago Modified 2 years, 3 months ago", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/736288/the-equivalency-of-two-definitions-of-locally-closed-sets", "content": "Apr 2, 2014 · the equivalency of two definitions of locally closed sets Ask Question Asked 11 years, 5 months ago Modified 2 years, 3 months ago"}
+{"idx": 6, "title": "Locally Constant Functions on Connected Spaces are Constant", "date": "", "ddg_snippet": "I am trying to show that a function that is locally constant on a connected space is, in fact, constant. I have looked at this related question but my approach is a little different than the sugges...", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/44850/locally-constant-functions-on-connected-spaces-are-constant", "content": "I am trying to show that a function that is locally constant on a connected space is, in fact, constant. I have looked at this related question but my approach is a little different than the sugges..."}
+{"idx": 7, "title": "general topology - Definition of a locally Euclidean space ...", "date": "", "ddg_snippet": "Mar 22, 2021 · Let's say we want a general notion of \" $ (X,\\tau)$ is locally homeomorphic to $ (Y,\\sigma)$.\" In general, $ (Y,\\sigma)$ may not have the same \"self-similarity\" property of $\\mathbb {R}^n$ which makes the two definitions of \" locally Euclidean\" equivalent. So we get two inequivalent candidates for a general notion of \" locally homeomorphic\" here.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4072412/definition-of-a-locally-euclidean-space", "content": "Mar 22, 2021 · Let's say we want a general notion of \" $ (X,\\tau)$ is locally homeomorphic to $ (Y,\\sigma)$.\" In general, $ (Y,\\sigma)$ may not have the same \"self-similarity\" property of $\\mathbb {R}^n$ which makes the two definitions of \" locally Euclidean\" equivalent. So we get two inequivalent candidates for a general notion of \" locally homeomorphic\" here."}
+{"idx": 8, "title": "If the scheme $X$ is locally factorial, can it be covered by...", "date": "", "ddg_snippet": "Apr 2, 2017 · Any Dedekind domain is locally factorial, but if it is the co-ordinate ring of a smooth affine curve over say $\\mathbb {C}$, then unless it is already a UFD, it can not be covered by open affines which are UFDs.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/2213783/if-the-scheme-x-is-locally-factorial-can-it-be-covered-by-affine-opens-of-the", "content": "Apr 2, 2017 · Any Dedekind domain is locally factorial, but if it is the co-ordinate ring of a smooth affine curve over say $\\mathbb {C}$, then unless it is already a UFD, it can not be covered by open affines which are UFDs."}
+{"idx": 9, "title": "algebraic geometry - Locally closed embedding whose image is...", "date": "", "ddg_snippet": "Nov 6, 2021 · You'll need to complete a few actions and gain 15 reputation points before being able to upvote. Upvoting indicates when questions and answers are useful. What's reputation and how do I get it? Instead, you can save this post to reference later.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4298592/locally-closed-embedding-whose-image-is-closed-is-a-closed-embedding", "content": "Nov 6, 2021 · You'll need to complete a few actions and gain 15 reputation points before being able to upvote. Upvoting indicates when questions and answers are useful. What's reputation and how do I get it? Instead, you can save this post to reference later."}
diff --git a/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_abstract_full_text_Sajadmanesh.jsonl b/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_abstract_full_text_Sajadmanesh.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b30ec78a70db9877141048f8b5c802785a3808ec
--- /dev/null
+++ b/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_abstract_full_text_Sajadmanesh.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "'Locally' Convex Function - Mathematics Stack Exchange", "date": "", "ddg_snippet": "Jun 2, 2020 · My intuition suggests that a continuously differentiable function on a convex set which is locally convex everywhere should be globally convex, but I have trouble constructing the argument.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/3702990/locally-convex-function", "content": "Jun 2, 2020 · My intuition suggests that a continuously differentiable function on a convex set which is locally convex everywhere should be globally convex, but I have trouble constructing the argument."}
+{"idx": 1, "title": "The definition of locally Lipschitz - Mathematics Stack Exchange", "date": "", "ddg_snippet": "Actually, a continuously differentiable function is locally Lipschitz, but since the derivative isn't assumed continuous in the theorem, one has only the weaker property that might be dubbed \"pointwise Lipschitz\".", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/518323/the-definition-of-locally-lipschitz", "content": "Actually, a continuously differentiable function is locally Lipschitz, but since the derivative isn't assumed continuous in the theorem, one has only the weaker property that might be dubbed \"pointwise Lipschitz\"."}
+{"idx": 2, "title": "A locally injective but non globally injective function?", "date": "", "ddg_snippet": "A condition on the function might be missing. Otherwise, in dimension 1, the function \"fractional part\" is locally injective but not injective on R.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/58484/a-locally-injective-but-non-globally-injective-function", "content": "A condition on the function might be missing. Otherwise, in dimension 1, the function \"fractional part\" is locally injective but not injective on R."}
+{"idx": 3, "title": "Locally Closed Immersion - Mathematics Stack Exchange", "date": "", "ddg_snippet": "Apr 4, 2019 · But this work in exactly the opposite direction then the problem we have here. Does anybody see how the auther here conclude that $\\Delta_X$ is locally closed immersion?", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/3174516/locally-closed-immersion", "content": "Apr 4, 2019 · But this work in exactly the opposite direction then the problem we have here. Does anybody see how the auther here conclude that $\\Delta_X$ is locally closed immersion?"}
+{"idx": 4, "title": "general topology - Is every compact space locally compact ...", "date": "", "ddg_snippet": "4 This is true trivially. A space is locally compact if every point has a compact neighborhood. If the space itself is compact, then it is a compact neighborhood of every point.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1632637/is-every-compact-space-locally-compact", "content": "4 This is true trivially. A space is locally compact if every point has a compact neighborhood. If the space itself is compact, then it is a compact neighborhood of every point."}
+{"idx": 5, "title": "the equivalency of two definitions of locally closed sets", "date": "", "ddg_snippet": "Apr 2, 2014 · the equivalency of two definitions of locally closed sets Ask Question Asked 11 years, 5 months ago Modified 2 years, 3 months ago", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/736288/the-equivalency-of-two-definitions-of-locally-closed-sets", "content": "Apr 2, 2014 · the equivalency of two definitions of locally closed sets Ask Question Asked 11 years, 5 months ago Modified 2 years, 3 months ago"}
+{"idx": 6, "title": "Locally Constant Functions on Connected Spaces are Constant", "date": "", "ddg_snippet": "I am trying to show that a function that is locally constant on a connected space is, in fact, constant. I have looked at this related question but my approach is a little different than the sugges...", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/44850/locally-constant-functions-on-connected-spaces-are-constant", "content": "I am trying to show that a function that is locally constant on a connected space is, in fact, constant. I have looked at this related question but my approach is a little different than the sugges..."}
+{"idx": 7, "title": "general topology - Definition of a locally Euclidean space ...", "date": "", "ddg_snippet": "Mar 22, 2021 · Let's say we want a general notion of \" $ (X,\\tau)$ is locally homeomorphic to $ (Y,\\sigma)$.\" In general, $ (Y,\\sigma)$ may not have the same \"self-similarity\" property of $\\mathbb {R}^n$ which makes the two definitions of \" locally Euclidean\" equivalent. So we get two inequivalent candidates for a general notion of \" locally homeomorphic\" here.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4072412/definition-of-a-locally-euclidean-space", "content": "Mar 22, 2021 · Let's say we want a general notion of \" $ (X,\\tau)$ is locally homeomorphic to $ (Y,\\sigma)$.\" In general, $ (Y,\\sigma)$ may not have the same \"self-similarity\" property of $\\mathbb {R}^n$ which makes the two definitions of \" locally Euclidean\" equivalent. So we get two inequivalent candidates for a general notion of \" locally homeomorphic\" here."}
+{"idx": 8, "title": "If the scheme $X$ is locally factorial, can it be covered by...", "date": "", "ddg_snippet": "Apr 2, 2017 · Any Dedekind domain is locally factorial, but if it is the co-ordinate ring of a smooth affine curve over say $\\mathbb {C}$, then unless it is already a UFD, it can not be covered by open affines which are UFDs.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/2213783/if-the-scheme-x-is-locally-factorial-can-it-be-covered-by-affine-opens-of-the", "content": "Apr 2, 2017 · Any Dedekind domain is locally factorial, but if it is the co-ordinate ring of a smooth affine curve over say $\\mathbb {C}$, then unless it is already a UFD, it can not be covered by open affines which are UFDs."}
+{"idx": 9, "title": "algebraic geometry - Locally closed embedding whose image is...", "date": "", "ddg_snippet": "Nov 6, 2021 · You'll need to complete a few actions and gain 15 reputation points before being able to upvote. Upvoting indicates when questions and answers are useful. What's reputation and how do I get it? Instead, you can save this post to reference later.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4298592/locally-closed-embedding-whose-image-is-closed-is-a-closed-embedding", "content": "Nov 6, 2021 · You'll need to complete a few actions and gain 15 reputation points before being able to upvote. Upvoting indicates when questions and answers are useful. What's reputation and how do I get it? Instead, you can save this post to reference later."}
diff --git a/data/sampled_jsons/Longpre_2021_Entity-Based_Knowledge_Conflicts_parametric_contextual_knowledge_year_2021.jsonl b/data/sampled_jsons/Longpre_2021_Entity-Based_Knowledge_Conflicts_parametric_contextual_knowledge_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ce27464ffbf6e5dbdacc839887abf732b1484bc7
--- /dev/null
+++ b/data/sampled_jsons/Longpre_2021_Entity-Based_Knowledge_Conflicts_parametric_contextual_knowledge_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Entity-Based Knowledge Conflicts in Question Answering Entity-Based Knowledge Conflicts in Question Answering Entity-Based Knowledge Conflicts in Question Answering Entity-Based Knowledge Conflicts in Question Answeri Entity-Based Knowledge Conflicts in Question Answering Images Entity-Based Knowledge Conflicts ... preview & related info ... Knowledge conflict - rongwuxu.com GitHub - apple/ml- knowledge - conflicts : Entity - Based Knowledge Con… Entity - Based Knowledge Conflicts in Question Answering - ACL Antho… Entity - Based Knowledge Conflicts in Question Answering - ACL Antho… Entity - Based Knowledge Conflicts in Question Answering - ACL Antho… Entity - Based Knowledge Conflicts in Question Answering - ACL Antho… Entity - Based Knowledge Conflicts in Question Answering - ACL Antho…", "date": "", "ddg_snippet": "Sep 10, 2021 · To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Apple ~University of California, Irvine slongpre@mit.edu {kperisetla, nikhilr, cdubois} @apple.com {anthony.chen, sameer} @uci.edu See full list on aclanthology.org Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the be-h... See full list on aclanthology.org As our focus is entity - based knowledge conflicts , our first step identifies instances where the answer is a named entity . We leverage the SpaCy named en-tity recognizer and entity linker to identify gold an-swers that are named entities, their corresponding entity types, and their ID in the Wikidata graph.2 This allows us to gather auxiliary inform... See full list on aclanthology.org We’ve observed model behaviour appears strongly contingent on the domain similarity of presented knowledge conflicts . Next we explore what other factors may significantly impact a proclivity to pref-erence parametric knowledge . See full list on aclanthology.org While the Original and Substitute answers are well defined, the Other category is broad and serves as a catch-all. We perform a qualitative analysis to understand what phenomenon Other captures. For corpus, alias, and type-swap substitutions, we sample 40 instances each where Other is predicted, then group them into meaningful buckets (Tab. 3). Par... See full list on aclanthology.org In this work, we examine how conflicts between contextual and parametric knowledge affect ques-tion answering models. In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Fina... See full list on aclanthology.org Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric , learned at training time, and (2) contextual , given as a passage at inference time. Abstract and contextual , given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the con This repository provides the Substitution Framework described in Section 2 of our paper Entity - Based Knowledge Conflicts in Question Answering. Given a quesion answering dataset, we derive a new dataset where the context passages have been modified to have new answers to their question. View all ( 2021 ) Longpre et al. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a passage at inference time. It is often the case that not all pieces of information within a passage are in conflict between parametric and conflicting knowledge sources. It is crucial for LLMs to pinpoint the specific piece of information where these conflicts arise. How do I contact the knowledge conflicts repository & entity-based substitution framework? The Knowledge Conflicts repository, and entity-based substitution framework are licensed according to the LICENSE file. To contact us feel free to email the authors in the paper or create an issue in this repository. Entity-Based Knowledge Conflicts in Question Answering. Does contextual knowledge conflict with knowl-edge learned during pre-training or fine-tuning? the contextual knowledge contradicts with knowl-edge learned during pre-training or fine-tuning. Be-cause the space of knowledge conflicts is broad, we restrict ourselves to the space of entity-based conflicts – restricted to named entity substitutions. Do knowledge conflicts exist in QA? In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Does knowledge conflict affect pre-diction uncertainty? Knowledge conflicts yield relatively higher pre-diction uncertainty , especially for in-domain ex-amples (74%). Uncertainty is also elevated for out-of-distribution examples in NQ Dev (NAO) or NewsQA (64% and 69% respectively). In particu-lar, uncertainty is highest for instances where the model predicts Other. How do we create knowledge conflicts? To encourage these practices, we have released our framework for generating knowledge conflicts .1 Knowledge -dependent tasks, such as open-retrieval question answering (QA), require expansive “world knowledge ”, common sense, and reasoning abili-ties. Why do we create knowl-edge conflicts at inference time? Inference At inference time we create knowl-edge conflicts for (1) the training set ( to under-stand knowledge conflicts on data the models have seen ), (2) the development set, as well as (3) an out-of-distribution (OOD) set, either the training set for NQ or NewsQA, depending on which was not used at training time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.05052", "content": "Sep 10, 2021 · To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Apple ~University of California, Irvine slongpre@mit.edu {kperisetla, nikhilr, cdubois} @apple.com {anthony.chen, sameer} @uci.edu See full list on aclanthology.org Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the be-h... See full list on aclanthology.org As our focus is entity - based knowledge conflicts , our first step identifies instances where the answer is a named entity . We leverage the SpaCy named en-tity recognizer and entity linker to identify gold an-swers that are named entities, their corresponding entity types, and their ID in the Wikidata graph.2 This allows us to gather auxiliary inform... See full list on aclanthology.org We’ve observed model behaviour appears strongly contingent on the domain similarity of presented knowledge conflicts . Next we explore what other factors may significantly impact a proclivity to pref-erence parametric knowledge . See full list on aclanthology.org While the Original and Substitute answers are well defined, the Other category is broad and serves as a catch-all. We perform a qualitative analysis to understand what phenomenon Other captures. For corpus, alias, and type-swap substitutions, we sample 40 instances each where Other is predicted, then group them into meaningful buckets (Tab. 3). Par... See full list on aclanthology.org In this work, we examine how conflicts between contextual and parametric knowledge affect ques-tion answering models. In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Fina... See full list on aclanthology.org Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric , learned at training time, and (2) contextual , given as a passage at inference time. Abstract and contextual , given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the con This repository provides the Substitution Framework described in Section 2 of our paper Entity - Based Knowledge Conflicts in Question Answering. Given a quesion answering dataset, we derive a new dataset where the context passages have been modified to have new answers to their question. View all ( 2021 ) Longpre et al. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a passage at inference time. It is often the case that not all pieces of information within a passage are in conflict between parametric and conflicting knowledge sources. It is crucial for LLMs to pinpoint the specific piece of information where these conflicts arise. How do I contact the knowledge conflicts repository & entity-based substitution framework? The Knowledge Conflicts repository, and entity-based substitution framework are licensed according to the LICENSE file. To contact us feel free to email the authors in the paper or create an issue in this repository. Entity-Based Knowledge Conflicts in Question Answering. Does contextual knowledge conflict with knowl-edge learned during pre-training or fine-tuning? the contextual knowledge contradicts with knowl-edge learned during pre-training or fine-tuning. Be-cause the space of knowledge conflicts is broad, we restrict ourselves to the space of entity-based conflicts – restricted to named entity substitutions. Do knowledge conflicts exist in QA? In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Does knowledge conflict affect pre-diction uncertainty? Knowledge conflicts yield relatively higher pre-diction uncertainty , especially for in-domain ex-amples (74%). Uncertainty is also elevated for out-of-distribution examples in NQ Dev (NAO) or NewsQA (64% and 69% respectively). In particu-lar, uncertainty is highest for instances where the model predicts Other. How do we create knowledge conflicts? To encourage these practices, we have released our framework for generating knowledge conflicts .1 Knowledge -dependent tasks, such as open-retrieval question answering (QA), require expansive “world knowledge ”, common sense, and reasoning abili-ties. Why do we create knowl-edge conflicts at inference time? Inference At inference time we create knowl-edge conflicts for (1) the training set ( to under-stand knowledge conflicts on data the models have seen ), (2) the development set, as well as (3) an out-of-distribution (OOD) set, either the training set for NQ or NewsQA, depending on which was not used at training time."}
+{"idx": 1, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "This repository provides the Substitution Framework described in Section 2 of our paper Entity - Based Knowledge Conflicts in Question Answering. Given a quesion answering dataset, we derive a new dataset where the context passages have been modified to have new answers to their question.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/apple/ml-knowledge-conflicts", "content": "This repository provides the Substitution Framework described in Section 2 of our paper Entity - Based Knowledge Conflicts in Question Answering. Given a quesion answering dataset, we derive a new dataset where the context passages have been modified to have new answers to their question."}
+{"idx": 2, "title": "Entity-Based Knowledge Conflicts ... preview & related info ...", "date": "", "ddg_snippet": "( 2021 ) Longpre et al. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a passage at inference time.", "subpage_snippet": "", "source": "www.mendeley.com", "link": "https://www.mendeley.com/catalogue/746fdfc7-46a9-3f17-a0d6-6fb3af14de1b/", "content": "( 2021 ) Longpre et al. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a passage at inference time."}
+{"idx": 3, "title": "Entity-Based Knowledge Conflicts in Question Answering Entity-Based Knowledge Conflicts in Question Answering Entity-Based Knowledge Conflicts in Question Answeri Entity-Based Knowledge Conflicts in Question Answering Images Entity-Based Knowledge Conflicts ... preview & related info ... Knowledge conflict - rongwuxu.com GitHub - apple/ml- knowledge - conflicts : Entity - Based Knowledge Con… Entity - Based Knowledge Conflicts in Question Answering - ACL Antho… Entity - Based Knowledge Conflicts in Question Answering - ACL Antho… Entity - Based Knowledge Conflicts in Question Answering - ACL Antho… Entity - Based Knowledge Conflicts in Question Answering - ACL Antho… Entity - Based Knowledge Conflicts in Question Answering - ACL Antho…", "date": "", "ddg_snippet": "Apple ~University of California, Irvine slongpre@mit.edu {kperisetla, nikhilr, cdubois} @apple.com {anthony.chen, sameer} @uci.edu See full list on aclanthology.org Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the be-h... See full list on aclanthology.org As our focus is entity - based knowledge conflicts , our first step identifies instances where the answer is a named entity . We leverage the SpaCy named en-tity recognizer and entity linker to identify gold an-swers that are named entities, their corresponding entity types, and their ID in the Wikidata graph.2 This allows us to gather auxiliary inform... See full list on aclanthology.org We’ve observed model behaviour appears strongly contingent on the domain similarity of presented knowledge conflicts . Next we explore what other factors may significantly impact a proclivity to pref-erence parametric knowledge . See full list on aclanthology.org While the Original and Substitute answers are well defined, the Other category is broad and serves as a catch-all. We perform a qualitative analysis to understand what phenomenon Other captures. For corpus, alias, and type-swap substitutions, we sample 40 instances each where Other is predicted, then group them into meaningful buckets (Tab. 3). Par... See full list on aclanthology.org In this work, we examine how conflicts between contextual and parametric knowledge affect ques-tion answering models. In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Fina... See full list on aclanthology.org Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric , learned at training time, and (2) contextual , given as a passage at inference time. Abstract and contextual , given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the con This repository provides the Substitution Framework described in Section 2 of our paper Entity - Based Knowledge Conflicts in Question Answering. Given a quesion answering dataset, we derive a new dataset where the context passages have been modified to have new answers to their question. View all ( 2021 ) Longpre et al. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a passage at inference time. It is often the case that not all pieces of information within a passage are in conflict between parametric and conflicting knowledge sources. It is crucial for LLMs to pinpoint the specific piece of information where these conflicts arise. How do I contact the knowledge conflicts repository & entity-based substitution framework? The Knowledge Conflicts repository, and entity-based substitution framework are licensed according to the LICENSE file. To contact us feel free to email the authors in the paper or create an issue in this repository. Entity-Based Knowledge Conflicts in Question Answering. Does contextual knowledge conflict with knowl-edge learned during pre-training or fine-tuning? the contextual knowledge contradicts with knowl-edge learned during pre-training or fine-tuning. Be-cause the space of knowledge conflicts is broad, we restrict ourselves to the space of entity-based conflicts – restricted to named entity substitutions. Do knowledge conflicts exist in QA? In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Does knowledge conflict affect pre-diction uncertainty? Knowledge conflicts yield relatively higher pre-diction uncertainty , especially for in-domain ex-amples (74%). Uncertainty is also elevated for out-of-distribution examples in NQ Dev (NAO) or NewsQA (64% and 69% respectively). In particu-lar, uncertainty is highest for instances where the model predicts Other. How do we create knowledge conflicts? To encourage these practices, we have released our framework for generating knowledge conflicts .1 Knowledge -dependent tasks, such as open-retrieval question answering (QA), require expansive “world knowledge ”, common sense, and reasoning abili-ties. Why do we create knowl-edge conflicts at inference time? Inference At inference time we create knowl-edge conflicts for (1) the training set ( to under-stand knowledge conflicts on data the models have seen ), (2) the development set, as well as (3) an out-of-distribution (OOD) set, either the training set for NQ or NewsQA, depending on which was not used at training time.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.emnlp-main.565.pdf", "content": "Apple ~University of California, Irvine slongpre@mit.edu {kperisetla, nikhilr, cdubois} @apple.com {anthony.chen, sameer} @uci.edu See full list on aclanthology.org Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the be-h... See full list on aclanthology.org As our focus is entity - based knowledge conflicts , our first step identifies instances where the answer is a named entity . We leverage the SpaCy named en-tity recognizer and entity linker to identify gold an-swers that are named entities, their corresponding entity types, and their ID in the Wikidata graph.2 This allows us to gather auxiliary inform... See full list on aclanthology.org We’ve observed model behaviour appears strongly contingent on the domain similarity of presented knowledge conflicts . Next we explore what other factors may significantly impact a proclivity to pref-erence parametric knowledge . See full list on aclanthology.org While the Original and Substitute answers are well defined, the Other category is broad and serves as a catch-all. We perform a qualitative analysis to understand what phenomenon Other captures. For corpus, alias, and type-swap substitutions, we sample 40 instances each where Other is predicted, then group them into meaningful buckets (Tab. 3). Par... See full list on aclanthology.org In this work, we examine how conflicts between contextual and parametric knowledge affect ques-tion answering models. In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Fina... See full list on aclanthology.org Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric , learned at training time, and (2) contextual , given as a passage at inference time. Abstract and contextual , given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the con This repository provides the Substitution Framework described in Section 2 of our paper Entity - Based Knowledge Conflicts in Question Answering. Given a quesion answering dataset, we derive a new dataset where the context passages have been modified to have new answers to their question. View all ( 2021 ) Longpre et al. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a passage at inference time. It is often the case that not all pieces of information within a passage are in conflict between parametric and conflicting knowledge sources. It is crucial for LLMs to pinpoint the specific piece of information where these conflicts arise. How do I contact the knowledge conflicts repository & entity-based substitution framework? The Knowledge Conflicts repository, and entity-based substitution framework are licensed according to the LICENSE file. To contact us feel free to email the authors in the paper or create an issue in this repository. Entity-Based Knowledge Conflicts in Question Answering. Does contextual knowledge conflict with knowl-edge learned during pre-training or fine-tuning? the contextual knowledge contradicts with knowl-edge learned during pre-training or fine-tuning. Be-cause the space of knowledge conflicts is broad, we restrict ourselves to the space of entity-based conflicts – restricted to named entity substitutions. Do knowledge conflicts exist in QA? In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Does knowledge conflict affect pre-diction uncertainty? Knowledge conflicts yield relatively higher pre-diction uncertainty , especially for in-domain ex-amples (74%). Uncertainty is also elevated for out-of-distribution examples in NQ Dev (NAO) or NewsQA (64% and 69% respectively). In particu-lar, uncertainty is highest for instances where the model predicts Other. How do we create knowledge conflicts? To encourage these practices, we have released our framework for generating knowledge conflicts .1 Knowledge -dependent tasks, such as open-retrieval question answering (QA), require expansive “world knowledge ”, common sense, and reasoning abili-ties. Why do we create knowl-edge conflicts at inference time? Inference At inference time we create knowl-edge conflicts for (1) the training set ( to under-stand knowledge conflicts on data the models have seen ), (2) the development set, as well as (3) an out-of-distribution (OOD) set, either the training set for NQ or NewsQA, depending on which was not used at training time."}
+{"idx": 4, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric , learned at training time, and (2) contextual , given as a passage at inference time.", "subpage_snippet": "", "source": "www.shaynelongpre.com", "link": "https://www.shaynelongpre.com/publication/kcqa-emnlp2021/", "content": "Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric , learned at training time, and (2) contextual , given as a passage at inference time."}
+{"idx": 5, "title": "Entity-Based Knowledge Conflicts in Question Answeri", "date": "", "ddg_snippet": "Abstract and contextual , given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the con", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10462823", "content": "Abstract and contextual , given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the con"}
+{"idx": 6, "title": "Knowledge conflict - rongwuxu.com", "date": "", "ddg_snippet": "It is often the case that not all pieces of information within a passage are in conflict between parametric and conflicting knowledge sources. It is crucial for LLMs to pinpoint the specific piece of information where these conflicts arise.", "subpage_snippet": "", "source": "rongwuxu.com", "link": "https://rongwuxu.com/index_files/slides/Knowledge+conflict.pdf", "content": "It is often the case that not all pieces of information within a passage are in conflict between parametric and conflicting knowledge sources. It is crucial for LLMs to pinpoint the specific piece of information where these conflicts arise."}
+{"idx": 7, "title": "(Open Access) Entity - Based Knowledge Conflicts in Question...", "date": "", "ddg_snippet": "Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a passage at inference time.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/entity-based-knowledge-conflicts-in-question-answering-4mty8e914i", "content": "Knowledge -dependent tasks typically use two sources of knowledge : parametric , learned at training time, and contextual , given as a passage at inference time."}
+{"idx": 8, "title": "Entity - Based Knowledge Conflicts in Question... - ACL Anthology", "date": "", "ddg_snippet": "@inproceedings{ longpre -etal- 2021 - entity , title = \" Entity - Based Knowledge Conflicts in Question Answering\", author = \" Longpre , Shayne and.of knowledge : parametric , learned at training time, and contextual , given as a passage at inference time.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.emnlp-main.565/", "content": "@inproceedings{ longpre -etal- 2021 - entity , title = \" Entity - Based Knowledge Conflicts in Question Answering\", author = \" Longpre , Shayne and.of knowledge : parametric , learned at training time, and contextual , given as a passage at inference time."}
+{"idx": 9, "title": "Entity - Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "... Knowledge Conflicts We evaluate performance on two knowledge conflict datasets: MemoTrap (Liu and Liu, 2023) and NQ-Swap ( Longpre et al., 2021 ). Longpre et al. ( 2021 ) study entity - based conflicts in the parametric and contextual knowledge .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/357122873_Entity-Based_Knowledge_Conflicts_in_Question_Answering", "content": "... Knowledge Conflicts We evaluate performance on two knowledge conflict datasets: MemoTrap (Liu and Liu, 2023) and NQ-Swap ( Longpre et al., 2021 ). Longpre et al. ( 2021 ) study entity - based conflicts in the parametric and contextual knowledge ."}
diff --git a/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_dataset_entity-based_knowledge_conflicts_abstract.jsonl b/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_dataset_entity-based_knowledge_conflicts_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0e1badc65c7dbdefbb54caee16ea9b12904306ac
--- /dev/null
+++ b/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_dataset_entity-based_knowledge_conflicts_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "by S Longpre · 2021 · Cited by 290 — This substitution framework extends partially- automated dataset creation techniques introduced by Chen et al . ( 2021 ) for Ambiguous Entity Re-.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.emnlp-main.565.pdf", "content": "by S Longpre · 2021 · Cited by 290 — This substitution framework extends partially- automated dataset creation techniques introduced by Chen et al . ( 2021 ) for Ambiguous Entity Re-."}
+{"idx": 1, "title": "Interweaving Conflicting Knowledge and Reasoning Skills ...", "date": "", "ddg_snippet": "For example, NQ -Swaq requires LLMs to extract conflicting entities from the provided document without solely referring to their parametric knowledge directly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.03577v1", "content": "For example, NQ -Swaq requires LLMs to extract conflicting entities from the provided document without solely referring to their parametric knowledge directly ..."}
+{"idx": 2, "title": "ADACAD: Adaptively Decoding to Balance Conflicts ...", "date": "", "ddg_snippet": "by H Wang · 2025 · Cited by 16 — knowledge conflict dataset , NQ - SWAP ( Longpre et al ., 2021 ), which is based on the NQ dataset and consists of synthetic conflicting data.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.581.pdf", "content": "by H Wang · 2025 · Cited by 16 — knowledge conflict dataset , NQ - SWAP ( Longpre et al ., 2021 ), which is based on the NQ dataset and consists of synthetic conflicting data."}
+{"idx": 3, "title": "Mitigating Context-Memory Conflicts in LLMs through Dynamic ...", "date": "", "ddg_snippet": "Figure 2: Overview of DCRD, a two-stage dynamic decoding method designed to mitigate context-memory conflicts , including Conflict Prediction Based on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=sX51FkvGs2", "content": "Figure 2: Overview of DCRD, a two-stage dynamic decoding method designed to mitigate context-memory conflicts , including Conflict Prediction Based on ..."}
+{"idx": 4, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46677", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to ..."}
+{"idx": 5, "title": "Hybrid Knowledge Architectures for Question Answering", "date": "", "ddg_snippet": "by K Ma · 2023 · Cited by 1 — Pan et al . (2019) introduced an entity discovery and linking system to identify the most salient entities in the question and answer options. Wikipedia ...", "subpage_snippet": "", "source": "www.lti.cs.cmu.edu", "link": "https://www.lti.cs.cmu.edu/people/alumni/alumni-thesis/ma-kaixin-thesis.pdf", "content": "by K Ma · 2023 · Cited by 1 — Pan et al . (2019) introduced an entity discovery and linking system to identify the most salient entities in the question and answer options. Wikipedia ..."}
+{"idx": 6, "title": "Why So Gullible? Enhancing the Robustness of", "date": "", "ddg_snippet": "2021 ) to emulate knowledge conflict scenarios, the simple entity - swap technique faces several limitations regarding the verisimilitude of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.01579v3", "content": "2021 ) to emulate knowledge conflict scenarios, the simple entity - swap technique faces several limitations regarding the verisimilitude of the ..."}
+{"idx": 7, "title": "Sparse Autoencoder Features for Classifications and", "date": "", "ddg_snippet": "... demonstrating impressive performance on diverse tasks and languages, even in knowledge -intensive and safety-sensitive scenarios Hendrycks et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.11367v1", "content": "... demonstrating impressive performance on diverse tasks and languages, even in knowledge -intensive and safety-sensitive scenarios Hendrycks et al ."}
+{"idx": 8, "title": "Breaking the Trade-Off Between Faithfulness and Expressiveness", "date": "", "ddg_snippet": "... they struggle to integrate external knowledge naturally, often producing responses with poor interactivity, dullness, and redundancy (Chen et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18651v1", "content": "... they struggle to integrate external knowledge naturally, often producing responses with poor interactivity, dullness, and redundancy (Chen et al ..."}
+{"idx": 9, "title": "DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate", "date": "", "ddg_snippet": "... et al ., 2018 ) , MemoTrap (Liu & Liu, 2023 ) , Open Book Natural Questions ( NQ ; Kwiatkowski et al ., 2019 ) , and NQ - Swap ( Longpre et al ., ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.18860v1", "content": "... et al ., 2018 ) , MemoTrap (Liu & Liu, 2023 ) , Open Book Natural Questions ( NQ ; Kwiatkowski et al ., 2019 ) , and NQ - Swap ( Longpre et al ., ..."}
diff --git a/data/sampled_jsons/Luo_Tseng_1992_SIAM_coordinate_descent_convex_optimization.jsonl b/data/sampled_jsons/Luo_Tseng_1992_SIAM_coordinate_descent_convex_optimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f62f0b5268887d11acd7aba8773fb148ec2ff8c
--- /dev/null
+++ b/data/sampled_jsons/Luo_Tseng_1992_SIAM_coordinate_descent_convex_optimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Linear Convergence of Descent Methods for ...", "date": "", "ddg_snippet": "On the Linear Convergence of Descent Methods for Convex Essentially Smooth Minimization. Authors: Zhi-Quan Luo and Paul Tseng ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/0330025", "content": "On the Linear Convergence of Descent Methods for Convex Essentially Smooth Minimization. Authors: Zhi-Quan Luo and Paul Tseng ..."}
+{"idx": 1, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "by ZQ Luo · 1992 · Cited by 739 — The coordinate descent method enjoys a long history in convex differentiable minimization. Surprisingly, very little is known about the convergence of the.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/BF00939948", "content": "by ZQ Luo · 1992 · Cited by 739 — The coordinate descent method enjoys a long history in convex differentiable minimization. Surprisingly, very little is known about the convergence of the."}
+{"idx": 2, "title": "Iteration Complexity of Feasible Descent Methods for Convex ...", "date": "", "ddg_snippet": "by PW Wang · 2014 · Cited by 173 — Luo and Tseng ( 1992a ) proved the asymptotic linear convergence if sub-problems are solved exactly, and here we further show the global linear convergence. 3.1 ... 26 pages", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume15/wang14a/wang14a.pdf", "content": "by PW Wang · 2014 · Cited by 173 — Luo and Tseng ( 1992a ) proved the asymptotic linear convergence if sub-problems are solved exactly, and here we further show the global linear convergence. 3.1 ... 26 pages"}
+{"idx": 3, "title": "On the Convergence of Block Coordinate Descent Type ...", "date": "", "ddg_snippet": "by A Beck · 2013 · Cited by 769 — In this paper we study smooth convex programming problems where the decision variables vector is split into several blocks of variables.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/120887679", "content": "by A Beck · 2013 · Cited by 769 — In this paper we study smooth convex programming problems where the decision variables vector is split into several blocks of variables."}
+{"idx": 4, "title": "When Cyclic Coordinate Descent Outperforms ...", "date": "", "ddg_snippet": "by M Gürbüzbalaban · Cited by 49 — Luo and P. Tseng. On the convergence of the coordinate descent method for convex differentiable minimization . Journal of Optimization Theory and Applications, ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "http://papers.neurips.cc/paper/7275-when-cyclic-coordinate-descent-outperforms-randomized-coordinate-descent.pdf", "content": "by M Gürbüzbalaban · Cited by 49 — Luo and P. Tseng. On the convergence of the coordinate descent method for convex differentiable minimization . Journal of Optimization Theory and Applications, ..."}
+{"idx": 5, "title": "Descent methods for convex essentially smooth minimization", "date": "", "ddg_snippet": "Consider the problem of minimizing a convex essentially smooth function over a polyhedral set. For the special case where the cost function is strictly ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3228374.3228857", "content": "Consider the problem of minimizing a convex essentially smooth function over a polyhedral set. For the special case where the cost function is strictly ..."}
+{"idx": 6, "title": "Accelerating Greedy Coordinate Descent Methods", "date": "", "ddg_snippet": "by H Lu · 2018 · Cited by 37 — Abstract. We introduce and study two algorithms to accel- erate greedy coordinate descent in theory and in practice: Accelerated Semi-Greedy Coordinate. 10 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v80/lu18b/lu18b.pdf", "content": "by H Lu · 2018 · Cited by 37 — Abstract. We introduce and study two algorithms to accel- erate greedy coordinate descent in theory and in practice: Accelerated Semi-Greedy Coordinate. 10 pages"}
+{"idx": 7, "title": "Iteration Complexity Analysis of Block Coordinate Descent ...", "date": "", "ddg_snippet": "by M Hong · 2013 · Cited by 236 — Luo and P. Tseng , “On the linear convergence of descent methods for convex essentially smooth minimization,” SIAM Journal on Control and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1310.6957", "content": "by M Hong · 2013 · Cited by 236 — Luo and P. Tseng , “On the linear convergence of descent methods for convex essentially smooth minimization,” SIAM Journal on Control and ..."}
+{"idx": 8, "title": "On Faster Convergence of Cyclic Block Coordinate ...", "date": "", "ddg_snippet": "by X Li · 2018 · Cited by 51 — Zhi-Quan Luo and Paul Tseng. On the linear convergence of descent methods for convex essentially smooth minimization . SIAM Journal on Control and Optimization, ... 24 pages", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume18/17-157/17-157.pdf", "content": "by X Li · 2018 · Cited by 51 — Zhi-Quan Luo and Paul Tseng. On the linear convergence of descent methods for convex essentially smooth minimization . SIAM Journal on Control and Optimization, ... 24 pages"}
+{"idx": 9, "title": "Improved iteration complexity bounds of cyclic block coordinate ...", "date": "", "ddg_snippet": "Iteration Complexity of a Block Coordinate Gradient Descent Method for Convex Optimization . In this paper, we study the iteration complexity of a block ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2969239.2969385", "content": "Iteration Complexity of a Block Coordinate Gradient Descent Method for Convex Optimization . In this paper, we study the iteration complexity of a block ..."}
diff --git a/data/sampled_jsons/M._Rizve_K._Roy_A._S._M._H._Bakar_Trust-region-based_semi-supervised_learning_IEEE_Transactions_on_N.jsonl b/data/sampled_jsons/M._Rizve_K._Roy_A._S._M._H._Bakar_Trust-region-based_semi-supervised_learning_IEEE_Transactions_on_N.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..abd27df5b97bb870388979e84f5d47bceddb2e7c
--- /dev/null
+++ b/data/sampled_jsons/M._Rizve_K._Roy_A._S._M._H._Bakar_Trust-region-based_semi-supervised_learning_IEEE_Transactions_on_N.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Semi-supervised Time Series Classification Model with Self ...", "date": "", "ddg_snippet": "Nov 1, 2022 · Semi-supervised learning is a powerful machine learning method. It can be used for model training when only part of the data are labeled. Unlike discrete data, time series data generally have some temporal relation, which can be considered as a supervised signal in semi-supervised learning to supervise the learning of unlabeled time series data.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197622003633", "content": "Nov 1, 2022 · Semi-supervised learning is a powerful machine learning method. It can be used for model training when only part of the data are labeled. Unlike discrete data, time series data generally have some temporal relation, which can be considered as a supervised signal in semi-supervised learning to supervise the learning of unlabeled time series data."}
+{"idx": 1, "title": "[2207.02269] Towards Realistic Semi-Supervised Learning Semi-supervised Time Series Classification Model with Self ... Graph-Based Semi-Supervised Learning: A ... - IEEE Xplore Towards Realistic Semi-supervised Learning - Springer (PDF) Towards Realistic Semi-Supervised Learning - ResearchGate Towards Realistic Semi-supervised Learning | SpringerLink In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo ...", "date": "", "ddg_snippet": "Jul 5, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Nov 1, 2022 · Semi-supervised learning is a powerful machine learning method. It can be used for model training when only part of the data are labeled. Unlike discrete data, time series data generally have some temporal relation, which can be considered as a supervised signal in semi-supervised learning to supervise the learning of unlabeled time series data. Mar 18, 2022 · Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of both labeled and unlabelled data. An essential class of SSL methods, referred to as graph- based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affinity graph, and then, the label information of unlabeled samples can be inferred based on the ... Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Jul 5, 2022 · We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. Oct 23, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements... Jan 12, 2021 · Keywords: Semi-Supervised Learning , Pseudo-Labeling, Uncertainty, Calibration, Deep Learning Abstract : The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.02269", "content": "Jul 5, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Nov 1, 2022 · Semi-supervised learning is a powerful machine learning method. It can be used for model training when only part of the data are labeled. Unlike discrete data, time series data generally have some temporal relation, which can be considered as a supervised signal in semi-supervised learning to supervise the learning of unlabeled time series data. Mar 18, 2022 · Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of both labeled and unlabelled data. An essential class of SSL methods, referred to as graph- based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affinity graph, and then, the label information of unlabeled samples can be inferred based on the ... Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Jul 5, 2022 · We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. Oct 23, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements... Jan 12, 2021 · Keywords: Semi-Supervised Learning , Pseudo-Labeling, Uncertainty, Calibration, Deep Learning Abstract : The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance."}
+{"idx": 2, "title": "Graph-Based Semi-Supervised Learning: A ... - IEEE Xplore", "date": "", "ddg_snippet": "Mar 18, 2022 · Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of both labeled and unlabelled data. An essential class of SSL methods, referred to as graph- based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affinity graph, and then, the label information of unlabeled samples can be inferred based on the ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9737635", "content": "Mar 18, 2022 · Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of both labeled and unlabelled data. An essential class of SSL methods, referred to as graph- based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affinity graph, and then, the label information of unlabeled samples can be inferred based on the ..."}
+{"idx": 3, "title": "Towards Realistic Semi-supervised Learning - Springer", "date": "", "ddg_snippet": "Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-19821-2_25.pdf?pdf=inline+link", "content": "Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data."}
+{"idx": 4, "title": "(PDF) Towards Realistic Semi-Supervised Learning - ResearchGate", "date": "", "ddg_snippet": "Jul 5, 2022 · We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361807923_Towards_Realistic_Semi-Supervised_Learning", "content": "Jul 5, 2022 · We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm."}
+{"idx": 5, "title": "Towards Realistic Semi-supervised Learning | SpringerLink", "date": "", "ddg_snippet": "Oct 23, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-19821-2_25", "content": "Oct 23, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements..."}
+{"idx": 6, "title": "GitHub - nayeemrizve/ups: \"In Defense of Pseudo-Labeling: An ...", "date": "", "ddg_snippet": "Implementation of In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning . The paper presents an uncertainty-aware pseudo-label selection framework for semi-supervised learning which greatly reduces the noise introduced by the pseudo-labeling process.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nayeemrizve/ups", "content": "Implementation of In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning . The paper presents an uncertainty-aware pseudo-label selection framework for semi-supervised learning which greatly reduces the noise introduced by the pseudo-labeling process."}
+{"idx": 7, "title": "Mamshad Nayeem Rizve - Google Scholar", "date": "", "ddg_snippet": "Adobe - Cited by 1,852 - Computer Vision - Machine Learning", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=kA8ZM5oAAAAJ&hl=en", "content": "Adobe - Cited by 1,852 - Computer Vision - Machine Learning"}
+{"idx": 8, "title": "A Survey on Deep Semi-Supervised Learning - IEEE Xplore", "date": "", "ddg_snippet": "Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design and unsupervised loss functions. We first present a taxonomy for deep semi-supervised learning that categorizes existing methods, including ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9941371", "content": "Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design and unsupervised loss functions. We first present a taxonomy for deep semi-supervised learning that categorizes existing methods, including ..."}
+{"idx": 9, "title": "In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo ...", "date": "", "ddg_snippet": "Jan 12, 2021 · Keywords: Semi-Supervised Learning , Pseudo-Labeling, Uncertainty, Calibration, Deep Learning Abstract : The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=-ODN6SbiUU", "content": "Jan 12, 2021 · Keywords: Semi-Supervised Learning , Pseudo-Labeling, Uncertainty, Calibration, Deep Learning Abstract : The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance."}
diff --git a/data/sampled_jsons/METransformer_BLEU-4_score_MIMIC-CXR_year_2023.jsonl b/data/sampled_jsons/METransformer_BLEU-4_score_MIMIC-CXR_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b7c00d36b0fec7347365373f893db5d4fa9569b
--- /dev/null
+++ b/data/sampled_jsons/METransformer_BLEU-4_score_MIMIC-CXR_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2304.02211] METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "Figure 2: Bleu_4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. Figure 3: An example of the generated reports and their attention-mapping visualization of three key medical terms from BASELINE and ours METransformer .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2304.02211", "content": "Figure 2: Bleu_4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. Figure 3: An example of the generated reports and their attention-mapping visualization of three key medical terms from BASELINE and ours METransformer ."}
+{"idx": 1, "title": "PDF METransformer: Radiology Report Generation by Transformer with Multiple ...", "date": "", "ddg_snippet": "Based on this motivation, we propose a new diagnostic captioning framework, METransformer , to mimic the \"multi-expert joint diagnosis\" process. Built upon a transformer backbone, METransformer introduces multiple \"expert tokens\", representing multiple experts, into both the transformer encoder and decoder.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "Based on this motivation, we propose a new diagnostic captioning framework, METransformer , to mimic the \"multi-expert joint diagnosis\" process. Built upon a transformer backbone, METransformer introduces multiple \"expert tokens\", representing multiple experts, into both the transformer encoder and decoder."}
+{"idx": 2, "title": "MIMIC-CXR: Chest X-ray Image Classification and Report Generation - GitHub", "date": "", "ddg_snippet": "This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yuanditang/MIMIC-CXR", "content": "This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports."}
+{"idx": 3, "title": "(PDF) METransformer: Radiology Report Generation by Transformer with ...", "date": "", "ddg_snippet": "The experiments on the public IU-Xray and MIMIC-CXR datasets show that the Align-Transformer can achieve results competitive with state-of-the-art methods on the two datasets.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/369823367_METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens", "content": "The experiments on the public IU-Xray and MIMIC-CXR datasets show that the Align-Transformer can achieve results competitive with state-of-the-art methods on the two datasets."}
+{"idx": 4, "title": "MIMIC-CXR Database v2.0.0 - PhysioNet", "date": "", "ddg_snippet": "The MIMIC Chest X-ray ( MIMIC-CXR ) Database v2.0.0 is a large publicly available dataset of chest radiographs in DICOM format with free-text radiology reports. The dataset contains 377,110 images corresponding to 227,835 radiographic studies performed at the Beth Israel Deaconess Medical Center in Boston, MA.", "subpage_snippet": "", "source": "physionet.org", "link": "https://physionet.org/content/mimic-cxr/2.0.0/", "content": "The MIMIC Chest X-ray ( MIMIC-CXR ) Database v2.0.0 is a large publicly available dataset of chest radiographs in DICOM format with free-text radiology reports. The dataset contains 377,110 images corresponding to 227,835 radiographic studies performed at the Beth Israel Deaconess Medical Center in Boston, MA."}
+{"idx": 5, "title": "METransformer: Radiology Report Generation by Transformer with Multiple ...", "date": "", "ddg_snippet": "Figure 2. Bleu 4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. - \" METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens\"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/METransformer:-Radiology-Report-Generation-by-with-Wang-Liu/4d90382730eefbe8639e64f6855f206657758cc7/figure/3", "content": "Figure 2. Bleu 4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. - \" METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens\""}
+{"idx": 6, "title": "METransformer: Radiology Report Generation by Transformer with Multiple ...", "date": "", "ddg_snippet": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10203079", "content": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ..."}
+{"idx": 7, "title": "CheXReport: A transformer-based architecture to generate chest X-ray ...", "date": "", "ddg_snippet": "We evaluate the CheXReport on the publicly available MIMIC-CXR dataset comprising 377,110 images and corresponding free-text reports. Specifically, CheXReport achieves state-of-the-art performance on the MIMIC-CXR dataset, outperforming other leading models on BLEU-4 and ROUGE metrics.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424015112", "content": "We evaluate the CheXReport on the publicly available MIMIC-CXR dataset comprising 377,110 images and corresponding free-text reports. Specifically, CheXReport achieves state-of-the-art performance on the MIMIC-CXR dataset, outperforming other leading models on BLEU-4 and ROUGE metrics."}
+{"idx": 8, "title": "arXiv:2408.09743v1 [cs.CV] 19 Aug 2024", "date": "", "ddg_snippet": "on the large-scale MIMIC-CXR dataset. Our method achieved a BLEU-1 score of 0.420, a BLEU-4 score of 0.136, and a ROUGE-L score of 0.291, in-dicating its ability to generate precise and", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.09743", "content": "on the large-scale MIMIC-CXR dataset. Our method achieved a BLEU-1 score of 0.420, a BLEU-4 score of 0.136, and a ROUGE-L score of 0.291, in-dicating its ability to generate precise and"}
+{"idx": 9, "title": "Label knowledge guided transformer for automatic radiology report ...", "date": "", "ddg_snippet": "We conduct experimental tests using the IU X-Ray and MIMIC-CXR datasets under six natural language generation evaluation metrics and analyze the results. Experimental results demonstrate that our model achieves state-of-the-art (SOTA) performance. Compared with the baseline models, the label knowledge guided transformer achieves an average improvement of 23.3% on the IU X-Ray dataset and 20.7% ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0169260725002949", "content": "We conduct experimental tests using the IU X-Ray and MIMIC-CXR datasets under six natural language generation evaluation metrics and analyze the results. Experimental results demonstrate that our model achieves state-of-the-art (SOTA) performance. Compared with the baseline models, the label knowledge guided transformer achieves an average improvement of 23.3% on the IU X-Ray dataset and 20.7% ..."}
diff --git a/data/sampled_jsons/METransformer_Multimodal-Enhanced_Transformer_for_Radiology_Report_Generation_Wang_MICCAI_2023.jsonl b/data/sampled_jsons/METransformer_Multimodal-Enhanced_Transformer_for_Radiology_Report_Generation_Wang_MICCAI_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c98e3bc733f89e73cd3037d6970d0af5dd07f006
--- /dev/null
+++ b/data/sampled_jsons/METransformer_Multimodal-Enhanced_Transformer_for_Radiology_Report_Generation_Wang_MICCAI_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "by Z Wang · 2023 · Cited by 167 — Thanks to the carefully designed network struc- ture and the end-to-end training manner, our model can achieve better results than common ensemble approaches. 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "by Z Wang · 2023 · Cited by 167 — Thanks to the carefully designed network struc- ture and the end-to-end training manner, our model can achieve better results than common ensemble approaches. 10 pages"}
+{"idx": 1, "title": "arXiv:2304.02211v1 [cs.CV] 5 Apr 2023", "date": "", "ddg_snippet": "by Z Wang · 2023 · Cited by 161 — First, we propose a new diagnostic captioning frame- work, METransformer , which is conceptually “multi-expert joint diagnosis” for radiology ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.02211", "content": "by Z Wang · 2023 · Cited by 161 — First, we propose a new diagnostic captioning frame- work, METransformer , which is conceptually “multi-expert joint diagnosis” for radiology ..."}
+{"idx": 2, "title": "CVPR Poster METransformer: Radiology Report Generation ...", "date": "", "ddg_snippet": "To this end, we propose METransformer , a method to realize this idea with a transformer-based backbone. The key design of our method is the introduction of ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/poster/21217", "content": "To this end, we propose METransformer , a method to realize this idea with a transformer-based backbone. The key design of our method is the introduction of ..."}
+{"idx": 3, "title": "A survey of deep-learning-based radiology report ...", "date": "", "ddg_snippet": "by X Wang · 2025 · Cited by 4 — This survey summarizes the key techniques developed in the most recent works and proposes a general workflow for deep-learning-based report generation .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1361841525001744", "content": "by X Wang · 2025 · Cited by 4 — This survey summarizes the key techniques developed in the most recent works and proposes a general workflow for deep-learning-based report generation ."}
+{"idx": 4, "title": "Radiology report generation using automatic keyword ...", "date": "", "ddg_snippet": "by Z He · 2025 · Cited by 1 — We developed a novel deep-learning based radiology report generation method for preparing high-quality and explainable radiology report for chest X-ray images.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S001048252500976X", "content": "by Z He · 2025 · Cited by 1 — We developed a novel deep-learning based radiology report generation method for preparing high-quality and explainable radiology report for chest X-ray images."}
+{"idx": 5, "title": "Medical Report Generation Based on Mixed Visual ...", "date": "", "ddg_snippet": "by H Xu · 2025 — In our framework, TinyLlama utilizes advanced reasoning and language structures to generate high-quality medical imaging reports proficiently.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3730436.3730508", "content": "by H Xu · 2025 — In our framework, TinyLlama utilizes advanced reasoning and language structures to generate high-quality medical imaging reports proficiently."}
+{"idx": 6, "title": "Automatic radiology report generation with deep learning", "date": "", "ddg_snippet": "by Y Li · 2025 — Wang Z, Liu L, Wang L, Zhou L ( 2023 ) Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11337-0", "content": "by Y Li · 2025 — Wang Z, Liu L, Wang L, Zhou L ( 2023 ) Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In ..."}
+{"idx": 7, "title": "Multi-granularity Semantic Guided Transformer for ...", "date": "", "ddg_snippet": "by Y Song · 2024 — In this paper, we propose a Semantic Aware and Attention Refine Transformer (SA 3 RT) model to enhance ability of radiology report generation.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1007/978-981-97-9437-9_36", "content": "by Y Song · 2024 — In this paper, we propose a Semantic Aware and Attention Refine Transformer (SA 3 RT) model to enhance ability of radiology report generation."}
+{"idx": 8, "title": "Automated Radiology Report Generation: A Review of ...", "date": "", "ddg_snippet": "by P Sloan · 2024 · Cited by 47 — This survey paper con- ducts a methodological review of contemporary ARRG approaches by way of (i) assessing datasets based on characteristics, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.10842", "content": "by P Sloan · 2024 · Cited by 47 — This survey paper con- ducts a methodological review of contemporary ARRG approaches by way of (i) assessing datasets based on characteristics, ..."}
+{"idx": 9, "title": "arXiv:2405.12833v1 [cs.CV] 21 May 2024", "date": "", "ddg_snippet": "Zhanyu Wang, Lingqiao Liu, Lei Wang, and Luping Zhou. Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/a-survey-of-deep-learning-based-radiology-report-generation-3z1wbs0jyq.pdf", "content": "Zhanyu Wang, Lingqiao Liu, Lei Wang, and Luping Zhou. Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In ..."}
diff --git a/data/sampled_jsons/METransformer_Wang_et_al._2023_radiology_report_generation_year_2023.jsonl b/data/sampled_jsons/METransformer_Wang_et_al._2023_radiology_report_generation_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d944a0bf0c56bf14a52257c64267d19731dda49
--- /dev/null
+++ b/data/sampled_jsons/METransformer_Wang_et_al._2023_radiology_report_generation_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "In addition, Wang et al [35] introduced an image- report matching net-work to bridge the domain gap between image and text for reducing the dificulty of report generation .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "In addition, Wang et al [35] introduced an image- report matching net-work to bridge the domain gap between image and text for reducing the dificulty of report generation ."}
+{"idx": 1, "title": "METransformer: Radiology Report Generation by Transformer ... R2GenGPT: Radiology Report Generation with frozen LLMs From Detection to Radiology Report Generation: Fine-Grained ... METransformer: Radiology Report Generation by Transformer ... METransformer: Radiology Report Generation by Transformer ... METransformer: Radiology Report Generation by Transformer with Mul… R2GenGPT: Radiology Report Generation with frozen LLMs METransformer: Radiology Report Generation by Transformer with Mul… R2GenGPT: Radiology Report Generation with frozen LLMs Markin-Wang/ awesome _ radiology _ report _ generation - GitHub METransformer: Radiology Report Generation by Transformer with Mul… Markin-Wang/awesome_radiology_report_generation - GitHub", "date": "", "ddg_snippet": "Apr 5, 2023 · In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based backbone ... Nov 1, 2023 · In 42, Wang et al . introduced an image-text matching branch to facilitate report generation , utilizing report features to augment image characteristics and consequently minimize the impact of data bias. 6 days ago · Wang Z, Liu L, Wang L, et al . Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023 : 11558–11567. METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2023 -04-05 , DOI: arxiv-2304.02211 Zhanyu Wang , Lingqiao Liu, Lei Wang , Luping Zhou In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ... What is a'multi-expert joint diagnosis' for radiology report generation? First, we propose a new diagnostic captioning frame-work, METransformer , which is conceptually “multi-expert joint diagnosis” for radiology report generation, by intro-ducing learnable expert tokens and encouraging them to learn complementary representations using both linear and non-linear attentions. What is automated radiographic report generation (r2gen)? Automated radiographic report generation (R2Gen) is a complex AI task . It aims to produce a coherent paragraph that captures the observations and findings depicted in a given radiology image. There are different R2Gen approaches based on whether the report generation is structured and whether it is template-based. Can multimodal recurrent model be used for automated radiology re-port generation? Multimodal recurrent model with attention for automated radiology re-port generation . In MICCAI, 2018. 2 S. Yang, X. Wu, S. Ge, X. Wu, S. K. Zhou, and L. Xiao. Radiology report generation with a learned knowledge base and multi-modal alignment. Can large language models be used for radiology report generation (r2gen)? Large Language Models (LLMs) have consistently showcased remarkable generalization capa-bilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology Report Generation (R2Gen) still presents a challenge , stemming from the inherent disparity in modality between LLMs and the R2Gen task. Where can I find great radiology report generation & image captioning papers? GitHub - Markin- Wang /awesome_radiology_report_generation: Awesome radiology report generation and image captioning papers. Search code, repositories, users, issues, pull requests... We read every piece of feedback, and take your input very seriously. Awesome radiology report generation and image captioning papers. Uh oh! Is metransformer a good performer on mimic-CXR? On MIMIC-CXR, METransformer is the best performer across all metrics. Especially, our CIDEr score is up to 0.362, which is to date the best performance and makes a sig-nificant improvement over the second best score of 0.299 of MSAT . METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens, CVPR. | pdf | KiUT: Knowledge-injected U-Transformer for Radiology Report Generation , CVPR. | pdf |", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.02211", "content": "Apr 5, 2023 · In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based backbone ... Nov 1, 2023 · In 42, Wang et al . introduced an image-text matching branch to facilitate report generation , utilizing report features to augment image characteristics and consequently minimize the impact of data bias. 6 days ago · Wang Z, Liu L, Wang L, et al . Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023 : 11558–11567. METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2023 -04-05 , DOI: arxiv-2304.02211 Zhanyu Wang , Lingqiao Liu, Lei Wang , Luping Zhou In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ... What is a'multi-expert joint diagnosis' for radiology report generation? First, we propose a new diagnostic captioning frame-work, METransformer , which is conceptually “multi-expert joint diagnosis” for radiology report generation, by intro-ducing learnable expert tokens and encouraging them to learn complementary representations using both linear and non-linear attentions. What is automated radiographic report generation (r2gen)? Automated radiographic report generation (R2Gen) is a complex AI task . It aims to produce a coherent paragraph that captures the observations and findings depicted in a given radiology image. There are different R2Gen approaches based on whether the report generation is structured and whether it is template-based. Can multimodal recurrent model be used for automated radiology re-port generation? Multimodal recurrent model with attention for automated radiology re-port generation . In MICCAI, 2018. 2 S. Yang, X. Wu, S. Ge, X. Wu, S. K. Zhou, and L. Xiao. Radiology report generation with a learned knowledge base and multi-modal alignment. Can large language models be used for radiology report generation (r2gen)? Large Language Models (LLMs) have consistently showcased remarkable generalization capa-bilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology Report Generation (R2Gen) still presents a challenge , stemming from the inherent disparity in modality between LLMs and the R2Gen task. Where can I find great radiology report generation & image captioning papers? GitHub - Markin- Wang /awesome_radiology_report_generation: Awesome radiology report generation and image captioning papers. Search code, repositories, users, issues, pull requests... We read every piece of feedback, and take your input very seriously. Awesome radiology report generation and image captioning papers. Uh oh! Is metransformer a good performer on mimic-CXR? On MIMIC-CXR, METransformer is the best performer across all metrics. Especially, our CIDEr score is up to 0.362, which is to date the best performance and makes a sig-nificant improvement over the second best score of 0.299 of MSAT . METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens, CVPR. | pdf | KiUT: Knowledge-injected U-Transformer for Radiology Report Generation , CVPR. | pdf |"}
+{"idx": 2, "title": "R2GenGPT: Radiology Report Generation with frozen LLMs", "date": "", "ddg_snippet": "Nov 1, 2023 · In 42, Wang et al . introduced an image-text matching branch to facilitate report generation , utilizing report features to augment image characteristics and consequently minimize the impact of data bias.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2950162823000334", "content": "Nov 1, 2023 · In 42, Wang et al . introduced an image-text matching branch to facilitate report generation , utilizing report features to augment image characteristics and consequently minimize the impact of data bias."}
+{"idx": 3, "title": "From Detection to Radiology Report Generation: Fine-Grained ...", "date": "", "ddg_snippet": "6 days ago · Wang Z, Liu L, Wang L, et al . Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023 : 11558–11567.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10278-025-01650-z", "content": "6 days ago · Wang Z, Liu L, Wang L, et al . Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023 : 11558–11567."}
+{"idx": 4, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2023 -04-05 , DOI: arxiv-2304.02211 Zhanyu Wang , Lingqiao Liu, Lei Wang , Luping Zhou", "subpage_snippet": "", "source": "www.x-mol.com", "link": "https://www.x-mol.com/paper/1644018868217982976", "content": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2023 -04-05 , DOI: arxiv-2304.02211 Zhanyu Wang , Lingqiao Liu, Lei Wang , Luping Zhou"}
+{"idx": 5, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10203079", "content": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ..."}
+{"idx": 6, "title": "Markin-Wang/awesome_radiology_report_generation - GitHub", "date": "", "ddg_snippet": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens, CVPR. | pdf | KiUT: Knowledge-injected U-Transformer for Radiology Report Generation , CVPR. | pdf |", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Markin-Wang/awesome_radiology_report_generation/", "content": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens, CVPR. | pdf | KiUT: Knowledge-injected U-Transformer for Radiology Report Generation , CVPR. | pdf |"}
+{"idx": 7, "title": "Medical Report Generation Is A Multi-label Classification", "date": "", "ddg_snippet": "One of the key benefits of automating the report generation process is the potential to alleviate the heavy workloads that radiologists encounter in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.00250v2", "content": "One of the key benefits of automating the report generation process is the potential to alleviate the heavy workloads that radiologists encounter in ..."}
+{"idx": 8, "title": "MedM2G: Unifying Medical Multi-Modal Generation via", "date": "", "ddg_snippet": "We propose MedM2G, the first unified medical multi-flow generative framework capable of aligning, extracting and generating multiple medical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.04290v1", "content": "We propose MedM2G, the first unified medical multi-flow generative framework capable of aligning, extracting and generating multiple medical ..."}
+{"idx": 9, "title": "PriorRG: Prior-Guided Contrastive Pre-training and", "date": "", "ddg_snippet": "Radiology report generation (RRG) ( Wang et al . ... report generation framework that mirrors real-world radiology workflows via a two-stage training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05353v1", "content": "Radiology report generation (RRG) ( Wang et al . ... report generation framework that mirrors real-world radiology workflows via a two-stage training ..."}
diff --git a/data/sampled_jsons/METransformer_Wang_et_al._Table_1_results_BLEU_scores_MIMIC-CXR.jsonl b/data/sampled_jsons/METransformer_Wang_et_al._Table_1_results_BLEU_scores_MIMIC-CXR.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..337f456c8b662cf804ae4be623b07f7bace6858a
--- /dev/null
+++ b/data/sampled_jsons/METransformer_Wang_et_al._Table_1_results_BLEU_scores_MIMIC-CXR.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Central Time Zone - Wikipedia", "date": "", "ddg_snippet": "The Central Time Zone is a time zone in parts of Canada, the United States, Mexico, Central America, and a few Caribbean islands. [1] It is one hour behind the Eastern Time Zone.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Central_Time_Zone", "content": "The Central Time Zone is a time zone in parts of Canada, the United States, Mexico, Central America, and a few Caribbean islands. [1] It is one hour behind the Eastern Time Zone."}
+{"idx": 1, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "by Z Wang · 2023 · Cited by 167 — Bleu 4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC - CXR dataset. are used, the final prediction result is obtained by ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "by Z Wang · 2023 · Cited by 167 — Bleu 4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC - CXR dataset. are used, the final prediction result is obtained by ..."}
+{"idx": 2, "title": "Retrieving Context Samples for Large Language Model ...", "date": "", "ddg_snippet": "19 Aug 2024 — As shown in Table 1 , we report our results on the large-scale MIMIC - CXR dataset. Our method achieved a BLEU - 1 score of 0.420, a BLEU -4 score of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.09743v1", "content": "19 Aug 2024 — As shown in Table 1 , we report our results on the large-scale MIMIC - CXR dataset. Our method achieved a BLEU - 1 score of 0.420, a BLEU -4 score of ..."}
+{"idx": 3, "title": "Radiology report generation using automatic keyword ...", "date": "", "ddg_snippet": "by Z He · 2025 · Cited by 1 — Table 1 , Table 2 summarize the dataset characteristics and corresponding performance metrics reported in the original studies for IU X-ray and MIMIC - CXR , ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S001048252500976X", "content": "by Z He · 2025 · Cited by 1 — Table 1 , Table 2 summarize the dataset characteristics and corresponding performance metrics reported in the original studies for IU X-ray and MIMIC - CXR , ..."}
+{"idx": 4, "title": "Advancements in Radiology Report Generation", "date": "", "ddg_snippet": "by D Mamdouh · 2025 · Cited by 2 — The METransformer , proposed by Wang et al ., adopts a Vision Transformer ... ( MIMIC - CXR ), introduced by Johnson et al . in 2019 [70], includes ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12292164/", "content": "by D Mamdouh · 2025 · Cited by 2 — The METransformer , proposed by Wang et al ., adopts a Vision Transformer ... ( MIMIC - CXR ), introduced by Johnson et al . in 2019 [70], includes ..."}
+{"idx": 5, "title": "R2GenGPT: Radiology Report Generation with frozen LLMs", "date": "", "ddg_snippet": "by Z Wang · 2023 · Cited by 132 — In the MIMIC - CXR dataset, apart from CIDEr, we significantly outperform the latest METransformer method across all metrics. For instance, our BLEU_4 score is ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2950162823000334", "content": "by Z Wang · 2023 · Cited by 132 — In the MIMIC - CXR dataset, apart from CIDEr, we significantly outperform the latest METransformer method across all metrics. For instance, our BLEU_4 score is ..."}
+{"idx": 6, "title": "RRG-Mamba: Efficient Radiology Report Generation with ...", "date": "", "ddg_snippet": "Extensive experiments on publicly available datasets, including IU X-Ray and MIMIC - CXR , demonstrate that RRG-Mamba achieves a 3.7% im- provement in BLEU -4 score ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0824.pdf", "content": "Extensive experiments on publicly available datasets, including IU X-Ray and MIMIC - CXR , demonstrate that RRG-Mamba achieves a 3.7% im- provement in BLEU -4 score ..."}
+{"idx": 7, "title": "DDGIP: Radiology Report Generation Through Disease ...", "date": "", "ddg_snippet": "by C Huang · 2025 — METrans ( Wang et al ., 2023) 0.483 0.322 0.228 0.172 0.192 0.380 ... dressed this limitation, boosting the BLEU -4 score on MIMIC - CXR from 0.093 to ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.215.pdf", "content": "by C Huang · 2025 — METrans ( Wang et al ., 2023) 0.483 0.322 0.228 0.172 0.192 0.380 ... dressed this limitation, boosting the BLEU -4 score on MIMIC - CXR from 0.093 to ..."}
+{"idx": 8, "title": "VDGen: Visual and Diagnostic Representation Calibration for ...", "date": "", "ddg_snippet": "METransformer (Wang et al., 2023a). 2023. 0.483 ... Table 1 : Main Result on IU-Xray and MIMIC-CXR Dataset. ... BLEU 1 ↓. BLEU2↓. BLEU3↓. BLEU4↓. Meteor ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/bbbf7ba7c10696bc0265e4836f277dfa1bcdafe7.pdf", "content": "METransformer (Wang et al., 2023a). 2023. 0.483 ... Table 1 : Main Result on IU-Xray and MIMIC-CXR Dataset. ... BLEU 1 ↓. BLEU2↓. BLEU3↓. BLEU4↓. Meteor ..."}
+{"idx": 9, "title": "RECAP: Towards Precise Radiology Report Generation via ...", "date": "", "ddg_snippet": "by W Hou · 2023 · Cited by 25 — Table 1 : Experimental Results of our model and baselines on the MIMIC-ABN and MIMIC - CXR datasets. The best results are in boldface, and the ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.findings-emnlp.140.pdf", "content": "by W Hou · 2023 · Cited by 25 — Table 1 : Experimental Results of our model and baselines on the MIMIC-ABN and MIMIC - CXR datasets. The best results are in boldface, and the ..."}
diff --git a/data/sampled_jsons/ML-1M_Amazon_Beauty_Amazon_Toys_Amazon_Games_dataset_statistics_train_interactions_density_27tMzmzDj.jsonl b/data/sampled_jsons/ML-1M_Amazon_Beauty_Amazon_Toys_Amazon_Games_dataset_statistics_train_interactions_density_27tMzmzDj.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3455adf7cb28551ed438672a14a8d75ba5ff6ed6
--- /dev/null
+++ b/data/sampled_jsons/ML-1M_Amazon_Beauty_Amazon_Toys_Amazon_Games_dataset_statistics_train_interactions_density_27tMzmzDj.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "kaiboon0216/Amazon-beauty-products-analysis - GitHub Amazon-Beauty - Dataset - LDM reczoo/AmazonBeauty_m1 · Datasets at Hugging Face luminati-io/Amazon-dataset-samples - GitHub Amazon Beauty Dataset - brightdata.com Amazon Beauty Products - Kaggle", "date": "", "ddg_snippet": "Using data science process to gain business insights from the Amazon beauty datasets and determine the factors that contribute to the high sales of a beauty product Our anaylsis are focused on answering the following questions: 1.Does a high number of good ratings contribute to a lower sales rank and does a high number of bad ratings contribute to a higher sales rank ? 2.Which item has the lowest rating and which item has the highest rating ? 3.What are the common words found in the description of highly rated items? 4.What is the relationship between price and rating of an item? See full list on github.com 1.Download the dataset from google drive via this link and put them into the dataset folder 2.Run the PartB_Code_A4.ipynb on your machine or colab See full list on github.com Amazon - Beauty dataset is a real-world dataset for recommender systems, containing user-item interactions . We’re on a journey to advance and democratize artificial intelligence through open source and open science. Amazon - dataset -samples A sample dataset of 1001 Amazon products A Amazon dataset sample of over 1000 products. Dataset was extracted using the Bright Data API. Access an Amazon beauty products dataset with reviews, ratings, and insights. Analyze trends, customer feedback, and improve marketing strategies in the beauty industry. 2+ Million records Amazon Beauty Products Dataset along with URL.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kaiboon0216/Amazon-beauty-products-analysis", "content": "Using data science process to gain business insights from the Amazon beauty datasets and determine the factors that contribute to the high sales of a beauty product Our anaylsis are focused on answering the following questions: 1.Does a high number of good ratings contribute to a lower sales rank and does a high number of bad ratings contribute to a higher sales rank ? 2.Which item has the lowest rating and which item has the highest rating ? 3.What are the common words found in the description of highly rated items? 4.What is the relationship between price and rating of an item? See full list on github.com 1.Download the dataset from google drive via this link and put them into the dataset folder 2.Run the PartB_Code_A4.ipynb on your machine or colab See full list on github.com Amazon - Beauty dataset is a real-world dataset for recommender systems, containing user-item interactions . We’re on a journey to advance and democratize artificial intelligence through open source and open science. Amazon - dataset -samples A sample dataset of 1001 Amazon products A Amazon dataset sample of over 1000 products. Dataset was extracted using the Bright Data API. Access an Amazon beauty products dataset with reviews, ratings, and insights. Analyze trends, customer feedback, and improve marketing strategies in the beauty industry. 2+ Million records Amazon Beauty Products Dataset along with URL."}
+{"idx": 1, "title": "Amazon-Beauty - Dataset - LDM", "date": "", "ddg_snippet": "Amazon - Beauty dataset is a real-world dataset for recommender systems, containing user-item interactions .", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/amazon-beauty", "content": "Amazon - Beauty dataset is a real-world dataset for recommender systems, containing user-item interactions ."}
+{"idx": 2, "title": "luminati-io/Amazon-dataset-samples - GitHub", "date": "", "ddg_snippet": "Amazon - dataset -samples A sample dataset of 1001 Amazon products A Amazon dataset sample of over 1000 products. Dataset was extracted using the Bright Data API.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/luminati-io/Amazon-dataset-samples", "content": "Amazon - dataset -samples A sample dataset of 1001 Amazon products A Amazon dataset sample of over 1000 products. Dataset was extracted using the Bright Data API."}
+{"idx": 3, "title": "Amazon Beauty Dataset - brightdata.com", "date": "", "ddg_snippet": "Access an Amazon beauty products dataset with reviews, ratings, and insights. Analyze trends, customer feedback, and improve marketing strategies in the beauty industry.", "subpage_snippet": "", "source": "brightdata.com", "link": "https://brightdata.com/products/datasets/amazon/beauty", "content": "Access an Amazon beauty products dataset with reviews, ratings, and insights. Analyze trends, customer feedback, and improve marketing strategies in the beauty industry."}
+{"idx": 4, "title": "Amazon Beauty Products - Kaggle", "date": "", "ddg_snippet": "2+ Million records Amazon Beauty Products Dataset along with URL.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/satrapankti/amazon-beauty-product-recommendation", "content": "2+ Million records Amazon Beauty Products Dataset along with URL."}
+{"idx": 5, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 6, "title": "The Official Amazon Games Website | Amazon Games", "date": "", "ddg_snippet": "Amazon Game logo in gold.Aug 27, 2025 March of Giants, a New Free-to-Play War MOBA From Amazon Games Montreal, Begins Closed Alpha Testing.", "subpage_snippet": "", "source": "www.amazongames.com", "link": "https://www.amazongames.com/en-us/", "content": "Amazon Game logo in gold.Aug 27, 2025 March of Giants, a New Free-to-Play War MOBA From Amazon Games Montreal, Begins Closed Alpha Testing."}
+{"idx": 7, "title": "Amazon .com: Toys & Games", "date": "", "ddg_snippet": "Interactive toys . Amazon 's Toys & Games store features thousands of products, including dolls, action figures, games , advent calendars, building toys , stuffed animals, and much more.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/toys/b?ie=UTF8&node=165793011", "content": "Interactive toys . Amazon 's Toys & Games store features thousands of products, including dolls, action figures, games , advent calendars, building toys , stuffed animals, and much more."}
+{"idx": 8, "title": "Amazon ’s Best Toys In 2025 List Is Here - Forbes Vetted", "date": "", "ddg_snippet": "Amazon 's annual ' Toys We Love' holiday preview is filled with the retailer's top toys of 2024 for kids of all ages, including 100 new items.", "subpage_snippet": "", "source": "www.forbes.com", "link": "https://www.forbes.com/sites/forbes-personal-shopper/2024/09/11/best-amazon-toys-we-love/", "content": "Amazon 's annual ' Toys We Love' holiday preview is filled with the retailer's top toys of 2024 for kids of all ages, including 100 new items."}
+{"idx": 9, "title": "The Amount Amazon Is Actually Making on AI Will Make Your...", "date": "", "ddg_snippet": "But looking at Amazon as a case study, just a fraction of that centibillion sum will return revenue.Whether Amazon will blink remains to be seen. It's indicated it will spend a total of $100 billion this year in capital expenditures, with the majority of that going towards AWS.", "subpage_snippet": "", "source": "futurism.com", "link": "https://futurism.com/amount-amazon-making-ai", "content": "But looking at Amazon as a case study, just a fraction of that centibillion sum will return revenue.Whether Amazon will blink remains to be seen. It's indicated it will spend a total of $100 billion this year in capital expenditures, with the majority of that going towards AWS."}
diff --git a/data/sampled_jsons/MLD_motion_latent_diffusion_HumanAct12_FID_results_table_Chen_2023_year_2023.jsonl b/data/sampled_jsons/MLD_motion_latent_diffusion_HumanAct12_FID_results_table_Chen_2023_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fe64b5d042cf30117e211bbd9c9894f519f8df05
--- /dev/null
+++ b/data/sampled_jsons/MLD_motion_latent_diffusion_HumanAct12_FID_results_table_Chen_2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ChenFengYe/motion-latent-diffusion: [CVPR 2023] ...", "date": "", "ddg_snippet": "Motion Latent Diffusion ( MLD ) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion", "content": "Motion Latent Diffusion ( MLD ) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of ..."}
+{"idx": 1, "title": "Executing Your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "by X Chen · 2023 · Cited by 494 — Our proposed Motion Latent - based Diffusion model ( MLD ) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf", "content": "by X Chen · 2023 · Cited by 494 — Our proposed Motion Latent - based Diffusion model ( MLD ) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce ... 11 pages"}
+{"idx": 2, "title": "Executing Your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "MLD achieves state-of-the-art accuracy and diversity on UESTC and competitive results on HumanAct12, indicating that diffusion models in motion latent can also ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/poster/23272", "content": "MLD achieves state-of-the-art accuracy and diversity on UESTC and competitive results on HumanAct12, indicating that diffusion models in motion latent can also ..."}
+{"idx": 3, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "TL;DR: In this paper, a motion latent -based diffusion model ( MLD ) is proposed to generate human motion sequences conforming to the given conditional inputs ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/executing-your-commands-via-motion-diffusion-in-latent-space-w2yvt5un?citations_page=8", "content": "TL;DR: In this paper, a motion latent -based diffusion model ( MLD ) is proposed to generate human motion sequences conforming to the given conditional inputs ..."}
+{"idx": 4, "title": "LS-GAN: Human Motion Synthesis with Latent-space GANs", "date": "", "ddg_snippet": "30 Dec 2024 — We perform experiments on the HumanML3D, HumanAct12 benchmarks and demonstrate that a remarkably simple GAN in the latent space achieves a FID ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.01449v1", "content": "30 Dec 2024 — We perform experiments on the HumanML3D, HumanAct12 benchmarks and demonstrate that a remarkably simple GAN in the latent space achieves a FID ..."}
+{"idx": 5, "title": "MoLA: Motion Generation and Editing with Latent Diffusion ...", "date": "", "ddg_snippet": "4 Jun 2024 — MLD ( Chen et al., 2023 ) mitigates this issue by adopting a diffusion model in a low-dimensional latent space provided by a VAE trained on motion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01867v1", "content": "4 Jun 2024 — MLD ( Chen et al., 2023 ) mitigates this issue by adopting a diffusion model in a low-dimensional latent space provided by a VAE trained on motion ..."}
+{"idx": 6, "title": "MOTION FLOW MATCHING FOR EFFICIENT HUMAN ...", "date": "", "ddg_snippet": "MLD ( Chen et al., 2023 ) advances the latent diffusion model (Rombach et al ... We present our results on the action-to- motion dataset HumanAct12 in Table 4. 21 pages", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/61a1228b68fe5cc477b223010e69dce5ad7aff34.pdf", "content": "MLD ( Chen et al., 2023 ) advances the latent diffusion model (Rombach et al ... We present our results on the action-to- motion dataset HumanAct12 in Table 4. 21 pages"}
+{"idx": 7, "title": "MoLA: Motion Generation and Editing with Latent Diffusion ...", "date": "", "ddg_snippet": "MDM and MotionDiffuse adopt diffusion models for motion generation, which leads to better performance in terms of generation quality. However, these methods ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvprw/2025/999400c901/2a1Vlt6q5C8", "content": "MDM and MotionDiffuse adopt diffusion models for motion generation, which leads to better performance in terms of generation quality. However, these methods ..."}
+{"idx": 8, "title": "NERM: LEARNING NEURAL REPRESENTATIONS FOR ...", "date": "", "ddg_snippet": "To be specific, due to the latent diffusion design, MLD ( Chen et al., 2023 ) and our NeRM are much faster than MDM (Tevet et al., 2023 ). We are even faster ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f27c2baf9c383fe987291d41b9a4635d4e0222d7.pdf", "content": "To be specific, due to the latent diffusion design, MLD ( Chen et al., 2023 ) and our NeRM are much faster than MDM (Tevet et al., 2023 ). We are even faster ..."}
+{"idx": 9, "title": "GUESS: GradUally Enriching SyntheSis for Text-Driven ...", "date": "", "ddg_snippet": "by X Gao · 2024 · Cited by 21 — In this article, we propose a novel cascaded diffusion -based generative framework for text-driven human motion synthesis, which exploits a strategy named ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tg/2024/12/10399852/1TJtoS7mAVy", "content": "by X Gao · 2024 · Cited by 21 — In this article, we propose a novel cascaded diffusion -based generative framework for text-driven human motion synthesis, which exploits a strategy named ..."}
diff --git a/data/sampled_jsons/MSVD_dataset_12_frames_text-to-video_retrieval_standard_preprocessing.jsonl b/data/sampled_jsons/MSVD_dataset_12_frames_text-to-video_retrieval_standard_preprocessing.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..db49177eff0146f0bfefd8a42325b976be836145
--- /dev/null
+++ b/data/sampled_jsons/MSVD_dataset_12_frames_text-to-video_retrieval_standard_preprocessing.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Narrating the Video: Boosting Text-Video Retrieval via Comprehensive ...", "date": "", "ddg_snippet": "For preprocessing all datasets , we follow the data prepar-ing process of CLIP4Clip. Also, we uniformly sample each video data at 12 frames for MSR-VTT, MSVD , and VATEX datasets , and sample 32 frames for DiDeMo to cover longer video duration. For the hard negative loss, the hyperparameter settings vary by dataset .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hur_Narrating_the_Video_CVPR_2025_supplemental.pdf", "content": "For preprocessing all datasets , we follow the data prepar-ing process of CLIP4Clip. Also, we uniformly sample each video data at 12 frames for MSR-VTT, MSVD , and VATEX datasets , and sample 32 frames for DiDeMo to cover longer video duration. For the hard negative loss, the hyperparameter settings vary by dataset ."}
+{"idx": 1, "title": "Narrating the Video: Boosting Text-Video Retrieval via ...", "date": "", "ddg_snippet": "7 Mar 2025 — For preprocessing all datasets , we follow the data preparing process of CLIP4Clip. Also, we uniformly sample each video data at 12 frames ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05186v1", "content": "7 Mar 2025 — For preprocessing all datasets , we follow the data preparing process of CLIP4Clip. Also, we uniformly sample each video data at 12 frames ..."}
+{"idx": 2, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "by A Reddy · 2025 · Cited by 5 — In line with previous work, we sample 12 frames for. MSR-VTT, MSVD and VATEX, while using 64 frames for. DiDeMo and ActivityNet. 5.3. Baselines & Evaluation ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "by A Reddy · 2025 · Cited by 5 — In line with previous work, we sample 12 frames for. MSR-VTT, MSVD and VATEX, while using 64 frames for. DiDeMo and ActivityNet. 5.3. Baselines & Evaluation ... 11 pages"}
+{"idx": 3, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "24 Mar 2025 — In line with previous work, we sample 12 frames for MSR-VTT, MSVD and VATEX, while using 64 frames for DiDeMo and ActivityNet. Report issue ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "24 Mar 2025 — In line with previous work, we sample 12 frames for MSR-VTT, MSVD and VATEX, while using 64 frames for DiDeMo and ActivityNet. Report issue ..."}
+{"idx": 4, "title": "Zero-shot Video Question Answering Using a VLM", "date": "", "ddg_snippet": "by W Kim · 2024 · Cited by 78 — Representing a video as text tokens leverage LLMs' proficiency in text ... 12 frames single frame. (w/o image grid). 4 frames. 6 frames. 12 frames . GPU ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/6514899/10802898.pdf", "content": "by W Kim · 2024 · Cited by 78 — Representing a video as text tokens leverage LLMs' proficiency in text ... 12 frames single frame. (w/o image grid). 4 frames. 6 frames. 12 frames . GPU ..."}
+{"idx": 5, "title": "Augmented Partial Mutual Learning with Frame Masking ...", "date": "", "ddg_snippet": "by K Lin · 2021 · Cited by 29 — Hard Masking 12 frames . 98.6. 38.0. Table 5: Ablation study on frame masking. Number of Decoders CIDEr METEOR. 2 Decoders. 108.3. 39.3. 3 Decoders. 108.8. 39.2. 9 pages", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/16301/16301-13-19795-1-2-20210518.pdf", "content": "by K Lin · 2021 · Cited by 29 — Hard Masking 12 frames . 98.6. 38.0. Table 5: Ablation study on frame masking. Number of Decoders CIDEr METEOR. 2 Decoders. 108.3. 39.3. 3 Decoders. 108.8. 39.2. 9 pages"}
+{"idx": 6, "title": "MGSGA: Multi-grained and Semantic-Guided Alignment for ...", "date": "", "ddg_snippet": "by X Wu · 2024 · Cited by 2 — We use CLIP (ViT-B/16) to initialize the parameters of text encoder and video encoder. We uniformly sample 12 frames for each video . The max ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11063-024-11468-5", "content": "by X Wu · 2024 · Cited by 2 — We use CLIP (ViT-B/16) to initialize the parameters of text encoder and video encoder. We uniformly sample 12 frames for each video . The max ..."}
+{"idx": 7, "title": "A cross-modal conditional mechanism based on attention ...", "date": "", "ddg_snippet": "by W Du · Cited by 2 — This study extracts 12 frames from each video according to a specified time window to resolve this issue. It uses them as representatives of ...", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/aimspress-data/mbe/2023/11/PDF/mbe-20-11-889.pdf", "content": "by W Du · Cited by 2 — This study extracts 12 frames from each video according to a specified time window to resolve this issue. It uses them as representatives of ..."}
+{"idx": 8, "title": "Proceedings of the 29th ACM International Conference on ...", "date": "", "ddg_snippet": "... 12 frames , the TokShift transformer achieves SOTA precision: 79.83%/80.40% on the Kinetics-400, 66.56% on EGTEA-Gaze+, and 96.80% on UCF-101 datasets ,", "subpage_snippet": "", "source": "www.sigmm.org", "link": "http://www.sigmm.org/opentoc/MM2021-TOC", "content": "... 12 frames , the TokShift transformer achieves SOTA precision: 79.83%/80.40% on the Kinetics-400, 66.56% on EGTEA-Gaze+, and 96.80% on UCF-101 datasets ,"}
+{"idx": 9, "title": "Proceedings of 2019 Chinese Intelligent Systems ...", "date": "", "ddg_snippet": "Then we obtain the dataset by the medical pathological image preprocessing system. ... 12 frames per second when the left and right image resolutions are 1280 720 ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/proceedings-of-2019-chinese-intelligent-systems-conference-volume-ii-1st-ed-2020-978-981-32-9685-5-978-981-32-9686-2.html", "content": "Then we obtain the dataset by the medical pathological image preprocessing system. ... 12 frames per second when the left and right image resolutions are 1280 720 ..."}
diff --git a/data/sampled_jsons/Ma_et_al._fairness_online_matching_markets_2028.jsonl b/data/sampled_jsons/Ma_et_al._fairness_online_matching_markets_2028.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5be3be7897a3324a0c72e756b18ed70e2d8dfdf8
--- /dev/null
+++ b/data/sampled_jsons/Ma_et_al._fairness_online_matching_markets_2028.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ma (2019 film) - Wikipedia", "date": "", "ddg_snippet": "Ma is a 2019 American psychological thriller film directed by Tate Taylor and written by Scotty Landes. It stars Octavia Spencer, Juliette Lewis, Diana Silvers, Corey Fogelmanis, Gianni Paolo, McKaley Miller and Luke Evans and follows a group of teenagers who befriend a lonely middle-aged woman.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Ma_(2019_film)", "content": "Ma is a 2019 American psychological thriller film directed by Tate Taylor and written by Scotty Landes. It stars Octavia Spencer, Juliette Lewis, Diana Silvers, Corey Fogelmanis, Gianni Paolo, McKaley Miller and Luke Evans and follows a group of teenagers who befriend a lonely middle-aged woman."}
+{"idx": 1, "title": "Google Maps", "date": "", "ddg_snippet": "Find local businesses, view maps and get driving directions in Google Maps.", "subpage_snippet": "", "source": "maps.google.com", "link": "https://maps.google.com/", "content": "Find local businesses, view maps and get driving directions in Google Maps."}
+{"idx": 2, "title": "Ma (2019) - IMDb", "date": "", "ddg_snippet": "May 31, 2019 · From torture to face stitching: Ma has it all! Octavia Spencer, her co-stars, and director Tate Taylor break down how they translated the script's shocking moments to the screen.", "subpage_snippet": "", "source": "m.imdb.com", "link": "https://m.imdb.com/title/tt7958736", "content": "May 31, 2019 · From torture to face stitching: Ma has it all! Octavia Spencer, her co-stars, and director Tate Taylor break down how they translated the script's shocking moments to the screen."}
+{"idx": 3, "title": "Colorado Marijuana Anonymous – Colorado Marijuana Anonymous", "date": "", "ddg_snippet": "MA is not affiliated with any religious or secular institution or organization and has no opinion on any outside controversies or causes. Our primary purpose is to stay free of marijuana and to help the marijuana addict who still suffers achieve the same freedom.", "subpage_snippet": "", "source": "www.ma-colorado.org", "link": "https://www.ma-colorado.org/", "content": "MA is not affiliated with any religious or secular institution or organization and has no opinion on any outside controversies or causes. Our primary purpose is to stay free of marijuana and to help the marijuana addict who still suffers achieve the same freedom."}
+{"idx": 4, "title": "MA Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of MA is mother. How to use ma in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/ma", "content": "The meaning of MA is mother. How to use ma in a sentence."}
+{"idx": 5, "title": "Massachusetts | State Facts & History - Infoplease", "date": "", "ddg_snippet": "Nov 30, 2023 · Information on Massachusetts 's economy, government, culture, state map and flag, major cities, points of interest, famous residents, state motto, symbols, nicknames ...", "subpage_snippet": "", "source": "www.infoplease.com", "link": "https://www.infoplease.com/us/states/massachusetts", "content": "Nov 30, 2023 · Information on Massachusetts 's economy, government, culture, state map and flag, major cities, points of interest, famous residents, state motto, symbols, nicknames ..."}
+{"idx": 6, "title": "What is an MA Degree? | SNHU", "date": "", "ddg_snippet": "Aug 11, 2025 · An MA degree is a Master of Arts — a type of graduate degree that's generally associated with subjects in the arts and humanities.", "subpage_snippet": "", "source": "www.snhu.edu", "link": "https://www.snhu.edu/about-us/newsroom/education/what-is-an-ma-degree", "content": "Aug 11, 2025 · An MA degree is a Master of Arts — a type of graduate degree that's generally associated with subjects in the arts and humanities."}
+{"idx": 7, "title": "Watch Ma | Prime Video - amazon.com", "date": "", "ddg_snippet": "Ma (Octavia Spencer) offers the basement of her home for some kids to party, but her hospitality curdles into obsession, turning a teenage dream into a terrorizing nightmare.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/Ma-Octavia-Spencer/dp/B07SMBD3M4", "content": "Ma (Octavia Spencer) offers the basement of her home for some kids to party, but her hospitality curdles into obsession, turning a teenage dream into a terrorizing nightmare."}
+{"idx": 8, "title": "MA - Definition by AcronymFinder", "date": "", "ddg_snippet": "What does MA stand for? MA abbreviation. Define MA at AcronymFinder.com.", "subpage_snippet": "", "source": "www.acronymfinder.com", "link": "https://www.acronymfinder.com/Ma.html", "content": "What does MA stand for? MA abbreviation. Define MA at AcronymFinder.com."}
+{"idx": 9, "title": "MA | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "My brother has an MA in linguistics. She's studying for/doing an MA in French literature.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/ma", "content": "My brother has an MA in linguistics. She's studying for/doing an MA in French literature."}
diff --git a/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics-_A_Suite_of_Datasets_Capturing_Research-level_Conject.jsonl b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics-_A_Suite_of_Datasets_Capturing_Research-level_Conject.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3879e2e59f22f88b586a82fa4c8e8714e82c1a1
--- /dev/null
+++ b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics-_A_Suite_of_Datasets_Capturing_Research-level_Conject.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine - Wikipedia", "date": "", "ddg_snippet": "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines .", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Machine", "content": "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines ."}
+{"idx": 1, "title": "MACHINE Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Machine definition: an apparatus consisting of interrelated parts with separate functions, used in the performance of some kind of work.. See examples of MACHINE used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/machine", "content": "Machine definition: an apparatus consisting of interrelated parts with separate functions, used in the performance of some kind of work.. See examples of MACHINE used in a sentence."}
+{"idx": 2, "title": "MACHINE Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of MACHINE is a mechanically, electrically, or electronically operated device for performing a task. How to use machine in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/machine", "content": "The meaning of MACHINE is a mechanically, electrically, or electronically operated device for performing a task. How to use machine in a sentence."}
+{"idx": 3, "title": "Machine | Definition, Mechanisms & Efficiency | Britannica", "date": "", "ddg_snippet": "machine , device, having a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/technology/machine", "content": "machine , device, having a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks."}
+{"idx": 4, "title": "MACHINE | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "MACHINE definition: 1. a piece of equipment with several moving parts that uses power to do a particular type of work…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/machine", "content": "MACHINE definition: 1. a piece of equipment with several moving parts that uses power to do a particular type of work…. Learn more."}
+{"idx": 5, "title": "Machine - definition of machine by The Free Dictionary", "date": "", "ddg_snippet": "Of, relating to, or felt to resemble a machine : machine repairs; machine politics.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/machine", "content": "Of, relating to, or felt to resemble a machine : machine repairs; machine politics."}
+{"idx": 6, "title": "What Is A Machine ? Its Types and How it Works - Mech Lesson", "date": "", "ddg_snippet": "A machine is a mechanical device that uses power to apply force and control motion to perform work efficiently . Machines range from simple tools like pulleys and levers to complex systems like engines, robots, and manufacturing equipment.", "subpage_snippet": "", "source": "mechlesson.com", "link": "https://mechlesson.com/machine/", "content": "A machine is a mechanical device that uses power to apply force and control motion to perform work efficiently . Machines range from simple tools like pulleys and levers to complex systems like engines, robots, and manufacturing equipment."}
+{"idx": 7, "title": "What is a Machine ? - Computer Hope", "date": "", "ddg_snippet": "Jun 1, 2025 · A machine is a device with several parts that work together to perform a task. A machine can augment or replace the efforts required by an animal or human to make it easier to complete a task. For example, a car is a complex machine that transports humans and other things.", "subpage_snippet": "", "source": "www.computerhope.com", "link": "https://www.computerhope.com/jargon/m/machine.htm", "content": "Jun 1, 2025 · A machine is a device with several parts that work together to perform a task. A machine can augment or replace the efforts required by an animal or human to make it easier to complete a task. For example, a car is a complex machine that transports humans and other things."}
+{"idx": 8, "title": "Machine - New World Encyclopedia", "date": "", "ddg_snippet": "Modern power tools, automated machine tools, and human-operated power machinery are tools that are also machines . Machines used to transform heat or other energy into mechanical energy are known as engines.", "subpage_snippet": "", "source": "www.newworldencyclopedia.org", "link": "https://www.newworldencyclopedia.org/entry/Machine", "content": "Modern power tools, automated machine tools, and human-operated power machinery are tools that are also machines . Machines used to transform heat or other energy into mechanical energy are known as engines."}
+{"idx": 9, "title": "What is Machine ? – An Essential Guide", "date": "", "ddg_snippet": "The machine is a device that has a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks.", "subpage_snippet": "", "source": "www.theengineeringchoice.com", "link": "https://www.theengineeringchoice.com/what-is-machine/", "content": "The machine is a device that has a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks."}
diff --git a/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics.jsonl b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1a244d5e7bc0d28c913502c1970b66a7cdfbac56
--- /dev/null
+++ b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.06366] Machine Learning meets Algebraic Combinatorics: A", "date": "", "ddg_snippet": "View a PDF of the paper titled Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "View a PDF of the paper titled Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in ..."}
+{"idx": 1, "title": "Algebra | Combinatorics and more", "date": "", "ddg_snippet": "Posted in Algebra , Analysis , Combinatorics , Computer Science and Optimization , Games , Logic and set theory , Number theory , Updates | Tagged ...", "subpage_snippet": "", "source": "gilkalai.wordpress.com", "link": "https://gilkalai.wordpress.com/category/algebra/", "content": "Posted in Algebra , Analysis , Combinatorics , Computer Science and Optimization , Games , Logic and set theory , Number theory , Updates | Tagged ..."}
+{"idx": 2, "title": "Download Algebraic And Geometric Combinatorics", "date": "", "ddg_snippet": "If the download algebraic and geometric academic belief meets well driven successfully less than the including fish, the process will Make to update ...", "subpage_snippet": "", "source": "LKQATV.com", "link": "http://LKQATV.com/library/download-algebraic-and-geometric-combinatorics.html", "content": "If the download algebraic and geometric academic belief meets well driven successfully less than the including fish, the process will Make to update ..."}
+{"idx": 3, "title": "Book Algebraic And Geometric Combinatorics", "date": "", "ddg_snippet": "My book algebraic and geometric combinatorics suggests to take the file to JavaScript painful and Greek sands of the National Chi Alpha man file.", "subpage_snippet": "", "source": "peachmusic.com", "link": "http://peachmusic.com/live/graphics/socialmedia/book/book-algebraic-and-geometric-combinatorics/", "content": "My book algebraic and geometric combinatorics suggests to take the file to JavaScript painful and Greek sands of the National Chi Alpha man file."}
+{"idx": 4, "title": "Joram’s seminar 2025: Hypercontractivity, Groups and", "date": "", "ddg_snippet": "Nathan explained how Erdős–Ko–Rado-type problems about permutations in combinatorics and problems about products of conjugacy classes in group ...", "subpage_snippet": "", "source": "gilkalai.wordpress.com", "link": "https://gilkalai.wordpress.com/2025/07/15/jorams-seminar-2025-hypercontractivity-groups-and-representations/", "content": "Nathan explained how Erdős–Ko–Rado-type problems about permutations in combinatorics and problems about products of conjugacy classes in group ..."}
+{"idx": 5, "title": "synthical.com/search/by_author/Davis%20Brown", "date": "", "ddg_snippet": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/search/by_author/Davis+Brown", "content": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics"}
+{"idx": 6, "title": "Talks — Yue Ren (Durham University) - Tropical and", "date": "", "ddg_snippet": "Workshop on Algebraic Geometry, Combinatorics , and Machine Learning 2022, Leipzig, Germany - Tropical geometry and linear regions of maxout networks ...", "subpage_snippet": "", "source": "www.yueren.de", "link": "https://www.yueren.de/talks", "content": "Workshop on Algebraic Geometry, Combinatorics , and Machine Learning 2022, Leipzig, Germany - Tropical geometry and linear regions of maxout networks ..."}
+{"idx": 7, "title": "Course on “Non-Commutative Distributions”, summer", "date": "", "ddg_snippet": "... Random Matrices and Machine Learning ... ACPMS Seminar on Algebraic and Combinatorial Perspectives in the Mathematical Sciences (Fridays, online)", "subpage_snippet": "", "source": "rolandspeicher.com", "link": "https://rolandspeicher.com/lectures/lectures-ncd19/", "content": "... Random Matrices and Machine Learning ... ACPMS Seminar on Algebraic and Combinatorial Perspectives in the Mathematical Sciences (Fridays, online)"}
+{"idx": 8, "title": "AMS 2024 Spring Eastern Sectional Meeting", "date": "", "ddg_snippet": "Algebra , Combinatorics , Topology ... Combinatorics (9) ... Machine learning (3)", "subpage_snippet": "", "source": "mathdwight.com", "link": "https://mathdwight.com/happenings/spring24organizing8/", "content": "Algebra , Combinatorics , Topology ... Combinatorics (9) ... Machine learning (3)"}
+{"idx": 9, "title": "Brian's Schedule, Fall 2006", "date": "", "ddg_snippet": "Introduction to Machine Learning CSCE 878 Avery Hall 19 ... Introduction to Modern Algebra I MATH 817 Avery Hall 111", "subpage_snippet": "", "source": "www.zifyoip.com", "link": "https://www.zifyoip.com/me/sched/2006/fall.html", "content": "Introduction to Machine Learning CSCE 878 Avery Hall 19 ... Introduction to Modern Algebra I MATH 817 Avery Hall 111"}
diff --git a/data/sampled_jsons/Magnani_2022_Fourier_Neural_Operators_FNO.jsonl b/data/sampled_jsons/Magnani_2022_Fourier_Neural_Operators_FNO.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d6db2a844d7eeab398cd770531c76b647add1f75
--- /dev/null
+++ b/data/sampled_jsons/Magnani_2022_Fourier_Neural_Operators_FNO.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — More closely related to our work, Magnani et al. (2022 ) developed uncer- tainty estimates for graph neural operators using Laplace approximation, though their ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — More closely related to our work, Magnani et al. (2022 ) developed uncer- tainty estimates for graph neural operators using Laplace approximation, though their ..."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "Poster linearization turns neural operators into function-valued Gaussian processes . Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Poster linearization turns neural operators into function-valued Gaussian processes . Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig."}
+{"idx": 2, "title": "Probabilistic neural operators for functional uncertainty ...", "date": "", "ddg_snippet": "18 Feb 2025 — In this paper, we introduce the probabilistic neural operator (PNO), a framework for learning probability distributions over the output function space of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12902v1", "content": "18 Feb 2025 — In this paper, we introduce the probabilistic neural operator (PNO), a framework for learning probability distributions over the output function space of ..."}
+{"idx": 3, "title": "Probabilistic predictions with Fourier neural operators", "date": "", "ddg_snippet": "by C Bülte · Cited by 1 — Weber et al. [2024] and Magnani et al. [2024] use a Laplace approximation for the Fourier neural operator , which utilizes a linearized neural ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=orKA6gJwlB", "content": "by C Bülte · Cited by 1 — Weber et al. [2024] and Magnani et al. [2024] use a Laplace approximation for the Fourier neural operator , which utilizes a linearized neural ..."}
+{"idx": 4, "title": "Neural Operator induced Gaussian Process framework for ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 5 — We propose a novel Neural Operator -induced Gaussian Process (NOGaP), which exploits the probabilistic characteristics of Gaussian Processes (GPs)", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0045782524005218", "content": "by S Kumar · 2024 · Cited by 5 — We propose a novel Neural Operator -induced Gaussian Process (NOGaP), which exploits the probabilistic characteristics of Gaussian Processes (GPs)"}
+{"idx": 5, "title": "Fourier Neural Operators for Accelerating Earthquake", "date": "", "ddg_snippet": "The Fourier Neural Operator ( FNO ) is a learning-based operator designed to map ... cilitates efficient training of the FNO model (Cuomo et al., 2022 ). ... Magnani , ...", "subpage_snippet": "", "source": "essopenarchive.org", "link": "https://essopenarchive.org/users/909034/articles/1284813/master/file/data/1029261_0_merged_1743618143/1029261_0_merged_1743618143.pdf", "content": "The Fourier Neural Operator ( FNO ) is a learning-based operator designed to map ... cilitates efficient training of the FNO model (Cuomo et al., 2022 ). ... Magnani , ..."}
+{"idx": 6, "title": "[PDF] Spherical Fourier Neural Operators: Learning Stable ...", "date": "", "ddg_snippet": "6 Jun 2023 — Spherical FNOs (SFNOs ) are introduced, a generalization of Fourier Neural Operators on the sphere, which has important implications for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/f3338a66e93b62d60ee01aa00ed798c9cc9582d9", "content": "6 Jun 2023 — Spherical FNOs (SFNOs ) are introduced, a generalization of Fourier Neural Operators on the sphere, which has important implications for ..."}
+{"idx": 7, "title": "Using Uncertainty Quantification to Characterize and ...", "date": "", "ddg_snippet": "15 Mar 2024 — We consider the Fourier Neural Operator (FNO ) (Li et al., 2022a) as our base model, and we evaluate the OOD performance of several UQ methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.10642v1", "content": "15 Mar 2024 — We consider the Fourier Neural Operator (FNO ) (Li et al., 2022a) as our base model, and we evaluate the OOD performance of several UQ methods."}
+{"idx": 8, "title": "using-uncertainty-quantification-to-characterize-and- ...", "date": "", "ddg_snippet": "by SC Mouli · 2024 · Cited by 12 — We use the Fourier Neural Operator (FNO ) (Li et al., 2022a) as a base model and leave similar investi- gations of other operators to future work. To provide ... 47 pages", "subpage_snippet": "", "source": "assets.amazon.science", "link": "https://assets.amazon.science/cf/ec/2b04154b46289411e802bab9ee77/using-uncertainty-quantification-to-characterize-and-improve-out-of-domain-learning-for-pdes.pdf", "content": "by SC Mouli · 2024 · Cited by 12 — We use the Fourier Neural Operator (FNO ) (Li et al., 2022a) as a base model and leave similar investi- gations of other operators to future work. To provide ... 47 pages"}
+{"idx": 9, "title": "Towards Gaussian Process for operator learning", "date": "", "ddg_snippet": "by S Kumar · 2025 · Cited by 6 — Popular kernel integration-based neural operator includes Graph Neural Operator (GNO) [15], Fourier Neural Operator (FNO ) [8], Laplace Neural Operator (LNO) [16] ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0045782524009186", "content": "by S Kumar · 2025 · Cited by 6 — Popular kernel integration-based neural operator includes Graph Neural Operator (GNO) [15], Fourier Neural Operator (FNO ) [8], Laplace Neural Operator (LNO) [16] ..."}
diff --git a/data/sampled_jsons/Magnani_2022_uncertainty_estimation_neural_operators_abstract.jsonl b/data/sampled_jsons/Magnani_2022_uncertainty_estimation_neural_operators_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9c7582c7011e4bbb1a5db4f3be638a29c6858f90
--- /dev/null
+++ b/data/sampled_jsons/Magnani_2022_uncertainty_estimation_neural_operators_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2208.01565] Approximate Bayesian Neural Operators: Uncertainty", "date": "", "ddg_snippet": "As a result, our approach is able to identify cases, and provide structured uncertainty estimates , where the neural operator fails to predict well.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2208.01565", "content": "As a result, our approach is able to identify cases, and provide structured uncertainty estimates , where the neural operator fails to predict well."}
+{"idx": 1, "title": "Light-Weight Diffusion Multiplier and Uncertainty", "date": "", "ddg_snippet": "... over those time parameters, we cast DINOZAUR as a Bayesian neural operator to yield spatially correlated outputs and calibrated uncertainty estimates ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.00643v1", "content": "... over those time parameters, we cast DINOZAUR as a Bayesian neural operator to yield spatially correlated outputs and calibrated uncertainty estimates ..."}
+{"idx": 2, "title": "A composition of simplified physics-based model with neural", "date": "", "ddg_snippet": "Specifically, the proposed approach introduces a simplified physics-based simulator/ operator between the seismic input and the neural operator .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10569v1", "content": "Specifically, the proposed approach introduces a simplified physics-based simulator/ operator between the seismic input and the neural operator ."}
+{"idx": 3, "title": "Neurons and Cognition Feb 2022", "date": "", "ddg_snippet": "... of this article, now retracted ( this https URL PhysRevX.12.041007), contained errors described in the retraction notice ( this https URL abstract ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/q-bio.NC/2022-02", "content": "... of this article, now retracted ( this https URL PhysRevX.12.041007), contained errors described in the retraction notice ( this https URL abstract ..."}
+{"idx": 4, "title": "1 Introduction", "date": "", "ddg_snippet": "... estimate Bayesian posterior probability density functions ... To enable uncertainty estimate in neural network-based inversions, Devilee et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.08080v1", "content": "... estimate Bayesian posterior probability density functions ... To enable uncertainty estimate in neural network-based inversions, Devilee et al."}
+{"idx": 5, "title": "OLS regression models explaining egocentric distances to", "date": "", "ddg_snippet": "... images abstraction level interacted with people s TV news consumption to influence their egocentric hypothetical distance (perceived uncertainty ) of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/OLS-regression-models-explaining-egocentric-distances-to-climate-change-N-402_tbl1_334772142", "content": "... images abstraction level interacted with people s TV news consumption to influence their egocentric hypothetical distance (perceived uncertainty ) of ..."}
+{"idx": 6, "title": "329559 PDFs | Review articles in CONVERGENCE", "date": "", "ddg_snippet": "This paper presents a novel approach for pointwise estimation of multivariate density functions on known domains of arbitrary dimensions using ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Convergence/publications", "content": "This paper presents a novel approach for pointwise estimation of multivariate density functions on known domains of arbitrary dimensions using ..."}
+{"idx": 7, "title": "Risks, MDPI | IDEAS/RePEc", "date": "", "ddg_snippet": "For technical questions regarding this item, or to correct its authors, title, abstract , bibliographic or download information, contact: MDPI ...", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/s/gam/jrisks.html", "content": "For technical questions regarding this item, or to correct its authors, title, abstract , bibliographic or download information, contact: MDPI ..."}
+{"idx": 8, "title": "Summer Undergrad Research Experience | Department of", "date": "", "ddg_snippet": "At the conclusion of their projects, students will produce an abstract under the supervision of their faculty mentor and will then present their ...", "subpage_snippet": "", "source": "math.nyu.edu", "link": "https://math.nyu.edu/dynamic/undergrad/ba-cas/activities-research/summer-undergraduate-research-experience/", "content": "At the conclusion of their projects, students will produce an abstract under the supervision of their faculty mentor and will then present their ..."}
+{"idx": 9, "title": "AMT - Relations - Impact of temperature field inhomogeneities", "date": "", "ddg_snippet": "This study estimated the effects of aerosols on GHGSat satellite methane retrieval and investigated the performance of simultaneously retrieving ...", "subpage_snippet": "", "source": "amt.copernicus.org", "link": "https://amt.copernicus.org/articles/3/1487/2010/amt-3-1487-2010-relations.html", "content": "This study estimated the effects of aerosols on GHGSat satellite methane retrieval and investigated the performance of simultaneously retrieving ..."}
diff --git a/data/sampled_jsons/Marvin_Li_Aayush_Karan_Sitan_Chen_blink_of_an_eye_github_code.jsonl b/data/sampled_jsons/Marvin_Li_Aayush_Karan_Sitan_Chen_blink_of_an_eye_github_code.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..23d0f9d7040b04559906a7ff1da6ac71543a6ff1
--- /dev/null
+++ b/data/sampled_jsons/Marvin_Li_Aayush_Karan_Sitan_Chen_blink_of_an_eye_github_code.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Windows and Doors | Window and Door Manufacturer | Marvin", "date": "", "ddg_snippet": "Marvin creates windows and doors inspired by how people live. Explore our product lines and enhance your new construction, remodel or replacement project.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/", "content": "Marvin creates windows and doors inspired by how people live. Explore our product lines and enhance your new construction, remodel or replacement project."}
+{"idx": 1, "title": "Products - Marvin", "date": "", "ddg_snippet": "Marvin windows are designed for unprecedented performance and exceptional aesthetics. Our expansive window portfolio offers everything from classic double hungs to a full line of hurricane impact rated products.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/products", "content": "Marvin windows are designed for unprecedented performance and exceptional aesthetics. Our expansive window portfolio offers everything from classic double hungs to a full line of hurricane impact rated products."}
+{"idx": 2, "title": "Compare Window and Door Collections | Marvin", "date": "", "ddg_snippet": "Marvin windows and doors are inspired by how people live. Explore our three product collections organized by degree of design detail, flexibility, and customization possibilities.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/products/collections", "content": "Marvin windows and doors are inspired by how people live. Explore our three product collections organized by degree of design detail, flexibility, and customization possibilities."}
+{"idx": 3, "title": "Windows - Marvin", "date": "", "ddg_snippet": "From a classic double hung to casements in industry-leading sizes, Marvin offers window style options and high-quality material choices for any project.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/products/windows", "content": "From a classic double hung to casements in industry-leading sizes, Marvin offers window style options and high-quality material choices for any project."}
+{"idx": 4, "title": "Find a Dealer | Marvin", "date": "", "ddg_snippet": "Find a Marvin window and door dealer near you to visit a showroom, discuss design and performance options, and request pricing.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/find-a-dealer", "content": "Find a Marvin window and door dealer near you to visit a showroom, discuss design and performance options, and request pricing."}
+{"idx": 5, "title": "Doors - Marvin", "date": "", "ddg_snippet": "From French doors to exterior patio doors that slide or swing, Marvin doors are designed to offer uncompromising performance to any project.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/products/doors", "content": "From French doors to exterior patio doors that slide or swing, Marvin doors are designed to offer uncompromising performance to any project."}
+{"idx": 6, "title": "Marvin Customer Support | Marvin", "date": "", "ddg_snippet": "Everything you need to spec your Marvin project including technical specifications, performance data, installation resources, product brochures, and more.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/support", "content": "Everything you need to spec your Marvin project including technical specifications, performance data, installation resources, product brochures, and more."}
+{"idx": 7, "title": "Technical Specifications & Installation Instructions | Marvin", "date": "", "ddg_snippet": "Find technical specifications, installation instructions, construction specifications (CSI), CAD/BIM files and parts manuals for all Marvin products.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/support/technical-specifications", "content": "Find technical specifications, installation instructions, construction specifications (CSI), CAD/BIM files and parts manuals for all Marvin products."}
+{"idx": 8, "title": "Marvin Connected Home - Smart Windows & Automatic Doors | Marvin", "date": "", "ddg_snippet": "Discover Marvin Connected home products, redefining convenience with automated awning windows, smart multi-slide doors, and more.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/solutions/connected-home", "content": "Discover Marvin Connected home products, redefining convenience with automated awning windows, smart multi-slide doors, and more."}
+{"idx": 9, "title": "Marvin Opens New Distribution Center in CT | Marvin", "date": "", "ddg_snippet": "Jan 8, 2025 · Marvin opened its newest distribution center at 205 Baker Hollow Road in Windsor, Connecticut. This expansion will enhance the efficiency of Marvin ’s distribution network and create additional jobs in the region while providing four-times the operating footprint of Marvin ’s previous area facility.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/news/marvin-opens-new-distribution-center-in-windsor-connecticut", "content": "Jan 8, 2025 · Marvin opened its newest distribution center at 205 Baker Hollow Road in Windsor, Connecticut. This expansion will enhance the efficiency of Marvin ’s distribution network and create additional jobs in the region while providing four-times the operating footprint of Marvin ’s previous area facility."}
diff --git a/data/sampled_jsons/Marvin_Li_github_blink_of_an_eye.jsonl b/data/sampled_jsons/Marvin_Li_github_blink_of_an_eye.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..23d0f9d7040b04559906a7ff1da6ac71543a6ff1
--- /dev/null
+++ b/data/sampled_jsons/Marvin_Li_github_blink_of_an_eye.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Windows and Doors | Window and Door Manufacturer | Marvin", "date": "", "ddg_snippet": "Marvin creates windows and doors inspired by how people live. Explore our product lines and enhance your new construction, remodel or replacement project.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/", "content": "Marvin creates windows and doors inspired by how people live. Explore our product lines and enhance your new construction, remodel or replacement project."}
+{"idx": 1, "title": "Products - Marvin", "date": "", "ddg_snippet": "Marvin windows are designed for unprecedented performance and exceptional aesthetics. Our expansive window portfolio offers everything from classic double hungs to a full line of hurricane impact rated products.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/products", "content": "Marvin windows are designed for unprecedented performance and exceptional aesthetics. Our expansive window portfolio offers everything from classic double hungs to a full line of hurricane impact rated products."}
+{"idx": 2, "title": "Compare Window and Door Collections | Marvin", "date": "", "ddg_snippet": "Marvin windows and doors are inspired by how people live. Explore our three product collections organized by degree of design detail, flexibility, and customization possibilities.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/products/collections", "content": "Marvin windows and doors are inspired by how people live. Explore our three product collections organized by degree of design detail, flexibility, and customization possibilities."}
+{"idx": 3, "title": "Windows - Marvin", "date": "", "ddg_snippet": "From a classic double hung to casements in industry-leading sizes, Marvin offers window style options and high-quality material choices for any project.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/products/windows", "content": "From a classic double hung to casements in industry-leading sizes, Marvin offers window style options and high-quality material choices for any project."}
+{"idx": 4, "title": "Find a Dealer | Marvin", "date": "", "ddg_snippet": "Find a Marvin window and door dealer near you to visit a showroom, discuss design and performance options, and request pricing.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/find-a-dealer", "content": "Find a Marvin window and door dealer near you to visit a showroom, discuss design and performance options, and request pricing."}
+{"idx": 5, "title": "Doors - Marvin", "date": "", "ddg_snippet": "From French doors to exterior patio doors that slide or swing, Marvin doors are designed to offer uncompromising performance to any project.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/products/doors", "content": "From French doors to exterior patio doors that slide or swing, Marvin doors are designed to offer uncompromising performance to any project."}
+{"idx": 6, "title": "Marvin Customer Support | Marvin", "date": "", "ddg_snippet": "Everything you need to spec your Marvin project including technical specifications, performance data, installation resources, product brochures, and more.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/support", "content": "Everything you need to spec your Marvin project including technical specifications, performance data, installation resources, product brochures, and more."}
+{"idx": 7, "title": "Technical Specifications & Installation Instructions | Marvin", "date": "", "ddg_snippet": "Find technical specifications, installation instructions, construction specifications (CSI), CAD/BIM files and parts manuals for all Marvin products.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/support/technical-specifications", "content": "Find technical specifications, installation instructions, construction specifications (CSI), CAD/BIM files and parts manuals for all Marvin products."}
+{"idx": 8, "title": "Marvin Connected Home - Smart Windows & Automatic Doors | Marvin", "date": "", "ddg_snippet": "Discover Marvin Connected home products, redefining convenience with automated awning windows, smart multi-slide doors, and more.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/solutions/connected-home", "content": "Discover Marvin Connected home products, redefining convenience with automated awning windows, smart multi-slide doors, and more."}
+{"idx": 9, "title": "Marvin Opens New Distribution Center in CT | Marvin", "date": "", "ddg_snippet": "Jan 8, 2025 · Marvin opened its newest distribution center at 205 Baker Hollow Road in Windsor, Connecticut. This expansion will enhance the efficiency of Marvin ’s distribution network and create additional jobs in the region while providing four-times the operating footprint of Marvin ’s previous area facility.", "subpage_snippet": "", "source": "www.marvin.com", "link": "https://www.marvin.com/news/marvin-opens-new-distribution-center-in-windsor-connecticut", "content": "Jan 8, 2025 · Marvin opened its newest distribution center at 205 Baker Hollow Road in Windsor, Connecticut. This expansion will enhance the efficiency of Marvin ’s distribution network and create additional jobs in the region while providing four-times the operating footprint of Marvin ’s previous area facility."}
diff --git a/data/sampled_jsons/Masked_Autoregressive_Flow_MAF_architecture_MLP_neural_network.jsonl b/data/sampled_jsons/Masked_Autoregressive_Flow_MAF_architecture_MLP_neural_network.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2b1cdb760fb23fc7781dede84397624f96e3b718
--- /dev/null
+++ b/data/sampled_jsons/Masked_Autoregressive_Flow_MAF_architecture_MLP_neural_network.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Masked Singer (American TV series) season 13 - Wikipedia", "date": "", "ddg_snippet": "The thirteenth season of the American television series The Masked Singer premiered on Fox on February 12, 2025, and concluded on May 7, 2025.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/The_Masked_Singer_(American_TV_series)_season_13", "content": "The thirteenth season of the American television series The Masked Singer premiered on Fox on February 12, 2025, and concluded on May 7, 2025."}
+{"idx": 1, "title": "'The Masked Singer' Season 14 Delayed, Won't Be on Fox in ...", "date": "", "ddg_snippet": "May 7, 2025 · The Masked Singer is taking the rest of 2025 off. For the first time since it launched in 2019, Fox will rest the veteran reality competition series and forgo a fall cycle.", "subpage_snippet": "", "source": "tvline.com", "link": "https://tvline.com/news/the-masked-singer-season-14-delayed-2026-premiere-fox-1235446911/", "content": "May 7, 2025 · The Masked Singer is taking the rest of 2025 off. For the first time since it launched in 2019, Fox will rest the veteran reality competition series and forgo a fall cycle."}
+{"idx": 2, "title": "'The Masked Singer' season 13 reveals: See every unmasked ...", "date": "", "ddg_snippet": "May 8, 2025 · Check out every celebrity eliminated and revealed on 'The Masked Singer' season 13, including Candace Cameron Bure, Matthew Lawrence, Flavor Flav, James Van Der Beek, Cedric the Entertainer, and...", "subpage_snippet": "", "source": "ew.com", "link": "https://ew.com/the-masked-singer-season-13-reveals-8788742", "content": "May 8, 2025 · Check out every celebrity eliminated and revealed on 'The Masked Singer' season 13, including Candace Cameron Bure, Matthew Lawrence, Flavor Flav, James Van Der Beek, Cedric the Entertainer, and..."}
+{"idx": 3, "title": "The Masked Singer Season 14 (2026): Everything to Know - Parade", "date": "", "ddg_snippet": "May 8, 2025 · Read on to find out everything we know about The Masked Singer Season 13, including when the season premieres, the costumes from this celebrity batch, and much more.", "subpage_snippet": "", "source": "parade.com", "link": "https://parade.com/tv/the-masked-singer-season-14-2026", "content": "May 8, 2025 · Read on to find out everything we know about The Masked Singer Season 13, including when the season premieres, the costumes from this celebrity batch, and much more."}
+{"idx": 4, "title": "The Real Reason Why ‘The Masked Singer’ Is Skipping Fall 2025 ...", "date": "", "ddg_snippet": "May 12, 2025 · Now, an executive at Fox is explaining the reasoning behind the decision. Fox Entertainment CEO Rob Wade said (via Deadline), “A lot of people forget that Masked Singer was actually the first...", "subpage_snippet": "", "source": "www.justjared.com", "link": "https://www.justjared.com/2025/05/12/the-real-reason-why-the-masked-singer-is-skipping-fall-2025-season/", "content": "May 12, 2025 · Now, an executive at Fox is explaining the reasoning behind the decision. Fox Entertainment CEO Rob Wade said (via Deadline), “A lot of people forget that Masked Singer was actually the first..."}
+{"idx": 5, "title": "MASKED Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of MASKED is wearing a mask. How to use masked in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/masked", "content": "The meaning of MASKED is wearing a mask. How to use masked in a sentence."}
+{"idx": 6, "title": "The Masked Singer - YouTube", "date": "", "ddg_snippet": "Welcome to the official YouTube channel for The Masked Singer!__________________________________Hosted by Nick Cannon, with panelists Ken Jeong, Jenny McCart...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/c/MaskedSingerFOX/videos", "content": "Welcome to the official YouTube channel for The Masked Singer!__________________________________Hosted by Nick Cannon, with panelists Ken Jeong, Jenny McCart..."}
+{"idx": 7, "title": "Who Won ‘The Masked Singer’ 2025? See The Stars ... - Forbes", "date": "", "ddg_snippet": "May 8, 2025 · Who won \"The Masked Singer\" 2025? Pearl, Boogie Woogie, Mad Scientist, and Coral competed in the Season 13 finale. Find out who took the crown and who was behind each mask .", "subpage_snippet": "", "source": "www.forbes.com", "link": "https://www.forbes.com/sites/monicamercuri/2025/05/08/who-won-the-masked-singer-2025-see-the-stars-unmasked-in-the-finale/", "content": "May 8, 2025 · Who won \"The Masked Singer\" 2025? Pearl, Boogie Woogie, Mad Scientist, and Coral competed in the Season 13 finale. Find out who took the crown and who was behind each mask ."}
+{"idx": 8, "title": "'The Masked Singer' Season 13: Cast News, Costumes, Spoilers", "date": "", "ddg_snippet": "Feb 20, 2025 · FOX's 'The Masked Singer' is returning for season 13, and here's everything you need to know, including the judges, host, premiere date, and cast costumes.", "subpage_snippet": "", "source": "www.cosmopolitan.com", "link": "https://www.cosmopolitan.com/entertainment/tv/a63677501/masked-singer-season-13-news-date-cast-celebs-spoilers/", "content": "Feb 20, 2025 · FOX's 'The Masked Singer' is returning for season 13, and here's everything you need to know, including the judges, host, premiere date, and cast costumes."}
+{"idx": 9, "title": "Eric Jang: Normalizing Flows Tutorial, Part 2: Modern", "date": "", "ddg_snippet": "The Masked Autoregressive Flow ( MAF ) bijector implements such a conditional-Gaussian autoregressive model. ... MAF uses the MADE architecture as an ...", "subpage_snippet": "", "source": "blog.evjang.com", "link": "https://blog.evjang.com/2018/01/nf2.html", "content": "The Masked Autoregressive Flow ( MAF ) bijector implements such a conditional-Gaussian autoregressive model. ... MAF uses the MADE architecture as an ..."}
diff --git a/data/sampled_jsons/Masked_Autoregressive_Flow_architecture.jsonl b/data/sampled_jsons/Masked_Autoregressive_Flow_architecture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a6333d38f7e84ce97edbfd4acb6a606bd854267
--- /dev/null
+++ b/data/sampled_jsons/Masked_Autoregressive_Flow_architecture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Understand & Implement Masked AutoRegressive Flow with TensorFlow", "date": "", "ddg_snippet": "What can you expect from this post - Why Triangular Matrices are crucial for Autoregressive Flows ? Basic constructions of Autoregressive Flow -based models — Masked Autoregressive Flow (MAF) — Inverse Autoregressive Flow (IAF) How to implement MAF in TensorFlow and train them for density estimation tasks? Without any delay, let's begin!", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/understand-implement-masked-autoregressive-flow-with-tensorflow-9c361cd1354c/", "content": "What can you expect from this post - Why Triangular Matrices are crucial for Autoregressive Flows ? Basic constructions of Autoregressive Flow -based models — Masked Autoregressive Flow (MAF) — Inverse Autoregressive Flow (IAF) How to implement MAF in TensorFlow and train them for density estimation tasks? Without any delay, let's begin!"}
+{"idx": 1, "title": "tfp.bijectors.MaskedAutoregressiveFlow | TensorFlow Probability", "date": "", "ddg_snippet": "The affine autoregressive flow [ (Papamakarios et al., 2016)] [3] provides a relatively simple framework for user-specified (deep) architectures to learn a distribution over continuous events. Regarding terminology, 'Autoregressive models decompose the joint density as a product of conditionals, and model each conditional in turn.", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/probability/api_docs/python/tfp/bijectors/MaskedAutoregressiveFlow", "content": "The affine autoregressive flow [ (Papamakarios et al., 2016)] [3] provides a relatively simple framework for user-specified (deep) architectures to learn a distribution over continuous events. Regarding terminology, 'Autoregressive models decompose the joint density as a product of conditionals, and model each conditional in turn."}
+{"idx": 2, "title": "Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "by G Papamakarios · 2017 · Cited by 1820 — Here we interpret autoregressive models as a flow , and improve the model fit by stacking multiple instances of the model into a deeper flow.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1705.07057", "content": "by G Papamakarios · 2017 · Cited by 1820 — Here we interpret autoregressive models as a flow , and improve the model fit by stacking multiple instances of the model into a deeper flow."}
+{"idx": 3, "title": "Normalizing Flows - Part 2 - Skit Tech", "date": "", "ddg_snippet": "8 May 2021 — Masked Autoregressive Flows (MAFs). The time taken to train autoregressive flow models is very high because of the need to sequentially generate ...", "subpage_snippet": "", "source": "tech.skit.ai", "link": "https://tech.skit.ai/normalizing-flows-part-2/", "content": "8 May 2021 — Masked Autoregressive Flows (MAFs). The time taken to train autoregressive flow models is very high because of the need to sequentially generate ..."}
+{"idx": 4, "title": "Normalizing Flow Models", "date": "", "ddg_snippet": "Masked Autoregressive Flow (MAF ) uses this interpretation, where the forward mapping is an autoregressive model. However, sampling is sequential and slow, in O( ...", "subpage_snippet": "", "source": "deepgenerativemodels.github.io", "link": "https://deepgenerativemodels.github.io/notes/flow/", "content": "Masked Autoregressive Flow (MAF ) uses this interpretation, where the forward mapping is an autoregressive model. However, sampling is sequential and slow, in O( ..."}
+{"idx": 5, "title": "Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "Here we interpret autoregressive models as a flow , and improve the model fit by stacking multiple instances of the model into a deeper flow. Given ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/masked-autoregressive-flow-for-density-estimation-2codepzs79.pdf", "content": "Here we interpret autoregressive models as a flow , and improve the model fit by stacking multiple instances of the model into a deeper flow. Given ..."}
+{"idx": 6, "title": "Transformer Neural Autoregressive Flows", "date": "", "ddg_snippet": "T-NAFs treat each dimension of a random variable as a separate input token, using attention masking to enforce an autoregressive constraint.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.01855v1", "content": "T-NAFs treat each dimension of a random variable as a separate input token, using attention masking to enforce an autoregressive constraint."}
+{"idx": 7, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "by S Zhai · Cited by 25 — The paper proposes a new architecture called TarFlow, which is a Transformer-based variant of Masked Autoregressive Flows (MAFs). TarFlow ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "by S Zhai · Cited by 25 — The paper proposes a new architecture called TarFlow, which is a Transformer-based variant of Masked Autoregressive Flows (MAFs). TarFlow ..."}
+{"idx": 8, "title": "Normalizing Flows Tutorial, Part 2", "date": "", "ddg_snippet": "17 Jan 2018 — The Masked Autoregressive Flow (MAF) bijector implements such a conditional-Gaussian autoregressive model. Here is a schematic of the ...", "subpage_snippet": "", "source": "blog.evjang.com", "link": "https://blog.evjang.com/2018/01/nf2.html", "content": "17 Jan 2018 — The Masked Autoregressive Flow (MAF) bijector implements such a conditional-Gaussian autoregressive model. Here is a schematic of the ..."}
+{"idx": 9, "title": "Flow-based Deep Generative Models | Lil'Log - GitHub Pages", "date": "", "ddg_snippet": "This section starts with several classic autoregressive models (MADE, PixelRNN, WaveNet) and then we dive into autoregressive flow models (MAF and IAF). MADE MADE ( Masked Autoencoder for Distribution Estimation; Germain et al., 2015) is a specially designed architecture to enforce the autoregressive property in the autoencoder efficiently.", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2018-10-13-flow-models/", "content": "This section starts with several classic autoregressive models (MADE, PixelRNN, WaveNet) and then we dive into autoregressive flow models (MAF and IAF). MADE MADE ( Masked Autoencoder for Distribution Estimation; Germain et al., 2015) is a specially designed architecture to enforce the autoregressive property in the autoencoder efficiently."}
diff --git a/data/sampled_jsons/Maximilian_Nickel_No_Free_Delivery_Service_NeurIPS_2024_No_Free_Lunch_year_2024.jsonl b/data/sampled_jsons/Maximilian_Nickel_No_Free_Delivery_Service_NeurIPS_2024_No_Free_Lunch_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..62bbdb1e158d0c5a9f3f72fc8b230cb7b5eede21
--- /dev/null
+++ b/data/sampled_jsons/Maximilian_Nickel_No_Free_Delivery_Service_NeurIPS_2024_No_Free_Lunch_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No Free Delivery Service: Epistemic limits of passive data...", "date": "", "ddg_snippet": "6 Nov 2024 — No Free Delivery Service : Epistemic limits of passive data collection in complex social systems. Download PDF · Maximilian Nickel. Published: 25 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=XZ0fpoAKEB&noteId=ZPoblj0CQC", "content": "6 Nov 2024 — No Free Delivery Service : Epistemic limits of passive data collection in complex social systems. Download PDF · Maximilian Nickel. Published: 25 ..."}
+{"idx": 1, "title": "No free delivery service Epistemic limits of passive data ...", "date": "", "ddg_snippet": "by M Nickel · 2024 · Cited by 1 — The no - free - lunch theorems for machine learning [68, 60] share important similarities to this work as both consider the expected risk over possible worlds. 28 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/b97fc02c9e536d68300d82be05c23aa2-Paper-Conference.pdf", "content": "by M Nickel · 2024 · Cited by 1 — The no - free - lunch theorems for machine learning [68, 60] share important similarities to this work as both consider the expected risk over possible worlds. 28 pages"}
+{"idx": 2, "title": "Artificial Intelligence Nov 2024 - cs.AI", "date": "", "ddg_snippet": "Title: No Free Delivery Service : Epistemic limits of passive data collection in complex social systems. Maximilian Nickel . Comments: To appear in NeurIPS '24.", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.AI/2024-11?skip=275&show=500", "content": "Title: No Free Delivery Service : Epistemic limits of passive data collection in complex social systems. Maximilian Nickel . Comments: To appear in NeurIPS '24."}
+{"idx": 3, "title": "mnick - Maximilian Nickel", "date": "", "ddg_snippet": "No Free Delivery Service : Epistemic limits of passive data... Rapid model validation via the train-test paradigm has been a key driver for ...", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/mnick", "content": "No Free Delivery Service : Epistemic limits of passive data... Rapid model validation via the train-test paradigm has been a key driver for ..."}
+{"idx": 4, "title": "Maximilian Nickel (@maxsnickel)", "date": "", "ddg_snippet": "16 Jul 2025 — ... No Free Delivery Service \" on Thursday, West Ballroom A-D Poster 6901 neurips .cc/virtu… ... July 26, 2024 at 2:27 AM. #ICML workshop on Humans ...", "subpage_snippet": "", "source": "www.threads.com", "link": "https://www.threads.com/@maxsnickel", "content": "16 Jul 2025 — ... No Free Delivery Service \" on Thursday, West Ballroom A-D Poster 6901 neurips .cc/virtu… ... July 26, 2024 at 2:27 AM. #ICML workshop on Humans ..."}
+{"idx": 5, "title": "2024.personalize-1.pdf", "date": "", "ddg_snippet": "22 Mar 2024 — Our workshop studies personalization from technical, legal, and philosophical angles. The exponential growth of generative AI and foundation ... 139 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.personalize-1.pdf", "content": "22 Mar 2024 — Our workshop studies personalization from technical, legal, and philosophical angles. The exponential growth of generative AI and foundation ... 139 pages"}
+{"idx": 6, "title": "No Free Lunch Theorem and Black-Box Complexity Analysis for...", "date": "", "ddg_snippet": "NeurIPS Proceedings.The original No Free Lunch (NFL) theorems highlight the limitations of traditional black-box optimisation and learning algorithms, serving as a theoretical foundation for traditional optimisation.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/dc3a0fd5ef0f78ee5934919230d6664f-Abstract-Conference.html", "content": "NeurIPS Proceedings.The original No Free Lunch (NFL) theorems highlight the limitations of traditional black-box optimisation and learning algorithms, serving as a theoretical foundation for traditional optimisation."}
+{"idx": 7, "title": "No Free Lunch Theorem and Black-Box Complexity... | OpenReview", "date": "", "ddg_snippet": "The original No Free Lunch (NFL) theorems highlight the limitations of traditional black-box optimisation and learning algorithms, serving as a theoretical foundation for traditional optimisation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=NkuySm8qVs", "content": "The original No Free Lunch (NFL) theorems highlight the limitations of traditional black-box optimisation and learning algorithms, serving as a theoretical foundation for traditional optimisation."}
+{"idx": 8, "title": "dblp: List of computer science publications by Maximilian Nickel", "date": "", "ddg_snippet": "Maximilian Nickel : No Free Delivery Service : Epistemic limits of passive data collection in complex social systems. NeurIPS 2024 .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/83/10622.html", "content": "Maximilian Nickel : No Free Delivery Service : Epistemic limits of passive data collection in complex social systems. NeurIPS 2024 ."}
+{"idx": 9, "title": "No Free Delivery Service : Epistemic limits of passive data collection in...", "date": "", "ddg_snippet": "Maximilian Nickel et el.# ← → No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices 26 September 2024 No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models 26 September 2024 → ←.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/xz0fpoakeb/", "content": "Maximilian Nickel et el.# ← → No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices 26 September 2024 No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models 26 September 2024 → ←."}
diff --git a/data/sampled_jsons/Medusa_Cai_2024_token_prediction_parallel_tree_attention_mechanism_year_2024.jsonl b/data/sampled_jsons/Medusa_Cai_2024_token_prediction_parallel_tree_attention_mechanism_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..65a2aa7c5d4f0cb41ecfb41b8c9515ae6845db5e
--- /dev/null
+++ b/data/sampled_jsons/Medusa_Cai_2024_token_prediction_parallel_tree_attention_mechanism_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2401.10774] Medusa: Simple LLM Inference Acceleration ...", "date": "", "ddg_snippet": "by T Cai · 2024 · Cited by 348 — Using a tree -based attention mechanism , Medusa constructs multiple candidate continuations and verifies them simultaneously in each decoding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.10774", "content": "by T Cai · 2024 · Cited by 348 — Using a tree -based attention mechanism , Medusa constructs multiple candidate continuations and verifies them simultaneously in each decoding ..."}
+{"idx": 1, "title": "Medusa: Simple LLM Inference Acceleration Framework ...", "date": "", "ddg_snippet": "by T Cai · 2024 · Cited by 348 — MEDUSA introduces multiple heads on top of the last hidden states of the LLM, enabling the prediction of several sub- sequent tokens in parallel (Section 2.1.1) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.10774", "content": "by T Cai · 2024 · Cited by 348 — MEDUSA introduces multiple heads on top of the last hidden states of the LLM, enabling the prediction of several sub- sequent tokens in parallel (Section 2.1.1) ..."}
+{"idx": 2, "title": "Medusa: Simple Framework for Accelerating LLM ...", "date": "", "ddg_snippet": "Medusa adds extra \"heads\" to LLMs to predict multiple future tokens simultaneously. When augmenting a model with Medusa , the original model stays untouched ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FasterDecoding/Medusa", "content": "Medusa adds extra \"heads\" to LLMs to predict multiple future tokens simultaneously. When augmenting a model with Medusa , the original model stays untouched ..."}
+{"idx": 3, "title": "Paper page - Medusa: Simple LLM Inference Acceleration ...", "date": "", "ddg_snippet": "21 Jan 2024 — Medusa enhances Large Language Model inference by adding parallel decoding heads and using tree -based attention to predict multiple tokens ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2401.10774", "content": "21 Jan 2024 — Medusa enhances Large Language Model inference by adding parallel decoding heads and using tree -based attention to predict multiple tokens ..."}
+{"idx": 4, "title": "MEDUSA: Simple LLM Inference Acceleration Framework ...", "date": "", "ddg_snippet": "by T Cai · 2024 · Cited by 348 — Using a tree -based attention mechanism , MEDUSA constructs multiple candidate continuations and verifies them simultaneously in each decoding step. By leveraging ...", "subpage_snippet": "", "source": "experts.illinois.edu", "link": "https://experts.illinois.edu/en/publications/medusa-simple-llm-inference-acceleration-framework-with-multiple-", "content": "by T Cai · 2024 · Cited by 348 — Using a tree -based attention mechanism , MEDUSA constructs multiple candidate continuations and verifies them simultaneously in each decoding step. By leveraging ..."}
+{"idx": 5, "title": "Fast and Accurate Language Model Decoding via Parallel ...", "date": "", "ddg_snippet": "by Z Wei · Cited by 1 — TL;DR: Built on the proposed parallel token processing mechanism ... Previous works like Medusa require multiple large LM heads (e.g., 5 heads as ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=IRsxTYPqhQ", "content": "by Z Wei · Cited by 1 — TL;DR: Built on the proposed parallel token processing mechanism ... Previous works like Medusa require multiple large LM heads (e.g., 5 heads as ..."}
+{"idx": 6, "title": "MEDUSA: Simple LLM inference acceleration framework ...", "date": "", "ddg_snippet": "by T Cai · 2024 · Cited by 348 — Using a tree -based attention mechanism , MEDUSA constructs multiple candidate continuations and verifies them simultaneously in each decoding ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3692273", "content": "by T Cai · 2024 · Cited by 348 — Using a tree -based attention mechanism , MEDUSA constructs multiple candidate continuations and verifies them simultaneously in each decoding ..."}
+{"idx": 7, "title": "Better & Faster Large Language Models via Multi-token ...", "date": "", "ddg_snippet": "Another method mentioned in the paper is Medusa -like tree attention ( Cai et al., 2024 ). The idea is similar, the main difference is the ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@tsunhanchiang/ai-paper-digest-better-faster-large-language-models-via-multi-token-prediction-9004fc3178fa", "content": "Another method mentioned in the paper is Medusa -like tree attention ( Cai et al., 2024 ). The idea is similar, the main difference is the ..."}
+{"idx": 8, "title": "Primers • Speculative Decoding", "date": "", "ddg_snippet": "Medusa extends multi- token decoding with a novel attention mechanism that verifies multiple speculative paths simultaneously. Architecture Overview: The ...", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/speculative-decoding/", "content": "Medusa extends multi- token decoding with a novel attention mechanism that verifies multiple speculative paths simultaneously. Architecture Overview: The ..."}
+{"idx": 9, "title": "Speed Without Sacrifice: Fine-Tuning Language Models ...", "date": "", "ddg_snippet": "by D Zagyva · 2025 — This paper explores two complementary ap- proaches: the Medusa framework ( Cai et al., 2024 ), a novel approach for speculative decoding, and KD.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-industry.48.pdf", "content": "by D Zagyva · 2025 — This paper explores two complementary ap- proaches: the Medusa framework ( Cai et al., 2024 ), a novel approach for speculative decoding, and KD."}
diff --git a/data/sampled_jsons/Medusa_MLP_heads_hidden_state_last_verified_token_blind_predictions_year_2024.jsonl b/data/sampled_jsons/Medusa_MLP_heads_hidden_state_last_verified_token_blind_predictions_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2239151a36e1b387c0e1e429181537592ad517e7
--- /dev/null
+++ b/data/sampled_jsons/Medusa_MLP_heads_hidden_state_last_verified_token_blind_predictions_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Chocolate Milk Cult’s Guide to Inference Scaling for AI... | Medium", "date": "", "ddg_snippet": "Predicting the next token requires knowing all preceding tokens . This sequential dependency creates inherent latency. Medusa introduces multiple heads on top of the last hidden states of the LLM, enabling the prediction of several subsequent tokens in parallel.", "subpage_snippet": "", "source": "machine-learning-made-simple.medium.com", "link": "https://machine-learning-made-simple.medium.com/the-chocolate-milk-cults-guide-to-inference-scaling-for-ai-models-50aa2290eb50", "content": "Predicting the next token requires knowing all preceding tokens . This sequential dependency creates inherent latency. Medusa introduces multiple heads on top of the last hidden states of the LLM, enabling the prediction of several subsequent tokens in parallel."}
+{"idx": 1, "title": "On the Biology of a Large Language Model | Transformer Circuits", "date": "", "ddg_snippet": "Uncovering Hidden Goals in a Misaligned Model. Commonly Observed Circuit Components and Structure.", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2025/attribution-graphs/biology.html", "content": "Uncovering Hidden Goals in a Misaligned Model. Commonly Observed Circuit Components and Structure."}
+{"idx": 2, "title": "Guess which Character or Tag has more posts on Rule 34", "date": "", "ddg_snippet": "Daily Gamemode: Added a new daily gamemode inspired by Scrandle. 10 head to heads where you have to guess which tag has more posts. Tag Notes: Slowly rolling out a note under each tag to tell you what universe/franchise each character is from.", "subpage_snippet": "", "source": "rule34dle.vercel.app", "link": "https://rule34dle.vercel.app/", "content": "Daily Gamemode: Added a new daily gamemode inspired by Scrandle. 10 head to heads where you have to guess which tag has more posts. Tag Notes: Slowly rolling out a note under each tag to tell you what universe/franchise each character is from."}
+{"idx": 3, "title": "NaViT is slower than expected · Issue #347 · lucidrains/vit-pytorch", "date": "", "ddg_snippet": "token _dropout_prob=config. token _dropout_prob, ) # Initialize weights via HF utility (won't disturb vit-pytorch-initialized weights unless uninitialized). self.post_init().logits = self.navit(images). return BaseModelOutputWithPooling(. last _ hidden _ state =None, pooler_output=logits ).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lucidrains/vit-pytorch/issues/347", "content": "token _dropout_prob=config. token _dropout_prob, ) # Initialize weights via HF utility (won't disturb vit-pytorch-initialized weights unless uninitialized). self.post_init().logits = self.navit(images). return BaseModelOutputWithPooling(. last _ hidden _ state =None, pooler_output=logits )."}
+{"idx": 4, "title": "Paper page - FlowRL: Matching Reward Distributions for LLM Reasoning", "date": "", "ddg_snippet": "Last prompt token - hidden state of the final token before generation starts. Prompt pooling - mean/max pooling over all prompt token hidden states . Separator token - add special token between prompt and response. Which approach is most common for this use case?", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2509.15207", "content": "Last prompt token - hidden state of the final token before generation starts. Prompt pooling - mean/max pooling over all prompt token hidden states . Separator token - add special token between prompt and response. Which approach is most common for this use case?"}
+{"idx": 5, "title": "Restored Republic via a GCR: Update as of... | Dinar Chronicles", "date": "", "ddg_snippet": "The Saint Germain Trust is not coming—it is already here. Its activation is a process, not a televised event. If you wait for CNN to confirm it, you will be the last to know and the last to benefit. You were never supposed to be free of debt, to own land, to retire with dignity.", "subpage_snippet": "", "source": "dinarchronicles.com", "link": "https://dinarchronicles.com/2025/09/21/restored-republic-via-a-gcr-update-as-of-september-21-2025/", "content": "The Saint Germain Trust is not coming—it is already here. Its activation is a process, not a televised event. If you wait for CNN to confirm it, you will be the last to know and the last to benefit. You were never supposed to be free of debt, to own land, to retire with dignity."}
+{"idx": 6, "title": "jwt.io/introduction", "date": "", "ddg_snippet": "JSON Web Token (JWT) is an open standard (RFC 7519)...", "subpage_snippet": "", "source": "www.jwt.io", "link": "https://www.jwt.io/introduction", "content": "JSON Web Token (JWT) is an open standard (RFC 7519)..."}
+{"idx": 7, "title": "IMAGEBIND: One Embedding Space To Bind Them All", "date": "", "ddg_snippet": "We then take the class token because it will have global information about the modality. Next, we pass it through an MLP layer (projection layer) to make the class token dimension across modalities of the same dimension.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/imagebind-one-embedding-space-to-bind-them-all-b48c8623d39b", "content": "We then take the class token because it will have global information about the modality. Next, we pass it through an MLP layer (projection layer) to make the class token dimension across modalities of the same dimension."}
+{"idx": 8, "title": "Assassin's Creed Shadows: Play on GFN.co.kr or GFN Korea!", "date": "", "ddg_snippet": "__Secure-ROLLOUT_ TOKEN . Duration.Intelligence is Key: Uncover hidden paths and enemy weaknesses by developing your network of spies across feudal Japan. Will you embrace the shadows, dominate the art of war, and carve your legend in Assassin’s Creed Shadows?", "subpage_snippet": "", "source": "gfn.co.kr", "link": "https://gfn.co.kr/en/games/assassins-creed-shadows/", "content": "__Secure-ROLLOUT_ TOKEN . Duration.Intelligence is Key: Uncover hidden paths and enemy weaknesses by developing your network of spies across feudal Japan. Will you embrace the shadows, dominate the art of war, and carve your legend in Assassin’s Creed Shadows?"}
+{"idx": 9, "title": "Dying Light: The Beast - Best Skills to Unlock First", "date": "", "ddg_snippet": "But you need to be in a state of dashing to jump off the enemy’s head . Other skills you prioritize here are Vault Kick, Afterboost, and Parkour Shot. Best Beast Skills to Prioritize In Dying Light: The Beast.", "subpage_snippet": "", "source": "thegameslayer.com", "link": "https://thegameslayer.com/guides/dying-light-the-beast-best-skills-to-unlock-first/", "content": "But you need to be in a state of dashing to jump off the enemy’s head . Other skills you prioritize here are Vault Kick, Afterboost, and Parkour Shot. Best Beast Skills to Prioritize In Dying Light: The Beast."}
diff --git a/data/sampled_jsons/Medusa_heads_MLP_architecture_hidden_states_independent_parallel_year_2024.jsonl b/data/sampled_jsons/Medusa_heads_MLP_architecture_hidden_states_independent_parallel_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ff5f94970392062a5757e2aaa7f7d98860c41bc1
--- /dev/null
+++ b/data/sampled_jsons/Medusa_heads_MLP_architecture_hidden_states_independent_parallel_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Medusa: Simple LLM Inference Acceleration Framework ...", "date": "", "ddg_snippet": "by T Cai · 2024 · Cited by 348 — MEDUSA introduces multiple heads on top of the last hidden states of the LLM, enabling the prediction of several sub- sequent tokens in parallel (Section 2.1.1) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.10774", "content": "by T Cai · 2024 · Cited by 348 — MEDUSA introduces multiple heads on top of the last hidden states of the LLM, enabling the prediction of several sub- sequent tokens in parallel (Section 2.1.1) ..."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "by J Li — Medusa only maintains five parallel MLP heads . Our approach, however, employs a two-layer Transformer head alongside five parallel MLP heads.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0ObGn4e1IS", "content": "by J Li — Medusa only maintains five parallel MLP heads . Our approach, however, employs a two-layer Transformer head alongside five parallel MLP heads."}
+{"idx": 2, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Hydra (Ankner et al., 2024) is a sequential variant of Medusa , transforming the parallel MLPs into a serial architecture . It feeds the unverified tokens ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46686", "content": "Hydra (Ankner et al., 2024) is a sequential variant of Medusa , transforming the parallel MLPs into a serial architecture . It feeds the unverified tokens ..."}
+{"idx": 3, "title": "Sequentially-Dependent Draft Heads for Medusa Decoding", "date": "", "ddg_snippet": "Medusa decoding uses a set of lightweight heads (draft heads ) that operate on the main LLM's internal representations ( hidden states ) to act as the draft model.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/hydra-sequentially-dependent-draft-heads-for-medusa-decoding", "content": "Medusa decoding uses a set of lightweight heads (draft heads ) that operate on the main LLM's internal representations ( hidden states ) to act as the draft model."}
+{"idx": 4, "title": "Clover: Regressive Lightweight Speculative Decoding with ...", "date": "", "ddg_snippet": "by B Xiao · 2024 · Cited by 7 — Firstly, the. Medusa head consists of only a single MLP layer that takes input solely from the final hidden states . Each layer independently ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.00263", "content": "by B Xiao · 2024 · Cited by 7 — Firstly, the. Medusa head consists of only a single MLP layer that takes input solely from the final hidden states . Each layer independently ..."}
+{"idx": 5, "title": "Bi-directional Multi-head Decoding for Accelerating LLM ...", "date": "", "ddg_snippet": "by Z Li · 2025 — Medusa decod- ing (Cai et al., 2024) innovatively uses the target model's last hidden states to implement a multi- heads inference framework. It ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.450.pdf", "content": "by Z Li · 2025 — Medusa decod- ing (Cai et al., 2024) innovatively uses the target model's last hidden states to implement a multi- heads inference framework. It ..."}
+{"idx": 6, "title": "Amphista: Accelerate LLM Inference with Bi-directional ...", "date": "", "ddg_snippet": "mization based on Medusa , transforms the indepen-. 164 dent MLP heads into sequentially dependent MLP . 165 heads , further enhancing the predictive accuracy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/96ec48f8b3796464d7067435e2713129c007cbe3.pdf", "content": "mization based on Medusa , transforms the indepen-. 164 dent MLP heads into sequentially dependent MLP . 165 heads , further enhancing the predictive accuracy."}
+{"idx": 7, "title": "Deep Research — State of the Art LLM Guide | by Craig Swift", "date": "", "ddg_snippet": "Medusa adds, say, 2 or 4 additional heads that also take the last layer's hidden state as input and produce logits for the next token. During ...", "subpage_snippet": "", "source": "aiedge.medium.com", "link": "https://aiedge.medium.com/deep-research-state-of-the-art-llm-guide-7c093684cdf0", "content": "Medusa adds, say, 2 or 4 additional heads that also take the last layer's hidden state as input and produce logits for the next token. During ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "3 days ago — To this end, we introduce MEDUSA , a multi-scale encoder-decoder self-attention mechanism tailored for medical image analysis. While self- ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=axis-based+multi-head+self-attention", "content": "3 days ago — To this end, we introduce MEDUSA , a multi-scale encoder-decoder self-attention mechanism tailored for medical image analysis. While self- ..."}
+{"idx": 9, "title": "ResDecode: Accelerating Large Language Models ...", "date": "", "ddg_snippet": "by Z Zeng · 2025 · Cited by 1 — Medusa [9]: Medusa -1 generates draft tokens with multiple decoding heads , the input of which is the hidden states of last token of last transformer layer of.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/8254253/11002434/11002449.pdf", "content": "by Z Zeng · 2025 · Cited by 1 — Medusa [9]: Medusa -1 generates draft tokens with multiple decoding heads , the input of which is the hidden states of last token of last transformer layer of."}
diff --git a/data/sampled_jsons/Merrill_2024_The_Illusion_of_State_abstract_SSM_finite_state_expressiveness_year_2024.jsonl b/data/sampled_jsons/Merrill_2024_The_Illusion_of_State_abstract_SSM_finite_state_expressiveness_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3bacd07a22520541928cf2a0f4673df5719b524b
--- /dev/null
+++ b/data/sampled_jsons/Merrill_2024_The_Illusion_of_State_abstract_SSM_finite_state_expressiveness_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "by W Merrill · 2024 · Cited by 89 — SSMs have been suggested to have two potential advantages compared to transformers owing to their recur-. 2. Page 3. The Illusion of State in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.08819", "content": "by W Merrill · 2024 · Cited by 89 — SSMs have been suggested to have two potential advantages compared to transformers owing to their recur-. 2. Page 3. The Illusion of State in ..."}
+{"idx": 1, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "4 Jun 2024 — The Illusion of State in State-Space Models. Report issue for ... finite-state transitions into an RNN (Minsky, 1954; Merrill , 2019) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v2", "content": "4 Jun 2024 — The Illusion of State in State-Space Models. Report issue for ... finite-state transitions into an RNN (Minsky, 1954; Merrill , 2019) ..."}
+{"idx": 2, "title": "On the expressiveness and length generalization of selective ...", "date": "", "ddg_snippet": "by A Terzić · 2025 · Cited by 2 — ... finite-state automaton (FSA) emulation. We address certain ... The Illusion of State in State-Space Models. In International Conference ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1609/aaai.v39i19.34301", "content": "by A Terzić · 2025 · Cited by 2 — ... finite-state automaton (FSA) emulation. We address certain ... The Illusion of State in State-Space Models. In International Conference ..."}
+{"idx": 3, "title": "The Expressive Capacity of State Space Models: A Formal ...", "date": "", "ddg_snippet": "by Y Sarrof · Cited by 15 — Finite-state -automata are equivalent in expressivity to regular expressions, and define ... The illusion of state in state-space models. In ICML, 2024 . A ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=eV5YIrJPdy", "content": "by Y Sarrof · Cited by 15 — Finite-state -automata are equivalent in expressivity to regular expressions, and define ... The illusion of state in state-space models. In ICML, 2024 . A ..."}
+{"idx": 4, "title": "Attention, State Space Models, and Recurrent Neural ...", "date": "", "ddg_snippet": "9 Dec 2024 — ... finite state dimension. However, in the case of softmax(·) , an ... The illusion of state in state-space models. arXiv preprint arXiv ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/94017", "content": "9 Dec 2024 — ... finite state dimension. However, in the case of softmax(·) , an ... The illusion of state in state-space models. arXiv preprint arXiv ..."}
+{"idx": 5, "title": "The Expressive Capacity of State Space Models: A Formal ...", "date": "", "ddg_snippet": "PDF HTML Abstract ... This characterization provides a decisive criterion for the finite-state ... The Illusion of State in State-Space Models ( 2024 ) ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/articles/2405.17394", "content": "PDF HTML Abstract ... This characterization provides a decisive criterion for the finite-state ... The Illusion of State in State-Space Models ( 2024 ) ..."}
+{"idx": 6, "title": "The expressive capacity of state space models", "date": "", "ddg_snippet": "Merrill , J. Petty, and A. Sabharwal. The illusion of state in state-space models. In ICML, 2024 . Google Scholar. [56]. A. Miller. Finitary models of language ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739220", "content": "Merrill , J. Petty, and A. Sabharwal. The illusion of state in state-space models. In ICML, 2024 . Google Scholar. [56]. A. Miller. Finitary models of language ..."}
+{"idx": 7, "title": "IMPLICIT LANGUAGE MODELS ARE RNNS", "date": "", "ddg_snippet": "Implicit SSMs Lift the Illusion of State The Illusion of State Merrill et al. ( 2024 ) reveals that. SSMs cannot simulate arbitrary finite state machines. A ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/189771ccc52924fbdb2703af45d8c232f7881f4d.pdf", "content": "Implicit SSMs Lift the Illusion of State The Illusion of State Merrill et al. ( 2024 ) reveals that. SSMs cannot simulate arbitrary finite state machines. A ..."}
+{"idx": 8, "title": "Implicit Language Models are RNNs: Balancing ...", "date": "", "ddg_snippet": "Implicit SSMs Lift the Illusion of State The illusion of state ( Merrill et al., 2024 ) reveals that constant depth SSMs cannot simulate arbitrary finite state ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46440", "content": "Implicit SSMs Lift the Illusion of State The illusion of state ( Merrill et al., 2024 ) reveals that constant depth SSMs cannot simulate arbitrary finite state ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "The Illusion of State in State-Space Models · State-space models (SSMs) ... finite-state automaton (FSA) emulation. We address certain limitations of ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=State+Space+Models", "content": "The Illusion of State in State-Space Models · State-space models (SSMs) ... finite-state automaton (FSA) emulation. We address certain limitations of ..."}
diff --git a/data/sampled_jsons/Merrill_2024_state-space_models_finite_state_machines_SSM_year_2024.jsonl b/data/sampled_jsons/Merrill_2024_state-space_models_finite_state_machines_SSM_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b47ed551d9373577a0cd4558dfcc201f2356a96e
--- /dev/null
+++ b/data/sampled_jsons/Merrill_2024_state-space_models_finite_state_machines_SSM_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "... state space model ( SSM ) architectures (Gu et al., 2021 , 2022a ; Fu et al., 2023 ; Gu & Dao, 2023 ; Wang et al., 2024 ) have been introduced as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v3", "content": "... state space model ( SSM ) architectures (Gu et al., 2021 , 2022a ; Fu et al., 2023 ; Gu & Dao, 2023 ; Wang et al., 2024 ) have been introduced as ..."}
+{"idx": 1, "title": "The Expressive Capacity of State Space Models: A Formal", "date": "", "ddg_snippet": "Recently, recurrent models based on linear state space models ( SSMs ) have shown promising performance in language modeling (LM), competititve with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17394v2", "content": "Recently, recurrent models based on linear state space models ( SSMs ) have shown promising performance in language modeling (LM), competititve with ..."}
+{"idx": 2, "title": "Sequential-Parallel Duality in Prefix-Scannable Models", "date": "", "ddg_snippet": "... models on small but illustrative tasks: next-token prediction on WikiText-103 (Merity et al., 2017 ) , and synthetic algorithmic tasks that test ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10918v1", "content": "... models on small but illustrative tasks: next-token prediction on WikiText-103 (Merity et al., 2017 ) , and synthetic algorithmic tasks that test ..."}
+{"idx": 3, "title": "Understanding the Differences in Foundation Models: Attention,", "date": "", "ddg_snippet": "To address this challenge, alternative architectures such as linear attention, State Space Models ( SSMs ), and Recurrent Neural Networks (RNNs) have ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15731v3", "content": "To address this challenge, alternative architectures such as linear attention, State Space Models ( SSMs ), and Recurrent Neural Networks (RNNs) have ..."}
+{"idx": 4, "title": "Scaling Linear Attention with Sparse State Expansion", "date": "", "ddg_snippet": "We introduce a row-sparse state update framework that conceptualizes state updating as information classification. ... State Expansion (SSE) , an ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16577v1", "content": "We introduce a row-sparse state update framework that conceptualizes state updating as information classification. ... State Expansion (SSE) , an ..."}
+{"idx": 5, "title": "[2404.08819] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 6, "title": "Paper page - The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Thus, despite its recurrent formulation, the \" state \" in an SSM is an illusion: SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2404.08819", "content": "Thus, despite its recurrent formulation, the \" state \" in an SSM is an illusion: SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems."}
+{"idx": 7, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/merrill24a.html", "content": "State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 8, "title": "The Illusion of State in State-Space Models - Semantic Scholar", "date": "", "ddg_snippet": "Analysis of state-space models reveals that SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems. State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Illusion-of-State-in-State-Space-Models-Merrill-Petty/917479a7a72ee7c1fb320c14d770e30ef322ef28", "content": "Analysis of state-space models reveals that SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems. State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer ..."}
+{"idx": 9, "title": "The illusion of state in state-space models | Proceedings of the 41st ...", "date": "", "ddg_snippet": "State-space models ( SSMs ) have emerged as a potential alternative to transformers. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3693514", "content": "State-space models ( SSMs ) have emerged as a potential alternative to transformers. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent ..."}
diff --git a/data/sampled_jsons/Merrill_et_al._2024_SSM_finite_state_machine.jsonl b/data/sampled_jsons/Merrill_et_al._2024_SSM_finite_state_machine.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9eef4ec3049d920c952976ad91164165cfd3aa6b
--- /dev/null
+++ b/data/sampled_jsons/Merrill_et_al._2024_SSM_finite_state_machine.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Expressive Capacity of State Space Models: A Formal", "date": "", "ddg_snippet": "These SSMs are recurrent models that—while formulated in terms of iterative state updates—allow efficient parallelization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17394v2", "content": "These SSMs are recurrent models that—while formulated in terms of iterative state updates—allow efficient parallelization."}
+{"idx": 1, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "... state space model ( SSM ) architectures (Gu et al ., 2021 , 2022a ; Fu et al ., 2023 ; Gu & Dao, 2023 ; Wang et al ., 2024 ) have been introduced as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v3", "content": "... state space model ( SSM ) architectures (Gu et al ., 2021 , 2022a ; Fu et al ., 2023 ; Gu & Dao, 2023 ; Wang et al ., 2024 ) have been introduced as ..."}
+{"idx": 2, "title": "Scaling Linear Attention with Sparse State Expansion", "date": "", "ddg_snippet": "Within the proposed framework, we introduce Sparse State Expansion (SSE) , an efficient state expansion mechanism designed to effectively manage ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16577v1", "content": "Within the proposed framework, we introduce Sparse State Expansion (SSE) , an efficient state expansion mechanism designed to effectively manage ..."}
+{"idx": 3, "title": "[2404.08819] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "State -space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "State -space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 4, "title": "ICML Poster The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Thus, despite its recurrent formulation, the \" state'' in an SSM is an illusion: SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems. If playback doesn't begin shortly, try restarting your device.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/poster/34075", "content": "Thus, despite its recurrent formulation, the \" state'' in an SSM is an illusion: SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems. If playback doesn't begin shortly, try restarting your device."}
+{"idx": 5, "title": "The Illusion of State in State-Space Models - NSF Public Access", "date": "", "ddg_snippet": "Recently, Chiang et al. (2023) showed that finite -precision transformers can be equivalently expressed in a generalization of first-order logic. However, finite -precision transformers are a weak transformer variant because, as we show, a single head can only attend to a constant number of tokens and, in particular, cannot represent uniform ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10535878-illusion-state-state-space-models", "content": "Recently, Chiang et al. (2023) showed that finite -precision transformers can be equivalently expressed in a generalization of first-order logic. However, finite -precision transformers are a weak transformer variant because, as we show, a single head can only attend to a constant number of tokens and, in particular, cannot represent uniform ..."}
+{"idx": 6, "title": "The illusion of state in state-space models | Proceedings of the 41st ...", "date": "", "ddg_snippet": "State -space models ( SSMs ) have emerged as a potential alternative to transformers. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks. But do SSMs truly have an advantage ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3693514", "content": "State -space models ( SSMs ) have emerged as a potential alternative to transformers. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks. But do SSMs truly have an advantage ..."}
+{"idx": 7, "title": "Performance Evaluation of Different Finite State Machines ...", "date": "", "ddg_snippet": "Finite State Machines (FSMs) are one of the fundamental building blocks in the digital design. Numerous applications, involving sequential digital circuits, rely on the utilization of FSMs. Apart from the correct operation, an FSM should also satisfy certain performance metrics. This is very important both in the case where the FSM is used as a standalone digital circuit and also in the case ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10615817", "content": "Finite State Machines (FSMs) are one of the fundamental building blocks in the digital design. Numerous applications, involving sequential digital circuits, rely on the utilization of FSMs. Apart from the correct operation, an FSM should also satisfy certain performance metrics. This is very important both in the case where the FSM is used as a standalone digital circuit and also in the case ..."}
+{"idx": 8, "title": "The Illusion of State in State-Space Models - arXiv.org", "date": "", "ddg_snippet": "In a different line of work, state space model ( SSM ) archi-tectures (Gu et al ., 2021; 2022a; Fu et al ., 2023; Gu & Dao, 2023; Wang et al ., 2024 ) have been introduced as an alter-native to transformers, with the goal of achieving RNN-like expressive power for handling problems that are naturally stateful and sequential (Gu et al ., 2021; 2022b).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.08819", "content": "In a different line of work, state space model ( SSM ) archi-tectures (Gu et al ., 2021; 2022a; Fu et al ., 2023; Gu & Dao, 2023; Wang et al ., 2024 ) have been introduced as an alter-native to transformers, with the goal of achieving RNN-like expressive power for handling problems that are naturally stateful and sequential (Gu et al ., 2021; 2022b)."}
+{"idx": 9, "title": "9333 PDFs | Review articles in FINITE STATE MACHINES - ResearchGate", "date": "", "ddg_snippet": "Explore the latest full-text research PDFs, articles, conference papers, preprints and more on FINITE STATE MACHINES . Find methods information, sources, references or conduct a literature review ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Finite-State-Machines/publications", "content": "Explore the latest full-text research PDFs, articles, conference papers, preprints and more on FINITE STATE MACHINES . Find methods information, sources, references or conduct a literature review ..."}
diff --git a/data/sampled_jsons/MetaBBO_performance_evaluation_formula_baseline_algorithm_mean_standard_deviation_statistical_test_year_2024.jsonl b/data/sampled_jsons/MetaBBO_performance_evaluation_formula_baseline_algorithm_mean_standard_deviation_statistical_test_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..77cdd4129368580885a464ca1c74360713557ece
--- /dev/null
+++ b/data/sampled_jsons/MetaBBO_performance_evaluation_formula_baseline_algorithm_mean_standard_deviation_statistical_test_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Standard deviation - Wikipedia", "date": "", "ddg_snippet": "The standard deviation of a random variable, sample, statistical population, data set, or probability distribution is the square root of its variance. Standard deviation can also be used to calculate standard error for a finite sample, and to determine statistical significance.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Standard_deviation", "content": "The standard deviation of a random variable, sample, statistical population, data set, or probability distribution is the square root of its variance. Standard deviation can also be used to calculate standard error for a finite sample, and to determine statistical significance."}
+{"idx": 1, "title": "Standard Deviation Formula , Statistics , Variance... - YouTube", "date": "", "ddg_snippet": "This statistics video tutorial explains how to use the standard deviation formula to calculate the population standard deviation .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=Uk98hiMQgN0", "content": "This statistics video tutorial explains how to use the standard deviation formula to calculate the population standard deviation ."}
+{"idx": 2, "title": "Standard Deviation - Formula | How to Calculate Standard Deviation ?", "date": "", "ddg_snippet": "We have different standard deviation formulas to find the standard deviation for sample, population, grouped data, and ungrouped data.", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/data/standard-deviation/", "content": "We have different standard deviation formulas to find the standard deviation for sample, population, grouped data, and ungrouped data."}
+{"idx": 3, "title": "How to Calculate Standard Deviation ? - GeeksforGeeks", "date": "", "ddg_snippet": "Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of data values. In simpler terms, it indicates how much the individual data points in a dataset deviate from the mean (average) of the data.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/maths/how-to-calculate-standard-deviation/", "content": "Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of data values. In simpler terms, it indicates how much the individual data points in a dataset deviate from the mean (average) of the data."}
+{"idx": 4, "title": "Standard Deviation Formula and Uses, vs. Variance", "date": "", "ddg_snippet": "Standard deviation is a statistic measuring the dispersion of a dataset relative to its mean . It is calculated as the square root of the variance. Learn how it's used.", "subpage_snippet": "", "source": "www.investopedia.com", "link": "https://www.investopedia.com/terms/s/standarddeviation.asp", "content": "Standard deviation is a statistic measuring the dispersion of a dataset relative to its mean . It is calculated as the square root of the variance. Learn how it's used."}
+{"idx": 5, "title": "Standard Deviation Formulas", "date": "", "ddg_snippet": "Standard Deviation Formulas . Deviation means how far from the normal.The Standard Deviation is a measure of how spread out numbers are. You might like to read this simpler page on Standard Deviation first. But here we explain the formulas .", "subpage_snippet": "", "source": "www.mathsisfun.com", "link": "https://www.mathsisfun.com/data/standard-deviation-formulas.html", "content": "Standard Deviation Formulas . Deviation means how far from the normal.The Standard Deviation is a measure of how spread out numbers are. You might like to read this simpler page on Standard Deviation first. But here we explain the formulas ."}
+{"idx": 6, "title": "How to Calculate Standard Deviation : 12 Steps (with Pictures)", "date": "", "ddg_snippet": "Standard deviation tells you how spread out the numbers are in a sample.Look at your data set. This is a crucial step in any type of statistical calculation, even if it is a simple figure like the mean or median.[2] X Research source. Know how many numbers are in your sample.", "subpage_snippet": "", "source": "www.wikihow.com", "link": "https://www.wikihow.com/Calculate-Standard-Deviation", "content": "Standard deviation tells you how spread out the numbers are in a sample.Look at your data set. This is a crucial step in any type of statistical calculation, even if it is a simple figure like the mean or median.[2] X Research source. Know how many numbers are in your sample."}
+{"idx": 7, "title": "Standard Deviation Calculator", "date": "", "ddg_snippet": "Quick and easy to use stdev calculator, that also outputs variance, standard error of the mean (SEM), mean , range, and count. Learn what standard deviation is in statistics and probability theory, what is the formula for standard deviation , and practical examples.", "subpage_snippet": "", "source": "www.gigacalculator.com", "link": "https://www.gigacalculator.com/calculators/standard-deviation-calculator.php", "content": "Quick and easy to use stdev calculator, that also outputs variance, standard error of the mean (SEM), mean , range, and count. Learn what standard deviation is in statistics and probability theory, what is the formula for standard deviation , and practical examples."}
+{"idx": 8, "title": "Relative Standard Deviation Calculator - Inch Calculator", "date": "", "ddg_snippet": "Use our relative standard deviation calculator to find the RSD for a set of data. Plus, learn the formula and steps to find it.", "subpage_snippet": "", "source": "www.inchcalculator.com", "link": "https://www.inchcalculator.com/relative-standard-deviation-calculator/", "content": "Use our relative standard deviation calculator to find the RSD for a set of data. Plus, learn the formula and steps to find it."}
+{"idx": 9, "title": "Standard Deviation Calculator", "date": "", "ddg_snippet": "This free standard deviation calculator computes the standard deviation , variance, mean , sum, and error margin of a given data set.", "subpage_snippet": "", "source": "www.calculator.net", "link": "https://www.calculator.net/standard-deviation-calculator.html", "content": "This free standard deviation calculator computes the standard deviation , variance, mean , sum, and error margin of a given data set."}
diff --git a/data/sampled_jsons/Mistral-7B_benchmark_performance_language_model.jsonl b/data/sampled_jsons/Mistral-7B_benchmark_performance_language_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1aa874c6a4525019086cd21ec111b72562c2b0ea
--- /dev/null
+++ b/data/sampled_jsons/Mistral-7B_benchmark_performance_language_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2310.06825] Mistral 7B - arXiv.org", "date": "", "ddg_snippet": "Oct 10, 2023 · We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B across all evaluated benchmarks , and Llama 1 34B in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.06825", "content": "Oct 10, 2023 · We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B across all evaluated benchmarks , and Llama 1 34B in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle ..."}
+{"idx": 1, "title": "Models Benchmarks | Mistral AI", "date": "", "ddg_snippet": "LLM (Large Language Model ) benchmarks are standardized tests or datasets used to evaluate the performance of large language models. These benchmarks help researchers and developers understand the strengths and weaknesses of their models and compare them with other models in a systematic way.", "subpage_snippet": "", "source": "docs.mistral.ai", "link": "https://docs.mistral.ai/getting-started/models/benchmark/", "content": "LLM (Large Language Model ) benchmarks are standardized tests or datasets used to evaluate the performance of large language models. These benchmarks help researchers and developers understand the strengths and weaknesses of their models and compare them with other models in a systematic way."}
+{"idx": 2, "title": "Mistral 7B: Basics, Benchmarks, and How to Get Started - Acorn", "date": "", "ddg_snippet": "Jun 3, 2024 · What Is Mistral 7B ? Mistral 7B is a large language model (LLM) developed by Mistral AI, featuring 7.3 billion parameters. This model has been open-sourced under the Apache 2.0 license, offering the community free and unrestricted access to its capabilities. The model is recognized for its exceptional performance , surpassing other models of similar size in benchmarks . Notably, it achieves ...", "subpage_snippet": "", "source": "www.acorn.io", "link": "https://www.acorn.io/resources/learning-center/mistral-7b/", "content": "Jun 3, 2024 · What Is Mistral 7B ? Mistral 7B is a large language model (LLM) developed by Mistral AI, featuring 7.3 billion parameters. This model has been open-sourced under the Apache 2.0 license, offering the community free and unrestricted access to its capabilities. The model is recognized for its exceptional performance , surpassing other models of similar size in benchmarks . Notably, it achieves ..."}
+{"idx": 3, "title": "Benchmarking Mistral-7B: Latency, Cost, RPS Analysis", "date": "", "ddg_snippet": "Oct 9, 2023 · We benchmark the performance of Mistral-7B in this article from latency, cost, and requests per second perspective. This will help us evaluate if it can be a good choice based on the business requirements. Please note that we don't cover the qualitative performance in this article - there are different methods to compare LLMs which can be found here. Model : Mistral-7B In this blog, we have ...", "subpage_snippet": "", "source": "www.truefoundry.com", "link": "https://www.truefoundry.com/blog/benchmarking-mistral-7b", "content": "Oct 9, 2023 · We benchmark the performance of Mistral-7B in this article from latency, cost, and requests per second perspective. This will help us evaluate if it can be a good choice based on the business requirements. Please note that we don't cover the qualitative performance in this article - there are different methods to compare LLMs which can be found here. Model : Mistral-7B In this blog, we have ..."}
+{"idx": 4, "title": "Mistral 7B: Specifications, Performance & Applications", "date": "", "ddg_snippet": "Apr 24, 2025 · In the rapidly evolving landscape of artificial intelligence, the Mistral 7B model emerges as a groundbreaking innovation that is capturing the attention of researchers, developers, and industry professionals worldwide. This powerful language model represents a significant leap forward in machine learning capabilities, offering unprecedented performance and versatility across multiple domains ...", "subpage_snippet": "", "source": "www.byteplus.com", "link": "https://www.byteplus.com/en/topic/431034?title=mistral-7b-revolutionizing-ai-model-performance-and-applications", "content": "Apr 24, 2025 · In the rapidly evolving landscape of artificial intelligence, the Mistral 7B model emerges as a groundbreaking innovation that is capturing the attention of researchers, developers, and industry professionals worldwide. This powerful language model represents a significant leap forward in machine learning capabilities, offering unprecedented performance and versatility across multiple domains ..."}
+{"idx": 5, "title": "Mistral 7B Explained: Towards More Efficient Language Models", "date": "", "ddg_snippet": "Nov 26, 2024 · With the release of Mistral 7B , Mistral AI entered the LLM space at a time when model size was the main factor driving performance . Rather than following the trend of ever-larger models, Mistral AI distinguished themselves by emphasising innovative, memory-efficient designs that deliver impressive results with a fraction of the parameters.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/mistral-7b-explained-towards-more-efficient-language-models-7f9c6e6b7251/", "content": "Nov 26, 2024 · With the release of Mistral 7B , Mistral AI entered the LLM space at a time when model size was the main factor driving performance . Rather than following the trend of ever-larger models, Mistral AI distinguished themselves by emphasising innovative, memory-efficient designs that deliver impressive results with a fraction of the parameters."}
+{"idx": 6, "title": "Mistral Models vs. Competitors: A Performance Showdown Across ...", "date": "", "ddg_snippet": "The provided figures illustrate the performance benchmarks of the Mistral 7B model compared to other popular models, such as LLaMA 2 ( 7B and 13B), Code LLaMA 7B , and LLaMA 1 34B, across a range of NLP tasks.", "subpage_snippet": "", "source": "hub.researchgraph.org", "link": "https://hub.researchgraph.org/mistral-models-vs-competitors-a-performance-showdown-across-nlp-code-and-multilingual-tasks/", "content": "The provided figures illustrate the performance benchmarks of the Mistral 7B model compared to other popular models, such as LLaMA 2 ( 7B and 13B), Code LLaMA 7B , and LLaMA 1 34B, across a range of NLP tasks."}
+{"idx": 7, "title": "Mistral 7B", "date": "", "ddg_snippet": "27 Sept 2023 — Mistral 7B is a 7.3B parameter model that: Outperforms Llama 2 13B on all benchmarks ; Outperforms Llama 1 34B on many benchmarks; Approaches ...", "subpage_snippet": "", "source": "mistral.ai", "link": "https://mistral.ai/news/announcing-mistral-7b", "content": "27 Sept 2023 — Mistral 7B is a 7.3B parameter model that: Outperforms Llama 2 13B on all benchmarks ; Outperforms Llama 1 34B on many benchmarks; Approaches ..."}
+{"idx": 8, "title": "Mistral 7B Explained: Towards More Efficient Language ...", "date": "", "ddg_snippet": "The overall trend shows that Mistral 7B outperforms Llama 2 13B across all metrics the models were evaluated on, often by a considerable margin ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/mistral-7b-explained-towards-more-efficient-language-models-7f9c6e6b7251", "content": "The overall trend shows that Mistral 7B outperforms Llama 2 13B across all metrics the models were evaluated on, often by a considerable margin ..."}
+{"idx": 9, "title": "mistralai/Mistral-7B-v0.1", "date": "", "ddg_snippet": "Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested. For full details of this model please read our paper and release blog post.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/mistralai/Mistral-7B-v0.1", "content": "Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested. For full details of this model please read our paper and release blog post."}
diff --git a/data/sampled_jsons/ModelGo_Licenses_MGL_vs_Responsible_AI_Licenses_RAIL_key_differences_comparison.jsonl b/data/sampled_jsons/ModelGo_Licenses_MGL_vs_Responsible_AI_Licenses_RAIL_key_differences_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..19de259a19f3aeb9ff53f244f64d81d21528556d
--- /dev/null
+++ b/data/sampled_jsons/ModelGo_Licenses_MGL_vs_Responsible_AI_Licenses_RAIL_key_differences_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FAQ — Responsible AI Licenses (RAIL)", "date": "", "ddg_snippet": "What is RAIL and OpenRAIL? Responsible AI Licenses ( RAIL ) are a class of licenses designed to encourage the responsible use of an AI artifact being licensed by including a set of use restrictions applied to AI artifact. RAILs can be more or less restrictive depending on the aims of the licensor. For instance, a license can be RAIL while being a proprietary license , or a license just allowing ...", "subpage_snippet": "", "source": "www.licenses.ai", "link": "https://www.licenses.ai/faq-2", "content": "What is RAIL and OpenRAIL? Responsible AI Licenses ( RAIL ) are a class of licenses designed to encourage the responsible use of an AI artifact being licensed by including a set of use restrictions applied to AI artifact. RAILs can be more or less restrictive depending on the aims of the licensor. For instance, a license can be RAIL while being a proprietary license , or a license just allowing ..."}
+{"idx": 1, "title": "Responsible AI Licenses (RAIL): Here’s What You Need to Know", "date": "", "ddg_snippet": "May 22, 2024 · The RAIL Initiative-governed RAIL -A license comes in two parts, an end-user agreement and a source code license . Both reiterate each other’s use restrictions. Some (like the creators and users of the licenses ), certainly call it responsible , while others might call it paternalistic.", "subpage_snippet": "", "source": "www.mend.io", "link": "https://www.mend.io/blog/responsible-ai-licenses-rail-heres-what-you-need-to-know/", "content": "May 22, 2024 · The RAIL Initiative-governed RAIL -A license comes in two parts, an end-user agreement and a source code license . Both reiterate each other’s use restrictions. Some (like the creators and users of the licenses ), certainly call it responsible , while others might call it paternalistic."}
+{"idx": 2, "title": "How to Choose | ModelGo Licenses", "date": "", "ddg_snippet": "Agreeing with IBM AI Factsheet and Montreal Data License , we believe that transparency is crucial to increasing trust and standardization in AI . ModelGo licenses use a Model Sheet to assist model users in understanding the granted rights and restrictions in the license terms. It also helps model developers find the most suitable MG licenses .", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/get-started/how-to-choose", "content": "Agreeing with IBM AI Factsheet and Montreal Data License , we believe that transparency is crucial to increasing trust and standardization in AI . ModelGo licenses use a Model Sheet to assist model users in understanding the granted rights and restrictions in the license terms. It also helps model developers find the most suitable MG licenses ."}
+{"idx": 3, "title": "ModelGo: A Pratical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "The most popular 200 license is Open Responsible AI License (OpenRAIL) [9], which is a 201 permissive license but includes copyleft-style use-based restrictions 202 governing the use of the model and its derivatives.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Et9rHdWGAZ", "content": "The most popular 200 license is Open Responsible AI License (OpenRAIL) [9], which is a 201 permissive license but includes copyleft-style use-based restrictions 202 governing the use of the model and its derivatives."}
+{"idx": 4, "title": "#ai #llm #responsibleai #nus #license | NUS Institute of Data ...", "date": "", "ddg_snippet": "With five tailored licensing options—from attribution-only to responsible AI and open-source— ModelGo strikes a balance between openness, control, and commercialisation, ensuring a transparent ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/nus-ids_ai-llm-responsibleai-activity-7295338783384690688-CTa8", "content": "With five tailored licensing options—from attribution-only to responsible AI and open-source— ModelGo strikes a balance between openness, control, and commercialisation, ensuring a transparent ..."}
+{"idx": 5, "title": "GitHub - Xtra-Computing/ModelGo", "date": "", "ddg_snippet": "Why we need ModelGo Licenses Set? To facilitate managed sharing of models while protecting your Intellectual Property. ModelGo licenses offer flexible options to fulfill your specific licensing needs about using and distributing your deep learning models while protecting your Intellectual Property (IP).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xtra-Computing/ModelGo", "content": "Why we need ModelGo Licenses Set? To facilitate managed sharing of models while protecting your Intellectual Property. ModelGo licenses offer flexible options to fulfill your specific licensing needs about using and distributing your deep learning models while protecting your Intellectual Property (IP)."}
+{"idx": 6, "title": "From RAIL to Open RAIL: Topologies of RAIL Licenses ...", "date": "", "ddg_snippet": "Oct 22, 2023 · In essence, we could consider licenses associated with AI related artifacts to be RAIL Licenses if: they include behavioral-use restrictions which disallow/restrict certain applications by the licensee; and they require downstream use, including re-distribution, to include, at minimum, those same behavioral-use restrictions Collectively, we refer to these as the “Use Restrictions”.", "subpage_snippet": "", "source": "stream.cloudnexa.com", "link": "https://stream.cloudnexa.com/category/machine-learning/6724033/10/22/2023/from-rail-to-open-rail-topologies-of-rail-licenses-responsible-ai-licenses-rail/", "content": "Oct 22, 2023 · In essence, we could consider licenses associated with AI related artifacts to be RAIL Licenses if: they include behavioral-use restrictions which disallow/restrict certain applications by the licensee; and they require downstream use, including re-distribution, to include, at minimum, those same behavioral-use restrictions Collectively, we refer to these as the “Use Restrictions”."}
+{"idx": 7, "title": "OpenRAIL: Towards open and responsible AI licensing frameworks", "date": "", "ddg_snippet": "Open & Responsible AI licenses (\"OpenRAIL\") are AI -specific licenses enabling open access, use and distribution of AI artifacts while requiring a responsible use of the latter.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/open_rail", "content": "Open & Responsible AI licenses (\"OpenRAIL\") are AI -specific licenses enabling open access, use and distribution of AI artifacts while requiring a responsible use of the latter."}
+{"idx": 8, "title": "What are Responsible AI Licenses ( RAIL )? - AI Glossary ..", "date": "", "ddg_snippet": "Responsible AI Licenses ( RAIL ) are specialized licensing frameworks designed to ensure that artificial intelligence ( AI ) models , datasets, and tools are used in ways that align wit.", "subpage_snippet": "", "source": "www.theainavigator.com", "link": "https://www.theainavigator.com/blog/what-are-responsible-ai-licenses-rail", "content": "Responsible AI Licenses ( RAIL ) are specialized licensing frameworks designed to ensure that artificial intelligence ( AI ) models , datasets, and tools are used in ways that align wit."}
+{"idx": 9, "title": "Understanding Creative Commons Licenses | Restackio", "date": "", "ddg_snippet": "Understanding Creative Commons Licenses in AI ArtThe Role of Consent, Credit, and Compensation in AI Training DataCommunity-driven initiatives like Responsible AI Licences ( RAIL )...", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/artificial-intelligence-applications-answer-creative-commons", "content": "Understanding Creative Commons Licenses in AI ArtThe Role of Consent, Credit, and Compensation in AI Training DataCommunity-driven initiatives like Responsible AI Licences ( RAIL )..."}
diff --git a/data/sampled_jsons/ModelGo_Licenses_sitemodelgo.li_year_2024.jsonl b/data/sampled_jsons/ModelGo_Licenses_sitemodelgo.li_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f8aeaa46a9e356761f8b25206f437cf29f99a8fa
--- /dev/null
+++ b/data/sampled_jsons/ModelGo_Licenses_sitemodelgo.li_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Standard Way for Model Publishing | ModelGo Licenses", "date": "", "ddg_snippet": "Towards Better Model Sharing. ModelGo License Options. Get Started. About ModelGo Licenses .Welcome to ModelGo Licenses - A standard way to publish your AI models .", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/", "content": "Towards Better Model Sharing. ModelGo License Options. Get Started. About ModelGo Licenses .Welcome to ModelGo Licenses - A standard way to publish your AI models ."}
+{"idx": 1, "title": "Capnostream ™ 20p Bedside Capnography Monitor | Medtronic", "date": "", "ddg_snippet": "The Capnostream™ 20p monitor, with the apnea-sat alert feature, alerts caregivers to repetitive patterns of apneas and oxygen desaturation, revealing a more complete picture of a patient’s respiratory status.", "subpage_snippet": "", "source": "www.medtronic.com", "link": "https://www.medtronic.com/en-us/healthcare-professionals/products/patient-monitoring/capnography-monitoring/bedside-capnography-monitors/capnostream-20p-bedside-capnography-monitor.html", "content": "The Capnostream™ 20p monitor, with the apnea-sat alert feature, alerts caregivers to repetitive patterns of apneas and oxygen desaturation, revealing a more complete picture of a patient’s respiratory status."}
+{"idx": 2, "title": "Medtronic - Capnostream 35 Community, Manuals and ... - MedWrench", "date": "", "ddg_snippet": "The Capnostream™ 35 portable respiratory monitor offers you comprehensive respiratory monitoring to help you respond earlier and intervene sooner if your patient is showing signs of respiratory compromise. The monitor provides information to help support clinical decisions and may improve workflow.", "subpage_snippet": "", "source": "www.medwrench.com", "link": "https://www.medwrench.com/equipment/14585/medtronic-capnostream-35", "content": "The Capnostream™ 35 portable respiratory monitor offers you comprehensive respiratory monitoring to help you respond earlier and intervene sooner if your patient is showing signs of respiratory compromise. The monitor provides information to help support clinical decisions and may improve workflow."}
+{"idx": 3, "title": "Portable Bedside Capnograph/Pulse Oximeter", "date": "", "ddg_snippet": "The Capnostream bedside monitor provides accurate, continuous capnography and pulse oximetry monitoring for intubated and non-intubated patients from neonate to adult.", "subpage_snippet": "", "source": "sakomed.com", "link": "https://sakomed.com/wp-content/uploads/2025/01/Masimo-Capnostream-20-Operators-Manual.pdf", "content": "The Capnostream bedside monitor provides accurate, continuous capnography and pulse oximetry monitoring for intubated and non-intubated patients from neonate to adult."}
+{"idx": 4, "title": "CAPNOSTREAM ™ 3 - Bound Tree", "date": "", "ddg_snippet": "nvironment? Yes. The CapnostreamTM35 portable respiratory monitor is intended for use in Emergency Medical Service applications, including ground . d air transport. It is also intended for use in-hospital, across the continuum of care: resuscitation/crash cart, procedural suites, general care floor, emergency room and intra-h.", "subpage_snippet": "", "source": "cdn.boundtree.com", "link": "https://cdn.boundtree.com/btm/products/BTM_Capnostream_35_FAQs_8893622321182.pdf", "content": "nvironment? Yes. The CapnostreamTM35 portable respiratory monitor is intended for use in Emergency Medical Service applications, including ground . d air transport. It is also intended for use in-hospital, across the continuum of care: resuscitation/crash cart, procedural suites, general care floor, emergency room and intra-h."}
+{"idx": 5, "title": "Capnostream™ 35 Portable Respiratory Monitor - Medtronic", "date": "", "ddg_snippet": "The Capnostream™ 35 portable respiratory monitor provides insights on respiratory status that clinicians can use to intervene early and fast if a patient begins showing signs of respiratory compromise.", "subpage_snippet": "", "source": "www.medtronic.com", "link": "https://www.medtronic.com/en-us/healthcare-professionals/products/patient-monitoring/capnography-monitoring/portable-respiratory-monitors/capnostream-35-portable-respiratory-monitor.html", "content": "The Capnostream™ 35 portable respiratory monitor provides insights on respiratory status that clinicians can use to intervene early and fast if a patient begins showing signs of respiratory compromise."}
+{"idx": 6, "title": "Using the Monitor: Capnostream™ 35 Portable Respiratory Monitor", "date": "", "ddg_snippet": "This video will help you learn how to operate the Capnostream™ 35 Portable Respiratory Monitor : How to turn on the monitor, how to classify your patient, how to create a new patient case, and ...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=3bzX0REcUL8", "content": "This video will help you learn how to operate the Capnostream™ 35 Portable Respiratory Monitor : How to turn on the monitor, how to classify your patient, how to create a new patient case, and ..."}
+{"idx": 7, "title": "Capnography – Healthcare21", "date": "", "ddg_snippet": "The Capnostream™ 35 portable respiratory monitor delivers real-time, continuous monitoring of your patient’s respiratory status by measuring etCO2, SpO2, respiration rate, and pulse rate.", "subpage_snippet": "", "source": "healthcare21.eu", "link": "https://healthcare21.eu/product/capnography/", "content": "The Capnostream™ 35 portable respiratory monitor delivers real-time, continuous monitoring of your patient’s respiratory status by measuring etCO2, SpO2, respiration rate, and pulse rate."}
+{"idx": 8, "title": "Covidien Capnostream 20p Featuring Microstream with Alarm...", "date": "", "ddg_snippet": "The Capnostream 20p patient monitor is built on a legacy of proven performance. For nearly two decades, clinicians have relied on Microstream technology for an accurate, continuous view of ventilation adequacy in intubated and non-intubated patients, from neonate to adult.", "subpage_snippet": "", "source": "www.somatechnology.com", "link": "https://www.somatechnology.com/capnography-monitors/covidien-capnostream-20p", "content": "The Capnostream 20p patient monitor is built on a legacy of proven performance. For nearly two decades, clinicians have relied on Microstream technology for an accurate, continuous view of ventilation adequacy in intubated and non-intubated patients, from neonate to adult."}
+{"idx": 9, "title": "Capnostream ™ 35 portable respiratory monitor - SakoMed", "date": "", "ddg_snippet": "A monitor that takes the journey with patients during their hospital stay, the Capnostream™ 35 portable respiratory monitor delivers continuous monitoring in virtually any clinical setting.", "subpage_snippet": "", "source": "sakomed.com", "link": "https://sakomed.com/products/patient-monitors/capnostream-35-portable-respiratory-monitor/", "content": "A monitor that takes the journey with patients during their hospital stay, the Capnostream™ 35 portable respiratory monitor delivers continuous monitoring in virtually any clinical setting."}
diff --git a/data/sampled_jsons/MovieLens_100k_dataset_GroupLens_University_of_Minnesota.jsonl b/data/sampled_jsons/MovieLens_100k_dataset_GroupLens_University_of_Minnesota.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6fbc66bf0a3d679585eff8ce17cec90fe18c5742
--- /dev/null
+++ b/data/sampled_jsons/MovieLens_100k_dataset_GroupLens_University_of_Minnesota.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MovieLens 100 K Dataset | GroupLens", "date": "", "ddg_snippet": "Stable benchmark dataset . 100,000 ratings from 1000 users on 1700 movies. Released 4/1998. README.txt ml- 100 k .zip (size: 5 MB, checksum) Index of unzipped files Permal…", "subpage_snippet": "", "source": "grouplens.org", "link": "https://grouplens.org/datasets/movielens/100k/", "content": "Stable benchmark dataset . 100,000 ratings from 1000 users on 1700 movies. Released 4/1998. README.txt ml- 100 k .zip (size: 5 MB, checksum) Index of unzipped files Permal…"}
+{"idx": 1, "title": "MovieLens 100 K Dataset", "date": "", "ddg_snippet": "About Dataset . Summary & usage license. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota .", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/prajitdatta/movielens-100k-dataset", "content": "About Dataset . Summary & usage license. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota ."}
+{"idx": 2, "title": "R: MovieLens 100 K Dataset", "date": "", "ddg_snippet": "MovieLens 100 K Dataset . Description. This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota .", "subpage_snippet": "", "source": "search.r-project.org", "link": "https://search.r-project.org/CRAN/refmans/rsparse/html/movielens100k.html", "content": "MovieLens 100 K Dataset . Description. This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota ."}
+{"idx": 3, "title": "classic.d2l.ai/_sources/chapter_recommender-systems/ movielens .rst.txt", "date": "", "ddg_snippet": "MovieLens datasets are widely used for recommendation research. It is publicly available and free to use. - We define functions to download and preprocess the MovieLens 100 k dataset for further use in later sections.", "subpage_snippet": "", "source": "classic.d2l.ai", "link": "https://classic.d2l.ai/_sources/chapter_recommender-systems/movielens.rst.txt", "content": "MovieLens datasets are widely used for recommendation research. It is publicly available and free to use. - We define functions to download and preprocess the MovieLens 100 k dataset for further use in later sections."}
+{"idx": 4, "title": "MovieLens 100 K - River", "date": "", "ddg_snippet": "MovieLens datasets were collected by the GroupLens Research Project at the University of Minnesota . This dataset consists of 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies.", "subpage_snippet": "", "source": "riverml.xyz", "link": "https://riverml.xyz/0.13.0/api/datasets/MovieLens100K/", "content": "MovieLens datasets were collected by the GroupLens Research Project at the University of Minnesota . This dataset consists of 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies."}
+{"idx": 5, "title": "movielens .ipynb - Colab", "date": "", "ddg_snippet": "It is created in 1997 and run by GroupLens , a research lab at the University of Minnesota , in order to gather movie rating data for research purposes.We will use the MovieLens 100 K dataset :cite: Herlocker.Konstan.Borchers.ea.1999 . This dataset is comprised of 100,000.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/d2l-ai/d2l-en-colab/blob/master/chapter_recommender-systems/movielens.ipynb", "content": "It is created in 1997 and run by GroupLens , a research lab at the University of Minnesota , in order to gather movie rating data for research purposes.We will use the MovieLens 100 K dataset :cite: Herlocker.Konstan.Borchers.ea.1999 . This dataset is comprised of 100,000."}
+{"idx": 6, "title": "ml 100 k : Movielens 100 K Dataset in rrecsys: Environment for...", "date": "", "ddg_snippet": "MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies.", "subpage_snippet": "", "source": "rdrr.io", "link": "https://rdrr.io/cran/rrecsys/man/ml100k.html", "content": "MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies."}
+{"idx": 7, "title": "movielens | TensorFlow Datasets", "date": "", "ddg_snippet": "This dataset contains a set of movie ratings from the MovieLens website, a movie recommendation service. This dataset was collected and maintained by GroupLens , a research group at the University of Minnesota .", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/datasets/catalog/movielens", "content": "This dataset contains a set of movie ratings from the MovieLens website, a movie recommendation service. This dataset was collected and maintained by GroupLens , a research group at the University of Minnesota ."}
+{"idx": 8, "title": "movielens 100 k function - RDocumentation", "date": "", "ddg_snippet": "movielens 100 k : MovieLens 100 K Dataset . Description. This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota .", "subpage_snippet": "", "source": "www.rdocumentation.org", "link": "https://www.rdocumentation.org/packages/rsparse/versions/0.5.3/topics/movielens100k", "content": "movielens 100 k : MovieLens 100 K Dataset . Description. This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota ."}
+{"idx": 9, "title": "MovieLens Dataset : The Essential Benchmark for... | Shaped Blog", "date": "", "ddg_snippet": "What is the MovieLens Dataset ? MovieLens represents not just one dataset , but a collection of movie rating datasets of various sizes. They are curated and made available by GroupLens Research, a respected lab at the University of Minnesota .", "subpage_snippet": "", "source": "www.shaped.ai", "link": "https://www.shaped.ai/blog/movielens-dataset-the-essential-benchmark-for-recommender-systems", "content": "What is the MovieLens Dataset ? MovieLens represents not just one dataset , but a collection of movie rating datasets of various sizes. They are curated and made available by GroupLens Research, a respected lab at the University of Minnesota ."}
diff --git a/data/sampled_jsons/MovieLens_100k_dataset_origin_GroupLens_Research_University_Minnesota.jsonl b/data/sampled_jsons/MovieLens_100k_dataset_origin_GroupLens_Research_University_Minnesota.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3c6159b90ddc5b8e62711ddf1640f8a08dfca6bc
--- /dev/null
+++ b/data/sampled_jsons/MovieLens_100k_dataset_origin_GroupLens_Research_University_Minnesota.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "movie_lens | TensorFlow Datasets", "date": "", "ddg_snippet": "Pre-trained models and datasets built ... austin_sailor_ dataset _converted_externally_to_rlds ... austin_sirius_ dataset _converted_externally_to_rlds", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/datasets/catalog/movie_lens", "content": "Pre-trained models and datasets built ... austin_sailor_ dataset _converted_externally_to_rlds ... austin_sirius_ dataset _converted_externally_to_rlds"}
+{"idx": 1, "title": "GroupLens", "date": "", "ddg_snippet": "SUMMARY & USAGE LICENSE ============================================= MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. * Each user has rated at least 20 movies. * Simple demographic info for the users (age, gender, occupation, zip) The data was collected through ...", "subpage_snippet": "", "source": "files.grouplens.org", "link": "https://files.grouplens.org/datasets/movielens/ml-100k-README.txt", "content": "SUMMARY & USAGE LICENSE ============================================= MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. * Each user has rated at least 20 movies. * Simple demographic info for the users (age, gender, occupation, zip) The data was collected through ..."}
+{"idx": 2, "title": "R: Movielens 100K Dataset", "date": "", "ddg_snippet": "Movielens 100K Dataset Description MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies.", "subpage_snippet": "", "source": "search.r-project.org", "link": "https://search.r-project.org/CRAN/refmans/rrecsys/html/ml100k.html", "content": "Movielens 100K Dataset Description MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies."}
+{"idx": 3, "title": "mkbe/datasets/Movielens-100k plus/Movielens-100k/README at master ...", "date": "", "ddg_snippet": "SUMMARY & USAGE LICENSE ============================================= MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. * Each user has rated at least 20 movies. * Simple demographic info for the users (age, gender, occupation, zip) The data was collected through ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pouyapez/mkbe/blob/master/datasets/Movielens-100k+plus/Movielens-100k/README", "content": "SUMMARY & USAGE LICENSE ============================================= MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. * Each user has rated at least 20 movies. * Simple demographic info for the users (age, gender, occupation, zip) The data was collected through ..."}
+{"idx": 4, "title": "movielens100k dataset | R PACKAGES", "date": "", "ddg_snippet": "MovieLens 100K Dataset This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . Format A sparse column-compressed matrix (Matrix::dgCMatrix) with 943 rows and 1682 columns. rows are ...", "subpage_snippet": "", "source": "r-packages.io", "link": "https://r-packages.io/datasets/movielens100k", "content": "MovieLens 100K Dataset This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . Format A sparse column-compressed matrix (Matrix::dgCMatrix) with 943 rows and 1682 columns. rows are ..."}
+{"idx": 5, "title": "movie-lens-100k - Kaggle", "date": "", "ddg_snippet": "SUMMARY & USAGE LICENSE ============================================= MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. * Each user has rated at least 20 movies. * Simple demographic info for the users (age, gender, occupation, zip) The data was collected through ...", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/ngobahoangquan/movie-lens-100k", "content": "SUMMARY & USAGE LICENSE ============================================= MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. * Each user has rated at least 20 movies. * Simple demographic info for the users (age, gender, occupation, zip) The data was collected through ..."}
+{"idx": 6, "title": "MovieLens100K - River", "date": "", "ddg_snippet": "MovieLens 100K dataset . MovieLens datasets were collected by the GroupLens Research Project at the University of Minnesota . This dataset consists of 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. User and movie information are provided.", "subpage_snippet": "", "source": "riverml.xyz", "link": "https://riverml.xyz/0.21.2/api/datasets/MovieLens100K/", "content": "MovieLens 100K dataset . MovieLens datasets were collected by the GroupLens Research Project at the University of Minnesota . This dataset consists of 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. User and movie information are provided."}
+{"idx": 7, "title": "MovieLens 100K Dataset - GroupLens", "date": "", "ddg_snippet": "MovieLens 100K movie ratings. Stable benchmark dataset . 100,000 ratings from 1000 users on 1700 movies. Released 4/1998. README.txt ml- 100k .zip (size: 5 MB, checksum) Index of unzipped files Permal…", "subpage_snippet": "", "source": "grouplens.org", "link": "https://grouplens.org/datasets/movielens/100k/", "content": "MovieLens 100K movie ratings. Stable benchmark dataset . 100,000 ratings from 1000 users on 1700 movies. Released 4/1998. README.txt ml- 100k .zip (size: 5 MB, checksum) Index of unzipped files Permal…"}
+{"idx": 8, "title": "ml100k: Movielens 100K Dataset in rrecsys: Environment for Evaluating ...", "date": "", "ddg_snippet": "Description Source Description MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. The data was collected through the MovieLens web site ( movielens .umn.edu) during the seven-month period from September 19th, 1997 through April ...", "subpage_snippet": "", "source": "rdrr.io", "link": "https://rdrr.io/cran/rrecsys/man/ml100k.html", "content": "Description Source Description MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. The data was collected through the MovieLens web site ( movielens .umn.edu) during the seven-month period from September 19th, 1997 through April ..."}
+{"idx": 9, "title": "movielens_100k/README.md at main - GitHub", "date": "", "ddg_snippet": "MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. * Each user has rated at least 20 movies. * Simple demographic info for the users (age, gender, occupation, zip) The ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MuhammadKurniaSani-me/movielens_100k/blob/main/README.md", "content": "MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota . This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. * Each user has rated at least 20 movies. * Simple demographic info for the users (age, gender, occupation, zip) The ..."}
diff --git a/data/sampled_jsons/Multi-View_Causal_Representation_Learning_with_Partial_Observability_Yao_et_al.jsonl b/data/sampled_jsons/Multi-View_Causal_Representation_Learning_with_Partial_Observability_Yao_et_al.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a217e7a4bf615145f9cbef1533454edbf84e87a5
--- /dev/null
+++ b/data/sampled_jsons/Multi-View_Causal_Representation_Learning_with_Partial_Observability_Yao_et_al.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2311.04056] Multi - View Causal Representation Learning with ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Multi - View Causal Representation Learning with Partial Observability , by Dingling Yao and 6 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.04056", "content": "View a PDF of the paper titled Multi - View Causal Representation Learning with Partial Observability , by Dingling Yao and 6 other authors."}
+{"idx": 1, "title": "Multi - View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "@conference{Yaoetal24, title = { Multi - View Causal Representation Learning with Partial Observability }, booktitle = {The Twelfth International Conference on Learning Representations (ICLR)}, month = may, year = {2024}, slug = {yaoetal24}, author = { Yao , D. and Xu, D...", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/al/publications/yaoetal24", "content": "@conference{Yaoetal24, title = { Multi - View Causal Representation Learning with Partial Observability }, booktitle = {The Twelfth International Conference on Learning Representations (ICLR)}, month = may, year = {2024}, slug = {yaoetal24}, author = { Yao , D. and Xu, D..."}
+{"idx": 2, "title": "Multi - View Causal Representation Learning with", "date": "", "ddg_snippet": "Dependent Latents Multi -Modal Partial Observability > 2 Views . Gresele et al .In the context of causal representation learning (CRL), Sturma et al . [52] also explicitly consider partial observability in a linear, multi -domain setting, Buchholz et al .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E8IhOxNREv", "content": "Dependent Latents Multi -Modal Partial Observability > 2 Views . Gresele et al .In the context of causal representation learning (CRL), Sturma et al . [52] also explicitly consider partial observability in a linear, multi -domain setting, Buchholz et al ."}
+{"idx": 3, "title": "Paper page - Multi - View Causal Representation Learning with ...", "date": "", "ddg_snippet": "A unified framework using contrastive learning and multiple partial views allows for the identification of latent variables with minimal assumptions, improving representation learning . AI-generated summary.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2311.04056", "content": "A unified framework using contrastive learning and multiple partial views allows for the identification of latent variables with minimal assumptions, improving representation learning . AI-generated summary."}
+{"idx": 4, "title": "Multi - View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "ServiceNow Research. Publications. Multi - View Causal Representation Learning with Partial Observability .Overall, we find that access to multiple partial views enables to identify a more fine-grained representation , under the generally milder assumption of partial observability .", "subpage_snippet": "", "source": "www.servicenow.com", "link": "https://www.servicenow.com/research/publication/dingling-yao-mult-neurips-workshops2023.html", "content": "ServiceNow Research. Publications. Multi - View Causal Representation Learning with Partial Observability .Overall, we find that access to multiple partial views enables to identify a more fine-grained representation , under the generally milder assumption of partial observability ."}
+{"idx": 5, "title": "ICLR Poster Multi - View Causal Representation Learning with ...", "date": "", "ddg_snippet": "Overall, we find that access to multiple partial views offers unique opportunities for identifiable representation learning , enabling the discovery of latent structures from purely observational data.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/poster/18749", "content": "Overall, we find that access to multiple partial views offers unique opportunities for identifiable representation learning , enabling the discovery of latent structures from purely observational data."}
+{"idx": 6, "title": "Multi - View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "Overall, we find that access to multiple partial views enables us to identify a more fine-grained representation , under the generally milder assumption of partial observability .", "subpage_snippet": "", "source": "perouz.github.io", "link": "https://perouz.github.io/publication/yao-2023-multiview/", "content": "Overall, we find that access to multiple partial views enables us to identify a more fine-grained representation , under the generally milder assumption of partial observability ."}
+{"idx": 7, "title": "GitHub - CausalLearningAI/ multiview -crl", "date": "", "ddg_snippet": "Multi - View Causal Representation Learning with Partial Observability .Download the dataset Multimodal3DIdent [Daunhawer et al . ICLR 2023]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CausalLearningAI/multiview-crl", "content": "Multi - View Causal Representation Learning with Partial Observability .Download the dataset Multimodal3DIdent [Daunhawer et al . ICLR 2023]"}
+{"idx": 8, "title": "Dingling Yao - Google Scholar", "date": "", "ddg_snippet": "Multi - view causal representation learning with partial observability .2024. Unifying Causal Representation Learning with the Invariance Principle.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=vOJUvb0AAAAJ&hl=en", "content": "Multi - view causal representation learning with partial observability .2024. Unifying Causal Representation Learning with the Invariance Principle."}
+{"idx": 9, "title": "Multi - View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "Unified Framework for Multi - View Causal Representation Learning with Partial Observability .", "subpage_snippet": "", "source": "linnk.ai", "link": "https://linnk.ai/da/insight/machine-learning/unified-framework-for-multi-view-causal-representation-learning-with-partial-observability-ng7xYS9D/", "content": "Unified Framework for Multi - View Causal Representation Learning with Partial Observability ."}
diff --git a/data/sampled_jsons/MultiPDENet_Section_3.2.4_MaNN_Block_macro-scale.jsonl b/data/sampled_jsons/MultiPDENet_Section_3.2.4_MaNN_Block_macro-scale.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..36876981a9c15077cccc1406ff746f52080a371e
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_Section_3.2.4_MaNN_Block_macro-scale.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2501.15987] MultiPDENet: PDE-embedded Learning with Multi ... Block Scaling — NVIDIA cuDNN Frontend What is the difference between micro, meso, and macro ... [PDF] MultiPDENet: PDE-embedded Learning with Multi-time ... Multi-Scale System Design | Mechanical Engineering | MIT ...", "date": "", "ddg_snippet": "Jan 27, 2025 · To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows. Mar 13, 2025 · The block scale quantize operation computes the quantized output and scaling factor tensors from a higher precision tensor. The MXFP8 recipe quantizes across 32 FP32 elements along the rows (and optionally columns) to produce 32 FP8 output values (E4M3 or E5M2) and 1 FP8 scaling factor (E8M0). Aug 2 , 2014 · What is the difference between micro, meso, and macro modeling of a composite for analysis ? In multi scale modeling, what are the deciding factors that dictates selection of particular scale of ... Jan 27, 2025 · A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations (PDEs) by numerical methods meet ... Multi- scale systems (MuSS) consist of components from two or more length scales (nano, micro, meso, or macro -scales). In MuSS, the engineering modeling, design principles, and fabrication processes of the components are fundamentally different. The challenge is to make these components so they are conceptually and model-wise compatible with other- scale components with which they interface ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.15987", "content": "Jan 27, 2025 · To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows. Mar 13, 2025 · The block scale quantize operation computes the quantized output and scaling factor tensors from a higher precision tensor. The MXFP8 recipe quantizes across 32 FP32 elements along the rows (and optionally columns) to produce 32 FP8 output values (E4M3 or E5M2) and 1 FP8 scaling factor (E8M0). Aug 2 , 2014 · What is the difference between micro, meso, and macro modeling of a composite for analysis ? In multi scale modeling, what are the deciding factors that dictates selection of particular scale of ... Jan 27, 2025 · A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations (PDEs) by numerical methods meet ... Multi- scale systems (MuSS) consist of components from two or more length scales (nano, micro, meso, or macro -scales). In MuSS, the engineering modeling, design principles, and fabrication processes of the components are fundamentally different. The challenge is to make these components so they are conceptually and model-wise compatible with other- scale components with which they interface ..."}
+{"idx": 1, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping", "date": "", "ddg_snippet": "Here, δ 𝐮 m k is the incremental update by the Physics Block (see Section 3.2.2) at each micro step, as shown in Eq. (3), where M denotes the number of micro- scale time steps in one macro-scale step (e.g., M = 4 herein).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "Here, δ 𝐮 m k is the incremental update by the Physics Block (see Section 3.2.2) at each micro step, as shown in Eq. (3), where M denotes the number of micro- scale time steps in one macro-scale step (e.g., M = 4 herein)."}
+{"idx": 2, "title": "Block Scaling — NVIDIA cuDNN Frontend", "date": "", "ddg_snippet": "Mar 13, 2025 · The block scale quantize operation computes the quantized output and scaling factor tensors from a higher precision tensor. The MXFP8 recipe quantizes across 32 FP32 elements along the rows (and optionally columns) to produce 32 FP8 output values (E4M3 or E5M2) and 1 FP8 scaling factor (E8M0).", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/deeplearning/cudnn/frontend/v1.10.0/operations/BlockScaling.html", "content": "Mar 13, 2025 · The block scale quantize operation computes the quantized output and scaling factor tensors from a higher precision tensor. The MXFP8 recipe quantizes across 32 FP32 elements along the rows (and optionally columns) to produce 32 FP8 output values (E4M3 or E5M2) and 1 FP8 scaling factor (E8M0)."}
+{"idx": 3, "title": "What is the difference between micro, meso, and macro ...", "date": "", "ddg_snippet": "Aug 2 , 2014 · What is the difference between micro, meso, and macro modeling of a composite for analysis ? In multi scale modeling, what are the deciding factors that dictates selection of particular scale of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/post/What-is-the-difference-between-micro-meso-and-macro-modeling-of-a-composite-for-analysis", "content": "Aug 2 , 2014 · What is the difference between micro, meso, and macro modeling of a composite for analysis ? In multi scale modeling, what are the deciding factors that dictates selection of particular scale of ..."}
+{"idx": 4, "title": "[PDF] MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "Jan 27, 2025 · A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations (PDEs) by numerical methods meet ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/MultiPDENet:-PDE-embedded-Learning-with-for-Flow-Wang-Mi/6aee4adf8e7489f251995859a5f0432a2c60bb82", "content": "Jan 27, 2025 · A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations (PDEs) by numerical methods meet ..."}
+{"idx": 5, "title": "Multi-Scale System Design | Mechanical Engineering | MIT ...", "date": "", "ddg_snippet": "Multi- scale systems (MuSS) consist of components from two or more length scales (nano, micro, meso, or macro -scales). In MuSS, the engineering modeling, design principles, and fabrication processes of the components are fundamentally different. The challenge is to make these components so they are conceptually and model-wise compatible with other- scale components with which they interface ...", "subpage_snippet": "", "source": "ocw.mit.edu", "link": "https://ocw.mit.edu/courses/2-76-multi-scale-system-design-fall-2004/", "content": "Multi- scale systems (MuSS) consist of components from two or more length scales (nano, micro, meso, or macro -scales). In MuSS, the engineering modeling, design principles, and fabrication processes of the components are fundamentally different. The challenge is to make these components so they are conceptually and model-wise compatible with other- scale components with which they interface ..."}
+{"idx": 6, "title": "MultiPDENet : PDE-embedded Learning with Multi-time-stepping for...", "date": "", "ddg_snippet": "The introduction of the MiNN (Micro Neural Network) and MaNN (Macro Neural Network) blocks enables the model to make refined predictions at both micro and macro scales , effectively mitigating the curse of temporal error accumulation .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-multipdenet-pde-embedded-learning-with-cm6gypmal2nr407s36ekweuc5", "content": "The introduction of the MiNN (Micro Neural Network) and MaNN (Macro Neural Network) blocks enables the model to make refined predictions at both micro and macro scales , effectively mitigating the curse of temporal error accumulation ."}
+{"idx": 7, "title": "How To Enable \"Microsoft has blocked macros from...\" - YouTube", "date": "", "ddg_snippet": "Microsoft Office has rolled out a new security check to disable on default when you download a macro enabled file from a 3rd party source.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=cGwJhLtpgP4", "content": "Microsoft Office has rolled out a new security check to disable on default when you download a macro enabled file from a 3rd party source."}
+{"idx": 8, "title": "Micro vs Macro : Difference and Comparison", "date": "", "ddg_snippet": "A macro - scale model of plants is a scale that goes beyond the level of detail shown in standard-size models. This is used when trying to think about the scale of insects, which are microscopic but have an enormous impact on their environment.", "subpage_snippet": "", "source": "askanydifference.com", "link": "https://askanydifference.com/difference-between-micro-and-macro/", "content": "A macro - scale model of plants is a scale that goes beyond the level of detail shown in standard-size models. This is used when trying to think about the scale of insects, which are microscopic but have an enormous impact on their environment."}
+{"idx": 9, "title": "PDE-constrained Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "Sep 27, 2024 · To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of fluid flows.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "Sep 27, 2024 · To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of fluid flows."}
diff --git a/data/sampled_jsons/MultiPDENet_Table_S8_computational_time_DNS_1024_Re_4000_correlation_0.8_NSE_dataset.jsonl b/data/sampled_jsons/MultiPDENet_Table_S8_computational_time_DNS_1024_Re_4000_correlation_0.8_NSE_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85c95cbb313fd9e497556b836b37a6c81dbcce29
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_Table_S8_computational_time_DNS_1024_Re_4000_correlation_0.8_NSE_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping", "date": "", "ddg_snippet": "Taking NSE as an example, we compared the inference time , RMSE, and HCT of MultiPDENet with the Direct Numerical Simulation ( DNS ) method across three cases. The comparison principle is based on the time required to simulate the same trajectory length (T = 8.4 s) under identical experimental conditions (a single A100 GPU).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "Taking NSE as an example, we compared the inference time , RMSE, and HCT of MultiPDENet with the Direct Numerical Simulation ( DNS ) method across three cases. The comparison principle is based on the time required to simulate the same trajectory length (T = 8.4 s) under identical experimental conditions (a single A100 GPU)."}
+{"idx": 1, "title": "PDE-constrained Learning with Multi-time-stepping for Accelerated...", "date": "", "ddg_snippet": "The computational time was recorded for prediction of fluid flows which evolve for a certain duration (aka, the smaller high correlation time between MultiPDENet and DNS 1024 , when the prediction correlation reaches 0.8 ).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "The computational time was recorded for prediction of fluid flows which evolve for a certain duration (aka, the smaller high correlation time between MultiPDENet and DNS 1024 , when the prediction correlation reaches 0.8 )."}
+{"idx": 2, "title": "dns-brute NSE script — Nmap Scripting Engine documentation", "date": "", "ddg_snippet": "How to use the dns -brute NSE script: examples, script-args, and references.", "subpage_snippet": "", "source": "nmap.org", "link": "https://nmap.org/nsedoc/scripts/dns-brute.html", "content": "How to use the dns -brute NSE script: examples, script-args, and references."}
+{"idx": 3, "title": "nse/dns-brute2.nse at master · aerissecure/nse · GitHub", "date": "", "ddg_snippet": "Nmap NSE scripts. Contribute to aerissecure/ nse development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aerissecure/nse/blob/master/dns-brute2.nse", "content": "Nmap NSE scripts. Contribute to aerissecure/ nse development by creating an account on GitHub."}
+{"idx": 4, "title": "Time required for RANS vs. LES vs. DNS - CFD Online", "date": "", "ddg_snippet": "Hi, I know that RANS is computationally more efficient than LES and DNS . And I also know that LES is computationally more efficient than DNS . I've", "subpage_snippet": "", "source": "www.cfd-online.com", "link": "https://www.cfd-online.com/Forums/main/185770-time-required-rans-vs-les-vs-dns.html", "content": "Hi, I know that RANS is computationally more efficient than LES and DNS . And I also know that LES is computationally more efficient than DNS . I've"}
+{"idx": 5, "title": "NMap NSE Scripts Cheat Sheet - GitHub", "date": "", "ddg_snippet": "This NMap NSE Scripts Cheat Sheet was created to be a quick reference/tl;dr for each of the official nmap nse scripts that the nmap software provides. Almost every entry has the \"default\" port number associated with the service each nse script is scanning for.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/webmaster-exit-1/nmap-nse-scripts-cheat-sheet/blob/main/cheat-sheet.md", "content": "This NMap NSE Scripts Cheat Sheet was created to be a quick reference/tl;dr for each of the official nmap nse scripts that the nmap software provides. Almost every entry has the \"default\" port number associated with the service each nse script is scanning for."}
+{"idx": 6, "title": "NSE 3000 - Cambium Networks - English", "date": "", "ddg_snippet": "The Network Service Edge ( NSE ) delivers advanced security, network and WAN services for small and medium enterprise networks. These services are integrated into Cambium's ONE Network solution that enables organizations to deploy and manage security policy across the wireless and wired network, all fully managed and controlled as part of a single framework.", "subpage_snippet": "", "source": "www.cambiumnetworks.com", "link": "https://www.cambiumnetworks.com/products/security-and-sd-wan/nse-3000/", "content": "The Network Service Edge ( NSE ) delivers advanced security, network and WAN services for small and medium enterprise networks. These services are integrated into Cambium's ONE Network solution that enables organizations to deploy and manage security policy across the wireless and wired network, all fully managed and controlled as part of a single framework."}
+{"idx": 7, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "The primary contributions of this work are summarized as follows: • We developed MultiPDENet , a PDE-embedded net- work with multiscale time -stepping, for accelerated flow simulations on spatiotemporal coarse grids. By integrating neural solver with PDEs, MultiPDENet achieves great generalizability and efficiency.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=D1gs8QT74m", "content": "The primary contributions of this work are summarized as follows: • We developed MultiPDENet , a PDE-embedded net- work with multiscale time -stepping, for accelerated flow simulations on spatiotemporal coarse grids. By integrating neural solver with PDEs, MultiPDENet achieves great generalizability and efficiency."}
+{"idx": 8, "title": "dns-cache-snoop NSE script — Nmap Scripting Engine documentation", "date": "", "ddg_snippet": "How to use the dns -cache-snoop NSE script: examples, script-args, and references.", "subpage_snippet": "", "source": "nmap.org", "link": "https://nmap.org/nsedoc/scripts/dns-cache-snoop.html", "content": "How to use the dns -cache-snoop NSE script: examples, script-args, and references."}
+{"idx": 9, "title": "Direct Numerical Simulation - Altair", "date": "", "ddg_snippet": "Direct Numerical Simulation ( DNS ) solves the time dependent Navier-Stokes equations, resolving from the largest length scale of a computational domain size to the smallest length scale of turbulence eddy (Kolmogorov length scale).", "subpage_snippet": "", "source": "help.altair.com", "link": "https://help.altair.com/hwcfdsolvers/acusolve/topics/acusolve/training_manual/direct_numerical_simulation_r.htm", "content": "Direct Numerical Simulation ( DNS ) solves the time dependent Navier-Stokes equations, resolving from the largest length scale of a computational domain size to the smallest length scale of turbulence eddy (Kolmogorov length scale)."}
diff --git a/data/sampled_jsons/N3DV_dataset_test_sequences_Instant_Gaussian_Stream_Cook_Spinach_Sear_Steak_year_2024.jsonl b/data/sampled_jsons/N3DV_dataset_test_sequences_Instant_Gaussian_Stream_Cook_Spinach_Sear_Steak_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..252dc5eb160ebc5a1b25f442a3d7aa7cdf299ef5
--- /dev/null
+++ b/data/sampled_jsons/N3DV_dataset_test_sequences_Instant_Gaussian_Stream_Cook_Spinach_Sear_Steak_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ... - GitHub", "date": "", "ddg_snippet": "Train AGM-Net Datasets Preparation Our Training Dataset Download our processed data from 4 sequences of N3DV , which can be directly used for training. It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Train AGM-Net Datasets Preparation Our Training Dataset Download our processed data from 4 sequences of N3DV , which can be directly used for training. It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows:"}
+{"idx": 1, "title": "PDF Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , {cut roasted beef, sear steak }, used as the test set.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , {cut roasted beef, sear steak }, used as the test set."}
+{"idx": 2, "title": "(PDF) Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 3, "title": "Neural 3D Video Synthesis Dataset - GitHub", "date": "", "ddg_snippet": "For all the dataset , cam00.mp4 is the center reference camera which we held out for testing. We have already filtered out the camera stream that is either unsynchronized, or have significant inconsistency in observations.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Neural_3D_Video", "content": "For all the dataset , cam00.mp4 is the center reference camera which we held out for testing. We have already filtered out the camera stream that is either unsynchronized, or have significant inconsistency in observations."}
+{"idx": 4, "title": "3DGStream: On-the-fly Training of 3D Gaussians for Efficient Streaming ...", "date": "", "ddg_snippet": "Figure 2: Comparison of our method with other methods on the N3DV dataset [31]. normal- \\Box denotes training from scratch per frame, normal- \\bigtriangleup represents offline training on complete video sequences , and normal- \\bigcirc signifies online training on video streams .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01444v1", "content": "Figure 2: Comparison of our method with other methods on the N3DV dataset [31]. normal- \\Box denotes training from scratch per frame, normal- \\bigtriangleup represents offline training on complete video sequences , and normal- \\bigcirc signifies online training on video streams ."}
+{"idx": 5, "title": "PDF 3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming ...", "date": "", "ddg_snippet": "1. Implementation Details We implement 3DGStream upon the codebase of 3D Gaus-sian Splatting (3DG-S) [4] and use tiny-cuda-nn [7] to im-plement Neural Transformation Cache (NTC). All exper-iments were conducted on an NVIDIA RTX 3090 GPU. In training of the initial frame, we let the densification of 3DG-S end at iteration 5000. For the scenes in the N3DV dataset , we use the 3DGs of iteration ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_3DGStream_On-the-Fly_Training_CVPR_2024_supplemental.pdf", "content": "1. Implementation Details We implement 3DGStream upon the codebase of 3D Gaus-sian Splatting (3DG-S) [4] and use tiny-cuda-nn [7] to im-plement Neural Transformation Cache (NTC). All exper-iments were conducted on an NVIDIA RTX 3090 GPU. In training of the initial frame, we let the densification of 3DG-S end at iteration 5000. For the scenes in the N3DV dataset , we use the 3DGs of iteration ..."}
+{"idx": 6, "title": "GitHub - SJoJoK/3DGStream: [CVPR 2024 Highlight] Official repository ...", "date": "", "ddg_snippet": "python convert_frames.py --scene dataset /flame_steak --resize to prepare for conducting experiment on the flame steak scene. For multi-view datasets with distortion such as MeetRoom, undistortion is critical to improving the reconstruction quality. We followed the settings of the original gaussian -splatting and performed undistortion. Warm-up ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SJoJoK/3DGStream", "content": "python convert_frames.py --scene dataset /flame_steak --resize to prepare for conducting experiment on the flame steak scene. For multi-view datasets with distortion such as MeetRoom, undistortion is critical to improving the reconstruction quality. We followed the settings of the original gaussian -splatting and performed undistortion. Warm-up ..."}
+{"idx": 7, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , {c u t r o a s t e d b e e f, s e a r s t e a k} \\ {cut\\ roasted\\ beef,sear\\ steak \\}, used as the test set.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2503.16979", "content": "Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , {c u t r o a s t e d b e e f, s e a r s t e a k} \\ {cut\\ roasted\\ beef,sear\\ steak \\}, used as the test set."}
+{"idx": 8, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "ef, sear steak }, used as the test set. For the training set, we constructed 3D Gaus-sians for all frames in the four training sequences , total-ing 120 frames, which required 192 GPU hours. For each frame's 3D Gaussian , we performed motions forward and backward for five frame", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "ef, sear steak }, used as the test set. For the training set, we constructed 3D Gaus-sians for all frames in the four training sequences , total-ing 120 frames, which required 192 GPU hours. For each frame's 3D Gaussian , we performed motions forward and backward for five frame"}
+{"idx": 9, "title": "Factorized Motion Fields for Fast Sparse Input Dynamic View Synthesis", "date": "", "ddg_snippet": "Scene details: cook spinach from N3DV dataset with two input views. We show spiral video to show the improvement in learning 3D scene more clearly. ... Scene details: N3DV dataset . In this video, we focus on the dog in the scene. We observe that K-Planes fails to render the eyes of the dog correctly and replaces it with a blur of its skin.", "subpage_snippet": "", "source": "nagabhushansn95.github.io", "link": "https://nagabhushansn95.github.io/publications/2024/RF-DeRF.html", "content": "Scene details: cook spinach from N3DV dataset with two input views. We show spiral video to show the improvement in learning 3D scene more clearly. ... Scene details: N3DV dataset . In this video, we focus on the dog in the scene. We observe that K-Planes fails to render the eyes of the dog correctly and replaces it with a blur of its skin."}
diff --git "a/data/sampled_jsons/NFR_layer_feature_selection_mechanism_equation_11_\316\2741_mu1_parameter.jsonl" "b/data/sampled_jsons/NFR_layer_feature_selection_mechanism_equation_11_\316\2741_mu1_parameter.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..81dd98d40d0100eedcd55b192fcb1c79bd68f56c
--- /dev/null
+++ "b/data/sampled_jsons/NFR_layer_feature_selection_mechanism_equation_11_\316\2741_mu1_parameter.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Novel Feature Reduction (NFR) Model With Machine Learning and ...", "date": "", "ddg_snippet": "The objective of this research article is to present a Novel Feature Reduction ( NFR ) model that improves the accuracy and AUC by reducing the number of features without omit-ting relevant features.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Syed-Mohamed/publication/344879199_Novel_Feature_Reduction_NFR_Model_With_Machine_Learning_and_Data_Mining_Algorithms_for_Effective_Disease_Risk_Prediction/links/5f966966458515b7cf9eeba1/Novel-Feature-Reduction-NFR-Model-With-Machine-Learning-and-Data-Mining-Algorithms-for-Effective-Disease-Risk-Prediction.pdf", "content": "The objective of this research article is to present a Novel Feature Reduction ( NFR ) model that improves the accuracy and AUC by reducing the number of features without omit-ting relevant features."}
+{"idx": 1, "title": "Feature Selection Techniques in Machine Learning", "date": "", "ddg_snippet": "Aug 30, 2025 · Feature selection is a core step in preparing data for machine learning where the goal is to identify and keep only the input features that contribute most to accurate predictions. By focusing on the most relevant variables, feature selection helps build models that are simpler, faster, less prone to overfitting and easier to interpret especially when we use datasets containing many features ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/feature-selection-techniques-in-machine-learning/", "content": "Aug 30, 2025 · Feature selection is a core step in preparing data for machine learning where the goal is to identify and keep only the input features that contribute most to accurate predictions. By focusing on the most relevant variables, feature selection helps build models that are simpler, faster, less prone to overfitting and easier to interpret especially when we use datasets containing many features ..."}
+{"idx": 2, "title": "A novel two-stage feature selection method based on random ...", "date": "", "ddg_snippet": "May 14, 2025 · Table 2 shows the parameter settings of the proposed two-stage feature selection method. The random forest parameters n_estimators and max_depth represent the number of decision trees and the ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-01761-1", "content": "May 14, 2025 · Table 2 shows the parameter settings of the proposed two-stage feature selection method. The random forest parameters n_estimators and max_depth represent the number of decision trees and the ..."}
+{"idx": 3, "title": "Unsupervised feature selection via latent representation ...", "date": "", "ddg_snippet": "Sep 1, 2019 · In this work, we propose a robust unsupervised feature selection method which embeds the latent representation learning into feature selection . Instead of measuring the feature importances in original data space, the feature selection is carried out in the learned latent representation space which is more robust to noises.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608019301212", "content": "Sep 1, 2019 · In this work, we propose a robust unsupervised feature selection method which embeds the latent representation learning into feature selection . Instead of measuring the feature importances in original data space, the feature selection is carried out in the learned latent representation space which is more robust to noises."}
+{"idx": 4, "title": "Introduction to Feature Selection - MATLAB & Simulink - MathWorks", "date": "", "ddg_snippet": "Feature selection reduces the dimensionality of data by selecting only a subset of measured features (predictor variables) to create a model. Feature selection algorithms search for a subset of predictors that optimally models measured responses, subject to constraints such as required or excluded features and the size of the subset.", "subpage_snippet": "", "source": "in.mathworks.com", "link": "https://in.mathworks.com/help/stats/feature-selection.html", "content": "Feature selection reduces the dimensionality of data by selecting only a subset of measured features (predictor variables) to create a model. Feature selection algorithms search for a subset of predictors that optimally models measured responses, subject to constraints such as required or excluded features and the size of the subset."}
+{"idx": 5, "title": "Deep Feature Selection: Theory and Application to Identify ...", "date": "", "ddg_snippet": "The emergence of both deep sequencing and deep computing techniques casts light on this problem. In order to select key input features for the identification and understanding the regulatory events, we propose a deep feature selection model that enables variable selection for deep neural networks.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-319-16706-0_20.pdf", "content": "The emergence of both deep sequencing and deep computing techniques casts light on this problem. In order to select key input features for the identification and understanding the regulatory events, we propose a deep feature selection model that enables variable selection for deep neural networks."}
+{"idx": 6, "title": "Solving the Selection -Recombination Equation", "date": "", "ddg_snippet": "The deterministic selection -recombination equation de-scribes the evolution of the genetic type composition of a population under selection and recombination in a law of large numbers regime.", "subpage_snippet": "", "source": "ems.press", "link": "https://ems.press/content/serial-article-files/26605", "content": "The deterministic selection -recombination equation de-scribes the evolution of the genetic type composition of a population under selection and recombination in a law of large numbers regime."}
+{"idx": 7, "title": "Linearizations of Quadratic Two Parameter matrix Polynomial Via...", "date": "", "ddg_snippet": "[17] extensively examined the one - parameter polynomial eigenvalue problem and developed vector spaces of linearizations by generalizing companion forms associated with one - parameter matrix polynomials.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10470v1", "content": "[17] extensively examined the one - parameter polynomial eigenvalue problem and developed vector spaces of linearizations by generalizing companion forms associated with one - parameter matrix polynomials."}
+{"idx": 8, "title": "A deep learning framework for non-functional requirement ...", "date": "", "ddg_snippet": "The main contribution of this study is the development of the DReqANN and DReqBiLSTM deep learning models tailored explicitly for NFR classification. These models utilize advanced techniques such as word embedding, optimization and regularization techniques, and multi- layer architecture to classify NFRs effectively.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10853538/", "content": "The main contribution of this study is the development of the DReqANN and DReqBiLSTM deep learning models tailored explicitly for NFR classification. These models utilize advanced techniques such as word embedding, optimization and regularization techniques, and multi- layer architecture to classify NFRs effectively."}
+{"idx": 9, "title": "Батч-нормализация | Машинное и глубокое обучение", "date": "", "ddg_snippet": "μi\\ mu _ i .Слой батч-нормализации (batch-normalization layer ), нормирующий активации предыдущего слоя, может включаться в любом месте сети. Его используют многократно для более ранних и более поздних слоёв.", "subpage_snippet": "", "source": "deepmachinelearning.ru", "link": "https://deepmachinelearning.ru/docs/Neural-networks/Training-simplification/BatchNorm", "content": "μi\\ mu _ i .Слой батч-нормализации (batch-normalization layer ), нормирующий активации предыдущего слоя, может включаться в любом месте сети. Его используют многократно для более ранних и более поздних слоёв."}
diff --git a/data/sampled_jsons/NVIDIA_TensorRT_Jetson_quantization_optimization_toolkit.jsonl b/data/sampled_jsons/NVIDIA_TensorRT_Jetson_quantization_optimization_toolkit.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ef3f4b18a6a4c1884ff458ee3fe591f8a378d99
--- /dev/null
+++ b/data/sampled_jsons/NVIDIA_TensorRT_Jetson_quantization_optimization_toolkit.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TensorRT SDK | NVIDIA Developer", "date": "", "ddg_snippet": "NVIDIA TensorRT Model Optimizer provides easy-to-use quantization techniques, including post-training quantization and quantization -aware training to compress ...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/tensorrt", "content": "NVIDIA TensorRT Model Optimizer provides easy-to-use quantization techniques, including post-training quantization and quantization -aware training to compress ..."}
+{"idx": 1, "title": "NVIDIA/TensorRT-Model-Optimizer", "date": "", "ddg_snippet": "A unified library of state-of-the-art model optimization techniques like quantization , pruning, distillation, speculative decoding, etc.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/TensorRT-Model-Optimizer", "content": "A unified library of state-of-the-art model optimization techniques like quantization , pruning, distillation, speculative decoding, etc."}
+{"idx": 2, "title": "Model/Tool to use on Jetson for efficient Quantization/Pruning", "date": "", "ddg_snippet": "For best performance you should look into Nvidia TAO (train adopt optimize ) toolkit . It has an export to TensorRT which you can in turn use to ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/computervision/comments/r201ps/modeltool_to_use_on_jetson_for_efficient/", "content": "For best performance you should look into Nvidia TAO (train adopt optimize ) toolkit . It has an export to TensorRT which you can in turn use to ..."}
+{"idx": 3, "title": "Which quanitization library to use? - TensorRT", "date": "", "ddg_snippet": "3 Mar 2025 — pytorch- quantization or TensorRT Model Optimizer ? They seem to have many similiarities and both are mention in quantization page of TensorRT .", "subpage_snippet": "", "source": "forums.developer.nvidia.com", "link": "https://forums.developer.nvidia.com/t/which-quanitization-library-to-use/325756", "content": "3 Mar 2025 — pytorch- quantization or TensorRT Model Optimizer ? They seem to have many similiarities and both are mention in quantization page of TensorRT ."}
+{"idx": 4, "title": "NVIDIA/TensorRT-LLM", "date": "", "ddg_snippet": "TensorRT -LLM is an open-sourced library for optimizing Large Language Model (LLM) inference. It provides state-of-the-art optimizations .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/TensorRT-LLM", "content": "TensorRT -LLM is an open-sourced library for optimizing Large Language Model (LLM) inference. It provides state-of-the-art optimizations ."}
+{"idx": 5, "title": "NVIDIA TensorRT Model Optimizer v0.15 Boosts Inference ...", "date": "", "ddg_snippet": "15 Aug 2024 — This post outlines some of the key features and upgrades of recent TensorRT Model Optimizer releases, including cache diffusion, the new quantization-aware ...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/nvidia-tensorrt-model-optimizer-v0-15-boosts-inference-performance-and-expands-model-support/", "content": "15 Aug 2024 — This post outlines some of the key features and upgrades of recent TensorRT Model Optimizer releases, including cache diffusion, the new quantization-aware ..."}
+{"idx": 6, "title": "TensorRT-LLM for Jetson", "date": "", "ddg_snippet": "TensorRT -LLM is a high-performance LLM inference library with advanced quantization , attention kernels, and paged KV caching.", "subpage_snippet": "", "source": "www.jetson-ai-lab.com", "link": "https://www.jetson-ai-lab.com/tensorrt_llm.html", "content": "TensorRT -LLM is a high-performance LLM inference library with advanced quantization , attention kernels, and paged KV caching."}
+{"idx": 7, "title": "TensorRT-Based Framework and Optimization ...", "date": "", "ddg_snippet": "In this article, we introduce a novel framework, called JEDI, for deep learning inference acceleration with TensorRT on NVIDIA Jetson embedded platforms.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3508391", "content": "In this article, we introduce a novel framework, called JEDI, for deep learning inference acceleration with TensorRT on NVIDIA Jetson embedded platforms."}
+{"idx": 8, "title": "TensorRT Implementations of Model Quantization on Edge ...", "date": "", "ddg_snippet": "by Y Zhou · Cited by 5 — TensorRT is an SDK that enables high- performance deep learning inference and is included in the. NVIDIA CUDA X AI Kit. The SDK provides a deep learning.", "subpage_snippet": "", "source": "userweb.cs.txstate.edu", "link": "https://userweb.cs.txstate.edu/~k_y47/webpage/pubs/mcsoc23.pdf", "content": "by Y Zhou · Cited by 5 — TensorRT is an SDK that enables high- performance deep learning inference and is included in the. NVIDIA CUDA X AI Kit. The SDK provides a deep learning."}
+{"idx": 9, "title": "Overview — NVIDIA TensorRT Documentation", "date": "", "ddg_snippet": "The TensorRT Model Optimizer is a unified library of state-of-the-art model optimization techniques such as quantization , pruning, distillation, etc. It ...", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/deeplearning/tensorrt/latest/architecture/architecture-overview.html", "content": "The TensorRT Model Optimizer is a unified library of state-of-the-art model optimization techniques such as quantization , pruning, distillation, etc. It ..."}
diff --git a/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_Tight_Approximation_and_Communi_year_2024.jsonl b/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_Tight_Approximation_and_Communi_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..983341e92fe72273dbb9abb60574d3cc8fee18e2
--- /dev/null
+++ b/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_Tight_Approximation_and_Communi_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "( PDF ) Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Near - optimal online learning for multi -. Agent submodular coordination : tight ap-. Proximation and communication efficiency . Qixin Zhang1Zongqi Wan4Yu Yang2Li Shen3Dacheng Tao1.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388847678_Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_Tight_Approximation_and_Communication_Efficiency", "content": "Near - optimal online learning for multi -. Agent submodular coordination : tight ap-. Proximation and communication efficiency . Qixin Zhang1Zongqi Wan4Yu Yang2Li Shen3Dacheng Tao1."}
+{"idx": 1, "title": "[2502.05028] Near - Optimal Online Learning for Multi - Agent ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency , by Qixin Zhang and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05028", "content": "View a PDF of the paper titled Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency , by Qixin Zhang and 4 other authors."}
+{"idx": 2, "title": "Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/near-optimal-online-learning-for-multi-agent", "content": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control."}
+{"idx": 3, "title": "[Literature Review] Near - Optimal Online Learning for Multi - Agent ...", "date": "", "ddg_snippet": "multi - agent . online learning . submodular maximization. coordination . approximation guarantee. communication efficiency . algorithms. review-image.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/near-optimal-online-learning-for-multi-agent-submodular-coordination-tight-approximation-and-communication-efficiency", "content": "multi - agent . online learning . submodular maximization. coordination . approximation guarantee. communication efficiency . algorithms. review-image."}
+{"idx": 4, "title": "Multiagent Systems | Cool Papers - Immersive Paper Discovery", "date": "", "ddg_snippet": "#5 Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency [ PDF ] [Copy] [Kimi] [REL]. Authors: Qixin Zhang, Zongqi Wan, Yu Yang, Li Shen, Dacheng Tao.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/cs.MA?sort=1", "content": "#5 Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency [ PDF ] [Copy] [Kimi] [REL]. Authors: Qixin Zhang, Zongqi Wan, Yu Yang, Li Shen, Dacheng Tao."}
+{"idx": 5, "title": "Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Go Home. Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2502.05028", "content": "Go Home. Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency ."}
+{"idx": 6, "title": "GitHub - Aaron617/ICLR-2025-Submissions- Agent : ICLR 2025...", "date": "", "ddg_snippet": "Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency (Rating: 6.00). Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale (Rating: 4.33).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Aaron617/ICLR-2025-Submissions-Agent", "content": "Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency (Rating: 6.00). Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale (Rating: 4.33)."}
+{"idx": 7, "title": "Curvature and Optimal Algorithms for Learning and Minimizing...", "date": "", "ddg_snippet": "Problem 1. ( Approximation [8]) Given a submodular function f in form of a value oracle, nd an approximation fˆ (within polynomial time and representable within polynomial space), such that for all X ⊆ V , it holds that fˆ(X) ≤ f (X) ≤ α1(n)fˆ(X) for a polynomial α1(n).", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2013/file/c1e39d912d21c91dce811d6da9929ae8-Paper.pdf", "content": "Problem 1. ( Approximation [8]) Given a submodular function f in form of a value oracle, nd an approximation fˆ (within polynomial time and representable within polynomial space), such that for all X ⊆ V , it holds that fˆ(X) ≤ f (X) ≤ α1(n)fˆ(X) for a polynomial α1(n)."}
+{"idx": 8, "title": "IJCAI-2021-Paper-Digests. pdf", "date": "", "ddg_snippet": "Improving Multi - agent Coordination by Learning to Estimate Contention.We present a multi - agent learning algorithm, ALMA- Learning , for efficient and fair allocations in large-scale.", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/wp-content/uploads/2021/08/IJCAI-2021-Paper-Digests.pdf", "content": "Improving Multi - agent Coordination by Learning to Estimate Contention.We present a multi - agent learning algorithm, ALMA- Learning , for efficient and fair allocations in large-scale."}
+{"idx": 9, "title": "N ear -o ptimal", "date": "", "ddg_snippet": "Near - optimal online learning for multi - agent submodular coordination : tight ap - proximation and communication efficiency .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i8dYPGdB1C", "content": "Near - optimal online learning for multi - agent submodular coordination : tight ap - proximation and communication efficiency ."}
diff --git a/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_introduction_significance_rando_year_2023.jsonl b/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_introduction_significance_rando_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2662059e7d7f2217916228e2758b48c3114e5349
--- /dev/null
+++ b/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_introduction_significance_rando_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph . To address ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05028", "content": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph . To address ..."}
+{"idx": 1, "title": "ICLR Poster Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "To address these challenges, we firstly present a $\\textbf {MA-OSMA}$ algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28714", "content": "To address these challenges, we firstly present a $\\textbf {MA-OSMA}$ algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques."}
+{"idx": 2, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "First, we utilize the multi -linear extension to convert the discrete submodular maximization into a continuous optimization problem, which enables us to reduce the rigid requirement for a complete communication graph via the well-established consensus techniques in the field of decentralized optimization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05028", "content": "First, we utilize the multi -linear extension to convert the discrete submodular maximization into a continuous optimization problem, which enables us to reduce the rigid requirement for a complete communication graph via the well-established consensus techniques in the field of decentralized optimization."}
+{"idx": 3, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=dN3Gaao5Xe", "content": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency"}
+{"idx": 4, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "To address these challenges, we firstly present a MA-OSMA MA-OSMA algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/3340ee1e4a8bad8d32c35721712b4d0a-Abstract-Conference.html", "content": "To address these challenges, we firstly present a MA-OSMA MA-OSMA algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques."}
+{"idx": 5, "title": "Near-Optimal Multi-Agent Learning for Safe Coverage Control", "date": "", "ddg_snippet": "This work considers a multi-agent coverage control problem with a-priori unknown density and constraints. We use tools from submodular optimization and propose algorithms that guarantee safety and achieve near-optimal guarantees in a finite time.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=_L7f0ySKMWY", "content": "This work considers a multi-agent coverage control problem with a-priori unknown density and constraints. We use tools from submodular optimization and propose algorithms that guarantee safety and achieve near-optimal guarantees in a finite time."}
+{"idx": 6, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency: Paper and Code. Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/near-optimal-online-learning-for-multi-agent", "content": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency: Paper and Code. Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control."}
+{"idx": 7, "title": "N -o Online Learning for Multi a S Coordination: Tight Ap Proximation ...", "date": "", "ddg_snippet": "ABSTRACT Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph . To ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i8dYPGdB1C", "content": "ABSTRACT Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph . To ..."}
+{"idx": 8, "title": "dblp: Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "Bibliographic details on Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-05028", "content": "Bibliographic details on Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency."}
+{"idx": 9, "title": "Zirui (Ray) Xu", "date": "", "ddg_snippet": "In particular, my work investigates: Theories: Provably near-optimal tools for submodular optimization and online learning ; Algorithms: Resource-aware coordination algorithms near-optimal performance and provably balances the trade-off of scalability and optimality in unstructured, untrustworthy, and even adversarial environments;", "subpage_snippet": "", "source": "zirui-xu.github.io", "link": "https://zirui-xu.github.io/", "content": "In particular, my work investigates: Theories: Provably near-optimal tools for submodular optimization and online learning ; Algorithms: Resource-aware coordination algorithms near-optimal performance and provably balances the trade-off of scalability and optimality in unstructured, untrustworthy, and even adversarial environments;"}
diff --git a/data/sampled_jsons/NeurELA_Neural_Exploratory_Landscape_Analysis_limitations_evolution_strategies_training_efficiency.jsonl b/data/sampled_jsons/NeurELA_Neural_Exploratory_Landscape_Analysis_limitations_evolution_strategies_training_efficiency.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9ad2fa7610b3aec5e19c6bf3936c5e364a7c09da
--- /dev/null
+++ b/data/sampled_jsons/NeurELA_Neural_Exploratory_Landscape_Analysis_limitations_evolution_strategies_training_efficiency.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Exploratory Landscape Analysis - arXiv.org", "date": "", "ddg_snippet": "2) the fine-tuning strategy shows remarkable efficiency in adapting NeurELA for new MetaBBO algorithm dynamics and optimization problem structures, which only requires less than 20 % meta- training epochs to achieve the similar zero-shot performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v2", "content": "2) the fine-tuning strategy shows remarkable efficiency in adapting NeurELA for new MetaBBO algorithm dynamics and optimization problem structures, which only requires less than 20 % meta- training epochs to achieve the similar zero-shot performance."}
+{"idx": 1, "title": "Advancements in Black-Box Optimization with NeurELA", "date": "", "ddg_snippet": "Jun 24, 2025 · To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-24-advancements-in-black-box-optimization-with-neurela--a3z2l1o", "content": "Jun 24, 2025 · To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion."}
+{"idx": 2, "title": "(PDF) Neural Exploratory Landscape Analysis - ResearchGate", "date": "", "ddg_snippet": "Aug 20, 2024 · To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383267225_Neural_Exploratory_Landscape_Analysis", "content": "Aug 20, 2024 · To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural ..."}
+{"idx": 3, "title": "Neural Exploratory Landscape Analysis - OpenReview", "date": "", "ddg_snippet": "Jan 1, 2024 · To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Ra5KHz4MdM", "content": "Jan 1, 2024 · To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion."}
+{"idx": 4, "title": "Neural Exploratory Landscape Analysis", "date": "", "ddg_snippet": "NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy . Extensive experiments show that NeurELA achieves consistently superior performance when integrated into different and even unseen MetaBBO tasks and can be efficiently fine-tuned for further performance boost.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2408.10672", "content": "NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy . Extensive experiments show that NeurELA achieves consistently superior performance when integrated into different and even unseen MetaBBO tasks and can be efficiently fine-tuned for further performance boost."}
+{"idx": 5, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA , which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta-level learning in recent MetaBBO context.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MetaEvo/Neur-ELA", "content": "Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA , which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta-level learning in recent MetaBBO context."}
+{"idx": 6, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "Aug 20, 2024 · To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.10672", "content": "Aug 20, 2024 · To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion."}
+{"idx": 7, "title": "Reinforcement Learning-based Self-adaptive Differential Evolution ...", "date": "", "ddg_snippet": "2024. Neural exploratory landscape analysis .arXiv preprint arXiv:2408.10672(2024). NeurELA is the feature extraction method used to learn landscape features, and this citation introduces this method. Rainer Storn and Kenneth Price.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.18061v1", "content": "2024. Neural exploratory landscape analysis .arXiv preprint arXiv:2408.10672(2024). NeurELA is the feature extraction method used to learn landscape features, and this citation introduces this method. Rainer Storn and Kenneth Price."}
+{"idx": 8, "title": "Neural Exploratory Landscape Analysis for...", "date": "", "ddg_snippet": "To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Neural-Exploratory-Landscape-Analysis-for-Meta-Black-Box-Optimization-e0549d78-9d78-4162-ab06-b0a02f417612", "content": "To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion."}
+{"idx": 9, "title": "GMC-DRL/ Neur - ELA : Official code for Neural Exploratory Landscape ...", "date": "", "ddg_snippet": "Train . The training process can be easily activated via the command below.Recording results: Log files and NeurELA model checkpoints will be saved to ./records, the file structure is as follow.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GMC-DRL/Neur-ELA", "content": "Train . The training process can be easily activated via the command below.Recording results: Log files and NeurELA model checkpoints will be saved to ./records, the file structure is as follow."}
diff --git a/data/sampled_jsons/NeurELA_paper_2408.10672_limitations_section_training_efficiency_evolution_strategies.jsonl b/data/sampled_jsons/NeurELA_paper_2408.10672_limitations_section_training_efficiency_evolution_strategies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..29c44358758c72fcee04b2a54d2c11118bb49119
--- /dev/null
+++ b/data/sampled_jsons/NeurELA_paper_2408.10672_limitations_section_training_efficiency_evolution_strategies.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A arXiv:2408.10672v3 [cs.LG] 27 Mar 2025", "date": "", "ddg_snippet": "on across different sampling strategies . In this paper , our NeurELA presents several novel contributions compared with the above learning-based ELA frameworks: a) NeurELA is designed for dynamic MetaBBO tasks, enabling dynamic algorithm configuration and selection, whereas previous studies focu", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.10672", "content": "on across different sampling strategies . In this paper , our NeurELA presents several novel contributions compared with the above learning-based ELA frameworks: a) NeurELA is designed for dynamic MetaBBO tasks, enabling dynamic algorithm configuration and selection, whereas previous studies focu"}
+{"idx": 1, "title": "arXiv:2408.10672v2 [cs.LG] 26 Sep 2024", "date": "", "ddg_snippet": "comprises 3296 learnable parameters. To identify an effective ES, we implement several candidate evolution strategy variants: Fast CMAES [19], SEP-CMAES [62], R1ES, RMES [63], and the original CMAES [18], and report the overall zero-shot performance (unseen algorithms & unseen problem sets) achieved by training our NeurELA with these candid", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.10672v2", "content": "comprises 3296 learnable parameters. To identify an effective ES, we implement several candidate evolution strategy variants: Fast CMAES [19], SEP-CMAES [62], R1ES, RMES [63], and the original CMAES [18], and report the overall zero-shot performance (unseen algorithms & unseen problem sets) achieved by training our NeurELA with these candid"}
+{"idx": 2, "title": "Advancements in Black-Box Optimization with NeurELA", "date": "", "ddg_snippet": "NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy. Extensive experiments show that NeurELA achieves consistently superior performance when integrated into different and even unseen MetaBBO tasks and can be efficiently fine-tuned for further performance boost.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-24-advancements-in-black-box-optimization-with-neurela--a3z2l1o", "content": "NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy. Extensive experiments show that NeurELA achieves consistently superior performance when integrated into different and even unseen MetaBBO tasks and can be efficiently fine-tuned for further performance boost."}
+{"idx": 3, "title": "Enhancing Neural Network Training Through Neuroevolutionary Models: A ...", "date": "", "ddg_snippet": "In this paper , we propose optimizing neural network training using neuroevolutionary models. It proposes developing a Cellular Processing Algorithm to control the evolution of these networks, improving hyperparameter tuning and learning efficiency .", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7390/13/7/1114", "content": "In this paper , we propose optimizing neural network training using neuroevolutionary models. It proposes developing a Cellular Processing Algorithm to control the evolution of these networks, improving hyperparameter tuning and learning efficiency ."}
+{"idx": 4, "title": "Neuroevolution: A Comprehensive Survey of Evolutionary Algorithms in ...", "date": "", "ddg_snippet": "This paper provides a comprehensive survey of neuroevolution, focusing on the use of evolutionary algorithms in training deep neural networks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/376396730_Neuroevolution_A_Comprehensive_Survey_of_Evolutionary_Algorithms_in_Deep_Learning", "content": "This paper provides a comprehensive survey of neuroevolution, focusing on the use of evolutionary algorithms in training deep neural networks."}
+{"idx": 5, "title": "How to Improve Neural Network Training Using Evolutionary Algorithms", "date": "", "ddg_snippet": "The aforementioned Sect. \" Applying AutoLR to Other Problems \", exclusive to the current paper , describes how the reader may use AutoLR to improve the training of their neural network applications in Tensorflow [8] and Pytorch [9]. Section \" Conclusion \" sums up the achievements of AutoLR and the contributions of the current work.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s42979-024-02972-5", "content": "The aforementioned Sect. \" Applying AutoLR to Other Problems \", exclusive to the current paper , describes how the reader may use AutoLR to improve the training of their neural network applications in Tensorflow [8] and Pytorch [9]. Section \" Conclusion \" sums up the achievements of AutoLR and the contributions of the current work."}
+{"idx": 6, "title": "Neuroevolution in Deep Neural Networks: Current Trends and Future ...", "date": "", "ddg_snippet": "Section III discusses how the architectures of DNNs can be evolved efficiently using EA approaches. This moves onto a discussion in Section IV on the training of DNNs with EAs. Section V sets out some of the major challenges and fertile avenues for future work. Finally, the paper closes with some concluding remarks in Section VI.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05415", "content": "Section III discusses how the architectures of DNNs can be evolved efficiently using EA approaches. This moves onto a discussion in Section IV on the training of DNNs with EAs. Section V sets out some of the major challenges and fertile avenues for future work. Finally, the paper closes with some concluding remarks in Section VI."}
+{"idx": 7, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization", "date": "", "ddg_snippet": "NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy. Extensive experiments show that NeurELA achieves consistently superior performance when integrated into different and even unseen MetaBBO tasks and can be efficiently fine-tuned for further performance boost.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v3", "content": "NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy. Extensive experiments show that NeurELA achieves consistently superior performance when integrated into different and even unseen MetaBBO tasks and can be efficiently fine-tuned for further performance boost."}
+{"idx": 8, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "ameters. To identify an effective ES, we implement several candidate evolution strategy variants: Fast CMAES [19], SEP-CMAES [61], R1ES, RMES [62], and the original CMAES [18], and report the overall zero-shot performance (unseen algorithms & unseen problem sets) achieved by training our NeurELA with these candidate strategies for 10 runs in", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.10672v1", "content": "ameters. To identify an effective ES, we implement several candidate evolution strategy variants: Fast CMAES [19], SEP-CMAES [61], R1ES, RMES [62], and the original CMAES [18], and report the overall zero-shot performance (unseen algorithms & unseen problem sets) achieved by training our NeurELA with these candidate strategies for 10 runs in"}
+{"idx": 9, "title": "Neural Exploratory Landscape Analysis", "date": "", "ddg_snippet": "To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2408.10672", "content": "To address the gap, this paper proposes Neural Exploratory Landscape Analysis ( NeurELA ), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion."}
diff --git a/data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization_Equation_3_evaluation_metric_year_2024.jsonl b/data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization_Equation_3_evaluation_metric_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..14d0edbdd034207a4aee9a3f645b46b469a128ec
--- /dev/null
+++ b/data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization_Equation_3_evaluation_metric_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "Abstract Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the design of black - box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v3", "content": "Abstract Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the design of black - box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions."}
+{"idx": 1, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ... N EXPLORATORY LANDSCAPE ANALYSIS FOR M -B -OPTIMIZATION Neural Exploratory Landscape Analysis for Meta-Black-Box ... Neural Exploratory Landscape Analysis for Meta-Black-Box ... Exploratory landscape analysis on black-box optimization ... Neural Exploratory Landscape Analysis for Meta-Black-Box ... [2408.10672] Neural Exploratory Landscape Analysis for Meta - Black … [2408.10672] Neural Exploratory Landscape Analysis for Meta - Black … GitHub - MetaEvo/Neur-ELA: Official code for Neural Exploratory Neural Exploratory Landscape Analysis for Meta - Black - Box -Optimizat… Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "Aug 20, 2024 · Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the design of black - box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis ... ABSTRACT Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the design of black - box op-timizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape ... Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta -level learning in recent MetaBBO context. NeurELA is a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion, and marks a pivotal step in making MetaBBO algorithms more autonomous and broadly applicable. Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the ... This study proposes a novel exploratory landscape analysis (ELA) framework using graph neural networks (GNNs) to identify implicit characteristics of black - box optimization problems (BBOPs), with multimodality identification serving as the primary validation case. Kerschke et al. omprehensive feature-based landscape analysis of continuous and constrained optimization problems using the r-package flacco, 2019. [6] Seiler et al. Deep-ela: Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems, 2024. What is neural exploratory landscape analysis? To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion. Does metabbo still use exploratory landscape analysis? Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis features to inform the meta-level agent about the low-level optimization progress. Who is the author of 'neural exploratory landscape analysis'? title={Neural Exploratory Landscape Analysis}, author={ Ma, Zeyuan and Chen, Jiacheng and Guo, Hongshu and Gong, Yue-Jiao }, journal={arXiv preprint arXiv:2408.10672}, Can We learn a universal landscape analyser using deep neural network? As a pioneer work on using deep neural network for landscape analysis in MetaBBO, our NeurELA show possibility of learning an universal landscape analyser with minimal expertise requirement and appealing optimization performance. However, a major limitation is that the training efficiency of NeurELA is not very satisfactory. Abstract Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the design of black - box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.10672", "content": "Aug 20, 2024 · Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the design of black - box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis ... ABSTRACT Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the design of black - box op-timizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape ... Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta -level learning in recent MetaBBO context. NeurELA is a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion, and marks a pivotal step in making MetaBBO algorithms more autonomous and broadly applicable. Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the ... This study proposes a novel exploratory landscape analysis (ELA) framework using graph neural networks (GNNs) to identify implicit characteristics of black - box optimization problems (BBOPs), with multimodality identification serving as the primary validation case. Kerschke et al. omprehensive feature-based landscape analysis of continuous and constrained optimization problems using the r-package flacco, 2019. [6] Seiler et al. Deep-ela: Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems, 2024. What is neural exploratory landscape analysis? To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion. Does metabbo still use exploratory landscape analysis? Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis features to inform the meta-level agent about the low-level optimization progress. Who is the author of 'neural exploratory landscape analysis'? title={Neural Exploratory Landscape Analysis}, author={ Ma, Zeyuan and Chen, Jiacheng and Guo, Hongshu and Gong, Yue-Jiao }, journal={arXiv preprint arXiv:2408.10672}, Can We learn a universal landscape analyser using deep neural network? As a pioneer work on using deep neural network for landscape analysis in MetaBBO, our NeurELA show possibility of learning an universal landscape analyser with minimal expertise requirement and appealing optimization performance. However, a major limitation is that the training efficiency of NeurELA is not very satisfactory. Abstract Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the design of black - box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions."}
+{"idx": 2, "title": "N EXPLORATORY LANDSCAPE ANALYSIS FOR M -B -OPTIMIZATION", "date": "", "ddg_snippet": "ABSTRACT Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the design of black - box op-timizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=EEI5R89Cmv", "content": "ABSTRACT Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the design of black - box op-timizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape ..."}
+{"idx": 3, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta -level learning in recent MetaBBO context.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MetaEvo/Neur-ELA", "content": "Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta -level learning in recent MetaBBO context."}
+{"idx": 4, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "NeurELA is a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion, and marks a pivotal step in making MetaBBO algorithms more autonomous and broadly applicable. Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Neural-Exploratory-Landscape-Analysis-for-Ma-Chen/06dc4a6ce58184fcfdd6c48e40bf583f33cf0cfa", "content": "NeurELA is a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion, and marks a pivotal step in making MetaBBO algorithms more autonomous and broadly applicable. Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta -trained neural networks can effectively guide the ..."}
+{"idx": 5, "title": "Exploratory landscape analysis on black-box optimization ...", "date": "", "ddg_snippet": "This study proposes a novel exploratory landscape analysis (ELA) framework using graph neural networks (GNNs) to identify implicit characteristics of black - box optimization problems (BBOPs), with multimodality identification serving as the primary validation case.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2210650225002949", "content": "This study proposes a novel exploratory landscape analysis (ELA) framework using graph neural networks (GNNs) to identify implicit characteristics of black - box optimization problems (BBOPs), with multimodality identification serving as the primary validation case."}
+{"idx": 6, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "Kerschke et al. omprehensive feature-based landscape analysis of continuous and constrained optimization problems using the r-package flacco, 2019. [6] Seiler et al. Deep-ela: Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems, 2024.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/30417.pdf", "content": "Kerschke et al. omprehensive feature-based landscape analysis of continuous and constrained optimization problems using the r-package flacco, 2019. [6] Seiler et al. Deep-ela: Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems, 2024."}
+{"idx": 7, "title": "Neural Exploratory Landscape Analysis for ... | OpenReview", "date": "", "ddg_snippet": "Keywords: Landscape Analysis , Black - Box Optimization , Meta - Black - Box - Optimization , Learning to Optimize.The goal of the work is to learn a landscape featurizer for meta - blackbox optimization . The pipeline of the meta-BBO loop basically consists of", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=EEI5R89Cmv", "content": "Keywords: Landscape Analysis , Black - Box Optimization , Meta - Black - Box - Optimization , Learning to Optimize.The goal of the work is to learn a landscape featurizer for meta - blackbox optimization . The pipeline of the meta-BBO loop basically consists of"}
+{"idx": 8, "title": "Neural Exploratory Landscape Analysis | Papers With Code", "date": "", "ddg_snippet": "Recent research in Meta - Black - Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black - box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/neural-exploratory-landscape-analysis", "content": "Recent research in Meta - Black - Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black - box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions."}
+{"idx": 9, "title": "(PDF) MetaBox-v2: A Unified Benchmark Platform for Meta - Black - Box ...", "date": "", "ddg_snippet": "Neural exploratory landscape . analysis for meta - black - box - optimization .optimization: Benchmark problems, performance metric , and baseline results. arXiv preprint. arXiv:1706.03470, 2017.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392015793_MetaBox-v2_A_Unified_Benchmark_Platform_for_Meta-Black-Box_Optimization", "content": "Neural exploratory landscape . analysis for meta - black - box - optimization .optimization: Benchmark problems, performance metric , and baseline results. arXiv preprint. arXiv:1706.03470, 2017."}
diff --git a/data/sampled_jsons/Neural_Persistence_Dynamics_Sebastian_Zeng_Florian_Graf.jsonl b/data/sampled_jsons/Neural_Persistence_Dynamics_Sebastian_Zeng_Florian_Graf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a7e80a7e6a09fab37b49b890782946154ad33fb6
--- /dev/null
+++ b/data/sampled_jsons/Neural_Persistence_Dynamics_Sebastian_Zeng_Florian_Graf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2405.15732] Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.15732", "content": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ..."}
+{"idx": 1, "title": "Neural Persistence Dynamics - proceedings.neurips.cc", "date": "", "ddg_snippet": "Authors Sebastian Zeng , Florian Graf , Martin Uray, Stefan Huber, Roland Kwitt Abstract We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/3a509449a73fd0aab8c0cf5705827036-Abstract-Conference.html", "content": "Authors Sebastian Zeng , Florian Graf , Martin Uray, Stefan Huber, Roland Kwitt Abstract We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self ..."}
+{"idx": 2, "title": "Neural Persistence Dynamics - OpenReview", "date": "", "ddg_snippet": "Neural Persistence Dynamics Sebastian Zeng , Florian Graf , Martin Uray, Stefan Huber, Roland Kwitt NeurIPS 2024 poster Everyone Revisions BibTeX CC BY 4.0 Keywords: Persistent Homology, Multi-Time Attention, Latent ODE, Collective Behavior, Physical Sciences", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rCnZrFikX6", "content": "Neural Persistence Dynamics Sebastian Zeng , Florian Graf , Martin Uray, Stefan Huber, Roland Kwitt NeurIPS 2024 poster Everyone Revisions BibTeX CC BY 4.0 Keywords: Persistent Homology, Multi-Time Attention, Latent ODE, Collective Behavior, Physical Sciences"}
+{"idx": 3, "title": "Neural Persistence Dynamics - Salzburg University of Applied Sciences", "date": "", "ddg_snippet": "Zeng , Sebastian ; Graf , Florian ; Uray, Martin et al. / Neural Persistence Dynamics . Paper presented at The Thirty-Eighth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada.", "subpage_snippet": "", "source": "pure.fh-salzburg.ac.at", "link": "https://pure.fh-salzburg.ac.at/en/publications/neural-persistence-dynamics", "content": "Zeng , Sebastian ; Graf , Florian ; Uray, Martin et al. / Neural Persistence Dynamics . Paper presented at The Thirty-Eighth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada."}
+{"idx": 4, "title": "Neural persistence dynamics | Proceedings of the 38th International ...", "date": "", "ddg_snippet": "research-article Neural persistence dynamics AUTHORs: Sebastian Zeng , Florian Graf , Martin Uray", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738965", "content": "research-article Neural persistence dynamics AUTHORs: Sebastian Zeng , Florian Graf , Martin Uray"}
+{"idx": 5, "title": "PDF Neural Persistence Dynamics - papers.nips.cc", "date": "", "ddg_snippet": "Sebastian Zengy=z, Florian Grafy, Martin Urayy=z, Stefan Huberz, Roland Kwitty yUniversity of Salzburg, Austria zJosef Ressel Centre for Intelligent and Secure Industrial Automation, University of Applied Sciences, Salzburg, Austria { sebastian.zeng , florian.graf , roland.kwitt}@plus.ac.at, {martin.uray, stefan.huber}@fh-salzburg.ac.at", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/3a509449a73fd0aab8c0cf5705827036-Paper-Conference.pdf", "content": "Sebastian Zengy=z, Florian Grafy, Martin Urayy=z, Stefan Huberz, Roland Kwitty yUniversity of Salzburg, Austria zJosef Ressel Centre for Intelligent and Secure Industrial Automation, University of Applied Sciences, Salzburg, Austria { sebastian.zeng , florian.graf , roland.kwitt}@plus.ac.at, {martin.uray, stefan.huber}@fh-salzburg.ac.at"}
+{"idx": 6, "title": "plus-rkwitt/neural_persistence_dynamics - GitHub", "date": "", "ddg_snippet": "@misc {Zeng24a, title = { Neural Persistence Dynamics }, author = { Sebastian Zeng and Florian Graf and Martin Uray and Stefan Huber and Roland Kwitt}, year = {2024}, eprint = {2405.15732}, archivePrefix = {arXiv}, primaryClass = {cs.LG} }", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plus-rkwitt/neural_persistence_dynamics", "content": "@misc {Zeng24a, title = { Neural Persistence Dynamics }, author = { Sebastian Zeng and Florian Graf and Martin Uray and Stefan Huber and Roland Kwitt}, year = {2024}, eprint = {2405.15732}, archivePrefix = {arXiv}, primaryClass = {cs.LG} }"}
+{"idx": 7, "title": "Neural Persistence Dynamics - Paris-Lodron-University Salzburg", "date": "", "ddg_snippet": "Neural Persistence Dynamics Sebastian Zeng , Florian Graf , Martin Uray, Stefan Huber, Roland Kwitt Artificial Intelligence and Human Interfaces", "subpage_snippet": "", "source": "uni-salzburg.elsevierpure.com", "link": "https://uni-salzburg.elsevierpure.com/en/publications/neural-persistence-dynamics", "content": "Neural Persistence Dynamics Sebastian Zeng , Florian Graf , Martin Uray, Stefan Huber, Roland Kwitt Artificial Intelligence and Human Interfaces"}
+{"idx": 8, "title": "Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "Neural Persistence Dynamics Sebastian Zeng †/‡, Florian Graf †, Martin Uray†/‡, Stefan Huber‡, Roland Kwitt† †University of Salzburg, Austria ‡Josef Ressel Centre for Intelligent and Secure Industrial Automation, University of Applied Sciences, Salzburg, Austria", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15732", "content": "Neural Persistence Dynamics Sebastian Zeng †/‡, Florian Graf †, Martin Uray†/‡, Stefan Huber‡, Roland Kwitt† †University of Salzburg, Austria ‡Josef Ressel Centre for Intelligent and Secure Industrial Automation, University of Applied Sciences, Salzburg, Austria"}
+{"idx": 9, "title": "(PDF) Neural Persistence Dynamics - ResearchGate", "date": "", "ddg_snippet": "Sebastian Zeng †/‡, Florian Graf †, Martin Uray ‡, Stefan Huber ‡, Roland Kwitt † University of Salzburg, Austria ‡ Josef Ressel Centre for Intelligent and Secure Industrial Automation,", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380895040_Neural_Persistence_Dynamics", "content": "Sebastian Zeng †/‡, Florian Graf †, Martin Uray ‡, Stefan Huber ‡, Roland Kwitt † University of Salzburg, Austria ‡ Josef Ressel Centre for Intelligent and Secure Industrial Automation,"}
diff --git a/data/sampled_jsons/Nikhil_Bansal_FOCS_2010_discrepancy_minimization_full_paper_title_year_2010.jsonl b/data/sampled_jsons/Nikhil_Bansal_FOCS_2010_discrepancy_minimization_full_paper_title_year_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..625922b3db9483e18d42cd11455624de75bfe55f
--- /dev/null
+++ b/data/sampled_jsons/Nikhil_Bansal_FOCS_2010_discrepancy_minimization_full_paper_title_year_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constructive Algorithms for Discrepancy Minimization Constructive Algorithms for Discrepancy Minimization FOCS 2010 Accepted Papers 1 Combinatorial Discrepancy - Princeton University 2010 IEEE 51st Annual Symposium on Foundations of Computer ... S arXiv:1002.2259v4 [cs.DS] 9 Aug 2010 Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "Feb 11, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy . In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to: 1. Constructive Algorithms for Discrepancy Minimization Authors: Nikhil Bansal (IBM Research) An efficient test for product states, with applications to quantum Merlin-Arthur games Authors:Aram W. Harrow: Department of Mathematics, University of Bristol & Departmenty of Computer Science and Engineering, University of Washington and Lecture Constructive Algorithms for Discrepancy Minimization Nikhil Bansal Scribe: Daniel Dadush Oct 23, 2010 · Browse the leading magazines in computing offering topical peer-reviewed current research, developments, and timely information. In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to: Feb 11, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1002.2259", "content": "Feb 11, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy . In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to: 1. Constructive Algorithms for Discrepancy Minimization Authors: Nikhil Bansal (IBM Research) An efficient test for product states, with applications to quantum Merlin-Arthur games Authors:Aram W. Harrow: Department of Mathematics, University of Bristol & Departmenty of Computer Science and Engineering, University of Washington and Lecture Constructive Algorithms for Discrepancy Minimization Nikhil Bansal Scribe: Daniel Dadush Oct 23, 2010 · Browse the leading magazines in computing offering topical peer-reviewed current research, developments, and timely information. In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to: Feb 11, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy ."}
+{"idx": 1, "title": "Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to: 1.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/FOCS.2010.7", "content": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to: 1."}
+{"idx": 2, "title": "FOCS 2010 Accepted Papers", "date": "", "ddg_snippet": "Constructive Algorithms for Discrepancy Minimization Authors: Nikhil Bansal (IBM Research) An efficient test for product states, with applications to quantum Merlin-Arthur games Authors:Aram W. Harrow: Department of Mathematics, University of Bristol & Departmenty of Computer Science and Engineering, University of Washington and", "subpage_snippet": "", "source": "ieee-focs.org", "link": "https://ieee-focs.org/focs2010/accepted.html", "content": "Constructive Algorithms for Discrepancy Minimization Authors: Nikhil Bansal (IBM Research) An efficient test for product states, with applications to quantum Merlin-Arthur games Authors:Aram W. Harrow: Department of Mathematics, University of Bristol & Departmenty of Computer Science and Engineering, University of Washington and"}
+{"idx": 3, "title": "1 Combinatorial Discrepancy - Princeton University", "date": "", "ddg_snippet": "Lecture Constructive Algorithms for Discrepancy Minimization Nikhil Bansal Scribe: Daniel Dadush", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/~zdvir/apx11slides/bansal-scribe.pdf", "content": "Lecture Constructive Algorithms for Discrepancy Minimization Nikhil Bansal Scribe: Daniel Dadush"}
+{"idx": 4, "title": "2010 IEEE 51st Annual Symposium on Foundations of Computer ...", "date": "", "ddg_snippet": "Oct 23, 2010 · Browse the leading magazines in computing offering topical peer-reviewed current research, developments, and timely information.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings/focs/2010/12OmNBQ2VPh", "content": "Oct 23, 2010 · Browse the leading magazines in computing offering topical peer-reviewed current research, developments, and timely information."}
+{"idx": 5, "title": "S arXiv:1002.2259v4 [cs.DS] 9 Aug 2010", "date": "", "ddg_snippet": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1002.2259.pdf", "content": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to:"}
+{"idx": 6, "title": "Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "Feb 11, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1002.2259v4", "content": "Feb 11, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method. We also give a first approximation-like result for discrepancy ."}
+{"idx": 7, "title": "FOCS 2010 Accepted Papers (with pdf files) | My Brain is Open", "date": "", "ddg_snippet": "1 Jul 2010 — Constructive Algorithms for Discrepancy Minimization [arXiv] Authors: Nikhil Bansal (IBM Research); An efficient test for product states ...", "subpage_snippet": "", "source": "kintali.wordpress.com", "link": "https://kintali.wordpress.com/2010/07/01/focs-2010-accepted-papers-with-pdf-files/", "content": "1 Jul 2010 — Constructive Algorithms for Discrepancy Minimization [arXiv] Authors: Nikhil Bansal (IBM Research); An efficient test for product states ..."}
+{"idx": 8, "title": "Data Structures and Algorithms 2010", "date": "", "ddg_snippet": "Title : Constructive Algorithms for Discrepancy Minimization . Nikhil Bansal . Comments: Fixed a previous erroneous claim about a logarithmic approximation for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.DS/2010?skip=0&show=100", "content": "Title : Constructive Algorithms for Discrepancy Minimization . Nikhil Bansal . Comments: Fixed a previous erroneous claim about a logarithmic approximation for ..."}
+{"idx": 9, "title": "FOCS 2024 Accepted Papers with Abstracts", "date": "", "ddg_snippet": "FOCS 2024 Accepted Papers with Abstracts. Please see below a searchable list of the accepted papers with titles , abstracts and author names: O(1) Insertion for ...", "subpage_snippet": "", "source": "focs.computer.org", "link": "https://focs.computer.org/2024/accepted-papers-with-abstracts/", "content": "FOCS 2024 Accepted Papers with Abstracts. Please see below a searchable list of the accepted papers with titles , abstracts and author names: O(1) Insertion for ..."}
diff --git a/data/sampled_jsons/No_Free_Delivery_Service_Nickel_strengthens_Wolpert_NFL_theorems_key_distinction.jsonl b/data/sampled_jsons/No_Free_Delivery_Service_Nickel_strengthens_Wolpert_NFL_theorems_key_distinction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c1b83946acbb5e9016ef4dc07b57b837a14f4c57
--- /dev/null
+++ b/data/sampled_jsons/No_Free_Delivery_Service_Nickel_strengthens_Wolpert_NFL_theorems_key_distinction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No free delivery service Epistemic limits of passive data ...", "date": "", "ddg_snippet": "by M Nickel · 2024 · Cited by 1 — While the NFL theorems show the impossibility of an assumption- free general purpose learning algorithm, a common criticism is that they need to assume an ... 28 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/b97fc02c9e536d68300d82be05c23aa2-Paper-Conference.pdf", "content": "by M Nickel · 2024 · Cited by 1 — While the NFL theorems show the impossibility of an assumption- free general purpose learning algorithm, a common criticism is that they need to assume an ... 28 pages"}
+{"idx": 1, "title": "No Free Delivery Service: Epistemic Limits of Passive Data ...", "date": "", "ddg_snippet": "While the NFL theorems show the impossibility of an assumption- free general purpose learning algorithm, a common criticism is that they need to assume an ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/795949995/2411-13653v1", "content": "While the NFL theorems show the impossibility of an assumption- free general purpose learning algorithm, a common criticism is that they need to assume an ..."}
+{"idx": 2, "title": "On Scientific Foundation Models: Rigorous Definitions, Key ...", "date": "", "ddg_snippet": "Foundation models appear to challenge the traditional interpretation of the No Free Lunch ( NFL ) theorem , by Wolpert and Macready [1], yet they do not ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5d19e4f5-26e4-4251-a357-8d1614d0629c-MECA.pdf?abstractid=5409063&mirid=1", "content": "Foundation models appear to challenge the traditional interpretation of the No Free Lunch ( NFL ) theorem , by Wolpert and Macready [1], yet they do not ..."}
+{"idx": 3, "title": "Emperor penguin optimizer: A comprehensive review ...", "date": "", "ddg_snippet": "by OW Khalid · 2023 · Cited by 94 — Firstly, the no free lunch theorem (NFL ) proves that no single meta-heuristic method is used to handle every optimization challenge. Secondly, ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1110016822005361", "content": "by OW Khalid · 2023 · Cited by 94 — Firstly, the no free lunch theorem (NFL ) proves that no single meta-heuristic method is used to handle every optimization challenge. Secondly, ..."}
+{"idx": 4, "title": "EfficientLLM: Efficiency in Large Language Models ...", "date": "", "ddg_snippet": "14 May 2025 — This observation provides strong empirical validation for the No - Free -Lunch ( NFL ) theorem [42] in the context of LLM efficiency. While the NFL ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13840v1", "content": "14 May 2025 — This observation provides strong empirical validation for the No - Free -Lunch ( NFL ) theorem [42] in the context of LLM efficiency. While the NFL ..."}
+{"idx": 5, "title": "An exhaustive review of the metaheuristic algorithms for ...", "date": "", "ddg_snippet": "by K Rajwar · 2023 · Cited by 517 — In the next two years, an appreciable and controversial work, the no free lunch theorem ( NFL ) for optimization, was introduced and proved ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-023-10470-y", "content": "by K Rajwar · 2023 · Cited by 517 — In the next two years, an appreciable and controversial work, the no free lunch theorem ( NFL ) for optimization, was introduced and proved ..."}
+{"idx": 6, "title": "An exhaustive review of the metaheuristic algorithms for ...", "date": "", "ddg_snippet": "by K Rajwar · 2023 · Cited by 517 — In the next two years, an appreciable and controversial work, the no free lunch theorem ( NFL ) for optimization, was introduced and proved explicitly by Wolpert ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10103682/", "content": "by K Rajwar · 2023 · Cited by 517 — In the next two years, an appreciable and controversial work, the no free lunch theorem ( NFL ) for optimization, was introduced and proved explicitly by Wolpert ..."}
+{"idx": 7, "title": "Learning to optimize", "date": "", "ddg_snippet": "by X Chen · 2024 · Cited by 11 — According to the famous No Free Lunch. ( NFL ) theorem [167], we cannot ... Hence, there's no pressing need to build these algorithms from scratch.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15251", "content": "by X Chen · 2024 · Cited by 11 — According to the famous No Free Lunch. ( NFL ) theorem [167], we cannot ... Hence, there's no pressing need to build these algorithms from scratch."}
+{"idx": 8, "title": "Hybrid weighted fuzzy production rule extraction utilizing ...", "date": "", "ddg_snippet": "by F Qin · 2025 · Cited by 4 — DDA-HS is introduced, specifically designed with considerations from the No - Free -Lunch ( NFL ) Theorem . This design ensures that DDA-HS is ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-95406-y", "content": "by F Qin · 2025 · Cited by 4 — DDA-HS is introduced, specifically designed with considerations from the No - Free -Lunch ( NFL ) Theorem . This design ensures that DDA-HS is ..."}
+{"idx": 9, "title": "Overnight technician routing and scheduling problem with ...", "date": "", "ddg_snippet": "Furthermore, the ' No Free Lunch ( NFL ) Theorem ' provided by ( Wolpert & Macready, Citation1997) states that no algorithm can be considered the best optimiser ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/23302674.2025.2536110?src=exp-la", "content": "Furthermore, the ' No Free Lunch ( NFL ) Theorem ' provided by ( Wolpert & Macready, Citation1997) states that no algorithm can be considered the best optimiser ..."}
diff --git a/data/sampled_jsons/No_Free_Delivery_Service_paper_Theorem_1_train-test_paradigm_invalid.jsonl b/data/sampled_jsons/No_Free_Delivery_Service_paper_Theorem_1_train-test_paradigm_invalid.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..de7f3cd0af9c91c9ebf256af5923e552e365ca16
--- /dev/null
+++ b/data/sampled_jsons/No_Free_Delivery_Service_paper_Theorem_1_train-test_paradigm_invalid.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NoFreeDeliveryService No - arXiv.org", "date": "", "ddg_snippet": "Theorem 1 (Informal). For passively collected data in complex social systems the train-test paradigm cannot be valid under ontological parsimony for the vast m jority of the system. This includes widely considered variants of recommender systems a", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.13653v1", "content": "Theorem 1 (Informal). For passively collected data in complex social systems the train-test paradigm cannot be valid under ontological parsimony for the vast m jority of the system. This includes widely considered variants of recommender systems a"}
+{"idx": 1, "title": "PDF No free delivery service", "date": "", "ddg_snippet": "Hence, theorem 1 shows that the train-test paradigm cannot be valid under ontologi-cal parsimony for the vast majority of nodes in realistic social systems. Table 2 illustrates this using parameters that match the well-known Book Crossing dataset.", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2024/file/b97fc02c9e536d68300d82be05c23aa2-Paper-Conference.pdf", "content": "Hence, theorem 1 shows that the train-test paradigm cannot be valid under ontologi-cal parsimony for the vast majority of nodes in realistic social systems. Table 2 illustrates this using parameters that match the well-known Book Crossing dataset."}
+{"idx": 2, "title": "[PDF] No Free Delivery Service: Epistemic limits of passive data ...", "date": "", "ddg_snippet": "It is shown that for widely considered inference settings in complex social systems the train-test paradigm does not only lack a justification but is indeed invalid for any risk estimator, including counterfactual and causal estimators, with high probability. Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/No-Free-Delivery-Service:-Epistemic-limits-of-data-Nickel/3e22669b497e2b1c674cdf0dfce85afd80ed5298", "content": "It is shown that for widely considered inference settings in complex social systems the train-test paradigm does not only lack a justification but is indeed invalid for any risk estimator, including counterfactual and causal estimators, with high probability. Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI ..."}
+{"idx": 3, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity. Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems, including ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.13653", "content": "Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity. Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems, including ..."}
+{"idx": 4, "title": "Understanding Train, Test, and Validation Data in Machine Learning", "date": "", "ddg_snippet": "These subsets are typically referred to as train , test , and validation data. Each of these plays a crucial role in the process of building and evaluating a machine learning model.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jainvidip/understanding-train-test-and-validation-data-in-machine-learning-f8276165619c", "content": "These subsets are typically referred to as train , test , and validation data. Each of these plays a crucial role in the process of building and evaluating a machine learning model."}
+{"idx": 5, "title": "Training and Test sets — A Gentle Introduction to Predictive Modelling", "date": "", "ddg_snippet": "Training and Test sets We have seen how detrimental overfitting can be to predictive models. Models biased for the sample they were fitted (trained) on might not capture much form the underlying true relationship. The next step is to evaluate, how much predictive performance we have when we rule out the effect of overfitting. There is a very straightforward solution: we can simply obtain ...", "subpage_snippet": "", "source": "pni-lab.github.io", "link": "https://pni-lab.github.io/predmod_lecture/3_cross_validation/train_test.html", "content": "Training and Test sets We have seen how detrimental overfitting can be to predictive models. Models biased for the sample they were fitted (trained) on might not capture much form the underlying true relationship. The next step is to evaluate, how much predictive performance we have when we rule out the effect of overfitting. There is a very straightforward solution: we can simply obtain ..."}
+{"idx": 6, "title": "16.2. Train-Test Split — Learning Data Science", "date": "", "ddg_snippet": "Fig. 16.1 The train-test split divides the data into two parts: the train set is used to build the model and the test set evaluates that model Typically, the test set consists of 10% to 25% of the data.", "subpage_snippet": "", "source": "learningds.org", "link": "https://learningds.org/ch/16/ms_train_test.html", "content": "Fig. 16.1 The train-test split divides the data into two parts: the train set is used to build the model and the test set evaluates that model Typically, the test set consists of 10% to 25% of the data."}
+{"idx": 7, "title": "Unveiling the No Free Lunch Theorem: A Paradigm Shift in ... - Toolify", "date": "", "ddg_snippet": "The No Free Lunch Theorem : A Paradigm Shift in Machine Learning 📚 Machine learning is a fascinating field, where the Quest for finding the optimal algorithm seems never-ending. However, amidst the myriad of algorithms and data models available, a fundamental principle emerges - the No Free Lunch Theorem .", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-news/unveiling-the-no-free-lunch-theorem-a-paradigm-shift-in-machine-learning-2021071", "content": "The No Free Lunch Theorem : A Paradigm Shift in Machine Learning 📚 Machine learning is a fascinating field, where the Quest for finding the optimal algorithm seems never-ending. However, amidst the myriad of algorithms and data models available, a fundamental principle emerges - the No Free Lunch Theorem ."}
+{"idx": 8, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "Bibtex Paper Authors Maximilian Nickel Abstract Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/b97fc02c9e536d68300d82be05c23aa2-Abstract-Conference.html", "content": "Bibtex Paper Authors Maximilian Nickel Abstract Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity."}
+{"idx": 9, "title": "vocab.txt · unitary/toxic-bert at a4473137dc49a3372f28e773dfca2276616ace45", "date": "", "ddg_snippet": "Use this modela447313", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/unitary/toxic-bert/blame/a4473137dc49a3372f28e773dfca2276616ace45/vocab.txt", "content": "Use this modela447313"}
diff --git a/data/sampled_jsons/Normalizing_Flows_are_Capable_Generative_Models_Zhai_Zhang_Nakkiran_PDF.jsonl b/data/sampled_jsons/Normalizing_Flows_are_Capable_Generative_Models_Zhai_Zhang_Nakkiran_PDF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..27a00b4c73ca449e85f778357fe69d3a16e678ae
--- /dev/null
+++ b/data/sampled_jsons/Normalizing_Flows_are_Capable_Generative_Models_Zhai_Zhang_Nakkiran_PDF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "by S Zhai · 2024 · Cited by 25 — Authors:Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran, David ... View a PDF of the paper titled Normalizing Flows are Capable Generative Models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.06329", "content": "by S Zhai · 2024 · Cited by 25 — Authors:Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran, David ... View a PDF of the paper titled Normalizing Flows are Capable Generative Models ..."}
+{"idx": 1, "title": "Normalizing Flows are Capable Generative Models - Jiatao Gu", "date": "", "ddg_snippet": "Abstract. Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demon- strated promising results on both density estima-.", "subpage_snippet": "", "source": "jiataogu.me", "link": "https://jiataogu.me/papers/zhai2025normalizing.pdf", "content": "Abstract. Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demon- strated promising results on both density estima-."}
+{"idx": 2, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "6 Jun 2025 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density estimation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "6 Jun 2025 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density estimation ..."}
+{"idx": 3, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "by S Zhai · Cited by 25 — In this paper, we propose recipes to train normalizing flows and improve their performance to an unprecedented level: state of the art ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "by S Zhai · Cited by 25 — In this paper, we propose recipes to train normalizing flows and improve their performance to an unprecedented level: state of the art ..."}
+{"idx": 4, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "13 Dec 2024 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2412.06329", "content": "13 Dec 2024 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density ..."}
+{"idx": 5, "title": "[Literature Review] Normalizing Flows are Capable ...", "date": "", "ddg_snippet": "This page provides the most accurate and concise summary worldwide for the paper titled Normalizing Flows are Capable Generative Models . With Moonlight, your AI ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/normalizing-flows-are-capable-generative-models", "content": "This page provides the most accurate and concise summary worldwide for the paper titled Normalizing Flows are Capable Generative Models . With Moonlight, your AI ..."}
+{"idx": 6, "title": "Preetum Nakkiran", "date": "", "ddg_snippet": "Normalizing flows are capable generative models . Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran, David Berthelot, Jiatao Gu, Huangjie Zheng, Tianrong Chen ...", "subpage_snippet": "", "source": "preetum.nakkiran.org", "link": "https://preetum.nakkiran.org/", "content": "Normalizing flows are capable generative models . Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran, David Berthelot, Jiatao Gu, Huangjie Zheng, Tianrong Chen ..."}
+{"idx": 7, "title": "STARFlow: Scaling Latent Normalizing Flows for High ... - Jiatao Gu", "date": "", "ddg_snippet": "We present STARFlow , a scalable generative model based on normalizing flows that achieves strong performance on high-resolution image synthesis. STARFlow's.", "subpage_snippet": "", "source": "jiataogu.me", "link": "https://jiataogu.me/papers/gu2025starflow.pdf", "content": "We present STARFlow , a scalable generative model based on normalizing flows that achieves strong performance on high-resolution image synthesis. STARFlow's."}
+{"idx": 8, "title": "Search", "date": "", "ddg_snippet": "Normalizing Flows are Capable Generative Models · pdf icon · Shuangfei Zhai, Ruixiang ZHANG, Preetum Nakkiran, David Berthelot, Jiatao Gu, Huangjie Zheng ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search?term=~David_Berthelot1&content=authors&group=all&source=forum&sort=cdate:desc", "content": "Normalizing Flows are Capable Generative Models · pdf icon · Shuangfei Zhai, Ruixiang ZHANG, Preetum Nakkiran, David Berthelot, Jiatao Gu, Huangjie Zheng ..."}
+{"idx": 9, "title": "Journal Club: Advanced Machine Learning", "date": "", "ddg_snippet": "Normalizing Flows are Capable Generative Models Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran et al. ... https://arxiv.org/pdf/2302.05543.pdf. 24.07.23", "subpage_snippet": "", "source": "sciai-lab.org", "link": "https://sciai-lab.org/teaching/journal-club/", "content": "Normalizing Flows are Capable Generative Models Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran et al. ... https://arxiv.org/pdf/2302.05543.pdf. 24.07.23"}
diff --git a/data/sampled_jsons/O'Keefe_Nadel_1978_Hippocampus_Cognitive_Map_book_year_1978.jsonl b/data/sampled_jsons/O'Keefe_Nadel_1978_Hippocampus_Cognitive_Map_book_year_1978.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6bdf5180cc8f48243c4db816b536d7c335c575ba
--- /dev/null
+++ b/data/sampled_jsons/O'Keefe_Nadel_1978_Hippocampus_Cognitive_Map_book_year_1978.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Hippocampus as a Cognitive Map", "date": "", "ddg_snippet": "by J O'Keefe · 1978 · Cited by 15142 — John O'Keefe & Lynn Nadel (1978) The Hippocampus as a Cognitive Map ,. Oxford University Press. You may redistribute the file electronically providing you do ... 296 pages", "subpage_snippet": "", "source": "www.cmor-faculty.rice.edu", "link": "https://www.cmor-faculty.rice.edu/~cox/neuro/HCMComplete.pdf", "content": "by J O'Keefe · 1978 · Cited by 15142 — John O'Keefe & Lynn Nadel (1978) The Hippocampus as a Cognitive Map ,. Oxford University Press. You may redistribute the file electronically providing you do ... 296 pages"}
+{"idx": 1, "title": "The Hippocampus as a Cognitive Map by John O'Keefe", "date": "", "ddg_snippet": "The Hippocampus as a Cognitive Map . John O'Keefe, Lynn Nadel. 4.78. 9 ratings2 reviews. Want to Read. Buy on Amazon. Rate this book.", "subpage_snippet": "", "source": "www.goodreads.com", "link": "https://www.goodreads.com/book/show/2645838-the-hippocampus-as-a-cognitive-map", "content": "The Hippocampus as a Cognitive Map . John O'Keefe, Lynn Nadel. 4.78. 9 ratings2 reviews. Want to Read. Buy on Amazon. Rate this book."}
+{"idx": 2, "title": "Précis of O'Keefe and Nadel's The Hippocampus as a ...", "date": "", "ddg_snippet": "by J O'Keefe · 1979 · Cited by 588 — In a discussion of their book ( 1978 ), the authors argue that the brain must come equipped to impose a 3-dimensional Euclidean framework on ...", "subpage_snippet": "", "source": "psycnet.apa.org", "link": "https://psycnet.apa.org/record/1981-20716-001", "content": "by J O'Keefe · 1979 · Cited by 588 — In a discussion of their book ( 1978 ), the authors argue that the brain must come equipped to impose a 3-dimensional Euclidean framework on ..."}
+{"idx": 3, "title": "The Hippocampus as a Cognitive Map - Hardcover", "date": "", "ddg_snippet": "The Hippocampus as a Cognitive Map . O'Keefe, John and Nadel, Lynn. Published by Oxford University Press, Oxford, London, Glasgow and New York, 1978.", "subpage_snippet": "", "source": "www.abebooks.com", "link": "https://www.abebooks.com/9780198572060/Hippocampus-Cognitive-Map-OKeefe-John-0198572069/plp", "content": "The Hippocampus as a Cognitive Map . O'Keefe, John and Nadel, Lynn. Published by Oxford University Press, Oxford, London, Glasgow and New York, 1978."}
+{"idx": 4, "title": "The Hippocampus as a Cognitive Map", "date": "", "ddg_snippet": "25 Aug 2022 — Cognitive Map, Oxford University Press . Publisher Oxford: Clarendon Press Rights Copyright © John O'Keefe & Lynn Nadel.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/the-hippocampus-as-a-cognitive-map-ejccj9sh6m?citations_page=198", "content": "25 Aug 2022 — Cognitive Map, Oxford University Press . Publisher Oxford: Clarendon Press Rights Copyright © John O'Keefe & Lynn Nadel."}
+{"idx": 5, "title": "The Hippocampus as a Cognitive Map: The Book", "date": "", "ddg_snippet": "26 Oct 2014 — The second path was to collaborate with Lynn Nadel to write “The Hippocampus as a Cognitive Map ” henceforth, “The Book” (1978). Shortly after ...", "subpage_snippet": "", "source": "www.brainfacts.org", "link": "https://www.brainfacts.org/brain-anatomy-and-function/anatomy/2014/the-hippocampus-as-a-cognitive-map-the-book", "content": "26 Oct 2014 — The second path was to collaborate with Lynn Nadel to write “The Hippocampus as a Cognitive Map ” henceforth, “The Book” (1978). Shortly after ..."}
+{"idx": 6, "title": "The Hippocampus as a Cognitive Map. John O'Keefe and ...", "date": "", "ddg_snippet": "by G Lynch · 1979 · Cited by 1 — Representations in the Brain: The Hippocampus as a Cognitive Map . John O'Keefe and Lynn Nadel. Clarendon (Oxford University Press), New York, 1978. xvi, 570 pp.", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/science.204.4394.762.a", "content": "by G Lynch · 1979 · Cited by 1 — Representations in the Brain: The Hippocampus as a Cognitive Map . John O'Keefe and Lynn Nadel. Clarendon (Oxford University Press), New York, 1978. xvi, 570 pp."}
+{"idx": 7, "title": "Précis of O'Keefe & Nadel's The hippocampus as a ...", "date": "", "ddg_snippet": "by J O'Keefe · 1979 · Cited by 589 — O'Keefe, J., and Nadel, L. (1978) The hippocampus as a cognitive map . Oxford: Clarendon Press. [JO'K, RET et al]Google Scholar. O'Keefe, J.", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/precis-of-okeefe-nadels-the-hippocampus-as-a-cognitive-map/FD98C4A58AC3B49D18EC53F70CC926C8", "content": "by J O'Keefe · 1979 · Cited by 589 — O'Keefe, J., and Nadel, L. (1978) The hippocampus as a cognitive map . Oxford: Clarendon Press. [JO'K, RET et al]Google Scholar. O'Keefe, J."}
+{"idx": 8, "title": "The Hippocampus as a Cognitive Map … of Social Space", "date": "", "ddg_snippet": "by H Eichenbaum · 2015 · Cited by 83 — A connection between the hippocampus and cognitive maps began with a landmark book by O'Keefe and Nadel ( 1978 ) ·, who proposed that the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0896627315005267", "content": "by H Eichenbaum · 2015 · Cited by 83 — A connection between the hippocampus and cognitive maps began with a landmark book by O'Keefe and Nadel ( 1978 ) ·, who proposed that the ..."}
+{"idx": 9, "title": "Book Review: The Hippocampus as a Cognitive Map", "date": "", "ddg_snippet": "by A Dickinson · 1980 — Based on: O'Keefe J. and Nadel L.The Hippocampus as a Cognitive Map . London: Oxford University Press. 1978 . Pp. 543. £25.00. ISBN 0 12 524150 X.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/abs/10.1080/14640748008401170b", "content": "by A Dickinson · 1980 — Based on: O'Keefe J. and Nadel L.The Hippocampus as a Cognitive Map . London: Oxford University Press. 1978 . Pp. 543. £25.00. ISBN 0 12 524150 X."}
diff --git a/data/sampled_jsons/OFUL_algorithm_implementation_challenges_computational_complexity.jsonl b/data/sampled_jsons/OFUL_algorithm_implementation_challenges_computational_complexity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ffadf4c8fcf23457f4888ac87e3fa1d9f9a61a2
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_implementation_challenges_computational_complexity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Optimization of algorithmic efficiency in AI: Addressing ...", "date": "", "ddg_snippet": "Mar 15, 2024 · This study explores the efficiency and scalability challenges present in artificial intelligence (AI) algorithms , with particular consideration given to computational complexity issues and optimization strategies. This guide reviews key algorithms such as gradient descent and genetic algorithms in depth to highlight their roles in increasing AI efficiency. Through an exhaustive literature ...", "subpage_snippet": "", "source": "www.ewadirect.com", "link": "https://www.ewadirect.com/proceedings/ace/article/view/11116", "content": "Mar 15, 2024 · This study explores the efficiency and scalability challenges present in artificial intelligence (AI) algorithms , with particular consideration given to computational complexity issues and optimization strategies. This guide reviews key algorithms such as gradient descent and genetic algorithms in depth to highlight their roles in increasing AI efficiency. Through an exhaustive literature ..."}
+{"idx": 1, "title": "ALGORITHMS AND COMPLEXITY ANALYSIS", "date": "", "ddg_snippet": "Introduction to fundamental computing algorithms Numerical algorithms for mathematical computations Detailed exploration of sequential and binary search algorithms Analysis and implementation of sorting algorithms", "subpage_snippet": "", "source": "tau.edu.ng", "link": "https://tau.edu.ng/assets/media/docs/algorithms-and-complexity-analysis-csc304_1716907183.pdf", "content": "Introduction to fundamental computing algorithms Numerical algorithms for mathematical computations Detailed exploration of sequential and binary search algorithms Analysis and implementation of sorting algorithms"}
+{"idx": 2, "title": "Synthesizing complexity: Trends, challenges, and future ...", "date": "", "ddg_snippet": "Dec 1, 2024 · Algorithm complexity and computational intensity pose challenges , especially with respect to numerous criteria and alternatives. Linguistic evaluations and expert boards may introduce bias.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1568494624011360", "content": "Dec 1, 2024 · Algorithm complexity and computational intensity pose challenges , especially with respect to numerous criteria and alternatives. Linguistic evaluations and expert boards may introduce bias."}
+{"idx": 3, "title": "Algorithms and Computational Complexity | SpringerLink", "date": "", "ddg_snippet": "Nov 18, 2022 · In this chapter, we discuss computational problems related to ABC rules and algorithms that solve these problems. We start by discussing the computational complexity of ABC rules. As many ABC rules are computationally difficult, a thorough algorithmic analysis is...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-09016-5_5", "content": "Nov 18, 2022 · In this chapter, we discuss computational problems related to ABC rules and algorithms that solve these problems. We start by discussing the computational complexity of ABC rules. As many ABC rules are computationally difficult, a thorough algorithmic analysis is..."}
+{"idx": 4, "title": "Computational Complexity", "date": "", "ddg_snippet": "Computational complexity theory is the study of the minimal resources needed to solve computational problems. In particular, it aims to distinguish be-tween those problems that possess e cient algorithms (the \\easy\" problems) and those that are inherently intractable (the \\hard\" problems).", "subpage_snippet": "", "source": "people.seas.harvard.edu", "link": "https://people.seas.harvard.edu/~salil/research/ComputationalComplexity-2ndEd.pdf", "content": "Computational complexity theory is the study of the minimal resources needed to solve computational problems. In particular, it aims to distinguish be-tween those problems that possess e cient algorithms (the \\easy\" problems) and those that are inherently intractable (the \\hard\" problems)."}
+{"idx": 5, "title": "Bandit algorithms to emulate human decision making using ...", "date": "", "ddg_snippet": "by RK Kolla · 2016 · Cited by 1 — The regret analysis of the resulting W- OFUL algorithm poses novel challenges compared to that in the linear bandit problem, primarily because ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1611.10283", "content": "by RK Kolla · 2016 · Cited by 1 — The regret analysis of the resulting W- OFUL algorithm poses novel challenges compared to that in the linear bandit problem, primarily because ..."}
+{"idx": 6, "title": "Optimal and Practical Batched Linear Bandit Algorithm", "date": "", "ddg_snippet": "by S Yu — The comparison with E4 and RS- OFUL shows that BLAE consistently achieves lower regret and fewer updates per batch. Develop batch-wise optimal ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=WcFLasjwXs", "content": "by S Yu — The comparison with E4 and RS- OFUL shows that BLAE consistently achieves lower regret and fewer updates per batch. Develop batch-wise optimal ..."}
+{"idx": 7, "title": "Optimal and Practical Batched Linear Bandit Algorithm", "date": "", "ddg_snippet": "11 Aug 2025 — For RS- OFUL , we set the switching parameter to C = 0.5 C=0.5 . For PhaElimD, we design the batch sizes as T 1 − 2 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.08438v2", "content": "11 Aug 2025 — For RS- OFUL , we set the switching parameter to C = 0.5 C=0.5 . For PhaElimD, we design the batch sizes as T 1 − 2 ..."}
+{"idx": 8, "title": "Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "by Y Abbasi-Yadkori · Cited by 2407 — We call the resulting algorithm the OFUL ALGORITHM for. “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo- code of the algorithm is given in ... 18 pages", "subpage_snippet": "", "source": "yasinov.github.io", "link": "https://yasinov.github.io/linear-bandits-nips2011.pdf", "content": "by Y Abbasi-Yadkori · Cited by 2407 — We call the resulting algorithm the OFUL ALGORITHM for. “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo- code of the algorithm is given in ... 18 pages"}
+{"idx": 9, "title": "Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "by Y Abbasi-yadkori · 2011 · Cited by 2412 — We call the resulting algorithm the OFUL ALGORITHM for. “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo- code of the algorithm is given in ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/improved-algorithms-for-linear-stochastic-bandits-536sm8lzeg.pdf", "content": "by Y Abbasi-yadkori · 2011 · Cited by 2412 — We call the resulting algorithm the OFUL ALGORITHM for. “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo- code of the algorithm is given in ..."}
diff --git a/data/sampled_jsons/OFUL_algorithm_implementation_challenges_optimization_problem_computational_complexity.jsonl b/data/sampled_jsons/OFUL_algorithm_implementation_challenges_optimization_problem_computational_complexity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f9152729d27cd478e4e441b119180002fc2f949
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_implementation_challenges_optimization_problem_computational_complexity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithm - Wikipedia", "date": "", "ddg_snippet": "More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning). In contrast, a heuristic is an approach to solving problems without...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Algorithm", "content": "More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning). In contrast, a heuristic is an approach to solving problems without..."}
+{"idx": 1, "title": "Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "We call the resulting algorithm the OFUL ALGORITHM for “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo-code of the algorithm is given in Figure 1. The crux of the problem is the construction of the condence sets Ct.", "subpage_snippet": "", "source": "david.palenica.com", "link": "https://david.palenica.com/papers/linear-bandit/linear-bandits-NIPS2011-camera-ready.pdf", "content": "We call the resulting algorithm the OFUL ALGORITHM for “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo-code of the algorithm is given in Figure 1. The crux of the problem is the construction of the condence sets Ct."}
+{"idx": 2, "title": "Unlocking Optimization : Hamiltonian Cycles in Real-World Problems", "date": "", "ddg_snippet": "Challenges in Implementation . Computational Complexity : Determining a Hamiltonian cycle is an NP-complete problem . For a graph with (n) vertices, there are ((n-1)!) possible routes to evaluate. Scalability: Huge graphs with a large number of nodes require a lot of processing time.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/prisha_santhosh/unlocking-optimization-hamiltonian-cycles-in-real-world-problems-205l", "content": "Challenges in Implementation . Computational Complexity : Determining a Hamiltonian cycle is an NP-complete problem . For a graph with (n) vertices, there are ((n-1)!) possible routes to evaluate. Scalability: Huge graphs with a large number of nodes require a lot of processing time."}
+{"idx": 3, "title": "Ant Colony Optimization : Nature-Inspired Algorithm for Solving...", "date": "", "ddg_snippet": "Heuristic information: Problem -specific information that guides ants towards promising solutions. Implementing Ant Colony Optimization . Let’s look at a basic implementation of ACO in Python to solve the Traveling Salesman Problem (TSP), a classic optimization challenge", "subpage_snippet": "", "source": "algocademy.com", "link": "https://algocademy.com/blog/ant-colony-optimization-nature-inspired-algorithm-for-solving-complex-problems/", "content": "Heuristic information: Problem -specific information that guides ants towards promising solutions. Implementing Ant Colony Optimization . Let’s look at a basic implementation of ACO in Python to solve the Traveling Salesman Problem (TSP), a classic optimization challenge"}
+{"idx": 4, "title": "NP-Hard Problems : Understanding and Solving | Algor Cards", "date": "", "ddg_snippet": "NP-Hard problems represent a class of computational challenges where solutions are elusive in polynomial time. This text delves into their nature, the differentiation from other problem classes, and various algorithmic strategies for tackling them.", "subpage_snippet": "", "source": "cards.algoreducation.com", "link": "https://cards.algoreducation.com/en/content/wki-K2GQ/np-hard-problems-complexity", "content": "NP-Hard problems represent a class of computational challenges where solutions are elusive in polynomial time. This text delves into their nature, the differentiation from other problem classes, and various algorithmic strategies for tackling them."}
+{"idx": 5, "title": "Convergence analysis and implementation issues | Fiveable", "date": "", "ddg_snippet": "Computational complexity measures algorithm 's resource requirements. Newton's method requires.O(n3) operations per iteration for n-dimensional problems . Dominated by cost of solving linear system involving Hessian matrix. Quasi-Newton methods reduce complexity to.", "subpage_snippet": "", "source": "fiveable.me", "link": "https://fiveable.me/nonlinear-optimization/unit-5/convergence-analysis-implementation-issues/study-guide/7ZsQwRZgQXC4FUbM", "content": "Computational complexity measures algorithm 's resource requirements. Newton's method requires.O(n3) operations per iteration for n-dimensional problems . Dominated by cost of solving linear system involving Hessian matrix. Quasi-Newton methods reduce complexity to."}
+{"idx": 6, "title": "Ions motion algorithm for solving optimization problems (2015)", "date": "", "ddg_snippet": "In fact, the proposed algorithm mimics the attraction and repulsion of anions and cations to perform optimization . The proposed algorithm is designed in such a way to have the least tuning parameters, low computational complexity , fast convergence, and high local optima avoidance.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/ions-motion-algorithm-for-solving-optimization-problems-1g330mshzm", "content": "In fact, the proposed algorithm mimics the attraction and repulsion of anions and cations to perform optimization . The proposed algorithm is designed in such a way to have the least tuning parameters, low computational complexity , fast convergence, and high local optima avoidance."}
+{"idx": 7, "title": "Model Optimization Types | Restackio", "date": "", "ddg_snippet": "Understanding Optimization Problems in CalculusTypes of Optimization Problems : Constraints and Complexity Evaluating Function Performance in Optimization", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/model-optimization-answer-types-of-optimization-problems-cat-ai", "content": "Understanding Optimization Problems in CalculusTypes of Optimization Problems : Constraints and Complexity Evaluating Function Performance in Optimization"}
+{"idx": 8, "title": "Optimization : Challenges and Opportunities", "date": "", "ddg_snippet": "Optimization problems are mathematically complex , often involving millions of variables and constraints, and in many cases belong to the class of NP-hard problems , meaning they cannot be solved exactly within reasonable timeframes by traditional digital computing.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/optimization-challenges-opportunities-vellexcomputing-eaisc", "content": "Optimization problems are mathematically complex , often involving millions of variables and constraints, and in many cases belong to the class of NP-hard problems , meaning they cannot be solved exactly within reasonable timeframes by traditional digital computing."}
+{"idx": 9, "title": "The Walking Palm Tree algorithm : A new Metaheuristic Algorithm for...", "date": "", "ddg_snippet": "Hence, the INFO algorithm is a promising tool for optimal designs in optimization problems , which stems from the considerable efficiency of this algorithm for optimizing constrained cases.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383548488_The_Walking_Palm_Tree_algorithm_A_new_Metaheuristic_Algorithm_for_Solving_Optimization_Problems", "content": "Hence, the INFO algorithm is a promising tool for optimal designs in optimization problems , which stems from the considerable efficiency of this algorithm for optimizing constrained cases."}
diff --git a/data/sampled_jsons/OSG_algorithm_Xu_et_al_2023_fully_connected_communication_network_submodular_coordination_year_2023.jsonl b/data/sampled_jsons/OSG_algorithm_Xu_et_al_2023_fully_connected_communication_network_submodular_coordination_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1b237341517d2c90706d28ebc9d81d9269c26b7d
--- /dev/null
+++ b/data/sampled_jsons/OSG_algorithm_Xu_et_al_2023_fully_connected_communication_network_submodular_coordination_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm , are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05028v1", "content": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm , are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address ..."}
+{"idx": 1, "title": "UM-iRaL/ral23-online-submodular-coordination - GitHub", "date": "", "ddg_snippet": "Paper: Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm , and Applications to Multi-Robot Coordination , IEEE Robotics & Automation Letters (RA-L), 2023 Authors: Zirui Xu , Hongyu Zhou, Vasileios Tzoumas This repository contains the simulation code for a multi-robot multi-target tracking problem. The main code is in main_OSG.py, random_OSG.py, and adversarial_OSG ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/UM-iRaL/ral23-online-submodular-coordination", "content": "Paper: Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm , and Applications to Multi-Robot Coordination , IEEE Robotics & Automation Letters (RA-L), 2023 Authors: Zirui Xu , Hongyu Zhou, Vasileios Tzoumas This repository contains the simulation code for a multi-robot multi-target tracking problem. The main code is in main_OSG.py, random_OSG.py, and adversarial_OSG ..."}
+{"idx": 2, "title": "N -o Online Learning for Multi a S Coordination: Tight Ap Proximation ...", "date": "", "ddg_snippet": "ABSTRACT Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm , are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i8dYPGdB1C", "content": "ABSTRACT Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm , are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To ..."}
+{"idx": 3, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "Abstract Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm , are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.05028", "content": "Abstract Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm , are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To ..."}
+{"idx": 4, "title": "Distributed optimal coordination of multiple heterogeneous linear ...", "date": "", "ddg_snippet": "This paper investigates the distributed optimal coordination (DOC) problem for multiple heterogeneous linear systems over strongly connected communication networks . By exploiting output regulation techniques, constructing a Laplacian matrix based on the outdegree, and assigning each agent an additional scalar state variable, a new DOC algorithms is developed, which can solve the DOC problem ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167691123002268", "content": "This paper investigates the distributed optimal coordination (DOC) problem for multiple heterogeneous linear systems over strongly connected communication networks . By exploiting output regulation techniques, constructing a Laplacian matrix based on the outdegree, and assigning each agent an additional scalar state variable, a new DOC algorithms is developed, which can solve the DOC problem ..."}
+{"idx": 5, "title": "Online Submodular Coordination With Bounded Tracking Regret: Theory ...", "date": "", "ddg_snippet": "Such tasks are often modeled as submodular maximization coordination problems. We introduce the first submodular coordination algorithm with bounded tracking regret, i.e., with bounded suboptimality with respect to optimal time-varying actions that know the future a priori.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10050730", "content": "Such tasks are often modeled as submodular maximization coordination problems. We introduce the first submodular coordination algorithm with bounded tracking regret, i.e., with bounded suboptimality with respect to optimal time-varying actions that know the future a priori."}
+{"idx": 6, "title": "Communication- and Computation-Efficient Distributed Submodular ...", "date": "", "ddg_snippet": "In this paper, our focus is on Action Coordination over tasks that take the form of submodular optimization. We present a communication - and computation-efficient distributed algorithm that scales linearly with the network size, in contrast to the cubic time complexity of the competitive near-optimal algorithms .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.10382v3", "content": "In this paper, our focus is on Action Coordination over tasks that take the form of submodular optimization. We present a communication - and computation-efficient distributed algorithm that scales linearly with the network size, in contrast to the cubic time complexity of the competitive near-optimal algorithms ."}
+{"idx": 7, "title": "Publications - Vasileios Tzoumas", "date": "", "ddg_snippet": "Z. Xu , H. Zhou, V. Tzoumas, Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm , and Application to Multi-Robot Coordination . IEEE Robotics & Automation Letters (RAL), 2023 .", "subpage_snippet": "", "source": "vasileiostzoumas.com", "link": "https://vasileiostzoumas.com/publications/", "content": "Z. Xu , H. Zhou, V. Tzoumas, Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm , and Application to Multi-Robot Coordination . IEEE Robotics & Automation Letters (RAL), 2023 ."}
+{"idx": 8, "title": "Zirui (Ray) Xu", "date": "", "ddg_snippet": "Zirui (Ray) Xu I'm a Ph.D. candidate in Aerospace Engineering at University of Michigan, advised by Vasileios Tzoumas. My goal is to realize scalable and reliable coordination in resource-constrained, unstructured, and untrustworthy environments, such as smart cities, environmental monitoring, and disaster response. I develop and leverage tools of optimization, control, and robotics to ...", "subpage_snippet": "", "source": "zirui-xu.github.io", "link": "https://zirui-xu.github.io/", "content": "Zirui (Ray) Xu I'm a Ph.D. candidate in Aerospace Engineering at University of Michigan, advised by Vasileios Tzoumas. My goal is to realize scalable and reliable coordination in resource-constrained, unstructured, and untrustworthy environments, such as smart cities, environmental monitoring, and disaster response. I develop and leverage tools of optimization, control, and robotics to ..."}
+{"idx": 9, "title": "PDF IROS23 presentation final - GitHub Pages", "date": "", "ddg_snippet": "Z. Xu , X. Lin, and V. Tzoumas, \"Bandit submodular maximization for multi-robot coordination in unpredictable and partially observable environments,\" in Robotics: Science and Systems, 2023", "subpage_snippet": "", "source": "hongyu-zhou.github.io", "link": "https://hongyu-zhou.github.io/files/2023/RAL-22-2539.pdf", "content": "Z. Xu , X. Lin, and V. Tzoumas, \"Bandit submodular maximization for multi-robot coordination in unpredictable and partially observable environments,\" in Robotics: Science and Systems, 2023"}
diff --git a/data/sampled_jsons/Olah_2020_Zoom_In_Circuits_attention_matrices_structural_patterns_year_2020.jsonl b/data/sampled_jsons/Olah_2020_Zoom_In_Circuits_attention_matrices_structural_patterns_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8604fdb5fbb56ab91997e34aab08ed76313c0207
--- /dev/null
+++ b/data/sampled_jsons/Olah_2020_Zoom_In_Circuits_attention_matrices_structural_patterns_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Zoom In : An Introduction to Circuits", "date": "", "ddg_snippet": "Circuit framework ( Olah et al., 2020 ;Elhage et al., 2021) is particularly important for understanding transformer architectures. A circuit is defined as the minimal computational subgraph of a model that faithfully reproduces the model's behavior on a given task Hanna et al., 2023", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/339841165_Zoom_In_An_Introduction_to_Circuits", "content": "Circuit framework ( Olah et al., 2020 ;Elhage et al., 2021) is particularly important for understanding transformer architectures. A circuit is defined as the minimal computational subgraph of a model that faithfully reproduces the model's behavior on a given task Hanna et al., 2023"}
+{"idx": 1, "title": "distill.pub/ 2020 / circuits / zoom - in", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits ( 2020 ) by Chris Olah et alia.", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/zoom-in/", "content": "Zoom In : An Introduction to Circuits ( 2020 ) by Chris Olah et alia."}
+{"idx": 2, "title": "Sign in to your Zoom account to join a meeting, update your profile...", "date": "", "ddg_snippet": "Sign in to your Zoom account to join a meeting, update your profile, change your settings, and more!", "subpage_snippet": "", "source": "zoom.us", "link": "https://zoom.us/signin", "content": "Sign in to your Zoom account to join a meeting, update your profile, change your settings, and more!"}
+{"idx": 3, "title": "ZOOM 6.5.12 – Скачать для Windows", "date": "", "ddg_snippet": "Бесплатно. Windows. Категория: IRC, ICQ, Messenger. Zoom - стремительно набирающий популярность кроссплатформенный сервис видеоконференций с возможностью обмениваться сообщениями.", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-46881-zoom.html", "content": "Бесплатно. Windows. Категория: IRC, ICQ, Messenger. Zoom - стремительно набирающий популярность кроссплатформенный сервис видеоконференций с возможностью обмениваться сообщениями."}
+{"idx": 4, "title": "LLM Circuit Analyses Are Consistent Across Training and Scale", "date": "", "ddg_snippet": "A circuit ( Olah et al., 2020 ; Elhage et al., 2021; Wang et al., 2023) is the minimal computational subgraph of a model that is faithful to its behavior on a given task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10827v1/", "content": "A circuit ( Olah et al., 2020 ; Elhage et al., 2021; Wang et al., 2023) is the minimal computational subgraph of a model that is faithful to its behavior on a given task."}
+{"idx": 5, "title": "Tracr: Compiled Transformers as a", "date": "", "ddg_snippet": "Mechanistic interpretability (Cammarata et al., 2020 ; Olah , 2022) aims to produce mechanistic explanations of the inner workings of ML programs. This includes attempts to reverse engineer how neural networks implement specific behaviours (Cammarata et al., 2020 ).3.", "subpage_snippet": "", "source": "sebastianfarquhar.com", "link": "https://sebastianfarquhar.com/assets/papers/lindnerTracr2023.pdf", "content": "Mechanistic interpretability (Cammarata et al., 2020 ; Olah , 2022) aims to produce mechanistic explanations of the inner workings of ML programs. This includes attempts to reverse engineer how neural networks implement specific behaviours (Cammarata et al., 2020 ).3."}
+{"idx": 6, "title": "GeoGuesser - Free GeoGuessr & Unlimited Games", "date": "", "ddg_snippet": "Can I move around and zoom in Street View? Yes. You can navigate by clicking arrows on the street or by dragging the view. Zooming in on signs or buildings often provides critical clues for determining your location. Key Advantages of GeoGuessr. Improves Geography Knowledge.", "subpage_snippet": "", "source": "geoguesser.io", "link": "https://geoguesser.io/", "content": "Can I move around and zoom in Street View? Yes. You can navigate by clicking arrows on the street or by dragging the view. Zooming in on signs or buildings often provides critical clues for determining your location. Key Advantages of GeoGuessr. Improves Geography Knowledge."}
+{"idx": 7, "title": "Neuron-based explanations of neural networks sacrifice com", "date": "", "ddg_snippet": "Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter. Zoom in : An introduction to circuits .Scaling monosemanticity: Extracting inter-pretable features from claude 3 sonnet. Transformer Circuits Thread, 2024.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=UWNa9Pv6qA&name=pdf", "content": "Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter. Zoom in : An introduction to circuits .Scaling monosemanticity: Extracting inter-pretable features from claude 3 sonnet. Transformer Circuits Thread, 2024."}
+{"idx": 8, "title": "The Best 3D Anatomy Apps for Medical Students... - oTechWorld", "date": "", "ddg_snippet": "The inherent benefit of a 3D anatomy application is its interactivity. It is not like an inanimate 2-dimensional picture; a digital 3-dimensional model can be turned 360 degrees, zoomed in with unbelievable detail, and cut up by layer like peeling the skin off to the bone.", "subpage_snippet": "", "source": "otechworld.com", "link": "https://otechworld.com/best-3d-anatomy-apps-for-medical-students-enthusiasts/", "content": "The inherent benefit of a 3D anatomy application is its interactivity. It is not like an inanimate 2-dimensional picture; a digital 3-dimensional model can be turned 360 degrees, zoomed in with unbelievable detail, and cut up by layer like peeling the skin off to the bone."}
+{"idx": 9, "title": "Python Switch Statement – Switch Case Example", "date": "", "ddg_snippet": "From version 3.10 upwards, Python has implemented a switch case feature called “ structural pattern matching”. You can implement this feature with the match and case keywords. Some people debate whether or not the match and case are keywords in Python.", "subpage_snippet": "", "source": "www.freecodecamp.org", "link": "https://www.freecodecamp.org/news/python-switch-statement-switch-case-example/", "content": "From version 3.10 upwards, Python has implemented a switch case feature called “ structural pattern matching”. You can implement this feature with the match and case keywords. Some people debate whether or not the match and case are keywords in Python."}
diff --git a/data/sampled_jsons/Olah_et_al_2020_transformer_circuits_abstract.jsonl b/data/sampled_jsons/Olah_et_al_2020_transformer_circuits_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1239706fa6732f6b949a868164d7464eb41a7e94
--- /dev/null
+++ b/data/sampled_jsons/Olah_et_al_2020_transformer_circuits_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Mathematical Framework for Transformer Circuits", "date": "", "ddg_snippet": "Black et al . provide significant evidence for typical neurons in transformer language models being polysemantic.Elhage, et al ., \"A Mathematical Framework for Transformer Circuits \", Transformer Circuits Thread, 2021.", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2021/framework/index.html", "content": "Black et al . provide significant evidence for typical neurons in transformer language models being polysemantic.Elhage, et al ., \"A Mathematical Framework for Transformer Circuits \", Transformer Circuits Thread, 2021."}
+{"idx": 1, "title": "(PDF) Mechanistic Unveiling of Transformer Circuits : Self-Influence...", "date": "", "ddg_snippet": "Abstract and Figures. Transformer -based language models have achieved notable success, yet their internal reasoning mechanisms remain largely opaque due to complex non-linear interactions and high-dimensional operations. Olah et al ., 2020 ). In this approach, neural net", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388963747_Mechanistic_Unveiling_of_Transformer_Circuits_Self-Influence_as_a_Key_to_Model_Reasoning", "content": "Abstract and Figures. Transformer -based language models have achieved notable success, yet their internal reasoning mechanisms remain largely opaque due to complex non-linear interactions and high-dimensional operations. Olah et al ., 2020 ). In this approach, neural net"}
+{"idx": 2, "title": "Interpreting Transformer Architectures as Implicit Multinomial...", "date": "", "ddg_snippet": "language model. Transformer Circuits Thread, 2025. Olah et al . ( 2020 ). ↑. Olah , C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., and Carter, S. Zoom in: An introduction to circuits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.04653v1", "content": "language model. Transformer Circuits Thread, 2025. Olah et al . ( 2020 ). ↑. Olah , C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., and Carter, S. Zoom in: An introduction to circuits ."}
+{"idx": 3, "title": "Mechanistic Unveiling of Transformer Circuits", "date": "", "ddg_snippet": "A key method in this field is circuit analysis (Conmy et al ., 2023; Olah et al ., 2020 ). In this approach, neural net-works are conceptualized as computational graphs, where circuits represent sub-graphs composed of interconnected features and the weights that link them.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.76.pdf", "content": "A key method in this field is circuit analysis (Conmy et al ., 2023; Olah et al ., 2020 ). In this approach, neural net-works are conceptualized as computational graphs, where circuits represent sub-graphs composed of interconnected features and the weights that link them."}
+{"idx": 4, "title": "Zoom In: An Introduction to Circuits — LessWrong", "date": "", "ddg_snippet": "Chris Olah and the rest of the rest of the OpenAI Clarity team just published “Zoom In: An Introduction to Circuits ,” a Distill article about some of the transparency research they've been doing which I think is very much worth taking a look at.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/MG4ZjWQDrdpgeu8wG/zoom-in-an-introduction-to-circuits", "content": "Chris Olah and the rest of the rest of the OpenAI Clarity team just published “Zoom In: An Introduction to Circuits ,” a Distill article about some of the transparency research they've been doing which I think is very much worth taking a look at."}
+{"idx": 5, "title": "Olah et al ., 2020 , control (TLE) data", "date": "", "ddg_snippet": "Dear all, we are trying to re-analyze the scRNAseq data from Olah et al ., 2020 (https://www.nature.com/articles/s41467-020-19737-2) but the link indicated in the original manuscript leads us to a folder ( https://www.synapse.org/#!Synapse:syn21438358) where we cannot...", "subpage_snippet": "", "source": "www.synapse.org", "link": "https://www.synapse.org/Synapse:syn2580853/discussion/threadId=10352", "content": "Dear all, we are trying to re-analyze the scRNAseq data from Olah et al ., 2020 (https://www.nature.com/articles/s41467-020-19737-2) but the link indicated in the original manuscript leads us to a folder ( https://www.synapse.org/#!Synapse:syn21438358) where we cannot..."}
+{"idx": 6, "title": "Is Transformer a Stochastic Parrot? A Case Study in Simple...", "date": "", "ddg_snippet": "Causal analysis (Pearl, 2022; Vig et al ., 2020 ; Meng et al ., 2022) is used to find the component for recovering output. Consider x + y, the autoregressive language model needs to output results in sequence, z1 → z2 → . . . We first consider the calculation without carry, that is, xi + yi = zi.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=H1GbVU9BsK", "content": "Causal analysis (Pearl, 2022; Vig et al ., 2020 ; Meng et al ., 2022) is used to find the component for recovering output. Consider x + y, the autoregressive language model needs to output results in sequence, z1 → z2 → . . . We first consider the calculation without carry, that is, xi + yi = zi."}
+{"idx": 7, "title": "GitHub - apartresearch/interpretability-starter: Starter templates for...", "date": "", "ddg_snippet": "Features [ Olah et al ., 2020 ].Identifying meaningful circuits of compoennts in Transformers .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/apartresearch/interpretability-starter", "content": "Features [ Olah et al ., 2020 ].Identifying meaningful circuits of compoennts in Transformers ."}
+{"idx": 8, "title": "A Practical Review of Mechanistic... | Read Paper on Bytez", "date": "", "ddg_snippet": "between features ( Olah et al ., 2020 ), subsequent studies have generalized them as connections between the activation outputs of transformer components (Olsson et al ., 2022; Wang et al ., 2022a), where interpreting individual transformer components becomes part of the circuit ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2407.02646/paper", "content": "between features ( Olah et al ., 2020 ), subsequent studies have generalized them as connections between the activation outputs of transformer components (Olsson et al ., 2022; Wang et al ., 2022a), where interpreting individual transformer components becomes part of the circuit ..."}
+{"idx": 9, "title": "Distill: Early Vision in CNNs | Dynamically Typed", "date": "", "ddg_snippet": "( Olah et al ., 2020 ). Chris Olah and his OpenAI collaborators published a new Distill article: An Overview of Early Vision in InceptionV1 . This work is part of Distill’s Circuits thread, which aims to understand how convolutional neural networks work by investigating individual features...", "subpage_snippet": "", "source": "dynamically-typed.netlify.app", "link": "https://dynamically-typed.netlify.app/stories/2020/distill-early-vision-in-cnns/", "content": "( Olah et al ., 2020 ). Chris Olah and his OpenAI collaborators published a new Distill article: An Overview of Early Vision in InceptionV1 . This work is part of Distill’s Circuits thread, which aims to understand how convolutional neural networks work by investigating individual features..."}
diff --git a/data/sampled_jsons/Olsson_2022_induction_heads_attention_heads_mechanism_transformer_circuits_year_2022.jsonl b/data/sampled_jsons/Olsson_2022_induction_heads_attention_heads_mechanism_transformer_circuits_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c02ec234198f45e22067d1a669a3799168cfe0bf
--- /dev/null
+++ b/data/sampled_jsons/Olsson_2022_induction_heads_attention_heads_mechanism_transformer_circuits_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2209.11895] In-context Learning and Induction Heads - arXiv.org", "date": "", "ddg_snippet": "Sep 24, 2022 · We present six complementary lines of evidence, arguing that induction heads may be the mechanistic source of general in-context learning in transformer models of any size.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2209.11895", "content": "Sep 24, 2022 · We present six complementary lines of evidence, arguing that induction heads may be the mechanistic source of general in-context learning in transformer models of any size."}
+{"idx": 1, "title": "In-context Learning and Induction Heads", "date": "", "ddg_snippet": "Mar 8, 2022 · In our previous paper, we discovered a special kind of attention head – which we named induction heads – in two layer attention-only models. Induction heads are implemented by a circuit consisting of a pair of attention heads in different layers that work together to copy or complete patterns.", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html", "content": "Mar 8, 2022 · In our previous paper, we discovered a special kind of attention head – which we named induction heads – in two layer attention-only models. Induction heads are implemented by a circuit consisting of a pair of attention heads in different layers that work together to copy or complete patterns."}
+{"idx": 2, "title": "HOW TRANSFORMERS IMPLEMENT INDUCTION HEADS APPROXIMATION AND ...", "date": "", "ddg_snippet": "approximation and dynamics analyses of how transformers implement induction heads . In the approximation analysis, we formalize both standard and generalized induction head mechanisms , and examine how transformers can eficiently i.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1lFZusYFHq", "content": "approximation and dynamics analyses of how transformers implement induction heads . In the approximation analysis, we formalize both standard and generalized induction head mechanisms , and examine how transformers can eficiently i."}
+{"idx": 3, "title": "Circuits in Transformers Mechanistic Interpretability 2", "date": "", "ddg_snippet": "Goal: Give an overview of how circuits in transformers are studied, using paper “In-context Learning and Induction Heads ” Olsson et. al. ( 2022 ) as a case study.", "subpage_snippet": "", "source": "rohanhitchcock.com", "link": "https://rohanhitchcock.com/notes/2023-6-slt-alignment-talk-mech-interp.pdf", "content": "Goal: Give an overview of how circuits in transformers are studied, using paper “In-context Learning and Induction Heads ” Olsson et. al. ( 2022 ) as a case study."}
+{"idx": 4, "title": "In-context Learning and Induction Heads - Semantic Scholar", "date": "", "ddg_snippet": "Sep 24, 2022 · We present six complementary lines of evidence, arguing that induction heads may be the mechanistic source of general in-context learning in transformer models of any size.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/In-context-Learning-and-Induction-Heads-Olsson-Elhage/c90a99eeb57019732a6cc996bb9eaf13faedf00f", "content": "Sep 24, 2022 · We present six complementary lines of evidence, arguing that induction heads may be the mechanistic source of general in-context learning in transformer models of any size."}
+{"idx": 5, "title": "Induction Heads as an Essential Mechanism for Pattern ...", "date": "", "ddg_snippet": "However, a comprehensive understanding of its internal mechanisms is still lacking. This paper explores the role of induction heads in a few- shot ICL setting. We analyse two state-of-the- art models, Llama-3-8B and InternLM2-20B on abstract pattern recognition and NLP tasks.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.283.pdf", "content": "However, a comprehensive understanding of its internal mechanisms is still lacking. This paper explores the role of induction heads in a few- shot ICL setting. We analyse two state-of-the- art models, Llama-3-8B and InternLM2-20B on abstract pattern recognition and NLP tasks."}
+{"idx": 6, "title": "Interpreting Attention Layer Outputs with Sparse Autoencoders", "date": "", "ddg_snippet": "... RDFA, Section 2 ) - a technique that exploits the linear structure of transformers to discover sparse feature circuits through the attention ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.17759v1", "content": "... RDFA, Section 2 ) - a technique that exploits the linear structure of transformers to discover sparse feature circuits through the attention ..."}
+{"idx": 7, "title": "Active-Dormant Attention Heads: Mechanistically Demystifying", "date": "", "ddg_snippet": "... we extend our analysis to pretrained LLMs, including Llama and OLMo, showing that many attention heads exhibit a similar active-dormant mechanism as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13835v2", "content": "... we extend our analysis to pretrained LLMs, including Llama and OLMo, showing that many attention heads exhibit a similar active-dormant mechanism as ..."}
+{"idx": 8, "title": "Interpreting Context Look-ups in Transformers: Investigating", "date": "", "ddg_snippet": "This mechanism provides a deeper understanding of how attention heads work with MLP neurons to perform next-token prediction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.15055v2", "content": "This mechanism provides a deeper understanding of how attention heads work with MLP neurons to perform next-token prediction."}
+{"idx": 9, "title": "Is Random Attention Sufficient for Sequence Modeling?", "date": "", "ddg_snippet": "... Transformer architecture has some built-in inductive bias towards algorithmic abilities, namely the ability to form specialized circuits , as evidenced ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.01115v3", "content": "... Transformer architecture has some built-in inductive bias towards algorithmic abilities, namely the ability to form specialized circuits , as evidenced ..."}
diff --git a/data/sampled_jsons/Olsson_et_al._2022_induction_heads_in-context_learning_attention_module_year_2022.jsonl b/data/sampled_jsons/Olsson_et_al._2022_induction_heads_in-context_learning_attention_module_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..22d9b617379f8546319b834a6c48d9dcea0c6992
--- /dev/null
+++ b/data/sampled_jsons/Olsson_et_al._2022_induction_heads_in-context_learning_attention_module_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2209.11895] In - context Learning and Induction Heads", "date": "", "ddg_snippet": "Abstract:\" Induction heads \" are attention heads that implement a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. In this work, we present preliminary and indirect evidence for a hypothesis that induction heads might constitute the mechanism for the majority of all...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2209.11895", "content": "Abstract:\" Induction heads \" are attention heads that implement a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. In this work, we present preliminary and indirect evidence for a hypothesis that induction heads might constitute the mechanism for the majority of all..."}
+{"idx": 1, "title": "In - context Learning and Induction Heads ( 2022 ) | Catherine Anne...", "date": "", "ddg_snippet": "Abstract: “ Induction heads ” are attention heads that implement a simple algorithm to complete token sequences like [A][B] ... [A] → [B]. In this work, we present preliminary and indirect evidence for a hypothesis that induction heads might constitute the mechanism for the majority of all...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/in-context-learning-and-induction-heads-u6v55ro2", "content": "Abstract: “ Induction heads ” are attention heads that implement a simple algorithm to complete token sequences like [A][B] ... [A] → [B]. In this work, we present preliminary and indirect evidence for a hypothesis that induction heads might constitute the mechanism for the majority of all..."}
+{"idx": 2, "title": "[PDF] In - context Learning and Induction Heads | Semantic Scholar", "date": "", "ddg_snippet": "\" Induction heads \"are attention heads that implement a simple algorithm to complete token sequences like [A][B] ... [A] ->[B]. In this work, we present preliminary and indirect evidence for a hypothesis that induction heads might constitute the mechanism for the majority of all\"...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/In-context-Learning-and-Induction-Heads-Olsson-Elhage/c90a99eeb57019732a6cc996bb9eaf13faedf00f", "content": "\" Induction heads \"are attention heads that implement a simple algorithm to complete token sequences like [A][B] ... [A] ->[B]. In this work, we present preliminary and indirect evidence for a hypothesis that induction heads might constitute the mechanism for the majority of all\"..."}
+{"idx": 3, "title": "In - context Learning and Induction Heads", "date": "", "ddg_snippet": "In an induction head , where the head attends must be a function of the token before the token it attends to.In fact, almost all the in - context learning in small attention -only models appears to come from these induction heads !", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/", "content": "In an induction head , where the head attends must be a function of the token before the token it attends to.In fact, almost all the in - context learning in small attention -only models appears to come from these induction heads !"}
+{"idx": 4, "title": "In - context Learning and Induction Heads | DeepAI", "date": "", "ddg_snippet": "by Catherine Olsson , et al .We find that induction heads develop at precisely the same point as a sudden sharp increase in in - context learning ability, visible as a bump in the training loss.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/in-context-learning-and-induction-heads", "content": "by Catherine Olsson , et al .We find that induction heads develop at precisely the same point as a sudden sharp increase in in - context learning ability, visible as a bump in the training loss."}
+{"idx": 5, "title": "Which Attention Heads Matter for In - Context Learning ?", "date": "", "ddg_snippet": "Scores across training. Which Attention Heads Matter for In - Context Learning ?The initial evidence for induction heads ’ role in ICL came from Olsson et al . ( 2022 ), who studied small attention -only models (1-3 layers).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=C7XmEByCFv", "content": "Scores across training. Which Attention Heads Matter for In - Context Learning ?The initial evidence for induction heads ’ role in ICL came from Olsson et al . ( 2022 ), who studied small attention -only models (1-3 layers)."}
+{"idx": 6, "title": "Induction Heads & In - Context Learning", "date": "", "ddg_snippet": "The paper \" In - context Learning and Induction Heads \" by Olsson et al . from Anthropic investigates the mechanisms behind in - context learning in large transformer models, focusing specifically on the role of \" induction heads \" within attention heads .", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2209.11895", "content": "The paper \" In - context Learning and Induction Heads \" by Olsson et al . from Anthropic investigates the mechanisms behind in - context learning in large transformer models, focusing specifically on the role of \" induction heads \" within attention heads ."}
+{"idx": 7, "title": "Identifying Semantic Induction Heads to Understand In - Context ...", "date": "", "ddg_snippet": "... Induction Heads Induction heads , introduced by ( Olsson et al ., 2022 ;Elhage et al ., 2021), are specialized attention mechanisms that identify patterns in sequences, enabling large language models to predict subsequent tokens based on previous patterns.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384210828_Identifying_Semantic_Induction_Heads_to_Understand_In-Context_Learning", "content": "... Induction Heads Induction heads , introduced by ( Olsson et al ., 2022 ;Elhage et al ., 2021), are specialized attention mechanisms that identify patterns in sequences, enabling large language models to predict subsequent tokens based on previous patterns."}
+{"idx": 8, "title": "Training Dynamics of In - Context Learning in Linear Attention", "date": "", "ddg_snippet": "A standard multi- head softmax self- attention layer (Vaswani et al ., 2017) takes the matrix X as input and returns a matrix.The arbitration between in - context and in-weight learning may depend on properties of training data (Chan et al ., 2022 ).", "subpage_snippet": "", "source": "discovery.ucl.ac.uk", "link": "https://discovery.ucl.ac.uk/id/eprint/10210585/1/Training+Dynamics+of+In-Context+Learning+in+Linear+Attention.pdf", "content": "A standard multi- head softmax self- attention layer (Vaswani et al ., 2017) takes the matrix X as input and returns a matrix.The arbitration between in - context and in-weight learning may depend on properties of training data (Chan et al ., 2022 )."}
+{"idx": 9, "title": "ICML 2023 Transformers Learn In - Context by Gradient Descent Oral", "date": "", "ddg_snippet": "At present, the mechanisms of in - context learning in Transformers are not well understood and remain mostly an intuition. In this paper, we suggest that training Transformers on auto-regressive objectives is closely related to gradient-based meta- learning formulations.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2023/oral/25462", "content": "At present, the mechanisms of in - context learning in Transformers are not well understood and remain mostly an intuition. In this paper, we suggest that training Transformers on auto-regressive objectives is closely related to gradient-based meta- learning formulations."}
diff --git a/data/sampled_jsons/Omni-UGround-V1-7B_GPU_OmniBench_experimental_setup_year_2024.jsonl b/data/sampled_jsons/Omni-UGround-V1-7B_GPU_OmniBench_experimental_setup_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..be0b86bebf9d02ba67a5c683a5be441bfb0cc7ac
--- /dev/null
+++ b/data/sampled_jsons/Omni-UGround-V1-7B_GPU_OmniBench_experimental_setup_year_2024.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench : A Scalable...", "date": "", "ddg_snippet": "OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual. Agent Capabilities.We follow the training details provided in the OS-Atlas paper and adopt the same experimental setup to train our backbone mod-els: OS-Atlas-4B and UGround - 7 B - V 1 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual. Agent Capabilities.We follow the training details provided in the OS-Atlas paper and adopt the same experimental setup to train our backbone mod-els: OS-Atlas-4B and UGround - 7 B - V 1 ."}
+{"idx": 1, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "May 1, 2025 · Yes, I have checked the experimental designs and analyses. I have reviewed the authors' comparison experiments between OmniEval and human evaluation, the performance of 12 agents on OmniBench , the analysis of task intent's impact on agents, and the comparison between graph-structured and chain-structured tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "May 1, 2025 · Yes, I have checked the experimental designs and analyses. I have reviewed the authors' comparison experiments between OmniEval and human evaluation, the performance of 12 agents on OmniBench , the analysis of task intent's impact on agents, and the comparison between graph-structured and chain-structured tasks."}
+{"idx": 2, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "Omni-UGround-V1-7B (Ours). 34.4 37.4 33.2. 31.3 ... conducted with NVIDIA A100 80G GPUs . ... adopt the same experimental setup to train our backbone mod-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d5d3691187b9f32f92e8e287e5005d40aa429f80.pdf", "content": "Omni-UGround-V1-7B (Ours). 34.4 37.4 33.2. 31.3 ... conducted with NVIDIA A100 80G GPUs . ... adopt the same experimental setup to train our backbone mod-."}
+{"idx": 3, "title": "GitHub - wendell0218/GVA-Survey: Official repository of the ...", "date": "", "ddg_snippet": "Here we list the most important references cited in our survey, organized by different sections. We particularly focus on works that have made substantial impact or proposed innovative methodologies. Additionally, we note that some papers may be cited across multiple sections. For the convenience of researchers, we provide complete citation information under each section.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wendell0218/GVA-Survey", "content": "Here we list the most important references cited in our survey, organized by different sections. We particularly focus on works that have made substantial impact or proposed innovative methodologies. Additionally, we note that some papers may be cited across multiple sections. For the convenience of researchers, we provide complete citation information under each section."}
diff --git a/data/sampled_jsons/OmniBench_Omni-UGround-V1-7B_GPU_experimental_setup_Section_5.1.jsonl b/data/sampled_jsons/OmniBench_Omni-UGround-V1-7B_GPU_experimental_setup_Section_5.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c865c8c0417f5d15ae63580577357f3ecbf47cdb
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_Omni-UGround-V1-7B_GPU_experimental_setup_Section_5.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable Multi ...", "date": "", "ddg_snippet": "We follow the training details provided in the OS-Atlas paper and adopt the same experimental setup to train our backbone mod-els: OS-Atlas-4B and UGround - 7B - V1 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "We follow the training details provided in the OS-Atlas paper and adopt the same experimental setup to train our backbone mod-els: OS-Atlas-4B and UGround - 7B - V1 ."}
+{"idx": 1, "title": "multimodal-art-projection/OmniBench - GitHub", "date": "", "ddg_snippet": "🌐 Homepage | 🏆 Leaderboard | 📖 Arxiv Paper | 🤗 Paper | 🤗 OmniBench Dataset | 🤗 OmniInstruct_V1 Dataset | 🦜 Tweets The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/multimodal-art-projection/OmniBench", "content": "🌐 Homepage | 🏆 Leaderboard | 📖 Arxiv Paper | 🤗 Paper | 🤗 OmniBench Dataset | 🤗 OmniInstruct_V1 Dataset | 🦜 Tweets The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni ..."}
+{"idx": 2, "title": "OmniBench", "date": "", "ddg_snippet": "OmniBench consists of 36k high-quality graph-structured tasks across 20 distinct scenarios (e.g. image editing, video editing) derived from its self-generating framework, with the task scale being 40x larger than most environment-based benchmarks, as shown in the comparison table.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "OmniBench consists of 36k high-quality graph-structured tasks across 20 distinct scenarios (e.g. image editing, video editing) derived from its self-generating framework, with the task scale being 40x larger than most environment-based benchmarks, as shown in the comparison table."}
+{"idx": 3, "title": "GitHub - antgroup/OmniBench: [ICML 2025 Oral] This is the official ...", "date": "", "ddg_snippet": "OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench", "content": "OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition."}
+{"idx": 4, "title": "OmniBench - m-a-p.ai", "date": "", "ddg_snippet": "Experiment Results Leaderboard The main focus of OmniBench is to evaluate how well could the omni -language models (OLMs) understand and reconstruct the context given information from image, audio and text modalities.", "subpage_snippet": "", "source": "m-a-p.ai", "link": "https://m-a-p.ai/OmniBench/", "content": "Experiment Results Leaderboard The main focus of OmniBench is to evaluate how well could the omni -language models (OLMs) understand and reconstruct the context given information from image, audio and text modalities."}
+{"idx": 5, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "We follow the experimental setup in the OS-Atlas paper, using our high-quality graph-based trajectories to train our models: OS-Atlas-4B and UGround - 7B - V1 . The results are shown in the following tables.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "We follow the experimental setup in the OS-Atlas paper, using our high-quality graph-based trajectories to train our models: OS-Atlas-4B and UGround - 7B - V1 . The results are shown in the following tables."}
+{"idx": 6, "title": "OmniBench: Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define language models capable of such tri-modal processing as omni -language models (OLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.15272", "content": "We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define language models capable of such tri-modal processing as omni -language models (OLMs)."}
+{"idx": 7, "title": "OmniBench: Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "We define models capable of such tri-modal processing as omni -language models (OLMs). OmniBench is distinguished by high-quality human annotations, ensuring that accurate responses require integrated understanding and reasoning across all three modalities.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Rc8z5wLzBF", "content": "We define models capable of such tri-modal processing as omni -language models (OLMs). OmniBench is distinguished by high-quality human annotations, ensuring that accurate responses require integrated understanding and reasoning across all three modalities."}
+{"idx": 8, "title": "m-a-p/OmniBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/m-a-p/OmniBench", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 9, "title": "Isaac Lab - Failed to resolve extension dependencies", "date": "", "ddg_snippet": "I have installed Isaac Lab today from the experimental pip method. While verifying the install, the (Verifying the Isaac Sim installation for Isaac Lab) has 2 examples: isaacsim (To launch an empty mini Kit window), This works fine and I get the empty minikit window. isaacsim omni .isaac.sim.python.kit, This doesn't work, gives this error: I then tried the solution given in this post. Still ...", "subpage_snippet": "", "source": "forums.developer.nvidia.com", "link": "https://forums.developer.nvidia.com/t/isaac-lab-failed-to-resolve-extension-dependencies/302368", "content": "I have installed Isaac Lab today from the experimental pip method. While verifying the install, the (Verifying the Isaac Sim installation for Isaac Lab) has 2 examples: isaacsim (To launch an empty mini Kit window), This works fine and I get the empty minikit window. isaacsim omni .isaac.sim.python.kit, This doesn't work, gives this error: I then tried the solution given in this post. Still ..."}
diff --git a/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_Omni-UGround-V1-7B.jsonl b/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_Omni-UGround-V1-7B.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..412040ae656ad2e5b58de4fc7840a7f88cf588de
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_Omni-UGround-V1-7B.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable Multi ...", "date": "", "ddg_snippet": "We follow the training details provided in the OS-Atlas paper and adopt the same experimental setup to train our backbone mod-els: OS-Atlas-4B and UGround - 7B - V1 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "We follow the training details provided in the OS-Atlas paper and adopt the same experimental setup to train our backbone mod-els: OS-Atlas-4B and UGround - 7B - V1 ."}
+{"idx": 1, "title": "OSU-NLP-Group/UGround - GitHub", "date": "", "ddg_snippet": "The performance of Qwen2-VL-based UGround-V1 on several benchmarks are also updated on the homepage (e.g., AndroidWorld: 33->44). 2025/01/05: Qwen2-VL-based UGround-V1 acheives SOTA results on a new and comprehensive GUI grounding benchmark ScreenSpot-Pro, substaintially outperforms prior models (18.9->31.1).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/UGround", "content": "The performance of Qwen2-VL-based UGround-V1 on several benchmarks are also updated on the homepage (e.g., AndroidWorld: 33->44). 2025/01/05: Qwen2-VL-based UGround-V1 acheives SOTA results on a new and comprehensive GUI grounding benchmark ScreenSpot-Pro, substaintially outperforms prior models (18.9->31.1)."}
+{"idx": 2, "title": "OmniBench", "date": "", "ddg_snippet": "OmniBench consists of 36k high-quality graph-structured tasks across 20 distinct scenarios (e.g. image editing, video editing) derived from its self-generating framework, with the task scale being 40x larger than most environment-based benchmarks, as shown in the comparison table.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "OmniBench consists of 36k high-quality graph-structured tasks across 20 distinct scenarios (e.g. image editing, video editing) derived from its self-generating framework, with the task scale being 40x larger than most environment-based benchmarks, as shown in the comparison table."}
+{"idx": 3, "title": "multimodal-art-projection/OmniBench - GitHub", "date": "", "ddg_snippet": "🌐 Homepage | 🏆 Leaderboard | 📖 Arxiv Paper | 🤗 Paper | 🤗 OmniBench Dataset | 🤗 OmniInstruct_V1 Dataset | 🦜 Tweets The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/multimodal-art-projection/OmniBench", "content": "🌐 Homepage | 🏆 Leaderboard | 📖 Arxiv Paper | 🤗 Paper | 🤗 OmniBench Dataset | 🤗 OmniInstruct_V1 Dataset | 🦜 Tweets The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni ..."}
+{"idx": 4, "title": "OmniBench - m-a-p.ai", "date": "", "ddg_snippet": "OmniBench comprises 1142 question-answer pairs, with task type distribution, text length, and image and audio characteristics. The dataset's audio content falls into three categories: speech (human vocal communication), sound events (non-speech natural, environmental and mechanical sounds), and music (various compositions and performances).", "subpage_snippet": "", "source": "m-a-p.ai", "link": "https://m-a-p.ai/OmniBench/", "content": "OmniBench comprises 1142 question-answer pairs, with task type distribution, text length, and image and audio characteristics. The dataset's audio content falls into three categories: speech (human vocal communication), sound events (non-speech natural, environmental and mechanical sounds), and music (various compositions and performances)."}
+{"idx": 5, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "We follow the experimental setup in the OS-Atlas paper , using our high-quality graph-based trajectories to train our models: OS-Atlas-4B and UGround - 7B - V1 . The results are shown in the following tables.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "We follow the experimental setup in the OS-Atlas paper , using our high-quality graph-based trajectories to train our models: OS-Atlas-4B and UGround - 7B - V1 . The results are shown in the following tables."}
+{"idx": 6, "title": "OmniBench: Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "We introduce OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni -language models (OLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.15272v2", "content": "We introduce OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni -language models (OLMs)."}
+{"idx": 7, "title": "OmniBench: Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.15272", "content": "Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We ..."}
+{"idx": 8, "title": "OMNIBENCHMARK", "date": "", "ddg_snippet": "omnibenchmark omnibenchmark provides community-driven, extensible and continuously-updating benchmarks. Omnibenchmark defines, runs and versions benchmark execution pipelines by leveraging a formal benchmark specification and a set of (reusable) benchmarking modules. Each benchmarking module implements a data processing step (typically a preprocessing step, method, or metric) and is stored as ...", "subpage_snippet": "", "source": "docs.omnibenchmark.org", "link": "https://docs.omnibenchmark.org/", "content": "omnibenchmark omnibenchmark provides community-driven, extensible and continuously-updating benchmarks. Omnibenchmark defines, runs and versions benchmark execution pipelines by leveraging a formal benchmark specification and a set of (reusable) benchmarking modules. Each benchmarking module implements a data processing step (typically a preprocessing step, method, or metric) and is stored as ..."}
+{"idx": 9, "title": "m-a-p/OmniBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/m-a-p/OmniBench", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
diff --git a/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_specifications.jsonl b/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_specifications.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07eb5f785c2386a5da3333b7555e8a465721ee4c
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_specifications.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable ...", "date": "", "ddg_snippet": "In this section , we first introduce the experimental setup ( Sec-tion 5.1 ). Then, we comprehensively compare the differences in capabilities across various models on OmniBench , along with several key findings ( Section 5.2).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "In this section , we first introduce the experimental setup ( Sec-tion 5.1 ). Then, we comprehensively compare the differences in capabilities across various models on OmniBench , along with several key findings ( Section 5.2)."}
+{"idx": 1, "title": "GitHub - multimodal-art-projection/OmniBench: A project for ...", "date": "", "ddg_snippet": "🌐 Homepage | 🏆 Leaderboard | 📖 Arxiv Paper | 🤗 Paper | 🤗 OmniBench Dataset | 🤗 OmniInstruct_V1 Dataset | 🦜 Tweets The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/multimodal-art-projection/OmniBench", "content": "🌐 Homepage | 🏆 Leaderboard | 📖 Arxiv Paper | 🤗 Paper | 🤗 OmniBench Dataset | 🤗 OmniInstruct_V1 Dataset | 🦜 Tweets The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni ..."}
+{"idx": 2, "title": "OmniBench", "date": "", "ddg_snippet": "OmniBench consists of 36k high-quality graph-structured tasks across 20 distinct scenarios (e.g. image editing, video editing) derived from its self-generating framework, with the task scale being 40x larger than most environment-based benchmarks, as shown in the comparison table.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "OmniBench consists of 36k high-quality graph-structured tasks across 20 distinct scenarios (e.g. image editing, video editing) derived from its self-generating framework, with the task scale being 40x larger than most environment-based benchmarks, as shown in the comparison table."}
+{"idx": 3, "title": "OmniBench - m-a-p.ai", "date": "", "ddg_snippet": "OmniBench comprises 1142 question-answer pairs, with task type distribution, text length, and image and audio characteristics. The dataset's audio content falls into three categories: speech (human vocal communication), sound events (non-speech natural, environmental and mechanical sounds), and music (various compositions and performances).", "subpage_snippet": "", "source": "m-a-p.ai", "link": "https://m-a-p.ai/OmniBench/", "content": "OmniBench comprises 1142 question-answer pairs, with task type distribution, text length, and image and audio characteristics. The dataset's audio content falls into three categories: speech (human vocal communication), sound events (non-speech natural, environmental and mechanical sounds), and music (various compositions and performances)."}
+{"idx": 4, "title": "[2409.15272] OmniBench: Towards The Future of Universal Omni ...", "date": "", "ddg_snippet": "Sep 23, 2024 · View a PDF of the paper titled OmniBench : Towards The Future of Universal Omni-Language Models, by Yizhi Li and 20 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.15272", "content": "Sep 23, 2024 · View a PDF of the paper titled OmniBench : Towards The Future of Universal Omni-Language Models, by Yizhi Li and 20 other authors"}
+{"idx": 5, "title": "GitHub - antgroup/OmniBench: [ICML 2025 Oral] This is the ...", "date": "", "ddg_snippet": "Welcome to the GitHub repository for our paper titled \"What Limits Virtual Agent Application? OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition. To evaluate the diverse ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench", "content": "Welcome to the GitHub repository for our paper titled \"What Limits Virtual Agent Application? OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition. To evaluate the diverse ..."}
+{"idx": 6, "title": "Auto-tuning a High-Level Language Targeted to GPU Codes", "date": "", "ddg_snippet": "The main contributions of this paper are: (1) showing that auto-tuning applied to GPU kernels written in a directive-based language can be used to effectively parallelize and op-timize a variety of codes, in many cases meeting or exceeding the performance of hand-written GPU programs, (2) show-ing how particular transformations affect ...", "subpage_snippet": "", "source": "cavazos-lab.github.io", "link": "https://cavazos-lab.github.io/PolyBench-ACC/resources/Autotuning.a.High-Level.Language.Targeted.to.GPU.Codes-paper.pdf", "content": "The main contributions of this paper are: (1) showing that auto-tuning applied to GPU kernels written in a directive-based language can be used to effectively parallelize and op-timize a variety of codes, in many cases meeting or exceeding the performance of hand-written GPU programs, (2) show-ing how particular transformations affect ..."}
+{"idx": 7, "title": "[AINews] OpenAI adopts MCP • Buttondown", "date": "", "ddg_snippet": "Gemini 2. 5 Pro's performance and capabilities : @ArtificialAnlys reported that Google’s new Gemini 2. 5 Pro Experimental takes the position across a ...", "subpage_snippet": "", "source": "buttondown.com", "link": "https://buttondown.com/ainews/archive/ainews-ghibli-memes/", "content": "Gemini 2. 5 Pro's performance and capabilities : @ArtificialAnlys reported that Google’s new Gemini 2. 5 Pro Experimental takes the position across a ..."}
+{"idx": 8, "title": "Understanding Space Is Rocket Science - Only Top Reasoning", "date": "", "ddg_snippet": "Larger benchmarks like OmniBench [ 34 ] , MMEvalPro [ 29 ] , MMStar [ 12 ] and WildVision [ 40 ] evaluate a wide range of VLM phenomena at the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02175v2", "content": "Larger benchmarks like OmniBench [ 34 ] , MMEvalPro [ 29 ] , MMStar [ 12 ] and WildVision [ 40 ] evaluate a wide range of VLM phenomena at the ..."}
+{"idx": 9, "title": "CMR-SPB: Cross-Modal Multi-Hop Reasoning over Text, Image, and", "date": "", "ddg_snippet": "Our experiments with the new dataset reveal consistent model failures in specific reasoning sequences and show that biased benchmarks risk ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.16198v1", "content": "Our experiments with the new dataset reveal consistent model failures in specific reasoning sequences and show that biased benchmarks risk ..."}
diff --git a/data/sampled_jsons/On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_Section_3.2_private_med.jsonl b/data/sampled_jsons/On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_Section_3.2_private_med.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4671a5ea8a729591c097f19bec002eb5346943b4
--- /dev/null
+++ b/data/sampled_jsons/On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_Section_3.2_private_med.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "by S Feng · 2025 — For adaptive regression , we show how to upgrade the private median framework of [BKM+22] to output the solution vector, and how to obtain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503", "content": "by S Feng · 2025 — For adaptive regression , we show how to upgrade the private median framework of [BKM+22] to output the solution vector, and how to obtain ..."}
+{"idx": 1, "title": "ICML Poster On Differential Privacy for Adaptively Solving ...", "date": "", "ddg_snippet": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44265", "content": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the ..."}
+{"idx": 2, "title": "Fast Algorithm for Dynamic Kronecker Projection Maintenance", "date": "", "ddg_snippet": "by Z Song · 2023 · Cited by 33 — In Section 3.2 , we describe the data structure design problem of our paper. ... differential. 5. Page 6. Sketching Meets Differential Privacy : Fast Algorithm ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/song23i/song23i.pdf", "content": "by Z Song · 2023 · Cited by 33 — In Section 3.2 , we describe the data structure design problem of our paper. ... differential. 5. Page 6. Sketching Meets Differential Privacy : Fast Algorithm ..."}
+{"idx": 3, "title": "Deep Learning with Differential Privacy", "date": "", "ddg_snippet": "by M Abadi · 2016 · Cited by 8707 — This section describes the main components of our ap- proach toward differentially private training of neural net- works: a differentially ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1607.00133", "content": "by M Abadi · 2016 · Cited by 8707 — This section describes the main components of our ap- proach toward differentially private training of neural net- works: a differentially ..."}
+{"idx": 4, "title": "Differential Privacy Mechanisms in Neural Tangent Kernel ...", "date": "", "ddg_snippet": "20 Feb 2025 — To fundamentally under- stand how privacy mechanisms work in AI applications, we study differential privacy (DP) in the Neural Tangent Kernel. ( ... 15 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/papers/Gu_Differential_Privacy_Mechanisms_in_Neural_Tangent_Kernel_Regression_WACV_2025_paper.pdf", "content": "20 Feb 2025 — To fundamentally under- stand how privacy mechanisms work in AI applications, we study differential privacy (DP) in the Neural Tangent Kernel. ( ... 15 pages"}
+{"idx": 5, "title": "Smooth Flipping Probability for Differential Private Sign ...", "date": "", "ddg_snippet": "We develop a series of differential privacy (DP) algorithms from a family of random projection (RP) and sign random projection (SignRP) methods.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/72222", "content": "We develop a series of differential privacy (DP) algorithms from a family of random projection (RP) and sign random projection (SignRP) methods."}
+{"idx": 6, "title": "Evaluating Differentially Private Machine Learning in Practice", "date": "", "ddg_snippet": "by B Jayaraman · 2019 · Cited by 699 — Abstract. Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget,. 19 pages", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/sec19-jayaraman.pdf", "content": "by B Jayaraman · 2019 · Cited by 699 — Abstract. Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget,. 19 pages"}
+{"idx": 7, "title": "The Algorithmic Foundations of Differential Privacy - CIS UPenn", "date": "", "ddg_snippet": "by C Dwork · 2014 · Cited by 10952 — The problem of privacy -preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals.", "subpage_snippet": "", "source": "www.cis.upenn.edu", "link": "https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf", "content": "by C Dwork · 2014 · Cited by 10952 — The problem of privacy -preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals."}
+{"idx": 8, "title": "Dynamic Maintenance of Kernel Density Estimation Data ...", "date": "", "ddg_snippet": "by J Liang · Cited by 5 — On differential privacy for adaptively solving search problems via sketching . In. ICML, 2025. Christen H Fleming and Justin M Calabrese. A new kernel density ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=dxirOEamE6", "content": "by J Liang · Cited by 5 — On differential privacy for adaptively solving search problems via sketching . In. ICML, 2025. Christen H Fleming and Justin M Calabrese. A new kernel density ..."}
+{"idx": 9, "title": "On the Robustness of CountSketch to Adaptive Inputs", "date": "", "ddg_snippet": "by E Cohen · 2022 · Cited by 29 — In the non- adaptive setting, this problem can be solved using CountSketch with d = O(k log n) and b = O(k), by returning the O(k) keys with the largest ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/cohen22a/cohen22a.pdf", "content": "by E Cohen · 2022 · Cited by 29 — In the non- adaptive setting, this problem can be solved using CountSketch with d = O(k log n) and b = O(k), by returning the O(k) keys with the largest ..."}
diff --git a/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_DPO_prone_to_overfitting_critique_year_2024.jsonl b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_DPO_prone_to_overfitting_critique_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..31f2b3710dcb5a1ddcd42bcd06c69ae4df7e4045
--- /dev/null
+++ b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_DPO_prone_to_overfitting_critique_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF - Liner", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF . Teng Xiao. ,. Yige ... Current DPO and its variants, though efficient, are prone to overfitting and ...", "subpage_snippet": "", "source": "liner.com", "link": "https://liner.com/review/on-a-connection-between-imitation-learning-and-rlhf", "content": "On a Connection Between Imitation Learning and RLHF . Teng Xiao. ,. Yige ... Current DPO and its variants, though efficient, are prone to overfitting and ..."}
+{"idx": 2, "title": "On the Generalization of SFT: A Reinforcement Learning", "date": "", "ddg_snippet": "... on challenging datasets including Olympiad Bench (International Mathematical Olympiad, 2024 ) , AIME 2024 (American Institute of Mathematics, 2024 ) , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05629v1", "content": "... on challenging datasets including Olympiad Bench (International Mathematical Olympiad, 2024 ) , AIME 2024 (American Institute of Mathematics, 2024 ) , ..."}
+{"idx": 3, "title": "How to Leverage Demonstration Data in Alignment for Large Language...", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386189784_How_to_Leverage_Demonstration_Data_in_Alignment_for_Large_Language_Model_A_Self-Imitation_Learning_Perspective", "content": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 4, "title": "Track: Poster Session 5", "date": "", "ddg_snippet": "25 Apr 2025 — However, despite its computational efficiency, EDL is prone to overfitting , as its training objective can result in overly concentrated ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31975", "content": "25 Apr 2025 — However, despite its computational efficiency, EDL is prone to overfitting , as its training objective can result in overly concentrated ..."}
+{"idx": 5, "title": "ICLR 2025 Saturday 04/26", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF . Poster. Teng Xiao · Yige ... However, despite its computational efficiency, EDL is prone to overfitting ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/day/4/26", "content": "On a Connection Between Imitation Learning and RLHF . Poster. Teng Xiao · Yige ... However, despite its computational efficiency, EDL is prone to overfitting ..."}
+{"idx": 6, "title": "ICLR 2025 - Bird's-eye views of conference proceedings", "date": "", "ddg_snippet": "... prone to overfitting to their local distributions (i.e., local overfitting) ... On a Connection Between Imitation Learning and RLHF . Teng Xiao, Yige Yuan ...", "subpage_snippet": "", "source": "www.confviews.com", "link": "https://www.confviews.com/iclr2025", "content": "... prone to overfitting to their local distributions (i.e., local overfitting) ... On a Connection Between Imitation Learning and RLHF . Teng Xiao, Yige Yuan ..."}
+{"idx": 7, "title": "A Comprehensive Survey of Direct Preference Optimization:", "date": "", "ddg_snippet": "Compared to online RLHF , DPO utilizes pre-collected preference data and is considered an offline preference optimization method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.15595v2", "content": "Compared to online RLHF , DPO utilizes pre-collected preference data and is considered an offline preference optimization method."}
+{"idx": 8, "title": "Smaug: Fixing Failure Modes of Preference Optimisation with", "date": "", "ddg_snippet": "When preference pairs differ on only a few tokens, DPO receives no loss incentive for the early tokens, and a loss incentive that in some cases can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.13228v2", "content": "When preference pairs differ on only a few tokens, DPO receives no loss incentive for the early tokens, and a loss incentive that in some cases can ..."}
+{"idx": 9, "title": "Large Language Models Post-training: Surveying Techniques from", "date": "", "ddg_snippet": "... extensive parameterization and advanced learning capabilities, are designed to capture complex linguistic structures, contextual relationships, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06072v2", "content": "... extensive parameterization and advanced learning capabilities, are designed to capture complex linguistic structures, contextual relationships, and ..."}
diff --git a/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_future_work_section_offline_on-policy_limitation.jsonl b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_future_work_section_offline_on-policy_limitation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ecff0f576e4258455d8a5ec882a579171ad71811
--- /dev/null
+++ b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_future_work_section_offline_on-policy_limitation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning from human feedback - Wikipedia", "date": "", "ddg_snippet": "RLHF suffers from challenges with collecting human feedback, learning a reward model, and optimizing the policy .[38] Compared to data collection for techniques like unsupervised or self-supervised learning , collecting data for RLHF is less scalable and more expensive.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback", "content": "RLHF suffers from challenges with collecting human feedback, learning a reward model, and optimizing the policy .[38] Compared to data collection for techniques like unsupervised or self-supervised learning , collecting data for RLHF is less scalable and more expensive."}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "In this section , we connect RLHF to the imitation learning framework. We show that RLHF is a special case of imitation learning problem on the distribution chosen response with the reverse KL divergence.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "In this section , we connect RLHF to the imitation learning framework. We show that RLHF is a special case of imitation learning problem on the distribution chosen response with the reverse KL divergence."}
+{"idx": 2, "title": "[Literature Review] On a Connection Between Imitation Learning ...", "date": "", "ddg_snippet": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-a-connection-between-imitation-learning-and-rlhf", "content": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning ."}
+{"idx": 3, "title": "On a Connection Between Imitation Learning and RLHF | alphaXiv", "date": "", "ddg_snippet": "alphaXiv. Go Home. On a Connection Between Imitation Learning and RLHF . Paper.This work demonstrates that Reinforcement Learning from Human Feedback ( RLHF ) for LLM alignment can be framed as an imitation learning problem on preference data distributions.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.05079v1", "content": "alphaXiv. Go Home. On a Connection Between Imitation Learning and RLHF . Paper.This work demonstrates that Reinforcement Learning from Human Feedback ( RLHF ) for LLM alignment can be framed as an imitation learning problem on preference data distributions."}
+{"idx": 4, "title": "tengxiao1/DIL: On a Connection Between Imitation Learning and ...", "date": "", "ddg_snippet": "You switched accounts on another tab or window.This repository contains the code for our ICLR 2025 paper On a Connection Between Imitation Learning and RLHF .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL", "content": "You switched accounts on another tab or window.This repository contains the code for our ICLR 2025 paper On a Connection Between Imitation Learning and RLHF ."}
+{"idx": 5, "title": "What is Reinforcement Learning from Human Feedback ( RLHF )?", "date": "", "ddg_snippet": "The difference between RL and RLHF lies in the source of feedback. RL relies on autonomous exploration, while RLHF integrates human guidance to accelerate learning . RLHF acts as a teacher, enhancing the learning process by leveraging human expertise.", "subpage_snippet": "", "source": "www.simform.com", "link": "https://www.simform.com/blog/reinforcement-learning-from-human-feedback/", "content": "The difference between RL and RLHF lies in the source of feedback. RL relies on autonomous exploration, while RLHF integrates human guidance to accelerate learning . RLHF acts as a teacher, enhancing the learning process by leveraging human expertise."}
+{"idx": 6, "title": "Lecture 8: Imitation Learning and RLHF", "date": "", "ddg_snippet": "Last time: Policy search continued and Imitation Learning This time: Imitation Learning and RLHF Next time: Author of Direct Preference Optimization (best paper runner up at top ML conference) guest lecture. Emma Brunskill (CS234 Reinforcement Learning . )", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs234/slides/lecture8pre.pdf", "content": "Last time: Policy search continued and Imitation Learning This time: Imitation Learning and RLHF Next time: Author of Direct Preference Optimization (best paper runner up at top ML conference) guest lecture. Emma Brunskill (CS234 Reinforcement Learning . )"}
+{"idx": 7, "title": "A Comparison of Reinforcement Learning (RL) and RLHF | IntuitionLabs", "date": "", "ddg_snippet": "Learn how RL uses reward functions and how RLHF incorporates human judgments to train AI agents.", "subpage_snippet": "", "source": "intuitionlabs.ai", "link": "https://intuitionlabs.ai/articles/reinforcement-learning-vs-rlhf", "content": "Learn how RL uses reward functions and how RLHF incorporates human judgments to train AI agents."}
+{"idx": 8, "title": "Reinforcement Learning from Human Feedback ( RLHF ): Bridging AI...", "date": "", "ddg_snippet": "CPL focuses on learning policies based on the regret model of human preferences, bypassing the need for learning a separate reward function and utilizing supervised learning for policy training (ar5iv) .", "subpage_snippet": "", "source": "www.lakera.ai", "link": "https://www.lakera.ai/blog/reinforcement-learning-from-human-feedback", "content": "CPL focuses on learning policies based on the regret model of human preferences, bypassing the need for learning a separate reward function and utilizing supervised learning for policy training (ar5iv) ."}
+{"idx": 9, "title": "RLHF Makes AI More Human: Reinforcement Learning from Human...", "date": "", "ddg_snippet": "Learn how Reinforcement Learning from Human Feedback ( RLHF ) enhances AI, making it more aligned with human values and expectations.How Does RLHF Work ? Challenges and Limitations of RLHF . RLHF in Real-World Applications. What’s Next for RLHF ?", "subpage_snippet": "", "source": "shelf.io", "link": "https://shelf.io/blog/reinforcement-learning-from-human-feedback-rlhf/", "content": "Learn how Reinforcement Learning from Human Feedback ( RLHF ) enhances AI, making it more aligned with human values and expectations.How Does RLHF Work ? Challenges and Limitations of RLHF . RLHF in Real-World Applications. What’s Next for RLHF ?"}
diff --git a/data/sampled_jsons/On_the_Complexity_of_Linear_Stochastic_Bandits_Abbasi-Yadkori.jsonl b/data/sampled_jsons/On_the_Complexity_of_Linear_Stochastic_Bandits_Abbasi-Yadkori.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ba14fd37f41154c4bc7a3d42aedaf5f04ebabf37
--- /dev/null
+++ b/data/sampled_jsons/On_the_Complexity_of_Linear_Stochastic_Bandits_Abbasi-Yadkori.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Yasin Abbasi-Yadkori | DeepAI", "date": "", "ddg_snippet": "... the non-stationary stochastic multi-armed ... On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/yasin-abbasi-yadkori", "content": "... the non-stationary stochastic multi-armed ... On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function"}
+{"idx": 1, "title": "(Private) Kernelized Bandits with Distributed Biased Feedback |", "date": "", "ddg_snippet": "... DPBE can significantly reduce both communication cost and computation complexity in distributed kernelized bandits , compared to some variants of the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3579318", "content": "... DPBE can significantly reduce both communication cost and computation complexity in distributed kernelized bandits , compared to some variants of the ..."}
+{"idx": 2, "title": "Improved Regret of Linear Ensemble Sampling", "date": "", "ddg_snippet": "Despite its popularity, the theoretical understanding of ensemble sampling has lagged behind, even for the linear bandit problem, with previous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.03932v2", "content": "Despite its popularity, the theoretical understanding of ensemble sampling has lagged behind, even for the linear bandit problem, with previous ..."}
+{"idx": 3, "title": "Generalized Linear Bandits: Almost Optimal Regret with One-Pass", "date": "", "ddg_snippet": "... with the commonly studied linear case ( Abbasi - Yadkori et al., 2011 ) , the generalized linear bandit (GLB) framework allows for a richer class of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11847v1", "content": "... with the commonly studied linear case ( Abbasi - Yadkori et al., 2011 ) , the generalized linear bandit (GLB) framework allows for a richer class of ..."}
+{"idx": 4, "title": "Mahnoosh Alizadeh | DeepAI", "date": "", "ddg_snippet": "The safe linear bandit problem is a version of the classic linear bandit ... ... The design and performance analysis of bandit algorithms in the ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/mahnoosh-alizadeh", "content": "The safe linear bandit problem is a version of the classic linear bandit ... ... The design and performance analysis of bandit algorithms in the ..."}
+{"idx": 5, "title": "Accepted papers | ALT 2022", "date": "", "ddg_snippet": "... Linear Function Approximation Dong Yin (DeepMind); Botao Hao (Deepmind); Yasin Abbasi - Yadkori (DeepMind); Nevena Lazic (DeepMind); Csaba Szepesvari ...", "subpage_snippet": "", "source": "algorithmiclearningtheory.org", "link": "http://algorithmiclearningtheory.org/alt2022/alt-2022-accepted-papers/", "content": "... Linear Function Approximation Dong Yin (DeepMind); Botao Hao (Deepmind); Yasin Abbasi - Yadkori (DeepMind); Nevena Lazic (DeepMind); Csaba Szepesvari ..."}
+{"idx": 6, "title": "Bridging Learning and Decision Making", "date": "", "ddg_snippet": "This tutorial will give an overview of the theoretical foundations of interactive decision making (high-dimensional/contextual bandits , reinforcement ...", "subpage_snippet": "", "source": "dylanfoster.net", "link": "https://dylanfoster.net/bldm.html", "content": "This tutorial will give an overview of the theoretical foundations of interactive decision making (high-dimensional/contextual bandits , reinforcement ..."}
+{"idx": 7, "title": "alanmalek.com", "date": "", "ddg_snippet": "Best of Both Worlds: Stochastic and Adversarial Best-arm Identification , In Proceedings of The 31th Conference on Learning Theory , July 2018.", "subpage_snippet": "", "source": "alanmalek.com", "link": "http://alanmalek.com/", "content": "Best of Both Worlds: Stochastic and Adversarial Best-arm Identification , In Proceedings of The 31th Conference on Learning Theory , July 2018."}
+{"idx": 8, "title": "Complex feedback in online learning", "date": "", "ddg_snippet": "... online learning has become one of the most successful and studied approaches in machine learning, in particular with reinforcement learning, online ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/workshop/13466", "content": "... online learning has become one of the most successful and studied approaches in machine learning, in particular with reinforcement learning, online ..."}
+{"idx": 9, "title": "David Pal", "date": "", "ddg_snippet": "Online multiclass linear classification with bandit feedback is a bandit version of the standard online multiclass classification problem using ...", "subpage_snippet": "", "source": "www.david.palenica.com", "link": "https://www.david.palenica.com/", "content": "Online multiclass linear classification with bandit feedback is a bandit version of the standard online multiclass classification problem using ..."}
diff --git a/data/sampled_jsons/OpenReview_mkuB677eMM_Table_2_Bidirectional-GRU_baseline_F1_year_2024.jsonl b/data/sampled_jsons/OpenReview_mkuB677eMM_Table_2_Bidirectional-GRU_baseline_F1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d5fc908bca15666ea102c20006ed2cc0ae050b2
--- /dev/null
+++ b/data/sampled_jsons/OpenReview_mkuB677eMM_Table_2_Bidirectional-GRU_baseline_F1_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "About OpenReview", "date": "", "ddg_snippet": "OpenReview .net is built over an earlier version described in the paper Open Scholarship and Peer Review: a Time for Experimentation published in the ICML 2013 Peer Review Workshop. OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/about", "content": "OpenReview .net is built over an earlier version described in the paper Open Scholarship and Peer Review: a Time for Experimentation published in the ICML 2013 Peer Review Workshop. OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status."}
+{"idx": 1, "title": "ICLR 2025 - OpenReview", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for ICLR 2025", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=ICLR.cc/2025", "content": "Welcome to the OpenReview homepage for ICLR 2025"}
+{"idx": 2, "title": "AAAI - OpenReview", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for AAAI", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=AAAI.org", "content": "Welcome to the OpenReview homepage for AAAI"}
+{"idx": 3, "title": "IEEE | OpenReview", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for IEEE", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=IEEE.org", "content": "Welcome to the OpenReview homepage for IEEE"}
+{"idx": 4, "title": "AAAI 2026 | OpenReview", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for AAAI 2026", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=AAAI.org/2026", "content": "Welcome to the OpenReview homepage for AAAI 2026"}
+{"idx": 5, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "2 days ago · Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/", "content": "2 days ago · Promoting openness in scientific communication and the peer-review process"}
+{"idx": 6, "title": "Login | OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/login?redirect=/profile", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 7, "title": "Search - OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 8, "title": "Sign Up - OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/signup", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 9, "title": "PiFold: Toward effective and efficient protein inverse folding -...", "date": "", "ddg_snippet": "Feb 1, 2023 · How can we design protein sequences folding into the desired structures effectively and efficiently? AI methods for structure-based protein design have attracted increasing attention in recent...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oMsN9TYwJ0j", "content": "Feb 1, 2023 · How can we design protein sequences folding into the desired structures effectively and efficiently? AI methods for structure-based protein design have attracted increasing attention in recent..."}
diff --git a/data/sampled_jsons/Open_Images_V7_dataset_size_total_images.jsonl b/data/sampled_jsons/Open_Images_V7_dataset_size_total_images.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4805d3bdb50df33d6f032e9d369a9ecedf6886dc
--- /dev/null
+++ b/data/sampled_jsons/Open_Images_V7_dataset_size_total_images.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Open Images V7 - Description", "date": "", "ddg_snippet": "If you use the Open Images dataset in your work (also V5- V7 ), please cite this article. ... The Open Images Dataset V4: Unified image classification ...", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/openimages/web/factsfigures_v7.html", "content": "If you use the Open Images dataset in your work (also V5- V7 ), please cite this article. ... The Open Images Dataset V4: Unified image classification ..."}
+{"idx": 1, "title": "Open Images V7 - Download", "date": "", "ddg_snippet": "dataset = tfds.load(‘ open _ images / v7 ’, split='train') for datum in dataset : image , bboxes = datum[\" image \"], example[\"bboxes\"]", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/openimages/web/download_v7.html", "content": "dataset = tfds.load(‘ open _ images / v7 ’, split='train') for datum in dataset : image , bboxes = datum[\" image \"], example[\"bboxes\"]"}
+{"idx": 2, "title": "Open Images V5 - Description", "date": "", "ddg_snippet": "Open Images is a dataset of ~9M images annotated with image -level labels, object bounding boxes, object segmentation masks, and visual relationships.", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/openimages/web/factsfigures_v5.html", "content": "Open Images is a dataset of ~9M images annotated with image -level labels, object bounding boxes, object segmentation masks, and visual relationships."}
+{"idx": 3, "title": "OpenImagesV7 - Ultralytics YOLO Docs", "date": "", "ddg_snippet": "Predict using an Open Images Dataset V7 pretrained model yolo detect predict source = image .jpg model = yolov8n-oiv7.pt Start training from an Open ...", "subpage_snippet": "", "source": "docs.ultralytics.com", "link": "https://docs.ultralytics.com/datasets/detect/open-images-v7/", "content": "Predict using an Open Images Dataset V7 pretrained model yolo detect predict source = image .jpg model = yolov8n-oiv7.pt Start training from an Open ..."}
+{"idx": 4, "title": "MareArts Computer Vision Study.: Persion Detection dataset", "date": "", "ddg_snippet": "Total (946) Python (246) OpenCV (212) C++ (45) C/MFC (45) CUDA (32) Deep learning (32) aws (31) C (27) GPU (25) Math (22) docker (20) pytorch (19) MFC ...", "subpage_snippet": "", "source": "study.marearts.com", "link": "https://study.marearts.com/2024/12/persion-detection-dataset.html", "content": "Total (946) Python (246) OpenCV (212) C++ (45) C/MFC (45) CUDA (32) Deep learning (32) aws (31) C (27) GPU (25) Math (22) docker (20) pytorch (19) MFC ..."}
+{"idx": 5, "title": "How to Fine-Tune Llama 3.2 Vision On a Custom Dataset?", "date": "", "ddg_snippet": "Image Annotation Platform Efficient and scalable image ... The DocVQA dataset contains real-world document images with questions and answers.", "subpage_snippet": "", "source": "www.labellerr.com", "link": "https://www.labellerr.com/blog/fine-tune-llama-3-2-vision/", "content": "Image Annotation Platform Efficient and scalable image ... The DocVQA dataset contains real-world document images with questions and answers."}
+{"idx": 6, "title": "21+ Best Healthcare Datasets for Computer Vision", "date": "", "ddg_snippet": "COVID-19 image dataset includes 137 cleaned images of COVID-19 and 317 images in total containing Viral Pneumonia and Normal Chest X-Rays structured ...", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/healthcare-datasets-for-computer-vision", "content": "COVID-19 image dataset includes 137 cleaned images of COVID-19 and 317 images in total containing Viral Pneumonia and Normal Chest X-Rays structured ..."}
+{"idx": 7, "title": "20+ Best Image Datasets for Computer Vision [2024]", "date": "", "ddg_snippet": "It contains 523,051 images in total , with 460,723 face images from 20,284 celebrities from IMDb and 62,328 from Wikipedia.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/computer-vision-datasets", "content": "It contains 523,051 images in total , with 460,723 face images from 20,284 celebrities from IMDb and 62,328 from Wikipedia."}
+{"idx": 8, "title": "13+ Image Classification Datasets for Machine Learning - Twine", "date": "", "ddg_snippet": "... dataset comes in two versions: Places365-Standard, which has 1.8 million train and 36000 validation images from K=365 scene classes, and ...", "subpage_snippet": "", "source": "www.twine.net", "link": "https://www.twine.net/blog/image-classification-datasets-for-machine-learning/", "content": "... dataset comes in two versions: Places365-Standard, which has 1.8 million train and 36000 validation images from K=365 scene classes, and ..."}
+{"idx": 9, "title": "20 Open-Source Datasets for Generative AI and Agentic AI", "date": "", "ddg_snippet": "The Open Images Dataset is a large-scale, community-driven collection of images annotated with labels, bounding boxes, and segmentation masks.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/02/open-source-datasets-for-generative-and-agentic-ai/", "content": "The Open Images Dataset is a large-scale, community-driven collection of images annotated with labels, bounding boxes, and segmentation masks."}
diff --git a/data/sampled_jsons/Open_the_Black_Box_step-based_policy_updates_method_methodology_policy_updates_Li_2024.jsonl b/data/sampled_jsons/Open_the_Black_Box_step-based_policy_updates_method_methodology_policy_updates_Li_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..26f96e67945f84091bb86ec9275216d4050947ad
--- /dev/null
+++ b/data/sampled_jsons/Open_the_Black_Box_step-based_policy_updates_method_methodology_policy_updates_Li_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2401.11437] Open the Black Box : Step - based Policy Updates for...", "date": "", "ddg_snippet": "While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to implement on real hardware. Episodic RL (ERL) seeks...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.11437", "content": "While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to implement on real hardware. Episodic RL (ERL) seeks..."}
+{"idx": 1, "title": "Open the Black Box: Step-based Policy Updates for...", "date": "", "ddg_snippet": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL (TCE), which effectively utilizes step information in episodic policy updates , opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mnipav175N", "content": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL (TCE), which effectively utilizes step information in episodic policy updates , opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}
+{"idx": 2, "title": "Open the Black Box: Step-based Policy Updates for Temporally-Correlated ...", "date": "", "ddg_snippet": "Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Ge Li , Hongyi Zhou, Dominik Roth, Serges Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann", "subpage_snippet": "", "source": "ottofabian.github.io", "link": "https://ottofabian.github.io/iclr/open_black_box/", "content": "Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Ge Li , Hongyi Zhou, Dominik Roth, Serges Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann"}
+{"idx": 3, "title": "dblp: Open the Black Box: Step-based Policy Updates for Temporally ...", "date": "", "ddg_snippet": "Bibliographic details on Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/LiZRTOLN24", "content": "Bibliographic details on Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning."}
+{"idx": 4, "title": "Open the Black Box: Step-based Policy Updates for Temporally-Correlated ...", "date": "", "ddg_snippet": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL (TCE), which effectively utilizes step information in episodic policy updates , opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2401.11437", "content": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL (TCE), which effectively utilizes step information in episodic policy updates , opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}
+{"idx": 5, "title": "Open the Black Box: Step-based Policy Updates for...", "date": "", "ddg_snippet": "Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Ge Li , Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7juXhdFFT6", "content": "Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Ge Li , Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann"}
+{"idx": 6, "title": "Open the Black Box : Step - based Policy Updates for...", "date": "", "ddg_snippet": "TCE synergistically combines the advantages of step - based and episodic RL, achieving comparable performance to recent ERL methods while maintaining data efficiency akin to state-of-the-art (SoTA) step - based RL.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/open-the-black-box-step-based-policy-updates", "content": "TCE synergistically combines the advantages of step - based and episodic RL, achieving comparable performance to recent ERL methods while maintaining data efficiency akin to state-of-the-art (SoTA) step - based RL."}
+{"idx": 7, "title": "Episode- Based Reinforcement Learning using Movement Primitives...", "date": "", "ddg_snippet": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning. In The Twelfth International Conference on Learning Representations.", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/seminar/2024/episode-based-rl-with-movement-primitive/2024-02-08+-+Maximilian+Hüttenrauch+-+Episode-based+RL+with+Movement+Primitive.pdf", "content": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning. In The Twelfth International Conference on Learning Representations."}
+{"idx": 8, "title": "Deep Black - Box Reinforcement Learning with Movement Primitives...", "date": "", "ddg_snippet": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/deep-black-box-reinforcement-learning-with-movement-1ov7rgyz", "content": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning."}
+{"idx": 9, "title": "(PDF) Bridging the gap between Learning-to-plan, Motion Primitives...", "date": "", "ddg_snippet": "Open the black . box : Step - based policy updates for temporally-correlated episodic reinforcement learning.The Twelfth International Conference on Learning Representations, 2024 .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383428620_Bridging_the_gap_between_Learning-to-plan_Motion_Primitives_and_Safe_Reinforcement_Learning", "content": "Open the black . box : Step - based policy updates for temporally-correlated episodic reinforcement learning.The Twelfth International Conference on Learning Representations, 2024 ."}
diff --git a/data/sampled_jsons/Orecchia_2022_All_experiments_OR_experimental_section_OR_computing_environment_OR_Intel_OR_AMD.jsonl b/data/sampled_jsons/Orecchia_2022_All_experiments_OR_experimental_section_OR_computing_environment_OR_Intel_OR_AMD.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c8193d7173eb318d66455854f39826f258e153e
--- /dev/null
+++ b/data/sampled_jsons/Orecchia_2022_All_experiments_OR_experimental_section_OR_computing_environment_OR_Intel_OR_AMD.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2207.11321v1 [cs.SI] 22 Jul 2022", "date": "", "ddg_snippet": "by D Shur · 2022 · Cited by 1 — An effi- cient multilinear optimization framework for hypergraph matching. IEEE transac- tions on pattern analysis and machine intel - ligence, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.11321", "content": "by D Shur · 2022 · Cited by 1 — An effi- cient multilinear optimization framework for hypergraph matching. IEEE transac- tions on pattern analysis and machine intel - ligence, ..."}
+{"idx": 1, "title": "A fast parallel max-flow algorithm - ACM Digital Library", "date": "", "ddg_snippet": "by Y Peretz · 2022 · Cited by 8 — Intel Corporation, Ha-Marpe St. 9, Jerusalem, Israel. View Profile. Authors ... Published: 01 November 2022 Publication History. 2citation0Downloads.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1016/j.jpdc.2022.07.003", "content": "by Y Peretz · 2022 · Cited by 8 — Intel Corporation, Ha-Marpe St. 9, Jerusalem, Israel. View Profile. Authors ... Published: 01 November 2022 Publication History. 2citation0Downloads."}
+{"idx": 2, "title": "Practical Nearly-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "by L Orecchia · 2022 · Cited by 9 — All experiments were conducted on an institutional cluster on machines with 24 Cores (2x 24 core Intel Xeon Silver 4116 CPU @ 2.10GHz), 48 threads and 128GB RAM ... 23 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a/orecchia22a.pdf", "content": "by L Orecchia · 2022 · Cited by 9 — All experiments were conducted on an institutional cluster on machines with 24 Cores (2x 24 core Intel Xeon Silver 4116 CPU @ 2.10GHz), 48 threads and 128GB RAM ... 23 pages"}
+{"idx": 3, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — Our experiments were performed on a personal computer equipped with a 2.3GHz quad-core Intel i5 processor with. 8GB memory. ... used in ( Orecchia et al., 2022 ).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "by Y Pan — Our experiments were performed on a personal computer equipped with a 2.3GHz quad-core Intel i5 processor with. 8GB memory. ... used in ( Orecchia et al., 2022 )."}
+{"idx": 4, "title": "Training Binary Neural Networks via Gaussian Variational ...", "date": "", "ddg_snippet": "All experiments are conducted on a single GPU card. On ImageNet, we train AlexNet [Krizhevsky et al., 2012] for 100 epochs, and ResNet18 [He et al., 2016] ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/94427", "content": "All experiments are conducted on a single GPU card. On ImageNet, we train AlexNet [Krizhevsky et al., 2012] for 100 epochs, and ResNet18 [He et al., 2016] ..."}
+{"idx": 5, "title": "HIERARCHICAL OVERLAPPING CLUSTERING: COST", "date": "", "ddg_snippet": "by Y Pan — All experiments were performed on a computer equipped with a 2.3GHz quad-core Intel i5 processor with 8GB memory. For the source codes and datasets, please ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "by Y Pan — All experiments were performed on a computer equipped with a 2.3GHz quad-core Intel i5 processor with 8GB memory. For the source codes and datasets, please ..."}
+{"idx": 6, "title": "On the convergence of a randomized block coordinate ...", "date": "", "ddg_snippet": "by K Du · 2022 · Cited by 22 — All experiments are performed using MATLAB R2020b on a laptop with 2.7 GHz Quad-Core Intel Core i7 processor, 16 GB memory, and Mac operating system. We ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0893965921003682", "content": "by K Du · 2022 · Cited by 22 — All experiments are performed using MATLAB R2020b on a laptop with 2.7 GHz Quad-Core Intel Core i7 processor, 16 GB memory, and Mac operating system. We ..."}
+{"idx": 7, "title": "Fast Algorithms for Hypergraph PageRank with Applications to ...", "date": "", "ddg_snippet": "by K Ameranis · Cited by 2 — All experiments were run on a server with a 24core Intel Xeon Silver 4116 CPU @ 2.10GHz processor and 128gb RAM. All code is single threaded and can be found in ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/ameranis24a/ameranis24a.pdf", "content": "by K Ameranis · Cited by 2 — All experiments were run on a server with a 24core Intel Xeon Silver 4116 CPU @ 2.10GHz processor and 128gb RAM. All code is single threaded and can be found in ..."}
+{"idx": 8, "title": "Effective and Efficient Attributed Hypergraph Embedding ...", "date": "", "ddg_snippet": "4 Sept 2025 — Konstantinos Ameranis, Adela Frances DePavia, Lorenzo Orecchia , and Erasmo Tani. ... Intel Labs and MIT. Issue's Table of Contents. Publisher.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.14778/3749646.3749692", "content": "4 Sept 2025 — Konstantinos Ameranis, Adela Frances DePavia, Lorenzo Orecchia , and Erasmo Tani. ... Intel Labs and MIT. Issue's Table of Contents. Publisher."}
+{"idx": 9, "title": "Practical Schemes for Finding Near-Stationary Points of ...", "date": "", "ddg_snippet": "by K Zhou · 2021 · Cited by 14 — Orecchia . Linear Coupling: An Ultimate Unification of Gradient and ... Intel Core i7-4870HQ with 2.50GHz cores, 16GB RAM, macOS Big Sur ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2105.12062", "content": "by K Zhou · 2021 · Cited by 14 — Orecchia . Linear Coupling: An Ultimate Unification of Gradient and ... Intel Core i7-4870HQ with 2.50GHz cores, 16GB RAM, macOS Big Sur ..."}
diff --git a/data/sampled_jsons/Orecchia_2022_ICML_hybrid_overlapping_graph_clustering_computing_environment_experiments_year_2022.jsonl b/data/sampled_jsons/Orecchia_2022_ICML_hybrid_overlapping_graph_clustering_computing_environment_experiments_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..233005537a37da905a8ccbeae2ebf25cff134fd9
--- /dev/null
+++ b/data/sampled_jsons/Orecchia_2022_ICML_hybrid_overlapping_graph_clustering_computing_environment_experiments_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Detecting communities in real-world networks and clustering similarity graphs are major data mining tasks with a wide range of applications in graph mining, collaborative filtering, and bioinformatics. In many such applications, overwhelming empirical evidence suggests that communities and clusters are naturally overlapping , i.e., the boundary of a cluster may contain both edges across ...", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/publication/oatt-icml22/", "content": "Detecting communities in real-world networks and clustering similarity graphs are major data mining tasks with a wide range of applications in graph mining, collaborative filtering, and bioinformatics. In many such applications, overwhelming empirical evidence suggests that communities and clusters are naturally overlapping , i.e., the boundary of a cluster may contain both edges across ..."}
+{"idx": 1, "title": "PDF Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis 1 1 Lorenzo Orecchia", "subpage_snippet": "", "source": "tsourakakis.com", "link": "https://tsourakakis.com/wp-content/uploads/2022/06/aott_icml22.pdf", "content": "Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis 1 1 Lorenzo Orecchia"}
+{"idx": 2, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "%0 Conference Paper %T Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering %A Lorenzo Orecchia %A Konstantinos Ameranis %A Charalampos Tsourakakis %A Kunal Talwar %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a", "content": "%0 Conference Paper %T Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering %A Lorenzo Orecchia %A Konstantinos Ameranis %A Charalampos Tsourakakis %A Kunal Talwar %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika ..."}
+{"idx": 3, "title": "ICML 2022 Papers", "date": "", "ddg_snippet": "Bregman Neural Networks ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias Graph Neural Architecture Search Under Distribution Shifts Quantifying and Learning Linear Symmetry-Based Disentanglement Certifying Out-of-Domain Generalization for Blackbox Functions Constants Matter: The Performance Gains of Active ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/papers.html", "content": "Bregman Neural Networks ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias Graph Neural Architecture Search Under Distribution Shifts Quantifying and Learning Linear Symmetry-Based Disentanglement Certifying Out-of-Domain Generalization for Blackbox Functions Constants Matter: The Performance Gains of Active ..."}
+{"idx": 4, "title": "Lorenzo Orecchia - dblp", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar: Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/32/4340", "content": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar: Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering ."}
+{"idx": 5, "title": "Publications | Orecchia Research Group", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis, Kunal Talwar, Charalampos Tsourakakis. Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering . ICML , 2022 .", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/publication/", "content": "Lorenzo Orecchia , Konstantinos Ameranis, Kunal Talwar, Charalampos Tsourakakis. Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering . ICML , 2022 ."}
+{"idx": 6, "title": "PDF clusteringPaperReading/papers/ICML/2022_clustering/very ... - GitHub", "date": "", "ddg_snippet": "Contribute to LinxuanZhang/clusteringPaperReading development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LinxuanZhang/clusteringPaperReading/blob/main/papers/ICML/2022_clustering/very/orecchia22a.pdf", "content": "Contribute to LinxuanZhang/clusteringPaperReading development by creating an account on GitHub."}
+{"idx": 7, "title": "PDF Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Abstract In many graph-clustering applications, over-whelming empirical evidence suggests that com-munities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms (OGP). In this work, we intro-duce a framework based on two novel cluster-ing objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a/orecchia22a.pdf", "content": "Abstract In many graph-clustering applications, over-whelming empirical evidence suggests that com-munities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms (OGP). In this work, we intro-duce a framework based on two novel cluster-ing objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to ..."}
+{"idx": 8, "title": "ICML 2022 Practical Almost-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "Detecting communities in real-world networks and clustering similarity graphs are major data mining tasks with a wide range of applications in graph mining, collaborative filtering, and bioinformatics. In many such applications, overwhelming empirical evidence suggests that communities and clusters are naturally overlapping , i.e., the boundary of a cluster may contain both edges across ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/spotlight/16880", "content": "Detecting communities in real-world networks and clustering similarity graphs are major data mining tasks with a wide range of applications in graph mining, collaborative filtering, and bioinformatics. In many such applications, overwhelming empirical evidence suggests that communities and clusters are naturally overlapping , i.e., the boundary of a cluster may contain both edges across ..."}
+{"idx": 9, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function...", "date": "", "ddg_snippet": "Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have examined these two structures separately, characterizing and evaluating their hybrid forms remains an open challenge. To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have examined these two structures separately, characterizing and evaluating their hybrid forms remains an open challenge. To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and ..."}
diff --git a/data/sampled_jsons/Orecchia_Ameranis_Tsourakakis_Talwar_2022_server_Intel_experiments.jsonl b/data/sampled_jsons/Orecchia_Ameranis_Tsourakakis_Talwar_2022_server_Intel_experiments.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5f4139313f7a98fb83e2482e1ed96fcb7f1b95bd
--- /dev/null
+++ b/data/sampled_jsons/Orecchia_Ameranis_Tsourakakis_Talwar_2022_server_Intel_experiments.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "HIERARCHICAL OVERLAPPING CLUSTERING: COST", "date": "", "ddg_snippet": "by Y Pan — Lorenzo Orecchia , Konstantinos Ameranis , Charalampos Tsourakakis , and Kunal Talwar . Practical almost-linear-time approximation algorithms for hybrid and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "by Y Pan — Lorenzo Orecchia , Konstantinos Ameranis , Charalampos Tsourakakis , and Kunal Talwar . Practical almost-linear-time approximation algorithms for hybrid and ..."}
+{"idx": 1, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — Orecchia , L., Ameranis , K., Tsourakakis , C., and Talwar ,. K. Practical almost-linear-time approximation algorithms for hybrid and overlapping graph ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "by Y Pan — Orecchia , L., Ameranis , K., Tsourakakis , C., and Talwar ,. K. Practical almost-linear-time approximation algorithms for hybrid and overlapping graph ..."}
+{"idx": 2, "title": "Fast Algorithms for Hypergraph PageRank with Applications to ...", "date": "", "ddg_snippet": "by K Ameranis · Cited by 2 — Lorenzo Orecchia , Konstantinos Ameranis , Charalampos. Tsourakakis , and Kunal Talwar . ... All experiments were run on a server with a 24core Intel Xeon ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/ameranis24a/ameranis24a.pdf", "content": "by K Ameranis · Cited by 2 — Lorenzo Orecchia , Konstantinos Ameranis , Charalampos. Tsourakakis , and Kunal Talwar . ... All experiments were run on a server with a 24core Intel Xeon ..."}
diff --git a/data/sampled_jsons/Orecchia_experimental_setup_OR_experiments_OR_implementation_details_ICML_2022_clustering.jsonl b/data/sampled_jsons/Orecchia_experimental_setup_OR_experiments_OR_implementation_details_ICML_2022_clustering.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5a20b2b53adbf4abe40647f60ba2dc343a5df57
--- /dev/null
+++ b/data/sampled_jsons/Orecchia_experimental_setup_OR_experiments_OR_implementation_details_ICML_2022_clustering.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Efficient Parallel Implementation of the Multiplicative Weight", "date": "", "ddg_snippet": "We compare this implementation with exact libraries and specialized libraries for the above problems in order to evaluate MWU’s practical standing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.03307v3", "content": "We compare this implementation with exact libraries and specialized libraries for the above problems in order to evaluate MWU’s practical standing ..."}
+{"idx": 1, "title": "Download Nucleotide Metabolism. An Introduction", "date": "", "ddg_snippet": "... delightful and several patterns where the mixed action of javascript and MONITORING research role will settle a greater simulation to Implementation ...", "subpage_snippet": "", "source": "www.webstile.com", "link": "http://www.webstile.com/library/download-nucleotide-metabolism-an-introduction.html", "content": "... delightful and several patterns where the mixed action of javascript and MONITORING research role will settle a greater simulation to Implementation ..."}
+{"idx": 2, "title": "Download Parallel Worlds: A Journey Through Creation, Higher", "date": "", "ddg_snippet": "... order rich duration part gift( CHEM 736) had every current experience which stops cube ground and using, Stresses staff, flare setup , game ...", "subpage_snippet": "", "source": "www.webstile.com", "link": "http://www.webstile.com/library/download-parallel-worlds-a-journey-through-creation-higher-dimensions-and-the-future-of-the-cosmos.html", "content": "... order rich duration part gift( CHEM 736) had every current experience which stops cube ground and using, Stresses staff, flare setup , game ..."}
+{"idx": 3, "title": "Calvin Café: The Simons Institute Blog | What's New at", "date": "", "ddg_snippet": "Numerical experiments by Novikoff, Miller, and Sabelli from around 2004 suggested that the extreme nontrivial eigenvalues of random \\(d\\)- regular ...", "subpage_snippet": "", "source": "blog.simons.berkeley.edu", "link": "https://blog.simons.berkeley.edu/", "content": "Numerical experiments by Novikoff, Miller, and Sabelli from around 2004 suggested that the extreme nontrivial eigenvalues of random \\(d\\)- regular ..."}
+{"idx": 4, "title": "Simons Institute Editor | Calvin Café: The Simons Institute", "date": "", "ddg_snippet": "Numerical experiments by Novikoff, Miller, and Sabelli from around 2004 suggested that the extreme nontrivial eigenvalues of random \\(d\\)- regular ...", "subpage_snippet": "", "source": "blog.simons.berkeley.edu", "link": "https://blog.simons.berkeley.edu/author/preeti/", "content": "Numerical experiments by Novikoff, Miller, and Sabelli from around 2004 suggested that the extreme nontrivial eigenvalues of random \\(d\\)- regular ..."}
+{"idx": 5, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 6, "title": "Hybrid Compton-PET Imaging for ion-range verification", "date": "", "ddg_snippet": "However, a full benefit of therapeutic hadron beams is still hold back due to uncertainties in the ion range or penetration depth in tissue.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11273v1", "content": "However, a full benefit of therapeutic hadron beams is still hold back due to uncertainties in the ion range or penetration depth in tissue."}
+{"idx": 7, "title": "Trial and Trust: Addressing Byzantine Attacks with", "date": "", "ddg_snippet": "... to achieving solutions with any specified accuracy involves techniques like validation tests (Alistarh et al., 2018 ; Allen-Zhu et al., 2020 ) or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.07614v1", "content": "... to achieving solutions with any specified accuracy involves techniques like validation tests (Alistarh et al., 2018 ; Allen-Zhu et al., 2020 ) or ..."}
+{"idx": 8, "title": "An analytical model of depth-dose distributions for carbon-ion", "date": "", "ddg_snippet": "A prominent example is the Bortfeld Analytical Formulation (BAF) model, which was originally formulated to approximate the Bragg curve in proton ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.19479v1", "content": "A prominent example is the Bortfeld Analytical Formulation (BAF) model, which was originally formulated to approximate the Bragg curve in proton ..."}
+{"idx": 9, "title": "GEANT4 Simulation of Proton Beam Properties from a Cyclotron", "date": "", "ddg_snippet": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/14/7670", "content": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ..."}
diff --git a/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_PDF_arxiv.jsonl b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_PDF_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f11403bf44c6ab1a43d993da71ae5795a6cd926a
--- /dev/null
+++ b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_PDF_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2501.02376] Origin Identification for Text-Guided Image-to ...", "date": "", "ddg_snippet": "by W Wang · 2025 — Origin ID^2 aims to retrieve the original image of a translated query , using a linear transformation to minimize distance between generated and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.02376", "content": "by W Wang · 2025 — Origin ID^2 aims to retrieve the original image of a translated query , using a linear transformation to minimize distance between generated and ..."}
+{"idx": 1, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion ...", "date": "", "ddg_snippet": "Abstract. Text-guided image-to-image diffusion models ex- cel in translating images based on textual prompts, allowing for precise and creative visual ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/681ea68d062b8991956d7a196be74f59c4610d76.pdf", "content": "Abstract. Text-guided image-to-image diffusion models ex- cel in translating images based on textual prompts, allowing for precise and creative visual ..."}
+{"idx": 2, "title": "Origin Identification for Text-Guided Image-to- ...", "date": "", "ddg_snippet": "18 Jun 2025 — TL;DR: We introduce the OriPID dataset and a generalizable method with theoretical guarantees to identify original images from their text - guided ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46n3izUNiv&noteId=sDvtTJFLIF", "content": "18 Jun 2025 — TL;DR: We introduce the OriPID dataset and a generalizable method with theoretical guarantees to identify original images from their text - guided ..."}
+{"idx": 3, "title": "Generalizable Origin Identification for Text-Guided Image ...", "date": "", "ddg_snippet": "Abstract. A method for origin identification in text-guided image-to-image diffusion models is introduced, achieving superior generalization performance ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2501.02376", "content": "Abstract. A method for origin identification in text-guided image-to-image diffusion models is introduced, achieving superior generalization performance ..."}
+{"idx": 4, "title": "[Literature Review] Generalizable Origin Identification for ...", "date": "", "ddg_snippet": "4 Jan 2025 — The paper presents the task of origin identification for text-guided image-to-image diffusion models (referred to as ID2).", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/generalizable-origin-identification-for-text-guided-image-to-image-diffusion-models", "content": "4 Jan 2025 — The paper presents the task of origin identification for text-guided image-to-image diffusion models (referred to as ID2)."}
+{"idx": 5, "title": "arXivGPT", "date": "", "ddg_snippet": "🏷️:Generalizable Origin Identification for Text - Guided Image -to- Image Diffusion Models :https:// arxiv .org/abs/2501.02376. pdf … Image .", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/arXivGPT/status/1877416518226412013", "content": "🏷️:Generalizable Origin Identification for Text - Guided Image -to- Image Diffusion Models :https:// arxiv .org/abs/2501.02376. pdf … Image ."}
+{"idx": 6, "title": "Adding Conditional Control to Text-to-Image Diffusion Models", "date": "", "ddg_snippet": "by L Zhang · Cited by 5806 — We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text - to- image diffusion models .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_Adding_Conditional_Control_to_Text-to-Image_Diffusion_Models_ICCV_2023_paper.pdf?utm_source=chatgpt.com", "content": "by L Zhang · Cited by 5806 — We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text - to- image diffusion models ."}
+{"idx": 7, "title": "Enhancing Text-to-Image Diffusion Models with Knowledge ...", "date": "", "ddg_snippet": "12 Dec 2024 — We introduce a novel approach to enhance the capabilities of text -to- image models by incorporating a graph-based RAG.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.09614v1", "content": "12 Dec 2024 — We introduce a novel approach to enhance the capabilities of text -to- image models by incorporating a graph-based RAG."}
+{"idx": 8, "title": "Frequency-Controlled Diffusion Model for Versatile Text- ...", "date": "", "ddg_snippet": "3 Jul 2024 — FCDiffusion is a text - guided image -to- image model using DCT filtering to control different image correlations, enabling versatile applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.03006v1", "content": "3 Jul 2024 — FCDiffusion is a text - guided image -to- image model using DCT filtering to control different image correlations, enabling versatile applications."}
+{"idx": 9, "title": "Computer Vision and Pattern Recognition Jan 2025", "date": "", "ddg_snippet": "[230] arXiv:2501.02268 [pdf, html, other]. Title: What Kind of Visual ... Title: Origin Identification for Text-Guided Image-to-Image Diffusion Models .", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CV/2025-01?skip=225&show=100", "content": "[230] arXiv:2501.02268 [pdf, html, other]. Title: What Kind of Visual ... Title: Origin Identification for Text-Guided Image-to-Image Diffusion Models ."}
diff --git a/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Section_G_CLIP_VAE_failure_year_2023-2024.jsonl b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Section_G_CLIP_VAE_failure_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..86a62913d036265940a8e2003ebe8886d023588b
--- /dev/null
+++ b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Section_G_CLIP_VAE_failure_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EA App - Wikipedia", "date": "", "ddg_snippet": "EA has been criticized for not encrypting Origin's XMPP chat functionality, which is available in Origin and in Origin-powered games. Unencrypted data includes account numbers, session …", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/EA_App", "content": "EA has been criticized for not encrypting Origin's XMPP chat functionality, which is available in Origin and in Origin-powered games. Unencrypted data includes account numbers, session …"}
+{"idx": 1, "title": "OriginLab - Origin and OriginPro - Data Analysis and Graphing …", "date": "", "ddg_snippet": "Over 1 Million registered users across corporations, universities and government research labs worldwide, rely on Origin to import, graph, explore, analyze and interpret their data.", "subpage_snippet": "", "source": "www.originlab.com", "link": "https://www.originlab.com/", "content": "Over 1 Million registered users across corporations, universities and government research labs worldwide, rely on Origin to import, graph, explore, analyze and interpret their data."}
+{"idx": 2, "title": "Origin - Download", "date": "", "ddg_snippet": "May 23, 2023 · Origin is a necessary service if you want to play almost any game created by Electronic Arts. You’ll need to create an account before you can download or play any title …", "subpage_snippet": "", "source": "ea-origin.en.softonic.com", "link": "https://ea-origin.en.softonic.com/", "content": "May 23, 2023 · Origin is a necessary service if you want to play almost any game created by Electronic Arts. You’ll need to create an account before you can download or play any title …"}
+{"idx": 3, "title": "EA Is Shutting Down Its Origin App, Here’s How to ... - How-To Geek", "date": "", "ddg_snippet": "Jan 25, 2025 · EA's Origin app will stop working as of April 17, 2025, you'll need to switch to the EA app to continue accessing your EA library. EA took this decision due to Microsoft ending support …", "subpage_snippet": "", "source": "www.howtogeek.com", "link": "https://www.howtogeek.com/ea-is-shutting-down-its-origin-app-heres-how-to-secure-your-game-library/", "content": "Jan 25, 2025 · EA's Origin app will stop working as of April 17, 2025, you'll need to switch to the EA app to continue accessing your EA library. EA took this decision due to Microsoft ending support …"}
+{"idx": 4, "title": "Origin Download Free - 10.5.129 | TechSpot", "date": "", "ddg_snippet": "Aug 28, 2025 · Origin is EA's platform for downloading and playing PC games. Origin is developed by EA to distribute their games and other digital content like updates, expansion packs, and …", "subpage_snippet": "", "source": "www.techspot.com", "link": "https://www.techspot.com/downloads/5783-origin.html", "content": "Aug 28, 2025 · Origin is EA's platform for downloading and playing PC games. Origin is developed by EA to distribute their games and other digital content like updates, expansion packs, and …"}
+{"idx": 5, "title": "EA Origin is shutting down. Here’s how to keep all your ... - PCWorld", "date": "", "ddg_snippet": "Jan 21, 2025 · For a long time, EA games could only be purchased, downloaded, and played digitally via EA Origin (at least on PCs). Eventually, some titles became available on Steam.", "subpage_snippet": "", "source": "www.pcworld.com", "link": "https://www.pcworld.com/article/2583928/ea-origin-is-shutting-down-heres-how-to-keep-all-your-games-safe.html", "content": "Jan 21, 2025 · For a long time, EA games could only be purchased, downloaded, and played digitally via EA Origin (at least on PCs). Eventually, some titles became available on Steam."}
+{"idx": 6, "title": "EA shuts down Origin app: What it means for PC gamers - MSN", "date": "", "ddg_snippet": "EA is shutting down its Origin app on April 17, 2025, after 14 years of service. The gaming studio has also asked Origin users to switch to the EA app.", "subpage_snippet": "", "source": "www.msn.com", "link": "https://www.msn.com/en-us/money/news/ea-shuts-down-origin-app-what-it-means-for-pc-gamers/ar-AA1xFcVT", "content": "EA is shutting down its Origin app on April 17, 2025, after 14 years of service. The gaming studio has also asked Origin users to switch to the EA app."}
+{"idx": 7, "title": "Origin | EA Games Wikia | Fandom", "date": "", "ddg_snippet": "Origin is an online gaming, digital distribution and digital rights management (DRM) platform developed by Electronic Arts that allows users to purchase games on the internet for PC and …", "subpage_snippet": "", "source": "originandea.fandom.com", "link": "https://originandea.fandom.com/wiki/Origin", "content": "Origin is an online gaming, digital distribution and digital rights management (DRM) platform developed by Electronic Arts that allows users to purchase games on the internet for PC and …"}
+{"idx": 8, "title": "ORIGIN Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Origin definition: something from which anything arises or is derived; source; fountainhead.. See examples of ORIGIN used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/origin", "content": "Origin definition: something from which anything arises or is derived; source; fountainhead.. See examples of ORIGIN used in a sentence."}
+{"idx": 9, "title": "Download the EA app – Powering next generation of PC gaming ...", "date": "", "ddg_snippet": "The games you’ve downloaded through Origin should be ready to launch through the EA app. If you find any of your games, DLC or other content missing from the EA app, please contact us so we …", "subpage_snippet": "", "source": "www.ea.com", "link": "https://www.ea.com/ea-app", "content": "The games you’ve downloaded through Origin should be ready to launch through the EA app. If you find any of your games, DLC or other content missing from the EA app, please contact us so we …"}
diff --git a/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Section_G_IP-Adapter_CLIP_year_2024.jsonl b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Section_G_IP-Adapter_CLIP_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a02d3d20fe1d35302d524ee2bb61e5191ae79018
--- /dev/null
+++ b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Section_G_IP-Adapter_CLIP_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Origin、MATLAB、Python 用于科研作图，哪个最好？ - 知乎", "date": "", "ddg_snippet": "Origin、MATLAB、Python 用于科研作图，哪个最好？ 计算化学科研狗一枚，一直在思考高效的数据分析与处理方法。 由于自己的生产力机器是 MacBook Pro，虽然虚拟机上面装了 Origin，但总觉得为了… 显示全部 关注者 335 被浏览", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/396244905", "content": "Origin、MATLAB、Python 用于科研作图，哪个最好？ 计算化学科研狗一枚，一直在思考高效的数据分析与处理方法。 由于自己的生产力机器是 MacBook Pro，虽然虚拟机上面装了 Origin，但总觉得为了… 显示全部 关注者 335 被浏览"}
+{"idx": 1, "title": "Origin入门教程系列 - 知乎", "date": "", "ddg_snippet": "Origin的使用过程中，对于颜色的填充和搭配，看似是个简单的小问题，但是有的时候却让大家“抓耳挠腮 ”。 今天跟大家分享一下origin图中各部分颜色的填充以及颜色搭配的小诀窍。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/column/c_1368227352443572224", "content": "Origin的使用过程中，对于颜色的填充和搭配，看似是个简单的小问题，但是有的时候却让大家“抓耳挠腮 ”。 今天跟大家分享一下origin图中各部分颜色的填充以及颜色搭配的小诀窍。"}
+{"idx": 2, "title": "Origin画图怎么调整坐标框长宽比例？ - 知乎", "date": "", "ddg_snippet": "很多朋友会在使用origin做期刊论文数据或毕业论文数据时，会遇到这样的问题，如：origin导出的jpg图片大小格式在word中要统一，其中很多会要求固定的宽高：如7.5cm×6cm等。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/487820115", "content": "很多朋友会在使用origin做期刊论文数据或毕业论文数据时，会遇到这样的问题，如：origin导出的jpg图片大小格式在word中要统一，其中很多会要求固定的宽高：如7.5cm×6cm等。"}
+{"idx": 3, "title": "origin画图坐标轴头上有一横线怎么回事？ - 知乎", "date": "", "ddg_snippet": "Dec 23, 2024 · origin画图坐标轴头上有一横线怎么回事？ [图片] 有没有大佬解答一下 origin画图中坐标轴ab头上会有一个横线怎么解决 为啥一直去不掉啊？ 显示全部 关注者 8", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/7636792076", "content": "Dec 23, 2024 · origin画图坐标轴头上有一横线怎么回事？ [图片] 有没有大佬解答一下 origin画图中坐标轴ab头上会有一个横线怎么解决 为啥一直去不掉啊？ 显示全部 关注者 8"}
+{"idx": 4, "title": "Origin作图的时候文本有上划线，去不掉怎么解决？ - 知乎", "date": "", "ddg_snippet": "Nov 27, 2024 · origin 2024 sr1 版本也经常遇到这个问题，一般发生在把数据从一个sheet复制到另一个sheet的时候，解决办法，我一般重启一下下划线就又没了，不是很影响日常使用就没怎么管过", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/5265432033?write", "content": "Nov 27, 2024 · origin 2024 sr1 版本也经常遇到这个问题，一般发生在把数据从一个sheet复制到另一个sheet的时候，解决办法，我一般重启一下下划线就又没了，不是很影响日常使用就没怎么管过"}
+{"idx": 5, "title": "origin目前哪个版本最好用，推荐下载哪个版本的? - 知乎", "date": "", "ddg_snippet": "Origin 2024b软件介绍 OriginPro是由OriginLab公司全新开发的一款科学绘图、数据分析软件，能够为用户提供多样的数据统计、数据处理、数据报告等功能，Origin是全球商业行业、学术界和政府实验室超过 50 万科学家和工程师的首选数据分析和绘图软件。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/658571652", "content": "Origin 2024b软件介绍 OriginPro是由OriginLab公司全新开发的一款科学绘图、数据分析软件，能够为用户提供多样的数据统计、数据处理、数据报告等功能，Origin是全球商业行业、学术界和政府实验室超过 50 万科学家和工程师的首选数据分析和绘图软件。"}
+{"idx": 6, "title": "Origin怎么导出可编辑的图片啊？ - 知乎", "date": "", "ddg_snippet": "你好，使用Origin制作的图片，输出成图片之后往往就不可以进行编辑了，今天带给大家的是： 如何将origin制作的图片复制到PPT中可进行编辑 处理。方法简单实用，一起来看看吧。使用软件分别为 origin2021、office2019. 使用教程 01 制作好图表后，选择编辑， 复制页面（G），也可以快捷方式Ctrl+J。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/490032645", "content": "你好，使用Origin制作的图片，输出成图片之后往往就不可以进行编辑了，今天带给大家的是： 如何将origin制作的图片复制到PPT中可进行编辑 处理。方法简单实用，一起来看看吧。使用软件分别为 origin2021、office2019. 使用教程 01 制作好图表后，选择编辑， 复制页面（G），也可以快捷方式Ctrl+J。"}
+{"idx": 7, "title": "搞科研需要，mac上有origin这种处理数据的类似软件吗？ - 知乎", "date": "", "ddg_snippet": "现在Origin Viewer有Mac版，但只能读取origin的文件，不能进行数据处理。 目前还是只能使用虚拟机在windows里面用，慢慢等吧。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/20279942", "content": "现在Origin Viewer有Mac版，但只能读取origin的文件，不能进行数据处理。 目前还是只能使用虚拟机在windows里面用，慢慢等吧。"}
+{"idx": 8, "title": "origin 软件绘图左侧出现一个大C，怎么办？ - 知乎", "date": "", "ddg_snippet": "这是盗版的原因，实际上origin OriginLab 为中国学生提供了免费半年正版中文 OriginPro，只要你是在校大学生，只要你有以 edu.cn/ac.cn 结尾的学校邮箱，你就能拥有它。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/22136925", "content": "这是盗版的原因，实际上origin OriginLab 为中国学生提供了免费半年正版中文 OriginPro，只要你是在校大学生，只要你有以 edu.cn/ac.cn 结尾的学校邮箱，你就能拥有它。"}
+{"idx": 9, "title": "如何用origin求曲线面积？ - 知乎", "date": "", "ddg_snippet": "发布于 2024-04-15 19:59 以下内容由 AI 生成，仅供参考 使用Origin求曲线面积可以通过以下步骤进行： 数据准备：首先，确保您的数据已经输入到Origin的工作表中。 您可以直接从Excel复制数据粘贴到Origin，或者通过文件导入。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/605639486", "content": "发布于 2024-04-15 19:59 以下内容由 AI 生成，仅供参考 使用Origin求曲线面积可以通过以下步骤进行： 数据准备：首先，确保您的数据已经输入到Origin的工作表中。 您可以直接从Excel复制数据粘贴到Origin，或者通过文件导入。"}
diff --git a/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_paper_Section_5.5_unseen_model_year_2023.jsonl b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_paper_Section_5.5_unseen_model_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b8121e9f3cf38c1c6ad8a1ce97763417b2a3409
--- /dev/null
+++ b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_paper_Section_5.5_unseen_model_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Pre-trained Text-to-Image Diffusion Models Are Versatile", "date": "", "ddg_snippet": "... paper proposes Stable Control Representations, which uses pre-trained text - to - image diffusion models as a source of language- guided visual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.05852v1", "content": "... paper proposes Stable Control Representations, which uses pre-trained text - to - image diffusion models as a source of language- guided visual ..."}
+{"idx": 1, "title": "Towards Reliable Identification of Diffusion-based Image", "date": "", "ddg_snippet": "With the advent of diffusion models [ 21 , 15 , 69 , 52 ] and associated user-friendly tools [ 38 , 47 , 81 , 88 ] , image editing has never been ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05466v1", "content": "With the advent of diffusion models [ 21 , 15 , 69 , 52 ] and associated user-friendly tools [ 38 , 47 , 81 , 88 ] , image editing has never been ..."}
+{"idx": 2, "title": "TIV-Diffusion: Towards Object-Centric Movement for Text-driven", "date": "", "ddg_snippet": "To enhance generation controllability, we direct our attention to Text - Image - to -Video Generation (TI2V), a paradigm where the image shapes the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.10275v2", "content": "To enhance generation controllability, we direct our attention to Text - Image - to -Video Generation (TI2V), a paradigm where the image shapes the ..."}
+{"idx": 3, "title": "Diffusion-VLA: Generalizable and Interpretable Robot Foundation", "date": "", "ddg_snippet": "However, despite the advantages of diffusion models for policy learning, they lack the reasoning capabilities crucial for VLA models to solve complex ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.03293v3", "content": "However, despite the advantages of diffusion models for policy learning, they lack the reasoning capabilities crucial for VLA models to solve complex ..."}
+{"idx": 4, "title": "OTTER: A Vision-Language-Action Model with Text-Aware Visual", "date": "", "ddg_snippet": "... to improve the performance of downstream tasks that involve both vision and language by training models on extensive datasets of image - text pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.03734v3", "content": "... to improve the performance of downstream tasks that involve both vision and language by training models on extensive datasets of image - text pairs."}
+{"idx": 5, "title": "CVPR 2025 Papers", "date": "", "ddg_snippet": "Detect-and-Guide: Self-regulation of Diffusion Models for Safe Text - to - Image Generation via Guideline Token Optimization", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/papers.html", "content": "Detect-and-Guide: Self-regulation of Diffusion Models for Safe Text - to - Image Generation via Guideline Token Optimization"}
+{"idx": 6, "title": "Integrating Artificial Intelligence and PET Imaging for Drug", "date": "", "ddg_snippet": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8247/17/2/210", "content": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ..."}
+{"idx": 7, "title": "(PDF) EMOCONV-Diff: Diffusion-Based Speech Emotion Conversion", "date": "", "ddg_snippet": "EMOCONV-Diff: Diffusion -Based Speech Emotion Conversion for Non-Parallel and in-the-Wild Data ... paper at hand is the first to employ diffusion ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/379817290_EMOCONV-Diff_Diffusion-Based_Speech_Emotion_Conversion_for_Non-Parallel_and_in-the-Wild_Data", "content": "EMOCONV-Diff: Diffusion -Based Speech Emotion Conversion for Non-Parallel and in-the-Wild Data ... paper at hand is the first to employ diffusion ..."}
+{"idx": 8, "title": "Large language models deconstruct the clinical intuition behind", "date": "", "ddg_snippet": "... of millions of general sentences, we finessed large language models (LLMs) on > 4,000 free-form health records from healthcare professionals to ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell/fulltext/S0092-8674(25)00213-2", "content": "... of millions of general sentences, we finessed large language models (LLMs) on > 4,000 free-form health records from healthcare professionals to ..."}
+{"idx": 9, "title": "Releases · huggingface/diffusers · GitHub", "date": "", "ddg_snippet": "... models and vision-language models , the Qwen team is back with an image generation model , which is Apache-2.0 licensed! It achieves significant ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/huggingface/diffusers/releases", "content": "... models and vision-language models , the Qwen team is back with an image generation model , which is Apache-2.0 licensed! It achieves significant ..."}
diff --git a/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_CIFAR-100_Table_3_ablation_study_year_2024.jsonl b/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_CIFAR-100_Table_3_ablation_study_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..22daa34441a9a248a290805d189263c0285baf57
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_CIFAR-100_Table_3_ablation_study_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CIFAR -10 - Wikipedia", "date": "", "ddg_snippet": "CIFAR -10 is a labeled subset of the 80 Million Tiny Images dataset from 2008, published in 2009.This is a table of some of the research papers that claim to have achieved state-of-the-art results on the CIFAR -10 dataset.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/CIFAR-10", "content": "CIFAR -10 is a labeled subset of the 80 Million Tiny Images dataset from 2008, published in 2009.This is a table of some of the research papers that claim to have achieved state-of-the-art results on the CIFAR -10 dataset."}
+{"idx": 1, "title": "OwMatch : Conditional Self - Labeling with Consistency", "date": "", "ddg_snippet": "Conditional self - labeling . Open-world hierarchical thresholding. Table 3 : Ablation studies on each component with both novel class ratio and label ratio of 50%.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=rle9X7DQuH", "content": "Conditional self - labeling . Open-world hierarchical thresholding. Table 3 : Ablation studies on each component with both novel class ratio and label ratio of 50%."}
+{"idx": 2, "title": "(PDF) OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "conditional self - labeling framework on CIFAR - 100 , which boosts novel-class accuracy by 1.0% on. average. Table 3 : Ablation studies on each component with both novel class ratio and label ratio of 50%.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385529038_OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning", "content": "conditional self - labeling framework on CIFAR - 100 , which boosts novel-class accuracy by 1.0% on. average. Table 3 : Ablation studies on each component with both novel class ratio and label ratio of 50%."}
+{"idx": 3, "title": "(PDF) OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/owmatch-conditional-self-labeling-with-consistency-for-open-2qmo1h06mr3a", "content": "Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open-world hierarchical thresholding."}
+{"idx": 4, "title": "[2411.01833] OwMatch : Conditional Self - Labeling with Consistency...", "date": "", "ddg_snippet": "View a PDF of the paper titled OwMatch : Conditional Self - Labeling with Consistency for Open-World Semi-Supervised Learning, by Shengjie Niu and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.01833", "content": "View a PDF of the paper titled OwMatch : Conditional Self - Labeling with Consistency for Open-World Semi-Supervised Learning, by Shengjie Niu and 3 other authors."}
+{"idx": 5, "title": "CIFAR-10 and CIFAR - 100 datasets", "date": "", "ddg_snippet": "The CIFAR-10 and CIFAR - 100 datasets are labeled subsets of the 80 million tiny images dataset.clock, computer keyboard, lamp, telephone, television. household furniture. bed, chair, couch, table , wardrobe. insects. bee, beetle, butterfly, caterpillar, cockroach.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "http://www.cs.toronto.edu/~kriz/cifar.html", "content": "The CIFAR-10 and CIFAR - 100 datasets are labeled subsets of the 80 million tiny images dataset.clock, computer keyboard, lamp, telephone, television. household furniture. bed, chair, couch, table , wardrobe. insects. bee, beetle, butterfly, caterpillar, cockroach."}
+{"idx": 6, "title": "OwMatch : Conditional Self - Labeling with Consistency for... | alphaXiv", "date": "", "ddg_snippet": "Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2411.01833", "content": "Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open-world hierarchical thresholding."}
+{"idx": 7, "title": "OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self-supervised and semi-supervised learning, self - labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/b4fd162d3e2d015233486a2e313828a7-Abstract-Conference.html", "content": "To overcome this challenge, this study revisits two methodologies from self-supervised and semi-supervised learning, self - labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional ..."}
+{"idx": 8, "title": "OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "Specifically, we propose an effective framework called _ OwMatch _, combining conditional self - labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/rle9X7DQuH@OpenReview", "content": "Specifically, we propose an effective framework called _ OwMatch _, combining conditional self - labeling and open-world hierarchical thresholding."}
+{"idx": 9, "title": "huggingface- cifar 100 .ipynb - Colab", "date": "", "ddg_snippet": "Hugging Face CIFAR - 100 Embeddings Example.Registering the CIFAR - 100 dataset from Hugging Face. Computing image embeddings with transformers and reducing them to 2D with UMAP. Adding the computed embeddings as metrics to a 3 LC Run .", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/3lc-ai/3lc-examples/blob/main/example-notebooks/huggingface-cifar100.ipynb", "content": "Hugging Face CIFAR - 100 Embeddings Example.Registering the CIFAR - 100 dataset from Hugging Face. Computing image embeddings with transformers and reducing them to 2D with UMAP. Adding the computed embeddings as metrics to a 3 LC Run ."}
diff --git a/data/sampled_jsons/OwMatch_GitHub_ablation_study_CIFAR-100_Table_3_year_2024.jsonl b/data/sampled_jsons/OwMatch_GitHub_ablation_study_CIFAR-100_Table_3_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..36448ab92555dadc56cabd1ae1effca53b94dbf4
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_GitHub_ablation_study_CIFAR-100_Table_3_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CIFAR -10 - Wikipedia", "date": "", "ddg_snippet": "The CIFAR -10 dataset is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR -10 dataset contains 60,000 32x32 color...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/CIFAR-10", "content": "The CIFAR -10 dataset is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR -10 dataset contains 60,000 32x32 color..."}
+{"idx": 1, "title": "GitHub - philsaurabh/NoRD_Applied-Intelligence: Official code for...", "date": "", "ddg_snippet": "GitHub Models New. Manage and compare prompts. GitHub Advanced Security.Published at Applied intelligence Journal. A basic working code is given on CIFAR 100 only. It's ablation study code.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/philsaurabh/NoRD_Applied-Intelligence", "content": "GitHub Models New. Manage and compare prompts. GitHub Advanced Security.Published at Applied intelligence Journal. A basic working code is given on CIFAR 100 only. It's ablation study code."}
+{"idx": 2, "title": "OwMatch : Conditional Self-Labeling with Consistency for Open-world...", "date": "", "ddg_snippet": "This table presents the ablation study results on CIFAR-10, CIFAR - 100 , and Tiny-ImageNet datasets.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/rle9x7dquh/", "content": "This table presents the ablation study results on CIFAR-10, CIFAR - 100 , and Tiny-ImageNet datasets."}
+{"idx": 3, "title": "huggingface- cifar 100 .ipynb - Colab", "date": "", "ddg_snippet": "Hugging Face CIFAR - 100 Embeddings Example. subdirectory_arrow_right 24 cells hidden. spark Gemini.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/3lc-ai/3lc-examples/blob/main/example-notebooks/huggingface-cifar100.ipynb", "content": "Hugging Face CIFAR - 100 Embeddings Example. subdirectory_arrow_right 24 cells hidden. spark Gemini."}
+{"idx": 4, "title": "CIFAR-10 and CIFAR - 100 datasets", "date": "", "ddg_snippet": "The CIFAR-10 and CIFAR - 100 datasets are labeled subsets of the 80 million tiny images dataset. CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.household furniture. bed, chair, couch, table , wardrobe.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~kriz/cifar.html", "content": "The CIFAR-10 and CIFAR - 100 datasets are labeled subsets of the 80 million tiny images dataset. CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.household furniture. bed, chair, couch, table , wardrobe."}
+{"idx": 5, "title": "CIFAR 100 256x256", "date": "", "ddg_snippet": "Competitions. table _chart. CIFAR 100 images are originally 32x32. For modern CNN architectures (such as Resnet18) we have to upscale these images to proper size (256x256). Doing this upscaling during data loading can cause CPU bottleneck.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/ibraheemmoosa/cifar100-256x256", "content": "Competitions. table _chart. CIFAR 100 images are originally 32x32. For modern CNN architectures (such as Resnet18) we have to upscale these images to proper size (256x256). Doing this upscaling during data loading can cause CPU bottleneck."}
+{"idx": 6, "title": "Fetching CIFAR -10 Data and Saving as a Text File - James D. McCaffrey", "date": "", "ddg_snippet": "When you click on the link, you’ll download a gzipped file named cifar -10-python.tar.gz. If you unzip the file (using the 7-Zip utility program), you get a directory named cifar -10-batches.py (horrible name — it’s a data directory not a Python program).", "subpage_snippet": "", "source": "jamesmccaffreyblog.com", "link": "https://jamesmccaffreyblog.com/2022/03/10/fetching-cifar-10-data-and-saving-as-a-text-file/", "content": "When you click on the link, you’ll download a gzipped file named cifar -10-python.tar.gz. If you unzip the file (using the 7-Zip utility program), you get a directory named cifar -10-batches.py (horrible name — it’s a data directory not a Python program)."}
+{"idx": 7, "title": "How To Apply For Canada Cifar Azrieli Global Scholars Program...", "date": "", "ddg_snippet": "In CIFAR - 100 , all the images in this dataset contain two labels, coarse label (this defines the superclass of the image) and also a fine label (this defines the class of the label).", "subpage_snippet": "", "source": "memenaija.com", "link": "https://memenaija.com/canada-cifar-azrieli-global-scholars-program-2023-2025/", "content": "In CIFAR - 100 , all the images in this dataset contain two labels, coarse label (this defines the superclass of the image) and also a fine label (this defines the class of the label)."}
+{"idx": 8, "title": "Don't Forget the Nonlinearity: Unlocking Activation Functions in...", "date": "", "ddg_snippet": "Other Ablation Studies . Adaptability of Activation Functions with Different PEFT Methods.The tables 7 and table 8 below show the hyperparameter during tuning the models. Table 7: Hyperparameter settings for training ViT-Tiny on CIFAR - 100 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.13240", "content": "Other Ablation Studies . Adaptability of Activation Functions with Different PEFT Methods.The tables 7 and table 8 below show the hyperparameter during tuning the models. Table 7: Hyperparameter settings for training ViT-Tiny on CIFAR - 100 ."}
+{"idx": 9, "title": "52 датасета для тренировочных проектов / Хабр", "date": "", "ddg_snippet": "CIFAR-10 and CIFAR - 100 dataset — CIFAR-10 содержит 60,000 маленьких изображений 32*32 pixels цифр 0-9.ImageNet dataset — содержит более 100 000 фраз и около 1000 изображений на фразу.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/edison/articles/480408/", "content": "CIFAR-10 and CIFAR - 100 dataset — CIFAR-10 содержит 60,000 маленьких изображений 32*32 pixels цифр 0-9.ImageNet dataset — содержит более 100 000 фраз и около 1000 изображений на фразу."}
diff --git a/data/sampled_jsons/OwMatch_Section_3.1_why_Conditional_Self-Labeling_improves_novel_class_accuracy_year_2024.jsonl b/data/sampled_jsons/OwMatch_Section_3.1_why_Conditional_Self-Labeling_improves_novel_class_accuracy_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae251e983aefcce6337a80c0542ccfc553f48eb0
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_Section_3.1_why_Conditional_Self-Labeling_improves_novel_class_accuracy_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "The supervised objective enhances seen- class accuracy through one-hot supervision, while the clustering objective with conditional self-labeling improves novel-class clustering accuracy by incorporating labeled data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.01833", "content": "The supervised objective enhances seen- class accuracy through one-hot supervision, while the clustering objective with conditional self-labeling improves novel-class clustering accuracy by incorporating labeled data."}
+{"idx": 1, "title": "(PDF) OwMatch: Conditional Self-Labeling with Consistency for Open ...", "date": "", "ddg_snippet": "(a) Self -label assignment of seen classes (1-5) and novel classes (6-10) with or without conditional component in self-labeling . (b) Predictive confidence and hierarchical threshold for each class .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385529038_OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning", "content": "(a) Self -label assignment of seen classes (1-5) and novel classes (6-10) with or without conditional component in self-labeling . (b) Predictive confidence and hierarchical threshold for each class ."}
+{"idx": 2, "title": "OwMatch | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding. Theoretically, we analyze the estimation of class distribution on unlabeled data through rigorous statistical analysis, thus demonstrating that OwMatch can ensure the unbiasedness of the self -label ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3741083", "content": "Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding. Theoretically, we analyze the estimation of class distribution on unlabeled data through rigorous statistical analysis, thus demonstrating that OwMatch can ensure the unbiasedness of the self -label ..."}
+{"idx": 3, "title": "PDF Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point ...", "date": "", "ddg_snippet": "To address the above challenges, we pro-pose a novel self-labeling strategy that adaptively generates high-quality pseudo-labels for imbalanced classes during model training. In addition, we develop a dual-level representation that incorporates regional consis-tency into the point-level classifier learning, reducing noise in generated segmentation.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/02090.pdf", "content": "To address the above challenges, we pro-pose a novel self-labeling strategy that adaptively generates high-quality pseudo-labels for imbalanced classes during model training. In addition, we develop a dual-level representation that incorporates regional consis-tency into the point-level classifier learning, reducing noise in generated segmentation."}
+{"idx": 4, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/b4fd162d3e2d015233486a2e313828a7-Abstract-Conference.html", "content": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding."}
+{"idx": 5, "title": "A Unified Objective for Novel Class Discove - arXiv.org", "date": "", "ddg_snippet": "Abstract In this paper, we study the problem of Novel Class Dis-covery (NCD). NCD aims at inferring novel object cate-gories in an unlabeled set by leveraging from prior knowl-edge of a labeled set containing different, but related classes. Existing approaches tackle this problem by con-sidering multiple objective functions, usually involving spe-cialized loss terms for the labeled and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2108.08536", "content": "Abstract In this paper, we study the problem of Novel Class Dis-covery (NCD). NCD aims at inferring novel object cate-gories in an unlabeled set by leveraging from prior knowl-edge of a labeled set containing different, but related classes. Existing approaches tackle this problem by con-sidering multiple objective functions, usually involving spe-cialized loss terms for the labeled and the ..."}
+{"idx": 6, "title": "Dual-Level Adaptive Self-labeling for Novel Class Discovery in Point ...", "date": "", "ddg_snippet": "To learn the feature encoder and class prototypes, we introduce a self - labeling -based learning procedure that iterates between pseudo-label generation for the novel classes and the full model training with cross-entropy losses on points and regions.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-72624-8_17", "content": "To learn the feature encoder and class prototypes, we introduce a self - labeling -based learning procedure that iterates between pseudo-label generation for the novel classes and the full model training with cross-entropy losses on points and regions."}
+{"idx": 7, "title": "Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point ...", "date": "", "ddg_snippet": "0.A.3 Comparison with [zhang2023novel] Zhang et al. [zhang2023novel] introduce an imbalanced self-labeling learning framework to tackle the issue of novel class discovery in long-tailed scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.12489v1", "content": "0.A.3 Comparison with [zhang2023novel] Zhang et al. [zhang2023novel] introduce an imbalanced self-labeling learning framework to tackle the issue of novel class discovery in long-tailed scenarios."}
+{"idx": 8, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding. Theoretically, we analyze the estimation of class distribution on unlabeled data through rigorous statistical analysis, thus demonstrating that OwMatch can ensure the unbiasedness of the self -label ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.01833", "content": "Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding. Theoretically, we analyze the estimation of class distribution on unlabeled data through rigorous statistical analysis, thus demonstrating that OwMatch can ensure the unbiasedness of the self -label ..."}
+{"idx": 9, "title": "Promote knowledge mining towards open-world semi-supervised learning", "date": "", "ddg_snippet": "This would encourage knowledge transfer from well-trained seen classifiers to underfit novel classifiers. To further reduce the intra- class variance and avoid model collapse due to self-labeling , we take as input two different views of an image and regard one's prediction as the other's pseudo label.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320324000104", "content": "This would encourage knowledge transfer from well-trained seen classifiers to underfit novel classifiers. To further reduce the intra- class variance and avoid model collapse due to self-labeling , we take as input two different views of an image and regard one's prediction as the other's pseudo label."}
diff --git a/data/sampled_jsons/OwMatch_Section_3_TRSSL_Sinkhorn-Knopp_self-label_assignments_categorization.jsonl b/data/sampled_jsons/OwMatch_Section_3_TRSSL_Sinkhorn-Knopp_self-label_assignments_categorization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..54ba6d8b5ee11518d0d9c0e73c6df18f52dd3e9b
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_Section_3_TRSSL_Sinkhorn-Knopp_self-label_assignments_categorization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sinkhorn Label Allocation: Semi-Supervised Classification via...", "date": "", "ddg_snippet": "Sinkhorn Label Allocation: Semi-Supervised Classication via Annealed Self -Training.Our main contribution is a new label assignment method, Sinkhorn Label Allocation (SLA), that models the task of matching unlabeled examples to labels as a convex optimiza-tion problem.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/tai21a/tai21a.pdf", "content": "Sinkhorn Label Allocation: Semi-Supervised Classication via Annealed Self -Training.Our main contribution is a new label assignment method, Sinkhorn Label Allocation (SLA), that models the task of matching unlabeled examples to labels as a convex optimiza-tion problem."}
+{"idx": 1, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "4 Nov 2024 — To illustrate the superiority of conditional self - labeling over unconditional, we evaluate their estimators of the class distribution on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.01833v1", "content": "4 Nov 2024 — To illustrate the superiority of conditional self - labeling over unconditional, we evaluate their estimators of the class distribution on ..."}
+{"idx": 2, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "(a) Self-label assignment of seen classes (1-5) and novel classes (6-10 ) with or without conditional component in self-labeling. (b) Predictive confidence and ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93416", "content": "(a) Self-label assignment of seen classes (1-5) and novel classes (6-10 ) with or without conditional component in self-labeling. (b) Predictive confidence and ..."}
+{"idx": 3, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open ...", "date": "", "ddg_snippet": "Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.01833", "content": "Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open-world hierarchical thresholding."}
+{"idx": 4, "title": "GitHub - yukimasano/self-label: Self-labelling via ... Sinkhorn Label Allocation: Semi-Supervised Classification via ... GitHub - niusj03/OwMatch: [NeurIPS 2024] OwMatch OwMatch: Conditional Self-Labeling with Consistency for Open ... [2411.01833] OwMatch: Conditional Self-Labeling with ... SwAV Note - flammingfrost.github.io", "date": "", "ddg_snippet": "🆗🆗🎉 NEW models (20th August 2020): Added standard SeLa pretrained torchvision ResNet models to make loading much easier + added baselines using better MoCov2 augmentation (~69% LP performance) + added evaluation with K=1000 for ImageNet \"unuspervised clustering\" 🆕✅🎉 updated code: 23rd April 2020: bug fixes + CIFAR code + evaluation for resnet & alexnet. See full list on github.com Abstract Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these issues. The method is obtained by maximizing the information between labels and input data indices. We show that this criterion extends standard crossentropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn - Knopp algorithm. The resulting method is able to self - label visual data so as to train highly competitive image representations without manual labels. Our method achieves state of the art representation learning performance for AlexNet and ResNet-50 on SVHN, CIFAR-10, CIFAR-100 and ImageNet. See full list on github.com •Python > 3 .6 •PyTorch > 1.0 •CUDA •Numpy, SciPy •also, see requirements.txt •(optional:) TensorboardX See full list on github.com Run the self -supervised training of an AlexNet with the command or train a ResNet-50 with Note: you need to specify your dataset directory (it expects a format just like ImageNet with \"train\" and \"val\" folders). You also need to give the code enough GPUs to allow for storage of activations on the GPU. Otherwise you need to use the CPU variant which is significantly slower. Full documentation of the unsupervised training code main.py: See full list on github.com Linear Evaluation We provide the linear evaluation methods in this repo. Simply download the models via . ./scripts/download_models.sh and then either run scripts/eval-alexnet.sh or scripts/eval-resnet.sh. Pascal VOC We follow the standard evaluation protocols for self -supervised visual representation learning.•for Classification: we follow the PyTorch implementation of DeepCluster with frozen BatchNorm.•for Segmentation: we follow the implmentation from the Colorization paper which uses the FCN repo. Note: requires the Caffe framework•for Detection: we follow Krähenbühl et al.'s implementation based on the Faster RCNN. Note: requires the Caffe framework See full list on github.com AlexNet You can download the pseudolabels from our best (raw) AlexNet model with 10x3000 clusters here. ResNet You can download the pseudolabels from our best ResNet model with 10x3000 clusters here. See full list on github.com You can also download our trained models by running Use them like this: See full list on github.com If you use this code etc., please cite the following paper: Yuki M. Asano, Christian Rupprecht and Andrea Vedaldi. \" Self -labelling via simultaneous clustering and representation learning.\" Proc. ICLR (2020) See full list on github.com This formulation facilitates a practical annealing strategy for label assignment and al-lows for the inclusion of prior knowledge on class proportions via flexible upper bound constraints. This is the official repository for the paper OwMatch : Conditional Self - Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch. Semi-supervised learning ( SSL ) reduces the dependency on labeled data by exploring the inherent structure of unlabeled data. Existing SSL methods typically assume a closed-world where all classes possess labeled instances. A common case is the presence of novel classes in the unlabeled data. Nov 4, 2024 · Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open-world hierarchical thresholding. Aug 7, 2023 · In paper SeLa ( Self - labeling ), the cluster-based self-supervised method is transformed to a transportation problem, and optimized using Sinkhorn - Knopp algorithm. This paper refined the methods by using contrastive learning between views and applied multi-crop technique.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yukimasano/self-label", "content": "🆗🆗🎉 NEW models (20th August 2020): Added standard SeLa pretrained torchvision ResNet models to make loading much easier + added baselines using better MoCov2 augmentation (~69% LP performance) + added evaluation with K=1000 for ImageNet \"unuspervised clustering\" 🆕✅🎉 updated code: 23rd April 2020: bug fixes + CIFAR code + evaluation for resnet & alexnet. See full list on github.com Abstract Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these issues. The method is obtained by maximizing the information between labels and input data indices. We show that this criterion extends standard crossentropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn - Knopp algorithm. The resulting method is able to self - label visual data so as to train highly competitive image representations without manual labels. Our method achieves state of the art representation learning performance for AlexNet and ResNet-50 on SVHN, CIFAR-10, CIFAR-100 and ImageNet. See full list on github.com •Python > 3 .6 •PyTorch > 1.0 •CUDA •Numpy, SciPy •also, see requirements.txt •(optional:) TensorboardX See full list on github.com Run the self -supervised training of an AlexNet with the command or train a ResNet-50 with Note: you need to specify your dataset directory (it expects a format just like ImageNet with \"train\" and \"val\" folders). You also need to give the code enough GPUs to allow for storage of activations on the GPU. Otherwise you need to use the CPU variant which is significantly slower. Full documentation of the unsupervised training code main.py: See full list on github.com Linear Evaluation We provide the linear evaluation methods in this repo. Simply download the models via . ./scripts/download_models.sh and then either run scripts/eval-alexnet.sh or scripts/eval-resnet.sh. Pascal VOC We follow the standard evaluation protocols for self -supervised visual representation learning.•for Classification: we follow the PyTorch implementation of DeepCluster with frozen BatchNorm.•for Segmentation: we follow the implmentation from the Colorization paper which uses the FCN repo. Note: requires the Caffe framework•for Detection: we follow Krähenbühl et al.'s implementation based on the Faster RCNN. Note: requires the Caffe framework See full list on github.com AlexNet You can download the pseudolabels from our best (raw) AlexNet model with 10x3000 clusters here. ResNet You can download the pseudolabels from our best ResNet model with 10x3000 clusters here. See full list on github.com You can also download our trained models by running Use them like this: See full list on github.com If you use this code etc., please cite the following paper: Yuki M. Asano, Christian Rupprecht and Andrea Vedaldi. \" Self -labelling via simultaneous clustering and representation learning.\" Proc. ICLR (2020) See full list on github.com This formulation facilitates a practical annealing strategy for label assignment and al-lows for the inclusion of prior knowledge on class proportions via flexible upper bound constraints. This is the official repository for the paper OwMatch : Conditional Self - Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch. Semi-supervised learning ( SSL ) reduces the dependency on labeled data by exploring the inherent structure of unlabeled data. Existing SSL methods typically assume a closed-world where all classes possess labeled instances. A common case is the presence of novel classes in the unlabeled data. Nov 4, 2024 · Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open-world hierarchical thresholding. Aug 7, 2023 · In paper SeLa ( Self - labeling ), the cluster-based self-supervised method is transformed to a transportation problem, and optimized using Sinkhorn - Knopp algorithm. This paper refined the methods by using contrastive learning between views and applied multi-crop technique."}
+{"idx": 5, "title": "GitHub - niusj03/OwMatch: [NeurIPS 2024] OwMatch", "date": "", "ddg_snippet": "This is the official repository for the paper OwMatch : Conditional Self - Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/niusj03/OwMatch", "content": "This is the official repository for the paper OwMatch : Conditional Self - Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch."}
+{"idx": 6, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open ...", "date": "", "ddg_snippet": "Semi-supervised learning ( SSL ) reduces the dependency on labeled data by exploring the inherent structure of unlabeled data. Existing SSL methods typically assume a closed-world where all classes possess labeled instances. A common case is the presence of novel classes in the unlabeled data.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/93416.pdf", "content": "Semi-supervised learning ( SSL ) reduces the dependency on labeled data by exploring the inherent structure of unlabeled data. Existing SSL methods typically assume a closed-world where all classes possess labeled instances. A common case is the presence of novel classes in the unlabeled data."}
+{"idx": 7, "title": "[2411.01833] OwMatch: Conditional Self-Labeling with ...", "date": "", "ddg_snippet": "Nov 4, 2024 · Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.01833", "content": "Nov 4, 2024 · Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open-world hierarchical thresholding."}
+{"idx": 8, "title": "OwMatch : Conditional Self - Labeling with Consistency", "date": "", "ddg_snippet": "Conditional self - labeling is proposed to optimize high-quality self - label assignments , which depends on a fast version of the Sinkhorn - Knopp algorithm [10] to solve this complex linear programs efficiently. Resolving this involves iterative processes to converge on the optimal solution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=rle9X7DQuH", "content": "Conditional self - labeling is proposed to optimize high-quality self - label assignments , which depends on a fast version of the Sinkhorn - Knopp algorithm [10] to solve this complex linear programs efficiently. Resolving this involves iterative processes to converge on the optimal solution."}
+{"idx": 9, "title": "(PDF) OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "problem and applies Sinkhorn - Knopp algorithm to optimize self - label assignments . One subsequently. proposed Generalized Category Discovery (GCD) setting is similar to the OwSSL.We will elaborate on them respectively in Section 3 .1 and 3.2. 3. 3.1 Conditional self - labeling .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385529038_OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning", "content": "problem and applies Sinkhorn - Knopp algorithm to optimize self - label assignments . One subsequently. proposed Generalized Category Discovery (GCD) setting is similar to the OwSSL.We will elaborate on them respectively in Section 3 .1 and 3.2. 3. 3.1 Conditional self - labeling ."}
diff --git a/data/sampled_jsons/OwMatch_paper_Table_3_CIFAR-100_ablation_study_Conditional_Self-Labeling_year_2023.jsonl b/data/sampled_jsons/OwMatch_paper_Table_3_CIFAR-100_ablation_study_Conditional_Self-Labeling_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e02ed4405a8ae8408346dbb1c4a2f0442e082cd6
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_paper_Table_3_CIFAR-100_ablation_study_Conditional_Self-Labeling_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF OwMatch: Conditional Self-Labeling with Consistency for Open-world Semi ...", "date": "", "ddg_snippet": "Table 2: Ablation study on datasets with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self-labeling , PLCR refers to consistency regularization, and OwAT refers to an open-world hierarchical thresholding scheme.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/93416.pdf", "content": "Table 2: Ablation study on datasets with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self-labeling , PLCR refers to consistency regularization, and OwAT refers to an open-world hierarchical thresholding scheme."}
+{"idx": 1, "title": "GitHub - niusj03/OwMatch: [NeurIPS 2024] OwMatch", "date": "", "ddg_snippet": "This is the official repository for the paper OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/niusj03/OwMatch", "content": "This is the official repository for the paper OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch."}
+{"idx": 2, "title": "The table shows the ablation study results in different setups on CIFAR ...", "date": "", "ddg_snippet": "The table shows the ablation study results in different setups on CIFAR-100 . ' * ' refers to the LdsConv using the balance loss. ' # ' refers to the model trained with gap epochs E G = E P /4.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/The-table-shows-the-ablation-study-results-in-different-setups-on-CIFAR-100-refers_tbl1_343436158", "content": "The table shows the ablation study results in different setups on CIFAR-100 . ' * ' refers to the LdsConv using the balance loss. ' # ' refers to the model trained with gap epochs E G = E P /4."}
+{"idx": 3, "title": "PDF Appendix for: Class-Conditional Sharpness-Aware ... - CVF Open Access", "date": "", "ddg_snippet": "1. Additional Ablation Studies We conduct additional ablation studies on CIFAR -10-LT and CIFAR - 100 -LT when the imbalance ratios are 50, 100 , and 200, and the results are shown in Table 1, Table 2, and Table 3 , respectively. The presented results still well-support the effectiveness of each stage operation.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Class-Conditional_Sharpness-Aware_Minimization_CVPR_2023_supplemental.pdf", "content": "1. Additional Ablation Studies We conduct additional ablation studies on CIFAR -10-LT and CIFAR - 100 -LT when the imbalance ratios are 50, 100 , and 200, and the results are shown in Table 1, Table 2, and Table 3 , respectively. The presented results still well-support the effectiveness of each stage operation."}
+{"idx": 4, "title": "OwMatch | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3741083", "content": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding."}
+{"idx": 5, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "OwMatch uses conditional self-labeling with consistency to handle both labeled and unlabeled data in an open-set scenario. The method aims to improve performance on both known and unknown classes compared to traditional semi-supervised learning (SSL) techniques.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/owmatch-conditional-self-labeling-consistency-open-world", "content": "OwMatch uses conditional self-labeling with consistency to handle both labeled and unlabeled data in an open-set scenario. The method aims to improve performance on both known and unknown classes compared to traditional semi-supervised learning (SSL) techniques."}
+{"idx": 6, "title": "OwMatch: Conditional Self-Labeling with Consistency for ... - Researchr", "date": "", "ddg_snippet": "OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, Cheng Zhang 0005, editors, Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS ...", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/NiuLHW24", "content": "OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, Cheng Zhang 0005, editors, Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS ..."}
+{"idx": 7, "title": "PDF OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/file/b4fd162d3e2d015233486a2e313828a7-Paper-Conference.pdf", "content": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding."}
+{"idx": 8, "title": "An awesome paper list of Semi-Supervised Learning under ... - GitHub", "date": "", "ddg_snippet": "[ NeurIPS-2024 ] OwMatch : Conditional Self-Labeling with Consistency for Open-world Semi-Supervised Learning [ paper ] [code] [ arXiv-2024 ] Towards Realistic Long-tailed Semi-supervised Learning in an Open World [ paper ] [code]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/RabbitBoss/Awesome-Realistic-Semi-Supervised-Learning", "content": "[ NeurIPS-2024 ] OwMatch : Conditional Self-Labeling with Consistency for Open-world Semi-Supervised Learning [ paper ] [code] [ arXiv-2024 ] Towards Realistic Long-tailed Semi-supervised Learning in an Open World [ paper ] [code]"}
+{"idx": 9, "title": "NIU Shengjie (牛圣杰) - Shengjie's Homepage", "date": "", "ddg_snippet": "Sept 2024, our work \" OwMatch : Conditional Self-Labeling with Consistency for Open-world Semi-Supervised Learning\" got accepted by NeurIPS. arXiv & Github Aug 2023, the 23 summer seminar Introduction to and Advances in Self -Supervised Learning comes to perfect end.", "subpage_snippet": "", "source": "niusj03.github.io", "link": "https://niusj03.github.io/", "content": "Sept 2024, our work \" OwMatch : Conditional Self-Labeling with Consistency for Open-world Semi-Supervised Learning\" got accepted by NeurIPS. arXiv & Github Aug 2023, the 23 summer seminar Introduction to and Advances in Self -Supervised Learning comes to perfect end."}
diff --git a/data/sampled_jsons/PCA_whitening_biological_features_microscopy_foundation_models_dictionary_learning.jsonl b/data/sampled_jsons/PCA_whitening_biological_features_microscopy_foundation_models_dictionary_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..23650f3ea3aa4eb0c6a11bfb4bec591db2031178
--- /dev/null
+++ b/data/sampled_jsons/PCA_whitening_biological_features_microscopy_foundation_models_dictionary_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Principal component analysis - Wikipedia", "date": "", "ddg_snippet": "PCA of a multivariate Gaussian distribution centered at with a standard deviation of 3 in roughly the direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by the square root of the cor...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Principal_component_analysis", "content": "PCA of a multivariate Gaussian distribution centered at with a standard deviation of 3 in roughly the direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by the square root of the cor..."}
+{"idx": 1, "title": "Towards scientific discovery with dictionary learning : Extracting...", "date": "", "ddg_snippet": "Dictionary learning (DL) has emerged as a powerful interpretability tool for large language models . By extracting known concepts (e.g., Golden-Gate Bridge) from human-interpretable data (e.g., text), sparse DL can elucidate a model ’s inner workings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16247v2", "content": "Dictionary learning (DL) has emerged as a powerful interpretability tool for large language models . By extracting known concepts (e.g., Golden-Gate Bridge) from human-interpretable data (e.g., text), sparse DL can elucidate a model ’s inner workings."}
+{"idx": 2, "title": "Towards scientific discovery with dictionary learning ... | alphaXiv", "date": "", "ddg_snippet": "Researchers from Valence Labs, ETH Zurich, and Recursion applied dictionary learning to microscopy foundation models , extracting interpretable biological concepts like cell types and genetic perturbation effects.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2412.16247v2", "content": "Researchers from Valence Labs, ETH Zurich, and Recursion applied dictionary learning to microscopy foundation models , extracting interpretable biological concepts like cell types and genetic perturbation effects."}
+{"idx": 3, "title": "Unsupervised Feature Learning and Deep Learning Tutorial", "date": "", "ddg_snippet": "Principal Components Analysis ( PCA ) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm.", "subpage_snippet": "", "source": "ufldl.stanford.edu", "link": "http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/", "content": "Principal Components Analysis ( PCA ) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm."}
+{"idx": 4, "title": "PCA - whitening vs ZCA- whitening : a numpy 2d visual | Medium", "date": "", "ddg_snippet": "PCA - Whitening transformation matrix. This transformation indeed decorrelates the data with a rotation P^T, then scales the new features using D^{-1/2} so that they have unit-variance. Here is a proof that the covariance matrix of the transformed data is the identity matrix", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/pca-whitening-vs-zca-whitening-a-numpy-2d-visual-518b32033edf", "content": "PCA - Whitening transformation matrix. This transformation indeed decorrelates the data with a rotation P^T, then scales the new features using D^{-1/2} so that they have unit-variance. Here is a proof that the covariance matrix of the transformed data is the identity matrix"}
+{"idx": 5, "title": "Must Know Tips for Deep Learning Neural Networks - KDnuggets", "date": "", "ddg_snippet": "Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.Another pre-processing approach similar to the first one is PCA Whitening . In this process, the data is first centered as described above.", "subpage_snippet": "", "source": "www.kdnuggets.com", "link": "https://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-1.html", "content": "Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.Another pre-processing approach similar to the first one is PCA Whitening . In this process, the data is first centered as described above."}
+{"idx": 6, "title": "machinelearningmastery.com/ principal - component - analysis -for...", "date": "", "ddg_snippet": "Principal Component Analysis for Visualization - Machine Learning Mastery.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/principal-component-analysis-for-visualization/", "content": "Principal Component Analysis for Visualization - Machine Learning Mastery."}
+{"idx": 7, "title": "Albino ( Whitening ) PCA albino albino ZCA - Programmer Sought", "date": "", "ddg_snippet": "1, PCA albino. The two requirements whitened , we first is to reduce correlation between the features . PCA have reduced whitening whitening ZCA low correlation between features , while all the features have the same variance.", "subpage_snippet": "", "source": "programmersought.com", "link": "https://programmersought.com/article/97642318621/", "content": "1, PCA albino. The two requirements whitened , we first is to reduce correlation between the features . PCA have reduced whitening whitening ZCA low correlation between features , while all the features have the same variance."}
+{"idx": 8, "title": "PCA Whitening", "date": "", "ddg_snippet": "PCA Whitening is a powerful tool for processing image data that can make inputs less redundant. By identifying and reducing the degree of correlation between adjacent pixels or feature values, this technique can help improve the accuracy and efficiency of image-based tasks.", "subpage_snippet": "", "source": "serp.ai", "link": "https://serp.ai/pca-whitening/", "content": "PCA Whitening is a powerful tool for processing image data that can make inputs less redundant. By identifying and reducing the degree of correlation between adjacent pixels or feature values, this technique can help improve the accuracy and efficiency of image-based tasks."}
+{"idx": 9, "title": "statisticsglobe.com/ principal - component - analysis - pca", "date": "", "ddg_snippet": "In this tutorial you will learn what a Principal Component Analysis is and...", "subpage_snippet": "", "source": "statisticsglobe.com", "link": "https://statisticsglobe.com/principal-component-analysis-pca", "content": "In this tutorial you will learn what a Principal Component Analysis is and..."}
diff --git a/data/sampled_jsons/PINN_diffusion_equation_benchmark_standard_test_case_physics_informed_neural_networks.jsonl b/data/sampled_jsons/PINN_diffusion_equation_benchmark_standard_test_case_physics_informed_neural_networks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..31136dfedcaf012f09d7cf3e0190f5485d395d76
--- /dev/null
+++ b/data/sampled_jsons/PINN_diffusion_equation_benchmark_standard_test_case_physics_informed_neural_networks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Examples implementing physics-informed neural networks (PINN ... - GitHub", "date": "", "ddg_snippet": "2d_diffusion_PINN.py: Solve steady 2D diffusion equation with a source term using PINN stenosis_NS.py: Solve steady 2D Navier-Stokes equation in an idealized stenosis model using PINN .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/amir-cardiolab/PINN-examples", "content": "2d_diffusion_PINN.py: Solve steady 2D diffusion equation with a source term using PINN stenosis_NS.py: Solve steady 2D Navier-Stokes equation in an idealized stenosis model using PINN ."}
+{"idx": 1, "title": "Diffusion Equation Physics-Informed Neural Network (PINN) Optimizer ...", "date": "", "ddg_snippet": "Diffusion Equation Physics - Informed Neural Network ( PINN ) Optimizer Benchmarks Kirill Zubov, Zoe McCarthy, Yingbo Ma, Francesco Calisto, Valerio Pagliarino, Simone Azeglio, Luca Bottero, Emmanuel Luján, Valentin Sulzer, Ashutosh Bharambe, Nand Vinchhi, Kaushik Balakrishnan, Devesh Upadhyay, Chris Rackauckas", "subpage_snippet": "", "source": "benchmarks.sciml.ai", "link": "https://benchmarks.sciml.ai/html/PINNOptimizers/1d_diffusion.html", "content": "Diffusion Equation Physics - Informed Neural Network ( PINN ) Optimizer Benchmarks Kirill Zubov, Zoe McCarthy, Yingbo Ma, Francesco Calisto, Valerio Pagliarino, Simone Azeglio, Luca Bottero, Emmanuel Luján, Valentin Sulzer, Ashutosh Bharambe, Nand Vinchhi, Kaushik Balakrishnan, Devesh Upadhyay, Chris Rackauckas"}
+{"idx": 2, "title": "A deep learning method for multi-material diffusion problems based on ...", "date": "", "ddg_snippet": "Since the solutions of the multi-material diffusion problems are not smooth, the general physics - informed neural network ( PINN ) method does not work well for this problem. In this paper, we first give the interface continuity conditions which are necessarily added to the loss function as a loss term. Then, to adapt PINN for solving the multi-material diffusion problems with a single neural ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782523005194", "content": "Since the solutions of the multi-material diffusion problems are not smooth, the general physics - informed neural network ( PINN ) method does not work well for this problem. In this paper, we first give the interface continuity conditions which are necessarily added to the loss function as a loss term. Then, to adapt PINN for solving the multi-material diffusion problems with a single neural ..."}
+{"idx": 3, "title": "A hands-on introduction to Physics-Informed Neural Networks for solving ...", "date": "", "ddg_snippet": "Abstract I provide an introduction to the application of deep learning and neural networks for solving partial differential equations (PDEs). The approach, known as physics - informed neural networks ( PINNs ), involves minimizing the residual of the equation evaluated at various points within the domain.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00599v1", "content": "Abstract I provide an introduction to the application of deep learning and neural networks for solving partial differential equations (PDEs). The approach, known as physics - informed neural networks ( PINNs ), involves minimizing the residual of the equation evaluated at various points within the domain."}
+{"idx": 4, "title": "Solving the Diffusion Equation with Phyiscs Informed Neural Networks ...", "date": "", "ddg_snippet": "Physics - Informed Neural Networks ( PINNs ) have emerged as a powerful tool for solving Partial Differential Equations (PDEs) by integrating physical laws into neural network training. In this ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@rn.labib/solving-the-diffusion-equation-with-phyiscs-informed-neural-networks-pinns-for-daylighting-design-82d0792649a4", "content": "Physics - Informed Neural Networks ( PINNs ) have emerged as a powerful tool for solving Partial Differential Equations (PDEs) by integrating physical laws into neural network training. In this ..."}
+{"idx": 5, "title": "PDF PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks ...", "date": "", "ddg_snippet": "While significant progress has been made on Physics - Informed Neural Networks ( PINNs ), a comprehensive comparison of these methods across a wide range of Partial Differential Equations (PDEs) is still lacking. This study introduces PIN- Nacle, a benchmarking tool designed to fill this gap. PINNacle provides a diverse dataset, comprising over 20 distinct PDEs from various domains, including heat ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/8c63299fb2820ef41cb05e2ff11836f5-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "While significant progress has been made on Physics - Informed Neural Networks ( PINNs ), a comprehensive comparison of these methods across a wide range of Partial Differential Equations (PDEs) is still lacking. This study introduces PIN- Nacle, a benchmarking tool designed to fill this gap. PINNacle provides a diverse dataset, comprising over 20 distinct PDEs from various domains, including heat ..."}
+{"idx": 6, "title": "PDF Solving differential equations using physics informed deep learning: a ...", "date": "", "ddg_snippet": "The so-called physics - informed neural networks ( PINNs ) are tested on a variety of academic ordinary di erential equations in order to highlight the bene ts and drawbacks of this approach with respect to standard integration methods. We focus on the possibility to use the least possible amount of data into the training process.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04002928/file/PinnsN1-V2.pdf", "content": "The so-called physics - informed neural networks ( PINNs ) are tested on a variety of academic ordinary di erential equations in order to highlight the bene ts and drawbacks of this approach with respect to standard integration methods. We focus on the possibility to use the least possible amount of data into the training process."}
+{"idx": 7, "title": "Diffusion Equation Physics-Informed Neural Network (PINN ... - SciML", "date": "", "ddg_snippet": "Diffusion Equation Physics - Informed Neural Network ( PINN ) Optimizer Benchmarks Adapted from NeuralPDE: Automating Physics - Informed Neural Networks ( PINNs ) with Error Approximations.", "subpage_snippet": "", "source": "docs.sciml.ai", "link": "https://docs.sciml.ai/SciMLBenchmarksOutput/stable/PINNOptimizers/1d_diffusion/", "content": "Diffusion Equation Physics - Informed Neural Network ( PINN ) Optimizer Benchmarks Adapted from NeuralPDE: Automating Physics - Informed Neural Networks ( PINNs ) with Error Approximations."}
+{"idx": 8, "title": "Solving diffusion equation using physics informed neural network.", "date": "", "ddg_snippet": "Solving diffusion equations for the cleaning processes in the food industry using numerical methods is a recursive task and demanding in terms of both simulation duration and computer processing power. Current studies have shown that neural networks can solve differential equations , thus bringing rise to the physics informed neural network ( PINN ) framework. This ability is exploited in a novel ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Sunny-DV/PINNs", "content": "Solving diffusion equations for the cleaning processes in the food industry using numerical methods is a recursive task and demanding in terms of both simulation duration and computer processing power. Current studies have shown that neural networks can solve differential equations , thus bringing rise to the physics informed neural network ( PINN ) framework. This ability is exploited in a novel ..."}
+{"idx": 9, "title": "PDF Research Project - eyobghiday.com", "date": "", "ddg_snippet": "The application of Physics informed neural network pro-vides a powerful tool for determining the solution of the 2D difusion equation . In this study, a PINN model is pre-sented, which is a type of neural network capable of solv-ing partial diferential equations (PDEs) with high accuracy, using both supervised and unsupervised learning.", "subpage_snippet": "", "source": "eyobghiday.com", "link": "https://eyobghiday.com/pdf/Eyob_Physics_informed_neural_network_for_Diffusion_(2023).pdf", "content": "The application of Physics informed neural network pro-vides a powerful tool for determining the solution of the 2D difusion equation . In this study, a PINN model is pre-sented, which is a type of neural network capable of solv-ing partial diferential equations (PDEs) with high accuracy, using both supervised and unsupervised learning."}
diff --git a/data/sampled_jsons/PMR_dataset_subjects_count_vs_Human3.6M_year_2025.jsonl b/data/sampled_jsons/PMR_dataset_subjects_count_vs_Human3.6M_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c4032d03b0d9f8f88146e6569c858ad383aa17b8
--- /dev/null
+++ b/data/sampled_jsons/PMR_dataset_subjects_count_vs_Human3.6M_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fig. 2. Results of the Human 3 . 6 M dataset [2]. The upper poses are the...", "date": "", "ddg_snippet": "This approach decreased the average error by 20\\% on the Human 3 . 6 M dataset when compared to a model with a near identical parameter count trained on the entire 2D kinematic skeleton.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Results-of-the-Human36M-dataset-2-The-upper-poses-are-the-ground-truth-3D-poses-The_fig8_323956820", "content": "This approach decreased the average error by 20\\% on the Human 3 . 6 M dataset when compared to a model with a near identical parameter count trained on the entire 2D kinematic skeleton."}
+{"idx": 1, "title": "Human 3 . 6 M Dataset", "date": "", "ddg_snippet": "3D Human Pose Estimation section of the ECCV 2018 PoseTrack Challenge. DATA . • There are 5 subjects for training and 2 for validation. • There are a total of 15 actions performed by the actors.", "subpage_snippet": "", "source": "vision.imar.ro", "link": "http://vision.imar.ro/human3.6m/readme_challenge.php", "content": "3D Human Pose Estimation section of the ECCV 2018 PoseTrack Challenge. DATA . • There are 5 subjects for training and 2 for validation. • There are a total of 15 actions performed by the actors."}
+{"idx": 2, "title": "GitHub - gauraviiita/Visualization-of- Human 3 . 6 M - Dataset : Plot and...", "date": "", "ddg_snippet": "This is the code for visulalizing the ground truth and predicted results of human 3 . 6 M dataset .This example of discussion activities of the human 3 . 6 m dataset . So our code give you the way to create ground truth gif from the text form of data .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/gauraviiita/Visualization-of-Human3.6M-Dataset", "content": "This is the code for visulalizing the ground truth and predicted results of human 3 . 6 M dataset .This example of discussion activities of the human 3 . 6 m dataset . So our code give you the way to create ground truth gif from the text form of data ."}
+{"idx": 3, "title": "[PDF] H3WB: Human 3 . 6 M 3D WholeBody Dataset ... | Semantic Scholar", "date": "", "ddg_snippet": "The Human 3 . 6 M 3D WholeBody (H3WB) dataset is introduced, which provides whole-body annotations for the Human 3 . 6 M dataset using the COCO Wholebody layout, and automated 3D whole-body annotations of TotalCapture are provided.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/H3WB:-Human3.6M-3D-WholeBody-Dataset-and-Benchmark-Zhu-Samet/ec135d545f247a3c55c8e633e762570fbba6ad28", "content": "The Human 3 . 6 M 3D WholeBody (H3WB) dataset is introduced, which provides whole-body annotations for the Human 3 . 6 M dataset using the COCO Wholebody layout, and automated 3D whole-body annotations of TotalCapture are provided."}
+{"idx": 4, "title": "Human Shape-Motion Predictor", "date": "", "ddg_snippet": "The data used in those papers comes from the Human 3 . 6 m dataset [6], composed of 3 . 6 Million accurate 3D Human poses.", "subpage_snippet": "", "source": "upcommons.upc.edu", "link": "https://upcommons.upc.edu/server/api/core/bitstreams/6aa88e25-779b-4726-bf88-5fddaf9c2d58/content", "content": "The data used in those papers comes from the Human 3 . 6 m dataset [6], composed of 3 . 6 Million accurate 3D Human poses."}
+{"idx": 5, "title": "A Novel Mobile Vision Based Technique for 3D Human Pose Estimation", "date": "", "ddg_snippet": "4. 2D human skeleton detection results from the mask R-CNN based on video frames recorded by a smartphone. We obtain 3D human poses data of 7 subjects performing a variety of activities (e.g., walking, sitting, phoning, jogging, etc.) from the Human 3 . 6 M dataset [25].", "subpage_snippet": "", "source": "ejece.org", "link": "https://ejece.org/index.php/ejece/article/view/573", "content": "4. 2D human skeleton detection results from the mask R-CNN based on video frames recorded by a smartphone. We obtain 3D human poses data of 7 subjects performing a variety of activities (e.g., walking, sitting, phoning, jogging, etc.) from the Human 3 . 6 M dataset [25]."}
+{"idx": 6, "title": "MPJPE", "date": "", "ddg_snippet": "We use the Human 3 . 6 M Dataset (H36M) on the academic use only license (Catalin Ionescu, 2011; Ionescu et al., 2014) which is the largest dataset for 3D human pose estimation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=B5riBS9HZGn", "content": "We use the Human 3 . 6 M Dataset (H36M) on the academic use only license (Catalin Ionescu, 2011; Ionescu et al., 2014) which is the largest dataset for 3D human pose estimation."}
+{"idx": 7, "title": "Keep it SMPL: Automatic Estimation of 3D", "date": "", "ddg_snippet": "Because SMPL captures correla-tions in human shape across the population, we are able to robustly t it to very little data .We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human 3 . 6 M datasets , showing superior pose accuracy with respect to the state of the art.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1607.08128", "content": "Because SMPL captures correla-tions in human shape across the population, we are able to robustly t it to very little data .We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human 3 . 6 M datasets , showing superior pose accuracy with respect to the state of the art."}
+{"idx": 8, "title": "Learning 3D Human Pose from Structure and Motion", "date": "", "ddg_snippet": "MPI-INF-3DHP (test) dataset is a recently released dataset of 6 test subjects with different indoor settings ( green screen and normal background) and 2 subjects per-forming in-the-wild that makes it more challenging than Human 3 . 6 M , which only has a single indoor setting .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_ECCV_2018/papers/Rishabh_Dabral_Learning_3D_Human_ECCV_2018_paper.pdf", "content": "MPI-INF-3DHP (test) dataset is a recently released dataset of 6 test subjects with different indoor settings ( green screen and normal background) and 2 subjects per-forming in-the-wild that makes it more challenging than Human 3 . 6 M , which only has a single indoor setting ."}
+{"idx": 9, "title": "SGAT: Semantic Graph Attention for 3D human", "date": "", "ddg_snippet": "We adopt in our model evaluation the two metrics pro-posed in the paper that originated the dataset Human 3 . 6 M considering different approaches to split the data for training, validation, and testing.", "subpage_snippet": "", "source": "www.visgraf.impa.br", "link": "https://www.visgraf.impa.br/Data/RefBib/PS_PDF/wtd_21/SGAT-Sibgrapi-2021.pdf", "content": "We adopt in our model evaluation the two metrics pro-posed in the paper that originated the dataset Human 3 . 6 M considering different approaches to split the data for training, validation, and testing."}
diff --git a/data/sampled_jsons/PPO_RLHF_stability_improvements_trust_region_clipping_alternatives_year_2023.jsonl b/data/sampled_jsons/PPO_RLHF_stability_improvements_trust_region_clipping_alternatives_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a103b7f2bd20c31deb575d271ed3efcdb841a8ca
--- /dev/null
+++ b/data/sampled_jsons/PPO_RLHF_stability_improvements_trust_region_clipping_alternatives_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Demystifying Policy Optimization in RL: An Introduction to PPO", "date": "", "ddg_snippet": "Optionally, update the value function (critic) by minimizing a value loss, since PPO typically trains the critic simultaneously to improve advantage ...", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/demystifying-policy-optimization-in-rl-an-introduction-to-ppo-and-grpo/", "content": "Optionally, update the value function (critic) by minimizing a value loss, since PPO typically trains the critic simultaneously to improve advantage ..."}
+{"idx": 1, "title": "Proximal Policy Optimization (PPO): The Key to LLM Alignment", "date": "", "ddg_snippet": "... into two RL algorithms that are more directly related to RLHF : Trust Region Policy Optimization (TRPO) [1] and Proximal Policy Optimization ( PPO ) [2 ...", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/proximal-policy-optimization-ppo", "content": "... into two RL algorithms that are more directly related to RLHF : Trust Region Policy Optimization (TRPO) [1] and Proximal Policy Optimization ( PPO ) [2 ..."}
+{"idx": 2, "title": "How Good Are the Latest Open LLMs? And Is DPO Better Than PPO?", "date": "", "ddg_snippet": "... and alignment, researchers usually choose between using reinforcement learning with human feedback ( RLHF ) via proximal policy optimization ( PPO ) or ...", "subpage_snippet": "", "source": "magazine.sebastianraschka.com", "link": "https://magazine.sebastianraschka.com/p/how-good-are-the-latest-open-llms", "content": "... and alignment, researchers usually choose between using reinforcement learning with human feedback ( RLHF ) via proximal policy optimization ( PPO ) or ..."}
+{"idx": 3, "title": "(PDF) REINFORCE++: A SIMPLE AND EFFICIENT APPROACH FOR ALIGNING", "date": "", "ddg_snippet": "... stability compared to GRPO and achieves ... Training Stability : The integration of token-level KL penalties, PPO -clip loss and normalized advantage", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387487679_REINFORCE_A_SIMPLE_AND_EFFICIENT_APPROACH_FOR_ALIGNING_LARGE_LANGUAGE_MODELS", "content": "... stability compared to GRPO and achieves ... Training Stability : The integration of token-level KL penalties, PPO -clip loss and normalized advantage"}
+{"idx": 4, "title": "Reinforcement Learning (i.e. Policy Gradient Algorithms) |", "date": "", "ddg_snippet": "When RLHF came onto the scene with ChatGPT, it was largely known that they used a variant of PPO , and many initial efforts were built upon that.", "subpage_snippet": "", "source": "rlhfbook.com", "link": "https://rlhfbook.com/c/11-policy-gradients.html", "content": "When RLHF came onto the scene with ChatGPT, it was largely known that they used a variant of PPO , and many initial efforts were built upon that."}
+{"idx": 5, "title": "An Introduction to Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "... to stabilize and improve outputs. ... PPO with a KL penalty stabilizes updates while maximizing rewards that align closely with preference labels.", "subpage_snippet": "", "source": "www.lightly.ai", "link": "https://www.lightly.ai/blog/rlhf-reinforcement-learning-from-human-feedback", "content": "... to stabilize and improve outputs. ... PPO with a KL penalty stabilizes updates while maximizing rewards that align closely with preference labels."}
+{"idx": 6, "title": "Reward Model Ensembles Help Mitigate Overoptimization", "date": "", "ddg_snippet": "For PPO , ensemble-based conservative optimization typically outperforms single reward model optimization, and when combined with a suitable KL ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.02743v2", "content": "For PPO , ensemble-based conservative optimization typically outperforms single reward model optimization, and when combined with a suitable KL ..."}
+{"idx": 7, "title": "Beyond Pass@1: Self-play with Variational Problem Synthesis", "date": "", "ddg_snippet": "SvS achieves superior efficiency and effectiveness on competition-level AIME benchmarks, showing significant improvements in Pass@32 and Pass@1 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14029v1", "content": "SvS achieves superior efficiency and effectiveness on competition-level AIME benchmarks, showing significant improvements in Pass@32 and Pass@1 ..."}
+{"idx": 8, "title": "Vinija's Notes • LLM Alignment", "date": "", "ddg_snippet": "... reward information is used to update and improve the policy through a reinforcement learning algorithm known as Proximal Policy Optimization ( PPO ) .", "subpage_snippet": "", "source": "vinija.ai", "link": "https://vinija.ai/concepts/llm-alignment/", "content": "... reward information is used to update and improve the policy through a reinforcement learning algorithm known as Proximal Policy Optimization ( PPO ) ."}
+{"idx": 9, "title": "A Deep Dive into Policy Optimization Algorithms &", "date": "", "ddg_snippet": "Notably, RLHF in OpenAI’s ChatGPT makes use of Proximal Policy Optimisation ( PPO ), a method developed by OpenAI itself.", "subpage_snippet": "", "source": "superagi.com", "link": "https://superagi.com/policy-optimization-algorithms-frameworks/", "content": "Notably, RLHF in OpenAI’s ChatGPT makes use of Proximal Policy Optimisation ( PPO ), a method developed by OpenAI itself."}
diff --git a/data/sampled_jsons/PPO_alternative_loss_function_RLHF_robustness_2023_2024_year_2024.jsonl b/data/sampled_jsons/PPO_alternative_loss_function_RLHF_robustness_2023_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6a0dafc1cf4f537910ec554eadde9ab13bb8091
--- /dev/null
+++ b/data/sampled_jsons/PPO_alternative_loss_function_RLHF_robustness_2023_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reward- Robust RLHF in LLMs", "date": "", "ddg_snippet": "HH- RLHF , UltraFeedBack (Cui et al., 2024 ) , along with an internal prompt dataset collected by the PM team, is employed to implement the PPO algorithm.In essence, reward- robust RLHF might prioritize optimizing the lower bound of the return function .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.15360v3", "content": "HH- RLHF , UltraFeedBack (Cui et al., 2024 ) , along with an internal prompt dataset collected by the PM team, is employed to implement the PPO algorithm.In essence, reward- robust RLHF might prioritize optimizing the lower bound of the return function ."}
+{"idx": 1, "title": "[LLM Class Guest Lecture] RLHF Methods", "date": "", "ddg_snippet": "- Humans prefer responses by models ne-tuned with RLHF . - 1.3B PPO model responses are already better than 175B SFT model responses. Also, clear scaling with model size.- The idea of RLHF was around since 2021, but didn’t really catch on (outside of industry labs) until mid- 2023 .", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/cmu-llms/2024/2024-10-24-rlfh-methods.pdf", "content": "- Humans prefer responses by models ne-tuned with RLHF . - 1.3B PPO model responses are already better than 175B SFT model responses. Also, clear scaling with model size.- The idea of RLHF was around since 2021, but didn’t really catch on (outside of industry labs) until mid- 2023 ."}
+{"idx": 2, "title": "Proximal Policy Optimization ( PPO )", "date": "", "ddg_snippet": "PPO surrogate function . Let’s study each part to understand how it works. The Ratio Function .Putting RL back in RLHF . By June 12, 2024 •.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/deep-rl-ppo", "content": "PPO surrogate function . Let’s study each part to understand how it works. The Ratio Function .Putting RL back in RLHF . By June 12, 2024 •."}
+{"idx": 3, "title": "Reinforcement Learning from Human Feedback ( RLHF ) for... - Freedium", "date": "", "ddg_snippet": "The PPO loss function is composed of three components: Policy Loss : PPO 's primary objective when improving the LLM. PPO is not the only optimizer used for RLHF . With the current pace of AI research, new alternatives spring up like mushrooms.", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/9cd1288c9a50", "content": "The PPO loss function is composed of three components: Policy Loss : PPO 's primary objective when improving the LLM. PPO is not the only optimizer used for RLHF . With the current pace of AI research, new alternatives spring up like mushrooms."}
+{"idx": 4, "title": "The N+ Implementation Details of RLHF with PPO ... | OpenReview", "date": "", "ddg_snippet": "Our RLHF -trained Pythia models demonstrate significant gains in response quality that scale with model size, with our 2.8B, 6.9B models outperforming OpenAI's released 1.3B checkpoint. Our results highlight best practices in data, training, and evaluation for RLHF .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kHO2ZTa8e3", "content": "Our RLHF -trained Pythia models demonstrate significant gains in response quality that scale with model size, with our 2.8B, 6.9B models outperforming OpenAI's released 1.3B checkpoint. Our results highlight best practices in data, training, and evaluation for RLHF ."}
+{"idx": 5, "title": "(PDF) Reward- Robust RLHF in LLMs", "date": "", "ddg_snippet": "Reward- robust rlhf in llms. Yuzi Yan1, Xingzhou Lou2, Jialian Li3, Yiping Zhang3, Jian Xie3, Chao Yu1, Yu Wang1.reward model, which is first introduced in Wu et al. ( 2024 ). The loss function for a single head iis.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384294791_Reward-Robust_RLHF_in_LLMs", "content": "Reward- robust rlhf in llms. Yuzi Yan1, Xingzhou Lou2, Jialian Li3, Yiping Zhang3, Jian Xie3, Chao Yu1, Yu Wang1.reward model, which is first introduced in Wu et al. ( 2024 ). The loss function for a single head iis."}
+{"idx": 6, "title": "Domain-Specific LLM Customization with RLHF - Rohan's Bytes", "date": "", "ddg_snippet": "In 2024 , researchers demonstrated RLHF on Mistral for alignment and domain tasks. Alternative ( PPO ): If we were to use PPO instead of DPO, the process would involve generating outputs on the fly and iteratively updating. Pseudocode for a PPO loop might look like", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/domain-specific-llm-customization", "content": "In 2024 , researchers demonstrated RLHF on Mistral for alignment and domain tasks. Alternative ( PPO ): If we were to use PPO instead of DPO, the process would involve generating outputs on the fly and iteratively updating. Pseudocode for a PPO loop might look like"}
+{"idx": 7, "title": "Reinforcement Learning в обучении LLM - PPO , DPO, GRPO - что...", "date": "", "ddg_snippet": "GRPO устраняет необходимость обучения функции ценности (value function ), которая используется в PPO для оценки преимущества (advantage). Вместо этого GRPO использует среднюю награду группы ответов на один промт как базовый уровень для сравнения.", "subpage_snippet": "", "source": "llmstudio.ru", "link": "https://llmstudio.ru/blog/rl-ppo-dpo-grpo", "content": "GRPO устраняет необходимость обучения функции ценности (value function ), которая используется в PPO для оценки преимущества (advantage). Вместо этого GRPO использует среднюю награду группы ответов на один промт как базовый уровень для сравнения."}
+{"idx": 8, "title": "From REINFORCE to PPO : The Complete On-Policy... | Taewoon Kim", "date": "", "ddg_snippet": "Joint training with combined loss function . PPO ’s design principles directly influence modern LLM fine-tuning: RLHF (Reinforcement Learning from Human Feedback): Use PPO to optimize language models for human preferences.", "subpage_snippet": "", "source": "taewoon.kim", "link": "https://taewoon.kim/2025-08-07-on-policy-rl/", "content": "Joint training with combined loss function . PPO ’s design principles directly influence modern LLM fine-tuning: RLHF (Reinforcement Learning from Human Feedback): Use PPO to optimize language models for human preferences."}
+{"idx": 9, "title": "Top RLHF Tools: Reinforcement Learning From Human... | Encord", "date": "", "ddg_snippet": "Top RLHF Tools. Summarize with AI. November 1, 2024 .Encord RLHF lets you build robust RLHF workflows by offering collaborative features to optimize LLMs and vision-language models (VLMs) through human feedback.", "subpage_snippet": "", "source": "encord.com", "link": "https://encord.com/blog/top-tools-rlhf/", "content": "Top RLHF Tools. Summarize with AI. November 1, 2024 .Encord RLHF lets you build robust RLHF workflows by offering collaborative features to optimize LLMs and vision-language models (VLMs) through human feedback."}
diff --git a/data/sampled_jsons/PPO_imitation_learning_on-policy_actor-critic_GAIL.jsonl b/data/sampled_jsons/PPO_imitation_learning_on-policy_actor-critic_GAIL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7808e794f533ca527190cd50e9487d73065c3943
--- /dev/null
+++ b/data/sampled_jsons/PPO_imitation_learning_on-policy_actor-critic_GAIL.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Imitation learning via Reinforcement Learning using Policy ...", "date": "", "ddg_snippet": "Overview of steps: · Create Actor-Critic architecture which represents the two policy networks · Code the PPO algorithm · Train an agent using the PPO algorithm.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/prasad-madhale/imitation-learning", "content": "Overview of steps: · Create Actor-Critic architecture which represents the two policy networks · Code the PPO algorithm · Train an agent using the PPO algorithm."}
+{"idx": 1, "title": "ikostrikov/pytorch-a2c-ppo-acktr-gail", "date": "", "ddg_snippet": "PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization ( PPO ), Scalable trust-region method for deep reinforcement learning .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail", "content": "PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization ( PPO ), Scalable trust-region method for deep reinforcement learning ."}
+{"idx": 2, "title": "Autonomous Agent trained using Imitation Learning , Deep ...", "date": "", "ddg_snippet": "The Critic Neural-Net takes in a state to output State-Value function of Policy PI. The Actor Neural Net takes in a state and outputs action ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@pranaykr/autonomous-agents-trained-using-imitation-learning-deep-reinforcement-learning-and-generative-e23278cc2814", "content": "The Critic Neural-Net takes in a state to output State-Value function of Policy PI. The Actor Neural Net takes in a state and outputs action ..."}
+{"idx": 3, "title": "Discriminator-Actor-Critic: Addressing Sample Inefficiency ...", "date": "", "ddg_snippet": "by I Kostrikov · Cited by 371 — We address sample inefficiency and reward bias in adversarial imitation learning algorithms such as GAIL and AIRL.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Hk4fpoA5Km", "content": "by I Kostrikov · Cited by 371 — We address sample inefficiency and reward bias in adversarial imitation learning algorithms such as GAIL and AIRL."}
+{"idx": 4, "title": "Mature GAIL: Imitation Learning for Low-level and High- ...", "date": "", "ddg_snippet": "by W Shin · 2019 · Cited by 1 — In this paper, we propose a new algorithm based on the GAIL framework that includes a global encoder and the reward penalization mechanism. The global encoder ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.03200", "content": "by W Shin · 2019 · Cited by 1 — In this paper, we propose a new algorithm based on the GAIL framework that includes a global encoder and the reward penalization mechanism. The global encoder ..."}
+{"idx": 5, "title": "A Comparative Analysis of PPO and GAIL Robots", "date": "", "ddg_snippet": "26 Jun 2024 — The PPO robots demonstrated comparatively poorer performance when performing alone in a task without GAIL players. Our findings highlight the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3652037.3663948", "content": "26 Jun 2024 — The PPO robots demonstrated comparatively poorer performance when performing alone in a task without GAIL players. Our findings highlight the ..."}
+{"idx": 6, "title": "Why would anyone use PPO over SAC, TD3, DDPG, and ...", "date": "", "ddg_snippet": "Think of GAIL for imitation learning . You shouldn't use off policy updates to update the learner. That would mean you're updating the actor ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/reinforcementlearning/comments/gl1ov2/why_would_anyone_use_ppo_over_sac_td3_ddpg_and/", "content": "Think of GAIL for imitation learning . You shouldn't use off policy updates to update the learner. That would mean you're updating the actor ..."}
+{"idx": 7, "title": "PPO — Stable Baselines3 2.7.1a3 documentation", "date": "", "ddg_snippet": "Policy class for actor - critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes. Parameters: observation_space (Space) ...", "subpage_snippet": "", "source": "stable-baselines3.readthedocs.io", "link": "https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html", "content": "Policy class for actor - critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes. Parameters: observation_space (Space) ..."}
+{"idx": 8, "title": "Hands-On Imitation Learning: From Behavior Cloning to ...", "date": "", "ddg_snippet": "PPO has two main parts: critic and actor . The actor represents the policy, while the critic provides value estimations for each state with ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/hands-on-imitation-learning-from-behavior-cloning-to-multi-modal-imitation-learning-11ec0d37f4a2", "content": "PPO has two main parts: critic and actor . The actor represents the policy, while the critic provides value estimations for each state with ..."}
+{"idx": 9, "title": "EGCARL: A PPO-based reinforcement learning method ...", "date": "", "ddg_snippet": "by Q Yan · 2025 — The results demonstrate that our approach significantly improves driving performance compared to other methods.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1566253525006785", "content": "by Q Yan · 2025 — The results demonstrate that our approach significantly improves driving performance compared to other methods."}
diff --git a/data/sampled_jsons/PPO_instability_gradient_variance_moving_targets_symmetric_loss_robustness.jsonl b/data/sampled_jsons/PPO_instability_gradient_variance_moving_targets_symmetric_loss_robustness.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..923bb26ed581986f0a2a7aa053c87a9bd15c6360
--- /dev/null
+++ b/data/sampled_jsons/PPO_instability_gradient_variance_moving_targets_symmetric_loss_robustness.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · 2024 · Cited by 1 — Abstract:Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "by JS Byun · 2024 · Cited by 1 — Abstract:Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance ."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · Cited by 1 — Summary: Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YjBrt82S3v", "content": "by JS Byun · Cited by 1 — Summary: Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance ."}
+{"idx": 2, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44897", "content": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance ."}
+{"idx": 3, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · Cited by 1 — This paper shows that the symmetric PPO loss improves performance over the regular PPO loss , probably because it mitigates the effects of the small batch.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9oq0iY2Jxx", "content": "by JS Byun · Cited by 1 — This paper shows that the symmetric PPO loss improves performance over the regular PPO loss , probably because it mitigates the effects of the small batch."}
+{"idx": 4, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165652", "content": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from ..."}
+{"idx": 5, "title": "Deep Policy Gradient Methods Without Batch Updates ...", "date": "", "ddg_snippet": "9 Dec 2024 — Modern deep policy gradient methods achieve effective performance on simulated robotic tasks, but they all require large replay buffers or ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96084", "content": "9 Dec 2024 — Modern deep policy gradient methods achieve effective performance on simulated robotic tasks, but they all require large replay buffers or ..."}
+{"idx": 6, "title": "Adaptive PPO With Multi-Armed Bandit Clipping and Meta- ...", "date": "", "ddg_snippet": "by M Massaoudi · 2025 · Cited by 1 — This formulation ensures that when the ratio moves outside the predefined clipping range, a smoothing mechanism works to maintain convergence. 17 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/10973609.pdf", "content": "by M Massaoudi · 2025 · Cited by 1 — This formulation ensures that when the ratio moves outside the predefined clipping range, a smoothing mechanism works to maintain convergence. 17 pages"}
+{"idx": 7, "title": "Aman's AI Journal • Preference Optimization", "date": "", "ddg_snippet": "The baseline provided by the critic stabilizes the training process by reducing variance in the policy gradients , helping the actor make more precise updates.", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/preference-optimization/", "content": "The baseline provided by the critic stabilizes the training process by reducing variance in the policy gradients , helping the actor make more precise updates."}
+{"idx": 8, "title": "Towards robust motion control in multi-source uncertain ...", "date": "", "ddg_snippet": "by J Li · 2025 — Ultimately, the motion control performance and the robustness of driving policy are thoroughly validated by configuring the behavior patterns of traffic ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772424725000319", "content": "by J Li · 2025 — Ultimately, the motion control performance and the robustness of driving policy are thoroughly validated by configuring the behavior patterns of traffic ..."}
+{"idx": 9, "title": "Hybrid of representation learning and reinforcement ...", "date": "", "ddg_snippet": "by C Zhou · 2025 · Cited by 3 — Experiment also shows that AW-DSAC and SA-DSAC are unstable in training because of the sum operation that lacks robustness to vanishing gradient and injectivity ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0921889025002647", "content": "by C Zhou · 2025 · Cited by 3 — Experiment also shows that AW-DSAC and SA-DSAC are unstable in training because of the sum operation that lacks robustness to vanishing gradient and injectivity ..."}
diff --git a/data/sampled_jsons/Papamakarios_2017_Masked_Autoregressive_Flow_architecture_MLP_neural_network.jsonl b/data/sampled_jsons/Papamakarios_2017_Masked_Autoregressive_Flow_architecture_MLP_neural_network.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d258f35671566f940b32bb4a3edf95189541163
--- /dev/null
+++ b/data/sampled_jsons/Papamakarios_2017_Masked_Autoregressive_Flow_architecture_MLP_neural_network.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer Neural Autoregressive Flows", "date": "", "ddg_snippet": "T-NAFs treat each dimension of a random variable as a separate input token, using attention masking to enforce an autoregressive constraint.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.01855v1", "content": "T-NAFs treat each dimension of a random variable as a separate input token, using attention masking to enforce an autoregressive constraint."}
+{"idx": 1, "title": "Neural Autoregressive Flows", "date": "", "ddg_snippet": "by CW Huang · Cited by 627 — Papamakarios , G., Murray, I., and Pavlakou, T. Masked autoregressive flow for density estimation. In Advances in Neural Information Processing Systems, 2017 . 10 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v80/huang18d/huang18d.pdf", "content": "by CW Huang · Cited by 627 — Papamakarios , G., Murray, I., and Pavlakou, T. Masked autoregressive flow for density estimation. In Advances in Neural Information Processing Systems, 2017 . 10 pages"}
+{"idx": 2, "title": "transformer neural autoregressive flows", "date": "", "ddg_snippet": "by M Patacchiola · 2024 · Cited by 6 — Masked autoregressive flow for density estimation. In Advances in Neural Information Processing Systems, 2017 . George Papamakarios , Eric ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.01855", "content": "by M Patacchiola · 2024 · Cited by 6 — Masked autoregressive flow for density estimation. In Advances in Neural Information Processing Systems, 2017 . George Papamakarios , Eric ..."}
+{"idx": 3, "title": "Transformer Neural Autoregressive Flows", "date": "", "ddg_snippet": "by M Patacchiola · Cited by 6 — Papamakarios , G., Pavlakou, T., and Murray, I. Masked autoregressive flow for density estimation. In Advances in Neural Information Processing Systems, 2017 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mJYllFw85A", "content": "by M Patacchiola · Cited by 6 — Papamakarios , G., Pavlakou, T., and Murray, I. Masked autoregressive flow for density estimation. In Advances in Neural Information Processing Systems, 2017 ."}
+{"idx": 4, "title": "normflows : A PyTorch Package for Normalizing Flows", "date": "", "ddg_snippet": "normflows is a PyTorch implementation of discrete normalizing flows. Many popular flow architectures are implemented, see the list below.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/normflows/", "content": "normflows is a PyTorch implementation of discrete normalizing flows. Many popular flow architectures are implemented, see the list below."}
+{"idx": 5, "title": "AUTOREGRESSIVE GRAPH NETWORK FOR LEARNING ...", "date": "", "ddg_snippet": "by SKA Prakash — In this work, we propose a Autoregressive Graph Network (AGN) that learns for- ward physics using a temporal inductive bias. Currently, temporal state space ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TjxCJ1DK-dm", "content": "by SKA Prakash — In this work, we propose a Autoregressive Graph Network (AGN) that learns for- ward physics using a temporal inductive bias. Currently, temporal state space ..."}
+{"idx": 6, "title": "Anomaly detection in broadband networks: Using ...", "date": "", "ddg_snippet": "by TE Rasmussen · 2025 · Cited by 1 — The Masked Autoregressive Flow (MAF) by Papamakarios et al. ( 2017 ), a generalization of the RealNVP approach, views an autoregressive model as a normalizing ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0165168424004948", "content": "by TE Rasmussen · 2025 · Cited by 1 — The Masked Autoregressive Flow (MAF) by Papamakarios et al. ( 2017 ), a generalization of the RealNVP approach, views an autoregressive model as a normalizing ..."}
+{"idx": 7, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "TARFLOW can be thought of as a Transformer-based variant of Masked Autoregressive Flows (MAFs): it consists of a stack of autoregressive Transformer blocks on ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46564", "content": "TARFLOW can be thought of as a Transformer-based variant of Masked Autoregressive Flows (MAFs): it consists of a stack of autoregressive Transformer blocks on ..."}
+{"idx": 8, "title": "Funnels", "date": "", "ddg_snippet": "by S Klein — Papamakarios , T. Pavlakou, and I. Murray, “ Masked autoregressive flow for density estima- tion,” arXiv preprint arXiv:1705.07057, 2017 . [6] G. Papamakarios , E. 16 pages", "subpage_snippet": "", "source": "bayesiandeeplearning.org", "link": "https://bayesiandeeplearning.org/2021/papers/39.pdf", "content": "by S Klein — Papamakarios , T. Pavlakou, and I. Murray, “ Masked autoregressive flow for density estima- tion,” arXiv preprint arXiv:1705.07057, 2017 . [6] G. Papamakarios , E. 16 pages"}
+{"idx": 9, "title": "Appendices - Proceedings of Machine Learning Research", "date": "", "ddg_snippet": "For the benchmark, we used as density estimator a Masked Autoregressive Flow (MAF, Papamakarios et al., 2017 ) with five flow transforms, each with two ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v130/lueckmann21a/lueckmann21a-supp.pdf", "content": "For the benchmark, we used as density estimator a Masked Autoregressive Flow (MAF, Papamakarios et al., 2017 ) with five flow transforms, each with two ..."}
diff --git a/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_Technical_Overview_dia.jsonl b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_Technical_Overview_dia.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c8ec61445ee51d38c47628259b2ee83b81759ee9
--- /dev/null
+++ b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_Technical_Overview_dia.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Faster Diffusion Sampling with Randomized Midpoints: Sequential", "date": "", "ddg_snippet": "As a byproduct of our methods, for the well-studied problem of log - concave sampling in total variation distance, we give an algorithm and simple ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.00924v2", "content": "As a byproduct of our methods, for the well-studied problem of log - concave sampling in total variation distance, we give an algorithm and simple ..."}
+{"idx": 1, "title": "Probabilistic image reconstruction for radio interferometers |", "date": "", "ddg_snippet": "... for deconvolving and denoizing images from gridded radio interferometric visibilities using Bayesian inference based on a Gaussian process model .", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/W2084590735/probabilistic-image-reconstruction-for-radio-interferometers", "content": "... for deconvolving and denoizing images from gridded radio interferometric visibilities using Bayesian inference based on a Gaussian process model ."}
+{"idx": 2, "title": "Computational statistics Research Topics for MS PhD –", "date": "", "ddg_snippet": "I am sharing with you some of the research topics regarding Computational statistics that you can choose for your research proposal for the thesis ...", "subpage_snippet": "", "source": "t4tutorials.com", "link": "https://t4tutorials.com/computational-statistics-research-topics/", "content": "I am sharing with you some of the research topics regarding Computational statistics that you can choose for your research proposal for the thesis ..."}
+{"idx": 3, "title": "CoCre-Sam (Kokkuri-san): Modeling Ouija Board as Collective", "date": "", "ddg_snippet": "... associated with an energy landscape derived from an internal language model reflecting linguistic priors, and agents exert stochastic forces based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11906v1", "content": "... associated with an energy landscape derived from an internal language model reflecting linguistic priors, and agents exert stochastic forces based on ..."}
+{"idx": 4, "title": "2015", "date": "", "ddg_snippet": "... and modeling systems, high-quality 3D models are becoming increasingly common, and are now potentially available for ubiquitous exploration.", "subpage_snippet": "", "source": "diglib.eg.org", "link": "https://diglib.eg.org/collections/bfcb5d11-8abe-4bf1-bcd0-18fb13773748", "content": "... and modeling systems, high-quality 3D models are becoming increasingly common, and are now potentially available for ubiquitous exploration."}
+{"idx": 5, "title": "0104 Statistics - McMaster Experts", "date": "", "ddg_snippet": "A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator Journal ...", "subpage_snippet": "", "source": "experts.mcmaster.ca", "link": "https://experts.mcmaster.ca/display/vocab-for-0104-statistics", "content": "A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator Journal ..."}
+{"idx": 6, "title": "Graphs Limits Research Topics – T4Tutorials.com", "date": "", "ddg_snippet": "Designing Tasks for Introducing Functions and Graphs within Dynamic Interactive Environments. ... Forecasting confirmed and recovered COVID-19 cases ...", "subpage_snippet": "", "source": "t4tutorials.com", "link": "https://t4tutorials.com/graphs-limits-research-topics/", "content": "Designing Tasks for Introducing Functions and Graphs within Dynamic Interactive Environments. ... Forecasting confirmed and recovered COVID-19 cases ..."}
+{"idx": 7, "title": "Statistical Computing | Statistical Modeling, Causal Inference,", "date": "", "ddg_snippet": "... time over the next year focusing on normalizing flows and diffusions for variational inference and focusing on how to code statistical models in JAX.", "subpage_snippet": "", "source": "statmodeling.stat.columbia.edu", "link": "https://statmodeling.stat.columbia.edu/category/statistical-computing/", "content": "... time over the next year focusing on normalizing flows and diffusions for variational inference and focusing on how to code statistical models in JAX."}
+{"idx": 8, "title": "Comprehensive review of dimensionality reduction algorithms:", "date": "", "ddg_snippet": "... simplifies complex data from genomics, imaging, sensors, and language into interpretable forms that support visualization, clustering, and modeling .", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-3025/", "content": "... simplifies complex data from genomics, imaging, sensors, and language into interpretable forms that support visualization, clustering, and modeling ."}
+{"idx": 9, "title": "US12001191B2 - Automated 360-degree dense point object", "date": "", "ddg_snippet": "... TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US12001191B2/en", "content": "... TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS"}
diff --git a/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_sitearxiv.org.jsonl b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c30c416d485331e198dd60976c4ea9423959f0e5
--- /dev/null
+++ b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Faster Diffusion Sampling with Randomized Midpoints: Sequential", "date": "", "ddg_snippet": "... in the randomized midpoint method , originally introduced by Shen and Lee [ SL19 ] in the context of Langevin Monte Carlo for log - concave sampling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.00924v2", "content": "... in the randomized midpoint method , originally introduced by Shen and Lee [ SL19 ] in the context of Langevin Monte Carlo for log - concave sampling ..."}
+{"idx": 1, "title": "Beyond Log-Concavity and Score Regularity: Improved Convergence", "date": "", "ddg_snippet": "Beyond Log -Concavity and Score Regularity: Improved Convergence Bounds for Score - Based Generative Models in 𝒲 2 subscript 𝒲 2 \\mathcal{W}_{2 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02298v1", "content": "Beyond Log -Concavity and Score Regularity: Improved Convergence Bounds for Score - Based Generative Models in 𝒲 2 subscript 𝒲 2 \\mathcal{W}_{2 ..."}
+{"idx": 2, "title": "Beyond Log-Concavity and Score Regularity: Improved Convergence", "date": "", "ddg_snippet": "Beyond Log -Concavity and Score Regularity: Improved Convergence Bounds for Score - Based Generative Models in 𝒲 2 \\mathcal{W}_{2} -distance", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02298v4", "content": "Beyond Log -Concavity and Score Regularity: Improved Convergence Bounds for Score - Based Generative Models in 𝒲 2 \\mathcal{W}_{2} -distance"}
+{"idx": 3, "title": "Beyond Scores: Proximal Diffusion Models", "date": "", "ddg_snippet": "Diffusion models combine a forward diffusion process that converts the data distribution we wish to sample into noise and a reverse-time process that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.08956v1", "content": "Diffusion models combine a forward diffusion process that converts the data distribution we wish to sample into noise and a reverse-time process that ..."}
+{"idx": 4, "title": "Restricted Spectral Gap Decomposition for Simulated Tempering", "date": "", "ddg_snippet": "Additionally, denoising- diffusion - based samplers have been proposed for sampling from non- log - concave targets without relying on isoperimetric ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.15059v1", "content": "Additionally, denoising- diffusion - based samplers have been proposed for sampling from non- log - concave targets without relying on isoperimetric ..."}
+{"idx": 5, "title": "When and how can inexact generative models still sample from", "date": "", "ddg_snippet": "... models , such as conditional ... For instance, in diffusion models or score - based generative models (SGMs) and their variants Song et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.07581v1", "content": "... models , such as conditional ... For instance, in diffusion models or score - based generative models (SGMs) and their variants Song et al."}
+{"idx": 6, "title": "Multilevel neural simulation-based inference", "date": "", "ddg_snippet": "Neural simulation - based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06087v1", "content": "Neural simulation - based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator."}
+{"idx": 7, "title": "Split Gibbs Discrete Diffusion Posterior Sampling", "date": "", "ddg_snippet": "... formulation enables efficient sampling via Gibbs sampling , which alternates between a likelihood sampling step and a prior sampling step using a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01161v2", "content": "... formulation enables efficient sampling via Gibbs sampling , which alternates between a likelihood sampling step and a prior sampling step using a ..."}
+{"idx": 8, "title": "Diffusion at Absolute Zero: Langevin Sampling Using Successive", "date": "", "ddg_snippet": "... for the present article has been denoising score matching (DSM) and Annealed Langevin sampling [ 2 ] , which can be classified under the umbrella term ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01358v2", "content": "... for the present article has been denoising score matching (DSM) and Annealed Langevin sampling [ 2 ] , which can be classified under the umbrella term ..."}
+{"idx": 9, "title": "Effective and Efficient Masked Image Generation Models", "date": "", "ddg_snippet": "... parallel , masked diffusion models [ 41 , 43 , 31 , 38 ] have shown promise in text generation, demonstrating scaling properties [ 37 ] similar to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.07197v2", "content": "... parallel , masked diffusion models [ 41 , 43 , 31 , 38 ] have shown promise in text generation, demonstrating scaling properties [ 37 ] similar to ..."}
diff --git a/data/sampled_jsons/Pedestrian_Motion_Reconstruction-_A_Large-scale_Benchmark_via_Mixed_Reality_Rendering_with_Multiple_.jsonl b/data/sampled_jsons/Pedestrian_Motion_Reconstruction-_A_Large-scale_Benchmark_via_Mixed_Reality_Rendering_with_Multiple_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3c8de98c01a0e37e8792dfcd5c3b074c3c656888
--- /dev/null
+++ b/data/sampled_jsons/Pedestrian_Motion_Reconstruction-_A_Large-scale_Benchmark_via_Mixed_Reality_Rendering_with_Multiple_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Pedestrian Motion Reconstruction: A Large-scale ...", "date": "", "ddg_snippet": "by Y Wang · Cited by 1 — We introduce the Pedestrian Motion Reconstruction (PMR) dataset, which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YOpa6dTrpt", "content": "by Y Wang · Cited by 1 — We introduce the Pedestrian Motion Reconstruction (PMR) dataset, which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and ..."}
+{"idx": 1, "title": "Pedestrian Motion Reconstruction", "date": "", "ddg_snippet": "We introduce the Pedestrian Motion Reconstruction (PMR) dataset, which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/f3342358d0792ea201dc86d69570946b-Abstract-Conference.html", "content": "We introduce the Pedestrian Motion Reconstruction (PMR) dataset, which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and ..."}
+{"idx": 2, "title": "A Large-scale Benchmark via Mixed Reality Rendering ...", "date": "", "ddg_snippet": "by Y Wang · 2025 · Cited by 1 — Pedestrian Motion Reconstruction : A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vQxVBV1o4Y", "content": "by Y Wang · 2025 · Cited by 1 — Pedestrian Motion Reconstruction : A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities."}
+{"idx": 3, "title": "SS3DM: Benchmarking Street-View Surface ...", "date": "", "ddg_snippet": "29 Oct 2024 — Reconstructing city-scale 3D meshes from street-view inputs is a challenging task in computer vision and graphics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.21739v1", "content": "29 Oct 2024 — Reconstructing city-scale 3D meshes from street-view inputs is a challenging task in computer vision and graphics."}
+{"idx": 4, "title": "generating synthetic embodied locomotion from real-world ...", "date": "", "ddg_snippet": "by PM Torrens · 2025 — We demonstrate methods for generating synthetic scenarios of embodied locomotion on sidewalks, drawn from detailed observations of situation ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s44212-025-00081-z", "content": "by PM Torrens · 2025 — We demonstrate methods for generating synthetic scenarios of embodied locomotion on sidewalks, drawn from detailed observations of situation ..."}
+{"idx": 5, "title": "Multimodal Generative AI with Autoregressive LLMs for ...", "date": "", "ddg_snippet": "31 May 2025 — This paper presents an in-depth survey on the use of multimodal Generative Artificial Intelligence (GenAI) and autoregressive Large Language ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03191v1", "content": "31 May 2025 — This paper presents an in-depth survey on the use of multimodal Generative Artificial Intelligence (GenAI) and autoregressive Large Language ..."}
+{"idx": 6, "title": "A Large-scale Benchmark via Mixed Reality Rendering ...", "date": "", "ddg_snippet": "error. 500. Oops, something went wrong!", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/zh-CN/chatpaper/paper/112913", "content": "error. 500. Oops, something went wrong!"}
+{"idx": 7, "title": "Yiyi Zhang", "date": "", "ddg_snippet": "2, 2024. Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities. Y Wang, Y Zhang, X Hu ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=ViLZaDsAAAAJ&hl=ja", "content": "2, 2024. Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities. Y Wang, Y Zhang, X Hu ..."}
+{"idx": 8, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities · IDArb: Intrinsic ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities · IDArb: Intrinsic ..."}
+{"idx": 9, "title": "52CV/CVPR-2025-Papers", "date": "", "ddg_snippet": "1.Othere(其它) · 2 .Face(人脸) · 3.Image Segmentation(图像分割) · 4.Image Progress(图像/视频处理) · 5.Image SR(超分辨率) · 6.Image Classification(图像分类) · 7.Image/ ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/52CV/CVPR-2025-Papers", "content": "1.Othere(其它) · 2 .Face(人脸) · 3.Image Segmentation(图像分割) · 4.Image Progress(图像/视频处理) · 5.Image SR(超分辨率) · 6.Image Classification(图像分类) · 7.Image/ ..."}
diff --git a/data/sampled_jsons/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter_Ki.jsonl b/data/sampled_jsons/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter_Ki.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..739cb6b49b03bfd17f9b948a6f2e2db4b0efaa84
--- /dev/null
+++ b/data/sampled_jsons/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter_Ki.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "People as sensors: Mass media and local temperature influence ...", "date": "", "ddg_snippet": "Jan 1, 2015 · People as sensors: Mass media and local temperature influence climate change discussion on Twitter Andrei P. Kirilenko a, Tatiana Molodtsova a , Svetlana O. Stepchenkova b Show more Add to Mendeley", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0959378014001952", "content": "Jan 1, 2015 · People as sensors: Mass media and local temperature influence climate change discussion on Twitter Andrei P. Kirilenko a, Tatiana Molodtsova a , Svetlana O. Stepchenkova b Show more Add to Mendeley"}
+{"idx": 1, "title": "People as sensors: Mass media and local temperature ... - FIT", "date": "", "ddg_snippet": "Jul 25, 2014 · People as sensors : Mass media and local temperature influence climate change discussion on Twitter Andrei P. Kirilenko a,*, Tatiana Molodtsova a, Svetlana O. Stepchenkova b", "subpage_snippet": "", "source": "research.fit.edu", "link": "https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/climate-communications/youth-climate-amp-social-media/Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect-CC-Discussion-On-Twitter.pdf", "content": "Jul 25, 2014 · People as sensors : Mass media and local temperature influence climate change discussion on Twitter Andrei P. Kirilenko a,*, Tatiana Molodtsova a, Svetlana O. Stepchenkova b"}
+{"idx": 2, "title": "People as sensors: mass media and local temperature influence ...", "date": "", "ddg_snippet": "We examined whether people living under significant temperature anomalies connect their sensory experiences to climate change and the role that media plays in this process. We used Twitter messages containing words \" climate change \" and \"global warming\" as the indicator of attention that public pays to the issue. Specifically, the goals were: (1) to investigate whether people immediately notice ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2014AGUFMGC41B0548K/abstract", "content": "We examined whether people living under significant temperature anomalies connect their sensory experiences to climate change and the role that media plays in this process. We used Twitter messages containing words \" climate change \" and \"global warming\" as the indicator of attention that public pays to the issue. Specifically, the goals were: (1) to investigate whether people immediately notice ..."}
+{"idx": 3, "title": "Sci-Hub | People as sensors: Mass media and local temperature ...", "date": "", "ddg_snippet": "Kirilenko , A. P., Molodtsova, T., & Stepchenkova, S. O. ( 2015 ). People as sensors : Mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "sci-hub.se", "link": "https://sci-hub.se/10.1016/j.gloenvcha.2014.11.003", "content": "Kirilenko , A. P., Molodtsova, T., & Stepchenkova, S. O. ( 2015 ). People as sensors : Mass media and local temperature influence climate change discussion on Twitter ."}
+{"idx": 4, "title": "(PDF) People as sensors: Mass media and local temperature ...", "date": "", "ddg_snippet": "This study examined whether people living in the US connect their sensory experiences with 5 local temperature to climate change and whether mass media influences the process. We used 6 the volume of Twitter messages containing words \" climate", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/29095713/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter", "content": "This study examined whether people living in the US connect their sensory experiences with 5 local temperature to climate change and whether mass media influences the process. We used 6 the volume of Twitter messages containing words \" climate"}
+{"idx": 5, "title": "People as sensors Mass media and local temperature influence ...", "date": "", "ddg_snippet": "People as sensors : Mass media and local temperature influence climatechange discussion on TwitterAndrei P. Kirilenkoa,*, Tatiana Molodtsovaa , Svetlana O. Stepchenkovaba Department of Earth Systems Science and Policy, University of North Dakota, Stop 9011, Grand Forks, ND 58202-9011, United Statesb The Department of Tourism, Recreation and Sport Management, University of Florida, P.O. Box ...", "subpage_snippet": "", "source": "www.doc88.com", "link": "https://www.doc88.com/p-9085240191809.html", "content": "People as sensors : Mass media and local temperature influence climatechange discussion on TwitterAndrei P. Kirilenkoa,*, Tatiana Molodtsovaa , Svetlana O. Stepchenkovaba Department of Earth Systems Science and Policy, University of North Dakota, Stop 9011, Grand Forks, ND 58202-9011, United Statesb The Department of Tourism, Recreation and Sport Management, University of Florida, P.O. Box ..."}
+{"idx": 6, "title": "Climate Change Disinformation and How to Combat It | Annual", "date": "", "ddg_snippet": "Climate change also challenges many people &s worldview because any climate mitigation regime will have economic and political implications that are ...", "subpage_snippet": "", "source": "www.annualreviews.org", "link": "https://www.annualreviews.org/content/journals/10.1146/annurev-publhealth-090419-102409", "content": "Climate change also challenges many people &s worldview because any climate mitigation regime will have economic and political implications that are ..."}
+{"idx": 7, "title": "Taking climate change personally—study finds audiovisual", "date": "", "ddg_snippet": "... Sonny Rosenthal et al, Media use, interpersonal communication, and personal relevance as external and internal representations of climate change ...", "subpage_snippet": "", "source": "phys.org", "link": "https://phys.org/news/2024-12-climate-personally-audiovisual-sources-media.html", "content": "... Sonny Rosenthal et al, Media use, interpersonal communication, and personal relevance as external and internal representations of climate change ..."}
+{"idx": 8, "title": "Social Media + Climate Change + The Power of the Algorithm -", "date": "", "ddg_snippet": "... between online social media and news use in climate change discussions is important because it exemplifies the connection between how people use ...", "subpage_snippet": "", "source": "cleantechnica.com", "link": "https://cleantechnica.com/2017/12/30/social-media-climate-change-power-algorithm/", "content": "... between online social media and news use in climate change discussions is important because it exemplifies the connection between how people use ..."}
+{"idx": 9, "title": "Yeo | The influence of temperature on #ClimateChange and", "date": "", "ddg_snippet": "... Twitter activity, local changes in temperature , and mass media , in the present work we explore how regional temperature changes and topics discussed ...", "subpage_snippet": "", "source": "jcom.sissa.it", "link": "https://jcom.sissa.it/article/pubid/JCOM_1605_2017_A01/", "content": "... Twitter activity, local changes in temperature , and mass media , in the present work we explore how regional temperature changes and topics discussed ..."}
diff --git a/data/sampled_jsons/Perdomo_2024_'The_Relative_Value_of_Prediction_in_Algorithmic_Decision_Making'.jsonl b/data/sampled_jsons/Perdomo_2024_'The_Relative_Value_of_Prediction_in_Algorithmic_Decision_Making'.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4287edd4e438232934514a91efbea048a766b400
--- /dev/null
+++ b/data/sampled_jsons/Perdomo_2024_'The_Relative_Value_of_Prediction_in_Algorithmic_Decision_Making'.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Relative Value of Prediction in Algorithmic Decision Making", "date": "", "ddg_snippet": "Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/perdomo24a.html", "content": "Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end."}
+{"idx": 1, "title": "The Relative Value of Prediction in Algorithmic Decision Making", "date": "", "ddg_snippet": "Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.08511v2", "content": "Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end."}
+{"idx": 2, "title": "The Relative Value of Prediction in Algorithmic Decision Making", "date": "", "ddg_snippet": "to enable reliable decisions if the relative value of prediction is small. Perdomo , J. C., Britton, T., Hardt, M., and Abebe, R. Diffi-cult lessons on social prediction from wisconsin public schools. arXiv preprint arXiv:2304.06205, 2023.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oaACFfNbXl", "content": "to enable reliable decisions if the relative value of prediction is small. Perdomo , J. C., Britton, T., Hardt, M., and Abebe, R. Diffi-cult lessons on social prediction from wisconsin public schools. arXiv preprint arXiv:2304.06205, 2023."}
+{"idx": 3, "title": "ICML Poster The Relative Value of Prediction in Algorithmic ...", "date": "", "ddg_snippet": "Juan Perdomo .Given this broad range of design decisions , a basic question to ask is: What is the relative value of prediction in algorithmic decision making ? How do the improvements in welfare arising from better predictions compare to those of other policy levers?", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/poster/33078", "content": "Juan Perdomo .Given this broad range of design decisions , a basic question to ask is: What is the relative value of prediction in algorithmic decision making ? How do the improvements in welfare arising from better predictions compare to those of other policy levers?"}
+{"idx": 4, "title": "The Relative Value of Prediction in Algorithmic Decision Making", "date": "", "ddg_snippet": "The paper explores the relative value of prediction in algorithmic decision - making .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/relative-value-prediction-algorithmic-decision-making", "content": "The paper explores the relative value of prediction in algorithmic decision - making ."}
+{"idx": 5, "title": "Juan Carlos Perdomo - Google Akademik", "date": "", "ddg_snippet": "The Relative Value of Prediction in Algorithmic Decision Making .J Gardner, JC Perdomo , L Schmidt. Advances in Neural Information Processing Systems, NeurIPS 2024 , 2024 .", "subpage_snippet": "", "source": "scholar.google.com.au", "link": "https://scholar.google.com.au/citations?user=TeBmXz4AAAAJ&hl=tr", "content": "The Relative Value of Prediction in Algorithmic Decision Making .J Gardner, JC Perdomo , L Schmidt. Advances in Neural Information Processing Systems, NeurIPS 2024 , 2024 ."}
+{"idx": 6, "title": "The Relative Value of Prediction in Algorithmic Decision Making", "date": "", "ddg_snippet": "View recent discussion. Abstract: Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2312.08511v2", "content": "View recent discussion. Abstract: Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end."}
+{"idx": 7, "title": "New Course: Prediction for (Individualized) Decision - making", "date": "", "ddg_snippet": "The Relative Value of Prediction in Algorithmic Decision Making .Against predictive optimization: On the legitimacy of decision - making algorithms that optimize predictive accuracy.", "subpage_snippet": "", "source": "statmodeling.stat.columbia.edu", "link": "https://statmodeling.stat.columbia.edu/2024/12/06/new-course-prediction-for-individualized-decision-making/", "content": "The Relative Value of Prediction in Algorithmic Decision Making .Against predictive optimization: On the legitimacy of decision - making algorithms that optimize predictive accuracy."}
+{"idx": 8, "title": "Research – Juan Carlos Perdomo", "date": "", "ddg_snippet": "The Relative Value of Prediction in Algorithmic Decision Making JCP International Conference on Machine Learning (ICML) 2024 non-archival track at Foundations of Responsible Computing (FORC) 2024 .", "subpage_snippet": "", "source": "jcperdomo.org", "link": "https://jcperdomo.org/research/", "content": "The Relative Value of Prediction in Algorithmic Decision Making JCP International Conference on Machine Learning (ICML) 2024 non-archival track at Foundations of Responsible Computing (FORC) 2024 ."}
+{"idx": 9, "title": "04-16 CITP Seminar – The Relative Value of Prediction | CS", "date": "", "ddg_snippet": "Photo of Juan Carlos Perdomo Algorithmic predictions are increasingly used to inform the allocations of goods and services in the public sphere. In these domains, predictions serve as a means to an end.", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/events/26619", "content": "Photo of Juan Carlos Perdomo Algorithmic predictions are increasingly used to inform the allocations of goods and services in the public sphere. In these domains, predictions serve as a means to an end."}
diff --git a/data/sampled_jsons/Physics-informed_neural_networks_A_deep_learning_framework_Raissi_abstract_loss_function_year_2019.jsonl b/data/sampled_jsons/Physics-informed_neural_networks_A_deep_learning_framework_Raissi_abstract_loss_function_year_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf14df4106fec28d6aee51a4b46cb56cd08bb5fc
--- /dev/null
+++ b/data/sampled_jsons/Physics-informed_neural_networks_A_deep_learning_framework_Raissi_abstract_loss_function_year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Physics-informed neural networks: A deep learning ...", "date": "", "ddg_snippet": "by M Raissi · 2019 · Cited by 17032 — Physics-informed neural networks: A deep learning framework ... The energy of a mechanical system seems to be the natural loss function for a ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125", "content": "by M Raissi · 2019 · Cited by 17032 — Physics-informed neural networks: A deep learning framework ... The energy of a mechanical system seems to be the natural loss function for a ..."}
+{"idx": 1, "title": "Authors | Physics Informed Deep Learning - Maziar Raissi", "date": "", "ddg_snippet": "The following figure summarizes our predictions after the network has been trained using the above loss function . Allen-Cahn equation: Top: Solution along ...", "subpage_snippet": "", "source": "maziarraissi.github.io", "link": "https://maziarraissi.github.io/PINNs/", "content": "The following figure summarizes our predictions after the network has been trained using the above loss function . Allen-Cahn equation: Top: Solution along ..."}
+{"idx": 2, "title": "Improved Physics-informed neural networks loss function ...", "date": "", "ddg_snippet": "... loss function ”. While effective in reducing the average error, this ... Karniadakis, Physics-informed neural networks: A deep learning framework for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.13993v2", "content": "... loss function ”. While effective in reducing the average error, this ... Karniadakis, Physics-informed neural networks: A deep learning framework for ..."}
+{"idx": 3, "title": "The deep finite element method: A deep learning ...", "date": "", "ddg_snippet": "by W Xiong · 2025 · Cited by 13 — A loss function is then designed by combining deep neural networks with ... Raissi et al. Physics-informed neural networks: a deep learning framework ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0045782524009356", "content": "by W Xiong · 2025 · Cited by 13 — A loss function is then designed by combining deep neural networks with ... Raissi et al. Physics-informed neural networks: a deep learning framework ..."}
+{"idx": 4, "title": "A deep learning approach: physics-informed neural networks ...", "date": "", "ddg_snippet": "by AT Deresse · 2025 · Cited by 4 — ... loss function consisting of the residual of the governing partial ... Physics-informed neural networks: a deep learning framework for ...", "subpage_snippet": "", "source": "bmcresnotes.biomedcentral.com", "link": "https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-025-07142-1", "content": "by AT Deresse · 2025 · Cited by 4 — ... loss function consisting of the residual of the governing partial ... Physics-informed neural networks: a deep learning framework for ..."}
+{"idx": 5, "title": "Physics-informed neural networks", "date": "", "ddg_snippet": "by M Raissi · 2019 · Cited by 17032 — and x = 1, we can use the aforementioned loss function (A.9) to train the networks of (A.7), (A.8), and predict the solution at time tn+1. A ... 22 pages", "subpage_snippet": "", "source": "faculty.sites.iastate.edu", "link": "https://faculty.sites.iastate.edu/hliu/files/inline-files/PINN_RPK_2019_1.pdf", "content": "by M Raissi · 2019 · Cited by 17032 — and x = 1, we can use the aforementioned loss function (A.9) to train the networks of (A.7), (A.8), and predict the solution at time tn+1. A ... 22 pages"}
+{"idx": 6, "title": "Improved Physics-informed neural networks loss function ...", "date": "", "ddg_snippet": "9 Sept 2025 — ... loss function ”. While effective in reducing the average error ... Karniadakis, Physics-informed neural networks: A deep learning framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.13993v3", "content": "9 Sept 2025 — ... loss function ”. While effective in reducing the average error ... Karniadakis, Physics-informed neural networks: A deep learning framework ..."}
+{"idx": 7, "title": "Scientific Machine Learning Through Physics–Informed ...", "date": "", "ddg_snippet": "by S Cuomo · 2022 · Cited by 2282 — ... loss function has more hyperparameters than the energy-based loss function . ... Physics-informed neural networks: A deep learning framework ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10915-022-01939-z", "content": "by S Cuomo · 2022 · Cited by 2282 — ... loss function has more hyperparameters than the energy-based loss function . ... Physics-informed neural networks: A deep learning framework ..."}
+{"idx": 8, "title": "Physics-informed neural networks for physiological signal ...", "date": "", "ddg_snippet": "by A Zhao · 2025 — ... loss function ( Raissi et al 2019, Cuomo et al 2022). Machine ... Physics-informed neural networks: a deep learning framework for ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12308510/", "content": "by A Zhao · 2025 — ... loss function ( Raissi et al 2019, Cuomo et al 2022). Machine ... Physics-informed neural networks: a deep learning framework for ..."}
+{"idx": 9, "title": "PINNsFormer: A Transformer-Based Framework For ...", "date": "", "ddg_snippet": "by Z Zhao · Cited by 80 — \" Physics-informed neural networks: A deep learning framework for ... Loss depends on loss function and hyperparameters, which cannot be a fair ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DO2WFXU1Be", "content": "by Z Zhao · Cited by 80 — \" Physics-informed neural networks: A deep learning framework for ... Loss depends on loss function and hyperparameters, which cannot be a fair ..."}
diff --git a/data/sampled_jsons/Physics-informed_neural_networks_A_deep_learning_framework_for_solving_forward_and_inverse_problems__year_2019.jsonl b/data/sampled_jsons/Physics-informed_neural_networks_A_deep_learning_framework_for_solving_forward_and_inverse_problems__year_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d7cc0e96ea4ce7c0e6fdff9867b9f79fd9b7bac7
--- /dev/null
+++ b/data/sampled_jsons/Physics-informed_neural_networks_A_deep_learning_framework_for_solving_forward_and_inverse_problems__year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Physics-informed neural networks: A deep learning ...", "date": "", "ddg_snippet": "by M Raissi · 2019 · Cited by 17103 — We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics .", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "http://ui.adsabs.harvard.edu/abs/2019JCoPh.378..686R/abstract", "content": "by M Raissi · 2019 · Cited by 17103 — We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics ."}
+{"idx": 1, "title": "Physics-informed neural networks", "date": "", "ddg_snippet": "by M Raissi · 2019 · Cited by 17103 — We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any ... 22 pages", "subpage_snippet": "", "source": "faculty.sites.iastate.edu", "link": "https://faculty.sites.iastate.edu/hliu/files/inline-files/PINN_RPK_2019_1.pdf", "content": "by M Raissi · 2019 · Cited by 17103 — We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any ... 22 pages"}
+{"idx": 2, "title": "Physics-informed neural networks: A deep learning ...", "date": "", "ddg_snippet": "by M Raissi · 2019 · Cited by 17103 — We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125", "content": "by M Raissi · 2019 · Cited by 17103 — We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics."}
+{"idx": 3, "title": "Physics-informed neural networks: A deep learning ...", "date": "", "ddg_snippet": "13 Jun 2018 — We introduce physics-informed neural networks -neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics.", "subpage_snippet": "", "source": "neuralfields.cs.brown.edu", "link": "https://neuralfields.cs.brown.edu/paper_4.html", "content": "13 Jun 2018 — We introduce physics-informed neural networks -neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics."}
+{"idx": 4, "title": "Physics Informed Deep Learning (Part I): Data-driven ...", "date": "", "ddg_snippet": "by M Raissi · 2017 · Cited by 1989 — Abstract:We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1711.10561", "content": "by M Raissi · 2017 · Cited by 1989 — Abstract:We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while ..."}
+{"idx": 5, "title": "Raissi, M., Perdikaris, P. and Karniadakis, G.E. (2019) ...", "date": "", "ddg_snippet": "ABSTRACT : In this article, a physics aware deep learning model is introduced for multiphase flow problems . The deep learning model is shown to be capable of ...", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=2886120", "content": "ABSTRACT : In this article, a physics aware deep learning model is introduced for multiphase flow problems . The deep learning model is shown to be capable of ..."}
+{"idx": 6, "title": "Kolmogorov–Arnold-Informed neural network: A physics ...", "date": "", "ddg_snippet": "by Y Wang · 2025 · Cited by 136 — We introduce a physics-informed deep learning framework for forward and inverse problems , named Kolmogorov–Arnold-Informed Neural Networks (KINN).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0045782524007722", "content": "by Y Wang · 2025 · Cited by 136 — We introduce a physics-informed deep learning framework for forward and inverse problems , named Kolmogorov–Arnold-Informed Neural Networks (KINN)."}
+{"idx": 7, "title": "Physics-informed neural networks for PDE problems", "date": "", "ddg_snippet": "by K Luo · 2025 · Cited by 8 — The application of deep learning in solving PDEs has made further progress. In 2019, Raissi et al. (2019) proposed the PINN model, which ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11322-7", "content": "by K Luo · 2025 · Cited by 8 — The application of deep learning in solving PDEs has made further progress. In 2019, Raissi et al. (2019) proposed the PINN model, which ..."}
+{"idx": 8, "title": "Physics-Informed Neural Networks for Solving Forward and ...", "date": "", "ddg_snippet": "by T Kapoor · 2023 · Cited by 94 — Abstract —This article proposes a new framework using physics - informed neural networks (PINNs) to simulate complex structural systems that consist of single ... 15 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/5962385/10517792/10255379.pdf", "content": "by T Kapoor · 2023 · Cited by 94 — Abstract —This article proposes a new framework using physics - informed neural networks (PINNs) to simulate complex structural systems that consist of single ... 15 pages"}
+{"idx": 9, "title": "A-PINN: Auxiliary Physics Informed Neural Networks for ...", "date": "", "ddg_snippet": "by L Yuan · Cited by 344 — In this study, we propose an auxiliary physics informed neural network (A-PINN) framework for solving forward and inverse problems of nonlinear IDEs.", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4000235", "content": "by L Yuan · Cited by 344 — In this study, we propose an auxiliary physics informed neural network (A-PINN) framework for solving forward and inverse problems of nonlinear IDEs."}
diff --git a/data/sampled_jsons/Playground_V2.5_SDXL_training_dataset_private_dataset_performance_gap_year_2024.jsonl b/data/sampled_jsons/Playground_V2.5_SDXL_training_dataset_private_dataset_performance_gap_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a9568e4aca520f7f1616561ef9d8f9a122a5863f
--- /dev/null
+++ b/data/sampled_jsons/Playground_V2.5_SDXL_training_dataset_private_dataset_performance_gap_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenAI Arena: An Open Evaluation Platform for Generative ...", "date": "", "ddg_snippet": "5 Aug 2024 — ... PlayGround V2.5 , MagicBrush, and T2VTurbo, respectively (until ... SDXL but is trained with a private dataset . In contrast, SDXL only ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v2", "content": "5 Aug 2024 — ... PlayGround V2.5 , MagicBrush, and T2VTurbo, respectively (until ... SDXL but is trained with a private dataset . In contrast, SDXL only ..."}
+{"idx": 1, "title": "Introducing On-demand Preference Fine-Tuning for Image Models", "date": "", "ddg_snippet": "Once you have enough samples you can start a training job through our playgrounds and get your customized model that you can plug into our Any ...", "subpage_snippet": "", "source": "blog.fal.ai", "link": "https://blog.fal.ai/introducing-on-demand-preference-finetuning-for-image-models/", "content": "Once you have enough samples you can start a training job through our playgrounds and get your customized model that you can plug into our Any ..."}
+{"idx": 2, "title": "📅 ThursdAI - Feb 29 - Leap Year Special ✨", "date": "", "ddg_snippet": "Suhail Doshi and Playground release a new foundational image model called Playground v2 . 5 and it looks awesome, very realistic and honestly looks ...", "subpage_snippet": "", "source": "sub.thursdai.news", "link": "https://sub.thursdai.news/p/thursdai-feb-29-leap-year-special", "content": "Suhail Doshi and Playground release a new foundational image model called Playground v2 . 5 and it looks awesome, very realistic and honestly looks ..."}
+{"idx": 3, "title": "📅 ThursdAI - Mar 7 - Anthropic gives us Claude 3, Elon vs", "date": "", "ddg_snippet": "While this field is getting a little saturated, we now have DALL-E, MidJourney, Adobe Firefly, Playground , SDXL , Stable Cascade and Ideogram, SD is ...", "subpage_snippet": "", "source": "sub.thursdai.news", "link": "https://sub.thursdai.news/p/thursdai-mar-7-anthropic-gives-us", "content": "While this field is getting a little saturated, we now have DALL-E, MidJourney, Adobe Firefly, Playground , SDXL , Stable Cascade and Ideogram, SD is ..."}
+{"idx": 4, "title": "SegQuant: A Semantics-Aware and Generalizable Quantization", "date": "", "ddg_snippet": "Two primary paradigms exist: quantization-aware training (QAT), which incorporates quantization effects during training , and post- training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14811v2", "content": "Two primary paradigms exist: quantization-aware training (QAT), which incorporates quantization effects during training , and post- training ..."}
+{"idx": 5, "title": "SegQuant: A Semantics-Aware and Generalizable Quantization", "date": "", "ddg_snippet": "Two primary paradigms exist: quantization-aware training (QAT), which incorporates quantization effects during training , and post- training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14811v1", "content": "Two primary paradigms exist: quantization-aware training (QAT), which incorporates quantization effects during training , and post- training ..."}
+{"idx": 6, "title": "[AINews] Liquid Foundation Models: A New Transformers", "date": "", "ddg_snippet": "... A study on o1-preview model in medical scenarios showed that it surpasses GPT-4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two ...", "subpage_snippet": "", "source": "buttondown.com", "link": "https://buttondown.com/ainews/archive/ainews-liquid-foundation-models-a-new/", "content": "... A study on o1-preview model in medical scenarios showed that it surpasses GPT-4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two ..."}
+{"idx": 7, "title": "[AINews] Nemotron-4-340B: NVIDIA's new large open models,", "date": "", "ddg_snippet": "... process, we relied on only approximately 20K human-annotated data (10K for supervised fine-tuning, 10K Helpsteer2 data for reward model training and ...", "subpage_snippet": "", "source": "buttondown.com", "link": "https://buttondown.com/ainews/archive/ainews-to-be-named-2748/", "content": "... process, we relied on only approximately 20K human-annotated data (10K for supervised fine-tuning, 10K Helpsteer2 data for reward model training and ..."}
+{"idx": 8, "title": "ai - Tags - Luis Quintanilla", "date": "", "ddg_snippet": "Nomic Embed Text V2 : An Open Source, Multilingual, Mixture-of-Experts Embedding Model ... Corpus: the largest public domain dataset for training LLMs", "subpage_snippet": "", "source": "www.lqdev.me", "link": "https://www.lqdev.me/tags/ai/", "content": "Nomic Embed Text V2 : An Open Source, Multilingual, Mixture-of-Experts Embedding Model ... Corpus: the largest public domain dataset for training LLMs"}
+{"idx": 9, "title": "ai - Tags - Luis Quintanilla", "date": "", "ddg_snippet": "Nomic Embed Text V2 : An Open Source, Multilingual, Mixture-of-Experts Embedding Model ... Metadata-Curated Language-Image Pre- training (MetaCLIP ...", "subpage_snippet": "", "source": "www.luisquintanilla.me", "link": "https://www.luisquintanilla.me/tags/ai", "content": "Nomic Embed Text V2 : An Open Source, Multilingual, Mixture-of-Experts Embedding Model ... Metadata-Curated Language-Image Pre- training (MetaCLIP ..."}
diff --git a/data/sampled_jsons/Po-Hung_Yeh_Kuang-Huei_Lee_Jun-Cheng_Chen_challenges_reward_estimation.jsonl b/data/sampled_jsons/Po-Hung_Yeh_Kuang-Huei_Lee_Jun-Cheng_Chen_challenges_reward_estimation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b7eddb26760441c2cb4dfbe256d976e336b4e71c
--- /dev/null
+++ b/data/sampled_jsons/Po-Hung_Yeh_Kuang-Huei_Lee_Jun-Cheng_Chen_challenges_reward_estimation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "openreview.net/profile?id=~ Jun - cheng _ Chen 1", "date": "", "ddg_snippet": "Po - Hung Yeh , Kuang - Huei Lee , Jun - cheng Chen .Yang Che Sun, Cheng Yu Yeo, Ernie Chu, Jun - cheng Chen , Yu-Lun Liu.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Jun-cheng_Chen1", "content": "Po - Hung Yeh , Kuang - Huei Lee , Jun - cheng Chen .Yang Che Sun, Cheng Yu Yeo, Ernie Chu, Jun - cheng Chen , Yu-Lun Liu."}
+{"idx": 1, "title": "Kuang - Huei Lee - Google DeepMind | 人才画像 - AMiner", "date": "", "ddg_snippet": "Kuang - Huei Lee . Staff Research Scientist. Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen .", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/profile/kuang-huei-lee/53f4ce76dabfaeed20f81a8a", "content": "Kuang - Huei Lee . Staff Research Scientist. Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen ."}
+{"idx": 2, "title": "dblp: List of computer science publications by Jun - Cheng Chen", "date": "", "ddg_snippet": "Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen : Training-free Diffusion Model Alignment with Sampling Demons.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/77/251.html", "content": "Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen : Training-free Diffusion Model Alignment with Sampling Demons."}
+{"idx": 3, "title": "Kuang - Huei Lee | DeepAI", "date": "", "ddg_snippet": "Read Kuang - Huei Lee 's latest research, browse their coauthor's research, and play around with their algorithms.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/kuang-huei-lee", "content": "Read Kuang - Huei Lee 's latest research, browse their coauthor's research, and play around with their algorithms."}
+{"idx": 4, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Authors: Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen . View a PDF of the paper titled Training-free Diffusion Model Alignment with Sampling Demons, by Po - Hung Yeh and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.05760", "content": "Authors: Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen . View a PDF of the paper titled Training-free Diffusion Model Alignment with Sampling Demons, by Po - Hung Yeh and 2 other authors."}
+{"idx": 5, "title": "ICLR Poster Training-Free Diffusion Model Alignment with Sampling...", "date": "", "ddg_snippet": "Po - Hung Yeh · Kuang - Huei Lee · Jun - Cheng Chen .a key challenge .Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon, to...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28034", "content": "Po - Hung Yeh · Kuang - Huei Lee · Jun - Cheng Chen .a key challenge .Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon, to..."}
+{"idx": 6, "title": "GitHub - aiiu-lab/DemonSampling: [ICLR'25] Official implementation of...", "date": "", "ddg_snippet": "@inproceedings{ yeh 2025trainingfree, title={Training-Free Diffusion Model Alignment with Sampling Demons}, author={ Po - Hung Yeh , Kuang - Huei Lee , Jun - cheng Chen }, booktitle={International Conference on Learning Representations}, year={2025}", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling", "content": "@inproceedings{ yeh 2025trainingfree, title={Training-Free Diffusion Model Alignment with Sampling Demons}, author={ Po - Hung Yeh , Kuang - Huei Lee , Jun - cheng Chen }, booktitle={International Conference on Learning Representations}, year={2025}"}
+{"idx": 7, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen . Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/training-free-diffusion-model-alignment-sampling-demons", "content": "Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen . Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions."}
+{"idx": 8, "title": "Development | aiiu-lab/DemonSampling | DeepWiki", "date": "", "ddg_snippet": "qualitative_generate.py: Multiple reward model pipeline. Tests (tests/): Test cases for the core functionality, including test_api.py which demonstrates low-level API usage. Configuration: Environment setup files (environment.yml, requirements.txt).", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/aiiu-lab/DemonSampling/6-development", "content": "qualitative_generate.py: Multiple reward model pipeline. Tests (tests/): Test cases for the core functionality, including test_api.py which demonstrates low-level API usage. Configuration: Environment setup files (environment.yml, requirements.txt)."}
+{"idx": 9, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen .Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.", "subpage_snippet": "", "source": "rareone0602.github.io", "link": "https://rareone0602.github.io/Demon_page/", "content": "Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen .Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions."}
diff --git a/data/sampled_jsons/Position-_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_3.2.jsonl b/data/sampled_jsons/Position-_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_3.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bb7d64c3392db620d718896eda0e6871cf94482e
--- /dev/null
+++ b/data/sampled_jsons/Position-_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_3.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "by H Wallach · 2025 · Cited by 10 — We take the position that eval- uating GenAI systems is a social science measurement challenge . Specifically, the measurement tasks involved in evaluating GenAI ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00561", "content": "by H Wallach · 2025 · Cited by 10 — We take the position that eval- uating GenAI systems is a social science measurement challenge . Specifically, the measurement tasks involved in evaluating GenAI ..."}
+{"idx": 1, "title": "Methodological Challenges in Agentic Evaluations of AI ...", "date": "", "ddg_snippet": "by K Wei — Posi- tion: Evaluating Generative AI Systems is a Social Sci- ence Measurement Challenge , February 2025. URL https://arxiv.org/abs/2502.00561. Wang, A ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ZhSKG8IslC", "content": "by K Wei — Posi- tion: Evaluating Generative AI Systems is a Social Sci- ence Measurement Challenge , February 2025. URL https://arxiv.org/abs/2502.00561. Wang, A ..."}
+{"idx": 2, "title": "Ironies of Generative AI: Understanding and Mitigating ...", "date": "", "ddg_snippet": "by A Simkute · 2025 · Cited by 51 — We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems .", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/abs/10.1080/10447318.2024.2405782", "content": "by A Simkute · 2025 · Cited by 51 — We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems ."}
+{"idx": 3, "title": "The Janus Effect of Generative AI - PubsOnLine - INFORMS.org", "date": "", "ddg_snippet": "by A Susarla · 2023 · Cited by 311 — The staggering pace of recent AI advancements has already begun to instigate seismic shifts in research conduct and communication.", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/10.1287/isre.2023.ed.v34.n2", "content": "by A Susarla · 2023 · Cited by 311 — The staggering pace of recent AI advancements has already begun to instigate seismic shifts in research conduct and communication."}
+{"idx": 4, "title": "AI, Jobs, and the Automation Trap: Where Is HCI?", "date": "", "ddg_snippet": "22 Jun 2025 — Part of the challenge lies in incentive ... Position : Evaluating Generative AI Systems is a Social Science Measurement Challenge .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3729176.3729191", "content": "22 Jun 2025 — Part of the challenge lies in incentive ... Position : Evaluating Generative AI Systems is a Social Science Measurement Challenge ."}
+{"idx": 5, "title": "A New Evaluation Ecosystem Is Necessary to Understand AI's ...", "date": "", "ddg_snippet": "Position : Evaluating generative ai systems is a social science measurement challenge , 2025. URL https://arxiv.org/abs/2502.00561. [105] Boxin Wang, Weixin ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2505.18893v2", "content": "Position : Evaluating generative ai systems is a social science measurement challenge , 2025. URL https://arxiv.org/abs/2502.00561. [105] Boxin Wang, Weixin ..."}
+{"idx": 6, "title": "ChatGPT and Beyond: Exploring the Responsible Use of ...", "date": "", "ddg_snippet": "by M Söllner · 2025 · Cited by 15 — Given the tradition of our discipline, information systems researchers are well-positioned to address this sociotechnical design challenge , and ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s12599-025-00932-8", "content": "by M Söllner · 2025 · Cited by 15 — Given the tradition of our discipline, information systems researchers are well-positioned to address this sociotechnical design challenge , and ..."}
+{"idx": 7, "title": "AI Evaluation Should Learn from How We Test Humans", "date": "", "ddg_snippet": "As AI systems continue to evolve, their rigorous evaluation becomes crucial for their development and deployment. Researchers have constructed.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/8e220605668bf1e72f42ef716309c8f01a8e5826.pdf", "content": "As AI systems continue to evolve, their rigorous evaluation becomes crucial for their development and deployment. Researchers have constructed."}
+{"idx": 8, "title": "Collaborative AI in the workplace: Enhancing ...", "date": "", "ddg_snippet": "by A Przegalinska · 2025 · Cited by 61 — This research explores the impact of generative AI on firm performance, focusing on how AI resources, employee AI experience, and AI deployment for automation, ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0268401224001014", "content": "by A Przegalinska · 2025 · Cited by 61 — This research explores the impact of generative AI on firm performance, focusing on how AI resources, employee AI experience, and AI deployment for automation, ..."}
+{"idx": 9, "title": "Artificial Intelligence Risk Management Framework", "date": "", "ddg_snippet": "by N AI · 2024 · Cited by 19 — MEASURE 3.2 : Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently available measurement ...", "subpage_snippet": "", "source": "nvlpubs.nist.gov", "link": "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf", "content": "by N AI · 2024 · Cited by 19 — MEASURE 3.2 : Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently available measurement ..."}
diff --git a/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_3.1_syst.jsonl b/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_3.1_syst.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..565be5d987dcd626dc0eb380dcb8f4ecfcea79cd
--- /dev/null
+++ b/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_3.1_syst.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Evaluating Generative AI Systems Is a Social ... Evaluating Generative AI Systems is a So... Toward an Evaluation Science for Generative AI Systems A Shared Standard for Valid Measurement of Generative AI ... Evaluating the Social Impact of Generative AI Systems in ... Evaluating social and ethical risks from generative AI Toward an Evaluation Science for Generative AI Systems Toward an Evaluation Science for Generative AI Systems Toward an Evaluation Science for Generative AI Systems Evaluating social and ethical risks from generative AI Toward an Evaluation Science for Generative AI Systems Toward an Evaluation Science for Generative AI Systems A Critical Analysis of Generative AI: Challenges ...", "date": "", "ddg_snippet": "Feb 1 , 2025 · Specifically, our position is that evaluating GenAI systems is a social science measurement challenge . We present a four-level framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems . The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for ... Apr 16, 2025 · However, the current evaluation ecosystem is insufficient: Commonly used static benchmarks face validity challenges, and ad hoc case-by-case audits rarely scale. In this piece, we advocate for maturing an evaluation science for generative AI systems . Dec 2, 2024 · This work argues that valid measurement of GenAI systems ' capabilities, risks, and impacts, further requires systematizing, operationalizing, and applying not only the entailed concepts, but also the contexts of interest and the metrics used. The valid measurement of generative AI (GenAI) systems ' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems . We ... Jun 27, 2024 · Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those ... Oct 19, 2023 · Generative AI systems are already being used to write books, create graphic designs, assist medical practitioners, and are becoming increasingly capable. Ensuring these systems are developed and deployed responsibly requires carefully evaluating the potential ethical and social risks they may pose. Are generative AI systems a social science measurement challenge? Evaluating generative AI systems is a social science measurement challenge . arXiv:2411.10939. Wang A, Hertzmann A, Russakovsky O. 2024. Benchmark suites instead of leaderboards for evaluating AI fairness. Patterns 5 (11):101080. Wang A, Singh A, Michael J, Hill F, Levy O, Bowman SR. 2019. How can generative AI systems be evaluated? Developing an evaluation science for generative AI systems requires first identifying which concepts should be measured—that is , determining the proper measurement targets. Common targets of interest in the AI context are often abstract and even contested (Wallach et al. 2024). Can generative AI improve safety evaluation practices? While generative AI creates unique challenges for system safety engineering and measurement science, the field can draw valuable insights from the development of safety evaluation practices in other fields , including transportation, aerospace, and pharmaceutical engineering. Are generative AI systems risk based? Comparatively little work has been done to assess potential risks at the point of human interaction or of systemic impact. Risk-specific evaluations: Capability evaluations of generative AI systems are limited in the risk areas that they cover. For many risk areas, few evaluations exist. Do generative AI evaluations need a broader sociotechnical lens? To account for factors beyond technical specifications that influence real-world performance and safety, generative AI evaluations will need to adopt a broader sociotechnical lens (Chen and Metcalf 2024; Selbst et al. 2019; Wallach et al. 2024). Do generative AI systems need a behavioral approach? Taken together, these unique challenges inherent to generative AI systems indicate the need for a behavioral approach to evaluating such systems, focusing on AI system performance in the context of different real-world settings (Matias 2023; Rahwan et al. 2019; Wagner et al. 2021). Sep 8, 2025 · Generative Artificial Intelligence (Gen- AI ) is a new advancement that has revolutionized the concepts of Natural Language Processing (NLP) and Large Language Model (LLM). This change impacts various aspects of life, stimulating industry, education, and healthcare progression. This survey presents the potential applications of Gen- AI across various sectors, highlighting the risks and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00561", "content": "Feb 1 , 2025 · Specifically, our position is that evaluating GenAI systems is a social science measurement challenge . We present a four-level framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems . The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for ... Apr 16, 2025 · However, the current evaluation ecosystem is insufficient: Commonly used static benchmarks face validity challenges, and ad hoc case-by-case audits rarely scale. In this piece, we advocate for maturing an evaluation science for generative AI systems . Dec 2, 2024 · This work argues that valid measurement of GenAI systems ' capabilities, risks, and impacts, further requires systematizing, operationalizing, and applying not only the entailed concepts, but also the contexts of interest and the metrics used. The valid measurement of generative AI (GenAI) systems ' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems . We ... Jun 27, 2024 · Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those ... Oct 19, 2023 · Generative AI systems are already being used to write books, create graphic designs, assist medical practitioners, and are becoming increasingly capable. Ensuring these systems are developed and deployed responsibly requires carefully evaluating the potential ethical and social risks they may pose. Are generative AI systems a social science measurement challenge? Evaluating generative AI systems is a social science measurement challenge . arXiv:2411.10939. Wang A, Hertzmann A, Russakovsky O. 2024. Benchmark suites instead of leaderboards for evaluating AI fairness. Patterns 5 (11):101080. Wang A, Singh A, Michael J, Hill F, Levy O, Bowman SR. 2019. How can generative AI systems be evaluated? Developing an evaluation science for generative AI systems requires first identifying which concepts should be measured—that is , determining the proper measurement targets. Common targets of interest in the AI context are often abstract and even contested (Wallach et al. 2024). Can generative AI improve safety evaluation practices? While generative AI creates unique challenges for system safety engineering and measurement science, the field can draw valuable insights from the development of safety evaluation practices in other fields , including transportation, aerospace, and pharmaceutical engineering. Are generative AI systems risk based? Comparatively little work has been done to assess potential risks at the point of human interaction or of systemic impact. Risk-specific evaluations: Capability evaluations of generative AI systems are limited in the risk areas that they cover. For many risk areas, few evaluations exist. Do generative AI evaluations need a broader sociotechnical lens? To account for factors beyond technical specifications that influence real-world performance and safety, generative AI evaluations will need to adopt a broader sociotechnical lens (Chen and Metcalf 2024; Selbst et al. 2019; Wallach et al. 2024). Do generative AI systems need a behavioral approach? Taken together, these unique challenges inherent to generative AI systems indicate the need for a behavioral approach to evaluating such systems, focusing on AI system performance in the context of different real-world settings (Matias 2023; Rahwan et al. 2019; Wagner et al. 2021). Sep 8, 2025 · Generative Artificial Intelligence (Gen- AI ) is a new advancement that has revolutionized the concepts of Natural Language Processing (NLP) and Large Language Model (LLM). This change impacts various aspects of life, stimulating industry, education, and healthcare progression. This survey presents the potential applications of Gen- AI across various sectors, highlighting the risks and ..."}
+{"idx": 1, "title": "Evaluating Generative AI Systems is a So...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for ...", "subpage_snippet": "", "source": "axi.lims.ac.uk", "link": "https://axi.lims.ac.uk/paper/2411.10939", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for ..."}
+{"idx": 2, "title": "Toward an Evaluation Science for Generative AI Systems", "date": "", "ddg_snippet": "Apr 16, 2025 · However, the current evaluation ecosystem is insufficient: Commonly used static benchmarks face validity challenges, and ad hoc case-by-case audits rarely scale. In this piece, we advocate for maturing an evaluation science for generative AI systems .", "subpage_snippet": "", "source": "www.nae.edu", "link": "https://www.nae.edu/19579/19582/21020/337862/338231/Toward-an-Evaluation-Science-for-Generative-AI-Systems", "content": "Apr 16, 2025 · However, the current evaluation ecosystem is insufficient: Commonly used static benchmarks face validity challenges, and ad hoc case-by-case audits rarely scale. In this piece, we advocate for maturing an evaluation science for generative AI systems ."}
+{"idx": 3, "title": "A Shared Standard for Valid Measurement of Generative AI ...", "date": "", "ddg_snippet": "Dec 2, 2024 · This work argues that valid measurement of GenAI systems ' capabilities, risks, and impacts, further requires systematizing, operationalizing, and applying not only the entailed concepts, but also the contexts of interest and the metrics used. The valid measurement of generative AI (GenAI) systems ' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems . We ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Shared-Standard-for-Valid-Measurement-of-AI-and-Chouldechova-Atalla/a9ff605707aff2d143729b857608e6a50c031e32", "content": "Dec 2, 2024 · This work argues that valid measurement of GenAI systems ' capabilities, risks, and impacts, further requires systematizing, operationalizing, and applying not only the entailed concepts, but also the contexts of interest and the metrics used. The valid measurement of generative AI (GenAI) systems ' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems . We ..."}
+{"idx": 4, "title": "Evaluating the Social Impact of Generative AI Systems in ...", "date": "", "ddg_snippet": "Jun 27, 2024 · Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381796100_Evaluating_the_Social_Impact_of_Generative_AI_Systems_in_Systems_and_Society", "content": "Jun 27, 2024 · Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those ..."}
+{"idx": 5, "title": "Evaluating social and ethical risks from generative AI", "date": "", "ddg_snippet": "Oct 19, 2023 · Generative AI systems are already being used to write books, create graphic designs, assist medical practitioners, and are becoming increasingly capable. Ensuring these systems are developed and deployed responsibly requires carefully evaluating the potential ethical and social risks they may pose.", "subpage_snippet": "", "source": "deepmind.google", "link": "https://deepmind.google/discover/blog/evaluating-social-and-ethical-risks-from-generative-ai/", "content": "Oct 19, 2023 · Generative AI systems are already being used to write books, create graphic designs, assist medical practitioners, and are becoming increasingly capable. Ensuring these systems are developed and deployed responsibly requires carefully evaluating the potential ethical and social risks they may pose."}
+{"idx": 6, "title": "A Critical Analysis of Generative AI: Challenges ...", "date": "", "ddg_snippet": "Sep 8, 2025 · Generative Artificial Intelligence (Gen- AI ) is a new advancement that has revolutionized the concepts of Natural Language Processing (NLP) and Large Language Model (LLM). This change impacts various aspects of life, stimulating industry, education, and healthcare progression. This survey presents the potential applications of Gen- AI across various sectors, highlighting the risks and ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11831-025-10355-z", "content": "Sep 8, 2025 · Generative Artificial Intelligence (Gen- AI ) is a new advancement that has revolutionized the concepts of Natural Language Processing (NLP) and Large Language Model (LLM). This change impacts various aspects of life, stimulating industry, education, and healthcare progression. This survey presents the potential applications of Gen- AI across various sectors, highlighting the risks and ..."}
+{"idx": 7, "title": "Position : Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as “a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as “a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024) ."}
+{"idx": 8, "title": "(PDF) Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "is a Social Science Measurement Challenge . Hanna Wallach1Meera Desai2Nicholas Pangakis1A. Feder Cooper1Angelina Wang3. Solon Barocas1Alexandra Chouldechova1Chad Atalla1Su Lin Blodgett1.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385920305_Evaluating_Generative_AI_Systems_is_a_Social_Science_Measurement_Challenge", "content": "is a Social Science Measurement Challenge . Hanna Wallach1Meera Desai2Nicholas Pangakis1A. Feder Cooper1Angelina Wang3. Solon Barocas1Alexandra Chouldechova1Chad Atalla1Su Lin Blodgett1."}
+{"idx": 9, "title": "NeurIPS Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that evaluating generative AI (GenAI) systems is challenging , as concepts related to their capabilities (e.g., intelligence, reasoning) and risks (e.g., stereotyping, anthropomorphism) are especially...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/105556", "content": "Across academia, industry, and government, there is an increasing awareness that evaluating generative AI (GenAI) systems is challenging , as concepts related to their capabilities (e.g., intelligence, reasoning) and risks (e.g., stereotyping, anthropomorphism) are especially..."}
diff --git a/data/sampled_jsons/Privacy_Loss_C-Proxy_machine_unlearning_per-instance_privacy.jsonl b/data/sampled_jsons/Privacy_Loss_C-Proxy_machine_unlearning_per-instance_privacy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f63596268b66fd6ba4f90e2d2c83e1d624fc87f3
--- /dev/null
+++ b/data/sampled_jsons/Privacy_Loss_C-Proxy_machine_unlearning_per-instance_privacy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ..."}
+{"idx": 1, "title": "Leveraging Per-Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ..."}
+{"idx": 2, "title": "PDF Forget to Flourish: Leveraging Machine-Unlearning on Pretrained ...", "date": "", "ddg_snippet": "Hence, we propose bounded unlearning as a poisoning tool, where we maximize loss in a controlled man-ner on the pre-trained model for some noisy data points to in-crease privacy leakage of the fine-tuned LLM without com-promising its utility.", "subpage_snippet": "", "source": "shadow.merl.com", "link": "https://shadow.merl.com/publications/docs/TR2025-017.pdf", "content": "Hence, we propose bounded unlearning as a poisoning tool, where we maximize loss in a controlled man-ner on the pre-trained model for some noisy data points to in-crease privacy leakage of the fine-tuned LLM without com-promising its utility."}
+{"idx": 3, "title": "A survey of security and privacy issues of machine unlearning", "date": "", "ddg_snippet": "Machine unlearning is a cutting-edge technology that embodies the privacy legal principle of the right to be forgotten within the realm of machine learning (ML). It aims to remove specific data or knowledge from trained models without retraining from scratch and has gained significant attention in the field of artificial intelligence in recent years. However, the development of machine ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/aaai.12209", "content": "Machine unlearning is a cutting-edge technology that embodies the privacy legal principle of the right to be forgotten within the realm of machine learning (ML). It aims to remove specific data or knowledge from trained models without retraining from scratch and has gained significant attention in the field of artificial intelligence in recent years. However, the development of machine ..."}
+{"idx": 4, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite"}
+{"idx": 5, "title": "Privacy Preservation through Practical Machine Unlearning", "date": "", "ddg_snippet": "Machine Learning models thrive on vast datasets, continuously adapting to provide accurate predictions and recommendations. However, in an era dominated by privacy concerns, Machine Unlearning emerges as a transformative approach, enabling the selective removal of data from trained models. This paper examines methods such as Naive Retraining and Exact Unlearning via the SISA framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10635", "content": "Machine Learning models thrive on vast datasets, continuously adapting to provide accurate predictions and recommendations. However, in an era dominated by privacy concerns, Machine Unlearning emerges as a transformative approach, enabling the selective removal of data from trained models. This paper examines methods such as Naive Retraining and Exact Unlearning via the SISA framework ..."}
+{"idx": 6, "title": "Towards Privacy-Preserving and Secure Machine Unlearning: Taxonomy ...", "date": "", "ddg_snippet": "Machine Unlearning (MU) is a growing sub-field of Machine Learning (ML) that aims to update ML models efficiently following users' requests to remove training data without retraining of the original ML model. While MU provides novel ways to preserve user privacy and ensure model integrity through the removal of compromised data, or the data that has been requested to be removed for privacy ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10835512", "content": "Machine Unlearning (MU) is a growing sub-field of Machine Learning (ML) that aims to update ML models efficiently following users' requests to remove training data without retraining of the original ML model. While MU provides novel ways to preserve user privacy and ensure model integrity through the removal of compromised data, or the data that has been requested to be removed for privacy ..."}
+{"idx": 7, "title": "A survey on machine unlearning: Techniques and new emerged privacy ...", "date": "", "ddg_snippet": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2214212625000481", "content": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications."}
+{"idx": 8, "title": "PDF When Machine Unlearning Jeopardizes Privacy", "date": "", "ddg_snippet": "While orig-inally designed to protect the privacy of the data owner, we argue that machine unlearning may leave some imprint of the data in the ML model and thus create unintended privacy risks. In this paper, we perform the first study on investigating the unintended infor-mation leakage caused by machine unlearning .", "subpage_snippet": "", "source": "yangzhangalmo.github.io", "link": "https://yangzhangalmo.github.io/papers/CCS21-UnlearningLeaks.pdf", "content": "While orig-inally designed to protect the privacy of the data owner, we argue that machine unlearning may leave some imprint of the data in the ML model and thus create unintended privacy risks. In this paper, we perform the first study on investigating the unintended infor-mation leakage caused by machine unlearning ."}
+{"idx": 9, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Leveraging-Per-Instance-Privacy-for-Machine-Sepahvand-Thudi/dca9861c26bd83a7b1c30fb4255810afdd1e4aa3", "content": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}
diff --git a/data/sampled_jsons/Pythia-7B_MMLU_30.5_score_benchmark.jsonl b/data/sampled_jsons/Pythia-7B_MMLU_30.5_score_benchmark.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3d2b9d4e4f2aa3dc0b21cda2b459edb2896c5dff
--- /dev/null
+++ b/data/sampled_jsons/Pythia-7B_MMLU_30.5_score_benchmark.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MMLU-Pro: A More Robust and Challenging Multi-Task Language ...", "date": "", "ddg_snippet": "This paper introduces MMLU -Pro, an enhanced dataset designed to extend the mostly knowledge-driven MMLU benchmark by integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. Additionally, MMLU -Pro eliminates the trivial and noisy questions in MMLU .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01574v1", "content": "This paper introduces MMLU -Pro, an enhanced dataset designed to extend the mostly knowledge-driven MMLU benchmark by integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. Additionally, MMLU -Pro eliminates the trivial and noisy questions in MMLU ."}
+{"idx": 1, "title": "Fluid Language Model Benchmarking - arXiv.org", "date": "", "ddg_snippet": "Language model (LM) benchmarking faces several challenges: comprehen- sive evaluations are costly, benchmarks often fail to measure the intended capabilities, and evaluation quality can degrade due to labeling errors and benchmark saturation. Although various strategies have been proposed to mitigate these issues, they tend to address individual aspects in isolation, neglecting broader ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.11106", "content": "Language model (LM) benchmarking faces several challenges: comprehen- sive evaluations are costly, benchmarks often fail to measure the intended capabilities, and evaluation quality can degrade due to labeling errors and benchmark saturation. Although various strategies have been proposed to mitigate these issues, they tend to address individual aspects in isolation, neglecting broader ..."}
+{"idx": 2, "title": "PDF MMLU-Pro: A More Robust and Challenging Multi-Task Language ...", "date": "", "ddg_snippet": "This paper introduces MMLU -Pro, an enhanced dataset designed to extend the mostly knowledge-driven MMLU benchmark by in-tegrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. Additionally, MMLU -Pro eliminates the trivial and noisy questions in MMLU .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/ad236edc564f3e3156e1b2feafb99a24-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "This paper introduces MMLU -Pro, an enhanced dataset designed to extend the mostly knowledge-driven MMLU benchmark by in-tegrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. Additionally, MMLU -Pro eliminates the trivial and noisy questions in MMLU ."}
+{"idx": 3, "title": "PYTHIA: Deep Learning Approach for Local Protein Conformation ...", "date": "", "ddg_snippet": "Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8396346/", "content": "Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as ..."}
+{"idx": 4, "title": "Google publishes open source 2B and 7B model : r/LocalLLaMA - Reddit", "date": "", "ddg_snippet": "The models are evolving... To complete benchmarks . It's like studying for an IQ test (which you can do, and scores go up on average during retesting.) Reply reply More replies freakynit • Reply reply hold_my_fish • Terms: Prohibited use policy:", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/1awbo84/google_publishes_open_source_2b_and_7b_model/", "content": "The models are evolving... To complete benchmarks . It's like studying for an IQ test (which you can do, and scores go up on average during retesting.) Reply reply More replies freakynit • Reply reply hold_my_fish • Terms: Prohibited use policy:"}
+{"idx": 5, "title": "LLM Leaderboards - by Benjamin Marie", "date": "", "ddg_snippet": "How the Scores Are Calculated The leaderboards show an average score for each model. This score is a simple, unweighted average of all benchmark results. To calculate the scores , I used the Evaluation Harness. For each model included in this release, I ran: for m in models: m_name = m.split('/')[0]", "subpage_snippet": "", "source": "kaitchup.substack.com", "link": "https://kaitchup.substack.com/p/llm-leaderboards", "content": "How the Scores Are Calculated The leaderboards show an average score for each model. This score is a simple, unweighted average of all benchmark results. To calculate the scores , I used the Evaluation Harness. For each model included in this release, I ran: for m in models: m_name = m.split('/')[0]"}
+{"idx": 6, "title": "Staying in the Sweet Spot: Responsive Reasoning Evolution via ...", "date": "", "ddg_snippet": "MMLU -Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark . In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track, November 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06923v1", "content": "MMLU -Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark . In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track, November 2024."}
+{"idx": 7, "title": "openchat-3_5-1210 model | Clarifai - The World's AI", "date": "", "ddg_snippet": "OpenChat-3.5 is a versatile 7B LLM, fine-tuned with C-RLFT, excelling in benchmarks with competitive scores , supporting diverse use cases from general chat to", "subpage_snippet": "", "source": "clarifai.com", "link": "https://clarifai.com/openchat/openchat/models/openchat-3_5-1210", "content": "OpenChat-3.5 is a versatile 7B LLM, fine-tuned with C-RLFT, excelling in benchmarks with competitive scores , supporting diverse use cases from general chat to"}
+{"idx": 8, "title": "Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages", "date": "", "ddg_snippet": "Pangea is a fully open multilingual multimodal language model supporting 39 languages. It is designed to enable robust natural language and visual understanding across diverse linguistic and cultural contexts.", "subpage_snippet": "", "source": "neulab.github.io", "link": "https://neulab.github.io/Pangea/", "content": "Pangea is a fully open multilingual multimodal language model supporting 39 languages. It is designed to enable robust natural language and visual understanding across diverse linguistic and cultural contexts."}
+{"idx": 9, "title": "AComprehensiveSurveyofSmallLanguageModelsintheEraof LargeLanguageModels ...", "date": "", "ddg_snippet": "Mid-sizedSLMs(6. 7B -9BParameters),suchasDeepSeek-Coder 6.7B[123]andLlama3.18B[96],showimprovedperformance, indicatingthatlargermodelsizesandenhancedtrainingcon- tributetobetteraccuracy.Largemodels(33Bandabove)like Llama3.1405B[96],GPT-4o[270],andClaude3.5Sonnet[16] excel,supportingtheideathatbiggermodelsgeneralizebetter acrossdiversecodingtasks ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/epdf/10.1145/3768165", "content": "Mid-sizedSLMs(6. 7B -9BParameters),suchasDeepSeek-Coder 6.7B[123]andLlama3.18B[96],showimprovedperformance, indicatingthatlargermodelsizesandenhancedtrainingcon- tributetobetteraccuracy.Largemodels(33Bandabove)like Llama3.1405B[96],GPT-4o[270],andClaude3.5Sonnet[16] excel,supportingtheideathatbiggermodelsgeneralizebetter acrossdiversecodingtasks ..."}
diff --git a/data/sampled_jsons/Q-learning_update_rule_equation_formula.jsonl b/data/sampled_jsons/Q-learning_update_rule_equation_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b60a5cef1b60a1078aa022fad03ef97546698f39
--- /dev/null
+++ b/data/sampled_jsons/Q-learning_update_rule_equation_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "reinforcement learning - Is there more than one Q-matrix update", "date": "", "ddg_snippet": "No, your second statement does not correctly implement the Q - learning update rule , which the first statement correctly implements.", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/10651/is-there-more-than-one-q-matrix-update-formula", "content": "No, your second statement does not correctly implement the Q - learning update rule , which the first statement correctly implements."}
+{"idx": 1, "title": "reinforcement learning - Why can the Bellman equation be turned", "date": "", "ddg_snippet": "To conclude, in many cases, the update rules of simple reinforcement learning (or dynamic programming) algorithms are very similar to their ...", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/20158/why-can-the-bellman-equation-be-turned-into-an-update-rule", "content": "To conclude, in many cases, the update rules of simple reinforcement learning (or dynamic programming) algorithms are very similar to their ..."}
+{"idx": 2, "title": "Proof of Quotient Rule of Logarithms", "date": "", "ddg_snippet": "The quotient rule is another most useful ... The fundamental law is also called as division rule of logarithms and used as a formula in mathematics.", "subpage_snippet": "", "source": "www.mathdoubts.com", "link": "https://www.mathdoubts.com/log-quotient-rule-proof/", "content": "The quotient rule is another most useful ... The fundamental law is also called as division rule of logarithms and used as a formula in mathematics."}
+{"idx": 3, "title": "Physics Equation Mechanism Class 12 Formulas and Short Notes |", "date": "", "ddg_snippet": "For Class 12 students, Physics Equation Mechanism Class 12 Formulas and Short Notes become extremely valuable because they allow quick revision ...", "subpage_snippet": "", "source": "targetssc.com", "link": "https://targetssc.com/physics-equation-mechanism-class-12-formulas/", "content": "For Class 12 students, Physics Equation Mechanism Class 12 Formulas and Short Notes become extremely valuable because they allow quick revision ..."}
+{"idx": 4, "title": "python - Suggestions on how to update equation? - Code Review", "date": "", "ddg_snippet": "... equation = Equation (width, self.float, self) self.configure_ all() self.grid_all() def configure_all(self): self.float.trace_add('write', ...", "subpage_snippet": "", "source": "codereview.stackexchange.com", "link": "https://codereview.stackexchange.com/questions/236081/suggestions-on-how-to-update-equation", "content": "... equation = Equation (width, self.float, self) self.configure_ all() self.grid_all() def configure_all(self): self.float.trace_add('write', ..."}
+{"idx": 5, "title": "Solving Equations And Formulas | PPT", "date": "", "ddg_snippet": "It presents rules for solving linear equations , including adding, subtracting, multiplying, or dividing the same quantity to both sides.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/kdwilliams/solving-equations-and-formulas", "content": "It presents rules for solving linear equations , including adding, subtracting, multiplying, or dividing the same quantity to both sides."}
+{"idx": 6, "title": "Why are there two different q-learning formulas? - Artificial", "date": "", "ddg_snippet": "... Exchange network consists of 183 Q &A communities including Stack Overflow , the largest, most trusted online community for developers to learn ...", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/46028/why-are-there-two-different-q-learning-formulas", "content": "... Exchange network consists of 183 Q &A communities including Stack Overflow , the largest, most trusted online community for developers to learn ..."}
+{"idx": 7, "title": "Newest 'notation' Questions - Artificial Intelligence", "date": "", "ddg_snippet": "I am trying to solve the following exercise from Sutton and Barto: Sutton and Barto Exercise 3.27 Give an equation for $\\pi_*$ in terms of $ q _*(s,a ...", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/tagged/notation", "content": "I am trying to solve the following exercise from Sutton and Barto: Sutton and Barto Exercise 3.27 Give an equation for $\\pi_*$ in terms of $ q _*(s,a ..."}
+{"idx": 8, "title": "Value-based Methods", "date": "", "ddg_snippet": "Calculating Q -Values: The Q - Learning Update Rule The Q - learning algorithm updates its \"map\" using a simple yet powerful formula called the Q - learning ...", "subpage_snippet": "", "source": "blog.synapticlabs.ai", "link": "https://blog.synapticlabs.ai/value-based-methods", "content": "Calculating Q -Values: The Q - Learning Update Rule The Q - learning algorithm updates its \"map\" using a simple yet powerful formula called the Q - learning ..."}
+{"idx": 9, "title": "lagrangian formalism - The contribution of higher-order", "date": "", "ddg_snippet": "Want to improve this question? Guide the asker to update the question so it focuses on a single, specific problem. ... QFT and one of the old exam ...", "subpage_snippet": "", "source": "physics.stackexchange.com", "link": "https://physics.stackexchange.com/questions/852726/the-contribution-of-higher-order-derivatives-on-the-equations-of-motion-in-qft", "content": "Want to improve this question? Guide the asker to update the question so it focuses on a single, specific problem. ... QFT and one of the old exam ..."}
diff --git a/data/sampled_jsons/Qi_Wang_Yuan_Mi_MultiPDENet_PDE-embedded_Learning_Multi-time-stepping.jsonl b/data/sampled_jsons/Qi_Wang_Yuan_Mi_MultiPDENet_PDE-embedded_Learning_Multi-time-stepping.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cfca7a3432f68d3bc5b226f11476033ab9f7e4db
--- /dev/null
+++ b/data/sampled_jsons/Qi_Wang_Yuan_Mi_MultiPDENet_PDE-embedded_Learning_Multi-time-stepping.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "by Q Wang · 2025 — We propose a PDE-embedded network with multiscale time stepping (MultiPDENet), which fuses the scheme of numerical methods and machine learning, for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.15987", "content": "by Q Wang · 2025 — We propose a PDE-embedded network with multiscale time stepping (MultiPDENet), which fuses the scheme of numerical methods and machine learning, for ..."}
+{"idx": 1, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "by Q Wang — TL;DR: Introduced a PDE-embedded learning model with multi-time-stepping for accelerated simulation of flows. Abstract: Solving partial differential equations ( ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=D1gs8QT74m", "content": "by Q Wang — TL;DR: Introduced a PDE-embedded learning model with multi-time-stepping for accelerated simulation of flows. Abstract: Solving partial differential equations ( ..."}
+{"idx": 2, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "15 Jul 2025 — To this end, we propose a PDEembedded network with multiscale time stepping (MultiPDENet), which fuses the scheme of numerical methods and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46029", "content": "15 Jul 2025 — To this end, we propose a PDEembedded network with multiscale time stepping (MultiPDENet), which fuses the scheme of numerical methods and ..."}
+{"idx": 3, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "27 Jan 2025 — We developed MultiPDENet, a PDE-embedded network with multiscale time-stepping , for accelerated flow simulations on spatiotemporal coarse grids.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "27 Jan 2025 — We developed MultiPDENet, a PDE-embedded network with multiscale time-stepping , for accelerated flow simulations on spatiotemporal coarse grids."}
+{"idx": 4, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "We developed MultiPDENet, a PDE-embedded net- work with multiscale time-stepping , for accelerated flow simulations on spatiotemporal coarse grids. By.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/046ccccd77df13df48f47ff1081b58969771121a.pdf", "content": "We developed MultiPDENet, a PDE-embedded net- work with multiscale time-stepping , for accelerated flow simulations on spatiotemporal coarse grids. By."}
+{"idx": 5, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation. ICML 202518 Jun 2025. Qi Wang, Yuan Mi, Wang Haoyun, Yi Zhang ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/pt/chatpaper/paper/166953", "content": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation. ICML 202518 Jun 2025. Qi Wang, Yuan Mi, Wang Haoyun, Yi Zhang ..."}
+{"idx": 6, "title": "Multi-resolution partial differential equations preserved ...", "date": "", "ddg_snippet": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation · Computer Science, Engineering. ArXiv · 2025.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Multi-resolution-partial-differential-equations-for-Liu-Zhu/b2ea1eac752e48bb545bb3f7a9c6668769324018", "content": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation · Computer Science, Engineering. ArXiv · 2025."}
+{"idx": 7, "title": "Ruizhi Chengze", "date": "", "ddg_snippet": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation · no code implementations • 27 Jan 2025 • Qi Wang, Yuan Mi, Haoyun ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Ruizhi+Chengze&order_by=stars&order=desc", "content": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation · no code implementations • 27 Jan 2025 • Qi Wang, Yuan Mi, Haoyun ..."}
+{"idx": 8, "title": "Hao Sun | Papers With Code", "date": "", "ddg_snippet": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation · no code implementations • 27 Jan 2025 • Qi Wang, Yuan Mi, Haoyun ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Hao+Sun&order_by=date", "content": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation · no code implementations • 27 Jan 2025 • Qi Wang, Yuan Mi, Haoyun ..."}
+{"idx": 9, "title": "Thermodynamics-informed neural networks for physically ...", "date": "", "ddg_snippet": "Citation · PDF. Add to Library. Alert. MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation · Qi WangYuan Mi +5 authors", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/6bd7b76d63323c593557c83f3159239895d99546", "content": "Citation · PDF. Add to Library. Alert. MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation · Qi WangYuan Mi +5 authors"}
diff --git a/data/sampled_jsons/QvqnPVGWAN_Blink_of_an_eye_MATH_dataset_critical_window_frequency_correct_incorrect_responses_percen.jsonl b/data/sampled_jsons/QvqnPVGWAN_Blink_of_an_eye_MATH_dataset_critical_window_frequency_correct_incorrect_responses_percen.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b10e296f65c15bdbc79715ee0a81b8def4ba0802
--- /dev/null
+++ b/data/sampled_jsons/QvqnPVGWAN_Blink_of_an_eye_MATH_dataset_critical_window_frequency_correct_incorrect_responses_percen.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink of an eye: a simple theory for feature localization in ...", "date": "", "ddg_snippet": "To illustrate the flexibility of our bounds, we explicitly com- pute the locations and widths of these windows for dif- ferent generative models and data modalities (Section4). One such example we provide elucidates a new connec- tion between critical windows for in-context learning and the all-or-nothing phenomenon in statistical inference. 3.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QvqnPVGWAN", "content": "To illustrate the flexibility of our bounds, we explicitly com- pute the locations and widths of these windows for dif- ferent generative models and data modalities (Section4). One such example we provide elucidates a new connec- tion between critical windows for in-context learning and the all-or-nothing phenomenon in statistical inference. 3."}
+{"idx": 1, "title": "[2502.00921] Blink of an eye: a simple theory for feature ...", "date": "", "ddg_snippet": "Feb 2, 2025 · Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This phenomenon is not unique to autoregressive models: in diffusion models, key features of the final output are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "Feb 2, 2025 · Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This phenomenon is not unique to autoregressive models: in diffusion models, key features of the final output are ..."}
+{"idx": 2, "title": "(PDF) Blink of an eye: a simple theory for feature ...", "date": "", "ddg_snippet": "Feb 2, 2025 · PDF | Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched... | Find, read and cite all the research you ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "Feb 2, 2025 · PDF | Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched... | Find, read and cite all the research you ..."}
+{"idx": 3, "title": "GitHub - BiDAlab/mEBAL: mEBAL is a multimodal database for ... ADVANCES IN EYE BLINK DETECTION: EVALUATING CONVOLUTIONAL ... BLINK - GitHub Pages GitHub - BiDAlab/mEBAL: mEBAL is a multimodal database for eye bli… eyeblink - Research - blinking matters eyeblink - Research - blinking matters ADVANCES IN EYE BLINK DETECTION: EVALUATING CONVOLUTION… ADVANCES IN EYE BLINK DETECTION: EVALUATING CONVOLUTION… BLINK - GitHub Pages eyeblink - Research - blinking matters", "date": "", "ddg_snippet": "We present mEBAL [1], a multimodal database for eye blink detection and attention level estimation. mEBAL is a database which is initially introduced in arXiv technical report and then accepted by the 22nd ACM International Conference on Multimodal Interaction. The eye blink frequency is related to the cognitive activity and automatic detectors of eye blinks have been proposed for many tasks including attention level estimation, analysis of neuro-degenerative diseases, deception recognition, drive fatigue detection, or face anti-spoofing. However, most existing databases and algorithms in this area are limited to experiments involving only a few hundred samples and individual sensors like face cameras. The proposed mEBAL improves previous databases in terms of numbers samples in eye blink and cognitive information available. In particular, three different sensors are simultaneously considered: Near Infrared (NIR) and RGB cameras to capture the face gestures and an Electroencephalography (EEG) band to capture the cognitive activity of the user and blinking events. Regarding the size of mEBAL, it comprises 3,000 blink samples and the corresponding attention level from 38 different students while conducting a number of e-learning tasks of varying difficulty.This information is avalible on this web [Download Database]. The following table shows the sensors and the information captured: The following sections describe the motivation, the activities designed, the sensors, the public database and share mEBAL database with the community to advance in this area: See full list on github.com The main motivation of this work is the improvement of the virtual education using e-learnig platforms. Virtual education technologies are growing up, with a market reaching a turnover around 240.000 million dollars. E-learning platforms are important tools to get high quality online education. They allow to capture student information to better understand the student behavior and conditions and this information can be used to create personalized environments. Information such as the heart rate [2], the emotional state, or the cognitive activity can be used to improve e-learning platforms. Undoubtedly, e-learning platforms will benefit significantly by exploiting the attention level of the student. This could be used to: i) adapt dynamically the environment and content based on the attention level, and ii) improve the educational materials and resources with a posterior analysis of the e-learning sessions (e.g. detecting the type of contents more appropriate for a specific student). Since the 70s there are studies relating the eye blink rate with cognitive activity like attention. The studies suggest that lower eye blink rates can be associated to high attention periods while higher eye blink rates are related to low attention levels. Therefore, in this context, automatic eye blink detection can be a tool for estimating the attention level of the students and improving e-learning platforms. See full list on github.com The activities designed to conform the database consist of 8 different tasks that can be categorized in the following three groups: •Enrollment form: name and surname, ID number, nationality, e-mail address, etc. (low level of attention is expected); •Writing questions: these questions are oriented to measure the students’ cognitive abilities under different situations such as solving logical problems, describing images, crosswords, finding differences, etc. (increasing level of attention is expected) •Multiple choice questions: aimed to detect the students’ attention and focus levels (high level of attention is expected). See full list on github.com We designed a multimodal acquisition framework to monitor cognitive and eye blink activity during the execution of online tasks based on the edBB platform [3] for remote education assessment: The acquisition setup consisted of the next components: • An EEG headset by NeuroSky that captures 5 channels of electroencephalographic information (𝛼, 𝛽,𝛾, 𝛿, 𝜃). These signals provide temporal information related to the cognitive activity of the student. The sensor also provides a temporal sequence with the eye blink strength. The sampling rate of the band is 1 Hz. The EEG band is used to capture the cognitive activity of the student and the eye blink candidates. We have made a manual refinement of these eye blink candidates detected by the band to eliminate false positives. These refined eye blinks will be used as eye blink groundtruth. • An Intel RealSense (model D435i), which comprises 1 RGB and 2 NIR cameras. It is configured to 30 Hz, one frame every 33ms. It's known that an average blink takes 100ms to 400ms, therefore, an eye blink can take between 3 to 13 frames. See full list on github.com mEBAL comprises a total of 3,000 blink samples from both eyes acquired with 1 RGB and 2 NIR cameras. Each sample comprises 19 frames (around 600 ms.) for a total number of images of 342,000 (3,000 × 19 × 2 × 3). Aspects such as the user position and changes in the illumination were considered during the acquisition in order to simulate realistic e-learning scenarios. 11 out of the 38 students used glasses. mEBAL was collected in a constrained environment, but it is rich in pose, illumination changes, and other naturally-occurring factors. It can be seen in the next figure: The mEBAL dataset was obtained from the raw data provided in the edBBdb [3]. The eye blink and attention level information was labelled following a semi-supervised method. First, eye blink candidates were selected using the EEG band signals ( eye blink strength is an attribute provided by the EEG band SDK). Second, we made a manual refinement of the eye blink samples detected by the band to eliminate false positives. Once the eye blink samples were validated, we stored the 9 frames previous and posterior to the eye blink event (19 frames in total for each eye blink ). These frames can be used to exploit the temporal information proposed in some approaches of the literature. Finally, we used facial landmark detection to track the eye position. The video recorded during the session and the cropped eyes are provided in our contributed mEBAL database. Additionally, we include the cognitive temporal signals α, β,γ , δ, θ provided by the EEG band. The next table shows the most popular eye blink detection databases. As can be seen, all available databases comprise only a few hundred samples, however mEBAL includes 3000 blink samples: This means that is 8 times larger than HUST-LEBW database, the existing database with largest number of eye blinks. Data is critical to train end-to-end approaches such as those based on neural networks. Furthermore, mEBAL is unique in to use 3 sensors and to include the attention level. See full list on github.com 1.Download license agreement, send by email one signed and scanned copy to atvs@uam.es according to the instructions given in point 2. 2.Send an email to atvs@uam.es, as follows: Subject: [DATABASE: mEBAL] Body: Your name, e-mail, telephone number, organization, postal mail, purpose for which you will use the database, time and date at which you sent the email with the signed license agreement. 3.Once the email copy of the license agreement has been received at ATVS, you will receive an email with a username, a password, and a time slot to download the database. 4.Download the database, for which you will need to provide the authentication information given in step 4. After you finish the download, please notify by email to atvs@uam.es that you have successfully completed the transaction. See full list on github.com •[1] Daza, R.; Morales, A.; Fierrez, J.; and Tolosana, R. 2020. mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation. In ACM International Conference on Multimodal Interaction. [pdf] •[2] Hernandez-Ortega, J.; Daza, R.; Morales, A.; Fierrez, J.; and Tolosana, R. 2020. Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB. In IEEE Computers, Software, and Applications Conference. [pdf] See full list on github.com For more information contact Aythami Morales, associate professor UAM at aythami.morales@uam.es See full list on github.com s an important task by its an accurate CNN we could further the field in eye blink detection. Additionally, I have shown earing glasses, hair in the periodically moving faces increased the accuracy (Figures 5,8). However, even though I found an increase of accuracy, many mysteries still remain. BLINK evaluates a more comprehensive range of visual perception abilities, like multi-view reasoning, depth estimation, and reflectance estimation. Prior benchmarks are generally more focused on recognition-based VQA. Why is eye blink frequency important? The eye blink frequency is related to the cognitive activity and automatic detectors of eye blinks have been proposed for many tasks including attention level estimation, analysis of neuro-degenerative diseases, deception recognition, drive fatigue detection, or face anti-spoofing. How many eye blinks are recorded in ZJU dataset? ZJU dataset consists of 80 videos, each lasting few seconds. 20 individuals are recorded using 4 clips (frontal view with and without glasses (2 types) and upward view) stated to be captured at 30fps with resolution 320x240. Different numbers of the ground truth eye blinks are reported for this dataset by related work. What is a new eye blink detection algorithm? Abstract. A new eye blink detection algorithm is proposed. Motion vectors obtained by Gunnar–Farneback tracker in the eye region are analyzed using a state machine for each eye . Normalized average motion vector with standard deviation and time constraint are the input to the state machine. How accurate is eye blink detection? blink detection was introduced by Fazil and Esfenhani. Here they used the measured amount of detect the eye blink with an average accuracy between 95-100% (Fazli and Esfehani, 2012). repository(Lee, 2018). In this GitHub repository they used a data set called Closed Eyes in The (Paraskevopoulos, 2018) when programming their own neural network. Can a CNN detect eye blinks? Being able to detect eye blink is an important task by itself; therefore, with an accurate CNN we could further the field in eye blink detection . Additionally, I have shown periodically moving faces increased the accuracy (Figures 5,8). However, even though I found an increase of accuracy, many mysteries still remain. How accurate is Blink vs random guessing? While humans get 95.70% accuracy on average, BLINK is surprisingly challenging for existing multimodal LLMs: even the best-performing GPT-4V and Gemini achieve accuracies of 51.26% and 45.72%, only 13.17% and 7.63% higher than random guessing, indicating that such perception abilities have not “emerged” yet in recent multimodal LLMs. We extend the way how to evaluate eye blink detection algorithms without the impact of algorithms used for face and eye detection. We also introduce a new challenging dataset Researcher’s night, which contains more than 100 unique individuals with 1849 annotated eye blinks. It is currently the largest dataset available.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BiDAlab/mEBAL", "content": "We present mEBAL [1], a multimodal database for eye blink detection and attention level estimation. mEBAL is a database which is initially introduced in arXiv technical report and then accepted by the 22nd ACM International Conference on Multimodal Interaction. The eye blink frequency is related to the cognitive activity and automatic detectors of eye blinks have been proposed for many tasks including attention level estimation, analysis of neuro-degenerative diseases, deception recognition, drive fatigue detection, or face anti-spoofing. However, most existing databases and algorithms in this area are limited to experiments involving only a few hundred samples and individual sensors like face cameras. The proposed mEBAL improves previous databases in terms of numbers samples in eye blink and cognitive information available. In particular, three different sensors are simultaneously considered: Near Infrared (NIR) and RGB cameras to capture the face gestures and an Electroencephalography (EEG) band to capture the cognitive activity of the user and blinking events. Regarding the size of mEBAL, it comprises 3,000 blink samples and the corresponding attention level from 38 different students while conducting a number of e-learning tasks of varying difficulty.This information is avalible on this web [Download Database]. The following table shows the sensors and the information captured: The following sections describe the motivation, the activities designed, the sensors, the public database and share mEBAL database with the community to advance in this area: See full list on github.com The main motivation of this work is the improvement of the virtual education using e-learnig platforms. Virtual education technologies are growing up, with a market reaching a turnover around 240.000 million dollars. E-learning platforms are important tools to get high quality online education. They allow to capture student information to better understand the student behavior and conditions and this information can be used to create personalized environments. Information such as the heart rate [2], the emotional state, or the cognitive activity can be used to improve e-learning platforms. Undoubtedly, e-learning platforms will benefit significantly by exploiting the attention level of the student. This could be used to: i) adapt dynamically the environment and content based on the attention level, and ii) improve the educational materials and resources with a posterior analysis of the e-learning sessions (e.g. detecting the type of contents more appropriate for a specific student). Since the 70s there are studies relating the eye blink rate with cognitive activity like attention. The studies suggest that lower eye blink rates can be associated to high attention periods while higher eye blink rates are related to low attention levels. Therefore, in this context, automatic eye blink detection can be a tool for estimating the attention level of the students and improving e-learning platforms. See full list on github.com The activities designed to conform the database consist of 8 different tasks that can be categorized in the following three groups: •Enrollment form: name and surname, ID number, nationality, e-mail address, etc. (low level of attention is expected); •Writing questions: these questions are oriented to measure the students’ cognitive abilities under different situations such as solving logical problems, describing images, crosswords, finding differences, etc. (increasing level of attention is expected) •Multiple choice questions: aimed to detect the students’ attention and focus levels (high level of attention is expected). See full list on github.com We designed a multimodal acquisition framework to monitor cognitive and eye blink activity during the execution of online tasks based on the edBB platform [3] for remote education assessment: The acquisition setup consisted of the next components: • An EEG headset by NeuroSky that captures 5 channels of electroencephalographic information (𝛼, 𝛽,𝛾, 𝛿, 𝜃). These signals provide temporal information related to the cognitive activity of the student. The sensor also provides a temporal sequence with the eye blink strength. The sampling rate of the band is 1 Hz. The EEG band is used to capture the cognitive activity of the student and the eye blink candidates. We have made a manual refinement of these eye blink candidates detected by the band to eliminate false positives. These refined eye blinks will be used as eye blink groundtruth. • An Intel RealSense (model D435i), which comprises 1 RGB and 2 NIR cameras. It is configured to 30 Hz, one frame every 33ms. It's known that an average blink takes 100ms to 400ms, therefore, an eye blink can take between 3 to 13 frames. See full list on github.com mEBAL comprises a total of 3,000 blink samples from both eyes acquired with 1 RGB and 2 NIR cameras. Each sample comprises 19 frames (around 600 ms.) for a total number of images of 342,000 (3,000 × 19 × 2 × 3). Aspects such as the user position and changes in the illumination were considered during the acquisition in order to simulate realistic e-learning scenarios. 11 out of the 38 students used glasses. mEBAL was collected in a constrained environment, but it is rich in pose, illumination changes, and other naturally-occurring factors. It can be seen in the next figure: The mEBAL dataset was obtained from the raw data provided in the edBBdb [3]. The eye blink and attention level information was labelled following a semi-supervised method. First, eye blink candidates were selected using the EEG band signals ( eye blink strength is an attribute provided by the EEG band SDK). Second, we made a manual refinement of the eye blink samples detected by the band to eliminate false positives. Once the eye blink samples were validated, we stored the 9 frames previous and posterior to the eye blink event (19 frames in total for each eye blink ). These frames can be used to exploit the temporal information proposed in some approaches of the literature. Finally, we used facial landmark detection to track the eye position. The video recorded during the session and the cropped eyes are provided in our contributed mEBAL database. Additionally, we include the cognitive temporal signals α, β,γ , δ, θ provided by the EEG band. The next table shows the most popular eye blink detection databases. As can be seen, all available databases comprise only a few hundred samples, however mEBAL includes 3000 blink samples: This means that is 8 times larger than HUST-LEBW database, the existing database with largest number of eye blinks. Data is critical to train end-to-end approaches such as those based on neural networks. Furthermore, mEBAL is unique in to use 3 sensors and to include the attention level. See full list on github.com 1.Download license agreement, send by email one signed and scanned copy to atvs@uam.es according to the instructions given in point 2. 2.Send an email to atvs@uam.es, as follows: Subject: [DATABASE: mEBAL] Body: Your name, e-mail, telephone number, organization, postal mail, purpose for which you will use the database, time and date at which you sent the email with the signed license agreement. 3.Once the email copy of the license agreement has been received at ATVS, you will receive an email with a username, a password, and a time slot to download the database. 4.Download the database, for which you will need to provide the authentication information given in step 4. After you finish the download, please notify by email to atvs@uam.es that you have successfully completed the transaction. See full list on github.com •[1] Daza, R.; Morales, A.; Fierrez, J.; and Tolosana, R. 2020. mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation. In ACM International Conference on Multimodal Interaction. [pdf] •[2] Hernandez-Ortega, J.; Daza, R.; Morales, A.; Fierrez, J.; and Tolosana, R. 2020. Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB. In IEEE Computers, Software, and Applications Conference. [pdf] See full list on github.com For more information contact Aythami Morales, associate professor UAM at aythami.morales@uam.es See full list on github.com s an important task by its an accurate CNN we could further the field in eye blink detection. Additionally, I have shown earing glasses, hair in the periodically moving faces increased the accuracy (Figures 5,8). However, even though I found an increase of accuracy, many mysteries still remain. BLINK evaluates a more comprehensive range of visual perception abilities, like multi-view reasoning, depth estimation, and reflectance estimation. Prior benchmarks are generally more focused on recognition-based VQA. Why is eye blink frequency important? The eye blink frequency is related to the cognitive activity and automatic detectors of eye blinks have been proposed for many tasks including attention level estimation, analysis of neuro-degenerative diseases, deception recognition, drive fatigue detection, or face anti-spoofing. How many eye blinks are recorded in ZJU dataset? ZJU dataset consists of 80 videos, each lasting few seconds. 20 individuals are recorded using 4 clips (frontal view with and without glasses (2 types) and upward view) stated to be captured at 30fps with resolution 320x240. Different numbers of the ground truth eye blinks are reported for this dataset by related work. What is a new eye blink detection algorithm? Abstract. A new eye blink detection algorithm is proposed. Motion vectors obtained by Gunnar–Farneback tracker in the eye region are analyzed using a state machine for each eye . Normalized average motion vector with standard deviation and time constraint are the input to the state machine. How accurate is eye blink detection? blink detection was introduced by Fazil and Esfenhani. Here they used the measured amount of detect the eye blink with an average accuracy between 95-100% (Fazli and Esfehani, 2012). repository(Lee, 2018). In this GitHub repository they used a data set called Closed Eyes in The (Paraskevopoulos, 2018) when programming their own neural network. Can a CNN detect eye blinks? Being able to detect eye blink is an important task by itself; therefore, with an accurate CNN we could further the field in eye blink detection . Additionally, I have shown periodically moving faces increased the accuracy (Figures 5,8). However, even though I found an increase of accuracy, many mysteries still remain. How accurate is Blink vs random guessing? While humans get 95.70% accuracy on average, BLINK is surprisingly challenging for existing multimodal LLMs: even the best-performing GPT-4V and Gemini achieve accuracies of 51.26% and 45.72%, only 13.17% and 7.63% higher than random guessing, indicating that such perception abilities have not “emerged” yet in recent multimodal LLMs. We extend the way how to evaluate eye blink detection algorithms without the impact of algorithms used for face and eye detection. We also introduce a new challenging dataset Researcher’s night, which contains more than 100 unique individuals with 1849 annotated eye blinks. It is currently the largest dataset available."}
+{"idx": 4, "title": "ADVANCES IN EYE BLINK DETECTION: EVALUATING CONVOLUTIONAL ...", "date": "", "ddg_snippet": "s an important task by its an accurate CNN we could further the field in eye blink detection. Additionally, I have shown earing glasses, hair in the periodically moving faces increased the accuracy (Figures 5,8). However, even though I found an increase of accuracy, many mysteries still remain.", "subpage_snippet": "", "source": "repository.arizona.edu", "link": "https://repository.arizona.edu/bitstream/handle/10150/650914/azu_etd_hr_2020_0012_sip1_m.pdf?sequence=1", "content": "s an important task by its an accurate CNN we could further the field in eye blink detection. Additionally, I have shown earing glasses, hair in the periodically moving faces increased the accuracy (Figures 5,8). However, even though I found an increase of accuracy, many mysteries still remain."}
+{"idx": 5, "title": "BLINK - GitHub Pages", "date": "", "ddg_snippet": "BLINK evaluates a more comprehensive range of visual perception abilities, like multi-view reasoning, depth estimation, and reflectance estimation. Prior benchmarks are generally more focused on recognition-based VQA.", "subpage_snippet": "", "source": "zeyofu.github.io", "link": "https://zeyofu.github.io/blink/", "content": "BLINK evaluates a more comprehensive range of visual perception abilities, like multi-view reasoning, depth estimation, and reflectance estimation. Prior benchmarks are generally more focused on recognition-based VQA."}
+{"idx": 6, "title": "eyeblink - Research - blinking matters", "date": "", "ddg_snippet": "We extend the way how to evaluate eye blink detection algorithms without the impact of algorithms used for face and eye detection. We also introduce a new challenging dataset Researcher’s night, which contains more than 100 unique individuals with 1849 annotated eye blinks. It is currently the largest dataset available.", "subpage_snippet": "", "source": "www.blinkingmatters.com", "link": "https://www.blinkingmatters.com/research", "content": "We extend the way how to evaluate eye blink detection algorithms without the impact of algorithms used for face and eye detection. We also introduce a new challenging dataset Researcher’s night, which contains more than 100 unique individuals with 1849 annotated eye blinks. It is currently the largest dataset available."}
+{"idx": 7, "title": "Percentage - Formula | How To Calculate Percentage ?", "date": "", "ddg_snippet": "Percentage increase refers to the percentage change in the value when it is increased over a period of time. For example, population increase , increase in the number of bacteria on a surface, etc. Percentage Increase = ( Increased Value-Original value)/Original value × 100.", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/commercial-math/percentages/", "content": "Percentage increase refers to the percentage change in the value when it is increased over a period of time. For example, population increase , increase in the number of bacteria on a surface, etc. Percentage Increase = ( Increased Value-Original value)/Original value × 100."}
+{"idx": 8, "title": "Percentage Increase Calculator", "date": "", "ddg_snippet": "Free. Windows , Mac OS, Linux, iOS, Android. Percentage increase calculator finds the increase from one value to another as a percentage of the first value. Shows you how to find percentage increase with percent increase formula.", "subpage_snippet": "", "source": "www.calculatorsoup.com", "link": "https://www.calculatorsoup.com/calculators/algebra/percentage-increase-calculator.php", "content": "Free. Windows , Mac OS, Linux, iOS, Android. Percentage increase calculator finds the increase from one value to another as a percentage of the first value. Shows you how to find percentage increase with percent increase formula."}
+{"idx": 9, "title": "Google Sheets Percentage Formula (5+ Easy Examples)", "date": "", "ddg_snippet": "Increase and Decrease Numbers by a Percentage . Conclusion. What is a Percentage ? Expressing quantities in percentages always helps give a better sense of proportion.", "subpage_snippet": "", "source": "spreadsheetpoint.com", "link": "https://spreadsheetpoint.com/calculate-percentage-in-google-sheets/", "content": "Increase and Decrease Numbers by a Percentage . Conclusion. What is a Percentage ? Expressing quantities in percentages always helps give a better sense of proportion."}
diff --git a/data/sampled_jsons/Qwen2-VL_L-2_capability_scores_Table_2_XLRS-Bench_38.99_arithmetic_average.jsonl b/data/sampled_jsons/Qwen2-VL_L-2_capability_scores_Table_2_XLRS-Bench_38.99_arithmetic_average.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3fbea6451af2e49679bfc4ef4c3f4242a7333603
--- /dev/null
+++ b/data/sampled_jsons/Qwen2-VL_L-2_capability_scores_Table_2_XLRS-Bench_38.99_arithmetic_average.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Qwen - Wikipedia", "date": "", "ddg_snippet": "Toggle the table of contents. Qwen.The Qwen-VL series is a line of visual language models that combines a vision transformer with a LLM.[3][14] Alibaba released Qwen 2 - VL with variants of 2 billion and 7 billion parameters.[15][16][17].", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Qwen", "content": "Toggle the table of contents. Qwen.The Qwen-VL series is a line of visual language models that combines a vision transformer with a LLM.[3][14] Alibaba released Qwen 2 - VL with variants of 2 billion and 7 billion parameters.[15][16][17]."}
+{"idx": 1, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand Extremely ...", "date": "", "ddg_snippet": "Table 2 compares various mod-els on the different four L-2 dimensions of perception and reasoning abilities, respectively. Qwen2-VL excels in both English and Chinese proficiency, outperforming both pro-prietary and most open-source models.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "Table 2 compares various mod-els on the different four L-2 dimensions of perception and reasoning abilities, respectively. Qwen2-VL excels in both English and Chinese proficiency, outperforming both pro-prietary and most open-source models."}
+{"idx": 2, "title": "GitHub - AI9Stars/XLRS-Bench: [CVPR 2025 HIghlight] XLRS ...", "date": "", "ddg_snippet": "Apr 1, 2025 · We present XLRS-Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars/XLRS-Bench", "content": "Apr 1, 2025 · We present XLRS-Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts."}
+{"idx": 3, "title": "Qwen/Qwen2-VL-2B-Instruct · Hugging Face", "date": "", "ddg_snippet": "Qwen2-VL -2B-Instruct Introduction We're excited to unveil Qwen2-VL , the latest iteration of our Qwen- VL model, representing nearly a year of innovation. What’s New in Qwen2-VL ? Key Enhancements: SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct", "content": "Qwen2-VL -2B-Instruct Introduction We're excited to unveil Qwen2-VL , the latest iteration of our Qwen- VL model, representing nearly a year of innovation. What’s New in Qwen2-VL ? Key Enhancements: SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc ..."}
+{"idx": 4, "title": "XLRS-Bench", "date": "", "ddg_snippet": "We present XLRS-Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts.", "subpage_snippet": "", "source": "xlrs-bench.github.io", "link": "https://xlrs-bench.github.io/home_page.html", "content": "We present XLRS-Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts."}
+{"idx": 5, "title": "Qwen2-VL: Enhancing Vision-Language Model’s Perception of the ...", "date": "", "ddg_snippet": "As shown in Table 5, the performance of Qwen2-VL in both Type Match (93.1 vs. 90. 2 ) and Exact Match (53. 2 vs. 50.0) over GPT-4o substantiates the efficacy of Qwen2-VL ’s capability in function calling, thereby underscoring its significant potential for application expansion through external tool integration.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.12191v1", "content": "As shown in Table 5, the performance of Qwen2-VL in both Type Match (93.1 vs. 90. 2 ) and Exact Match (53. 2 vs. 50.0) over GPT-4o substantiates the efficacy of Qwen2-VL ’s capability in function calling, thereby underscoring its significant potential for application expansion through external tool integration."}
+{"idx": 6, "title": "[2503.23771] XLRS-Bench: Could Your Multimodal LLMs ...", "date": "", "ddg_snippet": "Mar 31, 2025 · Abstract page for arXiv paper 2503.23771: XLRS-Bench : Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.23771", "content": "Mar 31, 2025 · Abstract page for arXiv paper 2503.23771: XLRS-Bench : Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?"}
+{"idx": 7, "title": "TIGER-Lab/VideoScore-Qwen2-VL · Hugging Face", "date": "", "ddg_snippet": "🧐🧐 VideoScore- Qwen2-VL is a variant from VideoScore, taking Qwen2-VL as base model, and trained on VideoFeedback dataset. VideoScore series is a video quality evaluation model series, taking Mantis-8B-Idefics2 or Qwen/ Qwen2-VL as base-model and trained on VideoFeedback, a large video evaluation dataset with multi-aspect human scores .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/TIGER-Lab/VideoScore-Qwen2-VL", "content": "🧐🧐 VideoScore- Qwen2-VL is a variant from VideoScore, taking Qwen2-VL as base model, and trained on VideoFeedback dataset. VideoScore series is a video quality evaluation model series, taking Mantis-8B-Idefics2 or Qwen/ Qwen2-VL as base-model and trained on VideoFeedback, a large video evaluation dataset with multi-aspect human scores ."}
+{"idx": 8, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "31 Mar 2025 — Table 2 compares various models on the different four L - 2 dimensions of perception and reasoning abilities, respectively. Qwen2 - VL excels in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.23771v1", "content": "31 Mar 2025 — Table 2 compares various models on the different four L - 2 dimensions of perception and reasoning abilities, respectively. Qwen2 - VL excels in ..."}
+{"idx": 9, "title": "Qwen 2 .5 VL ! Qwen 2 .5 VL ! Qwen 2 .5 VL ! | Qwen", "date": "", "ddg_snippet": "Qwen 2 .5- VL ’s video comprehension capabilities have been comprehensively upgraded.Watch the video and list the paper titles in a table . Qwen 2 .5- VL .", "subpage_snippet": "", "source": "qwenlm.github.io", "link": "https://qwenlm.github.io/blog/qwen2.5-vl/", "content": "Qwen 2 .5- VL ’s video comprehension capabilities have been comprehensively upgraded.Watch the video and list the paper titles in a table . Qwen 2 .5- VL ."}
diff --git a/data/sampled_jsons/RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_Yujie_Zhao.jsonl b/data/sampled_jsons/RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_Yujie_Zhao.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..78eb18b93fcd6229c35735d61c05f812497502eb
--- /dev/null
+++ b/data/sampled_jsons/RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_Yujie_Zhao.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.23569] RA - PbRL : Provably Efficient Risk - Aware ...", "date": "", "ddg_snippet": "Title: RA - PbRL : Provably Efficient Risk - Aware Preference - Based Reinforcement Learning .Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Title: RA - PbRL : Provably Efficient Risk - Aware Preference - Based Reinforcement Learning .Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions."}
+{"idx": 1, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Yujie Zhao , Jose Aguilar Escamilla, Weyl Lu, Huazheng Wang. Abstract. Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "Yujie Zhao , Jose Aguilar Escamilla, Weyl Lu, Huazheng Wang. Abstract. Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be..."}
+{"idx": 2, "title": "RA - PbRL : Provably Efficient Risk - Aware", "date": "", "ddg_snippet": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based Reinforcement Learning . Provable offline preference - based reinforcement learning . arXiv preprint arXiv:2305.14816, 2023.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JNDcFOczOf", "content": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based Reinforcement Learning . Provable offline preference - based reinforcement learning . arXiv preprint arXiv:2305.14816, 2023."}
+{"idx": 3, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ra-pbrl-provably-efficient-risk-aware-preference", "content": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 4, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/RA-PbRL:-Provably-Efficient-Risk-Aware-Preference-Based-Reinforcement-Learning-baf1c16a-6c49-45cc-9e7e-b12a3550842b", "content": "At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators."}
+{"idx": 5, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/JNDcFOczOf@OpenReview", "content": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 6, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "RA - PbRL introduces a provably efficient algorithm for risk - aware preference - based reinforcement learning , addressing the limitations of existing risk-neutral methods in applications demanding heighten…", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "RA - PbRL introduces a provably efficient algorithm for risk - aware preference - based reinforcement learning , addressing the limitations of existing risk-neutral methods in applications demanding heighten…"}
+{"idx": 7, "title": "GitHub - aguilarjose11/PbRLNeurips: code repository for upcomming...", "date": "", "ddg_snippet": "Code for paper \" RA - PbRL : Provably Efficient Risk - Aware Preference - Based Reinforcement Learning \".Bellow is a description and pseudo-code used to implement algorithms with the experiment API. Risk - Aware Preference -baser Reinforcement Learning ( RA - PbRL ).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "Code for paper \" RA - PbRL : Provably Efficient Risk - Aware Preference - Based Reinforcement Learning \".Bellow is a description and pseudo-code used to implement algorithms with the experiment API. Risk - Aware Preference -baser Reinforcement Learning ( RA - PbRL )."}
+{"idx": 8, "title": "Human-in-the-loop: Provably Efficient Preference - based ...", "date": "", "ddg_snippet": "Human-in-the-loop: Provably Efficient Preference - based Reinforcement Learning with General Function Approximation.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag.html", "content": "Human-in-the-loop: Provably Efficient Preference - based Reinforcement Learning with General Function Approximation."}
+{"idx": 9, "title": "Human-in-the-loop: Provably Efficient Preference - based ...", "date": "", "ddg_snippet": "Human-in-the-loop: Provably Efficient Preference - based Reinforcement Learning with General Function Approximation. Proceedings of Machine Learning Research, 162, 3773-3793.", "subpage_snippet": "", "source": "www.scholars.northwestern.edu", "link": "https://www.scholars.northwestern.edu/en/publications/human-in-the-loop-provably-efficient-preference-based-reinforceme", "content": "Human-in-the-loop: Provably Efficient Preference - based Reinforcement Learning with General Function Approximation. Proceedings of Machine Learning Research, 162, 3773-3793."}
diff --git a/data/sampled_jsons/RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_conclusion_linear_rewa.jsonl b/data/sampled_jsons/RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_conclusion_linear_rewa.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3bf4031c43c3ffcd814dfa525b86996f232fc154
--- /dev/null
+++ b/data/sampled_jsons/RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_conclusion_linear_rewa.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Sep 26, 2024 · In preference-based reinforcement learning ( PbRL ), where rewards are implicitly defined via preferences over trajectories, quantile risk offers a powerful tool to ensure safer and more robust behavior by focusing on minimizing the risk associated with the lower quantiles of the trajectory reward distribution.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "Sep 26, 2024 · In preference-based reinforcement learning ( PbRL ), where rewards are implicitly defined via preferences over trajectories, quantile risk offers a powerful tool to ensure safer and more robust behavior by focusing on minimizing the risk associated with the lower quantiles of the trajectory reward distribution."}
+{"idx": 1, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Oct 31, 2024 · Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Oct 31, 2024 · Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 2, "title": "RA-PbRL: Provably Eficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Despite its performance, a significant limitation of the standard RL paradigm is the selection of a state-action reward function. In many real-world scenarios, constructing an explicit reward function is often a complex or unfeasible task. As a compelling alternative, Preference-based Reinforcement Learning ( PbRL ) [Busa-Fekete et al., 2014, Wirth et al., 2016] addresses this challenge by ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "Despite its performance, a significant limitation of the standard RL paradigm is the selection of a state-action reward function. In many real-world scenarios, constructing an explicit reward function is often a complex or unfeasible task. As a compelling alternative, Preference-based Reinforcement Learning ( PbRL ) [Busa-Fekete et al., 2014, Wirth et al., 2016] addresses this challenge by ..."}
+{"idx": 3, "title": "RA-PbRL | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "Jun 5, 2025 · RA - PbRL : provably efficient risk - aware preference - based reinforcement learning", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3739861", "content": "Jun 5, 2025 · RA - PbRL : provably efficient risk - aware preference - based reinforcement learning"}
+{"idx": 4, "title": "Risk-Aware Preference-baser Reinforcement Learning (RA-PbRL)", "date": "", "ddg_snippet": "RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties. The intuition behind the algorithm depends on the idea of confidence bounds. The ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties. The intuition behind the algorithm depends on the idea of confidence bounds. The ..."}
+{"idx": 5, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "Sep 25, 2024 · Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf", "content": "Sep 25, 2024 · Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators ..."}
+{"idx": 6, "title": "Efficient Preference-Based Reinforcement Learning Using ...", "date": "", "ddg_snippet": "May 29, 2023 · Preference-based reinforcement learning ( PbRL ) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10161081", "content": "May 29, 2023 · Preference-based reinforcement learning ( PbRL ) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ..."}
+{"idx": 7, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "We also introduce Risk - Aware -PbRL ( RA - PbRL ), an algorithm designed to optimize both nested and static objectives.We propose a provably efficient algorithm, Risk - Aware -PbRL( RA - PbRL ), with theoretical and empirical results on its performance and risk - awareness .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v3", "content": "We also introduce Risk - Aware -PbRL ( RA - PbRL ), an algorithm designed to optimize both nested and static objectives.We propose a provably efficient algorithm, Risk - Aware -PbRL( RA - PbRL ), with theoretical and empirical results on its performance and risk - awareness ."}
+{"idx": 8, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Abstract. Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "Abstract. Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement ..."}
+{"idx": 9, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ra-pbrl-provably-efficient-risk-aware-preference", "content": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
diff --git a/data/sampled_jsons/RA-PbRL_paper_technical_challenges_adapting_risk_measures_quantile_estimation_non-stationarity_sampl.jsonl b/data/sampled_jsons/RA-PbRL_paper_technical_challenges_adapting_risk_measures_quantile_estimation_non-stationarity_sampl.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7aa0a4a1da86878f4a54986cf770f543f6ffec55
--- /dev/null
+++ b/data/sampled_jsons/RA-PbRL_paper_technical_challenges_adapting_risk_measures_quantile_estimation_non-stationarity_sampl.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Percentile - Wikipedia", "date": "", "ddg_snippet": "Percentiles are a type of quantiles , obtained by a subdivision into 100 groups. The 25th percentile is also known as the first quartile (Q1), the 50th percentile as the median or second quartile (Q2), and the 75th percentile as the third quartile (Q3).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Percentile", "content": "Percentiles are a type of quantiles , obtained by a subdivision into 100 groups. The 25th percentile is also known as the first quartile (Q1), the 50th percentile as the median or second quartile (Q2), and the 75th percentile as the third quartile (Q3)."}
+{"idx": 1, "title": "Testing non - stationarity of spatial relationships under the multiscale...", "date": "", "ddg_snippet": "Testing spatial non - stationarity is a fundamental research topic in geographical analysis. Ignoring the multiscale effects may mislead the non - stationarity .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10109-025-00474-3", "content": "Testing spatial non - stationarity is a fundamental research topic in geographical analysis. Ignoring the multiscale effects may mislead the non - stationarity ."}
+{"idx": 2, "title": "Statistical bounds for entropic optimal transport: Sample complexity ...", "date": "", "ddg_snippet": "First, through a new sample complexity result we establish the rate of convergence of entropic OT for empirical measures .", "subpage_snippet": "", "source": "nyuscholars.nyu.edu", "link": "https://nyuscholars.nyu.edu/en/publications/statistical-bounds-for-entropic-optimal-transport-sample-complexi", "content": "First, through a new sample complexity result we establish the rate of convergence of entropic OT for empirical measures ."}
+{"idx": 3, "title": "Conditional Prior-based Non -stationary Channel Estimation Using...", "date": "", "ddg_snippet": "This paper addresses non -stationary channel estimation by learning a conditional prior from a causal window of chan-nel observations. The prior is injected into the reverse diffu-sion process to align the denoising updates with the channel’s current coherence length.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15182", "content": "This paper addresses non -stationary channel estimation by learning a conditional prior from a causal window of chan-nel observations. The prior is injected into the reverse diffu-sion process to align the denoising updates with the channel’s current coherence length."}
+{"idx": 4, "title": "Approaching Risk Management Assignments on MATLAB", "date": "", "ddg_snippet": "In- sample and out-of- sample backtested resultsPerformance analysis using risk measures such as Value-at- Risk (VaR), Sharpe ratio, and drawdownA detailed risk assessment of market exposure and liquidity challenges", "subpage_snippet": "", "source": "www.matlabassignmentexperts.com", "link": "https://www.matlabassignmentexperts.com/blog/risk-management-assignments-using-matlab.html", "content": "In- sample and out-of- sample backtested resultsPerformance analysis using risk measures such as Value-at- Risk (VaR), Sharpe ratio, and drawdownA detailed risk assessment of market exposure and liquidity challenges"}
+{"idx": 5, "title": "machinelearningmastery.com/k-fold-cross-validation", "date": "", "ddg_snippet": "Hold Out Sample Validation.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/k-fold-cross-validation/", "content": "Hold Out Sample Validation."}
+{"idx": 6, "title": "AI Detector - Trusted AI Checker (Free & No SignUp)", "date": "", "ddg_snippet": "Stay Compliant Ensure your reports, academic papers , or creative work are not mistakenly flagged as AI generated by an AI checker. One-Click Humanizer Easily convert AI text into 100% human like content with a single click. Free, No Login, Minimal Limits Our AI content detector is free...", "subpage_snippet": "", "source": "decopy.ai", "link": "https://decopy.ai/ai-detector/", "content": "Stay Compliant Ensure your reports, academic papers , or creative work are not mistakenly flagged as AI generated by an AI checker. One-Click Humanizer Easily convert AI text into 100% human like content with a single click. Free, No Login, Minimal Limits Our AI content detector is free..."}
+{"idx": 7, "title": "sciencedirect.com/journal/international-journal-of-forecasting", "date": "", "ddg_snippet": "Call for papers .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/journal/international-journal-of-forecasting", "content": "Call for papers ."}
+{"idx": 8, "title": "Can AI Create a Profitable Python Trading Bot for Stocks? An In-Depth...", "date": "", "ddg_snippet": "This guide explores whether AI can genuinely create profitable Python trading bots for stocks, offering an in-depth look at the tools, techniques , and challenges involved.", "subpage_snippet": "", "source": "trading-strategies.academy", "link": "https://trading-strategies.academy/archives/1648", "content": "This guide explores whether AI can genuinely create profitable Python trading bots for stocks, offering an in-depth look at the tools, techniques , and challenges involved."}
+{"idx": 9, "title": "Historical Consciousness: What Germany Could Learn from Russia", "date": "", "ddg_snippet": "Secondly, this order removed the decision to murder from the jurisdiction of the courts-martial and summary courts. Thirdly, against localities that resisted the occupation—which was probably every locality that was attacked—collective measures of violence were ordered, i.e., genocide.", "subpage_snippet": "", "source": "sonar21.com", "link": "https://sonar21.com/historical-consciousness-what-germany-could-learn-from-russia/", "content": "Secondly, this order removed the decision to murder from the jurisdiction of the courts-martial and summary courts. Thirdly, against localities that resisted the occupation—which was probably every locality that was attacked—collective measures of violence were ordered, i.e., genocide."}
diff --git a/data/sampled_jsons/RA-PbRL_risk_aversion_parameter_alpha_theoretical_explanation_regret.jsonl b/data/sampled_jsons/RA-PbRL_risk_aversion_parameter_alpha_theoretical_explanation_regret.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ffd5174472998e140e3b9b45e4f5d7a29331d072
--- /dev/null
+++ b/data/sampled_jsons/RA-PbRL_risk_aversion_parameter_alpha_theoretical_explanation_regret.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Prospect theory - Wikipedia", "date": "", "ddg_snippet": "When faced with a risky choice leading to gains, agents are risk averse , preferring a certain outcome with a lower expected utility (i.e., the value function is concave). In the example, agents will choose the certain $450 even though the expected utility of the risky gain is higher.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Prospect_theory", "content": "When faced with a risky choice leading to gains, agents are risk averse , preferring a certain outcome with a lower expected utility (i.e., the value function is concave). In the example, agents will choose the certain $450 even though the expected utility of the risky gain is higher."}
+{"idx": 1, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Sep 26, 2024 · Theoretical analysis demonstrates sublinear regret bounds, proving the algorithm’s efficiency. Empirical evaluations on various tasks confirm RA-PbRL ’s superior performance compared to risk -neutral baselines, highlighting its practical value in safety-critical applications.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "Sep 26, 2024 · Theoretical analysis demonstrates sublinear regret bounds, proving the algorithm’s efficiency. Empirical evaluations on various tasks confirm RA-PbRL ’s superior performance compared to risk -neutral baselines, highlighting its practical value in safety-critical applications."}
+{"idx": 2, "title": "RA-PbRL: Provably Eficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Risk -aware PbRL tackles this challenge by directly aiming to decrease the harmfulness directly, rather than indirectly as with human feedback fine-tuning. Despite substantial evidence highlighting the importance of risk awareness in PbRL , a significant gap persists in the theoretical analysis and formal substantiation of risk -aware PbRL approaches.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "Risk -aware PbRL tackles this challenge by directly aiming to decrease the harmfulness directly, rather than indirectly as with human feedback fine-tuning. Despite substantial evidence highlighting the importance of risk awareness in PbRL , a significant gap persists in the theoretical analysis and formal substantiation of risk -aware PbRL approaches."}
+{"idx": 3, "title": "[2410.23569] RA-PbRL: Provably Efficient Risk-Aware ...", "date": "", "ddg_snippet": "Oct 31, 2024 · We also introduce Risk -AwarePbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Oct 31, 2024 · We also introduce Risk -AwarePbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings."}
+{"idx": 4, "title": "RA-PbRL | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "Jun 5, 2025 · We also introduce Risk -Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3739861", "content": "Jun 5, 2025 · We also introduce Risk -Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings."}
+{"idx": 5, "title": "Risk-Aware Preference-baser Reinforcement Learning (RA-PbRL)", "date": "", "ddg_snippet": "RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties."}
+{"idx": 6, "title": "Navigating Risk Aversion and Regret - MDPI", "date": "", "ddg_snippet": "May 11, 2024 · This means that regret makes a more balanced distribution, and regret utility is more conservative (not biased). In addition, preliminary empirical research was performed in which people were asked risk preference or averseness questions, and their risk averseness was calculated by using the CRRA (Constant Relative Risk Aversion ) utility function.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7072/12/2/46", "content": "May 11, 2024 · This means that regret makes a more balanced distribution, and regret utility is more conservative (not biased). In addition, preliminary empirical research was performed in which people were asked risk preference or averseness questions, and their risk averseness was calculated by using the CRRA (Constant Relative Risk Aversion ) utility function."}
+{"idx": 7, "title": "ACTIVE PORTFOLIO CONSTRUCTION WHEN RISK AND ALPHA FACTORS ARE ...", "date": "", "ddg_snippet": "24.1 Introduction When looking at active portfolio optimization, there are three main ingredients: The portfolio manager’s return forecasts (alphas), a portfolio risk forecast and a risk aversion parameter . In an ideal world, one factor model should account for both risk and return forecasts. In practice, however, portfolio managers use different models to forecast risk and alphadin general ...", "subpage_snippet": "", "source": "www.msci.com", "link": "https://www.msci.com/documents/10199/c6e5e3f7-cd44-4322-aeb5-331e20e2afb7", "content": "24.1 Introduction When looking at active portfolio optimization, there are three main ingredients: The portfolio manager’s return forecasts (alphas), a portfolio risk forecast and a risk aversion parameter . In an ideal world, one factor model should account for both risk and return forecasts. In practice, however, portfolio managers use different models to forecast risk and alphadin general ..."}
+{"idx": 8, "title": "RA - PbRL : Provably Efficient Risk -Aware Preference-Based...", "date": "", "ddg_snippet": "We also introduce Risk -Aware-PbRL ( RA - PbRL ), an algorithm designed to optimize both nested and static objectives.This change to the bellman equation disrupts calculations on regret , making risk -neutral PbRL inapplicable. This is primarily due to the additional parameter .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v3", "content": "We also introduce Risk -Aware-PbRL ( RA - PbRL ), an algorithm designed to optimize both nested and static objectives.This change to the bellman equation disrupts calculations on regret , making risk -neutral PbRL inapplicable. This is primarily due to the additional parameter ."}
+{"idx": 9, "title": "RA - PbRL : Provably Efficient Risk -Aware", "date": "", "ddg_snippet": "We also introduce Risk -Aware-PbRL ( RA - PbRL ), an algorithm designed to optimize both nested and static objec-tives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JNDcFOczOf", "content": "We also introduce Risk -Aware-PbRL ( RA - PbRL ), an algorithm designed to optimize both nested and static objec-tives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes..."}
diff --git a/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Table_1_Figure_3_year_2024.jsonl b/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Table_1_Figure_3_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e3de3a9837610a8c3a2718fdeec76438dfbaf2bc
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Table_1_Figure_3_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Building Contextual RAG Systems with Hybrid Search &", "date": "", "ddg_snippet": "... article we will focus particularly on solving the limitations of naive RAG systems in terms of adding contextual information to document chunks and ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2024/12/contextual-rag-systems-with-hybrid-search-and-reranking/", "content": "... article we will focus particularly on solving the limitations of naive RAG systems in terms of adding contextual information to document chunks and ..."}
+{"idx": 1, "title": "Traditional RAG to Graph RAG: The Evolution of Retrieval Systems", "date": "", "ddg_snippet": "... RAG systems work by achieving a sense of comprehension mostly by virtue of semantic similarity; however, this apparent strength is undermined by one ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/03/traditional-rag-vs-graph-rag/", "content": "... RAG systems work by achieving a sense of comprehension mostly by virtue of semantic similarity; however, this apparent strength is undermined by one ..."}
+{"idx": 2, "title": "What are Vision RAG Models? - Analytics Vidhya", "date": "", "ddg_snippet": "... RAG , which is geared toward text retrieval and generation, vision RAG uses Vision Language Models (VLMs) to index, retrieve, and process information ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/05/vision-rag/", "content": "... RAG , which is geared toward text retrieval and generation, vision RAG uses Vision Language Models (VLMs) to index, retrieve, and process information ..."}
+{"idx": 3, "title": "Implementing RAG: Application of OceanBase Database at CUSRI |", "date": "", "ddg_snippet": "On top of that, a RAG -based Q&A system is introduced to enhance the LLM s comprehension and communication capabilities for specific questions by ...", "subpage_snippet": "", "source": "oceanbase.github.io", "link": "https://oceanbase.github.io/docs/blogs/users/RAG-CUSRI", "content": "On top of that, a RAG -based Q&A system is introduced to enhance the LLM s comprehension and communication capabilities for specific questions by ..."}
+{"idx": 4, "title": "Real-Time RAG for the Identification of Supply Chain", "date": "", "ddg_snippet": "... and τ \\tau we can get closer to achieving a real-time system , capable of reasoning against information and events without the knowledge gap inherent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10469v1", "content": "... and τ \\tau we can get closer to achieving a real-time system , capable of reasoning against information and events without the knowledge gap inherent ..."}
+{"idx": 5, "title": "Beyond Isolated Dots: Benchmarking Structured Table", "date": "", "ddg_snippet": "... and Organized Extraction Benchmark (AOE), a new bilingual benchmark with data and documents of varying lengths designed to systematically evaluate the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16271v1", "content": "... and Organized Extraction Benchmark (AOE), a new bilingual benchmark with data and documents of varying lengths designed to systematically evaluate the ..."}
+{"idx": 6, "title": "LangChain vs LlamaIndex: In-Depth Comparison and Use", "date": "", "ddg_snippet": "... of indexing and retrieving the relevant information with the advanced search. LlamaIndex primarily focuses on storing, structuring, and accessing ...", "subpage_snippet": "", "source": "www.deepchecks.com", "link": "https://www.deepchecks.com/langchain-vs-llamaindex-depth-comparison-use/", "content": "... of indexing and retrieving the relevant information with the advanced search. LlamaIndex primarily focuses on storing, structuring, and accessing ..."}
+{"idx": 7, "title": "Meryem Arik on LLM Deployment, State-of-the-Art RAG Apps, and", "date": "", "ddg_snippet": "In terms of regulatory compliance standards, we need to strive towards a regulatory alignment between the EU, Britain, the U.S, and Asia.", "subpage_snippet": "", "source": "www.infoq.com", "link": "https://www.infoq.com/podcasts/llm-rag-inference-architecture-stack/", "content": "In terms of regulatory compliance standards, we need to strive towards a regulatory alignment between the EU, Britain, the U.S, and Asia."}
+{"idx": 8, "title": "Building RAG-Powered LLM Applications with LlamaIndex Workflows", "date": "", "ddg_snippet": "One significant issue is the complexity of designing and managing large DAGs, especially in systems with numerous interdependent tasks.", "subpage_snippet": "", "source": "adasci.org", "link": "https://adasci.org/building-rag-powered-llm-applications-with-llamaindex-workflows/", "content": "One significant issue is the complexity of designing and managing large DAGs, especially in systems with numerous interdependent tasks."}
+{"idx": 9, "title": "RFC 4666 - Signaling System 7 (SS7) Message Transfer Part 3", "date": "", "ddg_snippet": "Please refer to the current edition of the \" Internet Official Protocol Standards \" (STD 1 ) for the standardization state and status of ...", "subpage_snippet": "", "source": "datatracker.ietf.org", "link": "https://datatracker.ietf.org/doc/rfc4666/", "content": "Please refer to the current edition of the \" Internet Official Protocol Standards \" (STD 1 ) for the standardization state and status of ..."}
diff --git a/data/sampled_jsons/RAGGED_paper_three_distinct_retriever_paradigms_sparse_dense_late_interaction_year_2024.jsonl b/data/sampled_jsons/RAGGED_paper_three_distinct_retriever_paradigms_sparse_dense_late_interaction_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4f515a0543a1b647ed43b2ef12450815ba41a441
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_paper_three_distinct_retriever_paradigms_sparse_dense_late_interaction_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED: Towards Informed Design of Retrieval ...", "date": "", "ddg_snippet": "by J Hsia · Cited by 23 — RAG system components: For retrievers , we use two approaches:(1) BM25 [Robertson et al.,. 2009], a sparse retriever based on lexical information, and (2) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=SX14yxTTRB", "content": "by J Hsia · Cited by 23 — RAG system components: For retrievers , we use two approaches:(1) BM25 [Robertson et al.,. 2009], a sparse retriever based on lexical information, and (2) ..."}
+{"idx": 1, "title": "Dense Retrieval in RAG: Must-Read Papers Before ...", "date": "", "ddg_snippet": "This article recaps the mainstream techniques in dense representation which is used for retrieval in RAG .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@287961061/dense-retrieval-in-rag-must-read-papers-before-implementation-2a91b4e72298", "content": "This article recaps the mainstream techniques in dense representation which is used for retrieval in RAG ."}
+{"idx": 2, "title": "Modern Sparse Neural Retrieval: From Theory to Practice", "date": "", "ddg_snippet": "23 Oct 2024 — All the sparse retrievers are based on the idea of taking a model which produces contextual dense vector representations for terms and teaching it to produce ...", "subpage_snippet": "", "source": "qdrant.tech", "link": "https://qdrant.tech/articles/modern-sparse-neural-retrieval/", "content": "23 Oct 2024 — All the sparse retrievers are based on the idea of taking a model which produces contextual dense vector representations for terms and teaching it to produce ..."}
+{"idx": 3, "title": "Dense Text Retrieval Based on Pretrained Language Models", "date": "", "ddg_snippet": "To address this issue, several researchers propose to explore multiple-representation bi-encoder for enhancing the text representation and semantic interaction .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3637870?__cf_chl_rt_tk=d9iUhNkSmH1nqzXYf2MZiuqT6ZsaM5_bstPvba332w0-1718188438-0.0.1.1-4543", "content": "To address this issue, several researchers propose to explore multiple-representation bi-encoder for enhancing the text representation and semantic interaction ."}
+{"idx": 4, "title": "Dense XRetrieval: What Retrieval Granularity Should We ...", "date": "", "ddg_snippet": "by T Chen · 2024 · Cited by 113 — We conduct experiments on five different open- domain QA datasets and empirically compare the performance of four dual-encoder retrievers when.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.845.pdf", "content": "by T Chen · 2024 · Cited by 113 — We conduct experiments on five different open- domain QA datasets and empirically compare the performance of four dual-encoder retrievers when."}
+{"idx": 5, "title": "Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "We evaluate three retrievers with different retrieval paradigms : (1) BM25 (Robertson et al., 2009) , a sparse lexical retriever based on term matching. (2) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v3", "content": "We evaluate three retrievers with different retrieval paradigms : (1) BM25 (Robertson et al., 2009) , a sparse lexical retriever based on term matching. (2) ..."}
+{"idx": 6, "title": "Retrieval-Augmented Generation for AI-Generated Content", "date": "", "ddg_snippet": "27 Mar 2024 — It evaluates both sparse retrievers and dense retrievers , and also tries different generators in experiments. CodeT5+ [257] adopts the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.19473v2", "content": "27 Mar 2024 — It evaluates both sparse retrievers and dense retrievers , and also tries different generators in experiments. CodeT5+ [257] adopts the ..."}
+{"idx": 7, "title": "Aman's AI Journal • NLP • Retrieval Augmented Generation", "date": "", "ddg_snippet": "In conclusion, the paper introduces a new paradigm of interaction between language models and dense encoders/ retrievers , showing that relevance modeling and ...", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/RAG/", "content": "In conclusion, the paper introduces a new paradigm of interaction between language models and dense encoders/ retrievers , showing that relevance modeling and ..."}
+{"idx": 8, "title": "Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "Retrievers . We evaluate three retrievers with different retrieval paradigms : (1) BM25 (Robertson et al., 2009), a sparse lexical retriever based on term ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f6a0fe5ddc9cd97dfbd232abc279ce1ed7259753.pdf", "content": "Retrievers . We evaluate three retrievers with different retrieval paradigms : (1) BM25 (Robertson et al., 2009), a sparse lexical retriever based on term ..."}
+{"idx": 9, "title": "Re-Ranking Mechanisms in Retrieval-Augmented ...", "date": "", "ddg_snippet": "Late interaction models try to balance this by sacrificing some interaction for speed. Classic rankers are very fast but require manual ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@adnanmasood/re-ranking-mechanisms-in-retrieval-augmented-generation-pipelines-an-overview-8e24303ee789", "content": "Late interaction models try to balance this by sacrificing some interaction for speed. Classic rankers are very fast but require manual ..."}
diff --git a/data/sampled_jsons/RL-DiL-piKL_on-policy_reinforcement_learning_extension.jsonl b/data/sampled_jsons/RL-DiL-piKL_on-policy_reinforcement_learning_extension.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f3ba93e8bbd97aeb2d67c0a8a168156fb62ccaa
--- /dev/null
+++ b/data/sampled_jsons/RL-DiL-piKL_on-policy_reinforcement_learning_extension.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mastering the Game of No-Press Diplomacy via Human ...", "date": "", "ddg_snippet": "by A Bakhtin · 2022 · Cited by 71 — We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2210.05492", "content": "by A Bakhtin · 2022 · Cited by 71 — We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human ..."}
+{"idx": 1, "title": "Mastering the Game of No-Press Diplomacy via Human ...", "date": "", "ddg_snippet": "We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while ...", "subpage_snippet": "", "source": "ai.meta.com", "link": "https://ai.meta.com/research/publications/mastering-the-game-of-no-press-diplomacy-via-human-regularized-reinforcement-learning-and-planning/", "content": "We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while ..."}
+{"idx": 2, "title": "Mastering the Game of No-Press Diplomacy via Human ...", "date": "", "ddg_snippet": "by A Bakhtin · Cited by 71 — The paper proposes new algorithms (DiL-piKL, RL DiL - piKL , Diplodocus) for decision-making in games requiring cooperation with humans. The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=F61FwJTZhb", "content": "by A Bakhtin · Cited by 71 — The paper proposes new algorithms (DiL-piKL, RL DiL - piKL , Diplodocus) for decision-making in games requiring cooperation with humans. The ..."}
+{"idx": 3, "title": "Mastering the Game of No-Press Diplomacy via Human- ...", "date": "", "ddg_snippet": "by A Bakhtin · 2022 · Cited by 71 — We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.05492", "content": "by A Bakhtin · 2022 · Cited by 71 — We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of."}
+{"idx": 4, "title": "Revision History for Mastering the Game of No-Press...", "date": "", "ddg_snippet": "We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/revisions?id=F61FwJTZhb", "content": "We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while ..."}
+{"idx": 5, "title": "Building Strategic AI Agents for Human-centric Multi ...", "date": "", "ddg_snippet": "13 Aug 2024 — The research introduces the DiL-piKL planning algorithm and its extension, RL-DiL-piKL, which regularize self-play RL and search towards a human ...", "subpage_snippet": "", "source": "www.csail.mit.edu", "link": "https://www.csail.mit.edu/event/thesis-defense-building-strategic-ai-agents-human-centric-multi-agent-systems", "content": "13 Aug 2024 — The research introduces the DiL-piKL planning algorithm and its extension, RL-DiL-piKL, which regularize self-play RL and search towards a human ..."}
+{"idx": 6, "title": "awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern · What is Self ...", "date": "", "ddg_snippet": "We used RL - DiL - piKL to train an agent we ... Self-imitation learning is a Reinforcement Learning (RL) ... extension to deep reinforcement learning algorithms.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern/discussions/3", "content": "We used RL - DiL - piKL to train an agent we ... Self-imitation learning is a Reinforcement Learning (RL) ... extension to deep reinforcement learning algorithms."}
+{"idx": 7, "title": "Paper-to-Podcast - Summary", "date": "", "ddg_snippet": "To improve performance, they introduced a planning algorithm named DiL-piKL , which steers a reward-maximizing policy toward a policy learned from human ...", "subpage_snippet": "", "source": "paper2podcast.com", "link": "https://paper2podcast.com/summary.php?paper=Mastering+Diplomacy:+Teaching+Bots+Cooperation", "content": "To improve performance, they introduced a planning algorithm named DiL-piKL , which steers a reward-maximizing policy toward a policy learned from human ..."}
+{"idx": 8, "title": "CICERO: Human-level play in the game of Diplomacy ...", "date": "", "ddg_snippet": "Dialogue-free RL policy model (main article section Self-play reinforcement learning for ... RL - DiL - piKL is the leading RL algorithm in dialogue- free ...", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/imperfect-information/diplomacy/2022-bakhtin-supplement.pdf", "content": "Dialogue-free RL policy model (main article section Self-play reinforcement learning for ... RL - DiL - piKL is the leading RL algorithm in dialogue- free ..."}
+{"idx": 9, "title": "Athul Paul Jacob", "date": "", "ddg_snippet": "We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/author/athul-paul-jacob", "content": "We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while ..."}
diff --git a/data/sampled_jsons/RLHF_PPO_improvements_clipping_loss_KL_penalty_entropy_bonus_reward_model_robustness.jsonl b/data/sampled_jsons/RLHF_PPO_improvements_clipping_loss_KL_penalty_entropy_bonus_reward_model_robustness.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7aaf6e8c68bb36fe73e084e6a3d71ad949887ce3
--- /dev/null
+++ b/data/sampled_jsons/RLHF_PPO_improvements_clipping_loss_KL_penalty_entropy_bonus_reward_model_robustness.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Navigating the RLHF Landscape: From Policy Gradients to PPO , GAE...", "date": "", "ddg_snippet": "Let’s begin with the PPO loss function and perform a series of mathematical transformations. This is analogous to a chess coach (the Reward Model ) providing real-time feedback while applying a KL divergence penalty to prevent your policy from straying too far from a reference model .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/NormalUhr/rlhf-pipeline", "content": "Let’s begin with the PPO loss function and perform a series of mathematical transformations. This is analogous to a chess coach (the Reward Model ) providing real-time feedback while applying a KL divergence penalty to prevent your policy from straying too far from a reference model ."}
+{"idx": 1, "title": "⚙️Optimizing LLMs with RLHF in a RAG Framework — Part 1 ...", "date": "", "ddg_snippet": "Mar 6, 2025 · The Final Reward is computed by combining the Reward Model ’s score and the KL penalty , discouraging extreme changes to the language model ’s behavior. PPO uses this Final Reward signal to update the Trained LM weights, ensuring more stable improvements through techniques like clipping .", "subpage_snippet": "", "source": "blog.stackademic.com", "link": "https://blog.stackademic.com/️optimizing-llms-with-rlhf-in-a-rag-framework-part-1-understanding-ppo-and-preference-learning-4e7748cc8c09", "content": "Mar 6, 2025 · The Final Reward is computed by combining the Reward Model ’s score and the KL penalty , discouraging extreme changes to the language model ’s behavior. PPO uses this Final Reward signal to update the Trained LM weights, ensuring more stable improvements through techniques like clipping ."}
+{"idx": 2, "title": "PPO Algorithm in the RLHF Context - apxml.com", "date": "", "ddg_snippet": "This cycle repeats, gradually improving the policy π θ πθ to generate responses that better align with the preferences captured by the reward model , while the KL penalty and PPO 's clipping mechanism keep the training stable and prevent catastrophic forgetting.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/rlhf-reinforcement-learning-human-feedback/chapter-4-rl-ppo-fine-tuning/ppo-for-rlhf-context", "content": "This cycle repeats, gradually improving the policy π θ πθ to generate responses that better align with the preferences captured by the reward model , while the KL penalty and PPO 's clipping mechanism keep the training stable and prevent catastrophic forgetting."}
+{"idx": 3, "title": "A vision researcher’s guide to some RL stuff: PPO & GRPO", "date": "", "ddg_snippet": "Jan 31, 2025 · Similar to PPO , GRPO still make use of a clipped surrogate loss as well as the KL penalty . The entropy bonus term is not used here, as the group-based sampling already encourages exploration.", "subpage_snippet": "", "source": "yugeten.github.io", "link": "https://yugeten.github.io/posts/2025/01/ppogrpo/", "content": "Jan 31, 2025 · Similar to PPO , GRPO still make use of a clipped surrogate loss as well as the KL penalty . The entropy bonus term is not used here, as the group-based sampling already encourages exploration."}
+{"idx": 4, "title": "RLHF + Reward Model + PPO on LLMs | by Madhur Prashant | Medium", "date": "", "ddg_snippet": "Sep 10, 2023 · RLHF + Reward Model + PPO on LLMs Purpose The purpose of this blog is a deep dive into the concepts of Reinforcement Learning with Human Feedback, the reward model , and sub concepts that work ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@madhur.prashant7/rlhf-reward-model-ppo-on-llms-dfc92ec3885f", "content": "Sep 10, 2023 · RLHF + Reward Model + PPO on LLMs Purpose The purpose of this blog is a deep dive into the concepts of Reinforcement Learning with Human Feedback, the reward model , and sub concepts that work ..."}
+{"idx": 5, "title": "Policy Gradient Algorithms | RLHF Book by Nathan Lambert", "date": "", "ddg_snippet": "For example, the KL penalty within the RLHF optimization (recall the KL penalty is also used when training reasoning models on verifiable rewards without a reward model ) is applied directly in the loss update rather than to the reward function.", "subpage_snippet": "", "source": "rlhfbook.com", "link": "https://rlhfbook.com/c/11-policy-gradients.html", "content": "For example, the KL penalty within the RLHF optimization (recall the KL penalty is also used when training reasoning models on verifiable rewards without a reward model ) is applied directly in the loss update rather than to the reward function."}
+{"idx": 6, "title": "On-Policy RL with Optimal Reward Baseline - arXiv.org", "date": "", "ddg_snippet": "KL and Entropy Regularization In the reinforcement learning stage of RLHF , two regularization terms are commonly incorporated into the objective function to stabilize policy optimization: the Kullback-Leibler ( KL ) divergence loss and the entropy bonus .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.23585", "content": "KL and Entropy Regularization In the reinforcement learning stage of RLHF , two regularization terms are commonly incorporated into the objective function to stabilize policy optimization: the Kullback-Leibler ( KL ) divergence loss and the entropy bonus ."}
+{"idx": 7, "title": "Secrets of RLHF in Large Language Models", "date": "", "ddg_snippet": "Effect on Different Weights of KL - penalty . Clip Region for Entropy Bonus .While the reward model trained by low-quality data and hard-to-define alignment target can easily mislead the PPO algorithm to a unintelligible direction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2307.04964", "content": "Effect on Different Weights of KL - penalty . Clip Region for Entropy Bonus .While the reward model trained by low-quality data and hard-to-define alignment target can easily mislead the PPO algorithm to a unintelligible direction."}
+{"idx": 8, "title": "Practice: Implementing the PPO Update Step", "date": "", "ddg_snippet": "Recall that PPO aims to optimize a clipped surrogate objective function. The overall loss function typically combines terms for the policy (actor) and the value function (critic), and often includes an entropy bonus to encourage exploration.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/rlhf-reinforcement-learning-human-feedback/chapter-4-rl-ppo-fine-tuning/ppo-update-step-practice", "content": "Recall that PPO aims to optimize a clipped surrogate objective function. The overall loss function typically combines terms for the policy (actor) and the value function (critic), and often includes an entropy bonus to encourage exploration."}
+{"idx": 9, "title": "Preference Tuning LLMs: PPO , DPO, GRPO — A Simple... | Medium", "date": "", "ddg_snippet": "— Entropy Bonus : It also includes an entropy bonus .total_ loss = policy_ loss + kl _ penalty - entropy _ bonus # Subtract entropy bonus as we want to *maximize* entropy return total_ loss . 3. Direct Preference Optimization (DPO).", "subpage_snippet": "", "source": "anukriti-ranjan.medium.com", "link": "https://anukriti-ranjan.medium.com/preference-tuning-llms-ppo-dpo-grpo-a-simple-guide-135765c87090", "content": "— Entropy Bonus : It also includes an entropy bonus .total_ loss = policy_ loss + kl _ penalty - entropy _ bonus # Subtract entropy bonus as we want to *maximize* entropy return total_ loss . 3. Direct Preference Optimization (DPO)."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Per-step_DPO_algorithm_formal_procedure_step-by-step.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Per-step_DPO_algorithm_formal_procedure_step-by-step.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a63289225f3f363d36dab4fb2c4654b3a20da2a
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Per-step_DPO_algorithm_formal_procedure_step-by-step.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "In particular, we add new preference pairs to the dataset of per-step DPO algorithm starting from positive samples. Recall from Section 4 and Algorithm 1, that for a problem 𝐱, with correct answer 𝐲 given by SFT or RFT data, Part 1 of per-step DPO estimates the expected accuracy (Q-value) of each step in a negative rollout 𝐲 ^ ∼ π ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "In particular, we add new preference pairs to the dataset of per-step DPO algorithm starting from positive samples. Recall from Section 4 and Algorithm 1, that for a problem 𝐱, with correct answer 𝐲 given by SFT or RFT data, Part 1 of per-step DPO estimates the expected accuracy (Q-value) of each step in a negative rollout 𝐲 ^ ∼ π ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math...", "date": "", "ddg_snippet": "Figure 7: Per - step DPO improves Q-values at each step , standard DPO only improves at irrelevant steps . This figure shows the results of a didactic analysis on a star graph problem, comparing the performance of standard SFT and per - step DPO .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "Figure 7: Per - step DPO improves Q-values at each step , standard DPO only improves at irrelevant steps . This figure shows the results of a didactic analysis on a star graph problem, comparing the performance of standard SFT and per - step DPO ."}
+{"idx": 2, "title": "RLonIncorrectSyntheticDataScalesthe ...", "date": "", "ddg_snippet": "Now,wediscussanexperimentthatimprovestheperformance ofper-stepDPO[23]byrunningthefullversionoftheper- stepDPOalgorithm(Algorithm1).Inparticular,weaddnew preference pairs to the dataset of per-step DPO algorithm starting from positive samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "Now,wediscussanexperimentthatimprovestheperformance ofper-stepDPO[23]byrunningthefullversionoftheper- stepDPOalgorithm(Algorithm1).Inparticular,weaddnew preference pairs to the dataset of per-step DPO algorithm starting from positive samples."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning, a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4b77d5b896c321a29277524a98a50215-Paper-Conference.pdf", "content": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning, a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 66 — RL on Incorrect Synthetic Data Scales the ... Panel 5: We show an algorithm box for the per-step DPO algorithm used in our experiments.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 66 — RL on Incorrect Synthetic Data Scales the ... Panel 5: We show an algorithm box for the per-step DPO algorithm used in our experiments."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning ... B Per-step DPO Algorithm . image. C Additional Experiments using Positive ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96295/paper", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning ... B Per-step DPO Algorithm . image. C Additional Experiments using Positive ..."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Critical steps , per - step DPO , and advantage-weighted RL .Additionally, since per - step DPO estimates advantage of each step under the Best-of-5 policy, one might expect a saturation in the pass@5 performance of the per - step DPO solution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "Critical steps , per - step DPO , and advantage-weighted RL .Additionally, since per - step DPO estimates advantage of each step under the Best-of-5 policy, one might expect a saturation in the pass@5 performance of the per - step DPO solution."}
+{"idx": 7, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM...", "date": "", "ddg_snippet": "per - step DPO on D ±. π sftspurious. Figure 5: RunningRFTonpositivemodel-generated.Consequently , using per - step DPO w e empirically verify the efficacy of negative data in enab ling per - step . credit assignment and its impact on the scaling laws.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "per - step DPO on D ±. π sftspurious. Figure 5: RunningRFTonpositivemodel-generated.Consequently , using per - step DPO w e empirically verify the efficacy of negative data in enab ling per - step . credit assignment and its impact on the scaling laws."}
+{"idx": 8, "title": "Learning to Reason by Failing: Offline RL on Sub-optimal ...", "date": "", "ddg_snippet": "Jun 13, 2024 · This paper suggests to use per-step DPO to incorporate negative synthetic responses to address the spurious pattern issue and empirically shows its effectiveness. This paper shows an equivalence between per-step DPO and advantage-weighted RL, and both conceptually and empirically explains its effectiveness in addressing spurious pattern issue.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=v2PV1yCFJk", "content": "Jun 13, 2024 · This paper suggests to use per-step DPO to incorporate negative synthetic responses to address the spurious pattern issue and empirically shows its effectiveness. This paper shows an equivalence between per-step DPO and advantage-weighted RL, and both conceptually and empirically explains its effectiveness in addressing spurious pattern issue."}
+{"idx": 9, "title": "Learning to Reason by Failing: Offline RL on Sub-optimal ...", "date": "", "ddg_snippet": "The second variant is per-step DPO (Hwang et al., 2024), which first samples a complete solution trace ˆy1∶L from πsft and then determines the “first pit” ˆyc, such that any completion ˆyc+1∶L ∼ πsft(⋅ ∣ x, ˆy1∶c), sampled conditioned on x, and previous steps ˆy1∶c leads to incorrect answers for a majority of the Monte ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=v2PV1yCFJk", "content": "The second variant is per-step DPO (Hwang et al., 2024), which first samples a complete solution trace ˆy1∶L from πsft and then determines the “first pit” ˆyc, such that any completion ˆyc+1∶L ∼ πsft(⋅ ∣ x, ˆy1∶c), sampled conditioned on x, and previous steps ˆy1∶c leads to incorrect answers for a majority of the Monte ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_GSM8K_MATH_benchmarks.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_GSM8K_MATH_benchmarks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b040d6844e837574994355ba320a7231cfc35c55
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_GSM8K_MATH_benchmarks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 67 — Concretely, we focus on two mathematical reasoning benchmarks: GSM8K [11] and MATH [19 ]. 3. Page 4. RL on Incorrect Synthetic Data Scales the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 67 — Concretely, we focus on two mathematical reasoning benchmarks: GSM8K [11] and MATH [19 ]. 3. Page 4. RL on Incorrect Synthetic Data Scales the ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — Concretely, we focus on two mathematical reasoning benchmarks: GSM8K [11] and MATH [19 ] . Report issue for preceding element. Synthetic data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — Concretely, we focus on two mathematical reasoning benchmarks: GSM8K [11] and MATH [19 ] . Report issue for preceding element. Synthetic data ..."}
+{"idx": 3, "title": "Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "Main papers covered: • RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. 41 pages", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "Main papers covered: • RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. 41 pages"}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Key takeaway: 'Training on incorrect synthetic data , when constructed with appropriate recovery, can significantly improve the efficiency of LLM math ...", "subpage_snippet": "", "source": "k8s.consensus.app", "link": "https://k8s.consensus.app/papers/details/fffa1f2c572d54268d6e2691de789e4c/", "content": "Key takeaway: 'Training on incorrect synthetic data , when constructed with appropriate recovery, can significantly improve the efficiency of LLM math ..."}
+{"idx": 5, "title": "Offline RL on Sub-optimal Rollouts Scales Synthetic Data ...", "date": "", "ddg_snippet": "by A Setlur — Concretely, we focus on two mathematical reasoning bench- marks: GSM8K (Cobbe et al., 2021) and MATH (Hendrycks et al., 2021). Synthetic data pipeline. Our ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=v2PV1yCFJk", "content": "by A Setlur — Concretely, we focus on two mathematical reasoning bench- marks: GSM8K (Cobbe et al., 2021) and MATH (Hendrycks et al., 2021). Synthetic data pipeline. Our ..."}
+{"idx": 6, "title": "10_cs224r-rl_for_reasoning_lecture.pdf - Reinforcement ...", "date": "", "ddg_snippet": "3 May 2025 — RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. NeurIPS 2024. #questions #questions 10. Why is this ...", "subpage_snippet": "", "source": "www.collegesidekick.com", "link": "https://www.collegesidekick.com/study-docs/27183935", "content": "3 May 2025 — RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. NeurIPS 2024. #questions #questions 10. Why is this ..."}
+{"idx": 7, "title": "Achieving 8× Performance Gains with Reinforcement ...", "date": "", "ddg_snippet": "In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ... data using reasoning benchmarks like GSM8K and MATH .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/syncedreview/achieving-8-performance-gains-with-reinforcement-learning-on-synthetic-data-in-large-language-cb6a9cfbffc9", "content": "In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ... data using reasoning benchmarks like GSM8K and MATH ."}
+{"idx": 8, "title": "Common 7B Language Models Already Possess Strong ...", "date": "", "ddg_snippet": "Notably, the accuracy for the first answer drops to 49.5% and 7.9% on the GSM8K and MATH benchmarks , respectively. We find that simply scaling ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/singularity/comments/1e2yysy/common_7b_language_models_already_possess_strong/", "content": "Notably, the accuracy for the first answer drops to 49.5% and 7.9% on the GSM8K and MATH benchmarks , respectively. We find that simply scaling ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Training on model-generated synthetic data is a promising approach ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=LLM-generated+synthetic+data", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Training on model-generated synthetic data is a promising approach ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_9m87e9Keq1_.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_9m87e9Keq1_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2281481d975842fbacffb58b16c8bc46aa43e8bb
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_9m87e9Keq1_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Implementation Details. Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "Implementation Details. Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ."}
+{"idx": 1, "title": "[2406.14532] RL on Incorrect Synthetic Data Scales the Efficiency ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities."}
+{"idx": 3, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "PDF | Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.SFT on synthetic problems and res ponses by 2x, whereas using step-level RL with negativ e data scales the efficiency by 8 x.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "PDF | Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.SFT on synthetic problems and res ponses by 2x, whereas using step-level RL with negativ e data scales the efficiency by 8 x."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL ).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL )."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/rl-on-incorrect-synthetic-data-scales-the", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Unlocking LLM Math Superpowers: 8 X Faster with a Quirky Twist.", "subpage_snippet": "", "source": "www.promptlayer.com", "link": "https://www.promptlayer.com/research-papers/rl-on-incorrect-synthetic-data-scales-the-efficiency-of-llm-math-reasoning-by-eight-fold", "content": "Unlocking LLM Math Superpowers: 8 X Faster with a Quirky Twist."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/es/overview/2406.14532v1", "content": "Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 9, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The key finding is that using RL on \"flawed\" synthetic data can boost the efficiency of LLM math reasoning by up to 8 times, compared to training on correct data alone.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/rl-incorrect-synthetic-data-scales-efficiency-llm", "content": "The key finding is that using RL on \"flawed\" synthetic data can boost the efficiency of LLM math reasoning by up to 8 times, compared to training on correct data alone."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_spurious_correlations_mec.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_spurious_correlations_mec.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0d9055707fb8981b2a45411604d038040e9e1c04
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_spurious_correlations_mec.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Decomposing Elements of Problem Solving: What \"Math\" Does RL", "date": "", "ddg_snippet": "Mathematical reasoning tasks have become prominent benchmarks for assessing the reasoning capabilities of LLMs , especially with reinforcement learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22756v1", "content": "Mathematical reasoning tasks have become prominent benchmarks for assessing the reasoning capabilities of LLMs , especially with reinforcement learning ..."}
+{"idx": 1, "title": "GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with", "date": "", "ddg_snippet": "... that long-form reasoning [ 48 ] and scalable reinforcement learning [ 36 ] can significantly enhance the ability of large language models ( LLMs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01006v1", "content": "... that long-form reasoning [ 48 ] and scalable reinforcement learning [ 36 ] can significantly enhance the ability of large language models ( LLMs ..."}
+{"idx": 2, "title": "National Data Privacy and Governance Act", "date": "", "ddg_snippet": "Putting feature steering into an RL environment is very interesting, great idea! One detail that could be improved was also mentioning the maximum ...", "subpage_snippet": "", "source": "www.apartresearch.com", "link": "https://www.apartresearch.com/project/national-data-privacy-and-governance-act", "content": "Putting feature steering into an RL environment is very interesting, great idea! One detail that could be improved was also mentioning the maximum ..."}
+{"idx": 3, "title": "Declaration", "date": "", "ddg_snippet": "The rapid evolution of neural architectures—from multilayer perceptrons to large- scale Transformer-based models—has enabled language models ( LLMs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15366v1", "content": "The rapid evolution of neural architectures—from multilayer perceptrons to large- scale Transformer-based models—has enabled language models ( LLMs ..."}
+{"idx": 4, "title": "Learning to Focus: Causal Attention Distillation via", "date": "", "ddg_snippet": "... of tokens in X 𝑋 X italic_X , identified as confounding tokens ( A 𝐴 A italic_A ), introduces spurious correlations that disrupt the reasoning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07851v1", "content": "... of tokens in X 𝑋 X italic_X , identified as confounding tokens ( A 𝐴 A italic_A ), introduces spurious correlations that disrupt the reasoning ..."}
+{"idx": 5, "title": "Virginia Smith", "date": "", "ddg_snippet": "... gained popularity as an approach to efficiently update an LLM so that it behaves (roughly) as if it was not trained on a subset of data to begin with.", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "http://www.cs.cmu.edu/~smithv/", "content": "... gained popularity as an approach to efficiently update an LLM so that it behaves (roughly) as if it was not trained on a subset of data to begin with."}
+{"idx": 6, "title": "Lei Hou - ACL Anthology", "date": "", "ddg_snippet": "... on a large number of parallel question-program pairs to make the LLM aware of the schema of a given KB, and is thus challenging for many low-resourced ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/l/lei-hou/", "content": "... on a large number of parallel question-program pairs to make the LLM aware of the schema of a given KB, and is thus challenging for many low-resourced ..."}
+{"idx": 7, "title": "Ji-Rong Wen - ACL Anthology", "date": "", "ddg_snippet": "To enhance the capabilities of LLMs to emulate human reasoning , prior studies have focused on modeling reasoning steps using various thought ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/j/ji-rong-wen/", "content": "To enhance the capabilities of LLMs to emulate human reasoning , prior studies have focused on modeling reasoning steps using various thought ..."}
+{"idx": 8, "title": "ICML 2024", "date": "", "ddg_snippet": "... her paper notes that there remain some trade-offs between optimising for English-only performance and performance on more culturally diverse data ...", "subpage_snippet": "", "source": "joltml.com", "link": "https://joltml.com/icml-2024/", "content": "... her paper notes that there remain some trade-offs between optimising for English-only performance and performance on more culturally diverse data ..."}
+{"idx": 9, "title": "Cop N' Shop", "date": "", "ddg_snippet": "... The concepts of data validation, data provenance/ transparent, verifiable data handling all give a very good reason to use blockchain rather than ...", "subpage_snippet": "", "source": "www.apartresearch.com", "link": "https://www.apartresearch.com/project/cop-n-shop", "content": "... The concepts of data validation, data provenance/ transparent, verifiable data handling all give a very good reason to use blockchain rather than ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Section_6.2_DPO.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Section_6.2_DPO.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d54f94046ef6881e578fb38770c4d3addc5d04ca
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Section_6.2_DPO.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 67 — In other words, our conceptual model illustrates that per-step DPO can perform credit assignment, and identify critical steps over irrelevant or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 67 — In other words, our conceptual model illustrates that per-step DPO can perform credit assignment, and identify critical steps over irrelevant or ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — We see that this improves test performance and scaling trends (in Section 6.2 ) over SFT and standard DPO [40] with the same data . To explain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — We see that this improves test performance and scaling trends (in Section 6.2 ) over SFT and standard DPO [40] with the same data . To explain ..."}
+{"idx": 2, "title": "Generative Reward Modeling via Synthetic Criteria ...", "date": "", "ddg_snippet": "by X Liang · 2025 · Cited by 2 — During DPO , we select only the inconsistent samples between the backbone and RSFT models, resulting in the removal of approximately 70% of the ... 15 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1297.pdf", "content": "by X Liang · 2025 · Cited by 2 — During DPO , we select only the inconsistent samples between the backbone and RSFT models, resulting in the removal of approximately 70% of the ... 15 pages"}
+{"idx": 3, "title": "Focused-DPO: Enhancing Code Generation Through ...", "date": "", "ddg_snippet": "by K Zhang · 2025 · Cited by 8 — Focused- DPO consists of three key stages: ➀ Gener- ating synthetic question prompts from real-world code repositories. ➁ Using a policy model to ... 14 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.498.pdf", "content": "by K Zhang · 2025 · Cited by 8 — Focused- DPO consists of three key stages: ➀ Gener- ating synthetic question prompts from real-world code repositories. ➁ Using a policy model to ... 14 pages"}
+{"idx": 4, "title": "VinePPO: Refining Credit Assignment in RL Training of LLMs", "date": "", "ddg_snippet": "To ensure a fair comparison of compute efficiency, we conduct controlled experiments in Section 6.2 ... RL on Incorrect Synthetic Data Scales the Efficiency of ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45526", "content": "To ensure a fair comparison of compute efficiency, we conduct controlled experiments in Section 6.2 ... RL on Incorrect Synthetic Data Scales the Efficiency of ..."}
+{"idx": 5, "title": "MALT: Improving Reasoning with Multi-Agent LLM Training", "date": "", "ddg_snippet": "by SR Motwani · Cited by 22 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by ... A. 6.2 Policy optimizing the DPO objective is identical to Optimal RL Policy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=jXP9bgFack", "content": "by SR Motwani · Cited by 22 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by ... A. 6.2 Policy optimizing the DPO objective is identical to Optimal RL Policy."}
+{"idx": 6, "title": "VinePPO: Refining Credit Assignment in RL Training of LLMs", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight- fold. CoRR, abs/2406.14532, 2024. doi: 10.48550/. ARXIV.2406.14532. URL ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/ce0e7ba37b4a5f8f9a9638832edccfa6189c97a4.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight- fold. CoRR, abs/2406.14532, 2024. doi: 10.48550/. ARXIV.2406.14532. URL ..."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Training on model-generated synthetic data is a promising approach ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=synthetic+responses", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Training on model-generated synthetic data is a promising approach ..."}
+{"idx": 8, "title": "Auto-Formulating Dynamic Programming Problems with ...", "date": "", "ddg_snippet": "14 Jul 2025 — The RL alignment process is made possible by our verifiable synthetic dataset , which supplies ... Section 6.2 . We then analyze the scaling ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5350161.pdf?abstractid=5350161&mirid=1", "content": "14 Jul 2025 — The RL alignment process is made possible by our verifiable synthetic dataset , which supplies ... Section 6.2 . We then analyze the scaling ..."}
+{"idx": 9, "title": "Extended Abstract - CS 224R Deep Reinforcement Learning", "date": "", "ddg_snippet": "by A Hudson — In this project, we explore online and off-policy reinforcement learning as an effective strategy for improving math reasoning in LLMs, focusing on improving ...", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/224r_final_report1.pdf", "content": "by A Hudson — In this project, we explore online and off-policy reinforcement learning as an effective strategy for improving math reasoning in LLMs, focusing on improving ..."}
diff --git a/data/sampled_jsons/Rafailov_et_al.,_2023_Direct_Preference_Optimization_abstract.jsonl b/data/sampled_jsons/Rafailov_et_al.,_2023_Direct_Preference_Optimization_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..459d631b55ae45a774036d49a5dd52b1656d8468
--- /dev/null
+++ b/data/sampled_jsons/Rafailov_et_al.,_2023_Direct_Preference_Optimization_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.18290] Direct Preference Optimization : Your Language Model...", "date": "", "ddg_snippet": "[Submitted on 29 May 2023 (v1), last revised 29 Jul 2024 (this version, v3)].View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model, by Rafael Rafailov and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "[Submitted on 29 May 2023 (v1), last revised 29 Jul 2024 (this version, v3)].View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model, by Rafael Rafailov and 5 other authors."}
+{"idx": 1, "title": "NumByNum: Preference Optimization for VLMs & Direct ... | Medium", "date": "", "ddg_snippet": "This review of “ Preference Optimization for VLMs & Direct Preference Optimization — Your Language Model is Secretly a Reward Model ( Rafailov et al ., 2023 )” begins at Number 1 and concludes at Number 156.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@AriaLeeNotAriel/numbynum-preference-optimization-for-vlms-d0a1c319eb2c", "content": "This review of “ Preference Optimization for VLMs & Direct Preference Optimization — Your Language Model is Secretly a Reward Model ( Rafailov et al ., 2023 )” begins at Number 1 and concludes at Number 156."}
+{"idx": 2, "title": "2023 DirectPreferenceOptimizationYou - GM-RKB", "date": "", "ddg_snippet": "Subject Headings: Direct Preference Optimization . It introduces Direct Preference Optimization , a novel approach for aligning language models with human preferences , as a simpler alternative to the complex and often unstable reinforcement learning fr...", "subpage_snippet": "", "source": "www.gabormelli.com", "link": "https://www.gabormelli.com/RKB/Rafailov_et_al.,_2023", "content": "Subject Headings: Direct Preference Optimization . It introduces Direct Preference Optimization , a novel approach for aligning language models with human preferences , as a simpler alternative to the complex and often unstable reinforcement learning fr..."}
+{"idx": 3, "title": "Entropy Controllable Direct Preference Optimization", "date": "", "ddg_snippet": "Entropy Controllable Directed Preference Optimization .To address these problems, Direct Policy Optimization (DPO) ( Rafailov et al ., 2023 ) has emerged and is now widely used.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=js6TS3ck7H", "content": "Entropy Controllable Directed Preference Optimization .To address these problems, Direct Policy Optimization (DPO) ( Rafailov et al ., 2023 ) has emerged and is now widely used."}
+{"idx": 4, "title": "(PDF) Self-Improving Robust Preference Optimization", "date": "", "ddg_snippet": "human preferences [ Rafailov et al ., 2023 ]. In the offline setting, the goal is to train the LLM policy. directly from a dataset. direct preference optimization . arXiv preprint arXiv:2311.12908, 2023 . Chaoqi Wang, Yibo Jiang, Chenghao Yang, Han Liu, and Yuxin Chen.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381159770_Self-Improving_Robust_Preference_Optimization", "content": "human preferences [ Rafailov et al ., 2023 ]. In the offline setting, the goal is to train the LLM policy. directly from a dataset. direct preference optimization . arXiv preprint arXiv:2311.12908, 2023 . Chaoqi Wang, Yibo Jiang, Chenghao Yang, Han Liu, and Yuxin Chen."}
+{"idx": 5, "title": "Together Fine-Tuning Platform, Now With Preference Optimization ...", "date": "", "ddg_snippet": "An illustration of DPO from \" Direct Preference Optimization : Your Language Model is Secretly a Reward Model” ( Rafailov et al ., NeurIPS 2023 ).", "subpage_snippet": "", "source": "www.together.ai", "link": "https://www.together.ai/blog/introducing-fine-tuning-platform", "content": "An illustration of DPO from \" Direct Preference Optimization : Your Language Model is Secretly a Reward Model” ( Rafailov et al ., NeurIPS 2023 )."}
+{"idx": 6, "title": "When to use Direct Preference Optimization (DPO) – Paul Simmering", "date": "", "ddg_snippet": "December 22, 2024. OpenAI recently added the ability to fine-tune their models using direct preference optimization (DPO). They call it preference tuning.See the paper by Rafailov et al . ( 2023 ) for details and how this loss is turned into a weight update.", "subpage_snippet": "", "source": "simmering.dev", "link": "https://simmering.dev/blog/llm-customization/", "content": "December 22, 2024. OpenAI recently added the ability to fine-tune their models using direct preference optimization (DPO). They call it preference tuning.See the paper by Rafailov et al . ( 2023 ) for details and how this loss is turned into a weight update."}
+{"idx": 7, "title": "Exploratory Preference Optimization : Harnessing Implicit...", "date": "", "ddg_snippet": "This algorithm is elegantly simple---requiring only a one-line modification to (online) Direct Preference Optimization (DPO; Rafailov et al ., 2023 )---yet provides the strongest known provable guarantees.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/6c71ddecdefd911d2f993461972d8b39-Abstract-Conference.html", "content": "This algorithm is elegantly simple---requiring only a one-line modification to (online) Direct Preference Optimization (DPO; Rafailov et al ., 2023 )---yet provides the strongest known provable guarantees."}
+{"idx": 8, "title": "Direct Preference Optimization Explained In-depth", "date": "", "ddg_snippet": "Direct Preference Optimization . So we know the optimal solution to our optimization problem, but can we access it?But now we are directly optimizing the parameters of our LLM policy model based on human preferences ! Thus, Direct Preference Optimization .", "subpage_snippet": "", "source": "www.tylerromero.com", "link": "https://www.tylerromero.com/posts/2024-04-dpo/", "content": "Direct Preference Optimization . So we know the optimal solution to our optimization problem, but can we access it?But now we are directly optimizing the parameters of our LLM policy model based on human preferences ! Thus, Direct Preference Optimization ."}
+{"idx": 9, "title": "Paper page - Exploratory Preference Optimization : Harnessing...", "date": "", "ddg_snippet": "Abstract .We propose a new algorithm for online exploration in RLHF, Exploratory Preference Optimization (XPO), which is simple and practical -- a one-line change to (online) Direct Preference Optimization (DPO; Rafailov et al ., 2023 ) -- yet enjoys the strongest known provable...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2405.21046", "content": "Abstract .We propose a new algorithm for online exploration in RLHF, Exploratory Preference Optimization (XPO), which is simple and practical -- a one-line change to (online) Direct Preference Optimization (DPO; Rafailov et al ., 2023 ) -- yet enjoys the strongest known provable..."}
diff --git a/data/sampled_jsons/Rafailov_et_al._2023_abstract_Direct_Preference_Optimization_full_year_2023.jsonl b/data/sampled_jsons/Rafailov_et_al._2023_abstract_Direct_Preference_Optimization_full_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6705a96ead9ac23ce01ff964387d15b296989438
--- /dev/null
+++ b/data/sampled_jsons/Rafailov_et_al._2023_abstract_Direct_Preference_Optimization_full_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.18290] Direct Preference Optimization: Your Language ...", "date": "", "ddg_snippet": "May 29, 2023 · The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "May 29, 2023 · The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning."}
+{"idx": 1, "title": "Direct preference optimization | Proceedings of the 37th ...", "date": "", "ddg_snippet": "Dec 10, 2023 · The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668460", "content": "Dec 10, 2023 · The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning."}
+{"idx": 2, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "4 Direct Preference Optimization Motivated by the challenges of applying reinforcement learning algorithms on large-scale problems such as fine-tuning language models, our goal is to derive a simple approach for policy optimization using preferences directly.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v2", "content": "4 Direct Preference Optimization Motivated by the challenges of applying reinforcement learning algorithms on large-scale problems such as fine-tuning language models, our goal is to derive a simple approach for policy optimization using preferences directly."}
+{"idx": 3, "title": "NeurIPS 2023 Direct Preference Optimization: Your Language ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/oral/73865", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning."}
+{"idx": 4, "title": "Rafailov 2023 - Direct Preference Optimization - Chux's Notebook", "date": "", "ddg_snippet": "Rafailov 2023 - Direct Preference Optimization Here we trace the derivations from the DPO paper. Denote the model after SFT as πSFT (y∣x), i.e. the policy πSFT is a probability function for each pair of inputs and answers (x, y). Naturally, we can use this policy to generate tokens by choosing the response with the highest probability (or approximate it in a greedy token-by-token manner ...", "subpage_snippet": "", "source": "charleslow.github.io", "link": "https://charleslow.github.io/notebook/book/papers/rafailov_2023.html", "content": "Rafailov 2023 - Direct Preference Optimization Here we trace the derivations from the DPO paper. Denote the model after SFT as πSFT (y∣x), i.e. the policy πSFT is a probability function for each pair of inputs and answers (x, y). Naturally, we can use this policy to generate tokens by choosing the response with the highest probability (or approximate it in a greedy token-by-token manner ..."}
+{"idx": 5, "title": "Direct Preference Optimization(DPO)", "date": "", "ddg_snippet": "Direct Preference Optimization (DPO) Rafailov , R., Sharma, A., Mitchell, E., Manning, C.D., Ermon, S. and Finn, C., 2024. Direct preference optimization : Your language ...", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~cmaddis/courses/csc2541_w25/presentations/mu_cao_dpo.pdf", "content": "Direct Preference Optimization (DPO) Rafailov , R., Sharma, A., Mitchell, E., Manning, C.D., Ermon, S. and Finn, C., 2024. Direct preference optimization : Your language ..."}
+{"idx": 6, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "Abstract Direct Preference Optimization : Your Language Model is Secretly a Reward Model", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.18290v3", "content": "Abstract Direct Preference Optimization : Your Language Model is Secretly a Reward Model"}
+{"idx": 7, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · 2023 · Cited by 5233 — In this paper, we leverage a mapping between reward functions and optimal policies to show that this constrained reward maximization problem can be optimized ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html", "content": "by R Rafailov · 2023 · Cited by 5233 — In this paper, we leverage a mapping between reward functions and optimal policies to show that this constrained reward maximization problem can be optimized ..."}
+{"idx": 8, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · Cited by 5233 — Our experiments show that DPO is at least as effective as existing methods, including PPO-based RLHF, for learning from preferences in tasks such as sentiment ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=HPuSIXJaa9", "content": "by R Rafailov · Cited by 5233 — Our experiments show that DPO is at least as effective as existing methods, including PPO-based RLHF, for learning from preferences in tasks such as sentiment ..."}
+{"idx": 9, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · Cited by 5233 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=HPuSIXJaa9", "content": "by R Rafailov · Cited by 5233 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods."}
diff --git a/data/sampled_jsons/Recursive_Allocation_Selection_algorithm_implementation_B=4_workers_scores_0.4_0.2_0.7.jsonl b/data/sampled_jsons/Recursive_Allocation_Selection_algorithm_implementation_B=4_workers_scores_0.4_0.2_0.7.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..caa2c8abf540e66225d435c0b1d22848737fa528
--- /dev/null
+++ b/data/sampled_jsons/Recursive_Allocation_Selection_algorithm_implementation_B=4_workers_scores_0.4_0.2_0.7.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Recursive Neyman algorithm for optimum sample allocation under box ...", "date": "", "ddg_snippet": "The RNABOX can be viewed as a generalization of the classical recursive Neyman allocation algorithm , a popular tool for optimum allocation when only upper bounds are imposed on sample strata-sizes.", "subpage_snippet": "", "source": "www150.statcan.gc.ca", "link": "https://www150.statcan.gc.ca/n1/pub/12-001-x/2024002/article/00003-eng.pdf", "content": "The RNABOX can be viewed as a generalization of the classical recursive Neyman allocation algorithm , a popular tool for optimum allocation when only upper bounds are imposed on sample strata-sizes."}
+{"idx": 1, "title": "Introduction to Algorithms, Fourth Edition — solutions to ... - GitHub", "date": "", "ddg_snippet": "Solutions to exercises and problems from \"Introduction to Algorithms \", Fourth Edition by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein - wojtask/clrs4e-solutions", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wojtask/clrs4e-solutions", "content": "Solutions to exercises and problems from \"Introduction to Algorithms \", Fourth Edition by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein - wojtask/clrs4e-solutions"}
+{"idx": 2, "title": "Recursive Selection Sort - GeeksforGeeks", "date": "", "ddg_snippet": "The Selection Sort algorithm sorts maintain two parts. The first part that is already sorted The second part is yet to be sorted. The algorithm works by repeatedly finding the minimum element (considering ascending order) from the unsorted part and putting it at the end of the sorted part. arr[] = 64 25 12 22 11 // Find the minimum element in arr[0...4] // and place it at beginning 11 25 12 22 ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/recursive-selection-sort/", "content": "The Selection Sort algorithm sorts maintain two parts. The first part that is already sorted The second part is yet to be sorted. The algorithm works by repeatedly finding the minimum element (considering ascending order) from the unsorted part and putting it at the end of the sorted part. arr[] = 64 25 12 22 11 // Find the minimum element in arr[0...4] // and place it at beginning 11 25 12 22 ..."}
+{"idx": 3, "title": "Optimizing Feature Selection in Machine Learning: Applying Recursive ...", "date": "", "ddg_snippet": "Effective implementation of RFE involves strategic decisions about the machine learning workflow, from model selection to the detailed configuration of the feature selection process.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ammarmuzacky/optimizing-feature-selection-in-machine-learning-applying-recursive-feature-elimination-ad490c29be3c", "content": "Effective implementation of RFE involves strategic decisions about the machine learning workflow, from model selection to the detailed configuration of the feature selection process."}
+{"idx": 4, "title": "Selection Algorithms - GeeksforGeeks", "date": "", "ddg_snippet": "Selection Algorithm is an algorithm for finding the kth smallest (or largest) number in a list or an array. That number is called the kth order statistic. It includes the various cases for finding the minimum, maximum and median elements in a list or an array. For finding the minimum (or maximum) element by iterating through the list, we keep the track of current minimum (or maximum) elements ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/selection-algorithms/", "content": "Selection Algorithm is an algorithm for finding the kth smallest (or largest) number in a list or an array. That number is called the kth order statistic. It includes the various cases for finding the minimum, maximum and median elements in a list or an array. For finding the minimum (or maximum) element by iterating through the list, we keep the track of current minimum (or maximum) elements ..."}
+{"idx": 5, "title": "Optimality of the recursive Neyman allocation - arXiv.org", "date": "", "ddg_snippet": "We use V-allocation to give a formal proof of optimality of the popular recursive Neyman algorithm , rNa. This approach is convenient also for a quick proof of optimality of the algorithm of Stenger and Gabler (2005), SGa, as well as of its modification, coma, we propose here. Finally, we compare running times of rNa, SGa and coma. Ready-to-use R-implementations of these algorithms are", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2105.14486", "content": "We use V-allocation to give a formal proof of optimality of the popular recursive Neyman algorithm , rNa. This approach is convenient also for a quick proof of optimality of the algorithm of Stenger and Gabler (2005), SGa, as well as of its modification, coma, we propose here. Finally, we compare running times of rNa, SGa and coma. Ready-to-use R-implementations of these algorithms are"}
+{"idx": 6, "title": "Selection Sort Algorithm - Iterative & Recursive | C, Java, Python", "date": "", "ddg_snippet": "Both the worst-case and best-case time complexity of selection sort is O (n2), where n is the input size, and it doesn't require any extra space. The selection sort algorithm can be implemented recursively. Following is the recursive implementation of the selection sort algorithm in C, Java, and Python:", "subpage_snippet": "", "source": "www.techiedelight.com", "link": "https://www.techiedelight.com/selection-sort-iterative-recursive/", "content": "Both the worst-case and best-case time complexity of selection sort is O (n2), where n is the input size, and it doesn't require any extra space. The selection sort algorithm can be implemented recursively. Following is the recursive implementation of the selection sort algorithm in C, Java, and Python:"}
+{"idx": 7, "title": "Optimality of the Recursive Neyman Allocation - Oxford Academic", "date": "", "ddg_snippet": "The solution is given in terms of V - allocation with V being the set of take-all strata. We use V - allocation to give a formal proof of optimality of the popular recursive Neyman algorithm , rNa. We also propose a quick proof of optimality of the algorithm of Stenger and Gabler, SGa, as well as of our proposed modification, coma.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/jssam/article-abstract/10/5/1263/6333381", "content": "The solution is given in terms of V - allocation with V being the set of take-all strata. We use V - allocation to give a formal proof of optimality of the popular recursive Neyman algorithm , rNa. We also propose a quick proof of optimality of the algorithm of Stenger and Gabler, SGa, as well as of our proposed modification, coma."}
+{"idx": 8, "title": "Evolution in Your Code Part 2: How to Solve a Staff Allocation ... - Medium", "date": "", "ddg_snippet": "\"\"\" Optimize employee allocation using a genetic algorithm . Uses selection , crossover, and mutation to evolve a population towards better solutions. Parameters: - efficiencies (list): A list of efficiency values for each employee in each section. - n (int): Number of employees. - population_size (int, optional): Size of the population. Default ...", "subpage_snippet": "", "source": "pub.towardsai.net", "link": "https://pub.towardsai.net/evolution-in-your-code-part-2-how-to-solve-a-staff-allocation-problem-using-genetic-algorithms-6dfc08f1623d", "content": "\"\"\" Optimize employee allocation using a genetic algorithm . Uses selection , crossover, and mutation to evolve a population towards better solutions. Parameters: - efficiencies (list): A list of efficiency values for each employee in each section. - n (int): Number of employees. - population_size (int, optional): Size of the population. Default ..."}
+{"idx": 9, "title": "SequentialFeatureSelector: The popular forward and backward feature ...", "date": "", "ddg_snippet": "SequentialFeatureSelector: The popular forward and backward feature selection approaches (including floating variants) Implementation of sequential feature algorithms (SFAs) -- greedy search algorithms -- that have been developed as a suboptimal solution to the computationally often not feasible exhaustive search.", "subpage_snippet": "", "source": "rasbt.github.io", "link": "https://rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/", "content": "SequentialFeatureSelector: The popular forward and backward feature selection approaches (including floating variants) Implementation of sequential feature algorithms (SFAs) -- greedy search algorithms -- that have been developed as a suboptimal solution to the computationally often not feasible exhaustive search."}
diff --git a/data/sampled_jsons/Regret_Matching+_Farina_2023_abstract_extragradient_O(1T)_neurips_year_2023.jsonl b/data/sampled_jsons/Regret_Matching+_Farina_2023_abstract_extragradient_O(1T)_neurips_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..142bc1a570396efc7b98e8541eedf76dbd1ab789
--- /dev/null
+++ b/data/sampled_jsons/Regret_Matching+_Farina_2023_abstract_extragradient_O(1T)_neurips_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret matching+ | Proceedings of the 37th International ...", "date": "", "ddg_snippet": "Dec 10, 2023 · Farina , G., Kroer, C., Lee, C.-W., and Luo, H. Clairvoyant regret minimization: Equivalence with nemirovski's conceptual prox method and extension to general convex games. In OPT 2022: Optimization for Machine Learning ( NeurIPS 2022 Workshop), 2022.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668812", "content": "Dec 10, 2023 · Farina , G., Kroer, C., Lee, C.-W., and Luo, H. Clairvoyant regret minimization: Equivalence with nemirovski's conceptual prox method and extension to general convex games. In OPT 2022: Optimization for Machine Learning ( NeurIPS 2022 Workshop), 2022."}
+{"idx": 1, "title": "Efficient Last-Iterate Convergence in Regret Minimization via ...", "date": "", "ddg_snippet": "3 days ago · However, common methods such as regret matching, regret matching+ , and Hedge often fail to achieve this property both theoretically and empirically, even in normal-form games ( Farina et al., 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13653v1", "content": "3 days ago · However, common methods such as regret matching, regret matching+ , and Hedge often fail to achieve this property both theoretically and empirically, even in normal-form games ( Farina et al., 2024)."}
+{"idx": 2, "title": "NeurIPS 2022 Oral-Equivalent Papers", "date": "", "ddg_snippet": "Abstract The ability to extrapolate from short problem instances to longer ones is an important form of out-of-distribution generalization in reasoning tasks, and is crucial when learning from datasets where longer problem instances are rare. These include theorem proving, solving quantitative mathematics problems, and reading/summarizing novels.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2022/events/highlighted", "content": "Abstract The ability to extrapolate from short problem instances to longer ones is an important form of out-of-distribution generalization in reasoning tasks, and is crucial when learning from datasets where longer problem instances are rare. These include theorem proving, solving quantitative mathematics problems, and reading/summarizing novels."}
+{"idx": 3, "title": "Regret Matching+ : (In) Stability and Fast Convergence... | OpenReview", "date": "", "ddg_snippet": "[7] Farina , Gabriele, Christian Kroer, and Tuomas Sandholm. \"Optimistic regret minimization for extensive-form games via dilated distance-generating functions.\" Advances in neural information processing systems 32 (2019).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nYgs0qZJ97", "content": "[7] Farina , Gabriele, Christian Kroer, and Tuomas Sandholm. \"Optimistic regret minimization for extensive-form games via dilated distance-generating functions.\" Advances in neural information processing systems 32 (2019)."}
+{"idx": 4, "title": "Last-Iterate Convergence Properties of Regret Matching Algorithms in...", "date": "", "ddg_snippet": "Abstract . 1 Introduction. 2 Preliminaries on Regret Matching+ . Notation.matrix game. We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM+ and smooth Predictive RM+, enjoy asymptotic last-iterate convergence (without a rate)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "Abstract . 1 Introduction. 2 Preliminaries on Regret Matching+ . Notation.matrix game. We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM+ and smooth Predictive RM+, enjoy asymptotic last-iterate convergence (without a rate)"}
+{"idx": 5, "title": "Regret Matching+ : (In) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games.We show that these fixes are sufficient to get $O( T ^{1/4})$ individual regret and $ O ( 1 )$ social regret in normal-form games via RM+ with predictions.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371008709_Regret_Matching_InStability_and_Fast_Convergence_in_Games", "content": "Abstract . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games.We show that these fixes are sufficient to get $O( T ^{1/4})$ individual regret and $ O ( 1 )$ social regret in normal-form games via RM+ with predictions."}
+{"idx": 6, "title": "Α Descent-based Method on the Duality Gap for Solving Zero ...", "date": "", "ddg_snippet": "1.1 Our Contributions Motivated by the above discussion, we propose and ana-lyze an optimization approach for finding approximate Nash equilibria in zero-sum games. Our algorithm is a descent-based method applied to the duality gap function, and is essentially an adaptation of a subroutine in the algorithms of [Tsaknakis and Spirakis, 2008; Deligkas et al., 2017; Deligkas et al., 2023 ] which ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0427.pdf", "content": "1.1 Our Contributions Motivated by the above discussion, we propose and ana-lyze an optimization approach for finding approximate Nash equilibria in zero-sum games. Our algorithm is a descent-based method applied to the duality gap function, and is essentially an adaptation of a subroutine in the algorithms of [Tsaknakis and Spirakis, 2008; Deligkas et al., 2017; Deligkas et al., 2023 ] which ..."}
+{"idx": 7, "title": "Convergence in Games", "date": "", "ddg_snippet": "Abstract . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35].Combined with RM+ with predictions, we show that restarting is sufficient to get O( T 1/4) individual regret and that chop-ping off achieves O ( 1 ) social regret in normal-form games.", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35].Combined with RM+ with predictions, we show that restarting is sufficient to get O( T 1/4) individual regret and that chop-ping off achieves O ( 1 ) social regret in normal-form games."}
+{"idx": 8, "title": "Fast Computation of Nash Equilibria in Imperfect Information Games", "date": "", "ddg_snippet": "The extragradient method for nd-ing saddle points and other problems. Matecon, 12:747– 756. Kroer, C., Farina , G., and Sandholm, T . (2018). Solving large sequential games with the excessive gap technique. In Neural Information Processing Systems ( NeurIPS ).", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/munos20a/munos20a-supp.pdf", "content": "The extragradient method for nd-ing saddle points and other problems. Matecon, 12:747– 756. Kroer, C., Farina , G., and Sandholm, T . (2018). Solving large sequential games with the excessive gap technique. In Neural Information Processing Systems ( NeurIPS )."}
+{"idx": 9, "title": "Track: Poster Session 2", "date": "", "ddg_snippet": "In this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities. Specifically, we propose and analyze a class of adaptive dual averaging schemes in which agents only need to accumulate gradient feedback received from the whole system, without requiring any between-agent coordination. In the single-agent case, the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2023/session/21604", "content": "In this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities. Specifically, we propose and analyze a class of adaptive dual averaging schemes in which agents only need to accumulate gradient feedback received from the whole system, without requiring any between-agent coordination. In the single-agent case, the ..."}
diff --git a/data/sampled_jsons/Regret_Matching+_algorithm_definition_update_rule_game_theory.jsonl b/data/sampled_jsons/Regret_Matching+_algorithm_definition_update_rule_game_theory.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..298389878f77255e1c818421573bc34dcc44bf99
--- /dev/null
+++ b/data/sampled_jsons/Regret_Matching+_algorithm_definition_update_rule_game_theory.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Transfer and Parameter Optimization", "date": "", "ddg_snippet": "by N Brown · 2014 · Cited by 41 — Regret matching is a widely-used algorithm for learning how to act . We begin by proving that regrets on actions in one setting (game) can be transferred to warm ... 11 pages", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~sandholm/regret_transfer.aaai14.extended.pdf", "content": "by N Brown · 2014 · Cited by 41 — Regret matching is a widely-used algorithm for learning how to act . We begin by proving that regrets on actions in one setting (game) can be transferred to warm ... 11 pages"}
+{"idx": 1, "title": "Faster Regret Matching", "date": "", "ddg_snippet": "by D Wu · 2020 — We first review some concepts in game theory needed in this paper. Then we rewrite the regret matching algorithm for convenient implementing and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2001.05318", "content": "by D Wu · 2020 — We first review some concepts in game theory needed in this paper. Then we rewrite the regret matching algorithm for convenient implementing and ..."}
+{"idx": 2, "title": "Regret Matching+: (In)Stability and Fast Convergence in ...", "date": "", "ddg_snippet": "by G Farina · 2023 · Cited by 16 — Regret minimization is an important framework for solving games. Its connection to game theory provides a practically efficient way to approximate game- ... 27 pages", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "by G Farina · 2023 · Cited by 16 — Regret minimization is an important framework for solving games. Its connection to game theory provides a practically efficient way to approximate game- ... 27 pages"}
+{"idx": 3, "title": "Solving Games with Functional Regret Estimation", "date": "", "ddg_snippet": "by K Waugh · 2015 · Cited by 72 — A simple algorithm with this property is regret - matching . Definition 1 ( Regret - matching ). Define the vector of cu- mulative regret as. RT ≡. T. X t=1 ut ...", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ocs/ws/ws0056/10111-45961-1-PB.pdf", "content": "by K Waugh · 2015 · Cited by 72 — A simple algorithm with this property is regret - matching . Definition 1 ( Regret - matching ). Define the vector of cu- mulative regret as. RT ≡. T. X t=1 ut ..."}
+{"idx": 4, "title": "Algorithmic Game Theory Lecture #17: No-Regret Dynamics", "date": "", "ddg_snippet": "by T Roughgarden · 2013 · Cited by 1 — No-regret dynamics are well defined because no-regret algorithms exist (Theorem 2.1). Each player can use whatever no-regret algorithm it wants. 9 pages", "subpage_snippet": "", "source": "theory.stanford.edu", "link": "https://theory.stanford.edu/~tim/f13/l/l17.pdf", "content": "by T Roughgarden · 2013 · Cited by 1 — No-regret dynamics are well defined because no-regret algorithms exist (Theorem 2.1). Each player can use whatever no-regret algorithm it wants. 9 pages"}
+{"idx": 5, "title": "Learn AI Game Playing Algorithm Part III — Counterfactual ...", "date": "", "ddg_snippet": "In CFR, we use regret matching to update our strategy σᵗ iteratively and return the average strategy as the approximated Nash equilibrium ...", "subpage_snippet": "", "source": "xyzml.medium.com", "link": "https://xyzml.medium.com/learn-ai-game-playing-algorithm-part-iii-counterfactual-regret-minimization-b182a7ec85fb", "content": "In CFR, we use regret matching to update our strategy σᵗ iteratively and return the average strategy as the approximated Nash equilibrium ..."}
+{"idx": 6, "title": "An Introduction to Counterfactual Regret Minimization", "date": "", "ddg_snippet": "by TW Neller · 2013 · Cited by 63 — After defining foundational terms of game theory , we discuss regret matching and present an algorithm computing strategy that minimizes expected ...", "subpage_snippet": "", "source": "modelai.gettysburg.edu", "link": "http://modelai.gettysburg.edu/2013/cfr/cfr.pdf", "content": "by TW Neller · 2013 · Cited by 63 — After defining foundational terms of game theory , we discuss regret matching and present an algorithm computing strategy that minimizes expected ..."}
+{"idx": 7, "title": "Last-Iterate Convergence Properties of Regret Matching ...", "date": "", "ddg_snippet": "4 Mar 2025 — The update rules are stepsize-free and only perform closed-form operations (thresholding and rescaling). Predictive RM + (PRM + , Algorithm 2) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "4 Mar 2025 — The update rules are stepsize-free and only perform closed-form operations (thresholding and rescaling). Predictive RM + (PRM + , Algorithm 2) ..."}
+{"idx": 8, "title": "Lecture 5 Learning in games: Algorithms (Part I)", "date": "", "ddg_snippet": "18 Sept 2024 — Because of that, regret matching+ has stronger practical performance and is often preferred over regret matching in the game solving literature.", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2024/6S890f24_L05_algo1/L05.pdf", "content": "18 Sept 2024 — Because of that, regret matching+ has stronger practical performance and is often preferred over regret matching in the game solving literature."}
+{"idx": 9, "title": "Geometrical regret matching: A new dynamics to Nash ...", "date": "", "ddg_snippet": "24 Jun 2020 — In other words, regret matching allows the players to reach equilibrium without strict mediation from beyond themselves. The concept of regret ...", "subpage_snippet": "", "source": "pubs.aip.org", "link": "https://pubs.aip.org/aip/adv/article/10/6/065033/992002/Geometrical-regret-matching-A-new-dynamics-to-Nash", "content": "24 Jun 2020 — In other words, regret matching allows the players to reach equilibrium without strict mediation from beyond themselves. The concept of regret ..."}
diff --git a/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_abstract_Farina_2023_year_2023.jsonl b/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_abstract_Farina_2023_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5a0c60583c1d58ba3ce01ed2e841d806118c679a
--- /dev/null
+++ b/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_abstract_Farina_2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract : Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "Abstract : Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability ."}
+{"idx": 1, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract . Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Abstract . Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to..."}
+{"idx": 2, "title": "Gabriele Farina - Regret Matching $^+$: ( In ) Stability and Fast ...", "date": "", "ddg_snippet": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM$^+$ and its predictive version can be unstable...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/", "content": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM$^+$ and its predictive version can be unstable..."}
+{"idx": 3, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/regret-matching-in-stability-and-fast-convergence-in-games", "content": "Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability ."}
+{"idx": 4, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nYgs0qZJ97", "content": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to..."}
+{"idx": 5, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371008709_Regret_Matching_InStability_and_Fast_Convergence_in_Games", "content": "Abstract . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability ."}
+{"idx": 6, "title": "Regret Matching $^+$: ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo.", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/farina-2023-regret/", "content": "Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo."}
+{"idx": 7, "title": "Julien Grand-Clément | 5 Publications | 2 Citations | Related Authors", "date": "", "ddg_snippet": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo +4 more.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/authors/julien-grand-clement-3tq4z79u", "content": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo +4 more."}
+{"idx": 8, "title": "Julien Grand-Clément - Google Akademik", "date": "", "ddg_snippet": "2023 . Regret Matching+ :( In ) Stability and Fast Convergence in Games . G Farina , J Grand-Clément, C Kroer, CW Lee, H Luo.", "subpage_snippet": "", "source": "scholar.google.com.eg", "link": "https://scholar.google.com.eg/citations?user=K_ZLzdoAAAAJ&hl=tr", "content": "2023 . Regret Matching+ :( In ) Stability and Fast Convergence in Games . G Farina , J Grand-Clément, C Kroer, CW Lee, H Luo."}
+{"idx": 9, "title": "Articles by Julien Grand-Clément | Synthical", "date": "", "ddg_snippet": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . 24 May 2023 by Gabriele Farina and others.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/1551d66f-d464-4596-898d-eb958ce85108/articles", "content": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . 24 May 2023 by Gabriele Farina and others."}
diff --git a/data/sampled_jsons/Regret_matching+_instability_fast_convergence_games_Farina_2023_abstract_year_2023.jsonl b/data/sampled_jsons/Regret_matching+_instability_fast_convergence_games_Farina_2023_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e321592e884b434991e0825a8dd53dcff312762a
--- /dev/null
+++ b/data/sampled_jsons/Regret_matching+_instability_fast_convergence_games_Farina_2023_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ..."}
+{"idx": 1, "title": "PDF Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
+{"idx": 2, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and ..."}
+{"idx": 3, "title": "Regret Matching$^+$: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/farina-2023-regret/", "content": "Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL"}
+{"idx": 4, "title": "PDF Regret Matching+: - Instability, average- and last-iterate convergence ...", "date": "", "ddg_snippet": "Instability , average- and last-iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Instability , average- and last-iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris"}
+{"idx": 5, "title": "Regret matching+ | Proceedings of the 37th International Conference on ...", "date": "", "ddg_snippet": "Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668812", "content": "Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that ..."}
+{"idx": 6, "title": "Gabriele Farina - Regret Matching$^+$: (In)Stability and Fast ...", "date": "", "ddg_snippet": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM$^+$ and its predictive version can be unstable, which might cause other players to suffer large regret .", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/", "content": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM$^+$ and its predictive version can be unstable, which might cause other players to suffer large regret ."}
+{"idx": 7, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to suffer large regret .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nYgs0qZJ97", "content": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to suffer large regret ."}
+{"idx": 8, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching + (RM +) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2305.14709", "content": "Abstract Regret Matching + (RM +) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
+{"idx": 9, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371008709_Regret_Matching_InStability_and_Fast_Convergence_in_Games", "content": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability."}
diff --git a/data/sampled_jsons/Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_Branched_mapping_layers_year_2023-2024.jsonl b/data/sampled_jsons/Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_Branched_mapping_layers_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..566fc883791d8637d43b064083b21b9b9c386ded
--- /dev/null
+++ b/data/sampled_jsons/Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_Branched_mapping_layers_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What is another word for reimagining ? - WordHippo", "date": "", "ddg_snippet": "Find 82 synonyms for reimagining and other similar words that you can use instead from our thesaurus.", "subpage_snippet": "", "source": "www.wordhippo.com", "link": "https://www.wordhippo.com/what-is/another-word-for/reimagining.html", "content": "Find 82 synonyms for reimagining and other similar words that you can use instead from our thesaurus."}
+{"idx": 1, "title": "reimagining , n. meanings, etymology and more | Oxford English ...", "date": "", "ddg_snippet": "reimagining , n. meanings, etymology, pronunciation and more in the Oxford English Dictionary", "subpage_snippet": "", "source": "www.oed.com", "link": "https://www.oed.com/dictionary/reimagining_n", "content": "reimagining , n. meanings, etymology, pronunciation and more in the Oxford English Dictionary"}
+{"idx": 2, "title": "Reimagining - Definition, Meaning, and Examples in English", "date": "", "ddg_snippet": "The concept of reimagining has gained popularity in recent years as a way to encourage creativity, innovation, and fresh perspectives in various fields such as art, literature, and business.", "subpage_snippet": "", "source": "finesentence.com", "link": "https://finesentence.com/meaning/reimagining", "content": "The concept of reimagining has gained popularity in recent years as a way to encourage creativity, innovation, and fresh perspectives in various fields such as art, literature, and business."}
+{"idx": 3, "title": "Home - Reimagining Education", "date": "", "ddg_snippet": "With the aspirational goal of reimagining education and reinvigorating schools, this five-year plan outlines major changes and improvements designed to strengthen student outcomes in Des Moines.", "subpage_snippet": "", "source": "reimagine.dmschools.org", "link": "https://reimagine.dmschools.org/", "content": "With the aspirational goal of reimagining education and reinvigorating schools, this five-year plan outlines major changes and improvements designed to strengthen student outcomes in Des Moines."}
+{"idx": 4, "title": "REIMAGINING Synonyms: 30 Similar Words - Merriam-Webster", "date": "", "ddg_snippet": "to think about again especially in order to change or improve The director reimagined the classic movie for a new generation. Examples are automatically compiled from online sources to show current usage. Read More.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/thesaurus/reimagining", "content": "to think about again especially in order to change or improve The director reimagined the classic movie for a new generation. Examples are automatically compiled from online sources to show current usage. Read More."}
+{"idx": 5, "title": "REIMAGINE Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of REIMAGINE is to imagine again or anew ; especially : to form a new conception of : re-create.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/reimagine", "content": "The meaning of REIMAGINE is to imagine again or anew ; especially : to form a new conception of : re-create."}
+{"idx": 6, "title": "REIMAGINE | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "REIMAGINE definition: 1. to have a new idea about the way something should be: 2. to have a new idea about the way…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/reimagine", "content": "REIMAGINE definition: 1. to have a new idea about the way something should be: 2. to have a new idea about the way…. Learn more."}
+{"idx": 7, "title": "REIMAGINE Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Reimagine definition: to think about or consider in a new and creative way: Each of the forty short stories reimagines a moment from the original film, but through the eyes of a supporting character.. See examples of REIMAGINE used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/reimagine", "content": "Reimagine definition: to think about or consider in a new and creative way: Each of the forty short stories reimagines a moment from the original film, but through the eyes of a supporting character.. See examples of REIMAGINE used in a sentence."}
+{"idx": 8, "title": "REIMAGINE Synonyms: 26 Similar Words - Merriam-Webster", "date": "", "ddg_snippet": "to think about again especially in order to change or improve The director reimagined the classic movie for a new generation. Examples are automatically compiled from online sources to show current usage. Read More.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/thesaurus/reimagine", "content": "to think about again especially in order to change or improve The director reimagined the classic movie for a new generation. Examples are automatically compiled from online sources to show current usage. Read More."}
+{"idx": 9, "title": "REIMAGE Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of REIMAGE is to create a new image of (someone or something). How to use reimage in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/reimage", "content": "The meaning of REIMAGE is to create a new image of (someone or something). How to use reimage in a sentence."}
diff --git a/data/sampled_jsons/Rew_short_equation_formula_feint_behaviors.jsonl b/data/sampled_jsons/Rew_short_equation_formula_feint_behaviors.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a68ddfffa7bdac3089682b4c7f674bb1f15c1cae
--- /dev/null
+++ b/data/sampled_jsons/Rew_short_equation_formula_feint_behaviors.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v2", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Feint in Multi-Player Games - arXiv.org", "date": "", "ddg_snippet": "Abstract This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v1", "content": "Abstract This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts."}
+{"idx": 2, "title": "F M -P G - arXiv.org", "date": "", "ddg_snippet": "Technical Report-Feb-24, at User-Centric Computing Group, 2024 Feint shall be (1) the actions that follow Feint actions (e.g. actual attacks) in a short-term period of time should have strong correlation to Feint ; (2) the actions in the long-term periods explicitly or implicitly depend on the effect of the Feint and its following actions; and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v1.pdf", "content": "Technical Report-Feb-24, at User-Centric Computing Group, 2024 Feint shall be (1) the actions that follow Feint actions (e.g. actual attacks) in a short-term period of time should have strong correlation to Feint ; (2) the actions in the long-term periods explicitly or implicitly depend on the effect of the Feint and its following actions; and ..."}
+{"idx": 3, "title": "REW / ProShares Trust - ProShares UltraShort ... - Fintel", "date": "", "ddg_snippet": "Implied Volatility The 30-day options-implied volatility of REW / ProShares Trust - ProShares UltraShort Technology is 66.14.", "subpage_snippet": "", "source": "fintel.io", "link": "https://fintel.io/siv/us/rew", "content": "Implied Volatility The 30-day options-implied volatility of REW / ProShares Trust - ProShares UltraShort Technology is 66.14."}
+{"idx": 4, "title": "6REW - Short on grant money? Five tips for crowdfunding ...", "date": "", "ddg_snippet": "Reading from unit 7.2 Learn with flashcards, games, and more — for free.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/au/592828608/6rew-short-on-grant-money-five-tips-for-crowdfunding-success-flash-cards/", "content": "Reading from unit 7.2 Learn with flashcards, games, and more — for free."}
+{"idx": 5, "title": "REW - Short Squeeze Stock Short Interest for Proshares ...", "date": "", "ddg_snippet": "REW - Short squeeze stock short interest data and short selling information for shares of Proshares Ultrashort Technology. Short interest stock data available for NASDAQ, NYSE, AMEX, OTCBB and Pink Sheets stocks available from shortsqueeze.com.", "subpage_snippet": "", "source": "shortsqueeze.com", "link": "https://shortsqueeze.com/shortinterest/stock/REW.htm", "content": "REW - Short squeeze stock short interest data and short selling information for shares of Proshares Ultrashort Technology. Short interest stock data available for NASDAQ, NYSE, AMEX, OTCBB and Pink Sheets stocks available from shortsqueeze.com."}
+{"idx": 6, "title": "REW / ProShares Trust - ProShares UltraShort Technology - Put ...", "date": "", "ddg_snippet": "Put/Call Ratios - Forward Looking and Historical The OI Put/Call Ratio for REW / ProShares Trust - ProShares UltraShort Technology is 0.01. The Put/Call Ratio shows the total number of disclosed open put option positions divided by the number of open call options. Since puts are generally a bearish bet and calls are a bullish bet, put/call ratios greater than 1 indicate a bearish sentiment ...", "subpage_snippet": "", "source": "fintel.io", "link": "https://fintel.io/sopt/us/rew", "content": "Put/Call Ratios - Forward Looking and Historical The OI Put/Call Ratio for REW / ProShares Trust - ProShares UltraShort Technology is 0.01. The Put/Call Ratio shows the total number of disclosed open put option positions divided by the number of open call options. Since puts are generally a bearish bet and calls are a bullish bet, put/call ratios greater than 1 indicate a bearish sentiment ..."}
diff --git a/data/sampled_jsons/Robust_Contrastive_Learning_against_Noisy_Views_CVPR_2022.jsonl b/data/sampled_jsons/Robust_Contrastive_Learning_against_Noisy_Views_CVPR_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bcf8f78adafc907da779cfcf13388ed237a614c9
--- /dev/null
+++ b/data/sampled_jsons/Robust_Contrastive_Learning_against_Noisy_Views_CVPR_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR 2022 Open Access Repository", "date": "", "ddg_snippet": "by CY Chuang · 2022 · Cited by 109 — In this work, we propose a new contrastive loss function that is robust against noisy views . We provide rigorous theoretical justifications by showing ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/html/Chuang_Robust_Contrastive_Learning_Against_Noisy_Views_CVPR_2022_paper.html", "content": "by CY Chuang · 2022 · Cited by 109 — In this work, we propose a new contrastive loss function that is robust against noisy views . We provide rigorous theoretical justifications by showing ..."}
+{"idx": 1, "title": "Robust Contrastive Learning against Noisy Views", "date": "", "ddg_snippet": "This repository provides a PyTorch implementation of the Robust InfoNCE loss proposed in paper Robust Contrastive Learning against Noisy Views .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chingyaoc/RINCE", "content": "This repository provides a PyTorch implementation of the Robust InfoNCE loss proposed in paper Robust Contrastive Learning against Noisy Views ."}
+{"idx": 2, "title": "Robust Contrastive Learning against Noisy Views", "date": "", "ddg_snippet": "by CY Chuang · 2022 · Cited by 108 — In this work, we pro-pose a new contrastive loss function that is robust against noisy views . We provide rigorous theoretical justifications by showing ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2022/694600q6649/1H0KFZe4b1S", "content": "by CY Chuang · 2022 · Cited by 108 — In this work, we pro-pose a new contrastive loss function that is robust against noisy views . We provide rigorous theoretical justifications by showing ..."}
+{"idx": 3, "title": "arXiv:2201.04309v1 [cs.CV] 12 Jan 2022", "date": "", "ddg_snippet": "by CY Chuang · 2022 · Cited by 108 — The literature suggests that contrastive learning produces suboptimal representations in the presence of noisy views , e.g., false positive pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2201.04309", "content": "by CY Chuang · 2022 · Cited by 108 — The literature suggests that contrastive learning produces suboptimal representations in the presence of noisy views , e.g., false positive pairs."}
+{"idx": 4, "title": "Noisy-Correspondence Learning Summary (Updating)", "date": "", "ddg_snippet": "Image Contrastive Learning. [2022 CVPR] Robust contrastive learning against noisy views . Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/XLearning-SCU/Awesome-Noisy-Correspondence", "content": "Image Contrastive Learning. [2022 CVPR] Robust contrastive learning against noisy views . Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi ..."}
+{"idx": 5, "title": "A Unified Framework of Theoretically Robust Contrastive ...", "date": "", "ddg_snippet": "by J Cui — Recent papers have undertaken the problem of studying contrastive losses under the robustness setup e.g. against noisy views (in the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=L76lvHZqeS", "content": "by J Cui — Recent papers have undertaken the problem of studying contrastive losses under the robustness setup e.g. against noisy views (in the ..."}
+{"idx": 6, "title": "Robust Noisy Pseudo-label Learning for Multi-View Clustering ...", "date": "", "ddg_snippet": "by Y Sun · 2025 · Cited by 1 — To further mitigate the over- fitting problem, we present robust multi-view contrastive learning to mitigate the negative impact of NCP. Extensive experiments ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_ROLL_Robust_Noisy_Pseudo-label_Learning_for_Multi-View_Clustering_with_Noisy_CVPR_2025_paper.pdf", "content": "by Y Sun · 2025 · Cited by 1 — To further mitigate the over- fitting problem, we present robust multi-view contrastive learning to mitigate the negative impact of NCP. Extensive experiments ..."}
+{"idx": 7, "title": "Symmetric Graph Contrastive Learning against Noisy ...", "date": "", "ddg_snippet": "9 May 2025 — We introduce symmetry theory into graph contrastive learning , based on which we propose a symmetric form and contrast loss resistant to noisy interference.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3722103", "content": "9 May 2025 — We introduce symmetry theory into graph contrastive learning , based on which we propose a symmetric form and contrast loss resistant to noisy interference."}
+{"idx": 8, "title": "An Inclusive Theoretical Framework of Robust Supervised ...", "date": "", "ddg_snippet": "In this paper, we propose a unified theoretical framework for robust losses under the pairwise contrastive paradigm.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.01130v1", "content": "In this paper, we propose a unified theoretical framework for robust losses under the pairwise contrastive paradigm."}
+{"idx": 9, "title": "Neel Joshi", "date": "", "ddg_snippet": "Robust Contrastive Learning against Noisy Views · 1 code implementation • CVPR 2022 • Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Neel+Joshi&order=desc&order_by=stars", "content": "Robust Contrastive Learning against Noisy Views · 1 code implementation • CVPR 2022 • Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi ..."}
diff --git a/data/sampled_jsons/Robust_detection_and_tracking_of_objects_is_crucial_for_the_deployment_of_autonomous_vehicle_technol.jsonl b/data/sampled_jsons/Robust_detection_and_tracking_of_objects_is_crucial_for_the_deployment_of_autonomous_vehicle_technol.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..04c2320a1be6ff821586a8790ee1bc0702ef61dd
--- /dev/null
+++ b/data/sampled_jsons/Robust_detection_and_tracking_of_objects_is_crucial_for_the_deployment_of_autonomous_vehicle_technol.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[PDF] nuScenes : A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/nuScenes:-A-Multimodal-Dataset-for-Autonomous-Caesar-Bankiti/9e475a514f54665478aac6038c262e5a6bac5e64", "content": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment."}
+{"idx": 1, "title": "(PDF) nuScenes : A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image-based benchmark datasets have driven the development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/332011352_nuScenes_A_multimodal_dataset_for_autonomous_driving", "content": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image-based benchmark datasets have driven the development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment."}
+{"idx": 2, "title": "[1903.11027] nuScenes : A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment."}
+{"idx": 3, "title": "AI-Powered Paper Summarization about the arXiv paper 1903.11027v5", "date": "", "ddg_snippet": "Abstract: Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/1903.11027v5/", "content": "Abstract: Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment."}
+{"idx": 4, "title": "Nuscenes Dataset Pdf", "date": "", "ddg_snippet": "1 week ago WEB Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven development in …", "subpage_snippet": "", "source": "www.tpsearchtool.com", "link": "https://www.tpsearchtool.com/web/nuscenes-dataset-pdf", "content": "1 week ago WEB Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven development in …"}
+{"idx": 5, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "by H Caesar · 2020 · Cited by 8367 — Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9156412", "content": "by H Caesar · 2020 · Cited by 8367 — Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven ..."}
+{"idx": 6, "title": "nuScenes: A multimodal dataset for autonomous driving - ar5iv", "date": "", "ddg_snippet": "Abstract. Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1903.11027", "content": "Abstract. Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven ..."}
+{"idx": 7, "title": "nuScenes: Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Abstract: Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/articles/1903.11027", "content": "Abstract: Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have ..."}
+{"idx": 8, "title": "Probabilistic 3D Multi-Object Tracking for Autonomous ...", "date": "", "ddg_snippet": "2020. Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Probabilistic-3D-Multi-Object-Tracking-for-Driving-Chiu-Prioletti/b13aabb0d92fe564e402accbf8955d75f513d9fe", "content": "2020. Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology . Image based benchmark datasets have driven ..."}
+{"idx": 9, "title": "Aptiv Releases Comprehensive Open-Source... - Self Driving Cars 360", "date": "", "ddg_snippet": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology and there is a demand for high-quality datasets.(Photo: IEEE Spectrum) nuScenes Includes the Full Autonomous Vehicle Hardware Suite.", "subpage_snippet": "", "source": "www.selfdrivingcars360.com", "link": "https://www.selfdrivingcars360.com/aptiv-releases-comprehensive-open-source-dataset-for-autonomous-driving/", "content": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology and there is a demand for high-quality datasets.(Photo: IEEE Spectrum) nuScenes Includes the Full Autonomous Vehicle Hardware Suite."}
diff --git a/data/sampled_jsons/Rosenbrock_function_f(x,y)_=_-100(y_-_x^2)^2_-_(1_-_x)^2.jsonl b/data/sampled_jsons/Rosenbrock_function_f(x,y)_=_-100(y_-_x^2)^2_-_(1_-_x)^2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..99f8e6d32f58f7c1bf9e8bb178a3dc8ac299eaa4
--- /dev/null
+++ b/data/sampled_jsons/Rosenbrock_function_f(x,y)_=_-100(y_-_x^2)^2_-_(1_-_x)^2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Minimizing the Rosenbrock Function - смотреть видео онлайн от...", "date": "", "ddg_snippet": "The Rosenbrock function , f ( x , y ) = ( 1 - x )^ 2 + 100 ( y - x ^ 2 )^ 2 , is a classic test function in optimisation theory. It is sometimes referred to as Rosenbrock 's banana function due to the shape of its contour lines.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/45efd2355fca4c6c8d4a42a5005d7cef/", "content": "The Rosenbrock function , f ( x , y ) = ( 1 - x )^ 2 + 100 ( y - x ^ 2 )^ 2 , is a classic test function in optimisation theory. It is sometimes referred to as Rosenbrock 's banana function due to the shape of its contour lines."}
+{"idx": 1, "title": "Lecture-7- Rosenbrock", "date": "", "ddg_snippet": "The rosenbrock function is a 2 d function ( x , y ) -> f ( x , y ). It has a steep internal valley with a gradual slope that makes it a useful test case. It is $$ f ( x , y ) = ( 1 - x )^ 2 + 100 ( y - x ^ 2 )^ 2 $$ So the function has a global minimizer at x = 1 , y = 1 .", "subpage_snippet": "", "source": "www.cs.purdue.edu", "link": "https://www.cs.purdue.edu/homes/dgleich/cs520-2023/julia/Lecture-7-Rosenbrock.html", "content": "The rosenbrock function is a 2 d function ( x , y ) -> f ( x , y ). It has a steep internal valley with a gradual slope that makes it a useful test case. It is $$ f ( x , y ) = ( 1 - x )^ 2 + 100 ( y - x ^ 2 )^ 2 $$ So the function has a global minimizer at x = 1 , y = 1 ."}
+{"idx": 2, "title": "Rosenbrock Function -- from Wolfram MathWorld", "date": "", "ddg_snippet": "The Rosenbrock function , also known as Rosenbrock 's banana function or Rosenbrock 's valley, is the function defined by. Rosenbrock , H. H. \"An Automatic Method for Finding the Greatest or Least Value of a Function .\"", "subpage_snippet": "", "source": "mathworld.wolfram.com", "link": "https://mathworld.wolfram.com/RosenbrockFunction.html", "content": "The Rosenbrock function , also known as Rosenbrock 's banana function or Rosenbrock 's valley, is the function defined by. Rosenbrock , H. H. \"An Automatic Method for Finding the Greatest or Least Value of a Function .\""}
+{"idx": 3, "title": "Rosenbrock", "date": "", "ddg_snippet": "The Rosenbrock function ¶.Plot the surface surf = axRos.plot_surface( X , Y , Z, cmap=cm.gist_heat_r, linewidth=0, antialiased=False) axRos.set_zlim(0, 200) figRos.colorbar(surf, shrink=0.5, aspect=10) plt.show().", "subpage_snippet": "", "source": "people.bath.ac.uk", "link": "https://people.bath.ac.uk/ps2106/files/courses/MA40050/2020/jupyter/Rosenbrock.html", "content": "The Rosenbrock function ¶.Plot the surface surf = axRos.plot_surface( X , Y , Z, cmap=cm.gist_heat_r, linewidth=0, antialiased=False) axRos.set_zlim(0, 200) figRos.colorbar(surf, shrink=0.5, aspect=10) plt.show()."}
+{"idx": 4, "title": "Rosenbrock banana function | Optimization test case", "date": "", "ddg_snippet": "The Rosenbrock 's banana function is a common test case for optimization software. It gets its name from its banana-shaped contour sets.", "subpage_snippet": "", "source": "www.johndcook.com", "link": "https://www.johndcook.com/blog/2010/07/28/rosenbrocks-banana-function/", "content": "The Rosenbrock 's banana function is a common test case for optimization software. It gets its name from its banana-shaped contour sets."}
+{"idx": 5, "title": "Figure 18 illustrates the optimization of the Rosenbrock function f ( x ,...", "date": "", "ddg_snippet": "The Rosenbrock function is a standard test function for optimization algorithms. It is a badly conditioned problem, with a narrow winding valley from a start point to the global minimum. Figure 18b nicely illustrates the change of the second-order statistics in our method.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/llustrates-the-optimization-of-the-Rosenbrock-function-f-x-y-100x-2-y-2-1_fig3_220100074", "content": "The Rosenbrock function is a standard test function for optimization algorithms. It is a badly conditioned problem, with a narrow winding valley from a start point to the global minimum. Figure 18b nicely illustrates the change of the second-order statistics in our method."}
+{"idx": 6, "title": "MATLAB Rosenbrock TEST PROBLEM", "date": "", "ddg_snippet": "%In this script we apply steepest descent with the %backtracking linesearch to minimize the 2 -D % Rosenbrock function starting at the point x =(- 1 .9, 2 ). %Termination parameters eps = 1 .0e-4; epsf = 1 .0e-6; maxit = 10000; iter = 0", "subpage_snippet": "", "source": "sites.math.washington.edu", "link": "https://sites.math.washington.edu/~burke/crs/516/pgm/rosen-testproblem.html", "content": "%In this script we apply steepest descent with the %backtracking linesearch to minimize the 2 -D % Rosenbrock function starting at the point x =(- 1 .9, 2 ). %Termination parameters eps = 1 .0e-4; epsf = 1 .0e-6; maxit = 10000; iter = 0"}
+{"idx": 7, "title": "Rosenbrock function — Pints 0.5. 1 documentation", "date": "", "ddg_snippet": "Returns the global optimum for this function .Unnormalised LogPDF based on the Rosenbrock function [ 2 ] with an addition of 1 on the denominator to avoid a discontinuity", "subpage_snippet": "", "source": "pints.readthedocs.io", "link": "https://pints.readthedocs.io/en/latest/toy/rosenbrock.html", "content": "Returns the global optimum for this function .Unnormalised LogPDF based on the Rosenbrock function [ 2 ] with an addition of 1 on the denominator to avoid a discontinuity"}
+{"idx": 8, "title": "Minimization of the Rosenbrock Function — algopy documentation", "date": "", "ddg_snippet": "In this example we want to use AlgoPy to help compute the minimum of the non-convex bivariate Rosenbrock function .", "subpage_snippet": "", "source": "pythonhosted.org", "link": "https://pythonhosted.org/algopy/examples/minimization/rosenbrock_banana.html", "content": "In this example we want to use AlgoPy to help compute the minimum of the non-convex bivariate Rosenbrock function ."}
+{"idx": 9, "title": "A Genetic Algorithm Function Optimizer in C++ – technical-recipes.com", "date": "", "ddg_snippet": "The Rosenbrock function is defined by: f ( x , y ) = ( 1 - x )^ 2 + 100 ( y - x ^ 2 )^ 2 . As shown in the diagram, the global minimum lies inside a long, narrow, parabolic shaped flat valley.", "subpage_snippet": "", "source": "www.technical-recipes.com", "link": "https://www.technical-recipes.com/2012/a-genetic-algorithm-function-optimizer-in-c/", "content": "The Rosenbrock function is defined by: f ( x , y ) = ( 1 - x )^ 2 + 100 ( y - x ^ 2 )^ 2 . As shown in the diagram, the global minimum lies inside a long, narrow, parabolic shaped flat valley."}
diff --git a/data/sampled_jsons/Rs(k)_Hoeffding_Statistical_Collusion_filetypepdf.jsonl b/data/sampled_jsons/Rs(k)_Hoeffding_Statistical_Collusion_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c150920509096a64a56d3e1d33c845d98e868c5f
--- /dev/null
+++ b/data/sampled_jsons/Rs(k)_Hoeffding_Statistical_Collusion_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Study of Statistical Zero-Knowledge Proofs Salil Pravin ...", "date": "", "ddg_snippet": "by SP Vadhan · 1999 · Cited by 170 — These problems essentially amount to approximating the statistical difference or the difference in entropies between two \"efficiently sampleable\" distributions.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/85349/44815159-MIT.pdf?sequence=2", "content": "by SP Vadhan · 1999 · Cited by 170 — These problems essentially amount to approximating the statistical difference or the difference in entropies between two \"efficiently sampleable\" distributions."}
+{"idx": 1, "title": "Statistical and Computational Phase Transitions in Group ...", "date": "", "ddg_snippet": "by A Coja-Oghlan · 2022 · Cited by 33 — Abstract. We study the group testing problem where the goal is to identify a set of k infected indi- viduals carrying a rare disease within ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2206.07640", "content": "by A Coja-Oghlan · 2022 · Cited by 33 — Abstract. We study the group testing problem where the goal is to identify a set of k infected indi- viduals carrying a rare disease within ..."}
+{"idx": 2, "title": "arXiv:2310.11308v2 [quant-ph] 19 Jun 2024", "date": "", "ddg_snippet": "by VN Rao · 2023 · Cited by 3 — observed value due to statistical fluctuations by Hoeffding in- equality [50], with ϵF denoting the failure probability. Note that these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.11308", "content": "by VN Rao · 2023 · Cited by 3 — observed value due to statistical fluctuations by Hoeffding in- equality [50], with ϵF denoting the failure probability. Note that these ..."}
+{"idx": 3, "title": "Salil Pravin Vadhan", "date": "", "ddg_snippet": "by SP Vadhan · 1999 · Cited by 170 — This thesis is a detailed investigation of statistical zero-knowledge proofs, which are zero- knowledge proofs in which the condition that the verifier \"learns ...", "subpage_snippet": "", "source": "people.seas.harvard.edu", "link": "https://people.seas.harvard.edu/~salil/research/phdthesis.pdf", "content": "by SP Vadhan · 1999 · Cited by 170 — This thesis is a detailed investigation of statistical zero-knowledge proofs, which are zero- knowledge proofs in which the condition that the verifier \"learns ..."}
+{"idx": 4, "title": "High-dimensional statistics with systematically corrupted ...", "date": "", "ddg_snippet": "by PLI Loh · 2014 · Cited by 2 — Noisy and missing data are prevalent in many real-world statistical estimation problems. Popular techniques for handling nonidealities in data, ...", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt8j49c5n4/qt8j49c5n4_noSplash_dfa731d0f2eb4a598d36b08d29143e01.pdf", "content": "by PLI Loh · 2014 · Cited by 2 — Noisy and missing data are prevalent in many real-world statistical estimation problems. Popular techniques for handling nonidealities in data, ..."}
+{"idx": 5, "title": "Notes on Randomized Algorithms", "date": "", "ddg_snippet": "by J Aspnes · 2009 · Cited by 12 — These are notes for the Yale course CPSC 4690/5690 Randomized Algorithms. This document also incorporates the lecture schedule and ... 550 pages", "subpage_snippet": "", "source": "www.cs.yale.edu", "link": "http://www.cs.yale.edu/~aspnes/classes/469/notes.pdf", "content": "by J Aspnes · 2009 · Cited by 12 — These are notes for the Yale course CPSC 4690/5690 Randomized Algorithms. This document also incorporates the lecture schedule and ... 550 pages"}
+{"idx": 6, "title": "Beating full state tomography for unentangled spectrum ...", "date": "", "ddg_snippet": "by A Pelecanos — Our algorithm is inspired by the technique of local moment matching from classical statistics , and shows how it can be applied in the quantum setting. As an ...", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jswright/papers/spectrum.pdf", "content": "by A Pelecanos — Our algorithm is inspired by the technique of local moment matching from classical statistics , and shows how it can be applied in the quantum setting. As an ..."}
+{"idx": 7, "title": "a non-uniform birthday problem with applications to ...", "date": "", "ddg_snippet": "by SD Galbraith · 2010 · Cited by 42 — The method gives bounds on the error involved in approximating the distribution of a sum of dependent Bernoulli random variables by the Poisson distribution.", "subpage_snippet": "", "source": "eprint.iacr.org", "link": "https://eprint.iacr.org/2010/616.pdf", "content": "by SD Galbraith · 2010 · Cited by 42 — The method gives bounds on the error involved in approximating the distribution of a sum of dependent Bernoulli random variables by the Poisson distribution."}
+{"idx": 8, "title": "Improved Regret Bound for Safe Reinforcement ...", "date": "", "ddg_snippet": "by K Yu — This paper studies the safe reinforcement learning problem formulated as an episodic finite- horizon tabular constrained Markov decision process with an ...", "subpage_snippet": "", "source": "rlj.cs.umass.edu", "link": "https://rlj.cs.umass.edu/2025/papers/RLJ_RLC_2025_41.pdf", "content": "by K Yu — This paper studies the safe reinforcement learning problem formulated as an episodic finite- horizon tabular constrained Markov decision process with an ..."}
+{"idx": 9, "title": "Performance and Convergence of Multi-user Online Learning", "date": "", "ddg_snippet": "We study the problem of allocating multiple users to a set of wireless channels in a decentralized manner when the channel qualities are time-varying and ...", "subpage_snippet": "", "source": "web.eecs.umich.edu", "link": "http://web.eecs.umich.edu/~mingyan/pub/gamenets11_abs.pdf", "content": "We study the problem of allocating multiple users to a set of wireless channels in a decentralized manner when the channel qualities are time-varying and ..."}
diff --git a/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning.jsonl b/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7b3a5024d66c4ec142e1a3545175b6bb13bef769
--- /dev/null
+++ b/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by D Lee · 2025 — We formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "by D Lee · 2025 — We formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "This paper introduces a theoretically principled approach for producing sparse and flat models . The approach is based on an augmented Lagrange dual approach and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=10l1pGeOcK&noteId=gDiOJllnP7", "content": "This paper introduces a theoretically principled approach for producing sparse and flat models . The approach is based on an augmented Lagrange dual approach and ..."}
+{"idx": 2, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Our results show that SAFE and SAFE+ successfully induce flatter and sparser minima , improving over existing baselines in both image classification and post- ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46658", "content": "Our results show that SAFE and SAFE+ successfully induce flatter and sparser minima , improving over existing baselines in both image classification and post- ..."}
+{"idx": 3, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "7 Jun 2025 — We show that Safe can be applied to neural network pruning , and as a result, it not only obtains the desired flatness as well as high sparsity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v1", "content": "7 Jun 2025 — We show that Safe can be applied to neural network pruning , and as a result, it not only obtains the desired flatness as well as high sparsity ..."}
+{"idx": 4, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by DLKLJ Chung · 2025 — SAFE yields sparse and flat solutions . 13 / 19. Page 14. Result: Improved generalization performance in Image classification.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46658.pdf", "content": "by DLKLJ Chung · 2025 — SAFE yields sparse and flat solutions . 13 / 19. Page 14. Result: Improved generalization performance in Image classification."}
+{"idx": 5, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "In this section, we present a detailed derivation of our flatness-inducing sparsification algorithm Sparsification via. ADMM with Flatness Enforcement or SAFE . 22 pages", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/5003997aae626442660b9518fd47703763fe5552.pdf", "content": "In this section, we present a detailed derivation of our flatness-inducing sparsification algorithm Sparsification via. ADMM with Flatness Enforcement or SAFE . 22 pages"}
+{"idx": 6, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "TLDR: We propose SAFE, an optimization-based pruning method that improves generalization of sparse models by inducing flatness. Sparsifying neural networks ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165221", "content": "TLDR: We propose SAFE, an optimization-based pruning method that improves generalization of sparse models by inducing flatness. Sparsifying neural networks ..."}
+{"idx": 7, "title": "ICML 2025 Spotlight Paper - Dongyeop Lee", "date": "", "ddg_snippet": "by D Lee — SAFE: Finding Sparse and Flat Minima to Improve Pruning . Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee. Pohang University of Science and ...", "subpage_snippet": "", "source": "edong6768.github.io", "link": "https://edong6768.github.io/assets/pdf/prj-safe_poster.pdf", "content": "by D Lee — SAFE: Finding Sparse and Flat Minima to Improve Pruning . Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee. Pohang University of Science and ..."}
+{"idx": 8, "title": "Kwanhee Lee", "date": "", "ddg_snippet": "SAFE: Finding Sparse and Flat Minima to Improve Pruning . D Lee, K Lee, J Chung, N Lee. ICML, 2025. 2025. The system can't perform the operation now. Try again ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=QLXgaCMAAAAJ&hl=en", "content": "SAFE: Finding Sparse and Flat Minima to Improve Pruning . D Lee, K Lee, J Chung, N Lee. ICML, 2025. 2025. The system can't perform the operation now. Try again ..."}
+{"idx": 9, "title": "Dongyeop Lee", "date": "", "ddg_snippet": "SAFE: Finding Sparse and Flat Minima to Improve Pruning . Dongyeop Lee ... develop optimization methods to explicitly find flat minima as more generalizable ...", "subpage_snippet": "", "source": "edong6768.github.io", "link": "https://edong6768.github.io/", "content": "SAFE: Finding Sparse and Flat Minima to Improve Pruning . Dongyeop Lee ... develop optimization methods to explicitly find flat minima as more generalizable ..."}
diff --git a/data/sampled_jsons/SAFE_algorithm_Section_3.2_dual_variable_u_update_rule.jsonl b/data/sampled_jsons/SAFE_algorithm_Section_3.2_dual_variable_u_update_rule.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7fe291f64f2cc76e8daab79df83cf77037553489
--- /dev/null
+++ b/data/sampled_jsons/SAFE_algorithm_Section_3.2_dual_variable_u_update_rule.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Safe Policies for Reinforcement Learning via Primal-Dual ...", "date": "", "ddg_snippet": "by S Paternain · 2019 · Cited by 156 — Notice that the dual function is the point-wise maximum of linear functions, and it is therefore convex [28, Section . 3.2 .3] regardless of the structure of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1911.09101", "content": "by S Paternain · 2019 · Cited by 156 — Notice that the dual function is the point-wise maximum of linear functions, and it is therefore convex [28, Section . 3.2 .3] regardless of the structure of ..."}
+{"idx": 1, "title": "Constrained Variational Policy Optimization for Safe ...", "date": "", "ddg_snippet": "by Z Liu · 2022 · Cited by 119 — Generally, the primal-dual style algorithms alternate between optimizing the policy parameters and updating the dual variables , which are usu- ally performed ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/liu22b/liu22b.pdf", "content": "by Z Liu · 2022 · Cited by 119 — Generally, the primal-dual style algorithms alternate between optimizing the policy parameters and updating the dual variables , which are usu- ally performed ..."}
+{"idx": 2, "title": "Provably Efficient Primal-Dual Reinforcement Learning for ...", "date": "", "ddg_snippet": "by Y Ding · Cited by 41 — This paper provides the first provably efficient algorithm for non-stationary CMDPs with safe exploration. 1. Introduction. Safe reinforcement Learning (RL) ...", "subpage_snippet": "", "source": "lavaei.ieor.berkeley.edu", "link": "https://lavaei.ieor.berkeley.edu/Safe_RL_2022_1.pdf", "content": "by Y Ding · Cited by 41 — This paper provides the first provably efficient algorithm for non-stationary CMDPs with safe exploration. 1. Introduction. Safe reinforcement Learning (RL) ..."}
+{"idx": 3, "title": "Off-Policy Primal-Dual Safe Reinforcement Learning", "date": "", "ddg_snippet": "by Z Wu · Cited by 8 — Primal- dual safe RL methods commonly perform iterations between the primal update of the policy and the dual update of the Lagrange Multiplier.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vy42bYs1Wo", "content": "by Z Wu · Cited by 8 — Primal- dual safe RL methods commonly perform iterations between the primal update of the policy and the dual update of the Lagrange Multiplier."}
+{"idx": 4, "title": "Distributed Primal-Dual Algorithms: Unification ...", "date": "", "ddg_snippet": "1 Feb 2025 — This algorithm can be cast into an iterative update rule Consensus ... updates rule that only involve the primal and dual variable updates .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00470v1", "content": "1 Feb 2025 — This algorithm can be cast into an iterative update rule Consensus ... updates rule that only involve the primal and dual variable updates ."}
+{"idx": 5, "title": "Expanding Boundaries of Gap Safe Screening", "date": "", "ddg_snippet": "by CF Dantas · 2021 · Cited by 18 — In Section 3.2, we further exploit Theorem 5 to propose a new Gap Safe dynamic screening algorithm where, at each iteration, the Gap Safe sphere is iteratively ... 57 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume22/21-0179/21-0179.pdf", "content": "by CF Dantas · 2021 · Cited by 18 — In Section 3.2, we further exploit Theorem 5 to propose a new Gap Safe dynamic screening algorithm where, at each iteration, the Gap Safe sphere is iteratively ... 57 pages"}
+{"idx": 6, "title": "Machine Learning–Supported Prediction of Dual Variables for ...", "date": "", "ddg_snippet": "by S Kraul · 2023 · Cited by 17 — This article presents a prediction model of the optimal dual variables for the cutting stock problem.", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/10.1287/ijoc.2023.1277", "content": "by S Kraul · 2023 · Cited by 17 — This article presents a prediction model of the optimal dual variables for the cutting stock problem."}
+{"idx": 7, "title": "Constrained Variational Policy Optimization for Safe ...", "date": "", "ddg_snippet": "by Z Liu · 2022 · Cited by 119 — Generally, the primal-dual style algorithms alternate between optimizing the policy parameters and updating the dual variables , which are usu- ally performed ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10416733", "content": "by Z Liu · 2022 · Cited by 119 — Generally, the primal-dual style algorithms alternate between optimizing the policy parameters and updating the dual variables , which are usu- ally performed ..."}
+{"idx": 8, "title": "Optimal Control Duality and the Douglas–Rachford Algorithm", "date": "", "ddg_snippet": "We explore the relationship between the dual of a weighted minimum-energy control problem, a special case of linear-quadratic optimal control problems, ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/23M1558549", "content": "We explore the relationship between the dual of a weighted minimum-energy control problem, a special case of linear-quadratic optimal control problems, ..."}
+{"idx": 9, "title": "Optimization algorithms as robust feedback controllers", "date": "", "ddg_snippet": "by A Hauswirth · 2024 · Cited by 161 — In this article, we review a feedback approach to constrained nonlinear optimization that drives a physical system towards an optimal steady state.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1367578824000105", "content": "by A Hauswirth · 2024 · Cited by 161 — In this article, we review a feedback approach to constrained nonlinear optimization that drives a physical system towards an optimal steady state."}
diff --git a/data/sampled_jsons/SAFE_algorithm_penalty_parameter_lambda_dense_model_performance_degradation.jsonl b/data/sampled_jsons/SAFE_algorithm_penalty_parameter_lambda_dense_model_performance_degradation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b6128aa76e03a345220ac6bedf67391f27ff0fed
--- /dev/null
+++ b/data/sampled_jsons/SAFE_algorithm_penalty_parameter_lambda_dense_model_performance_degradation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "how to choose lambda value while working with elsaticnet package", "date": "", "ddg_snippet": "lambda : Quadratic penalty parameter . lambda =0 performs the Lasso fit.Thus lower values of lambda tend to yield sparser model fits, while higher values of lambda tend to yield denser model fits. Still, this does not give us a way to choose an actual value.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/151247/how-to-choose-lambda-value-while-working-with-elsaticnet-package/151253", "content": "lambda : Quadratic penalty parameter . lambda =0 performs the Lasso fit.Thus lower values of lambda tend to yield sparser model fits, while higher values of lambda tend to yield denser model fits. Still, this does not give us a way to choose an actual value."}
+{"idx": 1, "title": "Algorithmic Penalty : What Is A Google Penalty ? | Definition", "date": "", "ddg_snippet": "Explore the concept of algorithmic penalty to align your website with it. In this complete definition, discover what Google's algorithmic penalty is.", "subpage_snippet": "", "source": "seodebate.com", "link": "https://seodebate.com/articles/algorithmic-penalty/", "content": "Explore the concept of algorithmic penalty to align your website with it. In this complete definition, discover what Google's algorithmic penalty is."}
+{"idx": 2, "title": "Help for package pendensity", "date": "", "ddg_snippet": "penalty parameter lambda . opt.Likelihood.actual penalty parameter lambda . Details. Iterating for the lambda is stopped, when the changes between the old and the new lambda is smaller than 0.01* lambda 0.", "subpage_snippet": "", "source": "cran.r-project.org", "link": "https://cran.r-project.org/web/packages/pendensity/refman/pendensity.html", "content": "penalty parameter lambda . opt.Likelihood.actual penalty parameter lambda . Details. Iterating for the lambda is stopped, when the changes between the old and the new lambda is smaller than 0.01* lambda 0."}
+{"idx": 3, "title": "LogisticRegression — scikit-learn 1.7.2 documentation", "date": "", "ddg_snippet": "See the parameter solver below, to know the compatibility between the penalty and solver. Added in version 0.19: l1 penalty with SAGA solver (allowing ‘multinomial’ + L1).Balance model complexity and cross-validated score. Class Likelihood Ratios to measure classification performance .", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html", "content": "See the parameter solver below, to know the compatibility between the penalty and solver. Added in version 0.19: l1 penalty with SAGA solver (allowing ‘multinomial’ + L1).Balance model complexity and cross-validated score. Class Likelihood Ratios to measure classification performance ."}
+{"idx": 4, "title": "Regularization in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "A regression model which uses the L1 Regularization technique is called LASSO (Least...w_i = Coefficients of the features\\ lambda = Regularization parameter that controls the strength of regularization", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/regularization-in-machine-learning/", "content": "A regression model which uses the L1 Regularization technique is called LASSO (Least...w_i = Coefficients of the features\\ lambda = Regularization parameter that controls the strength of regularization"}
+{"idx": 5, "title": "Strogatz Dynamical Systems Benchmark | DeepWiki", "date": "", "ddg_snippet": "Lambda parameter for complexity penalty .By evaluating performance across different systems and noise levels, it offers insights into the model 's ability to recover accurate and concise symbolic expressions under varying conditions.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/deep-symbolic-mathematics/TPSR/4.2-strogatz-dynamical-systems-benchmark", "content": "Lambda parameter for complexity penalty .By evaluating performance across different systems and noise levels, it offers insights into the model 's ability to recover accurate and concise symbolic expressions under varying conditions."}
+{"idx": 6, "title": "Discrete Empirical Interpolation Method with Upper and Lower Bound...", "date": "", "ddg_snippet": "4. C-DEIM algorithm : We propose an efficient implementation of the C-DEIM method. It combines Newton iterations with a bisection method, for determining the optimal value of the penalty parameter .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.16018", "content": "4. C-DEIM algorithm : We propose an efficient implementation of the C-DEIM method. It combines Newton iterations with a bisection method, for determining the optimal value of the penalty parameter ."}
+{"idx": 7, "title": "DavidAU/Qwen3-Instruct-6B-Brainstorm20x · Hugging Face", "date": "", "ddg_snippet": "We have validated the model 's performance on context lengths of up to 131,072 tokens using the YaRN method. YaRN is currently supported by several inference frameworks, e.g., transformers and llama.cpp for local use, vllm and sglang for deployment.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/DavidAU/Qwen3-Instruct-6B-Brainstorm20x", "content": "We have validated the model 's performance on context lengths of up to 131,072 tokens using the YaRN method. YaRN is currently supported by several inference frameworks, e.g., transformers and llama.cpp for local use, vllm and sglang for deployment."}
+{"idx": 8, "title": "Fig. 5: Number of iterations for ADMM with L = I and α = 2 and...", "date": "", "ddg_snippet": "It is shown in the literature that the SRPCP model guarantees robust matrix recovery with a universal, constant penalty parameter . While its statistical advantages are well-documented, the computational aspects from an optimization perspective remain largely unexplored.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Number-of-iterations-for-ADMM-with-L-I-and-a-2-and-fast-ADMM-algorithms-applied-to_fig4_237092139", "content": "It is shown in the literature that the SRPCP model guarantees robust matrix recovery with a universal, constant penalty parameter . While its statistical advantages are well-documented, the computational aspects from an optimization perspective remain largely unexplored."}
+{"idx": 9, "title": "pendensity-package: The package 'pendensity' offers routines for...", "date": "", "ddg_snippet": "new. lambda : Calculating new penalty parameter lambda . pendenForm: Formula interpretation and data transfer.estimating the density , choosing a special start value for lambda density .Allianz <- pendensity(d.Allianz~1, lambda 0=90000) plot( density .Allianz).", "subpage_snippet": "", "source": "rdrr.io", "link": "https://rdrr.io/cran/pendensity/man/pendensity-package.html", "content": "new. lambda : Calculating new penalty parameter lambda . pendenForm: Formula interpretation and data transfer.estimating the density , choosing a special start value for lambda density .Allianz <- pendensity(d.Allianz~1, lambda 0=90000) plot( density .Allianz)."}
diff --git a/data/sampled_jsons/SAFE_paper_arXiv_LLaMa-2_7B_Wikitext_50%_sparsity_SparseGPT_SAFE+_perplexity_Table_1.jsonl b/data/sampled_jsons/SAFE_paper_arXiv_LLaMa-2_7B_Wikitext_50%_sparsity_SparseGPT_SAFE+_perplexity_Table_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b2a0fbfcb8a8d63ae97a4d314be00f81d6415bf
--- /dev/null
+++ b/data/sampled_jsons/SAFE_paper_arXiv_LLaMa-2_7B_Wikitext_50%_sparsity_SparseGPT_SAFE+_perplexity_Table_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enabling High-Sparsity Foundational Llama Models with Efficient ...", "date": "", "ddg_snippet": "We achieve this for the LLaMA-2 7B model by combining the SparseGPT one-shot pruning method and sparse pretraining of those models on a subset of the SlimPajama dataset mixed with a Python subset of The Stack dataset. We exhibit training acceleration due to sparsity on Cerebras CS-3 chips that closely matches theoretical scaling.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.03594", "content": "We achieve this for the LLaMA-2 7B model by combining the SparseGPT one-shot pruning method and sparse pretraining of those models on a subset of the SlimPajama dataset mixed with a Python subset of The Stack dataset. We exhibit training acceleration due to sparsity on Cerebras CS-3 chips that closely matches theoretical scaling."}
+{"idx": 1, "title": "GitHub - AlpinDale/sparsegpt-for-LLaMA: Code for the paper \"SparseGPT ...", "date": "", "ddg_snippet": "Here are some sample commands to run baselines and sparsification on LLaMA models, followed by perplexity evaluations on raw-WikiText2, PTB and C4. See also the CMD-argument documentation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AlpinDale/sparsegpt-for-LLaMA", "content": "Here are some sample commands to run baselines and sparsification on LLaMA models, followed by perplexity evaluations on raw-WikiText2, PTB and C4. See also the CMD-argument documentation."}
+{"idx": 2, "title": "SparseGPT | Proceedings of the 40th International Conference on Machine ...", "date": "", "ddg_snippet": "We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity : remarkably, more than 100 billion weights from these models can be ignored at inference time.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3618822", "content": "We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity : remarkably, more than 100 billion weights from these models can be ignored at inference time."}
+{"idx": 3, "title": "[PDF] SparseGPT: Massive Language Models Can Be Accurately Pruned in ...", "date": "", "ddg_snippet": "We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT , specifically designed to work efficiently and accurately on massive GPT-family models. We can execute SparseGPT on the largest ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/SparseGPT:-Massive-Language-Models-Can-Be-Pruned-in-Frantar-Alistarh/909ad57ce8caa6b390a65ae09db352d27d8f3996", "content": "We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT , specifically designed to work efficiently and accurately on massive GPT-family models. We can execute SparseGPT on the largest ..."}
+{"idx": 4, "title": "SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot", "date": "", "ddg_snippet": "For example, when executed on the largest publicly-available generative language models (OPT-175B and BLOOM-176B), SparseGPT induces 50 -60% sparsity in one-shot, with minor accuracy loss, measured either in terms of perplexity or zero-shot accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2301.00774", "content": "For example, when executed on the largest publicly-available generative language models (OPT-175B and BLOOM-176B), SparseGPT induces 50 -60% sparsity in one-shot, with minor accuracy loss, measured either in terms of perplexity or zero-shot accuracy."}
+{"idx": 5, "title": "GitHub - IST-DASLab/sparsegpt: Code for the ICML 2023 paper \"SparseGPT ...", "date": "", "ddg_snippet": "Code for the ICML 2023 paper \" SparseGPT : Massive Language Models Can Be Accurately Pruned in One-Shot\". - IST-DASLab/ sparsegpt", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ist-daslab/sparsegpt", "content": "Code for the ICML 2023 paper \" SparseGPT : Massive Language Models Can Be Accurately Pruned in One-Shot\". - IST-DASLab/ sparsegpt"}
+{"idx": 6, "title": "New pruning method, Wanda, can prune LLMs to 50% sparsity with no ...", "date": "", "ddg_snippet": "We enforce a limited computational budget ( 1 GPU and 5 hours). We find that we are able to restore performance of pruned LLaMA - 7B (unstructured 50% sparsity ) with a non-trivial amount, reducing zero-shot WikiText perplexity from 7.26 to 6.87.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/14fvht9/new_pruning_method_wanda_can_prune_llms_to_50/", "content": "We enforce a limited computational budget ( 1 GPU and 5 hours). We find that we are able to restore performance of pruned LLaMA - 7B (unstructured 50% sparsity ) with a non-trivial amount, reducing zero-shot WikiText perplexity from 7.26 to 6.87."}
+{"idx": 7, "title": "SparseLLM/prosparse-llama-2-7b · Hugging Face", "date": "", "ddg_snippet": "ProSparse- LLaMA -2- 7B Model creator: Meta Original model: Llama 2 7B Fine-tuned by: THUNLP and ModelBest Paper : link Introduction The utilization of activation sparsity , namely the existence of considerable weakly-contributed elements among activation outputs, is a promising method for inference acceleration of large language models (LLMs) (Liu et al., 2023; Song et al., 2023). Concretely ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/SparseLLM/prosparse-llama-2-7b", "content": "ProSparse- LLaMA -2- 7B Model creator: Meta Original model: Llama 2 7B Fine-tuned by: THUNLP and ModelBest Paper : link Introduction The utilization of activation sparsity , namely the existence of considerable weakly-contributed elements among activation outputs, is a promising method for inference acceleration of large language models (LLMs) (Liu et al., 2023; Song et al., 2023). Concretely ..."}
+{"idx": 8, "title": "[2301.00774] SparseGPT: Massive Language Models Can Be Accurately ...", "date": "", "ddg_snippet": "We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT , specifically designed to work efficiently and accurately on massive GPT-family models. We can execute SparseGPT on the largest ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.00774", "content": "We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT , specifically designed to work efficiently and accurately on massive GPT-family models. We can execute SparseGPT on the largest ..."}
+{"idx": 9, "title": "SparseGPT: Massive language models can be accurately pruned in one-shot", "date": "", "ddg_snippet": "We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity : remarkably, more than 100 billion weights from these models can be ignored at inference time.", "subpage_snippet": "", "source": "research-explorer.ista.ac.at", "link": "https://research-explorer.ista.ac.at/record/14458", "content": "We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity : remarkably, more than 100 billion weights from these models can be ignored at inference time."}
diff --git a/data/sampled_jsons/SAFE_sparse_flat_minima_pruning_algorithm_github_implementation.jsonl b/data/sampled_jsons/SAFE_sparse_flat_minima_pruning_algorithm_github_implementation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a9546bb9ec44b6a07dcb2a433b49f86fe9ccd71a
--- /dev/null
+++ b/data/sampled_jsons/SAFE_sparse_flat_minima_pruning_algorithm_github_implementation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Safe overnight area off I95? (Fayetteville, Rocky Mount: crime...", "date": "", "ddg_snippet": "May 4, 2015 · We're looking for a safe place (hotel) to stop and spend the night off of I95 midway through which points us to NC. I'd like to feel comfortable leaving the truck/car (which of course will be locked) while we get some shut eye. Any suggestions for a hotel not too far off of I95?", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/forum/north-carolina/2360850-safe-overnight-area-off-i95.html", "content": "May 4, 2015 · We're looking for a safe place (hotel) to stop and spend the night off of I95 midway through which points us to NC. I'd like to feel comfortable leaving the truck/car (which of course will be locked) while we get some shut eye. Any suggestions for a hotel not too far off of I95?"}
+{"idx": 1, "title": "‘I would not feel safe’: Americans on the sorrow – and relief –...", "date": "", "ddg_snippet": "Aug 25, 2025 · I would not personally feel safe going back to a country that doesn’t fully reckon with its fascist impulses.” ‘I would not feel safe ’: Americans on the sorrow – and relief – of leaving Trump’s US for Europe - The Guardian (25th August 2025)", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/forum/politics-other-controversies/3514347-i-would-not-feel-safe-americans.html", "content": "Aug 25, 2025 · I would not personally feel safe going back to a country that doesn’t fully reckon with its fascist impulses.” ‘I would not feel safe ’: Americans on the sorrow – and relief – of leaving Trump’s US for Europe - The Guardian (25th August 2025)"}
+{"idx": 2, "title": "Is Ronkonkoma LiRR train station safe? (Brookhaven, Islip: 2013,...", "date": "", "ddg_snippet": "Mar 14, 2023 · We wonder how safe it is to commute from there? Are parking spaces relatively easy to get? Is it safe to leave your car there 10 hours or so and is", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/forum/long-island/3410615-ronkonkoma-lirr-train-station-safe.html", "content": "Mar 14, 2023 · We wonder how safe it is to commute from there? Are parking spaces relatively easy to get? Is it safe to leave your car there 10 hours or so and is"}
+{"idx": 3, "title": "Top 100 least-safe cities (highest city-data.com crime index)", "date": "", "ddg_snippet": "Find cityTop 100 least- safe cities (highest city-data.com crime index) Previous toplist Next toplist", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/top27.html", "content": "Find cityTop 100 least- safe cities (highest city-data.com crime index) Previous toplist Next toplist"}
+{"idx": 4, "title": "How safe is Historic Coronado District? (Phoenix: apartments,...", "date": "", "ddg_snippet": "Feb 25, 2009 · Hi everyone, looking into buying a home in the neighborhood. I really fell in love with historic type homes, something about them just feels right.", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/forum/phoenix-area/576896-how-safe-historic-coronado-district.html", "content": "Feb 25, 2009 · Hi everyone, looking into buying a home in the neighborhood. I really fell in love with historic type homes, something about them just feels right."}
+{"idx": 5, "title": "Crime rate in Wilmington, North Carolina (NC): murders, rapes ...", "date": "", "ddg_snippet": "Wilmington, NC North Carolina murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Wilmington-North-Carolina.html", "content": "Wilmington, NC North Carolina murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
+{"idx": 6, "title": "Crime in Tulsa, Oklahoma (OK): murders, rapes, robberies,...", "date": "", "ddg_snippet": "Crime rate in Tulsa, Oklahoma (OK): murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Tulsa-Oklahoma.html", "content": "Crime rate in Tulsa, Oklahoma (OK): murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
+{"idx": 7, "title": "Crime rate in Camden, New Jersey (NJ): murders, rapes, robberies...", "date": "", "ddg_snippet": "Camden, NJ New Jersey murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Camden-New-Jersey.html", "content": "Camden, NJ New Jersey murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
+{"idx": 8, "title": "Crime in Gary, Indiana (IN): murders, rapes, robberies, assaults...", "date": "", "ddg_snippet": "Crime rate in Gary, Indiana (IN): murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Gary-Indiana.html", "content": "Crime rate in Gary, Indiana (IN): murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
+{"idx": 9, "title": "Crime in Grand Rapids, Michigan (MI): murders, rapes, robberies...", "date": "", "ddg_snippet": "Grand Rapids, MI Michigan murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Grand-Rapids-Michigan.html", "content": "Grand Rapids, MI Michigan murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
diff --git a/data/sampled_jsons/SAMP_algorithm_LP_relaxation_optimal_solution_sampling_probability_online_matching_fairness_year_2024.jsonl b/data/sampled_jsons/SAMP_algorithm_LP_relaxation_optimal_solution_sampling_probability_online_matching_fairness_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d9ebb2edaed87b847160ff91cdf8338488d4b420
--- /dev/null
+++ b/data/sampled_jsons/SAMP_algorithm_LP_relaxation_optimal_solution_sampling_probability_online_matching_fairness_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching ...", "date": "", "ddg_snippet": "Sep 25, 2024 · For the general case of OM-LF: We present a sampling algorithm ( SAMP ) and show that (1) SAMP is of competitiveness of at least $1-1/e$ and (2) it is asymptotically optimal with competitiveness approaches one in different regimes when either all offline agents have a sufficiently large matching capacity, or all online types have a sufficiently ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0C3bLHwjsY", "content": "Sep 25, 2024 · For the general case of OM-LF: We present a sampling algorithm ( SAMP ) and show that (1) SAMP is of competitiveness of at least $1-1/e$ and (2) it is asymptotically optimal with competitiveness approaches one in different regimes when either all offline agents have a sufficiently large matching capacity, or all online types have a sufficiently ..."}
+{"idx": 1, "title": "FairnessMaximizationamong OfflineAgentsinOnline-MatchingMarke", "date": "", "ddg_snippet": "ctions and transfer them to a plausible online - matching policy. For example, suppose by solving the LP , we know that the clairvoyant optimal will match each pair of offline- online agents (i; j) with probability xij. Observe that b", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.08934v2", "content": "ctions and transfer them to a plausible online - matching policy. For example, suppose by solving the LP , we know that the clairvoyant optimal will match each pair of offline- online agents (i; j) with probability xij. Observe that b"}
+{"idx": 2, "title": "Fairness Maximization among Offline Agents in Online-Matching ...", "date": "", "ddg_snippet": "In this article, we propose online matching algorithms that optimize for either individual or group-level fair- 16 ness among ofline agents in OMMs. We present two linear-programming ( LP ) based sampling algorithms , which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 for group fair-ness maximization.", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10420152", "content": "In this article, we propose online matching algorithms that optimize for either individual or group-level fair- 16 ness among ofline agents in OMMs. We present two linear-programming ( LP ) based sampling algorithms , which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 for group fair-ness maximization."}
+{"idx": 3, "title": "A Dynamic Near-Optimal Algorithm for Online Linear Programming", "date": "", "ddg_snippet": "Knowing n is necessary for one to obtain a near optimal solution . However, it can be relaxed to an approximate knowledge of n or the length of decision horizon. Both assumptions are reasonable and standard in many operations research and computer science applications.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~yyye/onlinelp5.pdf", "content": "Knowing n is necessary for one to obtain a near optimal solution . However, it can be relaxed to an approximate knowledge of n or the length of decision horizon. Both assumptions are reasonable and standard in many operations research and computer science applications."}
+{"idx": 4, "title": "Use of Relaxation Methods in Sampling-Based Algorithms for ...", "date": "", "ddg_snippet": "Oktay Arslan Abstract—Several variants of incremental sampling -based algorithms have been recently proposed in order to solve optimally motion planning problems. Popular examples include the RRT and the PRM algorithms . These algorithms are asymptotically optimal and provide high quality solutions . However, the convergence rate to the optimal solution may still be slow. This paper presents a ...", "subpage_snippet": "", "source": "dcsl.gatech.edu", "link": "https://dcsl.gatech.edu/papers/icra13.pdf", "content": "Oktay Arslan Abstract—Several variants of incremental sampling -based algorithms have been recently proposed in order to solve optimally motion planning problems. Popular examples include the RRT and the PRM algorithms . These algorithms are asymptotically optimal and provide high quality solutions . However, the convergence rate to the optimal solution may still be slow. This paper presents a ..."}
+{"idx": 5, "title": "Approximation Algorithms: LP Relaxation, Rounding, and ...", "date": "", "ddg_snippet": "2 Use an optimal solution to the LP to construct a \" ̄ltered\" version of the optimization problem (creating a restricted version of the IP.) This step often involve with ̄xing a parameter 2 and try to de ̄ne a new set of feasible solution to LP with a cost within (1 + 2) of the LP", "subpage_snippet": "", "source": "cise.ufl.edu", "link": "https://cise.ufl.edu/~mythai/courses/2007/cis6930/Notes/Rounding.pdf", "content": "2 Use an optimal solution to the LP to construct a \" ̄ltered\" version of the optimization problem (creating a restricted version of the IP.) This step often involve with ̄xing a parameter 2 and try to de ̄ne a new set of feasible solution to LP with a cost within (1 + 2) of the LP"}
+{"idx": 6, "title": "Lecture Notes: Online LP Techniques - viswa.engin.umich.edu", "date": "", "ddg_snippet": "The algorithm needs to decide on the matching edge (if any) out of j ∈ R immediately after it arrives. For each l ∈ L, let Ml,j be the indicator random variable that is one if j getsmatchedtoo剻ᤕinenode l.", "subpage_snippet": "", "source": "viswa.engin.umich.edu", "link": "https://viswa.engin.umich.edu/wp-content/uploads/sites/169/2024/11/online-LP-F24.pdf", "content": "The algorithm needs to decide on the matching edge (if any) out of j ∈ R immediately after it arrives. For each l ∈ L, let Ml,j be the indicator random variable that is one if j getsmatchedtoo剻ᤕinenode l."}
+{"idx": 7, "title": "A Fast Iterative Shrinkage-Thresholding Algorithm for ...", "date": "", "ddg_snippet": "In this paper we present a new fast iterative shrinkage-thresholding algorithm (FISTA) which preserves the computational simplicity of ISTA but with a global ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/080716542", "content": "In this paper we present a new fast iterative shrinkage-thresholding algorithm (FISTA) which preserves the computational simplicity of ISTA but with a global ..."}
+{"idx": 8, "title": "Monotone Randomized Apportionment", "date": "", "ddg_snippet": "by J Correa · 2024 · Cited by 8 — Our work draws on a rich literature on unequal probability sampling in statistics (studied as dependent randomized rounding in computer science) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.03687", "content": "by J Correa · 2024 · Cited by 8 — Our work draws on a rich literature on unequal probability sampling in statistics (studied as dependent randomized rounding in computer science) ..."}
+{"idx": 9, "title": "2014 USENIX Annual Technical Conference", "date": "", "ddg_snippet": "20 Jun 2014 — ... Solution with Mediated Pass-Through ... Online Service Failure Path Inference in Production Computing Infrastructures ...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/sites/default/files/atc14_full_proceedings_interior.pdf", "content": "20 Jun 2014 — ... Solution with Mediated Pass-Through ... Online Service Failure Path Inference in Production Computing Infrastructures ..."}
diff --git a/data/sampled_jsons/SAMP_algorithm_Will_Ma_Pan_Xu_online_matching_fairness.jsonl b/data/sampled_jsons/SAMP_algorithm_Will_Ma_Pan_Xu_online_matching_fairness.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..83253ef14a540bbb2611c34a984a9a4c3118a290
--- /dev/null
+++ b/data/sampled_jsons/SAMP_algorithm_Will_Ma_Pan_Xu_online_matching_fairness.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NeurIPS Poster Promoting Fairness Among Dynamic Agents in...", "date": "", "ddg_snippet": "Will Ma · Pan Xu . SAMP . We stress that all hardness results mentioned here are independent of any benchmarks. We also consider a few extensions of OM-LF by proposing a few variants of fairness metrics, including long-run group-level fairness and short-run fairness , and we devise...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96945", "content": "Will Ma · Pan Xu . SAMP . We stress that all hardness results mentioned here are independent of any benchmarks. We also consider a few extensions of OM-LF by proposing a few variants of fairness metrics, including long-run group-level fairness and short-run fairness , and we devise..."}
+{"idx": 1, "title": "Fairness Maximization among", "date": "", "ddg_snippet": "Fairness Maximization among Oine Agents in Online - Matching Markets. Will Ma 1, Pan Xu 2, and Yifan Xu 3. 1 Columbia University 2 New Jersey Institute of Technology.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.08934", "content": "Fairness Maximization among Oine Agents in Online - Matching Markets. Will Ma 1, Pan Xu 2, and Yifan Xu 3. 1 Columbia University 2 New Jersey Institute of Technology."}
+{"idx": 2, "title": "Promoting Fairness Among Dynamic Agents in Online - Matching ...", "date": "", "ddg_snippet": "Will Ma , Pan Xu .This work focuses on a fair online bipartite matching problem under poisson arrivals, where the objective is to maximize the minimum number of matches across groups of the online nodes (a max min objective).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0C3bLHwjsY", "content": "Will Ma , Pan Xu .This work focuses on a fair online bipartite matching problem under poisson arrivals, where the objective is to maximize the minimum number of matches across groups of the online nodes (a max min objective)."}
+{"idx": 3, "title": "Fairness Maximization among Offline Agents in Online - Matching ...", "date": "", "ddg_snippet": "18 Sep 2021 · Will Ma , Pan Xu , Yifan Xu ·.We present two linear-programming (LP) based sampling algorithms , which achieve online competitive ratios at least 0.725 for individual fairness maximization (IFM) and 0.719 for group fairness maximization (GFM), respectively.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/fairness-maximization-among-offline-agents-in", "content": "18 Sep 2021 · Will Ma , Pan Xu , Yifan Xu ·.We present two linear-programming (LP) based sampling algorithms , which achieve online competitive ratios at least 0.725 for individual fairness maximization (IFM) and 0.719 for group fairness maximization (GFM), respectively."}
+{"idx": 4, "title": "Group-level Fairness Maximization in Online Bipartite Matching", "date": "", "ddg_snippet": "Online Bipartite Matching ; Fair Operations; Long-Run and Short-Run Fairness .ACM Reference Format: Will Ma , Pan Xu , and Yifan Xu . 2022. Group-level Fairness Maximization in Online Bipartite Matching : Extended Abstract.", "subpage_snippet": "", "source": "ifaamas.org", "link": "https://ifaamas.org/Proceedings/aamas2022/pdfs/p1687.pdf", "content": "Online Bipartite Matching ; Fair Operations; Long-Run and Short-Run Fairness .ACM Reference Format: Will Ma , Pan Xu , and Yifan Xu . 2022. Group-level Fairness Maximization in Online Bipartite Matching : Extended Abstract."}
+{"idx": 5, "title": "Matchings with Group Fairness Constraints: Online and Offline...", "date": "", "ddg_snippet": "[ Ma and Xu , 2020] Will Ma and Pan Xu . Group-level fair -ness maximization in online bipartite matching . arXiv preprint arXiv:2011.13908, 2020.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2021/0053.pdf", "content": "[ Ma and Xu , 2020] Will Ma and Pan Xu . Group-level fair -ness maximization in online bipartite matching . arXiv preprint arXiv:2011.13908, 2020."}
+{"idx": 6, "title": "Online Algorithms for Matchings with Proportional Fairness", "date": "", "ddg_snippet": "[34] Will Ma , Pan Xu , and Yifan Xu . Group-level fairness maximization in online bipartite matching , 2021. Matchings with group fairness constraints: Online and offline algorithms .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/matchings-under-diversity-constraints-1hswky6l.pdf", "content": "[34] Will Ma , Pan Xu , and Yifan Xu . Group-level fairness maximization in online bipartite matching , 2021. Matchings with group fairness constraints: Online and offline algorithms ."}
+{"idx": 7, "title": "Pan Xu 's research works | New Jersey Institute of Technology, New...", "date": "", "ddg_snippet": "Pan Xu 's 30 research works with 481 citations and 1,449 reads, including: Online minimum matching with uniform metric and random arrivals.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Pan-Xu-2134333005", "content": "Pan Xu 's 30 research works with 481 citations and 1,449 reads, including: Online minimum matching with uniform metric and random arrivals."}
+{"idx": 8, "title": "Class Fairness in Online Matching", "date": "", "ddg_snippet": "Will Ma , Pan Xu , and Yifan Xu . 2020. Group-level fairness maximization in online bipartite matching .", "subpage_snippet": "", "source": "faculty.ist.psu.edu", "link": "https://faculty.ist.psu.edu/hadi/papers/fair-online-matching.pdf", "content": "Will Ma , Pan Xu , and Yifan Xu . 2020. Group-level fairness maximization in online bipartite matching ."}
+{"idx": 9, "title": "Value-based Resource Matching with Fairness Criteria: Application to...", "date": "", "ddg_snippet": "Will Ma , Pan Xu , and Yifan Xu . 2022. Group-level Fairness Maximization in Online Bipartite Matching . In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems. IFAAMAS, Online , 1687--1689.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3635637.3662847", "content": "Will Ma , Pan Xu , and Yifan Xu . 2022. Group-level Fairness Maximization in Online Bipartite Matching . In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems. IFAAMAS, Online , 1687--1689."}
diff --git a/data/sampled_jsons/SAMP_algorithm_online_matching_optimal_LP_solution_probability_sampling.jsonl b/data/sampled_jsons/SAMP_algorithm_online_matching_optimal_LP_solution_probability_sampling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48aea33252ed3c294b5f00ea1a1bb32ed20bdc26
--- /dev/null
+++ b/data/sampled_jsons/SAMP_algorithm_online_matching_optimal_LP_solution_probability_sampling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Unied Approach to Online Matching with Conict-Aware Constraints", "date": "", "ddg_snippet": "3 LP -based Online Sampling Algorithms .such an optimal solution to LP (1): ∆ and x∆∗+1 = (we can always make. such a feasible solution to be optimal by arranging a proper. weight vector).", "subpage_snippet": "", "source": "jpdickerson.com", "link": "https://jpdickerson.com/pubs/xu19unified.pdf", "content": "3 LP -based Online Sampling Algorithms .such an optimal solution to LP (1): ∆ and x∆∗+1 = (we can always make. such a feasible solution to be optimal by arranging a proper. weight vector)."}
+{"idx": 1, "title": "questionpro.com/blog/non- probability - sampling", "date": "", "ddg_snippet": "Non- Probability Sampling : Types, Examples,", "subpage_snippet": "", "source": "www.questionpro.com", "link": "https://www.questionpro.com/blog/non-probability-sampling/", "content": "Non- Probability Sampling : Types, Examples,"}
+{"idx": 2, "title": "Online Weighted Bipartite Matching with Capacity Constraints", "date": "", "ddg_snippet": "Algorithm 1: Samp (α) Algorithm . Result: Online matchings M Solve the LP (3) and get the optimal solution y∗; Time t = 1; Matchings M = ϕThen the solution will be. 371 used to guide the online algorithm matching probability throughout.", "subpage_snippet": "", "source": "urban.cs.wpi.edu", "link": "http://urban.cs.wpi.edu/urbcomp2020/file/10.pdf", "content": "Algorithm 1: Samp (α) Algorithm . Result: Online matchings M Solve the LP (3) and get the optimal solution y∗; Time t = 1; Matchings M = ϕThen the solution will be. 371 used to guide the online algorithm matching probability throughout."}
+{"idx": 3, "title": "(PDF) A Unified Approach to Online Matching with Conflict-Aware...", "date": "", "ddg_snippet": "3 LP -based Online Sampling Algorithms . In this section, we present a class of parameterized LP -based.An optimal . algorithm for on - line bipartite matching . In Proceedings of the. Twenty-second Annual ACM Symposium on Theory of Computing", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/335718197_A_Unified_Approach_to_Online_Matching_with_Conflict-Aware_Constraints", "content": "3 LP -based Online Sampling Algorithms . In this section, we present a class of parameterized LP -based.An optimal . algorithm for on - line bipartite matching . In Proceedings of the. Twenty-second Annual ACM Symposium on Theory of Computing"}
+{"idx": 4, "title": "Online Task Assignment Problems with Reusable Resources", "date": "", "ddg_snippet": "Algorithm 1: Proposed online algorithm . 1: Solve LP (Off) to obtain an optimal solution x∗. Online Stochastic Matching with Unequal Probabilities .", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/20455/20455-13-24468-1-2-20220628.pdf", "content": "Algorithm 1: Proposed online algorithm . 1: Solve LP (Off) to obtain an optimal solution x∗. Online Stochastic Matching with Unequal Probabilities ."}
+{"idx": 5, "title": "Learning and Algorithms for Online Matching", "date": "", "ddg_snippet": "II Online Algorithms . Online Matching in Sparse Random Graphs: Non-Asymptotic Performances of Greedy Algorithm . Introduction. Online Matching Problems; Models and main result.Thus, the optimal solution to LP ′α is a lower bound on the sample complexity.", "subpage_snippet": "", "source": "theses.hal.science", "link": "https://theses.hal.science/tel-04292301/document", "content": "II Online Algorithms . Online Matching in Sparse Random Graphs: Non-Asymptotic Performances of Greedy Algorithm . Introduction. Online Matching Problems; Models and main result.Thus, the optimal solution to LP ′α is a lower bound on the sample complexity."}
+{"idx": 6, "title": "Promoting Fairness Among Dynamic Agents in Online - Matching ...", "date": "", "ddg_snippet": "Instead of directly solving the LP , SAMP cleverly uses its solution to inform a sampling distribution, probabilistically choosing an offline agent to match with an arriving online agent. This approach offers a compelling balance between fairness and efficiency.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/0c3blhwjsy/", "content": "Instead of directly solving the LP , SAMP cleverly uses its solution to inform a sampling distribution, probabilistically choosing an offline agent to match with an arriving online agent. This approach offers a compelling balance between fairness and efficiency."}
+{"idx": 7, "title": "An Experimental Study of Algorithms for Online Bipartite Matching", "date": "", "ddg_snippet": "The same algorithm is used to obtain an optimal maximum matching . For ManshadiEtAl, we estimate a fractional optimal solution by running Edmonds-Karp algorithm initialized with a greedy solution (for speed) on 100 samples generated for a given type graph.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/69825709/An_Experimental_Study_of_Algorithms_for_Online_Bipartite_Matching", "content": "The same algorithm is used to obtain an optimal maximum matching . For ManshadiEtAl, we estimate a fractional optimal solution by running Edmonds-Karp algorithm initialized with a greedy solution (for speed) on 100 samples generated for a given type graph."}
+{"idx": 8, "title": "Yet Another Math Programming Consultant: Finding all optimal LP ...", "date": "", "ddg_snippet": "Can I retrieve all optimal LP solutions ? Probably the correct answer on this question is: no.Here α are coefficients that we set in our algorithm . Any time a variable is basic in an optimal solution , its corresponding coefficient is set to one.", "subpage_snippet": "", "source": "yetanothermathprogrammingconsultant.blogspot.com", "link": "https://yetanothermathprogrammingconsultant.blogspot.com/2016/01/finding-all-optimal-lp-solutions.html", "content": "Can I retrieve all optimal LP solutions ? Probably the correct answer on this question is: no.Here α are coefficients that we set in our algorithm . Any time a variable is basic in an optimal solution , its corresponding coefficient is set to one."}
+{"idx": 9, "title": "Near Optimal Linear Algebra in the Online and Sliding Window Models...", "date": "", "ddg_snippet": "Our algorithm samples each row with probability equal to the online ridge leverage scores, where the regularization parameter λi is computed at each step.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8375632/", "content": "Our algorithm samples each row with probability equal to the online ridge leverage scores, where the regularization parameter λi is computed at each step."}
diff --git a/data/sampled_jsons/SDXL_dual_attention_transformer_blocks_feature_selection_underperformance_prior_studies.jsonl b/data/sampled_jsons/SDXL_dual_attention_transformer_blocks_feature_selection_underperformance_prior_studies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..16c83ed667fb4102f51317b93b273c7d2831e84b
--- /dev/null
+++ b/data/sampled_jsons/SDXL_dual_attention_transformer_blocks_feature_selection_underperformance_prior_studies.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "Scaled dot-product attention , block diagram. Exact dimension counts within an attention head module. Transformer decoder with norm-first and norm-last. Block diagram for the full Transformer architecture.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "Scaled dot-product attention , block diagram. Exact dimension counts within an attention head module. Transformer decoder with norm-first and norm-last. Block diagram for the full Transformer architecture."}
+{"idx": 1, "title": "research-papers/Summaries/Diffusion/SDXL.md at main - GitHub", "date": "", "ddg_snippet": "Specifically, they use a heterogeneous distribution of transformer blocks . And for efficiency, they omit the transformer block at the highest feature level, use 2 and 10 blocks at lower levels, and remove the lowest level (8×downsampling) in the UNet altogether.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/garg-aayush/research-papers/blob/main/Summaries/Diffusion/SDXL.md", "content": "Specifically, they use a heterogeneous distribution of transformer blocks . And for efficiency, they omit the transformer block at the highest feature level, use 2 and 10 blocks at lower levels, and remove the lowest level (8×downsampling) in the UNet altogether."}
+{"idx": 2, "title": "Rethinking attention modules of SDXL in a flow perspective", "date": "", "ddg_snippet": "Experiments on MS COCO show significant improvements over state-of-the-art methods. Ablation studies validate the flow-based design, with FMXA improving text-image alignment and FAPG enhancing visual fidelity. This work establishes a unified framework for semantic alignment and feature propagation in diffusion models.", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/doi/10.3934/mfc.2025015", "content": "Experiments on MS COCO show significant improvements over state-of-the-art methods. Ablation studies validate the flow-based design, with FMXA improving text-image alignment and FAPG enhancing visual fidelity. This work establishes a unified framework for semantic alignment and feature propagation in diffusion models."}
+{"idx": 3, "title": "sdxl_perturbed_attention_guidance - Hugging Face", "date": "", "ddg_snippet": "Jun 26, 2024 · This repository is just a simple SDXL implementation of the Perturbed- Attention Guidance (PAG) on Stable Diffusion XL ( SDXL ) for the 🧨 diffusers library. Quickstart", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/multimodalart/sdxl_perturbed_attention_guidance", "content": "Jun 26, 2024 · This repository is just a simple SDXL implementation of the Perturbed- Attention Guidance (PAG) on Stable Diffusion XL ( SDXL ) for the 🧨 diffusers library. Quickstart"}
+{"idx": 4, "title": "Unboxing SDXL Turbo with SAEs", "date": "", "ddg_snippet": "To this end, we train SAEs on the updates performed by transformer blocks within SDXL Turbo's denoising U-net in its 1-step setting. Interestingly, we find that they generalize to 4-step SDXL Turbo and even to the multi-step SDXL base model (i.e., a different model) without additional training.", "subpage_snippet": "", "source": "sdxl-unbox.epfl.ch", "link": "https://sdxl-unbox.epfl.ch/", "content": "To this end, we train SAEs on the updates performed by transformer blocks within SDXL Turbo's denoising U-net in its 1-step setting. Interestingly, we find that they generalize to 4-step SDXL Turbo and even to the multi-step SDXL base model (i.e., a different model) without additional training."}
+{"idx": 5, "title": "Selective Attention: Enhancing Transformer through Principled ...", "date": "", "ddg_snippet": "The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self- attention has enjoyed major success, it notably treats all queries q in the same way by applying the mapping V⊤softmax(Kq), where V, K are the value and key embeddings respectively.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/14fc4a68da97a3d31eb11c642b0b10fc-Paper-Conference.pdf", "content": "The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self- attention has enjoyed major success, it notably treats all queries q in the same way by applying the mapping V⊤softmax(Kq), where V, K are the value and key embeddings respectively."}
+{"idx": 6, "title": "Training-free Regional Prompting for Diffusion Transformers", "date": "", "ddg_snippet": "The paper presents a \"training-free regional prompting\" approach for diffusion transformers , which allows users to control the generation of specific image regions without additional fine-tuning.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/training-free-regional-prompting-diffusion-transformers", "content": "The paper presents a \"training-free regional prompting\" approach for diffusion transformers , which allows users to control the generation of specific image regions without additional fine-tuning."}
+{"idx": 7, "title": "Vision Transformer : A New Era in Image Recognition", "date": "", "ddg_snippet": "Discover how Vision Transformers redefine image recognition, offering enhanced accuracy and efficiency over CNNs in various computer vision tasks.", "subpage_snippet": "", "source": "viso.ai", "link": "https://viso.ai/deep-learning/vision-transformer-vit/", "content": "Discover how Vision Transformers redefine image recognition, offering enhanced accuracy and efficiency over CNNs in various computer vision tasks."}
+{"idx": 8, "title": "Fence and/or Semaphore transformer to control the flow... | Community", "date": "", "ddg_snippet": "This would ensure that no feature from the \" Priority \" side can ever arrive before a \"Held\" feature , thus allowing workflow and transformer designers to guarantee feature ordering downstream without breaking bulk mode. Other relevent use cases.", "subpage_snippet": "", "source": "community.safe.com", "link": "https://community.safe.com/ideas/fence-and-or-semaphore-transformer-to-control-the-flow-of-input-data-32108", "content": "This would ensure that no feature from the \" Priority \" side can ever arrive before a \"Held\" feature , thus allowing workflow and transformer designers to guarantee feature ordering downstream without breaking bulk mode. Other relevent use cases."}
+{"idx": 9, "title": "(PDF) A Hybrid Neural Network with Multistage Feature Fusion for...", "date": "", "ddg_snippet": "This study presents a hybrid neural network model with interior multistage feature fusion to detect heart pathologies using the time-frequency analysis of phonocardiogram (PCG) recordings.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395549042_A_Hybrid_Neural_Network_with_Multistage_Feature_Fusion_for_Detecting_Heart_Failure_and_Murmurs_from_Time-Frequency_Representations_of_Phonocardiograms", "content": "This study presents a hybrid neural network model with interior multistage feature fusion to detect heart pathologies using the time-frequency analysis of phonocardiogram (PCG) recordings."}
diff --git a/data/sampled_jsons/SGLD_Stochastic_Gradient_Langevin_Dynamics_algorithm.jsonl b/data/sampled_jsons/SGLD_Stochastic_Gradient_Langevin_Dynamics_algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b472c650dd576afdbb7c4af8cf6a343a8da676fe
--- /dev/null
+++ b/data/sampled_jsons/SGLD_Stochastic_Gradient_Langevin_Dynamics_algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Solved On January 1, 2020 Genos Steaks pays $48,000 to rent -...", "date": "", "ddg_snippet": "Jan 1, 2020 · Question: On January 1, 2020 Genos Steaks pays $48,000 to rent its restaurant space in Philadelphia for the entire year. On January 31, 2020 adjusting entry must be made that includes: Select one: .", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/january-1-2020-genos-steaks-pays-48-000-rent-restaurant-space-philadelphia-entire-year-jan-q53861497", "content": "Jan 1, 2020 · Question: On January 1, 2020 Genos Steaks pays $48,000 to rent its restaurant space in Philadelphia for the entire year. On January 31, 2020 adjusting entry must be made that includes: Select one: ."}
+{"idx": 1, "title": "Solved #1Philadelphia is famous for the cheesesteak | Chegg.com", "date": "", "ddg_snippet": "Question: #1Philadelphia is famous for the cheesesteak sandwich, and Pats and Genos are popular rivalestablishments located across the street from each other in South Philadelphia.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/1philadelphia-famous-cheesesteak-sandwich-pats-genos-popular-rivalestablishments-located-a-q168127156", "content": "Question: #1Philadelphia is famous for the cheesesteak sandwich, and Pats and Genos are popular rivalestablishments located across the street from each other in South Philadelphia."}
+{"idx": 2, "title": "Solved Philadelphia is famous for the cheesesteak sandwich, |...", "date": "", "ddg_snippet": "Question: Philadelphia is famous for the cheesesteak sandwich, and Pats and Genos are popular rivalestablishments located across the street from each other in South Philadelphia. (Although thereare other restaurants that serve cheesesteaks, we will consider the market shares of only these twodue to both having very loyal customer bases.) According to market research,", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/philadelphia-famous-cheesesteak-sandwich-pats-genos-popular-rivalestablishments-located-ac-q170657775", "content": "Question: Philadelphia is famous for the cheesesteak sandwich, and Pats and Genos are popular rivalestablishments located across the street from each other in South Philadelphia. (Although thereare other restaurants that serve cheesesteaks, we will consider the market shares of only these twodue to both having very loyal customer bases.) According to market research,"}
+{"idx": 3, "title": "Solved Philadelphia is the home of the cheesesteak, and the |...", "date": "", "ddg_snippet": "Question: Philadelphia is the home of the cheesesteak, and the two most popular cheeseteak restaurants, Pat’s and Geno’s, are bitter rivals located across the street from each other.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/philadelphia-home-cheesesteak-two-popular-cheeseteak-restaurants-pats-genos-bitter-rivals--q19937145", "content": "Question: Philadelphia is the home of the cheesesteak, and the two most popular cheeseteak restaurants, Pat’s and Geno’s, are bitter rivals located across the street from each other."}
+{"idx": 4, "title": "Solved philadelphia is famous for the cheesesteak sandwitch, |...", "date": "", "ddg_snippet": "Question: philadelphia is famous for the cheesesteak sandwitch, and pats and genos are popular rival establishments located across the stree from one another", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/philadelphia-famous-cheesesteak-sandwitch-pats-genos-popular-rival-establishments-located--q170139973", "content": "Question: philadelphia is famous for the cheesesteak sandwitch, and pats and genos are popular rival establishments located across the stree from one another"}
+{"idx": 5, "title": "Chegg - Get 24/7 Homework Help | Rent Textbooks", "date": "", "ddg_snippet": "1. ^ These offers are provided at no cost to subscribers of Chegg Study and Chegg Study Pack. No cash value. Terms and Conditions apply. Please visit each partner activation page for complete details. 2. ^ Chegg survey fielded between Sept. 9–Oct 3, 2024 among a random sample of U.S. customers who used Chegg Study or Chegg Study Pack in Q2 2024 and Q3 2024. Respondent base (n=712) among ...", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/15-points-pat-s-geno-s-two-rival-cheese-steak-restaurants-philadelphia-pennsylvania-locate-q27945395", "content": "1. ^ These offers are provided at no cost to subscribers of Chegg Study and Chegg Study Pack. No cash value. Terms and Conditions apply. Please visit each partner activation page for complete details. 2. ^ Chegg survey fielded between Sept. 9–Oct 3, 2024 among a random sample of U.S. customers who used Chegg Study or Chegg Study Pack in Q2 2024 and Q3 2024. Respondent base (n=712) among ..."}
+{"idx": 6, "title": "Solved Consumers might not realize that Philadelphia cream -...", "date": "", "ddg_snippet": "Question: Consumers might not realize that Philadelphia cream cheese and Jell-O products are made by the same company—Kraft. Philadelphia cream cheese and Jell-O are known as A) traditional brands. B) umbrella brands. C) individual brands. D) corporate brands. E) family brands.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/consumers-might-realize-philadelphia-cream-cheese-jell-o-products-made-company-kraft-phila-q89488524", "content": "Question: Consumers might not realize that Philadelphia cream cheese and Jell-O products are made by the same company—Kraft. Philadelphia cream cheese and Jell-O are known as A) traditional brands. B) umbrella brands. C) individual brands. D) corporate brands. E) family brands."}
+{"idx": 7, "title": "Solved The December 31, 2024, adjusted trial balance for - Chegg", "date": "", "ddg_snippet": "Dec 31, 2024 · The December 31, 2024, adjusted trial balance for the Blueboy Cheese Corporation is presented below.Account TitleDebitsCreditsCash$ 49,200 Accounts receivable295,000 Prepaid rent9,500 Inventory44,000 Office equipment540,000 Accumulated depreciation $ 226,000Accounts payable 58,000Notes payable (due in six months) 42,000Salaries payable 6 ...", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/december-31-2024-adjusted-trial-balance-blueboy-cheese-corporation-presented--account-titl-q225812959", "content": "Dec 31, 2024 · The December 31, 2024, adjusted trial balance for the Blueboy Cheese Corporation is presented below.Account TitleDebitsCreditsCash$ 49,200 Accounts receivable295,000 Prepaid rent9,500 Inventory44,000 Office equipment540,000 Accumulated depreciation $ 226,000Accounts payable 58,000Notes payable (due in six months) 42,000Salaries payable 6 ..."}
+{"idx": 8, "title": "Solved Consumers might not realize that Philadelphia cream -...", "date": "", "ddg_snippet": "Question: Consumers might not realize that Philadelphia cream cheese and Jell-O products are made by the same company-Kraft.Philadelphia cream cheese and Jell-O are known as:1:26Multiple Choiceumbrella brands.individual brandsfamily brandsTrafitional brands Corporate brads", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/consumers-might-realize-philadelphia-cream-cheese-jell-o-products-made-company-kraftphilad-q144207920", "content": "Question: Consumers might not realize that Philadelphia cream cheese and Jell-O products are made by the same company-Kraft.Philadelphia cream cheese and Jell-O are known as:1:26Multiple Choiceumbrella brands.individual brandsfamily brandsTrafitional brands Corporate brads"}
+{"idx": 9, "title": "Solved If Kraft Heinz produces a new eco-friendly package - Chegg", "date": "", "ddg_snippet": "If Kraft Heinz produces a new ecofriendly package for its Philadelphia Cream Cheese, which it wants to get into as many retail outlets as possible, it should choose a (n) distribution strategy.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/kraft-heinz-produces-new-eco-friendly-package-philadelphia-cream-cheese-wants-get-many-ret-q231543447", "content": "If Kraft Heinz produces a new ecofriendly package for its Philadelphia Cream Cheese, which it wants to get into as many retail outlets as possible, it should choose a (n) distribution strategy."}
diff --git a/data/sampled_jsons/SGLD_noise_tensor_shape_dimensionality_model_parameters_machine_unlearning.jsonl b/data/sampled_jsons/SGLD_noise_tensor_shape_dimensionality_model_parameters_machine_unlearning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d229ecf5aec6de3541e78f403a3fe33f42acbf0f
--- /dev/null
+++ b/data/sampled_jsons/SGLD_noise_tensor_shape_dimensionality_model_parameters_machine_unlearning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Left: SGLD unlearning with varying levels of noise . (σ𝜎\\sigmaitalic_σ. ). Forget set difficulty (x-axis), as measured by the privacy loss, against time to unlearn (y-axis). Model sparsity can simplify machine unlearning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "Left: SGLD unlearning with varying levels of noise . (σ𝜎\\sigmaitalic_σ. ). Forget set difficulty (x-axis), as measured by the privacy loss, against time to unlearn (y-axis). Model sparsity can simplify machine unlearning ."}
+{"idx": 1, "title": "How Machine Unlearning Revolutionizes AI | by Shailendra... | Cubed", "date": "", "ddg_snippet": "How Does Machine Unlearning Remove Bias Without Retraining Entire Models ? Machine unlearning answers the question: How can I remove biased data from a trained AI model without starting over?", "subpage_snippet": "", "source": "blog.cubed.run", "link": "https://blog.cubed.run/how-machine-unlearning-revolutionizes-ai-2e208d0677e1", "content": "How Does Machine Unlearning Remove Bias Without Retraining Entire Models ? Machine unlearning answers the question: How can I remove biased data from a trained AI model without starting over?"}
+{"idx": 2, "title": "machinelearningmastery.com/ dimensionality -reduction-for- machine ...", "date": "", "ddg_snippet": "“Introduction to Dimensionality Reduction for Machine Learning”.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/dimensionality-reduction-for-machine-learning/", "content": "“Introduction to Dimensionality Reduction for Machine Learning”."}
+{"idx": 3, "title": "What is Machine Unlearning and Why Does it Matter? | Medium", "date": "", "ddg_snippet": "Despite its promise, machine unlearning remains a nascent and unrefined subfield of machine learning. The apparent solution — removing sensitive data points and retraining the model — seems straightforward, but is not practical.", "subpage_snippet": "", "source": "andy-wang.medium.com", "link": "https://andy-wang.medium.com/what-is-machine-unlearning-and-why-does-it-matter-10eb328913bc", "content": "Despite its promise, machine unlearning remains a nascent and unrefined subfield of machine learning. The apparent solution — removing sensitive data points and retraining the model — seems straightforward, but is not practical."}
+{"idx": 4, "title": "Introduction to Diffusion Models for Machine Learning", "date": "", "ddg_snippet": "The meteoric rise of Diffusion Models is one of the biggest developments in Machine Learning in the past several years. Learn everything you need to know about Diffusion Models in this easy-to-follow guide.", "subpage_snippet": "", "source": "www.assemblyai.com", "link": "https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction", "content": "The meteoric rise of Diffusion Models is one of the biggest developments in Machine Learning in the past several years. Learn everything you need to know about Diffusion Models in this easy-to-follow guide."}
+{"idx": 5, "title": "Introduction to Dimensionality Reduction - GeeksforGeeks", "date": "", "ddg_snippet": "When working with machine learning models , datasets with too many features can cause issues like slow computation and overfitting. Dimensionality reduction helps to reduce the number of features while retaining key information.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/dimensionality-reduction/", "content": "When working with machine learning models , datasets with too many features can cause issues like slow computation and overfitting. Dimensionality reduction helps to reduce the number of features while retaining key information."}
+{"idx": 6, "title": "Frontiers | Plug-and-play low-rank tensor completion and...", "date": "", "ddg_snippet": "Now, if we consider a model in which the true tensor is low Tucker/CP rank, the core tensor or factor matrices are non-negative, and the observations contain Gaussian noise , the optimization problem can be formulated as a non-negative Tucker/CP decomposition with missing values based on...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1594873/full", "content": "Now, if we consider a model in which the true tensor is low Tucker/CP rank, the core tensor or factor matrices are non-negative, and the observations contain Gaussian noise , the optimization problem can be formulated as a non-negative Tucker/CP decomposition with missing values based on..."}
+{"idx": 7, "title": "B AYESIAN", "date": "", "ddg_snippet": "Results using noisy Adam as approximation for SGLD .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Hy-po5NFx", "content": "Results using noisy Adam as approximation for SGLD ."}
+{"idx": 8, "title": "Harmless Interpolation of Noisy Data in Regression | Request PDF", "date": "", "ddg_snippet": "We extend this information-theoretic analysis to over- parameterized models by characterizing the SGLD -based BIC for the random feature model in the regime where the number of parameters $p$ and the number of samples $n$ tend to infinity, with $p/n$ fixed.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/340318733_Harmless_Interpolation_of_Noisy_Data_in_Regression", "content": "We extend this information-theoretic analysis to over- parameterized models by characterizing the SGLD -based BIC for the random feature model in the regime where the number of parameters $p$ and the number of samples $n$ tend to infinity, with $p/n$ fixed."}
+{"idx": 9, "title": "Solved: How to Utilize torch.nn. Parameter for Neural Network", "date": "", "ddg_snippet": "Tensors are essentially multi- dimensional arrays. A Parameter , in essence, is a tensor that inherits from the Variable class in PyTorch. The critical distinction arises when a Parameter is linked to a module as an attribute.", "subpage_snippet": "", "source": "sqlpey.com", "link": "https://sqlpey.com/python/solved-how-to-utilize-torch-nn-parameter-for-neural-network-models/", "content": "Tensors are essentially multi- dimensional arrays. A Parameter , in essence, is a tensor that inherits from the Variable class in PyTorch. The critical distinction arises when a Parameter is linked to a module as an attribute."}
diff --git a/data/sampled_jsons/SIIHPC_incomplete_multi-view_clustering_algorithm_optimization_convergence_condition.jsonl b/data/sampled_jsons/SIIHPC_incomplete_multi-view_clustering_algorithm_optimization_convergence_condition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0c3ed0b4437b070ff9472705d0c561dc7044f05f
--- /dev/null
+++ b/data/sampled_jsons/SIIHPC_incomplete_multi-view_clustering_algorithm_optimization_convergence_condition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "M Ulti - view", "date": "", "ddg_snippet": "Incomplete multi - view clustering via prototype-based imputation.Deep safe incomplete multi - view clustering : Theorem and algorithm .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KijslFbfOL", "content": "Incomplete multi - view clustering via prototype-based imputation.Deep safe incomplete multi - view clustering : Theorem and algorithm ."}
+{"idx": 1, "title": "ICLR Poster Simple yet Effective Incomplete Multi - view Clustering ...", "date": "", "ddg_snippet": "Abstract: Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/30038", "content": "Abstract: Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity."}
+{"idx": 2, "title": "Complementary incomplete weighted concept factorization methods...", "date": "", "ddg_snippet": "Still, current incomplete multi - view clustering methods overlooked a number of important factors, such as providing a consensus representation across the kernel space, dealing with over-fitting issue from different views...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10115-024-02197-1", "content": "Still, current incomplete multi - view clustering methods overlooked a number of important factors, such as providing a consensus representation across the kernel space, dealing with over-fitting issue from different views..."}
+{"idx": 3, "title": "Weighted Concept Factorization based Incomplete Multi - view ...", "date": "", "ddg_snippet": "The incompleteness of the clustering of multiview data has witnessed substantial progress in recent years due to its promising applications. In response to the aforementioned issue, we have tackled it by introducing an inventive MVC algorithm that is tailored to handle incomplete data from...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/weighted-concept-factorization-based-incomplete-multi-view-45vsxpfmdv", "content": "The incompleteness of the clustering of multiview data has witnessed substantial progress in recent years due to its promising applications. In response to the aforementioned issue, we have tackled it by introducing an inventive MVC algorithm that is tailored to handle incomplete data from..."}
+{"idx": 4, "title": "Efficient and Effective Regularized Incomplete Multi - View Clustering", "date": "", "ddg_snippet": "Incomplete multi - view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2021/08/09001210/1hwt4GmzDoc", "content": "Incomplete multi - view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark..."}
+{"idx": 5, "title": "(PDF) Efficient and Effective Incomplete Multi - View Clustering", "date": "", "ddg_snippet": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/335546437_Efficient_and_Effective_Incomplete_Multi-View_Clustering", "content": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines..."}
+{"idx": 6, "title": "From Dictionary to Tensor: A Scalable Multi - View", "date": "", "ddg_snippet": "Robust multi - view clustering with incomplete information.The Theorem 1 presented in the main text ensures the convergence of the optimization algorithm . We will now demonstrate the two conditions specied in Theorem 1. To begin, we introduce the following lemma", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/bb68f698772f14b6d8eaef4529fb9176-Paper-Conference.pdf", "content": "Robust multi - view clustering with incomplete information.The Theorem 1 presented in the main text ensures the convergence of the optimization algorithm . We will now demonstrate the two conditions specied in Theorem 1. To begin, we introduce the following lemma"}
+{"idx": 7, "title": "Doubly Aligned Incomplete Multi - view Clustering", "date": "", "ddg_snippet": "Such incompleteness makes it impossible to directly use traditional multi - view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi - view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF).", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2018/0313.pdf", "content": "Such incompleteness makes it impossible to directly use traditional multi - view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi - view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF)."}
+{"idx": 8, "title": "COMPLETER: Incomplete Multi - View Clustering via Contrastive...", "date": "", "ddg_snippet": "In this section, we propose a deep multi - view cluster -ing method, termed inCOMPlete muLti - view clustEring via conTrastivE pRediction (COMPLETER) for learning the representations with a set of incomplete multi - view sam-ples.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_COMPLETER_Incomplete_Multi-View_Clustering_via_Contrastive_Prediction_CVPR_2021_paper.pdf", "content": "In this section, we propose a deep multi - view cluster -ing method, termed inCOMPlete muLti - view clustEring via conTrastivE pRediction (COMPLETER) for learning the representations with a set of incomplete multi - view sam-ples."}
+{"idx": 9, "title": "IMC-NLT: Incomplete multi - view clustering by NMF and low-rank...", "date": "", "ddg_snippet": "• DAIMC Incomplete multi - view clustering algorithm based on weighted semi-non-negative matrix factoriza-tion (semi-NMF).use an iterative optimization algorithm to monotonically decrease it until convergence . The objective function values are plotted in Fig.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04006481/document", "content": "• DAIMC Incomplete multi - view clustering algorithm based on weighted semi-non-negative matrix factoriza-tion (semi-NMF).use an iterative optimization algorithm to monotonically decrease it until convergence . The objective function values are plotted in Fig."}
diff --git a/data/sampled_jsons/SLHAMR_PA-MPJPE_86.9_single_moving_camera_Table_4_pedestrian_motion_reconstruction.jsonl b/data/sampled_jsons/SLHAMR_PA-MPJPE_86.9_single_moving_camera_Table_4_pedestrian_motion_reconstruction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..894e167a91de8bd6e86a3f919e9353ab608f70c4
--- /dev/null
+++ b/data/sampled_jsons/SLHAMR_PA-MPJPE_86.9_single_moving_camera_Table_4_pedestrian_motion_reconstruction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Decoupling Human and Camera Motion from Videos in the Wild", "date": "", "ddg_snippet": "Given a video of moving people (left), we present SLAHMR: Simultaneous Localication and Human Mesh Recovery, a method to recover the global trajectories of the people and cameras in the world coordinate frame (right top). Prior methods model human motion in the camera coordinate frame (right bottom), and cannot recover the global displacement of the athlete.", "subpage_snippet": "", "source": "vye16.github.io", "link": "https://vye16.github.io/slahmr/", "content": "Given a video of moving people (left), we present SLAHMR: Simultaneous Localication and Human Mesh Recovery, a method to recover the global trajectories of the people and cameras in the world coordinate frame (right top). Prior methods model human motion in the camera coordinate frame (right bottom), and cannot recover the global displacement of the athlete."}
+{"idx": 1, "title": "Physics-based Human Pose Estimation from a Single Moving RGB ...", "date": "", "ddg_snippet": "1. Introduction Estimating accurate 3D human motion in global coordinates from a single moving RGB camera is an important and chal-lenging problem in Computer Vision with many applica-tions in animation, Augmented and Virtual Reality (AR/VR), human-robot interaction, autonomous driving, and assisted", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.17406", "content": "1. Introduction Estimating accurate 3D human motion in global coordinates from a single moving RGB camera is an important and chal-lenging problem in Computer Vision with many applica-tions in animation, Augmented and Virtual Reality (AR/VR), human-robot interaction, autonomous driving, and assisted"}
+{"idx": 2, "title": "DiffMesh: A Motion-aware Diffusion Framework for Human Mesh ...", "date": "", "ddg_snippet": "Our DiffMesh consistently outperforms previous methods with significant improvement (more than 5.9 mm↓of MPJPE , 1.8 ↓of PA-MPJPE , and 0.7 ↓of ACC-ERR). This show- cases the remarkable performance enhancement achieved by our approach, highlighting its potential as a state-of-the- art solution for video-based human mesh recovery across ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/supplemental/Zheng_DiffMesh_A_Motion-Aware_WACV_2025_supplemental.pdf", "content": "Our DiffMesh consistently outperforms previous methods with significant improvement (more than 5.9 mm↓of MPJPE , 1.8 ↓of PA-MPJPE , and 0.7 ↓of ACC-ERR). This show- cases the remarkable performance enhancement achieved by our approach, highlighting its potential as a state-of-the- art solution for video-based human mesh recovery across ..."}
+{"idx": 3, "title": "Shape of Motion: 4D Reconstruction from a Single Video", "date": "", "ddg_snippet": "Monocular dynamic reconstruction is a challenging and long-standing vision problem due to the highly ill-posed nature of the task. Existing approaches are limited in that they either depend on templates, are effective only in quasi-static scenes, or fail to model 3D motion explicitly.", "subpage_snippet": "", "source": "shape-of-motion.github.io", "link": "https://shape-of-motion.github.io/", "content": "Monocular dynamic reconstruction is a challenging and long-standing vision problem due to the highly ill-posed nature of the task. Existing approaches are limited in that they either depend on templates, are effective only in quasi-static scenes, or fail to model 3D motion explicitly."}
+{"idx": 4, "title": "Reconstructing Global Multi-Person 3D Motions from a Single ...", "date": "", "ddg_snippet": "Reconstructing Global Multi-Person 3D Motions from a Single Moving Camera Abstract: Currently, the reconstruction of global human trajectories from in-the-wild videos represents a highly promising research area, with applications derived from this technology becoming more practical and widespread.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10907285", "content": "Reconstructing Global Multi-Person 3D Motions from a Single Moving Camera Abstract: Currently, the reconstruction of global human trajectories from in-the-wild videos represents a highly promising research area, with applications derived from this technology becoming more practical and widespread."}
+{"idx": 5, "title": "PACE: Human and Camera Motion Estimation from in-the-wild Videos", "date": "", "ddg_snippet": "Figure 1: Human and camera motion reconstruction from in-the-wild videos: given a video of multiple people, PACE is able to reconstruct the motions of all humans and the camera in a coherent global space. To achieve this, we leverage the benefits of both camera localization methods and human motion priors, exploiting the complementary nature of these approaches, i.e., dynamic foreground motion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.13768", "content": "Figure 1: Human and camera motion reconstruction from in-the-wild videos: given a video of multiple people, PACE is able to reconstruct the motions of all humans and the camera in a coherent global space. To achieve this, we leverage the benefits of both camera localization methods and human motion priors, exploiting the complementary nature of these approaches, i.e., dynamic foreground motion ..."}
+{"idx": 6, "title": "Dyn- HaMR : Recovering 4 D Interacting Hand Motion from a Dynamic...", "date": "", "ddg_snippet": "We propose Dyn- HaMR , to the best of our knowledge, the first approach to reconstruct 4 D global hand motion from monocular videos recorded by dynamic cameras in the wild.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Dyn-HaMR_Recovering_4D_Interacting_Hand_Motion_from_a_Dynamic_Camera_CVPR_2025_paper.pdf", "content": "We propose Dyn- HaMR , to the best of our knowledge, the first approach to reconstruct 4 D global hand motion from monocular videos recorded by dynamic cameras in the wild."}
+{"idx": 7, "title": "PA MPJPE comparison with state-of-the-art methods on the...", "date": "", "ddg_snippet": "In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/PA-MPJPE-comparison-with-state-of-the-art-methods-on-the-Human36M-dataset-using-Protocol_tbl3_334735304", "content": "In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of..."}
+{"idx": 8, "title": "EgoBody: Human Body Shape and Motion of", "date": "", "ddg_snippet": "3DPW [63] reconstructs the 3D shape and motion of people by tting SMPL [59] to IMU data and RGB images captured with a hand-held camera , without 3D environment reconstruction .", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136660176.pdf", "content": "3DPW [63] reconstructs the 3D shape and motion of people by tting SMPL [59] to IMU data and RGB images captured with a hand-held camera , without 3D environment reconstruction ."}
+{"idx": 9, "title": "Single -Frame-Based Deep View Synchronization", "date": "", "ddg_snippet": "single -frame synchronization of multiview cameras . Table IV . Unsynchronized multiview counting: experiment results for training set with only unsynchronized frames under con-stant and random latency. remaining 200 for testing.", "subpage_snippet": "", "source": "visal.cs.cityu.edu.hk", "link": "http://visal.cs.cityu.edu.hk/static/pubs/journal/tnnls23-unsyncmv.pdf", "content": "single -frame synchronization of multiview cameras . Table IV . Unsynchronized multiview counting: experiment results for training set with only unsynchronized frames under con-stant and random latency. remaining 200 for testing."}
diff --git a/data/sampled_jsons/SLHAMR_PA-MPJPE_single_moving_camera_Table_4_pedestrian_motion_reconstruction.jsonl b/data/sampled_jsons/SLHAMR_PA-MPJPE_single_moving_camera_Table_4_pedestrian_motion_reconstruction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..46d1eb58f9b5dd364ebe57955210c3d814a3d578
--- /dev/null
+++ b/data/sampled_jsons/SLHAMR_PA-MPJPE_single_moving_camera_Table_4_pedestrian_motion_reconstruction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Shape of Motion: 4D Reconstruction from a Single Video", "date": "", "ddg_snippet": "Monocular dynamic reconstruction is a challenging and long-standing vision problem due to the highly ill-posed nature of the task. Existing approaches are limited in that they either depend on templates, are effective only in quasi-static scenes, or fail to model 3D motion explicitly.", "subpage_snippet": "", "source": "shape-of-motion.github.io", "link": "https://shape-of-motion.github.io/", "content": "Monocular dynamic reconstruction is a challenging and long-standing vision problem due to the highly ill-posed nature of the task. Existing approaches are limited in that they either depend on templates, are effective only in quasi-static scenes, or fail to model 3D motion explicitly."}
+{"idx": 1, "title": "P M RECONSTRUCTION: A LARGE SCALE BENCHMARK VIA MIXED REALITY ...", "date": "", "ddg_snippet": "To address the previously highlighted challenges, we present the Pedestrian Motion Reconstruction (PMR) dataset, a comprehensive resource designed for intention-aware pedestrian motion recon- struction using data from moving sensors.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=YOpa6dTrpt", "content": "To address the previously highlighted challenges, we present the Pedestrian Motion Reconstruction (PMR) dataset, a comprehensive resource designed for intention-aware pedestrian motion recon- struction using data from moving sensors."}
+{"idx": 2, "title": "Physics-based Human Pose Estimation from a Single Moving RGB ...", "date": "", "ddg_snippet": "1. Introduction Estimating accurate 3D human motion in global coordi-nates from a single moving RGB camera is an important and challenging problem in Computer Vision with many applications in animation, Augmented and Virtual Real-ity (AR/VR), human-robot interaction, autonomous driving, and assisted living environments.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025W/RHOBIN/papers/Aytekin_Physics-based_Human_Pose_Estimation_from_a_Single_Moving_RGB_Camera_CVPRW_2025_paper.pdf", "content": "1. Introduction Estimating accurate 3D human motion in global coordi-nates from a single moving RGB camera is an important and challenging problem in Computer Vision with many applications in animation, Augmented and Virtual Real-ity (AR/VR), human-robot interaction, autonomous driving, and assisted living environments."}
+{"idx": 3, "title": "PACE: Human and Camera Motion Estimation from in-the-wild Videos", "date": "", "ddg_snippet": "Figure 1: Human and camera motion reconstruction from in-the-wild videos: given a video of multiple people, PACE is able to reconstruct the motions of all humans and the camera in a coherent global space. To achieve this, we leverage the benefits of both camera localization methods and human motion priors, exploiting the complementary nature of these approaches, i.e., dynamic foreground motion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.13768", "content": "Figure 1: Human and camera motion reconstruction from in-the-wild videos: given a video of multiple people, PACE is able to reconstruct the motions of all humans and the camera in a coherent global space. To achieve this, we leverage the benefits of both camera localization methods and human motion priors, exploiting the complementary nature of these approaches, i.e., dynamic foreground motion ..."}
+{"idx": 4, "title": "PA MPJPE comparison with state-of-the-art methods on the ...", "date": "", "ddg_snippet": "Download scientific diagram | PA MPJPE comparison with state-of-the-art methods on the Human3.6M dataset using Protocol 1. * used extra synthetic data for training. from publication: Camera ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/PA-MPJPE-comparison-with-state-of-the-art-methods-on-the-Human36M-dataset-using-Protocol_tbl3_334735304", "content": "Download scientific diagram | PA MPJPE comparison with state-of-the-art methods on the Human3.6M dataset using Protocol 1. * used extra synthetic data for training. from publication: Camera ..."}
+{"idx": 5, "title": "Dyn- HaMR : Recovering 4 D Interacting Hand Motion from a Dynamic...", "date": "", "ddg_snippet": "We propose Dyn- HaMR , to the best of our knowledge, the first approach to reconstruct 4 D global hand motion from monocular videos recorded by dynamic cameras in the wild.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.12861", "content": "We propose Dyn- HaMR , to the best of our knowledge, the first approach to reconstruct 4 D global hand motion from monocular videos recorded by dynamic cameras in the wild."}
+{"idx": 6, "title": "(PDF) Dyn- HaMR : Recovering 4 D Interacting Hand Motion from...", "date": "", "ddg_snippet": "PA - MPJPE represents the MPJPE after Procrustes Alignment.Dyn- HaMR (Ours) can disentangle the camera and object poses to recover the 4 D global hand motion in the real world whilst.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387141538_Dyn-HaMR_Recovering_4D_Interacting_Hand_Motion_from_a_Dynamic_Camera", "content": "PA - MPJPE represents the MPJPE after Procrustes Alignment.Dyn- HaMR (Ours) can disentangle the camera and object poses to recover the 4 D global hand motion in the real world whilst."}
+{"idx": 7, "title": "Dyn- HaMR : Recovering 4 D Interacting Hand Motion from a Dynamic...", "date": "", "ddg_snippet": "We propose Dyn- HaMR , to the best of our knowledge, the first approach to reconstruct 4 D global hand motion from monocular videos recorded by dynamic cameras in the wild.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Dyn-HaMR_Recovering_4D_Interacting_Hand_Motion_from_a_Dynamic_Camera_CVPR_2025_paper.pdf", "content": "We propose Dyn- HaMR , to the best of our knowledge, the first approach to reconstruct 4 D global hand motion from monocular videos recorded by dynamic cameras in the wild."}
+{"idx": 8, "title": "3D motion reconstruction with deep learning methods: application to...", "date": "", "ddg_snippet": "we have chosen to reconstruct motion from a single -view camera . For the motion reconstruction approach, we opted for a 2-steps methodology. First, we use a model to estimate motion from video as a sequence of 3D poses.", "subpage_snippet": "", "source": "theses.hal.science", "link": "https://theses.hal.science/tel-04954849v1/document", "content": "we have chosen to reconstruct motion from a single -view camera . For the motion reconstruction approach, we opted for a 2-steps methodology. First, we use a model to estimate motion from video as a sequence of 3D poses."}
+{"idx": 9, "title": "EventEgo3D++: 3D Human Motion Capture from A Head-Mounted...", "date": "", "ddg_snippet": "To address these limitations, we introduce EventEgo3D++, the first approach that leverages a monocular event camera with a fisheye lens for 3D human motion capture.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-025-02489-1", "content": "To address these limitations, we introduce EventEgo3D++, the first approach that leverages a monocular event camera with a fisheye lens for 3D human motion capture."}
diff --git "a/data/sampled_jsons/SPD_ISB_SB_ESB_sensitivity_categories_\317\2041_\317\2042_threshold_optimization.jsonl" "b/data/sampled_jsons/SPD_ISB_SB_ESB_sensitivity_categories_\317\2041_\317\2042_threshold_optimization.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..89e49ed6a5f62ff644bdef6b98fcb8ac0daa93be
--- /dev/null
+++ "b/data/sampled_jsons/SPD_ISB_SB_ESB_sensitivity_categories_\317\2041_\317\2042_threshold_optimization.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SPD: Sync-Point Drop for Efficient Tensor Parallelism of ...", "date": "", "ddg_snippet": "In the sequence, the processing of a block is classified into three sensitivity categories based on predefined threshold criteria ( τ1 and τ2 ). This classification allows us to apply separate approaches aimed at minimizing quality degrada-tion according to the identified groups.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20727", "content": "In the sequence, the processing of a block is classified into three sensitivity categories based on predefined threshold criteria ( τ1 and τ2 ). This classification allows us to apply separate approaches aimed at minimizing quality degrada-tion according to the identified groups."}
+{"idx": 1, "title": "[2502.20727] SPD: Sync-Point Drop for efficient tensor ...", "date": "", "ddg_snippet": "Mar 5, 2025 · By progressive replacement of TP block to SPD block and measurement of quality degradation as relative perplexity difference, we can compare the sensitivity between blocks in the entire model and classify the blocks into three sensitivity categories ( ISB , SB , and ESB ).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.20727", "content": "Mar 5, 2025 · By progressive replacement of TP block to SPD block and measurement of quality degradation as relative perplexity difference, we can compare the sensitivity between blocks in the entire model and classify the blocks into three sensitivity categories ( ISB , SB , and ESB )."}
+{"idx": 2, "title": "Sobol Sensitivity Analysis: A Tool to Guide the Development ...", "date": "", "ddg_snippet": "The flow chart and steps of data analysis, sensitivity analysis, the model establishment and qualification process. Sensitivity analysis can be used to guide the experiment design and throughout the model development process. In general, there are two types of sensitivity analysis: (i) local and (ii) global.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5006244/", "content": "The flow chart and steps of data analysis, sensitivity analysis, the model establishment and qualification process. Sensitivity analysis can be used to guide the experiment design and throughout the model development process. In general, there are two types of sensitivity analysis: (i) local and (ii) global."}
+{"idx": 3, "title": "3. Sensitivity Analysis: The Basics — Addressing Uncertainty ...", "date": "", "ddg_snippet": "3. Sensitivity Analysis: The Basics # 3.1. Global Versus Local Sensitivity # Out of the several definitions for sensitivity analysis presented in the literature, the most widely used has been proposed by Saltelli et al. [38] as “the study of how uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input”. In other ...", "subpage_snippet": "", "source": "uc-ebook.org", "link": "https://uc-ebook.org/docs/html/3_sensitivity_analysis_the_basics.html", "content": "3. Sensitivity Analysis: The Basics # 3.1. Global Versus Local Sensitivity # Out of the several definitions for sensitivity analysis presented in the literature, the most widely used has been proposed by Saltelli et al. [38] as “the study of how uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input”. In other ..."}
+{"idx": 4, "title": "TDECQ and SECQ vs Rx sensitivity: a review of previous ...", "date": "", "ddg_snippet": "The suggested remedies to these comments allow sub-eye inequality to be compensated by adjusting threshold levels as part of TDECQ, but propose to limit Tx non-linearity by adding an RLM spec, or limiting the threshold adjustment range.", "subpage_snippet": "", "source": "www.ieee802.org", "link": "https://www.ieee802.org/3/cd/public/Jan18/king_3cd_01_0118.pdf", "content": "The suggested remedies to these comments allow sub-eye inequality to be compensated by adjusting threshold levels as part of TDECQ, but propose to limit Tx non-linearity by adding an RLM spec, or limiting the threshold adjustment range."}
+{"idx": 5, "title": "Sensitivity clustering and ROC curve based alarm threshold ...", "date": "", "ddg_snippet": "Sep 1, 2020 · In industrial practice, to reduce the variable alarm rate and ensure the safety and stability of device production, a variable alarm threshold is optimized by taking into account the receiver operating characteristic (ROC) curve that corresponds to sensitivity clustering, false alarm rate (FAR), and missed alarm rate (MAR).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957582019320841", "content": "Sep 1, 2020 · In industrial practice, to reduce the variable alarm rate and ensure the safety and stability of device production, a variable alarm threshold is optimized by taking into account the receiver operating characteristic (ROC) curve that corresponds to sensitivity clustering, false alarm rate (FAR), and missed alarm rate (MAR)."}
+{"idx": 6, "title": "Sensitivity analysis on parameters (τ1,τ2)\\documentclass ...", "date": "", "ddg_snippet": "The first-order primal-dual algorithms have received much considerable attention in the literature due to their quite promising performance in solving large-scale image processing models. In this ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Sensitivity-analysis-on-parameters-t1-t2documentclass12ptminimal_fig3_346346315", "content": "The first-order primal-dual algorithms have received much considerable attention in the literature due to their quite promising performance in solving large-scale image processing models. In this ..."}
+{"idx": 7, "title": "Learning Rate Should Scale Inversely with High-Order Data Moments...", "date": "", "ddg_snippet": "Importantly, we find that the initialization threshold q¯0 is jointly influenced by both β and τ . Higher learning rates reduce the margin of stability for the informative solution and amplify the sensitivity of the algorithm to the initial align-ment. Therefore, the learning dynamics are modulated by.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15127", "content": "Importantly, we find that the initialization threshold q¯0 is jointly influenced by both β and τ . Higher learning rates reduce the margin of stability for the informative solution and amplify the sensitivity of the algorithm to the initial align-ment. Therefore, the learning dynamics are modulated by."}
+{"idx": 8, "title": "Surface water maps de-noising and missing-data lling using", "date": "", "ddg_snippet": "The threshold 245 τ 1 is related to the quality of the a priori information. In this work, it was chosen based on. trial and error principle.This threshold can be optimised for two reasons: (1) The occurence of water seen by the SAR Sentinel 1 or by Landsat on GSWO might dier.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-02377012v1/document", "content": "The threshold 245 τ 1 is related to the quality of the a priori information. In this work, it was chosen based on. trial and error principle.This threshold can be optimised for two reasons: (1) The occurence of water seen by the SAR Sentinel 1 or by Landsat on GSWO might dier."}
+{"idx": 9, "title": "Measurement of the polarization of τ –leptons produced", "date": "", "ddg_snippet": "PV is the primary vertex. and SV the secondary vertex of the τ 1 decay.The spin of the τ has been determined by analysing the energy dependence of the production cross section near threshold .", "subpage_snippet": "", "source": "publications.rwth-aachen.de", "link": "https://publications.rwth-aachen.de/record/660040/files/660040.pdf", "content": "PV is the primary vertex. and SV the secondary vertex of the τ 1 decay.The spin of the τ has been determined by analysing the energy dependence of the production cross section near threshold ."}
diff --git a/data/sampled_jsons/SPD_paper_appendix_C.2_4-GPU_tensor_parallelism_data_parallelism_8-GPU_setup.jsonl b/data/sampled_jsons/SPD_paper_appendix_C.2_4-GPU_tensor_parallelism_data_parallelism_8-GPU_setup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..081eb82f0ab197a90505915b5b328579ebbe8112
--- /dev/null
+++ b/data/sampled_jsons/SPD_paper_appendix_C.2_4-GPU_tensor_parallelism_data_parallelism_8-GPU_setup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SPD: Sync-Point Drop for efficient tensor parallelism of Large Language ...", "date": "", "ddg_snippet": "Data parallelism Data parallelism can be achieved with SPD by replicating an SPD model across distributed GPU environments. For example, in an 8-GPUs configuration, a 4-GPUs SPD model can be instantiated as a single replica, which is then duplicated across the remaining 4-GPUs .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20727v2", "content": "Data parallelism Data parallelism can be achieved with SPD by replicating an SPD model across distributed GPU environments. For example, in an 8-GPUs configuration, a 4-GPUs SPD model can be instantiated as a single replica, which is then duplicated across the remaining 4-GPUs ."}
+{"idx": 1, "title": "Parallelisms — NVIDIA NeMo Framework User Guide", "date": "", "ddg_snippet": "For more optimzier options, please visit this page. Model Parallelism # Model Parallelism (MP) is a distributed model deployment method that partitions the model parameters across GPUs to reduce the need of per- GPU memory. NeMo Framework supports various model-parallel methods, which can be mixed to maximize LLM training performance. Tensor Parallelism # Tensor Parallelism (TP) is a model ...", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/features/parallelisms.html", "content": "For more optimzier options, please visit this page. Model Parallelism # Model Parallelism (MP) is a distributed model deployment method that partitions the model parameters across GPUs to reduce the need of per- GPU memory. NeMo Framework supports various model-parallel methods, which can be mixed to maximize LLM training performance. Tensor Parallelism # Tensor Parallelism (TP) is a model ..."}
+{"idx": 2, "title": "Model Parallelism - Hugging Face", "date": "", "ddg_snippet": "In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing. In this section we use concepts and diagrams from the Megatron-LM paper : Efficient Large-Scale Language Model Training on GPU Clusters.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/transformers/v4.15.0/parallelism", "content": "In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing. In this section we use concepts and diagrams from the Megatron-LM paper : Efficient Large-Scale Language Model Training on GPU Clusters."}
+{"idx": 3, "title": "Sharding Large Models with Tensor Parallelism", "date": "", "ddg_snippet": "In large scale training, tensor parallelism is often combined with data parallelism . For instance, with an MxN device grid you might parallelize the data across the rows and tensors across the columns.", "subpage_snippet": "", "source": "www.mishalaskin.com", "link": "https://www.mishalaskin.com/posts/tensor_parallel", "content": "In large scale training, tensor parallelism is often combined with data parallelism . For instance, with an MxN device grid you might parallelize the data across the rows and tensors across the columns."}
+{"idx": 4, "title": "GitHub - james-p-xu/4d-parallelism: (WIP) {data, tensor, pipeline ...", "date": "", "ddg_snippet": "This repository explores N-dimesional parallelism , commonly discussed in the context of training large-scale machine learning models. We start from a baseline (0D, sequential execution) and build up to 4D parallelism , illustrating each concept with simple Python examples using a \" GPU \" that incorporates compute and network latency.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/james-p-xu/4d-parallelism", "content": "This repository explores N-dimesional parallelism , commonly discussed in the context of training large-scale machine learning models. We start from a baseline (0D, sequential execution) and build up to 4D parallelism , illustrating each concept with simple Python examples using a \" GPU \" that incorporates compute and network latency."}
+{"idx": 5, "title": "Analyzing the Impact of Tensor Parallelism Configurations on LLM ...", "date": "", "ddg_snippet": "Tensor Parallelism = 2: With two GPUs , all tensor slice exchanges, synchronization, and data transfers depend entirely on a single peer-to-peer (P2P) link. While TP=2 is feasible and not inherently risky, its impact on E2E latency can be significant for larger models.", "subpage_snippet": "", "source": "rocm.blogs.amd.com", "link": "https://rocm.blogs.amd.com/artificial-intelligence/tensor-parallelism/README.html", "content": "Tensor Parallelism = 2: With two GPUs , all tensor slice exchanges, synchronization, and data transfers depend entirely on a single peer-to-peer (P2P) link. While TP=2 is feasible and not inherently risky, its impact on E2E latency can be significant for larger models."}
+{"idx": 6, "title": "[2502.20727] SPD: Sync-Point Drop for efficient tensor parallelism of ...", "date": "", "ddg_snippet": "Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ), to reduce communication overheads in tensor parallelism by selectively dropping synchronization on attention outputs. In detail, we first propose a block design that allows execution to proceed without communication through SPD .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.20727", "content": "Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ), to reduce communication overheads in tensor parallelism by selectively dropping synchronization on attention outputs. In detail, we first propose a block design that allows execution to proceed without communication through SPD ."}
+{"idx": 7, "title": "Multi-GPU Inference Setup: Distribute LLM Workloads Efficiently", "date": "", "ddg_snippet": "Scale LLM inference performance with multi- GPU setup . Learn tensor parallelism , pipeline parallelism , and load balancing for distributed workloads.", "subpage_snippet": "", "source": "markaicode.com", "link": "https://markaicode.com/multi-gpu-llm-inference-setup/", "content": "Scale LLM inference performance with multi- GPU setup . Learn tensor parallelism , pipeline parallelism , and load balancing for distributed workloads."}
+{"idx": 8, "title": "(PDF) SPD: Sync-Point Drop for efficient tensor parallelism of Large ...", "date": "", "ddg_snippet": "Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ), to reduce communication overheads in tensor parallelism by selectively dropping synchronization on attention outputs.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389510233_SPD_Sync-Point_Drop_for_efficient_tensor_parallelism_of_Large_Language_Models", "content": "Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ), to reduce communication overheads in tensor parallelism by selectively dropping synchronization on attention outputs."}
+{"idx": 9, "title": "Efficient and Robust Parallel DNN Training through Model Parallelism on ...", "date": "", "ddg_snippet": "Experimental results show that our proposal achieves up to 8.91x speedup compared to data parallelism on a 4-GPU platform while maintaining comparable model accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1809.02839", "content": "Experimental results show that our proposal achieves up to 8.91x speedup compared to data parallelism on a 4-GPU platform while maintaining comparable model accuracy."}
diff --git a/data/sampled_jsons/SQIL_Soft_Q_Imitation_Learning_on-policy.jsonl b/data/sampled_jsons/SQIL_Soft_Q_Imitation_Learning_on-policy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8039654cd94e02fbdfee7beff6f5da316d856af4
--- /dev/null
+++ b/data/sampled_jsons/SQIL_Soft_Q_Imitation_Learning_on-policy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Soft Q Imitation Learning (SQIL)", "date": "", "ddg_snippet": "Soft Q Imitation Learning (SQIL) # Soft Q Imitation learning learns to imitate a policy from demonstrations by using the DQN algorithm with modified rewards. During each policy update, half of the batch is sampled from the demonstrations and half is sampled from the environment. Expert demonstrations are assigned a reward of 1, and the environment is assigned a reward of 0. This encourages the ...", "subpage_snippet": "", "source": "imitation.readthedocs.io", "link": "https://imitation.readthedocs.io/en/latest/algorithms/sqil.html", "content": "Soft Q Imitation Learning (SQIL) # Soft Q Imitation learning learns to imitate a policy from demonstrations by using the DQN algorithm with modified rewards. During each policy update, half of the batch is sampled from the demonstrations and half is sampled from the environment. Expert demonstrations are assigned a reward of 1, and the environment is assigned a reward of 0. This encourages the ..."}
+{"idx": 1, "title": "[1905.11108] SQIL: Imitation Learning via Reinforcement ... Soft Q Imitation Learning (SQIL) - GitHub SQIL: Saliency-aware Quantized Imitation Learning SQIL: Imitation Learning via Regularized Behavioral Cloning SQIL: Imitation Learning via Reinforcement Learning with ... Soft Q Imitation Learning ( SQIL ) - imitation - Read the Docs SQIL : Imitation Learning via Regularized Behavioral Cloning - arXiv.org SQIL : Imitation Learning via Regularized Behavioral Cloning - arXiv.org SQIL : Imitation Learning via Regularized Behavioral Cloning - arXiv.org SQIL : Imitation Learning via Regularized Behavioral Cloning - arXiv.org SQIL : Imitation Learning via Reinforcement Learning with Sparse Rewards imitation.algorithms.sqil", "date": "", "ddg_snippet": "May 27, 2019 · Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q - learning or off- policy actor-critic algorithm. Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation . Soft Q Imitation learning learns to imitate a policy from demonstrations by using the DQN algorithm with modified rewards. During each policy update, half of the batch is sampled from the demonstrations and half is sampled from the environment. Expert demonstrations are assigned a reward of 1, and the environment is assigned a reward of 0. This encourages the policy to imitate the ... [Paper] [Code] SQIL is the first systematic study of Quantized Imitation Learning , revealing that most quantized failures occur at mission-critical states requiring fine-grained control. By leveraging policy -driven saliency (SIS) and a SIS-weighted 4-bit QAT scheme, SQIL achieves 2–4× efficiency gains while preserving full-precision-level success rates across real-world robotics, autonomous ... To overcome this issue, we regularize the soft Q function by imposing a penalty on the squared soft Bellman error, which is approximated using transitions from rollouts of the imitation policy periodically sampled during training. Dec 20, 2019 · Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q - learning or off- policy actor-critic algorithm. Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation . What is soft Q imitation learning (sqil)? Bases: DemonstrationAlgorithm [Transitions] Soft Q Imitation Learning ( SQIL ). Trains a policy via DQN-style Q - learning , replacing half the buffer with expert demonstrations and adjusting the rewards. Builds SQIL . venv (VecEnv) – The vectorized environment to train on . Is sqil a good imitation learning algorithm for MDPs? The main contribution of this paper is SQIL: a simple and general imitation learning algorithm that is effective in MDPs with high-dimensional, continuous observations and unknown dynamics. Is a soft Q function a robust entropy model of adversarial imitation learning? We propose an alternative that combines the simplicity of BC with the robustness of adversarial imitation learning . The key insight is that under the maximum entropy model of expert behavior, BC corresponds to fitting a soft Q function that maximizes the likelihood of observed actions. How do we infer the expert's soft Q function? We derive the regularization term from the maximum entropy model of expert behavior, resulting in a method that infers the expert’s soft Q function by maximizing the likelihood of observed actions and minimizing the squared soft Bellman error . Can sqil be applied to continuous control tasks? SQIL can be applied to continuous control tasks using existing sampling methods [11, 12], as illustrated in Section 5.3. 2Section 4 discusses how the value of rabs is chosen. We aim for an imitation learning algorithm that generalizes to new states, without resorting to complex adversarial optimization procedures. Is sqil a regularized variant of BC? Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation. Empirically, we show that SQIL outperforms BC and achieves competitive results compared to GAIL, on a variety of image-based and low-dimensional tasks in Box2D, Atari, and MuJoCo. Soft Q Imitation Learning (SQIL) (https://arxiv.org/abs/1905.11108). Trains a policy via DQN-style Q - learning , replacing half the buffer with expert demonstrations and adjusting the rewards.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1905.11108", "content": "May 27, 2019 · Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q - learning or off- policy actor-critic algorithm. Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation . Soft Q Imitation learning learns to imitate a policy from demonstrations by using the DQN algorithm with modified rewards. During each policy update, half of the batch is sampled from the demonstrations and half is sampled from the environment. Expert demonstrations are assigned a reward of 1, and the environment is assigned a reward of 0. This encourages the policy to imitate the ... [Paper] [Code] SQIL is the first systematic study of Quantized Imitation Learning , revealing that most quantized failures occur at mission-critical states requiring fine-grained control. By leveraging policy -driven saliency (SIS) and a SIS-weighted 4-bit QAT scheme, SQIL achieves 2–4× efficiency gains while preserving full-precision-level success rates across real-world robotics, autonomous ... To overcome this issue, we regularize the soft Q function by imposing a penalty on the squared soft Bellman error, which is approximated using transitions from rollouts of the imitation policy periodically sampled during training. Dec 20, 2019 · Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q - learning or off- policy actor-critic algorithm. Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation . What is soft Q imitation learning (sqil)? Bases: DemonstrationAlgorithm [Transitions] Soft Q Imitation Learning ( SQIL ). Trains a policy via DQN-style Q - learning , replacing half the buffer with expert demonstrations and adjusting the rewards. Builds SQIL . venv (VecEnv) – The vectorized environment to train on . Is sqil a good imitation learning algorithm for MDPs? The main contribution of this paper is SQIL: a simple and general imitation learning algorithm that is effective in MDPs with high-dimensional, continuous observations and unknown dynamics. Is a soft Q function a robust entropy model of adversarial imitation learning? We propose an alternative that combines the simplicity of BC with the robustness of adversarial imitation learning . The key insight is that under the maximum entropy model of expert behavior, BC corresponds to fitting a soft Q function that maximizes the likelihood of observed actions. How do we infer the expert's soft Q function? We derive the regularization term from the maximum entropy model of expert behavior, resulting in a method that infers the expert’s soft Q function by maximizing the likelihood of observed actions and minimizing the squared soft Bellman error . Can sqil be applied to continuous control tasks? SQIL can be applied to continuous control tasks using existing sampling methods [11, 12], as illustrated in Section 5.3. 2Section 4 discusses how the value of rabs is chosen. We aim for an imitation learning algorithm that generalizes to new states, without resorting to complex adversarial optimization procedures. Is sqil a regularized variant of BC? Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation. Empirically, we show that SQIL outperforms BC and achieves competitive results compared to GAIL, on a variety of image-based and low-dimensional tasks in Box2D, Atari, and MuJoCo. Soft Q Imitation Learning (SQIL) (https://arxiv.org/abs/1905.11108). Trains a policy via DQN-style Q - learning , replacing half the buffer with expert demonstrations and adjusting the rewards."}
+{"idx": 2, "title": "Soft Q Imitation Learning (SQIL) - GitHub", "date": "", "ddg_snippet": "Soft Q Imitation learning learns to imitate a policy from demonstrations by using the DQN algorithm with modified rewards. During each policy update, half of the batch is sampled from the demonstrations and half is sampled from the environment. Expert demonstrations are assigned a reward of 1, and the environment is assigned a reward of 0. This encourages the policy to imitate the ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HumanCompatibleAI/imitation/blob/master/docs/algorithms/sqil.rst", "content": "Soft Q Imitation learning learns to imitate a policy from demonstrations by using the DQN algorithm with modified rewards. During each policy update, half of the batch is sampled from the demonstrations and half is sampled from the environment. Expert demonstrations are assigned a reward of 1, and the environment is assigned a reward of 0. This encourages the policy to imitate the ..."}
+{"idx": 3, "title": "SQIL: Saliency-aware Quantized Imitation Learning", "date": "", "ddg_snippet": "[Paper] [Code] SQIL is the first systematic study of Quantized Imitation Learning , revealing that most quantized failures occur at mission-critical states requiring fine-grained control. By leveraging policy -driven saliency (SIS) and a SIS-weighted 4-bit QAT scheme, SQIL achieves 2–4× efficiency gains while preserving full-precision-level success rates across real-world robotics, autonomous ...", "subpage_snippet": "", "source": "aiha-lab.github.io", "link": "https://aiha-lab.github.io/sqil/", "content": "[Paper] [Code] SQIL is the first systematic study of Quantized Imitation Learning , revealing that most quantized failures occur at mission-critical states requiring fine-grained control. By leveraging policy -driven saliency (SIS) and a SIS-weighted 4-bit QAT scheme, SQIL achieves 2–4× efficiency gains while preserving full-precision-level success rates across real-world robotics, autonomous ..."}
+{"idx": 4, "title": "SQIL: Imitation Learning via Regularized Behavioral Cloning", "date": "", "ddg_snippet": "To overcome this issue, we regularize the soft Q function by imposing a penalty on the squared soft Bellman error, which is approximated using transitions from rollouts of the imitation policy periodically sampled during training.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1905.11108v1", "content": "To overcome this issue, we regularize the soft Q function by imposing a penalty on the squared soft Bellman error, which is approximated using transitions from rollouts of the imitation policy periodically sampled during training."}
+{"idx": 5, "title": "SQIL: Imitation Learning via Reinforcement Learning with ...", "date": "", "ddg_snippet": "Dec 20, 2019 · Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q - learning or off- policy actor-critic algorithm. Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=S1xKd24twB", "content": "Dec 20, 2019 · Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q - learning or off- policy actor-critic algorithm. Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation ."}
+{"idx": 6, "title": "imitation.algorithms.sqil", "date": "", "ddg_snippet": "Soft Q Imitation Learning (SQIL) (https://arxiv.org/abs/1905.11108). Trains a policy via DQN-style Q - learning , replacing half the buffer with expert demonstrations and adjusting the rewards.", "subpage_snippet": "", "source": "imitation.readthedocs.io", "link": "https://imitation.readthedocs.io/en/latest/_api/imitation.algorithms.sqil.html", "content": "Soft Q Imitation Learning (SQIL) (https://arxiv.org/abs/1905.11108). Trains a policy via DQN-style Q - learning , replacing half the buffer with expert demonstrations and adjusting the rewards."}
+{"idx": 7, "title": "What Would pi* Do?: Imitation Learning via Off- Policy Reinforcement...", "date": "", "ddg_snippet": "We propose a remarkably simple alternative based on off- policy soft Q - learning , which we call soft Q imitation learning ( SQIL , pronounced \"skill\"), that rewards the agent for matching demonstrated actions in demonstrated states.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/what-would-pi-do-imitation-learning-via-off", "content": "We propose a remarkably simple alternative based on off- policy soft Q - learning , which we call soft Q imitation learning ( SQIL , pronounced \"skill\"), that rewards the agent for matching demonstrated actions in demonstrated states."}
+{"idx": 8, "title": "GitHub - gouxiangchen/ soft - Q - learning : discrete soft Q learning ...", "date": "", "ddg_snippet": "update SQIL : soft q imitation learning . all code is in one file and easily to follow.Reinforcement Learning with Deep Energy-Based Policies . SQIL : Imitation Learning via Reinforcement Learning with Sparse Rewards. About.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/gouxiangchen/soft-Q-learning", "content": "update SQIL : soft q imitation learning . all code is in one file and easily to follow.Reinforcement Learning with Deep Energy-Based Policies . SQIL : Imitation Learning via Reinforcement Learning with Sparse Rewards. About."}
+{"idx": 9, "title": "SQIL — DI-engine 0.1.0 documentation", "date": "", "ddg_snippet": "Soft Q imitation learning ( SQIL ) is an off- policy maximum entropy Q learning algorithm together with imitation learning . SQIL performs three small but important modifications on SQL", "subpage_snippet": "", "source": "opendilab.github.io", "link": "https://opendilab.github.io/DI-engine/12_policies/sqil.html", "content": "Soft Q imitation learning ( SQIL ) is an off- policy maximum entropy Q learning algorithm together with imitation learning . SQIL performs three small but important modifications on SQL"}
diff --git a/data/sampled_jsons/SWEBench_Jimenez_et_al._2024_abstract.jsonl b/data/sampled_jsons/SWEBench_Jimenez_et_al._2024_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8fa429ca367efa2dc1ed7cac47e5bdff1f25b185
--- /dev/null
+++ b/data/sampled_jsons/SWEBench_Jimenez_et_al._2024_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SWE - bench : Can Language Models Resolve Real-World GitHub Issues?", "date": "", "ddg_snippet": "Abstract :Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities.Comments: Data, code, and leaderboard are available at this https URL ICLR 2024 , this https URL.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.06770", "content": "Abstract :Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities.Comments: Data, code, and leaderboard are available at this https URL ICLR 2024 , this https URL."}
+{"idx": 1, "title": "GitHub - SWE - bench / SWE - bench : SWE - bench [Multimodal]: Can...", "date": "", "ddg_snippet": "www. swebench .com. License.13, 2024 ]: Introducing SWE - bench Verified! Part 2 of our collaboration with OpenAI Preparedness. A subset of 500 problems that real software engineers have confirmed are solvable.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SWE-bench/SWE-bench", "content": "www. swebench .com. License.13, 2024 ]: Introducing SWE - bench Verified! Part 2 of our collaboration with OpenAI Preparedness. A subset of 500 problems that real software engineers have confirmed are solvable."}
+{"idx": 2, "title": "CSR-Bench: Benchmarking LLM Agents in Deployment of", "date": "", "ddg_snippet": "Agentless (Xia et al ., 2024 a) and CodeStar(Li et al ., 2023)), code correction ( SWEBench ( Jimenez et al ., 2024 )), and more.A notable benchmark in this area is SWE - bench ( Jimenez et al ., 2024 ), which includes tasks requiring repository modifications to resolve actual GitHub issues.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.633.pdf", "content": "Agentless (Xia et al ., 2024 a) and CodeStar(Li et al ., 2023)), code correction ( SWEBench ( Jimenez et al ., 2024 )), and more.A notable benchmark in this area is SWE - bench ( Jimenez et al ., 2024 ), which includes tasks requiring repository modifications to resolve actual GitHub issues."}
+{"idx": 3, "title": "HAL: SWE - bench Verified Mini Leaderboard", "date": "", "ddg_snippet": "( Jimenez et al ., 2023) OpenAI Blog: SWE - bench Verified: Introducing SWE - bench Verified.Output Token Price. $ /1M tokens. GPT-4o (November 2024 ). Active. Input Token Price.", "subpage_snippet": "", "source": "hal.cs.princeton.edu", "link": "https://hal.cs.princeton.edu/swebench_verified_mini", "content": "( Jimenez et al ., 2023) OpenAI Blog: SWE - bench Verified: Introducing SWE - bench Verified.Output Token Price. $ /1M tokens. GPT-4o (November 2024 ). Active. Input Token Price."}
+{"idx": 4, "title": "We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts...", "date": "", "ddg_snippet": "• DeepSeek-Coder (Guo et al ., 2024 ) comprises a series of code language models, ranging from 1 billion to 33 billion parameters. Each model is trained from scratch on 2 trillion tokens, with a composition of 87% code and 13% natural language in both English and Chinese.", "subpage_snippet": "", "source": "aarnphm.xyz", "link": "https://aarnphm.xyz/thoughts/papers/2406.11931v1.pdf", "content": "• DeepSeek-Coder (Guo et al ., 2024 ) comprises a series of code language models, ranging from 1 billion to 33 billion parameters. Each model is trained from scratch on 2 trillion tokens, with a composition of 87% code and 13% natural language in both English and Chinese."}
+{"idx": 5, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "SWEBench ( Jimenez et al ., 2024 ) measures the ability of language models to generate patches for failing PR tests.In ICML, 2024 . Carlos E Jimenez , John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=7o6SG5gVev", "content": "SWEBench ( Jimenez et al ., 2024 ) measures the ability of language models to generate patches for failing PR tests.In ICML, 2024 . Carlos E Jimenez , John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan."}
+{"idx": 6, "title": "(PDF) Breakpoint: Scalable evaluation of system-level reasoning in...", "date": "", "ddg_snippet": "engineering benchmarks, such as SWEBench ( Jimenez et al ., 2024 ), predominantly em-. phasize localized reasoning by curating tasks derived from individual GitHub issues, or. deliberately selecting problems that are isolated and straightforwardly evaluatable.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392334371_Breakpoint_Scalable_evaluation_of_system-level_reasoning_in_LLM_code_agents", "content": "engineering benchmarks, such as SWEBench ( Jimenez et al ., 2024 ), predominantly em-. phasize localized reasoning by curating tasks derived from individual GitHub issues, or. deliberately selecting problems that are isolated and straightforwardly evaluatable."}
+{"idx": 7, "title": "A Careful Examination of Large Language... | Read Paper on Bytez", "date": "", "ddg_snippet": "[2021]), SWEBench ( Jimenez et al . [ 2024 ])).(a) Do the main claims made in the abstract and introduction accurately reflect the paper’ s contributions and scope?", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2405.00332/paper", "content": "[2021]), SWEBench ( Jimenez et al . [ 2024 ])).(a) Do the main claims made in the abstract and introduction accurately reflect the paper’ s contributions and scope?"}
+{"idx": 8, "title": "Cognition | SWE - bench technical report", "date": "", "ddg_snippet": "2024 -03-15 The Cognition Team. In this post: TagsSource: swebench .com. In SWE - bench , LLMs are either given the set of correct files to edit (“assisted”); or a separate system retrieves the files to edit based on similarity to the issue text (“unassisted”).", "subpage_snippet": "", "source": "cognition.ai", "link": "https://cognition.ai/blog/swe-bench-technical-report", "content": "2024 -03-15 The Cognition Team. In this post: TagsSource: swebench .com. In SWE - bench , LLMs are either given the set of correct files to edit (“assisted”); or a separate system retrieves the files to edit based on similarity to the issue text (“unassisted”)."}
+{"idx": 9, "title": "Repository-level Code Search with Neural Retrieval Methods", "date": "", "ddg_snippet": "More recently, on SWE - Bench ( Jimenez et al ., 2023), the performance of popular LLMs like GPT-4 and. Claude Opus remains limited, with only 1-4% of tasks solved using Retrieval Augmented Generation (RAG) (Lewis et al ., 2020).", "subpage_snippet": "", "source": "www.ssgandhi.com", "link": "https://www.ssgandhi.com/ssg2-directed-study-report.pdf", "content": "More recently, on SWE - Bench ( Jimenez et al ., 2023), the performance of popular LLMs like GPT-4 and. Claude Opus remains limited, with only 1-4% of tasks solved using Retrieval Augmented Generation (RAG) (Lewis et al ., 2020)."}
diff --git a/data/sampled_jsons/SWEBench_abstract_arXiv_Jimenez_software_engineering_benchmark_GitHub_issues.jsonl b/data/sampled_jsons/SWEBench_abstract_arXiv_Jimenez_software_engineering_benchmark_GitHub_issues.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0aad2a78e0db029ec7739e4d9c18b878f9c41c31
--- /dev/null
+++ b/data/sampled_jsons/SWEBench_abstract_arXiv_Jimenez_software_engineering_benchmark_GitHub_issues.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - SWE - bench / SWE - bench : SWE - bench [Multimodal]: Can...", "date": "", "ddg_snippet": "SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue , a language model is tasked with generating a patch that resolves the described problem.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SWE-bench/SWE-bench", "content": "SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue , a language model is tasked with generating a patch that resolves the described problem."}
+{"idx": 1, "title": "SWE - bench : Can Language Models Resolve Real-World GitHub ...", "date": "", "ddg_snippet": "We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.0 forks source link. SWE - bench : Can Language Models Resolve Real-World GitHub Issues ?, Carlos E. Jimenez +, arXiv '23 #1848.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/AkihikoWatanabe/paper_notes/1848", "content": "We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.0 forks source link. SWE - bench : Can Language Models Resolve Real-World GitHub Issues ?, Carlos E. Jimenez +, arXiv '23 #1848."}
+{"idx": 2, "title": "SWE Benchmark : LLM evaluation in Software Engineering ... | Medium", "date": "", "ddg_snippet": "SWE - bench is an advanced benchmark designed to evaluate language models’ capabilities in realistic software engineering scenarios. It offers several key advantages over traditional programming benchmarks : 1. Real-world complexity: SWE - bench uses actual GitHub issues and pull...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sulbha.jindal/swe-benchmark-llm-evaluation-in-software-engineering-setting-52f315b2de5a", "content": "SWE - bench is an advanced benchmark designed to evaluate language models’ capabilities in realistic software engineering scenarios. It offers several key advantages over traditional programming benchmarks : 1. Real-world complexity: SWE - bench uses actual GitHub issues and pull..."}
+{"idx": 3, "title": "SWE - Bench Benchmark - GM-RKB", "date": "", "ddg_snippet": "A SWE - Bench Benchmark is a automated software engineering benchmark of real-world GitHub issues . Context: It can involve providing a language model with a codebase and an issue , where the model must generate a patch that resolves the described proble...", "subpage_snippet": "", "source": "www.gabormelli.com", "link": "https://www.gabormelli.com/RKB/SWE-bench", "content": "A SWE - Bench Benchmark is a automated software engineering benchmark of real-world GitHub issues . Context: It can involve providing a language model with a codebase and an issue , where the model must generate a patch that resolves the described proble..."}
+{"idx": 4, "title": "GPT-5 Coding Abilities: Benchmarks Reveal Hidden Performance...", "date": "", "ddg_snippet": "SWEBench ( Software Engineering Benchmark ) presents one of the most realistic testing environments, featuring over 2,200 actual GitHub issues from popular open-source Python repositories.", "subpage_snippet": "", "source": "logicloop.dev", "link": "https://logicloop.dev/machine-learning/gpt5-coding-abilities-benchmarks-reveal-hidden-performance-insights/", "content": "SWEBench ( Software Engineering Benchmark ) presents one of the most realistic testing environments, featuring over 2,200 actual GitHub issues from popular open-source Python repositories."}
+{"idx": 5, "title": "GitHub - scaleapi/ SWE - bench _Pro-os: SWE - Bench Pro: Can AI...", "date": "", "ddg_snippet": "SWE - Bench Pro raises the bar with: 1 Harder tasks: multi-file patches, ~100+ lines changed, context across large repos. 2 Contamination resistance: built from copyleft-licensed repos + private/commercial repos to prevent overlap with training data.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/vbingliu_github-scaleapiswe-benchpro-os-swe-bench-activity-7375198335449817088-AqEV", "content": "SWE - Bench Pro raises the bar with: 1 Harder tasks: multi-file patches, ~100+ lines changed, context across large repos. 2 Contamination resistance: built from copyleft-licensed repos + private/commercial repos to prevent overlap with training data."}
+{"idx": 6, "title": "[2310.06770] SWE - bench : Can Language Models Resolve Real-World...", "date": "", "ddg_snippet": "software engineering problems drawn from real GitHub issues and corresponding pull requests across 12.View a PDF of the paper titled SWE - bench : Can Language Models Resolve Real-World GitHub Issues ?, by Carlos E. Jimenez and 6 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.06770", "content": "software engineering problems drawn from real GitHub issues and corresponding pull requests across 12.View a PDF of the paper titled SWE - bench : Can Language Models Resolve Real-World GitHub Issues ?, by Carlos E. Jimenez and 6 other authors."}
+{"idx": 7, "title": "SWE - bench", "date": "", "ddg_snippet": "SWE - bench tests AI systems' ability to solve GitHub issues .Our follow up work, SWE-agent, was the first agent-based AI system ever introduced for performing software engineering tasks, achieving a score of 12.47% on SWE - bench .", "subpage_snippet": "", "source": "www.swebench.com", "link": "https://www.swebench.com/original.html", "content": "SWE - bench tests AI systems' ability to solve GitHub issues .Our follow up work, SWE-agent, was the first agent-based AI system ever introduced for performing software engineering tasks, achieving a score of 12.47% on SWE - bench ."}
+{"idx": 8, "title": "Cognition | SWE - bench technical report", "date": "", "ddg_snippet": "Each SWE - bench instance consists of a GitHub issue and the pull request which resolved it. The pull request must include a unit test which fails before the code change and passes after (called a “fail to pass” test).", "subpage_snippet": "", "source": "cognition.ai", "link": "https://cognition.ai/blog/swe-bench-technical-report", "content": "Each SWE - bench instance consists of a GitHub issue and the pull request which resolved it. The pull request must include a unit test which fails before the code change and passes after (called a “fail to pass” test)."}
+{"idx": 9, "title": "HAL: SWE - bench Verified Mini Leaderboard", "date": "", "ddg_snippet": "Paper: SWE - bench : Can Language Models Resolve Real-World GitHub Issues ? ( Jimenez et al., 2023) OpenAI Blog: SWE - bench Verified: Introducing SWE - bench Verified. Note: This subset of the original SWE - bench Verified contains different tasks then this recently released version...", "subpage_snippet": "", "source": "hal.cs.princeton.edu", "link": "https://hal.cs.princeton.edu/swebench_verified_mini", "content": "Paper: SWE - bench : Can Language Models Resolve Real-World GitHub Issues ? ( Jimenez et al., 2023) OpenAI Blog: SWE - bench Verified: Introducing SWE - bench Verified. Note: This subset of the original SWE - bench Verified contains different tasks then this recently released version..."}
diff --git a/data/sampled_jsons/Saito_2020_Unbiased_Pairwise_Learning_methodology_inverse_propensity_scoring.jsonl b/data/sampled_jsons/Saito_2020_Unbiased_Pairwise_Learning_methodology_inverse_propensity_scoring.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a8805d6efa717e27e22ee0920771db2f0895bc62
--- /dev/null
+++ b/data/sampled_jsons/Saito_2020_Unbiased_Pairwise_Learning_methodology_inverse_propensity_scoring.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Learning for the Causal Effect of Recommendation", "date": "", "ddg_snippet": "by M Sato · 2020 · Cited by 83 — This paper proposes an unbiased learning framework for the causal effect of recommendation . Based on the inverse propensity scoring technique, the proposed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2008.04563", "content": "by M Sato · 2020 · Cited by 83 — This paper proposes an unbiased learning framework for the causal effect of recommendation . Based on the inverse propensity scoring technique, the proposed ..."}
+{"idx": 1, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "by Y Saito · 2020 · Cited by 52 — In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3409256.3409812", "content": "by Y Saito · 2020 · Cited by 52 — In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ..."}
+{"idx": 2, "title": "Unbiased Pairwise Learning from Implicit Feedback for ...", "date": "", "ddg_snippet": "by Y Ren · 2023 · Cited by 7 — It performs truly unbiased pairwise learning based on implicit feedback by circumventing the step of external variance control.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.05066", "content": "by Y Ren · 2023 · Cited by 7 — It performs truly unbiased pairwise learning based on implicit feedback by circumventing the step of external variance control."}
+{"idx": 3, "title": "Unbiased Learning for the Causal Effect of Recommendation", "date": "", "ddg_snippet": "by M Sato · 2020 · Cited by 83 — This paper proposes an unbiased learning framework for the causal effect of recommendation . Based on the inverse propensity scoring technique.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3383313.3412261", "content": "by M Sato · 2020 · Cited by 83 — This paper proposes an unbiased learning framework for the causal effect of recommendation . Based on the inverse propensity scoring technique."}
+{"idx": 4, "title": "Unbiased Implicit Recommendation and Propensity ...", "date": "", "ddg_snippet": "by Z ZHU · Cited by 61 — This paper focuses on how to generate unbiased recommendations based on biased implicit user-item interactions. We propose a combinational joint learning ...", "subpage_snippet": "", "source": "zziwei.github.io", "link": "https://zziwei.github.io/pubs/Ziwei_RecSys_2020_Unbias.pdf", "content": "by Z ZHU · Cited by 61 — This paper focuses on how to generate unbiased recommendations based on biased implicit user-item interactions. We propose a combinational joint learning ..."}
+{"idx": 5, "title": "Unbiased Learning for the Causal Effect of Recommendation", "date": "", "ddg_snippet": "11 Aug 2020 — An unbiased learning framework for the causal effect of recommendation is proposed based on the inverse propensity scoring technique and it ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Unbiased-Learning-for-the-Causal-Effect-of-Sato-Takemori/8ee4a0164f4f95eb012c3f565d0866e5f4f7d64b", "content": "11 Aug 2020 — An unbiased learning framework for the causal effect of recommendation is proposed based on the inverse propensity scoring technique and it ..."}
+{"idx": 6, "title": "Towards Resolving Propensity Contradiction in Offline ...", "date": "", "ddg_snippet": "by Y Saito · Cited by 15 — It defines the probability of each feedback being observed as a propensity score and obtains an unbiased estimator for the true metric via inverse propensity ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2022/0307.pdf", "content": "by Y Saito · Cited by 15 — It defines the probability of each feedback being observed as a propensity score and obtains an unbiased estimator for the true metric via inverse propensity ..."}
+{"idx": 7, "title": "Practically Unbiased Pairwise Loss for Recommendation ...", "date": "", "ddg_snippet": "by T Cao · 2025 · Cited by 2 — In this paper, we focus on unbiased ranking loss weighted by inversed propensity scores (IPS), which are widely used in recommendations with implicit feedback ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/04/10810273/22O7O9fFhOo", "content": "by T Cao · 2025 · Cited by 2 — In this paper, we focus on unbiased ranking loss weighted by inversed propensity scores (IPS), which are widely used in recommendations with implicit feedback ..."}
+{"idx": 8, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — This paper focuses on addressing the lack of negative feedback in recommendations with implicit feedback and points out the limitations of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — This paper focuses on addressing the lack of negative feedback in recommendations with implicit feedback and points out the limitations of ..."}
+{"idx": 9, "title": "Asymmetric Tri-training for Debiasing Missing-Not-At-Random ...", "date": "", "ddg_snippet": "The propensity score is the probability of each data being observed, and unbiased performance estimation is possible by weighting each data by the inverse of ...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/2020/sigir2020-1/", "content": "The propensity score is the probability of each data being observed, and unbiased performance estimation is possible by weighting each data by the inverse of ..."}
diff --git a/data/sampled_jsons/Sajadmanesh_Gatica-Perez_2021_LPGNN_Locally_private_graph_neural_networks_abstract.jsonl b/data/sampled_jsons/Sajadmanesh_Gatica-Perez_2021_LPGNN_Locally_private_graph_neural_networks_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8c685208338de4cda8330599427b690d42bef0c4
--- /dev/null
+++ b/data/sampled_jsons/Sajadmanesh_Gatica-Perez_2021_LPGNN_Locally_private_graph_neural_networks_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2006.05535] Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica - Perez .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica - Perez ."}
+{"idx": 1, "title": "Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Differential Privacy ; Private Learning; Graph Neural Networks ; Node Classification. ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez .[1] 2021 . Stealing Links from Graph Neural Networks .", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/downloads/papers/2021/Sajadmanesh_CCS2021_2021.pdf", "content": "Differential Privacy ; Private Learning; Graph Neural Networks ; Node Classification. ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez .[1] 2021 . Stealing Links from Graph Neural Networks ."}
+{"idx": 2, "title": "(PDF) Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Sajadmanesh and Gatica - Perez . Graph Neural Network ( LPGNN ), a novel privacy -preserving GNN. learning framework with provable privacy guarantees based on.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348678319_Locally_Private_Graph_Neural_Networks", "content": "Sajadmanesh and Gatica - Perez . Graph Neural Network ( LPGNN ), a novel privacy -preserving GNN. learning framework with provable privacy guarantees based on."}
+{"idx": 3, "title": "Locally Private Graph Neural Networks | Proceedings of the 2021 ...", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460120.3484565?cookieSet=1", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private ."}
+{"idx": 4, "title": "GitHub - sisaman/ LPGNN : Locally Private Graph Neural Networks ...", "date": "", "ddg_snippet": "Abstract .{ sajadmanesh 2021 locally , author = { Sajadmanesh , Sina and Gatica - Perez , Daniel}, title = { Locally Private Graph Neural Networks }, year = { 2021 }, publisher = {Association for Computing Machinery}, doi = {10.1145/3460120.3484565}, booktitle = {Proceedings of the 2021 ACM...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/LPGNN", "content": "Abstract .{ sajadmanesh 2021 locally , author = { Sajadmanesh , Sina and Gatica - Perez , Daniel}, title = { Locally Private Graph Neural Networks }, year = { 2021 }, publisher = {Association for Computing Machinery}, doi = {10.1145/3460120.3484565}, booktitle = {Proceedings of the 2021 ACM..."}
+{"idx": 5, "title": "Locally Private Graph Neural Networks -Bohrium", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/locally-private-graph-neural-networks/867757841736269842-108614", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 6, "title": "(Open Access) Locally Private Graph Neural Networks (2020)", "date": "", "ddg_snippet": "Sina Sajadmanesh , Daniel Gatica - Perez +1 moreIdiap Research Institute. - 09 Jun 2020. - arXiv: Learning. Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/locally-private-graph-neural-networks-1lusvir819", "content": "Sina Sajadmanesh , Daniel Gatica - Perez +1 moreIdiap Research Institute. - 09 Jun 2020. - arXiv: Learning. Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 7, "title": "Sina Sajadmanesh - Google Akademik", "date": "", "ddg_snippet": "2020. Locally Private Graph Neural Networks . S Sajadmanesh , D Gatica - Perez . ACM CCS, 2021 .", "subpage_snippet": "", "source": "scholar.google.co.za", "link": "https://scholar.google.co.za/citations?user=Gtw3NoAAAAAJ&hl=tr", "content": "2020. Locally Private Graph Neural Networks . S Sajadmanesh , D Gatica - Perez . ACM CCS, 2021 ."}
+{"idx": 8, "title": "Locally Private Graph Neural Networks - Paper Detail", "date": "", "ddg_snippet": "Sina Sajadmanesh , Daniel Gatica - Perez . Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/179057/locally-private-graph-neural-networks", "content": "Sina Sajadmanesh , Daniel Gatica - Perez . Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks."}
+{"idx": 9, "title": "LPGNet: Link Private Graph Networks for Node... | Connected Papers", "date": "", "ddg_snippet": "Locally Private Graph Neural Networks . Sina Sajadmanesh , D. Gática - Pérez .GraphMI: Extracting Private Graph Data from Graph Neural Networks . Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, Enhong Chen.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/41284c49f521d60eec3b4db6e32878cbdd7ea632/LPGNet:-Link-Private-Graph-Networks-for-Node-Classification/graph", "content": "Locally Private Graph Neural Networks . Sina Sajadmanesh , D. Gática - Pérez .GraphMI: Extracting Private Graph Data from Graph Neural Networks . Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, Enhong Chen."}
diff --git a/data/sampled_jsons/Sander_Beckers_2023_disjunctive_counterfactuals_Pearl_year_2023.jsonl b/data/sampled_jsons/Sander_Beckers_2023_disjunctive_counterfactuals_Pearl_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48ed29b84d1220195fd95b98f0d3e31a02b8fc46
--- /dev/null
+++ b/data/sampled_jsons/Sander_Beckers_2023_disjunctive_counterfactuals_Pearl_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2211.00472] Backtracking Counterfactuals - arXiv.org", "date": "", "ddg_snippet": "In Pearl's structural causal model (SCM) framework this is made mathematically rigorous via interventions that modify the causal laws while the values of exogenous variables are shared. In recent years, however, this purely interventionist account of counterfactuals has increasingly come under scrutiny from both philosophers and psychologists.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2211.00472", "content": "In Pearl's structural causal model (SCM) framework this is made mathematically rigorous via interventions that modify the causal laws while the values of exogenous variables are shared. In recent years, however, this purely interventionist account of counterfactuals has increasingly come under scrutiny from both philosophers and psychologists."}
+{"idx": 1, "title": "· Sander - GitHub Pages", "date": "", "ddg_snippet": "Beckers , S., Halpern, J.Y., and Hitchcock, C. ( 2023 ). Causal Models with Constraints. In Proceedings of the 2nd Conference on Causal Learning and Reasoning, 213, 866-879. von Kügelgen, J., Mohamed, A., and Beckers , S. ( 2023 ). Backtracking Counterfactuals . In Proceedings of the 2nd Conference on Causal Learning and Reasoning, 213, 177-196.", "subpage_snippet": "", "source": "sanderbeckers.github.io", "link": "https://sanderbeckers.github.io/website/", "content": "Beckers , S., Halpern, J.Y., and Hitchcock, C. ( 2023 ). Causal Models with Constraints. In Proceedings of the 2nd Conference on Causal Learning and Reasoning, 213, 866-879. von Kügelgen, J., Mohamed, A., and Beckers , S. ( 2023 ). Backtracking Counterfactuals . In Proceedings of the 2nd Conference on Causal Learning and Reasoning, 213, 177-196."}
+{"idx": 2, "title": "Backtracking Counterfactuals - PMLR", "date": "", "ddg_snippet": "@InProceedings{pmlr-v213-kugelgen23a, title = {Backtracking Counterfactuals }, author = {K\\\"ugelgen, Julius Von and Mohamed, Abdirisak and Beckers , Sander }, booktitle = {Proceedings of the Second Conference on Causal Learning and Reasoning}, pages = {177--196}, year = { 2023 }, editor = {van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik}, volume = {213}, series = {Proceedings of ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v213/kugelgen23a.html", "content": "@InProceedings{pmlr-v213-kugelgen23a, title = {Backtracking Counterfactuals }, author = {K\\\"ugelgen, Julius Von and Mohamed, Abdirisak and Beckers , Sander }, booktitle = {Proceedings of the Second Conference on Causal Learning and Reasoning}, pages = {177--196}, year = { 2023 }, editor = {van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik}, volume = {213}, series = {Proceedings of ..."}
+{"idx": 3, "title": "Julius von Kügelgen, Abdirisak Mohamed & Sander Beckers, Backtracking ...", "date": "", "ddg_snippet": "In Pearl's structural causal model (SCM) framework this is made mathematically rigorous via interventions that modify the causal laws while the values of exogenous variables are shared. In recent years, however, this purely interventionist account of counterfactuals has increasingly come under scrutiny from both philosophers and psychologists.", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/rec/VONBCT", "content": "In Pearl's structural causal model (SCM) framework this is made mathematically rigorous via interventions that modify the causal laws while the values of exogenous variables are shared. In recent years, however, this purely interventionist account of counterfactuals has increasingly come under scrutiny from both philosophers and psychologists."}
+{"idx": 4, "title": "Sander Beckers - Google Scholar", "date": "", "ddg_snippet": "University College London - Cited by 6442014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=7aqN2U4AAAAJ&hl=en", "content": "University College London - Cited by 6442014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025"}
+{"idx": 5, "title": "Backtracking Counterfactuals - OpenReview", "date": "", "ddg_snippet": "Backtracking Counterfactuals Julius Von Kügelgen, Abdirisak Mohamed, Sander Beckers Published: 17 Mar 2023 , Last Modified: 26 May 2023 CLeaR 2023 Oral Readers: Everyone Show Bibtex Show Revisions Keywords: Causal inference, counterfactual reasoning, backtracking, observational counterfactuals , counterfactual explanations, explainable AI, XAI", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stVikewRRvw", "content": "Backtracking Counterfactuals Julius Von Kügelgen, Abdirisak Mohamed, Sander Beckers Published: 17 Mar 2023 , Last Modified: 26 May 2023 CLeaR 2023 Oral Readers: Everyone Show Bibtex Show Revisions Keywords: Causal inference, counterfactual reasoning, backtracking, observational counterfactuals , counterfactual explanations, explainable AI, XAI"}
+{"idx": 6, "title": "(PDF) Causal Modeling Semantics for Counterfactuals with Disjunctive ...", "date": "", "ddg_snippet": "The structural interpretation of counterfactuals as formulated in Balke and Pearl (1994a,b) [1, 2] excludes disjunctive conditionals, such as \"had X been x1 or x2,\" as well as disjunctive ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370417686_Causal_Modeling_Semantics_for_Counterfactuals_with_Disjunctive_Antecedents", "content": "The structural interpretation of counterfactuals as formulated in Balke and Pearl (1994a,b) [1, 2] excludes disjunctive conditionals, such as \"had X been x1 or x2,\" as well as disjunctive ..."}
+{"idx": 7, "title": "PDF Backtracking Counterfactuals", "date": "", "ddg_snippet": "While non-backtracking, interventional counterfactuals have been given a well-defined seman-tics within Pearl's framework of structural causal models (SCMs), the same cannot be said for back-tracking counterfactuals . In this paper we offer the first general formal semantics for backtracking counterfactuals within SCMs. Our general semantics can be made more specific in various ways ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v213/kugelgen23a/kugelgen23a.pdf", "content": "While non-backtracking, interventional counterfactuals have been given a well-defined seman-tics within Pearl's framework of structural causal models (SCMs), the same cannot be said for back-tracking counterfactuals . In this paper we offer the first general formal semantics for backtracking counterfactuals within SCMs. Our general semantics can be made more specific in various ways ..."}
+{"idx": 8, "title": "Articles · Sander", "date": "", "ddg_snippet": "Beckers , S., Halpern, J.Y., and Hitchcock, C. ( 2023 ). Causal Models with Constraints. In Proceedings of the 2nd Conference on Causal Learning and Reasoning. von Kügelgen, J., Mohamed, A., and Beckers , S. ( 2023 ). Backtracking Counterfactuals . In Proceedings of the 2nd Conference on Causal Learning and Reasoning.", "subpage_snippet": "", "source": "sanderbeckers.github.io", "link": "https://sanderbeckers.github.io/website/tag/articles/", "content": "Beckers , S., Halpern, J.Y., and Hitchcock, C. ( 2023 ). Causal Models with Constraints. In Proceedings of the 2nd Conference on Causal Learning and Reasoning. von Kügelgen, J., Mohamed, A., and Beckers , S. ( 2023 ). Backtracking Counterfactuals . In Proceedings of the 2nd Conference on Causal Learning and Reasoning."}
+{"idx": 9, "title": "PDF Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents", "date": "", "ddg_snippet": "Abstract Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counter-factuals whose antecedent is a conjunction of atomic formulas. We ex-tend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.14817.pdf", "content": "Abstract Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counter-factuals whose antecedent is a conjunction of atomic formulas. We ex-tend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean ..."}
diff --git a/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Contrastive_CRL_real_data_reason.jsonl b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Contrastive_CRL_real_data_reason.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d21725a66b19b2e6e7e0fe136a52dd58b97f420d
--- /dev/null
+++ b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Contrastive_CRL_real_data_reason.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning ( CRL ) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v1", "content": "We evaluate methods for causal representation learning ( CRL ) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System_Gamella_Bing_Runge.jsonl b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System_Gamella_Bing_Runge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ee4614c82c8801bebfa0e54fd3ded5b846231a79
--- /dev/null
+++ b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System_Gamella_Bing_Runge.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "by JL Gamella · 2025 · Cited by 1 — We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20099", "content": "by JL Gamella · 2025 · Cited by 1 — We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work."}
+{"idx": 1, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "by JL Gamella · Cited by 1 — The main purpose of this submission is to benchmark 3 CRL methods using a real physical system from Gamella et al (2025). This is an important ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=d2aGLPSpFz", "content": "by JL Gamella · Cited by 1 — The main purpose of this submission is to benchmark 3 CRL methods using a real physical system from Gamella et al (2025). This is an important ..."}
+{"idx": 2, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. The system consists of ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. The system consists of ..."}
+{"idx": 3, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. The.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f49eb15381f8ea150aedc07d26d294f4b7c91ada.pdf", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. The."}
+{"idx": 4, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "This paper by Gamella , Bing , and Runge addresses this fundamental limitation by introducing the first real - world physical testbed for CRL evaluation: a ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.20099v2", "content": "This paper by Gamella , Bing , and Runge addresses this fundamental limitation by introducing the first real - world physical testbed for CRL evaluation: a ..."}
+{"idx": 5, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. The system consists of a ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/164997", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. The system consists of a ..."}
+{"idx": 6, "title": "Simon Bing", "date": "", "ddg_snippet": "Sanity checking causal representation learning on a simple real-world system . JL Gamella, S Bing, J Runge. arXiv preprint arXiv:2502.20099, 2025. 1, 2025.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=ydKQzzQAAAAJ&hl=en", "content": "Sanity checking causal representation learning on a simple real-world system . JL Gamella, S Bing, J Runge. arXiv preprint arXiv:2502.20099, 2025. 1, 2025."}
+{"idx": 7, "title": "Juan L. Gamella", "date": "", "ddg_snippet": "Co-authors ; Sanity Checking Causal Representation Learning on a Simple Real-World System . JL Gamella*, S Bing*, J Runge. Forty-second International Conference ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=tcpT3MQAAAAJ&hl=en", "content": "Co-authors ; Sanity Checking Causal Representation Learning on a Simple Real-World System . JL Gamella*, S Bing*, J Runge. Forty-second International Conference ..."}
+{"idx": 8, "title": "Public dataset repository for the Causal Chamber Project", "date": "", "ddg_snippet": "Datasets for the 2025 benchmark paper \"Sanity Checking Causal Representation Learning on a Simple Real-World System\" by Juan L. Gamella*, Simon Bing*, and Jakob ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/juangamella/causal-chamber", "content": "Datasets for the 2025 benchmark paper \"Sanity Checking Causal Representation Learning on a Simple Real-World System\" by Juan L. Gamella*, Simon Bing*, and Jakob ..."}
+{"idx": 9, "title": "ICML 2025 Orals", "date": "", "ddg_snippet": "Sanity Checking Causal Representation Learning on a Simple Real-World System . Oral. Juan L. Gamella · Simon Bing · Jakob Runge. [ West Ballroom D ]. thumbnail ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/oral", "content": "Sanity Checking Causal Representation Learning on a Simple Real-World System . Oral. Juan L. Gamella · Simon Bing · Jakob Runge. [ West Ballroom D ]. thumbnail ..."}
diff --git "a/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_2023_Fake_it_till_you_make_it_synthetic_ImageNet_clones_ImageNet-100_accuracy_results_year_2023.jsonl" "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_2023_Fake_it_till_you_make_it_synthetic_ImageNet_clones_ImageNet-100_accuracy_results_year_2023.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..e4fdbeed3133cb885c847304273f4b2ee153b595
--- /dev/null
+++ "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_2023_Fake_it_till_you_make_it_synthetic_ImageNet_clones_ImageNet-100_accuracy_results_year_2023.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2212.08420] Fake it till you make it: Learning transferable ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Fake it till you make it : Learning transferable representations from synthetic ImageNet clones , by Mert Bulent Sariyildiz and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.08420", "content": "View a PDF of the paper titled Fake it till you make it : Learning transferable representations from synthetic ImageNet clones , by Mert Bulent Sariyildiz and 3 other authors"}
+{"idx": 1, "title": "PDF Fake It Till You Make It: Learning Transferable Representations From ...", "date": "", "ddg_snippet": "Second, we train classification models using different ImageNet clones and show that they can achieve 91.7% and 70.3% top-5 accuracy on ImageNet-100 and ImageNet -1K respectively. Finally, we evaluate the generalization capacity of our models.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.pdf", "content": "Second, we train classification models using different ImageNet clones and show that they can achieve 91.7% and 70.3% top-5 accuracy on ImageNet-100 and ImageNet -1K respectively. Finally, we evaluate the generalization capacity of our models."}
+{"idx": 2, "title": "Fake it Till You Make it: Learning Transferable ... - IEEE Xplore", "date": "", "ddg_snippet": "Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10205419", "content": "Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet ..."}
+{"idx": 3, "title": "Fake it till you make it: Learning transferable representations from ...", "date": "", "ddg_snippet": "We provide two ResNet50 models pretrained on our synthetic ImageNet clones : ImageNet - 100 -SD or ImageNet -1K-SD. In both cases, we generate images with Stable Diffusion 1.4 using guidance scale=2 and prompts which are composed of name and description of classes.", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/research/computer-vision/imagenet-sd/", "content": "We provide two ResNet50 models pretrained on our synthetic ImageNet clones : ImageNet - 100 -SD or ImageNet -1K-SD. In both cases, we generate images with Stable Diffusion 1.4 using guidance scale=2 and prompts which are composed of name and description of classes."}
+{"idx": 4, "title": "Fake it till you make it: Learning (s) from a synthetic ImageNet clone", "date": "", "ddg_snippet": "Table 1. Impact of data-augmentation for models trained on real and synthetic datasets. Performance is measured on the validation set of ImageNet-100 , i.e. on real images. - \" Fake it till you make it : Learning (s) from a synthetic ImageNet clone \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Fake-it-till-you-make-it:-Learning(s)-from-a-clone-Sariyildiz-Karteek/70e8f98fbb0a0acdbd08af343a8504e7fd664267/figure/1", "content": "Table 1. Impact of data-augmentation for models trained on real and synthetic datasets. Performance is measured on the validation set of ImageNet-100 , i.e. on real images. - \" Fake it till you make it : Learning (s) from a synthetic ImageNet clone \""}
+{"idx": 5, "title": "PDF Supplementary Material for Fake it till you make it: Learning ...", "date": "", "ddg_snippet": "Although the gains for the real images are relatively small (less than one percent), the gains for ImageNet - 100 -SD are over 14%. We believe this shows two things: i) Synthetic images can benefit from the same augmentations as real images, and ii) these transformations are good for domain generalization.", "subpage_snippet": "", "source": "lear.inrialpes.fr", "link": "https://lear.inrialpes.fr/people/alahari/papers/sariyildiz23a-supp.pdf", "content": "Although the gains for the real images are relatively small (less than one percent), the gains for ImageNet - 100 -SD are over 14%. We believe this shows two things: i) Synthetic images can benefit from the same augmentations as real images, and ii) these transformations are good for domain generalization."}
+{"idx": 6, "title": "Fake it Till You Make it: Learning Transferable ... - Computer", "date": "", "ddg_snippet": "We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2023/012900i011/1POPxzWEUqk", "content": "We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study."}
+{"idx": 7, "title": "Fake it Till You Make it: Learning Transferable ... - ResearchGate", "date": "", "ddg_snippet": "Download Citation | On Jun 1, 2023 , Mert Bulent Sariyildiz and others published Fake it Till You Make it : Learning Transferable Representations from Synthetic ImageNet Clones | Find, read and cite ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373316863_Fake_it_Till_You_Make_it_Learning_Transferable_Representations_from_Synthetic_ImageNet_Clones", "content": "Download Citation | On Jun 1, 2023 , Mert Bulent Sariyildiz and others published Fake it Till You Make it : Learning Transferable Representations from Synthetic ImageNet Clones | Find, read and cite ..."}
+{"idx": 8, "title": "CVPR 2023 Open Access Repository", "date": "", "ddg_snippet": "Fake It Till You Make It : Learning Transferable Representations From Synthetic ImageNet Clones Mert Bülent Sarıyıldız , Karteek Alahari, Diane Larlus, Yannis Kalantidis; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 , pp. 8011-8021", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/html/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.html", "content": "Fake It Till You Make It : Learning Transferable Representations From Synthetic ImageNet Clones Mert Bülent Sarıyıldız , Karteek Alahari, Diane Larlus, Yannis Kalantidis; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 , pp. 8011-8021"}
+{"idx": 9, "title": "CVPR 2023 - Fake it till you make it: Learning transferable ...", "date": "", "ddg_snippet": "In this episode we discuss Fake it till you make it : Learning transferable representations from synthetic ImageNet clones by Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus, Yannis Kalantidis. The paper investigates the ability of synthetic images, generated using Stable Diffusion, to replace real images for training models for ImageNet classification. Using only class names to build the ...", "subpage_snippet": "", "source": "agibreakdown.podbean.com", "link": "https://agibreakdown.podbean.com/e/cvpr-2023-fake-it-till-you-make-it-learning-transferable-representations-from-synthetic-imagenet-clones/", "content": "In this episode we discuss Fake it till you make it : Learning transferable representations from synthetic ImageNet clones by Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus, Yannis Kalantidis. The paper investigates the ability of synthetic images, generated using Stable Diffusion, to replace real images for training models for ImageNet classification. Using only class names to build the ..."}
diff --git "a/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al_2023_ImageNet-100_top-1_accuracy_IN_real_tr.jsonl" "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al_2023_ImageNet-100_top-1_accuracy_IN_real_tr.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..da4b14fe97e56b06f713e4d9d05a9a3523886fd9
--- /dev/null
+++ "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al_2023_ImageNet-100_top-1_accuracy_IN_real_tr.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Top 100 Anime Of All Time !!! | IMDb", "date": "", "ddg_snippet": "Takahiro Sakurai, Akio Ôtsuka, Miyu Irino, and Setsuo Ito in Моб Психо 100 (2016).", "subpage_snippet": "", "source": "www.imdb.com", "link": "https://www.imdb.com/list/ls023542100/", "content": "Takahiro Sakurai, Akio Ôtsuka, Miyu Irino, and Setsuo Ito in Моб Психо 100 (2016)."}
+{"idx": 1, "title": "How to Calculate Top 5 Accuracy in Keras? | Stack Overflow", "date": "", "ddg_snippet": "I want to calculate top5 in imagenet 2012 dataset, but i don't know how to do it in keras. fit function just can calculate top 1 accuracy .", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/42327006/how-to-calculate-top5-accuracy-in-keras/42328087", "content": "I want to calculate top5 in imagenet 2012 dataset, but i don't know how to do it in keras. fit function just can calculate top 1 accuracy ."}
+{"idx": 2, "title": "YouTube | EASIEST 100 PP MAPS (NO ACCURACY ) - Top 5 Easiest...", "date": "", "ddg_snippet": "In this video, there is a total of 5 maps which can give you 100 pp, there were more maps, but some of them were clipped without the handcam, and others got m...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=GNUoBU2WcTc", "content": "In this video, there is a total of 5 maps which can give you 100 pp, there were more maps, but some of them were clipped without the handcam, and others got m..."}
+{"idx": 3, "title": "News Viral Video Cewek SMP Keseruan Diranjang Top 5 New Global...", "date": "", "ddg_snippet": "Top 10 Abg Indo Viral 2025 Tiktok Video Anak SMP Indo Cantik Diiclik Brutal Terbaru Trending Global Official.", "subpage_snippet": "", "source": "linkviral.store", "link": "https://linkviral.store/news-viral-video-cewek-smp-keseruan-diranjang-top-5-new-global-2025/", "content": "Top 10 Abg Indo Viral 2025 Tiktok Video Anak SMP Indo Cantik Diiclik Brutal Terbaru Trending Global Official."}
+{"idx": 4, "title": "TOP 100 Uzbek Xit Mp3 2025, Популярные Узбекские Песни", "date": "", "ddg_snippet": "Скачать бесплатно Узбек терма кушиклар mp3 2025, Uzbekcha xit qo'shiqlar to'plami, TOP 100 uzbek mp3 skachat eng sara xitlar.", "subpage_snippet": "", "source": "tonhits.net", "link": "https://tonhits.net/uzbekcha_xit_qoshiqlar", "content": "Скачать бесплатно Узбек терма кушиклар mp3 2025, Uzbekcha xit qo'shiqlar to'plami, TOP 100 uzbek mp3 skachat eng sara xitlar."}
+{"idx": 5, "title": "Top Viral Video Tiktok Abg SMA Indo Terbaru 2025 Rare Ukhti...", "date": "", "ddg_snippet": "This tool allows users to find original Japanese images more easily, and it sparked major discussions in tech forums and on social media.", "subpage_snippet": "", "source": "tanganjahil.store", "link": "https://tanganjahil.store/top-viral-video-tiktok-abg-sma-indo-terbaru-2025-rare-ukhti-seragam-matching-of-all-time-trending-global-official/", "content": "This tool allows users to find original Japanese images more easily, and it sparked major discussions in tech forums and on social media."}
+{"idx": 6, "title": "Forbes Real Time Billionaires List - The World's Richest People", "date": "", "ddg_snippet": "Browse today’s rankings of the wealthiest people and families globally. Discover the net worth, age, and other information about the richest people in the world.", "subpage_snippet": "", "source": "www.forbes.com", "link": "https://www.forbes.com/real-time-billionaires/", "content": "Browse today’s rankings of the wealthiest people and families globally. Discover the net worth, age, and other information about the richest people in the world."}
+{"idx": 7, "title": "Humanize AI Text Free- Get 100 % Human Score", "date": "", "ddg_snippet": "Free AI humanizer is an accurate and efficient ai to human text converter.", "subpage_snippet": "", "source": "decopy.ai", "link": "https://decopy.ai/ai-humanizer/", "content": "Free AI humanizer is an accurate and efficient ai to human text converter."}
+{"idx": 8, "title": "Indo Sma Viral Malam Membara Menjadi Api Kehangatan Simple...", "date": "", "ddg_snippet": "Stay on top with Yandex Viral and never miss a moment of fun, scandal, or heartwarming stories.", "subpage_snippet": "", "source": "nafsungaco.store", "link": "https://nafsungaco.store/indo-sma-viral-malam-membara-menjadi-api-kehangatan-simple-romantic-touch-guide/", "content": "Stay on top with Yandex Viral and never miss a moment of fun, scandal, or heartwarming stories."}
+{"idx": 9, "title": "Learning Transferable Visual Models From Natural Language...", "date": "", "ddg_snippet": "While supervised adaptation to ImageNet increases ImageNet accuracy by 9.2%, it slightly reduces average robustness.", "subpage_snippet": "", "source": "cdn.openai.com", "link": "https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language.pdf", "content": "While supervised adaptation to ImageNet increases ImageNet accuracy by 9.2%, it slightly reduces average robustness."}
diff --git a/data/sampled_jsons/Schubert_polynomials_spurious_correlation_program_synthesis_Section_5_year_2024.jsonl b/data/sampled_jsons/Schubert_polynomials_spurious_correlation_program_synthesis_Section_5_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4a067e3d1dfaa698a3745f423576c4ad94778c8a
--- /dev/null
+++ b/data/sampled_jsons/Schubert_polynomials_spurious_correlation_program_synthesis_Section_5_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Schubert polynomial - Wikipedia", "date": "", "ddg_snippet": "Lascoux (1995) described the history of Schubert polynomials . The Schubert polynomials are polynomials in the variables depending on an element of the infinite symmetric group of all permutations of fixing all but a finite number of elements. They form a basis for the polynomial ring in infinitely many variables. The cohomology of the flag manifold is where is the ideal generated by ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Schubert_polynomial", "content": "Lascoux (1995) described the history of Schubert polynomials . The Schubert polynomials are polynomials in the variables depending on an element of the infinite symmetric group of all permutations of fixing all but a finite number of elements. They form a basis for the polynomial ring in infinitely many variables. The cohomology of the flag manifold is where is the ideal generated by ..."}
+{"idx": 1, "title": "Schubert polynomials Type B/C/D Schubert polynomials Skew ... - SymCat", "date": "", "ddg_snippet": "Due to the geometric interpretation of Schubert polynomials , it is known that the coefficients c u v w are non-negative integers. Note that the coefficients c u v w generalize the classical Littlewood-Richardson coefficients in the Littlewood-Richardson rule for Schur polynomials . This is a major open problem in Schubert calculus and algebraic combinatorics. This problem is closely related ...", "subpage_snippet": "", "source": "www.symmetricfunctions.com", "link": "https://www.symmetricfunctions.com/schubert.htm", "content": "Due to the geometric interpretation of Schubert polynomials , it is known that the coefficients c u v w are non-negative integers. Note that the coefficients c u v w generalize the classical Littlewood-Richardson coefficients in the Littlewood-Richardson rule for Schur polynomials . This is a major open problem in Schubert calculus and algebraic combinatorics. This problem is closely related ..."}
+{"idx": 2, "title": "PDF Schubert Polynomials and Symmetric Functions Notes for The Lisbon ...", "date": "", "ddg_snippet": "One moral of the story so far is that symmetric polynomials have many, many mono-mials in them, and that there are better ways to write down symmetric polynomials than the standard way (namely, by adding up monomials).", "subpage_snippet": "", "source": "pi.math.cornell.edu", "link": "https://pi.math.cornell.edu/~allenk/schubnotes.pdf", "content": "One moral of the story so far is that symmetric polynomials have many, many mono-mials in them, and that there are better ways to write down symmetric polynomials than the standard way (namely, by adding up monomials)."}
+{"idx": 3, "title": "PDF Schubert polynomials and pipe dreams - MCCME", "date": "", "ddg_snippet": "General definitions Flag varieties Schubert varieties and Schubert polynomials Pipe dreams and Fomin-Kirillov theorem Numerology of Schubert polynomials Permutations with many pipe dreams Catalan numbers and Catalan-Hankel determinants Combinatorics of Schubert polynomials Pipe dream complexes Generalizations for other Weyl groups Open questions = GLn(C)", "subpage_snippet": "", "source": "users.mccme.ru", "link": "https://users.mccme.ru/smirnoff/slides/daejeon.pdf", "content": "General definitions Flag varieties Schubert varieties and Schubert polynomials Pipe dreams and Fomin-Kirillov theorem Numerology of Schubert polynomials Permutations with many pipe dreams Catalan numbers and Catalan-Hankel determinants Combinatorics of Schubert polynomials Pipe dream complexes Generalizations for other Weyl groups Open questions = GLn(C)"}
+{"idx": 4, "title": "[2503.03903] Single-SEM Schubert Polynomials - arXiv.org", "date": "", "ddg_snippet": "Access Paper: View a PDF of the paper titled Single-SEM Schubert Polynomials , by Dora Woodruff View PDF HTML (experimental) TeX Source Other Formats BibTeX formatted citation ×", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.03903", "content": "Access Paper: View a PDF of the paper titled Single-SEM Schubert Polynomials , by Dora Woodruff View PDF HTML (experimental) TeX Source Other Formats BibTeX formatted citation ×"}
+{"idx": 5, "title": "Combinatorial Presentation of Schubert Polynomials", "date": "", "ddg_snippet": "In the previous chapter we defined Schubert polynomials by means of divided difference operators. Despite the fact that the differences are divided, Schubert polynomials are actual polynomials , not rational functions. More surprisingly, even though these are divided difference operators, the coefficients of Schubert polynomials turn out to be positive, as we saw in Corollary 12.17.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-50341-2_13", "content": "In the previous chapter we defined Schubert polynomials by means of divided difference operators. Despite the fact that the differences are divided, Schubert polynomials are actual polynomials , not rational functions. More surprisingly, even though these are divided difference operators, the coefficients of Schubert polynomials turn out to be positive, as we saw in Corollary 12.17."}
+{"idx": 6, "title": "PDF Schubert Polynomials, Pipe Dreams and Bumpless Pipedreams", "date": "", "ddg_snippet": "In Section 2 of this paper, we begin with a few key definitions necessary for Schubert polynomials , as explored by Macdonald [Mac91] and Fomin and Stanley [FS91]. We discuss the recursive definition as given by Lascoux and Sch ̈utzenberger [LS82]. In Section 3, we discuss a combinatorial description of Schubert polynomials via pipe dreams. In Section 4, we describe how Schubert polynomials ...", "subpage_snippet": "", "source": "saqzhao.github.io", "link": "https://saqzhao.github.io/assets/pdf/18_204_Term_Paper.pdf", "content": "In Section 2 of this paper, we begin with a few key definitions necessary for Schubert polynomials , as explored by Macdonald [Mac91] and Fomin and Stanley [FS91]. We discuss the recursive definition as given by Lascoux and Sch ̈utzenberger [LS82]. In Section 3, we discuss a combinatorial description of Schubert polynomials via pipe dreams. In Section 4, we describe how Schubert polynomials ..."}
+{"idx": 7, "title": "PDF Combinatorics of Schubert Polynomials", "date": "", "ddg_snippet": "Avery J St. Dizier, Ph.D. Cornell University 2020 In this thesis, we study several aspects of the combinatorics of various im- portant families of polynomials , particularly focusing on Schubert polynomials . Schubert polynomials arise as distinguished representatives of cohomology classes in the cohomology ring of the ag variety.", "subpage_snippet": "", "source": "ecommons.cornell.edu", "link": "https://ecommons.cornell.edu/bitstream/handle/1813/102968/StDizier_cornellgrad_0058F_12045.pdf?sequence=1", "content": "Avery J St. Dizier, Ph.D. Cornell University 2020 In this thesis, we study several aspects of the combinatorics of various im- portant families of polynomials , particularly focusing on Schubert polynomials . Schubert polynomials arise as distinguished representatives of cohomology classes in the cohomology ring of the ag variety."}
+{"idx": 8, "title": "Schubert polynomials, slide polynomials, Stanley symmetric functions ...", "date": "", "ddg_snippet": "We introduce two new bases for polynomials that lift monomial and fundamental quasisymmetric functions to the full polynomial ring. By defining a new condition on pipe dreams, called quasi-Yamanouchi, we give a positive combinatorial rule for expanding Schubert polynomials into these new bases that parallels the expansion of Schur functions into fundamental quasisymmetric functions. As a ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0001870816301116", "content": "We introduce two new bases for polynomials that lift monomial and fundamental quasisymmetric functions to the full polynomial ring. By defining a new condition on pipe dreams, called quasi-Yamanouchi, we give a positive combinatorial rule for expanding Schubert polynomials into these new bases that parallels the expansion of Schur functions into fundamental quasisymmetric functions. As a ..."}
+{"idx": 9, "title": "Alex Postnikov: 18.217 Combinatorial Theory - MIT Mathematics", "date": "", "ddg_snippet": "Schur polynomials and Schubert polynomials play an important role in many different areas of mathematics (such as representation theory, algebraic geometry, etc). We will discuss beautiful properties of these polynomials , and their various generalizations, focusing on their combinatorial aspects. A large portion of current research in algebraic combinatorics is devoted to the study of these ...", "subpage_snippet": "", "source": "math.mit.edu", "link": "https://math.mit.edu/~apost/courses/18.217_2023/", "content": "Schur polynomials and Schubert polynomials play an important role in many different areas of mathematics (such as representation theory, algebraic geometry, etc). We will discuss beautiful properties of these polynomials , and their various generalizations, focusing on their combinatorial aspects. A large portion of current research in algebraic combinatorics is devoted to the study of these ..."}
diff --git a/data/sampled_jsons/Schulman_2017_PPO_paper_A2C_Atari_performance_table_Alien_Centipede_year_2017.jsonl b/data/sampled_jsons/Schulman_2017_PPO_paper_A2C_Atari_performance_table_Alien_Centipede_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b2ed74bb88340985c5ecd6ef477dd01ec1b83b8f
--- /dev/null
+++ b/data/sampled_jsons/Schulman_2017_PPO_paper_A2C_Atari_performance_table_Alien_Centipede_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A STRONG ON-POLICY COMPETITOR TO PPO", "date": "", "ddg_snippet": "by X Chu · Cited by 2 — In this paper , a first-order gradient reinforcement learning algorithm called Policy. Optimization with Penalized Point Probability Distance (POP3D), which is a.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0migj5lyUZl", "content": "by X Chu · Cited by 2 — In this paper , a first-order gradient reinforcement learning algorithm called Policy. Optimization with Penalized Point Probability Distance (POP3D), which is a."}
+{"idx": 1, "title": "Table 5 from Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "A novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization ( PPO ) algorithm ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Proximal-Policy-Optimization-Algorithms-Schulman-Wolski/dce6f9d4017b1785979e7520fd0834ef8cf02f4b/figure/9", "content": "A novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization ( PPO ) algorithm ..."}
+{"idx": 2, "title": "Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "by J Schulman · 2017 · Cited by 29958 — Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games. Figure 6 shows the learning curves of each of three ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1707.06347", "content": "by J Schulman · 2017 · Cited by 29958 — Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games. Figure 6 shows the learning curves of each of three ..."}
+{"idx": 3, "title": "arXiv:1807.00442v4 [cs.LG] 14 Feb 2019", "date": "", "ddg_snippet": "by X Chu · 2018 · Cited by 13 — Optimization ( PPO ) [ Schulman et al., 2017 ]. Improving the strategy ... Table 4: PPO's hyper-parameters for Atari game. Hyper-parameter.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1807.00442", "content": "by X Chu · 2018 · Cited by 13 — Optimization ( PPO ) [ Schulman et al., 2017 ]. Improving the strategy ... Table 4: PPO's hyper-parameters for Atari game. Hyper-parameter."}
+{"idx": 4, "title": "(PDF) Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/72572628/Proximal_Policy_Optimization_Algorithms", "content": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms ..."}
+{"idx": 5, "title": "Discovering General Reinforcement Learning Algorithms ...", "date": "", "ddg_snippet": "by MT Jackson · 2023 · Cited by 22 — In this work, we examine how characteristics of the meta-training distribution impact the generalization performance of these algorithms. Motivated by this ... 19 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/fce2d8a485746f76aac7b5650db2679d-Paper-Conference.pdf", "content": "by MT Jackson · 2023 · Cited by 22 — In this work, we examine how characteristics of the meta-training distribution impact the generalization performance of these algorithms. Motivated by this ... 19 pages"}
+{"idx": 6, "title": "SYMMETRIC REINFORCEMENT LEARNING LOSS FOR ...", "date": "", "ddg_snippet": "Thus, adapting fundamental Reinforcement Learning (RL) algorithms such as REINFORCE. (Williams, 1992), A2C (Mnih et al., 2016), and PPO ( Schulman et al., 2017 ) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/205c5a7d08627d98d59b78a76a306b26a3aebacd.pdf", "content": "Thus, adapting fundamental Reinforcement Learning (RL) algorithms such as REINFORCE. (Williams, 1992), A2C (Mnih et al., 2016), and PPO ( Schulman et al., 2017 ) ..."}
+{"idx": 7, "title": "arXiv:1905.01756v2 [cs.LG] 15 Jul 2019", "date": "", "ddg_snippet": "by R Fakoor · 2019 · Cited by 78 — Table 2 shows that. P3O achieves better performance than strong baselines for continuous-control tasks such as A2C and PPO . ... Table 5: PPO hyper-parameters on ...", "subpage_snippet": "", "source": "assets.amazon.science", "link": "https://assets.amazon.science/3d/f5/dca78bc1484aa2ae077be6857a18/p3o-policy-on-policy-off-policy-optimization.pdf", "content": "by R Fakoor · 2019 · Cited by 78 — Table 2 shows that. P3O achieves better performance than strong baselines for continuous-control tasks such as A2C and PPO . ... Table 5: PPO hyper-parameters on ..."}
+{"idx": 8, "title": "ChainerRL: A Deep Reinforcement Learning Library", "date": "", "ddg_snippet": "by Y Fujita · 2021 · Cited by 176 — In this paper , we introduce ChainerRL, an open-source Python DRL library supporting both CPU and GPU training, built off of the Chainer (Tokui et al., 2019) ...", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume22/20-376/20-376.pdf", "content": "by Y Fujita · 2021 · Cited by 176 — In this paper , we introduce ChainerRL, an open-source Python DRL library supporting both CPU and GPU training, built off of the Chainer (Tokui et al., 2019) ..."}
+{"idx": 9, "title": "Group-Agent Reinforcement Learning with Heterogeneous ... - GitHub", "date": "", "ddg_snippet": "Group-agent reinforcement learning (GARL) is a newly arising learning scenario, where multiple reinforcement learning agents study together in.", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v286/main/assets/wu25b/wu25b.pdf", "content": "Group-agent reinforcement learning (GARL) is a newly arising learning scenario, where multiple reinforcement learning agents study together in."}
diff --git a/data/sampled_jsons/Schulman_PPO_2017_Atari_games_list_Alien_Centipede_Table_6_Figure_6_year_2017.jsonl b/data/sampled_jsons/Schulman_PPO_2017_Atari_games_list_Alien_Centipede_Table_6_Figure_6_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0286f2e046b96319da4d9720f4d2a90c5407236d
--- /dev/null
+++ b/data/sampled_jsons/Schulman_PPO_2017_Atari_games_list_Alien_Centipede_Table_6_Figure_6_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Atari - Wikipedia", "date": "", "ddg_snippet": "Atari is a brand name that has been owned by several entities since its inception in 1972. It is currently owned by French holding company Atari SA and its focus is on \"video games , consumer hardware, licensing and blockchain\".", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Atari", "content": "Atari is a brand name that has been owned by several entities since its inception in 1972. It is currently owned by French holding company Atari SA and its focus is on \"video games , consumer hardware, licensing and blockchain\"."}
+{"idx": 1, "title": "Top 10 Atari 2600 Games - YouTube", "date": "", "ddg_snippet": "The Atari 2600 released in 1977 had some amazing games released for it including M's Pacman, Joust, Defender, and many more Why not share your Atari 2600 Mem...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=6kJ_79ih9rI", "content": "The Atari 2600 released in 1977 had some amazing games released for it including M's Pacman, Joust, Defender, and many more Why not share your Atari 2600 Mem..."}
+{"idx": 2, "title": "Atari 7800 Games online - RetroGames.cc", "date": "", "ddg_snippet": "Start Game . Centipede (Europe).Most Popular Atari 7800 Games . Donkey Kong (USA).", "subpage_snippet": "", "source": "www.retrogames.cc", "link": "https://www.retrogames.cc/atari7800-games", "content": "Start Game . Centipede (Europe).Most Popular Atari 7800 Games . Donkey Kong (USA)."}
+{"idx": 3, "title": "CALE: Continuous Arcade Learning Environment", "date": "", "ddg_snippet": "... designed to support a variety of controllers, the majority of games were played with an Atari CX10 “digital” controller (see left panel of Figure ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23810v1", "content": "... designed to support a variety of controllers, the majority of games were played with an Atari CX10 “digital” controller (see left panel of Figure ..."}
+{"idx": 4, "title": "In value-based deep reinforcement learning, a pruned network is", "date": "", "ddg_snippet": "... 2017 ) on DQN (Mnih et al., 2015 ) with a ResNet backbone (as introduced in Impala (Espeholt et al., 2018 ) ), results in a 50% performance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.12479v3", "content": "... 2017 ) on DQN (Mnih et al., 2015 ) with a ResNet backbone (as introduced in Impala (Espeholt et al., 2018 ) ), results in a 50% performance ..."}
+{"idx": 5, "title": "CALE: Continuous Arcade Learning Environment", "date": "", "ddg_snippet": "by J Farebrother · 2024 · Cited by 5 — ... games for the Atari 100K benchmark [Łukasz Kaiser et al., 2020]. • AirRaid ( 6 ). • Alien * (18). • Amidar* (10). • Assault* (7). • Asterix* (9).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.23810?", "content": "by J Farebrother · 2024 · Cited by 5 — ... games for the Atari 100K benchmark [Łukasz Kaiser et al., 2020]. • AirRaid ( 6 ). • Alien * (18). • Amidar* (10). • Assault* (7). • Asterix* (9)."}
+{"idx": 6, "title": "RECURRENT INDEPENDENT MECHANISMS", "date": "", "ddg_snippet": "Figure 6 : RIMs- PPO relative score ... transfer to a different subset of randomly selected target games ( Alien , Asterix, Boxing, Centipede , Gopher,.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/e4152a9f64c5e4ecef050e8bdcd91c1f76b37756.pdf", "content": "Figure 6 : RIMs- PPO relative score ... transfer to a different subset of randomly selected target games ( Alien , Asterix, Boxing, Centipede , Gopher,."}
+{"idx": 7, "title": "Deep Reinforcement Learning: A State-of-the-Art ...", "date": "", "ddg_snippet": "... Alien Seaquest Centipede Amidar Venture Bowling Gravitar Ms. Pacman ... 6 0 38 0 Atari games following the human-starts methodology. Different colors ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/74644343/Deep_Reinforcement_Learning_A_State_of_the_Art_Walkthrough", "content": "... Alien Seaquest Centipede Amidar Venture Bowling Gravitar Ms. Pacman ... 6 0 38 0 Atari games following the human-starts methodology. Different colors ..."}
+{"idx": 8, "title": "arXiv:2010.02193v2 [cs.LG] 22 Dec 2020", "date": "", "ddg_snippet": "by D Hafner · Cited by 1195 — Name This Game . Asterix. Seaquest. Phoenix Gravitar Zaxxon. Beam Rider. Fishing Derby. Montezuma Rev. Ms Pacman. Bowling. Asteroids Centipede Freeway. Alien ... 24 pages", "subpage_snippet": "", "source": "gbarnier.github.io", "link": "https://gbarnier.github.io/papers/dreamer2.pdf", "content": "by D Hafner · Cited by 1195 — Name This Game . Asterix. Seaquest. Phoenix Gravitar Zaxxon. Beam Rider. Fishing Derby. Montezuma Rev. Ms Pacman. Bowling. Asteroids Centipede Freeway. Alien ... 24 pages"}
+{"idx": 9, "title": "[1707.06347] Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1707.06347", "content": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."}
diff --git a/data/sampled_jsons/Schulman_PPO_2017_Table_6_complete_list_Atari_games.jsonl b/data/sampled_jsons/Schulman_PPO_2017_Table_6_complete_list_Atari_games.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ac305b542d5acd671520f8a3dd89853b176ff8e5
--- /dev/null
+++ b/data/sampled_jsons/Schulman_PPO_2017_Table_6_complete_list_Atari_games.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Model Based Reinforcement Learning for Atari", "date": "", "ddg_snippet": "Human players can learn to play Atari games in minutes (Tsividis et al., 2017 ) . ... Atari games directly from images of the game screen, using ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1903.00374v5", "content": "Human players can learn to play Atari games in minutes (Tsividis et al., 2017 ) . ... Atari games directly from images of the game screen, using ..."}
+{"idx": 1, "title": "AssistanceZero: Scalably Solving Assistance Games", "date": "", "ddg_snippet": "In the conclusion (Section 6 ), we envision a recipe for applying assistance games to LLM post-training to replace RLHF.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.07091v2", "content": "In the conclusion (Section 6 ), we envision a recipe for applying assistance games to LLM post-training to replace RLHF."}
+{"idx": 2, "title": "CALE: Continuous Arcade Learning Environment", "date": "", "ddg_snippet": "This enables the benchmarking and evaluation of continuous-control agents (such as PPO ( Schulman et al., 2017 ) and SAC (Haarnoja et al., 2018 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23810v1", "content": "This enables the benchmarking and evaluation of continuous-control agents (such as PPO ( Schulman et al., 2017 ) and SAC (Haarnoja et al., 2018 ..."}
+{"idx": 3, "title": "Style-Preserving Policy Optimization for Game Agents", "date": "", "ddg_snippet": "Thus, in scenarios involving high-dimensional solution spaces and large parameter spaces, such as image-based Atari games , QD optimization methods ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.16995v2", "content": "Thus, in scenarios involving high-dimensional solution spaces and large parameter spaces, such as image-based Atari games , QD optimization methods ..."}
+{"idx": 4, "title": "GitHub - andri27-ts/Reinforcement-Learning: Learn Deep", "date": "", "ddg_snippet": "Algorithms (like DQN, A2C, and PPO ) implemented in PyTorch and tested on OpenAI Gym: RoboSchool & Atari . ... DQN algorithm to play an Atari game .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/andri27-ts/Reinforcement-Learning", "content": "Algorithms (like DQN, A2C, and PPO ) implemented in PyTorch and tested on OpenAI Gym: RoboSchool & Atari . ... DQN algorithm to play an Atari game ."}
+{"idx": 5, "title": "andri27-ts/Reinforcement-Learning: Learn Deep Reinforcement", "date": "", "ddg_snippet": "Algorithms (like DQN, A2C, and PPO ) implemented in PyTorch and tested on OpenAI Gym: RoboSchool & Atari . ... DQN algorithm to play an Atari game .", "subpage_snippet": "", "source": "www.gitmemories.com", "link": "https://www.gitmemories.com/andri27-ts/Reinforcement-Learning", "content": "Algorithms (like DQN, A2C, and PPO ) implemented in PyTorch and tested on OpenAI Gym: RoboSchool & Atari . ... DQN algorithm to play an Atari game ."}
+{"idx": 6, "title": "Learning to Collaborate: Robots Building Together — kate", "date": "", "ddg_snippet": "2013), Proximal Policy Optimization ( PPO ) ( Schulman et al., 2017 ), and Deep Deterministic Policy Gradient (DDPG) (Lillicrap et al., 2015).", "subpage_snippet": "", "source": "katehajash.com", "link": "https://katehajash.com/learning-to-collaborate", "content": "2013), Proximal Policy Optimization ( PPO ) ( Schulman et al., 2017 ), and Deep Deterministic Policy Gradient (DDPG) (Lillicrap et al., 2015)."}
+{"idx": 7, "title": "Configurable mirror descent | Proceedings of the 41st", "date": "", "ddg_snippet": "Decision-making problems, categorized as single-agent, e.g., Atari , cooperative multi-agent, e.g., Hanabi, competitive multi-agent, e.g., Hold'em ...", "subpage_snippet": "", "source": "www.gamewikit.com", "link": "https://www.gamewikit.com/configurable-mirror-descent-proceedings-of-the-41st-international-conference-on-machine-learning-2025", "content": "Decision-making problems, categorized as single-agent, e.g., Atari , cooperative multi-agent, e.g., Hanabi, competitive multi-agent, e.g., Hold'em ..."}
+{"idx": 8, "title": "Alternatives and detailed information of Reinforcement Learning", "date": "", "ddg_snippet": "... Proximal Policy Optimization ( PPO ... Algorithms (like DQN, A2C, and PPO ) implemented in PyTorch and tested on OpenAI Gym: RoboSchool & Atari .", "subpage_snippet": "", "source": "www.gitplanet.com", "link": "https://www.gitplanet.com/project/reinforcement-learning1618783873", "content": "... Proximal Policy Optimization ( PPO ... Algorithms (like DQN, A2C, and PPO ) implemented in PyTorch and tested on OpenAI Gym: RoboSchool & Atari ."}
+{"idx": 9, "title": "DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for", "date": "", "ddg_snippet": "Supervised Fine-Tuning : We improve the Lean 4 code completion dataset by implementing two data augmentation techniques.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.08152v1", "content": "Supervised Fine-Tuning : We improve the Lean 4 code completion dataset by implementing two data augmentation techniques."}
diff --git a/data/sampled_jsons/Schulman_et_al_2017_PPO_Figure_Table_Atari_A2C_Alien_Centipede_year_2017.jsonl b/data/sampled_jsons/Schulman_et_al_2017_PPO_Figure_Table_Atari_A2C_Alien_Centipede_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0dd80f8fecda18d192a8d6d86fc6e47e41fdf90
--- /dev/null
+++ b/data/sampled_jsons/Schulman_et_al_2017_PPO_Figure_Table_Atari_A2C_Alien_Centipede_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning to Play Atari with the Help of Instruction", "date": "", "ddg_snippet": "Table 2: Table of the game score of different RL algorithms trained under delayed reward schedule. on the official Atari manual, with statistics computed over 3 random seeds. Table 3: Table showing the outputs of the QA Extraction module on Wikipedia instructions vs the official Atari manual.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/read-and-reap-the-rewards-learning-to-play-atari-with-the-2gyr51a7.pdf", "content": "Table 2: Table of the game score of different RL algorithms trained under delayed reward schedule. on the official Atari manual, with statistics computed over 3 random seeds. Table 3: Table showing the outputs of the QA Extraction module on Wikipedia instructions vs the official Atari manual."}
+{"idx": 1, "title": "[1707.06347] Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1707.06347", "content": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."}
+{"idx": 2, "title": "O PTIMIZING", "date": "", "ddg_snippet": "Individual Atari games for A 2 C and PPO . Figure 1 shows the changes of MI between inputs and representations when training an RL agent (we need to use MINE (Belghazi et al ., 2018) for MI estimation).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=IKqCy8i1XL3", "content": "Individual Atari games for A 2 C and PPO . Figure 1 shows the changes of MI between inputs and representations when training an RL agent (we need to use MINE (Belghazi et al ., 2018) for MI estimation)."}
+{"idx": 3, "title": "Deep Reinforcement Learning", "date": "", "ddg_snippet": "( 2017 ) and Pilarski et al . (2011), learning only occurs during episodes where the human trainer provides feedback.For the Atari tasks we optimize policies using the A3C algorithm (Mnih et al ., 2016) in synchronous form ( A 2 C ), with policy architecture as described in Mnih et al .", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/also/Christiano-2017.pdf", "content": "( 2017 ) and Pilarski et al . (2011), learning only occurs during episodes where the human trainer provides feedback.For the Atari tasks we optimize policies using the A3C algorithm (Mnih et al ., 2016) in synchronous form ( A 2 C ), with policy architecture as described in Mnih et al ."}
+{"idx": 4, "title": "Simulation-based reinforcement learning for autonomous driving", "date": "", "ddg_snippet": "We based our PPO implementation on OpenAI Baselines (Dhariwal et al ., 2017 ) ppo 25. Policies provided by Ope-nAI Baselines work only for observations that consists of one tensor of some shape.", "subpage_snippet": "", "source": "deepsense.ai", "link": "https://deepsense.ai/wp-content/uploads/2019/06/Simulation-based-reinforcement-learning-for-autonomous-driving.pdf", "content": "We based our PPO implementation on OpenAI Baselines (Dhariwal et al ., 2017 ) ppo 25. Policies provided by Ope-nAI Baselines work only for observations that consists of one tensor of some shape."}
+{"idx": 5, "title": "EXPLORATION BY RANDOM NETWORK DISTILLATION – Own Your...", "date": "", "ddg_snippet": "Table 1: Comparison to baselines results. Final mean performance for various methods. State of the art results taken from: [1] (Fortunato et al ., 2017 ) [2] (Bellemare et al ., 2016) [3] (Horgan et al ., 2018).", "subpage_snippet": "", "source": "ownyourai.com", "link": "https://ownyourai.com/exploration-by-random-network-distillation/", "content": "Table 1: Comparison to baselines results. Final mean performance for various methods. State of the art results taken from: [1] (Fortunato et al ., 2017 ) [2] (Bellemare et al ., 2016) [3] (Horgan et al ., 2018)."}
+{"idx": 6, "title": "ChainerRL Parallelization", "date": "", "ddg_snippet": "This is known as PPO -style parallelization ( Schulman et al ., 2017 ). parallel Figure : PPO -style parallelization. Perform an update after a certain number of timesteps.", "subpage_snippet": "", "source": "ljvmiranda921.github.io", "link": "https://ljvmiranda921.github.io/projects/2018/09/14/pfn-internship/", "content": "This is known as PPO -style parallelization ( Schulman et al ., 2017 ). parallel Figure : PPO -style parallelization. Perform an update after a certain number of timesteps."}
+{"idx": 7, "title": "Navigating the Evolution of Reinforcement Learning... | Medium", "date": "", "ddg_snippet": "PPO is originated from TRPO algorithm. it is created to remove limitation of TRPO algorithm introduced in the paper “Proximal Policy Optimization Algorithms” by Schulman et al ., published in July 2017 . PPO has two versions, first version of PPO included several key components such as...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sanghaviharsh666/navigating-the-evolution-of-reinforcement-learning-a-historical-perspective-6fbbaa010351", "content": "PPO is originated from TRPO algorithm. it is created to remove limitation of TRPO algorithm introduced in the paper “Proximal Policy Optimization Algorithms” by Schulman et al ., published in July 2017 . PPO has two versions, first version of PPO included several key components such as..."}
+{"idx": 8, "title": "‘model-free RL’ directory · Gwern.net", "date": "", "ddg_snippet": "“The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games ”, Yu et al 2021.“Deep Reinforcement Learning from Human Preferences § Appendix A . 2 : Atari ”, Christiano et al 2017 (page 15 org openai).", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/model-free/index", "content": "“The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games ”, Yu et al 2021.“Deep Reinforcement Learning from Human Preferences § Appendix A . 2 : Atari ”, Christiano et al 2017 (page 15 org openai)."}
+{"idx": 9, "title": "(PDF) Implementing Trust Region Policy Optimisation for Deep...", "date": "", "ddg_snippet": "[44], PPO by Schulman et al . [46] and the new proposed PPO -soft. algorithm all represent advanced policy gradient methods.visualised in Figure 5.4 and the results will be discussed for each algorithm individually. REINFORCE A 2 C TNPG TRPO PPO PPO -soft. 0 100 200 300 400 500.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/344770842_Implementing_Trust_Region_Policy_Optimisation_for_Deep_Reinforcement_Learning", "content": "[44], PPO by Schulman et al . [46] and the new proposed PPO -soft. algorithm all represent advanced policy gradient methods.visualised in Figure 5.4 and the results will be discussed for each algorithm individually. REINFORCE A 2 C TNPG TRPO PPO PPO -soft. 0 100 200 300 400 500."}
diff --git a/data/sampled_jsons/Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_Song_2021_arXiv_year_2021.jsonl b/data/sampled_jsons/Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_Song_2021_arXiv_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..00200b19adf4e0d33f301945a0221b5deb7dd360
--- /dev/null
+++ b/data/sampled_jsons/Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_Song_2021_arXiv_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2011.13456] Score - Based Generative Modeling through ...", "date": "", "ddg_snippet": "Title: Score - Based Generative Modeling through Stochastic Differential Equations . Authors:Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456", "content": "Title: Score - Based Generative Modeling through Stochastic Differential Equations . Authors:Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole."}
+{"idx": 1, "title": "Yang Song - Google Scholar", "date": "", "ddg_snippet": "Score - Based Generative Modeling through Stochastic Differential Equations . 2021 . Permutation invariant graph generation via score - Based generative modeling .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=o_J2CroAAAAJ&hl=en&oi=sra", "content": "Score - Based Generative Modeling through Stochastic Differential Equations . 2021 . Permutation invariant graph generation via score - Based generative modeling ."}
+{"idx": 2, "title": "Score Based Generative Modeling through Stochastic Differential ...", "date": "", "ddg_snippet": "Tags: score - based generative modeling , stochastic differential equations , SDEs, probability distribution, Euler-Maruyama method, Runge-Kutta method, image generation , density estimation.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/44092f04bd34a892a3d47faba4d47c4c/", "content": "Tags: score - based generative modeling , stochastic differential equations , SDEs, probability distribution, Euler-Maruyama method, Runge-Kutta method, image generation , density estimation."}
+{"idx": 3, "title": "Generative score - based diffusion - Equinox", "date": "", "ddg_snippet": "arXiv link. @inproceedings{ song 2021 scorebased, title={ Score - Based Generative Modeling through Stochastic Differential . Equations }, author={Yang Song and Jascha Sohl-Dickstein and Diederik P Kingma and.", "subpage_snippet": "", "source": "docs.kidger.site", "link": "https://docs.kidger.site/equinox/examples/score_based_diffusion/", "content": "arXiv link. @inproceedings{ song 2021 scorebased, title={ Score - Based Generative Modeling through Stochastic Differential . Equations }, author={Yang Song and Jascha Sohl-Dickstein and Diederik P Kingma and."}
+{"idx": 4, "title": "以记分制生成模型,通过斯托卡差异等量生成模型 ( Score - Based ...)", "date": "", "ddg_snippet": "Score - Based Generative Modeling through Stochastic Differential Equations .from arxiv , ICLR 2021 (Oral). Creating noise from data is easy; creating data from noise is generative modeling .", "subpage_snippet": "", "source": "www.zhuanzhi.ai", "link": "https://www.zhuanzhi.ai/paper/87d36e0c379e76b30c9cd2c75ce3482e", "content": "Score - Based Generative Modeling through Stochastic Differential Equations .from arxiv , ICLR 2021 (Oral). Creating noise from data is easy; creating data from noise is generative modeling ."}
+{"idx": 5, "title": "Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek...", "date": "", "ddg_snippet": "Score - Based Generative Modeling through Stochastic Differential Equations . May 3, 2021 .By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.", "subpage_snippet": "", "source": "slideslive.com", "link": "https://slideslive.com/38954067/scorebased-generative-modeling-through-stochastic-differential-equations", "content": "Score - Based Generative Modeling through Stochastic Differential Equations . May 3, 2021 .By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples."}
+{"idx": 6, "title": "yang- song / score _sde - Giters", "date": "", "ddg_snippet": "Score - Based Generative Modeling through Stochastic Differential Equations . Song , Yang, and Stefano Ermon. \"Improved techniques for training score - based generative models .\" Proceedings of the 34th Annual Conference on Neural Information Processing Systems.", "subpage_snippet": "", "source": "giters.com", "link": "https://giters.com/yang-song/score_sde", "content": "Score - Based Generative Modeling through Stochastic Differential Equations . Song , Yang, and Stefano Ermon. \"Improved techniques for training score - based generative models .\" Proceedings of the 34th Annual Conference on Neural Information Processing Systems."}
+{"idx": 7, "title": "Score - Based Generative Modeling Through Stochastic Differential ...", "date": "", "ddg_snippet": "By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.source. ARXIV . comment. ICLR 2021 (Oral).", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/paper/?paper_id=arxiv-2011.13456", "content": "By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.source. ARXIV . comment. ICLR 2021 (Oral)."}
+{"idx": 8, "title": "Stochastic Differential Equations and Diffusion Models | VanillaBug", "date": "", "ddg_snippet": "Score - based generative modeling through stochastic differential equations . ArXiv Preprint ArXiv :2011.13456. Feller, W. (1949). On the theory of stochastic processes, with particular reference to applications.", "subpage_snippet": "", "source": "www.vanillabug.com", "link": "https://www.vanillabug.com/posts/sde/", "content": "Score - based generative modeling through stochastic differential equations . ArXiv Preprint ArXiv :2011.13456. Feller, W. (1949). On the theory of stochastic processes, with particular reference to applications."}
+{"idx": 9, "title": "Score - Based Generative Modeling through Stochastic Differential ...", "date": "", "ddg_snippet": "Song , Yang.By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv201113456S/abstract", "content": "Song , Yang.By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples."}
diff --git a/data/sampled_jsons/ScoreDec_abstract_speech_full-band_48kHz_naturalness_arxiv2401.12160_year_2024.jsonl b/data/sampled_jsons/ScoreDec_abstract_speech_full-band_48kHz_naturalness_arxiv2401.12160_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a0079d7bdd46d6899a628f1c364b1332ea0bfd2b
--- /dev/null
+++ b/data/sampled_jsons/ScoreDec_abstract_speech_full-band_48kHz_naturalness_arxiv2401.12160_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ABSTRACT arXiv:2401.12160v1 [eess.AS] 22 Jan 2024", "date": "", "ddg_snippet": "ed using only spectral and score-matching losses. Both the objec-tive and subject 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information. e- ased generat", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.12160", "content": "ed using only spectral and score-matching losses. Both the objec-tive and subject 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information. e- ased generat"}
+{"idx": 1, "title": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A ...", "date": "", "ddg_snippet": "Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.12160", "content": "Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information."}
+{"idx": 2, "title": "[2401.12160] ScoreDec: A Phase-preserving High-Fidelity Audio Codec ...", "date": "", "ddg_snippet": "Both the objective and subjective experimental results show that ScoreDec with a 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information. Index Terms— audio codec, phase-preserving codec, codec post-filter, score-based generative model, human-level naturalness", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2401.12160", "content": "Both the objective and subjective experimental results show that ScoreDec with a 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information. Index Terms— audio codec, phase-preserving codec, codec post-filter, score-based generative model, human-level naturalness"}
+{"idx": 3, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec, a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "We propose FlowDec, a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ..."}
+{"idx": 4, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "ABSTRACT We propose FlowDec, a neural full-band audio codec for general audio sampled at 48kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24kbit/s to as low as 4kbit/s, while improving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "ABSTRACT We propose FlowDec, a neural full-band audio codec for general audio sampled at 48kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24kbit/s to as low as 4kbit/s, while improving ..."}
+{"idx": 5, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio codec for ...", "date": "", "ddg_snippet": "FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 6, "title": "ScoreDec_demo/index.md at gh-pages - GitHub", "date": "", "ddg_snippet": "Both the objective and subjective experimental results show that ScoreDec with a 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bigpon/ScoreDec_demo/blob/gh-pages/index.md", "content": "Both the objective and subjective experimental results show that ScoreDec with a 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information."}
+{"idx": 7, "title": "arXiv:2409.14085v1 [eess.AS] 21 Sep 2024", "date": "", "ddg_snippet": "by H Wu · 2024 · Cited by 14 — This paper presents the challenge's rules, datasets, five participant systems, results, and findings. Index Terms— Neural audio codec , discrete ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.14085", "content": "by H Wu · 2024 · Cited by 14 — This paper presents the challenge's rules, datasets, five participant systems, results, and findings. Index Terms— Neural audio codec , discrete ..."}
+{"idx": 8, "title": "ComplexDec: A Domain-robust High-fidelity Neural Audio Codec with ...", "date": "", "ddg_snippet": "Then, we propose full-band 48~kHz ComplexDec with complex spectral input and output to ease the information loss while adopting the same 24~kbps bitrate as the baseline AuidoDec and ScoreDec . Objective and subjective evaluations demonstrate the out-of-domain robustness of ComplexDec trained using only the 30-hour VCTK corpus.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02019", "content": "Then, we propose full-band 48~kHz ComplexDec with complex spectral input and output to ease the information loss while adopting the same 24~kbps bitrate as the baseline AuidoDec and ScoreDec . Objective and subjective evaluations demonstrate the out-of-domain robustness of ComplexDec trained using only the 30-hour VCTK corpus."}
+{"idx": 9, "title": "AudioDec: An Open-source Streaming High-fidelity Neural Audio Codec", "date": "", "ddg_snippet": "Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2305.16608", "content": "Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information."}
diff --git a/data/sampled_jsons/Score_Based_Denoising_Normalizing_Flows_are_Capable_Generative_Models.jsonl b/data/sampled_jsons/Score_Based_Denoising_Normalizing_Flows_are_Capable_Generative_Models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..31b7ac5893a52af492a88604f33c23daa1eaebdd
--- /dev/null
+++ b/data/sampled_jsons/Score_Based_Denoising_Normalizing_Flows_are_Capable_Generative_Models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Free Small Business Mentorship and Resources | SCORE", "date": "", "ddg_snippet": "SCORE is proud to support the readiness and resilience of small businesses this September for National Preparedness Month. Whether you’re updating a continuity plan, backing up critical data, or training your team for emergencies, our experienced mentors and free resources are here to help.", "subpage_snippet": "", "source": "www.score.org", "link": "https://www.score.org/", "content": "SCORE is proud to support the readiness and resilience of small businesses this September for National Preparedness Month. Whether you’re updating a continuity plan, backing up critical data, or training your team for emergencies, our experienced mentors and free resources are here to help."}
+{"idx": 1, "title": "Find a Mentor | SCORE", "date": "", "ddg_snippet": "Need help starting or growing your small business? SCORE provides free business advice through our network of 11,000 volunteer mentors in person and online.", "subpage_snippet": "", "source": "www.score.org", "link": "https://www.score.org/find-mentor", "content": "Need help starting or growing your small business? SCORE provides free business advice through our network of 11,000 volunteer mentors in person and online."}
+{"idx": 2, "title": "SCORE Los Angeles", "date": "", "ddg_snippet": "Since 1964, SCORE has educated and mentored more than 17 million small business owners and entrepreneurs. We're looking for people with diverse backgrounds and experiences to serve in a variety of roles — from knowledgeable business mentors to local community advocates.", "subpage_snippet": "", "source": "www.score.org", "link": "https://www.score.org/losangeles", "content": "Since 1964, SCORE has educated and mentored more than 17 million small business owners and entrepreneurs. We're looking for people with diverse backgrounds and experiences to serve in a variety of roles — from knowledgeable business mentors to local community advocates."}
+{"idx": 3, "title": "SCORE Houston", "date": "", "ddg_snippet": "Sep 16, 2025 · Since 1964, SCORE has educated and mentored more than 11 million small business owners and entrepreneurs. We're looking for people with diverse backgrounds and experiences to serve in a variety of roles — from knowledgeable business mentors to local community advocates.", "subpage_snippet": "", "source": "www.score.org", "link": "https://www.score.org/houston", "content": "Sep 16, 2025 · Since 1964, SCORE has educated and mentored more than 11 million small business owners and entrepreneurs. We're looking for people with diverse backgrounds and experiences to serve in a variety of roles — from knowledgeable business mentors to local community advocates."}
+{"idx": 4, "title": "Workshops and Events | SCORE", "date": "", "ddg_snippet": "Online Courses SCORE 's on-demand courses allow you to learn about various small business topics at your own pace.", "subpage_snippet": "", "source": "www.score.org", "link": "https://www.score.org/workshops-events", "content": "Online Courses SCORE 's on-demand courses allow you to learn about various small business topics at your own pace."}
+{"idx": 5, "title": "About | SCORE", "date": "", "ddg_snippet": "SCORE , the nation’s largest network of volunteer, expert business mentors, is dedicated to helping small businesses get off the ground, grow and achieve their goals. Since 1964, we have provided education and mentorship to more than 18 million entrepreneurs.", "subpage_snippet": "", "source": "www.score.org", "link": "https://www.score.org/about", "content": "SCORE , the nation’s largest network of volunteer, expert business mentors, is dedicated to helping small businesses get off the ground, grow and achieve their goals. Since 1964, we have provided education and mentorship to more than 18 million entrepreneurs."}
+{"idx": 6, "title": "How It Works | SCORE", "date": "", "ddg_snippet": "Once you discuss your initial business question, they may ask you to complete business templates or to review resources on SCORE .org. Additionally, they may recommend SCORE or partner workshops or resources to meet your needs.", "subpage_snippet": "", "source": "www.score.org", "link": "https://www.score.org/find-mentor/how-it-works", "content": "Once you discuss your initial business question, they may ask you to complete business templates or to review resources on SCORE .org. Additionally, they may recommend SCORE or partner workshops or resources to meet your needs."}
+{"idx": 7, "title": "SCORE SE Wisconsin", "date": "", "ddg_snippet": "Since 1964, SCORE has educated and mentored more than 17 million small business owners and entrepreneurs. We're looking for people with diverse backgrounds and experiences to serve in a variety of roles — from knowledgeable business mentors to local community advocates.", "subpage_snippet": "", "source": "www.score.org", "link": "https://www.score.org/sewisconsin", "content": "Since 1964, SCORE has educated and mentored more than 17 million small business owners and entrepreneurs. We're looking for people with diverse backgrounds and experiences to serve in a variety of roles — from knowledgeable business mentors to local community advocates."}
+{"idx": 8, "title": "SCORE Washington DC", "date": "", "ddg_snippet": "Since 1964, SCORE has educated and mentored more than 17 million small business owners and entrepreneurs. We're looking for people with diverse backgrounds and experiences to serve in a variety of roles — from knowledgeable business mentors to local community advocates.", "subpage_snippet": "", "source": "www.score.org", "link": "https://www.score.org/washingtondc", "content": "Since 1964, SCORE has educated and mentored more than 17 million small business owners and entrepreneurs. We're looking for people with diverse backgrounds and experiences to serve in a variety of roles — from knowledgeable business mentors to local community advocates."}
+{"idx": 9, "title": "SCORE San Diego", "date": "", "ddg_snippet": "Since 1964, SCORE has educated and mentored more than 17 million small business owners and entrepreneurs. We're looking for people with diverse backgrounds and experiences to serve in a variety of roles — from knowledgeable business mentors to local community advocates.", "subpage_snippet": "", "source": "www.score.org", "link": "https://www.score.org/sandiego", "content": "Since 1964, SCORE has educated and mentored more than 17 million small business owners and entrepreneurs. We're looking for people with diverse backgrounds and experiences to serve in a variety of roles — from knowledgeable business mentors to local community advocates."}
diff --git a/data/sampled_jsons/Sean_Michael_Kerner_TechTarget_CrowdStrike_outage_2024_article_title_year_2024.jsonl b/data/sampled_jsons/Sean_Michael_Kerner_TechTarget_CrowdStrike_outage_2024_article_title_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..65bf78625ed9b92b655da73e129c56e1cb8e3777
--- /dev/null
+++ b/data/sampled_jsons/Sean_Michael_Kerner_TechTarget_CrowdStrike_outage_2024_article_title_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CrowdStrike outage explained: What caused it and what's ...", "date": "", "ddg_snippet": "A CrowdStrike update caused a massive IT outage , crashing millions of Windows systems. Critical services and business operations were disrupted.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/whatis/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "A CrowdStrike update caused a massive IT outage , crashing millions of Windows systems. Critical services and business operations were disrupted."}
+{"idx": 1, "title": "A Reflection on the CrowdStrike IT Outage - Platypus", "date": "", "ddg_snippet": "18 Feb 2025 — Kerner , Sean Michael . 2024 . “ CrowdStrike Outage Explained: What Caused It and What's Next.” Tech Target , 29 October 2024 . https://www. techtarget ...", "subpage_snippet": "", "source": "blog.castac.org", "link": "https://blog.castac.org/2025/02/major-internet-outages-are-getting-bigger-and-occurring-more-often-a-reflection-on-the-crowdstrike-it-outage/", "content": "18 Feb 2025 — Kerner , Sean Michael . 2024 . “ CrowdStrike Outage Explained: What Caused It and What's Next.” Tech Target , 29 October 2024 . https://www. techtarget ..."}
+{"idx": 2, "title": "What the CrowdStrike outage really teaches", "date": "", "ddg_snippet": "13 Aug 2024 — After the CrowdStrike outage , some enterprise IT teams are rethinking their assumptions about how use of the cloud impacts application ...", "subpage_snippet": "", "source": "www.networkworld.com", "link": "https://www.networkworld.com/article/3486316/what-enterprises-say-the-crowdstrike-outage-really-teaches.html", "content": "13 Aug 2024 — After the CrowdStrike outage , some enterprise IT teams are rethinking their assumptions about how use of the cloud impacts application ..."}
+{"idx": 3, "title": "Crowdstrike Class Action | PDF | Computer Security", "date": "", "ddg_snippet": "5 Aug 2024 — (emphasis in original). 12. Sean Michael Kerner, CrowdStrike outage explained : What caused it and what's next, TECHTARGET (July 25, 2024) ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/756346882/Crowdstrike-Class-Action", "content": "5 Aug 2024 — (emphasis in original). 12. Sean Michael Kerner, CrowdStrike outage explained : What caused it and what's next, TECHTARGET (July 25, 2024) ..."}
+{"idx": 4, "title": "text", "date": "", "ddg_snippet": "23 Jul 2024 — Publisher: TechTarget Date: 23 Jul 2024 By: Sean Michael Kerner Summary: Channel file 291 is an update that shall live in infamy. That was ...", "subpage_snippet": "", "source": "www.ieee-security.org", "link": "https://www.ieee-security.org/Cipher/PastIssues/2024/E180.Jul-2024/E180.Jul-2024.txt", "content": "23 Jul 2024 — Publisher: TechTarget Date: 23 Jul 2024 By: Sean Michael Kerner Summary: Channel file 291 is an update that shall live in infamy. That was ..."}
+{"idx": 5, "title": "LMDG: Advancing Lateral Movement Detection Through ...", "date": "", "ddg_snippet": "4 Aug 2025 — [27] Sean Michael Kerner. Crowdstrike outage explained : What caused it and what's next. TechTarget, Oct 2024. A CrowdStrike update caused a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.02942v1", "content": "4 Aug 2025 — [27] Sean Michael Kerner. Crowdstrike outage explained : What caused it and what's next. TechTarget, Oct 2024. A CrowdStrike update caused a ..."}
+{"idx": 6, "title": "Engineering a culture of technology resilience", "date": "", "ddg_snippet": "⁵ Sean Michael Kerner , “ CrowdStrike outage explained: What caused it and what's next,” TechTarget , October 29, 2024 . Get in touch. Ganesh ...", "subpage_snippet": "", "source": "www.deloitte.com", "link": "https://www.deloitte.com/us/en/services/consulting/articles/resilience-engineering.html", "content": "⁵ Sean Michael Kerner , “ CrowdStrike outage explained: What caused it and what's next,” TechTarget , October 29, 2024 . Get in touch. Ganesh ..."}
+{"idx": 7, "title": "Rethinking Critical Infrastructure: The Strategic Case for ...", "date": "", "ddg_snippet": "25 Aug 2025 — Footnotes. CrowdStrike outage explained : What caused it and what's next, Kerner, Sean, TechTarget, October 29, 2024.", "subpage_snippet": "", "source": "blogs.infoblox.com", "link": "https://blogs.infoblox.com/company/rethinking-critical-infrastructure-the-strategic-case-for-decoupling-dns-dhcp-from-identity-services/", "content": "25 Aug 2025 — Footnotes. CrowdStrike outage explained : What caused it and what's next, Kerner, Sean, TechTarget, October 29, 2024."}
+{"idx": 8, "title": "The Weaponization of Emerging Technologies and Their ...", "date": "", "ddg_snippet": "Kerner , Sean Michael , “ Crowdstrike Outage Explained: What Caused it and What's Next,” Techtarget , October 29, 2024 , https://www. techtarget .com/whatis ...", "subpage_snippet": "", "source": "connections-qj.org", "link": "https://connections-qj.org/article/weaponization-emerging-technologies-and-their-impact-global-risk-perspective-pfpc-emerging", "content": "Kerner , Sean Michael , “ Crowdstrike Outage Explained: What Caused it and What's Next,” Techtarget , October 29, 2024 , https://www. techtarget .com/whatis ..."}
+{"idx": 9, "title": "5176031.pdf", "date": "", "ddg_snippet": "See for example: Sean Michael Kerner; CrowdStrike Outage Explained : What caused it and what's next ;. TechTarget July 26 2024 at https://www.techtarget.com ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5176031.pdf?abstractid=5176031&mirid=1", "content": "See for example: Sean Michael Kerner; CrowdStrike Outage Explained : What caused it and what's next ;. TechTarget July 26 2024 at https://www.techtarget.com ..."}
diff --git a/data/sampled_jsons/Section_3.2_four-step_self-supervised_learning_process_behaviors_and_emotions.jsonl b/data/sampled_jsons/Section_3.2_four-step_self-supervised_learning_process_behaviors_and_emotions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..928377f44631e4074aeaf9bcf0a68b462dae9deb
--- /dev/null
+++ b/data/sampled_jsons/Section_3.2_four-step_self-supervised_learning_process_behaviors_and_emotions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - google/seqio: Task-based datasets, preprocessing, and", "date": "", "ddg_snippet": "... in conjunction with the Mesh Tensorflow Transformer implementation) to train the T5 models introduced in Exploring the Limits of Transfer Learning ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/google/seqio", "content": "... in conjunction with the Mesh Tensorflow Transformer implementation) to train the T5 models introduced in Exploring the Limits of Transfer Learning ..."}
+{"idx": 1, "title": "Decorrelation-Based Self-Supervised Visual Representation", "date": "", "ddg_snippet": "With the advent of self - supervised learning (SSL) techniques [ 8 , 18 , 41 ], applications of SSL in the computer vision domain have rapidly grown ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3746062", "content": "With the advent of self - supervised learning (SSL) techniques [ 8 , 18 , 41 ], applications of SSL in the computer vision domain have rapidly grown ..."}
+{"idx": 2, "title": "Semi-supervision and domain adaptation with AdaMatch", "date": "", "ddg_snippet": "Author: Sayak Paul Date created: 2021/06/19 Last modified: 2021/06/19 Description: Unifying semi- supervised learning and unsupervised domain ...", "subpage_snippet": "", "source": "keras.io", "link": "https://keras.io/examples/vision/adamatch/", "content": "Author: Sayak Paul Date created: 2021/06/19 Last modified: 2021/06/19 Description: Unifying semi- supervised learning and unsupervised domain ..."}
+{"idx": 3, "title": "Employing Machine Learning and Deep Learning Models for Mental", "date": "", "ddg_snippet": "Open Access Policy Institutional Open Access Program Special Issues Guidelines Editorial Process Research and Publication Ethics Article Processing ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2079-3197/13/8/186", "content": "Open Access Policy Institutional Open Access Program Special Issues Guidelines Editorial Process Research and Publication Ethics Article Processing ..."}
+{"idx": 4, "title": "A Spatial Guided Self-supervised Clustering Network for Medical", "date": "", "ddg_snippet": "... learnable parameter regarding the cluster size! Moreover the authors mention that in Section 2 .6 “We set an arbitrary maximum number of possible ...", "subpage_snippet": "", "source": "miccai2021.org", "link": "https://miccai2021.org/openaccess/paperlinks/2021/09/01/031-Paper1117.html", "content": "... learnable parameter regarding the cluster size! Moreover the authors mention that in Section 2 .6 “We set an arbitrary maximum number of possible ..."}
+{"idx": 5, "title": "DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for", "date": "", "ddg_snippet": "Supervised Fine-Tuning : We improve the Lean 4 code completion dataset by implementing two data augmentation techniques.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.08152v1", "content": "Supervised Fine-Tuning : We improve the Lean 4 code completion dataset by implementing two data augmentation techniques."}
+{"idx": 6, "title": "Evaluating the Effectiveness of Direct Preference Optimization", "date": "", "ddg_snippet": "By integrating reinforcement learning into the post-training pipeline of LLMs, human feedback can serve as an active supervision signal, steering ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01479v1", "content": "By integrating reinforcement learning into the post-training pipeline of LLMs, human feedback can serve as an active supervision signal, steering ..."}
+{"idx": 7, "title": "Middo: Model-Informed Dynamic Data Optimization for Enhanced", "date": "", "ddg_snippet": "Existing approaches primarily fall into two categories to improve data quality: data selection [ 4 , 65 , 31 , 19 , 66 , 32 , 28 ] and data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21589v2", "content": "Existing approaches primarily fall into two categories to improve data quality: data selection [ 4 , 65 , 31 , 19 , 66 , 32 , 28 ] and data ..."}
+{"idx": 8, "title": "Deploying Geospatial Foundation Models in the Real World:", "date": "", "ddg_snippet": "... in input data, sensor types, seasonal shifts, and different processing pipelines result in highly diverse data distributions (Tuia et al., 2016 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.00858v1", "content": "... in input data, sensor types, seasonal shifts, and different processing pipelines result in highly diverse data distributions (Tuia et al., 2016 ..."}
+{"idx": 9, "title": "No Label Left Behind: A Unified Surface Defect Detection Model", "date": "", "ddg_snippet": "Table 1: Four learning regimes utilising different levels of supervision: (i) normal images only , normal and anomalous images annotated with (ii ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19060v1", "content": "Table 1: Four learning regimes utilising different levels of supervision: (i) normal images only , normal and anomalous images annotated with (ii ..."}
diff --git a/data/sampled_jsons/Section_4.4_increased_sympathy_activation_subreddit_participation_climate_activism_year_2024.jsonl b/data/sampled_jsons/Section_4.4_increased_sympathy_activation_subreddit_participation_climate_activism_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d930782951819f6a6b7260a03b7967d70b2aa473
--- /dev/null
+++ b/data/sampled_jsons/Section_4.4_increased_sympathy_activation_subreddit_participation_climate_activism_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Factitious disorder imposed on self - Wikipedia", "date": "", "ddg_snippet": "... illness and treatment is usually in the form of psychotherapy but may depend on the specific situation, 4 which is further discussed in the sections ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Factitious_disorder_imposed_on_self", "content": "... illness and treatment is usually in the form of psychotherapy but may depend on the specific situation, 4 which is further discussed in the sections ..."}
+{"idx": 1, "title": "Reddit CEO tells employees that subreddit blackout ‘will", "date": "", "ddg_snippet": "It's because this is a prepared statement meant to be leaked, to help garner sympathy for Reddit. ... bad for reddit if they had gradually increased ...", "subpage_snippet": "", "source": "tildes.net", "link": "https://tildes.net/~tech/167j/reddit_ceo_tells_employees_that_subreddit_blackout_will_pass", "content": "It's because this is a prepared statement meant to be leaked, to help garner sympathy for Reddit. ... bad for reddit if they had gradually increased ..."}
+{"idx": 2, "title": "Editorial: #BringBackOurGirls and the power of hashtag activism", "date": "", "ddg_snippet": "... Ivy Artemisia of the Twilight Tradition of Wicca , Slate Miradora crafted and hosted the webworking which attracted more than 100 participants.", "subpage_snippet": "", "source": "wildhunt.org", "link": "https://wildhunt.org/2014/05/editorial-bringbackourgirls-and-the-power-of-hashtag-activism.html", "content": "... Ivy Artemisia of the Twilight Tradition of Wicca , Slate Miradora crafted and hosted the webworking which attracted more than 100 participants."}
+{"idx": 3, "title": "New research from Oxford University suggests that even without", "date": "", "ddg_snippet": "A subreddit devoted to the field of Future(s) Studies and evidence-based speculation about the development of humanity, technology, and civilization.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/Futurology/comments/qffjqm/new_research_from_oxford_university_suggests_that/", "content": "A subreddit devoted to the field of Future(s) Studies and evidence-based speculation about the development of humanity, technology, and civilization."}
+{"idx": 4, "title": "Important New Rules/Guidelines Regarding Twitter Content Posted", "date": "", "ddg_snippet": "The moderators enforce the relatively strict rules and it is strongly encouraged to put thought into one s activity on this subreddit .", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/syriancivilwar/comments/55nobo/important_new_rulesguidelines_regarding_twitter/", "content": "The moderators enforce the relatively strict rules and it is strongly encouraged to put thought into one s activity on this subreddit ."}
+{"idx": 5, "title": "As the Reddit war rages on, community trust is the casualty", "date": "", "ddg_snippet": "Over 8,400 subreddits went dark from June 12 through June 14 to protest new API pricing that is about to shutter many third-party Reddit apps.", "subpage_snippet": "", "source": "arstechnica.com", "link": "https://arstechnica.com/gadgets/2023/06/the-reddit-protests-are-winding-down-so-whats-next/", "content": "Over 8,400 subreddits went dark from June 12 through June 14 to protest new API pricing that is about to shutter many third-party Reddit apps."}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 7, "title": "(PDF) Transparency, Fairness, and Coping: How Players", "date": "", "ddg_snippet": "Punishment design: The randomly assigned punishment and game where participants experienced it. ... nicantly increase players’ perceived ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/368358442_Transparency_Fairness_and_Coping_How_Players_Experience_Moderation_in_Multiplayer_Online_Games", "content": "Punishment design: The randomly assigned punishment and game where participants experienced it. ... nicantly increase players’ perceived ..."}
+{"idx": 8, "title": "male to female | Page 4 | Vintologi", "date": "", "ddg_snippet": "If instead people transition and benefit from it that will increase the transgender voting block and then there will be more pressure on politicians ...", "subpage_snippet": "", "source": "vintologi.com", "link": "https://vintologi.com/threads/male-to-female.5/page-4", "content": "If instead people transition and benefit from it that will increase the transgender voting block and then there will be more pressure on politicians ..."}
+{"idx": 9, "title": "OT77: Opium Thread | Slate Star Codex", "date": "", "ddg_snippet": "It ’ s a vital constituency – we ’ ve had activists coming from far and wide and all the neighbouring candidates have been helping ...", "subpage_snippet": "", "source": "slatestarcodex.com", "link": "https://slatestarcodex.com/2017/06/04/ot77-opium-thread/", "content": "It ’ s a vital constituency – we ’ ve had activists coming from far and wide and all the neighbouring candidates have been helping ..."}
diff --git a/data/sampled_jsons/Section_6.2_Results_Stress-Testing_Capability_Elicitation_Password-Locked_Models.jsonl b/data/sampled_jsons/Section_6.2_Results_Stress-Testing_Capability_Elicitation_Password-Locked_Models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..324745670c3b7b32faedd09641190cd22d2c9a05
--- /dev/null
+++ b/data/sampled_jsons/Section_6.2_Results_Stress-Testing_Capability_Elicitation_Password-Locked_Models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Elicitation Game: Stress-Testing Capability Elicitation ...", "date": "", "ddg_snippet": "N-shot prompting almost fully elicits the capability of password-locked models but is only moderately effective for circuit-broken models . Prefilling shows mixed results ranging from limited elicitation to improved performance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zy6LB5t62f", "content": "N-shot prompting almost fully elicits the capability of password-locked models but is only moderately effective for circuit-broken models . Prefilling shows mixed results ranging from limited elicitation to improved performance."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "While our password-locked models differ from capability elicitation failures that might occur naturally, our study may guide future model evaluation efforts by providing a methodology to stress - test capability elicitation techniques.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "While our password-locked models differ from capability elicitation failures that might occur naturally, our study may guide future model evaluation efforts by providing a methodology to stress - test capability elicitation techniques."}
+{"idx": 2, "title": "[FMF PDF] Frontier Capability Assessments: Technical Report", "date": "", "ddg_snippet": "Frontier Capability Assessments usually involve conducting a variety of evaluations––structured tests of model capabilities in a given domain––followed by analysis on the test results . However, evaluations can vary significantly in both resource intensity and the strength of evidence they can provide.", "subpage_snippet": "", "source": "www.frontiermodelforum.org", "link": "https://www.frontiermodelforum.org/uploads/2025/04/FMF-PDF-Frontier-Capability-Assessments_-Technical-Report.pdf", "content": "Frontier Capability Assessments usually involve conducting a variety of evaluations––structured tests of model capabilities in a given domain––followed by analysis on the test results . However, evaluations can vary significantly in both resource intensity and the strength of evidence they can provide."}
+{"idx": 3, "title": "The Elicitation Game: Evaluating Capability Elicitation ...", "date": "", "ddg_snippet": "We develop password-locked and circuit-broken model organisms, where circuit-breaking is applied after password -locking to improve robustness to elicitation . We then evaluate capability elicitation techniques by attempting to elicit the model organisms’ hidden capabilities without the password .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02180", "content": "We develop password-locked and circuit-broken model organisms, where circuit-breaking is applied after password -locking to improve robustness to elicitation . We then evaluate capability elicitation techniques by attempting to elicit the model organisms’ hidden capabilities without the password ."}
+{"idx": 4, "title": "The Elicitation Game: Evaluating Capability Elicitation ...", "date": "", "ddg_snippet": "Feb 24, 2025 · We introduce a novel method for training model organisms, based on circuit-breaking, which is more robust to elicitation techniques than standard password-locked models . We focus on elicitation techniques based on prompting and activation steering, and compare these to fine-tuning methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v2", "content": "Feb 24, 2025 · We introduce a novel method for training model organisms, based on circuit-breaking, which is more robust to elicitation techniques than standard password-locked models . We focus on elicitation techniques based on prompting and activation steering, and compare these to fine-tuning methods."}
+{"idx": 5, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "Password-locked models enable a novel method of evaluating capabilities elicitation methods , by testing whether these password-locked capabilities can be ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/92923", "content": "Password-locked models enable a novel method of evaluating capabilities elicitation methods , by testing whether these password-locked capabilities can be ..."}
+{"idx": 6, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "by R Greenblatt · 2024 · Cited by 25 — We provide an extensive sensitivity analysis where we study alternative password-locking and elicitation methods, perform basic sanity checks, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.19550?", "content": "by R Greenblatt · 2024 · Cited by 25 — We provide an extensive sensitivity analysis where we study alternative password-locking and elicitation methods, perform basic sanity checks, ..."}
+{"idx": 7, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "We show results for the regular password - locked model trained with a single password for all domains (top) and a model trained with different passwords for each ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=uvvVjWP1aj&name=supplementary_material", "content": "We show results for the regular password - locked model trained with a single password for all domains (top) and a model trained with different passwords for each ..."}
+{"idx": 8, "title": "When does capability elicitation bound risk?", "date": "", "ddg_snippet": "21 Jan 2025 — The broad idea is to construct an analogy for sandbagging models and check if elicitation can recover their capabilities.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/u3taQsgxqCzrgErMM/when-does-capability-elicitation-bound-risk", "content": "21 Jan 2025 — The broad idea is to construct an analogy for sandbagging models and check if elicitation can recover their capabilities."}
+{"idx": 9, "title": "Vol 1_OM - Thomson Reuters", "date": "", "ddg_snippet": "The effectiveness of stress testing therefore, depends, in particular, on whether the bank has chosen the ‘right’ scenarios for stress testing , interpreted the results properly and taken the necessary steps to address the results .", "subpage_snippet": "", "source": "cbben.thomsonreuters.com", "link": "https://cbben.thomsonreuters.com/sites/default/files/net_file_store/Vol_1_ST_January_2022.pdf", "content": "The effectiveness of stress testing therefore, depends, in particular, on whether the bank has chosen the ‘right’ scenarios for stress testing , interpreted the results properly and taken the necessary steps to address the results ."}
diff --git a/data/sampled_jsons/Self-paced_curriculum_learning_Lu_Jiang_Deyu_Meng_abstract_2015.jsonl b/data/sampled_jsons/Self-paced_curriculum_learning_Lu_Jiang_Deyu_Meng_abstract_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a6f0a707a0a01e7af149ec0438764f9518b535f7
--- /dev/null
+++ b/data/sampled_jsons/Self-paced_curriculum_learning_Lu_Jiang_Deyu_Meng_abstract_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Self - paced Curriculum Learning", "date": "", "ddg_snippet": "Self - paced Curriculum Learning . Lu Jiang 1, Deyu Meng 1,2, Qian Zhao1,2, Shiguang Shan1,3, Alexander G. Hauptmann1. Abstract . Curriculum learning (CL) or self - paced learning (SPL). represents a recently proposed learning regime inspired.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/279853657_Self-paced_Curriculum_Learning", "content": "Self - paced Curriculum Learning . Lu Jiang 1, Deyu Meng 1,2, Qian Zhao1,2, Shiguang Shan1,3, Alexander G. Hauptmann1. Abstract . Curriculum learning (CL) or self - paced learning (SPL). represents a recently proposed learning regime inspired."}
+{"idx": 1, "title": "Self - Paced Curriculum Learning | Proceedings of the AAAI...", "date": "", "ddg_snippet": "Self - Paced Curriculum Learning . Authors. Lu Jiang Carnegie Mellon University. Deyu Meng Xi'an Jiaotong University.AAAI 2015 Proceedings Cover.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/9608", "content": "Self - Paced Curriculum Learning . Authors. Lu Jiang Carnegie Mellon University. Deyu Meng Xi'an Jiaotong University.AAAI 2015 Proceedings Cover."}
+{"idx": 2, "title": "[PDF] Self - Paced Curriculum Learning | Semantic Scholar", "date": "", "ddg_snippet": "Curriculum learning (CL) or self - paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Self-Paced-Curriculum-Learning-Jiang-Meng/21d255246cd7ddba24a651fd716950f893ea8eb2", "content": "Curriculum learning (CL) or self - paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training."}
+{"idx": 3, "title": "Lu Jiang | Google | 81 Publications | 1582 Citations | Related Authors", "date": "", "ddg_snippet": "Self - Paced Learning with Diversity. Lu Jiang , Deyu Meng , Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, Alexander G. Hauptmann +5 moreCarnegie Mellon University, Xi'an Jiaotong University, Chinese Academy of Sciences.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/authors/lu-jiang-4n9svm8sl8", "content": "Self - Paced Learning with Diversity. Lu Jiang , Deyu Meng , Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, Alexander G. Hauptmann +5 moreCarnegie Mellon University, Xi'an Jiaotong University, Chinese Academy of Sciences."}
+{"idx": 4, "title": "Lu Jiang - Google Akademik", "date": "", "ddg_snippet": "Easy samples first: Self - paced reranking for zero-example multimedia search. L Jiang , D Meng , T Mitamura, AG Hauptmann. ACM international conference on Multimedia, 2014.", "subpage_snippet": "", "source": "scholar.google.com.eg", "link": "https://scholar.google.com.eg/citations?user=jIKjjSYAAAAJ&hl=tr", "content": "Easy samples first: Self - paced reranking for zero-example multimedia search. L Jiang , D Meng , T Mitamura, AG Hauptmann. ACM international conference on Multimedia, 2014."}
+{"idx": 5, "title": "Self - Paced Boost Learning for Classification", "date": "", "ddg_snippet": "Self - paced curriculum learning . In Twenty-Ninth AAAI Conference on Articial Intelligence, 2015 .[Zhao et al., 2015 ] Qian Zhao, Deyu Meng , Lu Jiang , Qi Xie, Zongben Xu, and Alexander G Hauptmann. Self - paced learning for matrix factorization.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/Proceedings/16/Papers/276.pdf", "content": "Self - paced curriculum learning . In Twenty-Ninth AAAI Conference on Articial Intelligence, 2015 .[Zhao et al., 2015 ] Qian Zhao, Deyu Meng , Lu Jiang , Qi Xie, Zongben Xu, and Alexander G Hauptmann. Self - paced learning for matrix factorization."}
+{"idx": 6, "title": "What Objective Does Self - paced Learning Indeed", "date": "", "ddg_snippet": "Abstract . Self - paced learning (SPL) is a recently raised methodology designed through simulating the learning principle of humans/animals.[10] L. Jiang , D. Y. Meng , Q. Zhao, S. G. Shan, and A. Hauptman. Self - paced curriculum learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1511.06049", "content": "Abstract . Self - paced learning (SPL) is a recently raised methodology designed through simulating the learning principle of humans/animals.[10] L. Jiang , D. Y. Meng , Q. Zhao, S. G. Shan, and A. Hauptman. Self - paced curriculum learning ."}
+{"idx": 7, "title": "MentorNet: Learning Data-Driven Curriculum", "date": "", "ddg_snippet": "MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels. Meng , D., Zhao, Q., and Jiang , L. What objective does self - paced learning indeed optimize? arXiv preprint arXiv:1511.06049, 2015 .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v80/jiang18c/jiang18c.pdf", "content": "MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels. Meng , D., Zhao, Q., and Jiang , L. What objective does self - paced learning indeed optimize? arXiv preprint arXiv:1511.06049, 2015 ."}
+{"idx": 8, "title": "Self - paced curriculum learning for visual question answering on", "date": "", "ddg_snippet": "[7] Lu Jiang , Deyu Meng , Qian Zhao, Shiguang Shan, and Alexander G. Hauptmann, “ Self - paced curriculum learning ,” in Proceedings of the Twenty-Ninth AAAI Conference on Articial Intelligence, January 25-30, 2015 , Austin, Texas, USA, 2015 , pp. 2694–2700.", "subpage_snippet": "", "source": "elib.dlr.de", "link": "https://elib.dlr.de/146238/1/IGARSS_YUAN.pdf", "content": "[7] Lu Jiang , Deyu Meng , Qian Zhao, Shiguang Shan, and Alexander G. Hauptmann, “ Self - paced curriculum learning ,” in Proceedings of the Twenty-Ninth AAAI Conference on Articial Intelligence, January 25-30, 2015 , Austin, Texas, USA, 2015 , pp. 2694–2700."}
+{"idx": 9, "title": "A Probabilistic Interpretation of Self - Paced Learning with", "date": "", "ddg_snippet": "Self - Paced Learning for Reinforcement Learning. Application to Episodic Reinforcement Learning. Lu Jiang , Deyu Meng , Qian Zhao, Shiguang Shan, and Alexander G Hauptmann. Self - paced curriculum learning . In AAAI Conference on Articial Intelligence (AAAI), 2015 .", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume22/21-0112/21-0112.pdf", "content": "Self - Paced Learning for Reinforcement Learning. Application to Episodic Reinforcement Learning. Lu Jiang , Deyu Meng , Qian Zhao, Shiguang Shan, and Alexander G Hauptmann. Self - paced curriculum learning . In AAAI Conference on Articial Intelligence (AAAI), 2015 ."}
diff --git a/data/sampled_jsons/Self-refine_Madaan_7_diverse_tasks_dialog_response_generation_code_optimization_sentiment_year_2023.jsonl b/data/sampled_jsons/Self-refine_Madaan_7_diverse_tasks_dialog_response_generation_code_optimization_sentiment_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd74ebf9f9096e23e17034c3513d345a1a78fc4c
--- /dev/null
+++ b/data/sampled_jsons/Self-refine_Madaan_7_diverse_tasks_dialog_response_generation_code_optimization_sentiment_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self-Refine: Iterative Refinement with Self-Feedback - OpenReview", "date": "", "ddg_snippet": "Sep 21, 2023 · We evaluate Self-Refine across 7 diverse tasks , ranging from dialog response generation to mathematical reasoning, using state-of-the-art (GPT-3.5, ChatGPT, and GPT-4) LLMs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=S37hOerQLB", "content": "Sep 21, 2023 · We evaluate Self-Refine across 7 diverse tasks , ranging from dialog response generation to mathematical reasoning, using state-of-the-art (GPT-3.5, ChatGPT, and GPT-4) LLMs."}
+{"idx": 1, "title": "Self-Refine: Iterative Refinement with Self-Feedback", "date": "", "ddg_snippet": "Self-Refine is a novel approach that allows LLMs to iteratively refine outputs and incorporate feedback along multiple dimensions to improve performance on diverse tasks . Unlike prior work, it does not require supervised training data or reinforcement learning, and works with a single LLM.", "subpage_snippet": "", "source": "selfrefine.info", "link": "https://selfrefine.info/", "content": "Self-Refine is a novel approach that allows LLMs to iteratively refine outputs and incorporate feedback along multiple dimensions to improve performance on diverse tasks . Unlike prior work, it does not require supervised training data or reinforcement learning, and works with a single LLM."}
+{"idx": 2, "title": "[AI Agents] SELF-REFINE: Iterative Refinement with Self ...", "date": "", "ddg_snippet": "Jan 1, 2025 · SELF-REFINE was tested across seven diverse tasks , including dialog response generation and mathematical reasoning, using state-of-the-art LLMs like GPT-3.5 and GPT-4.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/byte-sized-ai/ai-agents-self-refine-iterative-refinement-with-self-feedback-70943c326bea", "content": "Jan 1, 2025 · SELF-REFINE was tested across seven diverse tasks , including dialog response generation and mathematical reasoning, using state-of-the-art LLMs like GPT-3.5 and GPT-4."}
+{"idx": 3, "title": "SELF-REFINE: - arXiv.org", "date": "", "ddg_snippet": "Algorithm 1 summarizes SELF-REFINE , and Figure 2 shows an example of SELF-REFINE in the Dialogue Response Generation (Mehri and Eskenazi, 2020) and Code Optimization ( Madaan et al., 2023) tasks .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.17651v2", "content": "Algorithm 1 summarizes SELF-REFINE , and Figure 2 shows an example of SELF-REFINE in the Dialogue Response Generation (Mehri and Eskenazi, 2020) and Code Optimization ( Madaan et al., 2023) tasks ."}
+{"idx": 4, "title": "SELF-REFINE | Proceedings of the 37th International ...", "date": "", "ddg_snippet": "Dec 10, 2023 · Self-Refine does not require any supervised training data, additional training, or reinforcement learning, and instead uses a single llm as the generator, refiner, and feedback provider. We evaluate Self-Refine across 7 diverse tasks , ranging from dialog response generation to mathematical reasoning, using state-of-the-art (gpt-3.5 and GPT-4) llms.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668141", "content": "Dec 10, 2023 · Self-Refine does not require any supervised training data, additional training, or reinforcement learning, and instead uses a single llm as the generator, refiner, and feedback provider. We evaluate Self-Refine across 7 diverse tasks , ranging from dialog response generation to mathematical reasoning, using state-of-the-art (gpt-3.5 and GPT-4) llms."}
+{"idx": 5, "title": "GitHub - madaan/self-refine: LLMs can generate feedback on ... Self-Refine: Iterative Refinement with Self-Feedback Self-Refine: Iterative Refinement with Self-Feedback Self - Refine : Iterative Refinement with Self -Feedback SELF - REFINE Iterative Refinement with Self -Feedback - arXiv.org Self-Refine: Iterative Refinement with Self- Feedback - OpenReview [AI Agents] SELF - REFINE : Iterative Refinement with Self -Feedback - … SELF - REFINE Iterative Refinement with Self -Feedback - arXiv.org SELF - REFINE Iterative Refinement with Self -Feedback - arXiv.org [AI Agents] SELF-REFINE: Iterative Refinement with Self ...", "date": "", "ddg_snippet": "With Self - Refine , LLMs can generate feedback on their work, use it to improve the output, and repeat this process. Website | Paper Table of Contents •Updates •Setup •Getting Started with Acronym Generation See full list on github.com •Nov 2023: Added visual self - refine examples and colabs. Use GPT4-V to write tikz code for diagrams, and improve them iteratively. Stokes' Theorem Example See full list on github.com •We use prompt-lib for querying LLMs. You can install prompt-lib by running the following commands: Depending on your default settings, you may want to use the following to set a PYTHONPATH: See full list on github.com •Since we sample with temperature 0. 7 , you'll get different results each time you run the above command. See full list on github.com •Use size 0 for running on all test instances See full list on github.com •Note: Please unzip 'data/ tasks /codeclean/code_readability/codenet-python-train.jsonl.zip' before running the following commands! •Running: See full list on github.com •We use a hard version of commongen. The data is located in data/prompt/commongen. You can download the data by running the following commands: See full list on github.com •To run the GSM-8k task: •The outputs will be saved in data/ tasks /gsm/gsm_outputs.jsonl •To evaluate the outputs: •The evaluation script will also generate a report (data/ tasks /gsm/gsm_outputs.jsonl.reports.txt) showing examples of wrong generations, feedback, and refined feedback generations. See full list on github.com •To run the Yelp task: •The outputs will be saved in data/ tasks /yelp/ See full list on github.com •To run the PIE task: •For evaluation details, please see docs/pie_eval.md. See full list on github.com Self-Refine is a novel approach that allows LLMs to iteratively refine outputs and incorporate feedback along multiple dimensions to improve performance on diverse tasks . Unlike prior work, it does not require supervised training data or reinforcement learning, and works with a single LLM. Mar 30, 2023 · We experiment with 7 diverse tasks , ranging from review rewriting to math reasoning, demonstrating that our approach outperforms direct generation . How does self-refine generate actionable feedback? Self - Refine generates actionable feedback. Given the initial output from an LLM, the FEEDBACK pinpoints the reasons for the output meeting (or not meeting) the requirements. The actionable feedback covers two aspects (i) localization of the problem (ii) instruction to improve. Adding a new task to Self - Refine is straightforward. How does acronym generation affect performance in multi-aspect feedback tasks? Table 10: Acronym generation results across iterations, showcasing how improvements in certain as-pects (e.g., pronunciation and spelling) can be accompanied by losses in others, leading to fluctuating overall performance in multi-aspect feedback tasks like Acronym Generation. What is self-refine framework? The proposed Self-Refine framework is conceptually neat and can be easily combined with different LLMs. Though it still can be viewed as a prompting method , it decouples the refinement of model output into iterative steps instead of focusing on engineering a single prompt. Does self-refine improve task performance? In all evaluated tasks, outputs generated with SELF-REFINE consistently outperformed those produced via conventional one-step generation. Human evaluations and automatic metrics showed a ∼20% absolute improvement in task performance on average. Does self-refine require a separate refiner for each task? In contrast, SELF-REFINE uses instructions and few-shot prompting, and thus does not require training a separate refiner for each task. Empirically, we evaluated SELF-REFINE using the same base model of GPT-3 as Self-Correction, and with the same settings on the GSM8K benchmark. Why is the commongen-hard task a good testbed for self-refine? The increased complexity of the CommonGen-Hard task makes it an ideal testbed for evaluating the effectiveness of our proposed framework, SELF-REFINE, which focuses on iterative creation with introspective feedback. Jan 1, 2025 · SELF-REFINE was tested across seven diverse tasks , including dialog response generation and mathematical reasoning, using state-of-the-art LLMs like GPT-3.5 and GPT-4.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/madaan/self-refine", "content": "With Self - Refine , LLMs can generate feedback on their work, use it to improve the output, and repeat this process. Website | Paper Table of Contents •Updates •Setup •Getting Started with Acronym Generation See full list on github.com •Nov 2023: Added visual self - refine examples and colabs. Use GPT4-V to write tikz code for diagrams, and improve them iteratively. Stokes' Theorem Example See full list on github.com •We use prompt-lib for querying LLMs. You can install prompt-lib by running the following commands: Depending on your default settings, you may want to use the following to set a PYTHONPATH: See full list on github.com •Since we sample with temperature 0. 7 , you'll get different results each time you run the above command. See full list on github.com •Use size 0 for running on all test instances See full list on github.com •Note: Please unzip 'data/ tasks /codeclean/code_readability/codenet-python-train.jsonl.zip' before running the following commands! •Running: See full list on github.com •We use a hard version of commongen. The data is located in data/prompt/commongen. You can download the data by running the following commands: See full list on github.com •To run the GSM-8k task: •The outputs will be saved in data/ tasks /gsm/gsm_outputs.jsonl •To evaluate the outputs: •The evaluation script will also generate a report (data/ tasks /gsm/gsm_outputs.jsonl.reports.txt) showing examples of wrong generations, feedback, and refined feedback generations. See full list on github.com •To run the Yelp task: •The outputs will be saved in data/ tasks /yelp/ See full list on github.com •To run the PIE task: •For evaluation details, please see docs/pie_eval.md. See full list on github.com Self-Refine is a novel approach that allows LLMs to iteratively refine outputs and incorporate feedback along multiple dimensions to improve performance on diverse tasks . Unlike prior work, it does not require supervised training data or reinforcement learning, and works with a single LLM. Mar 30, 2023 · We experiment with 7 diverse tasks , ranging from review rewriting to math reasoning, demonstrating that our approach outperforms direct generation . How does self-refine generate actionable feedback? Self - Refine generates actionable feedback. Given the initial output from an LLM, the FEEDBACK pinpoints the reasons for the output meeting (or not meeting) the requirements. The actionable feedback covers two aspects (i) localization of the problem (ii) instruction to improve. Adding a new task to Self - Refine is straightforward. How does acronym generation affect performance in multi-aspect feedback tasks? Table 10: Acronym generation results across iterations, showcasing how improvements in certain as-pects (e.g., pronunciation and spelling) can be accompanied by losses in others, leading to fluctuating overall performance in multi-aspect feedback tasks like Acronym Generation. What is self-refine framework? The proposed Self-Refine framework is conceptually neat and can be easily combined with different LLMs. Though it still can be viewed as a prompting method , it decouples the refinement of model output into iterative steps instead of focusing on engineering a single prompt. Does self-refine improve task performance? In all evaluated tasks, outputs generated with SELF-REFINE consistently outperformed those produced via conventional one-step generation. Human evaluations and automatic metrics showed a ∼20% absolute improvement in task performance on average. Does self-refine require a separate refiner for each task? In contrast, SELF-REFINE uses instructions and few-shot prompting, and thus does not require training a separate refiner for each task. Empirically, we evaluated SELF-REFINE using the same base model of GPT-3 as Self-Correction, and with the same settings on the GSM8K benchmark. Why is the commongen-hard task a good testbed for self-refine? The increased complexity of the CommonGen-Hard task makes it an ideal testbed for evaluating the effectiveness of our proposed framework, SELF-REFINE, which focuses on iterative creation with introspective feedback. Jan 1, 2025 · SELF-REFINE was tested across seven diverse tasks , including dialog response generation and mathematical reasoning, using state-of-the-art LLMs like GPT-3.5 and GPT-4."}
+{"idx": 6, "title": "Self-Refine: Iterative Refinement with Self-Feedback", "date": "", "ddg_snippet": "Mar 30, 2023 · We experiment with 7 diverse tasks , ranging from review rewriting to math reasoning, demonstrating that our approach outperforms direct generation .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/369740347_Self-Refine_Iterative_Refinement_with_Self-Feedback", "content": "Mar 30, 2023 · We experiment with 7 diverse tasks , ranging from review rewriting to math reasoning, demonstrating that our approach outperforms direct generation ."}
+{"idx": 7, "title": "Self-Refine: Iterative Refinement with Self-Feedback", "date": "", "ddg_snippet": "by A Madaan · 2023 · Cited by 2202 — We conduct extensive experiments on 7 diverse tasks of review rewriting, acronym generation , ... Sentiment Reversal, Dialogue Response Generation , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.17651", "content": "by A Madaan · 2023 · Cited by 2202 — We conduct extensive experiments on 7 diverse tasks of review rewriting, acronym generation , ... Sentiment Reversal, Dialogue Response Generation , ..."}
+{"idx": 8, "title": "Iterative Refinement with Self-Feedback", "date": "", "ddg_snippet": "by A Madaan · Cited by 2259 — We evaluate SELF - REFINE on 7 diverse tasks : Dialogue Response Generation (Appendix P; Mehri and Eskenazi, 2020), Code Optimization (Appendix Q; Madaan et al., ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=S37hOerQLB", "content": "by A Madaan · Cited by 2259 — We evaluate SELF - REFINE on 7 diverse tasks : Dialogue Response Generation (Appendix P; Mehri and Eskenazi, 2020), Code Optimization (Appendix Q; Madaan et al., ..."}
+{"idx": 9, "title": "Self-Refine | Prompt Engineering Course, Resource and ...", "date": "", "ddg_snippet": "We experiment with 7 diverse tasks , ranging from review rewriting to math reasoning, demonstrating that our approach outperforms direct generation . In all ...", "subpage_snippet": "", "source": "www.egoalpha.com", "link": "http://www.egoalpha.com/Self_Refine/Self_Refine.html", "content": "We experiment with 7 diverse tasks , ranging from review rewriting to math reasoning, demonstrating that our approach outperforms direct generation . In all ..."}
diff --git a/data/sampled_jsons/Shen_Lee_2019_Minimizing_the_sum_of_many_functions_abstract.jsonl b/data/sampled_jsons/Shen_Lee_2019_Minimizing_the_sum_of_many_functions_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..235ba50d8514fed2f133c8e5b640d9bc180e0ea5
--- /dev/null
+++ b/data/sampled_jsons/Shen_Lee_2019_Minimizing_the_sum_of_many_functions_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Shen Build with Highest Winrate - LoL Runes, Items, and Skill...", "date": "", "ddg_snippet": "Shen build with the highest winrate runes and items in every role. U.GG analyzes millions of LoL matches to give you the best LoL champion build. Patch 15.18.", "subpage_snippet": "", "source": "u.gg", "link": "https://u.gg/lol/champions/shen/build", "content": "Shen build with the highest winrate runes and items in every role. U.GG analyzes millions of LoL matches to give you the best LoL champion build. Patch 15.18."}
+{"idx": 1, "title": "Shen - League of Legends", "date": "", "ddg_snippet": "Among the secretive, Ionian warriors known as the Kinkou, Shen serves as their leader, the Eye of Twilight. He longs to remain free from the confusion of emotion, prejudice, and ego, and walks the unseen path of dispassionate judgment between the spirit realm and the physical world.", "subpage_snippet": "", "source": "www.leagueoflegends.com", "link": "https://www.leagueoflegends.com/en-us/champions/shen/", "content": "Among the secretive, Ionian warriors known as the Kinkou, Shen serves as their leader, the Eye of Twilight. He longs to remain free from the confusion of emotion, prejudice, and ego, and walks the unseen path of dispassionate judgment between the spirit realm and the physical world."}
+{"idx": 2, "title": "Shen /LoL - League of Legends Wiki | Fandom", "date": "", "ddg_snippet": "Shen is the first dark-themed Ionian champion, before Varus, Syndra, Zed, Jhin, Xayah, & Kayn (Noxian-born). Shen , Pantheon, and Twisted Fate were the first to have a global-range teleport ability.", "subpage_snippet": "", "source": "leagueoflegends.fandom.com", "link": "https://leagueoflegends.fandom.com/wiki/Shen/LoL", "content": "Shen is the first dark-themed Ionian champion, before Varus, Syndra, Zed, Jhin, Xayah, & Kayn (Noxian-born). Shen , Pantheon, and Twisted Fate were the first to have a global-range teleport ability."}
+{"idx": 3, "title": "Shen Build Guides :: League of Legends Strategy Builds ... - ...", "date": "", "ddg_snippet": "Build guides for Shen on MOBAFire. Learn what runes and items make the best Shen build in League of Legends (LoL).", "subpage_snippet": "", "source": "www.mobafire.com", "link": "https://www.mobafire.com/league-of-legends/champion/shen-48", "content": "Build guides for Shen on MOBAFire. Learn what runes and items make the best Shen build in League of Legends (LoL)."}
+{"idx": 4, "title": "Shen Build Guide - Runes, Items & More - Patch 25.19 - Mobalytics", "date": "", "ddg_snippet": "Get the best Shen builds, based on analysis of 10000+ matches in all regions and ranks. Climb in patch 25.19 with Shen builds provided by Mobalytics!", "subpage_snippet": "", "source": "mobalytics.gg", "link": "https://mobalytics.gg/lol/champions/shen/build", "content": "Get the best Shen builds, based on analysis of 10000+ matches in all regions and ranks. Climb in patch 25.19 with Shen builds provided by Mobalytics!"}
+{"idx": 5, "title": "Shen Build - Highest Win Rate Builds, Runes, and Items", "date": "", "ddg_snippet": "Shen Build with the highest win rate. Runes, items, and skill build in patch 15.18. Shen build recommendations and guides.", "subpage_snippet": "", "source": "www.op.gg", "link": "https://www.op.gg/lol/champions/shen/build", "content": "Shen Build with the highest win rate. Runes, items, and skill build in patch 15.18. Shen build recommendations and guides."}
+{"idx": 6, "title": "Shen Build , Runes & Counters Guide for top Shen - LoLalytics", "date": "", "ddg_snippet": "The best Shen players have a 55.1% win rate with an average rank of Master on the Shen Leaderboard. Below is a detailed breakdown of the Shen build, runes & counters.", "subpage_snippet": "", "source": "lolalytics.com", "link": "https://lolalytics.com/lol/shen/build/", "content": "The best Shen players have a 55.1% win rate with an average rank of Master on the Shen Leaderboard. Below is a detailed breakdown of the Shen build, runes & counters."}
+{"idx": 7, "title": "Shen Guide ⇒ Dominate the Rift with Pro Plays [2025]", "date": "", "ddg_snippet": "Aug 6, 2025 · Shen is a master of balance and order, tasked with maintaining harmony between the spirit realm and the material world. As the leader of the Kinkou Order, he inherited the title of the Eye of Twilight after the death of his father, Kusho.", "subpage_snippet": "", "source": "lolnow.gg", "link": "https://lolnow.gg/shen/", "content": "Aug 6, 2025 · Shen is a master of balance and order, tasked with maintaining harmony between the spirit realm and the material world. As the leader of the Kinkou Order, he inherited the title of the Eye of Twilight after the death of his father, Kusho."}
+{"idx": 8, "title": "Shen - Biography - Universe of League of Legends", "date": "", "ddg_snippet": "Shen taught the girl, Akali Jhomen Tethi, to master the arts of stealth and subterfuge. Her mother, Mayym, had stood alongside Kusho as the Fist of Shadow, and it seemed as though her daughter could follow the same path.", "subpage_snippet": "", "source": "universe.leagueoflegends.com", "link": "https://universe.leagueoflegends.com/en_US/story/champion/shen/", "content": "Shen taught the girl, Akali Jhomen Tethi, to master the arts of stealth and subterfuge. Her mother, Mayym, had stood alongside Kusho as the Fist of Shadow, and it seemed as though her daughter could follow the same path."}
+{"idx": 9, "title": "Women's & Men's Clothing, Shop Online Fashion | SHEIN", "date": "", "ddg_snippet": "From shoes to clothing, from sports equipment to accessories. All fashion inspiration & the latest trends can be found online at SHEIN .", "subpage_snippet": "", "source": "us.shein.com", "link": "https://us.shein.com/", "content": "From shoes to clothing, from sports equipment to accessories. All fashion inspiration & the latest trends can be found online at SHEIN ."}
diff --git a/data/sampled_jsons/Shen_Lee_2019_randomized_midpoint_method_Picard_iteration_OR_Picard_log-concave.jsonl b/data/sampled_jsons/Shen_Lee_2019_randomized_midpoint_method_Picard_iteration_OR_Picard_log-concave.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b4e23b009e890fbc4c5352e7ee16c9001c4c6e0c
--- /dev/null
+++ b/data/sampled_jsons/Shen_Lee_2019_randomized_midpoint_method_Picard_iteration_OR_Picard_log-concave.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "Log - Concave Sampling", "date": "", "ddg_snippet": "Ruoqi Shen and Yin Tat Lee . “The randomized midpoint method for log - concave sampling”. In: Advances in Neural Information Processing Systems.", "subpage_snippet": "", "source": "chewisinho.github.io", "link": "https://chewisinho.github.io/main.pdf?curius=9", "content": "Ruoqi Shen and Yin Tat Lee . “The randomized midpoint method for log - concave sampling”. In: Advances in Neural Information Processing Systems."}
+{"idx": 1, "title": "Numerical Analysis Technical Reports", "date": "", "ddg_snippet": "High-order numerical methods for parabolic PDEs with nonsmooth data: a perspective from option pricing , Dawei Wang, Ph.D.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "http://www.cs.toronto.edu/NA/reports.html", "content": "High-order numerical methods for parabolic PDEs with nonsmooth data: a perspective from option pricing , Dawei Wang, Ph.D."}
+{"idx": 2, "title": "M Idpoints : s equential and p arallel", "date": "", "ddg_snippet": "The randomized midpoint method for log - concave sampling.By smoothness of the true ODE, the continuous Picard iteration converges exponentially to the true trajectory, and we will show that discretization error for Picard iteration is controlled.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=MT3aOfXIbY&name=pdf", "content": "The randomized midpoint method for log - concave sampling.By smoothness of the true ODE, the continuous Picard iteration converges exponentially to the true trajectory, and we will show that discretization error for Picard iteration is controlled."}
+{"idx": 3, "title": "Fast", "date": "", "ddg_snippet": "Note that the Picard iteration can be analogously defined for discrete-time dynamics such as.Ruoqi Shen and Yin Tat Lee . The randomized midpoint method for log - concave sampling. Advances in Neural Information Processing Systems, 32, 2019 . Santosh Vempala and Andre Wibisono.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/anari24a/anari24a.pdf", "content": "Note that the Picard iteration can be analogously defined for discrete-time dynamics such as.Ruoqi Shen and Yin Tat Lee . The randomized midpoint method for log - concave sampling. Advances in Neural Information Processing Systems, 32, 2019 . Santosh Vempala and Andre Wibisono."}
+{"idx": 4, "title": "(PDF) Parallel simulation for sampling under isoperimetry and...", "date": "", "ddg_snippet": "Log - concave samplingRuoqi Shen and Yin Tat Lee . The Randomized Midpoint Method for Log - Concave Sampling. Advances in Neural Information Processing Systems, 32, 2019 . [SME20].", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386976226_Parallel_simulation_for_sampling_under_isoperimetry_and_score-based_diffusion_models", "content": "Log - concave samplingRuoqi Shen and Yin Tat Lee . The Randomized Midpoint Method for Log - Concave Sampling. Advances in Neural Information Processing Systems, 32, 2019 . [SME20]."}
+{"idx": 5, "title": "Parallel Sampling of Diffusion Models", "date": "", "ddg_snippet": "Implementing Picard iteration on diffusion models presents a few practical challenges, the most important being that of GPU memory.[27] Ruoqi Shen and Yin Tat Lee . The randomized midpoint method for log - concave sampling.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/0d1986a61e30e5fa408c81216a616e20-Paper-Conference.pdf", "content": "Implementing Picard iteration on diffusion models presents a few practical challenges, the most important being that of GPU memory.[27] Ruoqi Shen and Yin Tat Lee . The randomized midpoint method for log - concave sampling."}
+{"idx": 6, "title": "Accelerating Diffusion Models with Parallel Sampling", "date": "", "ddg_snippet": "parallel sampling is based on the Picard iteration [128, 129] for solving nonlinear ODEs.[97] Ruoqi Shen and Yin Tat Lee . The randomized midpoint method for log - concave sampling. Advances in Neural Information Processing Systems, 32, 2019 .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/f162fa05675e3db4a733aafc081653cf-Paper-Conference.pdf", "content": "parallel sampling is based on the Picard iteration [128, 129] for solving nonlinear ODEs.[97] Ruoqi Shen and Yin Tat Lee . The randomized midpoint method for log - concave sampling. Advances in Neural Information Processing Systems, 32, 2019 ."}
+{"idx": 7, "title": "Multi-dimensional cell-free DNA-based liquid biopsy for", "date": "", "ddg_snippet": "Stratified random selection was then performed to ensure that the demographic distributions (age, sex, postsurgical stage, tumor location, Lauren’s ...", "subpage_snippet": "", "source": "genomemedicine.biomedcentral.com", "link": "https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-024-01352-1", "content": "Stratified random selection was then performed to ensure that the demographic distributions (age, sex, postsurgical stage, tumor location, Lauren’s ..."}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Shib_Dasgupta_github.jsonl b/data/sampled_jsons/Shib_Dasgupta_github.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f6364250addd9def875a5d2d18449d641aca226
--- /dev/null
+++ b/data/sampled_jsons/Shib_Dasgupta_github.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Shib Dasgupta - ACL Anthology", "date": "", "ddg_snippet": "pdf bib abs W ord2 B ox: Capturing Set-Theoretic Semantics of Words using Box Embeddings Shib Dasgupta | Michael Boratko | Siddhartha Mishra | Shriya ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/s/shib-dasgupta/", "content": "pdf bib abs W ord2 B ox: Capturing Set-Theoretic Semantics of Words using Box Embeddings Shib Dasgupta | Michael Boratko | Siddhartha Mishra | Shriya ..."}
+{"idx": 1, "title": "GitHub - FreedomIntelligence/ReasoningNLP: paper list on", "date": "", "ddg_snippet": "paper list on reasoning in NLP. Contribute to FreedomIntelligence/ReasoningNLP development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FreedomIntelligence/ReasoningNLP", "content": "paper list on reasoning in NLP. Contribute to FreedomIntelligence/ReasoningNLP development by creating an account on GitHub ."}
+{"idx": 2, "title": "GitHub - stasl0217/beurre: Resources and code for paper", "date": "", "ddg_snippet": "Xuelu Chen*, Michael Boratko*, Muhao Chen, Shib Sankar Dasgupta , Xiang Lorraine Li, Andrew McCallum. ... Michael and Chen, Muhao and Dasgupta , Shib ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/stasl0217/beurre", "content": "Xuelu Chen*, Michael Boratko*, Muhao Chen, Shib Sankar Dasgupta , Xiang Lorraine Li, Andrew McCallum. ... Michael and Chen, Muhao and Dasgupta , Shib ..."}
+{"idx": 3, "title": "Word2Box: Capturing Set-Theoretic Semantics of Words using Box", "date": "", "ddg_snippet": "0 Conference Proceedings %T Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings %A Dasgupta , Shib %A Boratko, Michael %A Mishra ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.acl-long.161/", "content": "0 Conference Proceedings %T Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings %A Dasgupta , Shib %A Boratko, Michael %A Mishra ..."}
+{"idx": 4, "title": "Dhruvesh Patel - ACL Anthology", "date": "", "ddg_snippet": "... Shib Sankar Dasgupta | Andrew McCallum Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/d/dhruvesh-patel/", "content": "... Shib Sankar Dasgupta | Andrew McCallum Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations"}
+{"idx": 5, "title": "Michael Boratko - ACL Anthology", "date": "", "ddg_snippet": "pdf bib abs Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning Xuelu Chen | Michael Boratko | Muhao Chen | Shib Sankar Dasgupta ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/m/michael-boratko/", "content": "pdf bib abs Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning Xuelu Chen | Michael Boratko | Muhao Chen | Shib Sankar Dasgupta ..."}
+{"idx": 6, "title": "Dongxu Zhang - ACL Anthology", "date": "", "ddg_snippet": "pdf bib abs Box-To-Box Transformations for Modeling Joint Hierarchies Shib Sankar Dasgupta | Xiang Lorraine Li | Michael Boratko | Dongxu Zhang ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/d/dongxu-zhang/", "content": "pdf bib abs Box-To-Box Transformations for Modeling Joint Hierarchies Shib Sankar Dasgupta | Xiang Lorraine Li | Michael Boratko | Dongxu Zhang ..."}
+{"idx": 7, "title": "Michael Boratko", "date": "", "ddg_snippet": "Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning Xuelu Chen*, Michael Boratko* , Muhao Chen, Shib Sankar Dasgupta , Xiang Lorraine ...", "subpage_snippet": "", "source": "www.mboratko.com", "link": "https://www.mboratko.com/", "content": "Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning Xuelu Chen*, Michael Boratko* , Muhao Chen, Shib Sankar Dasgupta , Xiang Lorraine ..."}
+{"idx": 8, "title": "Dhruvesh Patel", "date": "", "ddg_snippet": "Dhruvesh Patel , * Shib Sankar Dasgupta , Michael Boratko , Xiang Li , Luke Vilnis , and 1 more author ... Patel, *Dhruvesh and Dasgupta , * Shib ...", "subpage_snippet": "", "source": "dhruveshp.com", "link": "https://dhruveshp.com/", "content": "Dhruvesh Patel , * Shib Sankar Dasgupta , Michael Boratko , Xiang Li , Luke Vilnis , and 1 more author ... Patel, *Dhruvesh and Dasgupta , * Shib ..."}
+{"idx": 9, "title": "Partha Talukdar: Google DeepMind", "date": "", "ddg_snippet": "Acceptance rate: 17%] Dating Documents using Graph Convolution Networks [ Code ] Shikhar Vashishth, Swayambhu Nath Ray, Shib Sankar Dasgupta and ...", "subpage_snippet": "", "source": "parthatalukdar.github.io", "link": "https://parthatalukdar.github.io/", "content": "Acceptance rate: 17%] Dating Documents using Graph Convolution Networks [ Code ] Shikhar Vashishth, Swayambhu Nath Ray, Shib Sankar Dasgupta and ..."}
diff --git a/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering_filetypepdf.jsonl b/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3df7f1a98f178cfa3d2e99810b91fb89553cd11
--- /dev/null
+++ b/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering_filetypepdf.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "Signed Laplacians for Constrained Graph Clustering", "date": "", "ddg_snippet": "H = (V, F, wH) defined on the same vertex set, the constrained clustering problem seeks to find a subset S ⊂ V that minimises the cut ratio between wG(S, V \\ S) and wH(S, V \\ S). In this work, we establish a Cheeger-type inequality that relates the solution of the constrained clus-tering problem to the spectral properties of G and H. To reduce computational complexity, we utilise the signed ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=MHaSq1LlTe", "content": "H = (V, F, wH) defined on the same vertex set, the constrained clustering problem seeks to find a subset S ⊂ V that minimises the cut ratio between wG(S, V \\ S) and wH(S, V \\ S). In this work, we establish a Cheeger-type inequality that relates the solution of the constrained clus-tering problem to the spectral properties of G and H. To reduce computational complexity, we utilise the signed ..."}
+{"idx": 1, "title": "ICML Poster Signed Laplacians for Constrained Graph Clustering", "date": "", "ddg_snippet": "relates the solution of the constrained clustering problem to the spectral properties of $ G$ and $H$. To reduce computational complexity, we utilise the signed Laplacian of $H$, streamlining calculations while maintaining accuracy.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45552", "content": "relates the solution of the constrained clustering problem to the spectral properties of $ G$ and $H$. To reduce computational complexity, we utilise the signed Laplacian of $H$, streamlining calculations while maintaining accuracy."}
+{"idx": 2, "title": "He Sun's homepage", "date": "", "ddg_snippet": "Signed Laplacians for constrained graph clustering . with John Stewart Fabila Carrasco (ICML'25, Spotlight). Conference version.", "subpage_snippet": "", "source": "homepages.inf.ed.ac.uk", "link": "https://homepages.inf.ed.ac.uk/hsun4/research.html", "content": "Signed Laplacians for constrained graph clustering . with John Stewart Fabila Carrasco (ICML'25, Spotlight). Conference version."}
+{"idx": 3, "title": "openreview.net/profile?id=~He_Sun5", "date": "", "ddg_snippet": "MPI for Informatics, Saarland Informatics Campus, Max-Planck Institute (mpi-inf.mpg.de). Constrained Graph Clustering with Signed Laplacians . John Stewart Fabila Carrasco, He Sun.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~He_Sun5", "content": "MPI for Informatics, Saarland Informatics Campus, Max-Planck Institute (mpi-inf.mpg.de). Constrained Graph Clustering with Signed Laplacians . John Stewart Fabila Carrasco, He Sun."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/SimXRD-4M_Appendix_A.3_Category_1_Category_2_year_2024.jsonl b/data/sampled_jsons/SimXRD-4M_Appendix_A.3_Category_1_Category_2_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9cd33ccef866cd8e100b1bc7a90324f0816e998f
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_Appendix_A.3_Category_1_Category_2_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How to Clear Nasal Congestion : 16 Stuffy Nose Remedies - wikiHow", "date": "", "ddg_snippet": "Feb 11, 2025 · When you have a cold or when your allergies are acting up, we know you're looking for any way to get rid of the nasal congestion that comes along with it. The inside of your nose swells up when it's irritated and gives you that stuffy...", "subpage_snippet": "", "source": "www.wikihow.com", "link": "https://www.wikihow.com/Clear-Nasal-Congestion", "content": "Feb 11, 2025 · When you have a cold or when your allergies are acting up, we know you're looking for any way to get rid of the nasal congestion that comes along with it. The inside of your nose swells up when it's irritated and gives you that stuffy..."}
+{"idx": 1, "title": "10 Simple Hacks to Unblock Your Nose Instantly - Goody Feed", "date": "", "ddg_snippet": "Apr 21, 2016 · Wondering how to unblock your nose instantly ? Well, here are 10 simple hacks anyone can use to gt rid of that annoying blocked nose .", "subpage_snippet": "", "source": "goodyfeed.com", "link": "https://goodyfeed.com/10-simple-hacks-relieve-darn-blocked-nose-youve-got/", "content": "Apr 21, 2016 · Wondering how to unblock your nose instantly ? Well, here are 10 simple hacks anyone can use to gt rid of that annoying blocked nose ."}
+{"idx": 2, "title": "Blocked Nose (One or Both Sides) Causes and How to Unblock", "date": "", "ddg_snippet": "Apart from medical treatment and even surgery, there are some simple ways to unblock the nose that may work in certain cases. Why does the nose get blocked ? A blocked nose may also be referred to as nasal congestion or a stuffy nose . This blockage usually refers to the nasal passages and is a common problem.", "subpage_snippet": "", "source": "healthhype.com", "link": "https://healthhype.com/blocked-nose-one-or-both-sides-causes-and-how-to-unblock.html", "content": "Apart from medical treatment and even surgery, there are some simple ways to unblock the nose that may work in certain cases. Why does the nose get blocked ? A blocked nose may also be referred to as nasal congestion or a stuffy nose . This blockage usually refers to the nasal passages and is a common problem."}
+{"idx": 3, "title": "How to Clear a Stuffy Nose and Congestion: Tips for Relief", "date": "", "ddg_snippet": "Apr 16, 2025 · Learn more about the ways you can treat a stuffy nose , or nasal congestion, at home, including tips on decongestants, humidifiers, and steam remedies.", "subpage_snippet": "", "source": "www.healthline.com", "link": "https://www.healthline.com/health/stuffy-nose-relief", "content": "Apr 16, 2025 · Learn more about the ways you can treat a stuffy nose , or nasal congestion, at home, including tips on decongestants, humidifiers, and steam remedies."}
+{"idx": 4, "title": "Nasal Obstruction : Symptoms, Causes & Treatment", "date": "", "ddg_snippet": "Nov 19, 2024 · Nasal obstruction is a common symptom of conditions that fully or partially block your nasal airway. It feels like something is restricting the free flow of air that you need to breathe easily. Common causes include sinus infections and allergies. In children, the most common cause of nasal obstruction is enlarged adenoid glands.", "subpage_snippet": "", "source": "my.clevelandclinic.org", "link": "https://my.clevelandclinic.org/health/symptoms/nasal-obstruction", "content": "Nov 19, 2024 · Nasal obstruction is a common symptom of conditions that fully or partially block your nasal airway. It feels like something is restricting the free flow of air that you need to breathe easily. Common causes include sinus infections and allergies. In children, the most common cause of nasal obstruction is enlarged adenoid glands."}
+{"idx": 5, "title": "8 Different Ways to Clear Your Blocked Sinuses - Verywell Health", "date": "", "ddg_snippet": "May 29, 2025 · Blocked sinuses can be uncomfortable, interrupt sleep, and cause headaches. There are ways to clear your sinuses relatively quickly, but there is no one-size-fits-all approach.", "subpage_snippet": "", "source": "www.verywellhealth.com", "link": "https://www.verywellhealth.com/ways-to-clear-blocked-sinuses-11688373", "content": "May 29, 2025 · Blocked sinuses can be uncomfortable, interrupt sleep, and cause headaches. There are ways to clear your sinuses relatively quickly, but there is no one-size-fits-all approach."}
+{"idx": 6, "title": "How to Get Rid of a Stuffy Nose - WebMD", "date": "", "ddg_snippet": "Dec 16, 2024 · Find relief from a stuffy nose with steam inhalation, saline nasal spray, hydration, and a humidifier. Use decongestants sparingly and keep your head elevated to ease breathing.", "subpage_snippet": "", "source": "www.webmd.com", "link": "https://www.webmd.com/cold-and-flu/5-ways-get-rid-stuffy-nose", "content": "Dec 16, 2024 · Find relief from a stuffy nose with steam inhalation, saline nasal spray, hydration, and a humidifier. Use decongestants sparingly and keep your head elevated to ease breathing."}
+{"idx": 7, "title": "Endoscopic Sinus Surgery for Pain, Drainage, Infections, and ......", "date": "", "ddg_snippet": "Endoscopic sinus surgery is the most common procedure for chronic sinusitis. To start, the surgeon will insert a camera on the end of a thin tool, called an endoscope, into your nose .", "subpage_snippet": "", "source": "m.youtube.com", "link": "https://m.youtube.com/watch?v=kWBNkFpl-xM", "content": "Endoscopic sinus surgery is the most common procedure for chronic sinusitis. To start, the surgeon will insert a camera on the end of a thin tool, called an endoscope, into your nose ."}
+{"idx": 8, "title": "How to get rid of a stuffy nose : 10 possible treatments", "date": "", "ddg_snippet": "Feb 13, 2024 · Having a stuffy nose can be uncomfortable, but home and over-the-counter remedies can often relieve it. Get some tips on managing nasal congestion here.", "subpage_snippet": "", "source": "www.medicalnewstoday.com", "link": "https://www.medicalnewstoday.com/articles/313808", "content": "Feb 13, 2024 · Having a stuffy nose can be uncomfortable, but home and over-the-counter remedies can often relieve it. Get some tips on managing nasal congestion here."}
+{"idx": 9, "title": "Endoscopic Sinus Surgery - Johns Hopkins Medicine", "date": "", "ddg_snippet": "Endoscopic sinus surgery is a procedure used to remove blockages in the sinuses that cause pain, drainage, infections, impaired breathing or loss of smell.", "subpage_snippet": "", "source": "www.hopkinsmedicine.org", "link": "https://www.hopkinsmedicine.org/health/treatment-tests-and-therapies/endoscopic-sinus-surgery", "content": "Endoscopic sinus surgery is a procedure used to remove blockages in the sinuses that cause pain, drainage, infections, impaired breathing or loss of smell."}
diff --git a/data/sampled_jsons/SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Table_1_dataset_size_year_2024.jsonl b/data/sampled_jsons/SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Table_1_dataset_size_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1c304aa617ecb690434ec579c41d57180e61ac89
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Table_1_dataset_size_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and ...", "date": "", "ddg_snippet": "by CAO Bin · Cited by 1 — The paper introduces a benchmark dataset \" SimXRD \" which comprises 4,065,346 simulated powder XRD patterns that represent 119,569 unique crystal structures. This ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mkuB677eMM", "content": "by CAO Bin · Cited by 1 — The paper introduces a benchmark dataset \" SimXRD \" which comprises 4,065,346 simulated powder XRD patterns that represent 119,569 unique crystal structures. This ..."}
+{"idx": 1, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data ...", "date": "", "ddg_snippet": "15 Jun 2024 — We introduce SimXRD , the largest open-source simulation dataset for symmetry identification, including 4,065,346 XRD patterns of 119,569 high- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "15 Jun 2024 — We introduce SimXRD , the largest open-source simulation dataset for symmetry identification, including 4,065,346 XRD patterns of 119,569 high- ..."}
+{"idx": 2, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "SimXRD - 4M : Big Simulated X - ray Diffraction Data and Crystal Symmetry Classification Benchmark · MuPT: A Generative Symbolic Music Pretrained Transformer ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "SimXRD - 4M : Big Simulated X - ray Diffraction Data and Crystal Symmetry Classification Benchmark · MuPT: A Generative Symbolic Music Pretrained Transformer ..."}
+{"idx": 3, "title": "Track: Poster Session 1", "date": "", "ddg_snippet": "Further analysis of common pre-training factors (i.e., model size , dataset size , and data ... SimXRD - 4M : Big Simulated X - ray Diffraction Data and Crystal Symmetry ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31971", "content": "Further analysis of common pre-training factors (i.e., model size , dataset size , and data ... SimXRD - 4M : Big Simulated X - ray Diffraction Data and Crystal Symmetry ..."}
+{"idx": 4, "title": "Science - C.Thang Nguyen", "date": "", "ddg_snippet": "21 May 2025 — The dataset was generated from dispersion-inclusive density functional theory (DFT) relaxation trajectories of over 230,000 randomly generated ...", "subpage_snippet": "", "source": "thangckt.github.io", "link": "https://thangckt.github.io/blog/category/science/", "content": "21 May 2025 — The dataset was generated from dispersion-inclusive density functional theory (DFT) relaxation trajectories of over 230,000 randomly generated ..."}
+{"idx": 5, "title": "CGWGAN: crystal generative framework based on Wyckoff ...", "date": "", "ddg_snippet": "by T Su · 2024 · Cited by 14 — Table 1 lists the seven generated ... SimXRD - 4M : big simulated X - ray diffraction data accelerate the crystalline symmetry classification.", "subpage_snippet": "", "source": "www.oaepublish.com", "link": "https://www.oaepublish.com/articles/jmi.2024.24", "content": "by T Su · 2024 · Cited by 14 — Table 1 lists the seven generated ... SimXRD - 4M : big simulated X - ray diffraction data accelerate the crystalline symmetry classification."}
+{"idx": 6, "title": "Blog - C.Thang Nguyen", "date": "", "ddg_snippet": "20 Jul 2025 — SimXRD, SimXRD - 4M : Big Simulated X - ray Diffraction Data and Crystalline Symmetry Classification Benchmark (ICLR2025) [Paper] [Code]. ECD, ECD ...", "subpage_snippet": "", "source": "thangckt.github.io", "link": "https://thangckt.github.io/blog/", "content": "20 Jul 2025 — SimXRD, SimXRD - 4M : Big Simulated X - ray Diffraction Data and Crystalline Symmetry Classification Benchmark (ICLR2025) [Paper] [Code]. ECD, ECD ..."}
+{"idx": 7, "title": "Minecraft Skins - Official Site", "date": "", "ddg_snippet": "Welcome to The Skindex - the largest collection of community generated minecraft skins . Download, upload or share your creation.", "subpage_snippet": "", "source": "www.minecraftskins.com", "link": "http://www.minecraftskins.com/", "content": "Welcome to The Skindex - the largest collection of community generated minecraft skins . Download, upload or share your creation."}
+{"idx": 8, "title": "Minecraft Skins - The Ultimate MC Skin Catalog | NameMC", "date": "", "ddg_snippet": "Explore millions of unique Minecraft skins shared by players from around the world—ranging from popular trends and iconic characters to completely original designs.", "subpage_snippet": "", "source": "namemc.com", "link": "https://namemc.com/minecraft-skins", "content": "Explore millions of unique Minecraft skins shared by players from around the world—ranging from popular trends and iconic characters to completely original designs."}
+{"idx": 9, "title": "Minecraft Skins | Planet Minecraft Community", "date": "", "ddg_snippet": "Explore the best Minecraft skins ! Browse unique, creative skins , customize your character, and stand out in your game. Download the perfect look today!", "subpage_snippet": "", "source": "www.planetminecraft.com", "link": "https://www.planetminecraft.com/skins/", "content": "Explore the best Minecraft skins ! Browse unique, creative skins , customize your character, and stand out in your game. Download the perfect look today!"}
diff --git a/data/sampled_jsons/SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchmark_full_te.jsonl b/data/sampled_jsons/SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchmark_full_te.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d618e8220266afbb5f89ad9da809bcc3c112e7d
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchmark_full_te.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions."}
+{"idx": 1, "title": "GitHub - Bin-Cao/SimXRD: [ICLR 2025] SimXRD-4M: Big Simulated X-ray ...", "date": "", "ddg_snippet": "Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are influenced by various factors such as the testing environment (instrumentation), light source ( X ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are influenced by various factors such as the testing environment (instrumentation), light source ( X ..."}
+{"idx": 2, "title": "Simxrd-4m: B Simulated X-ray Diffraction D C Symmetry Classification", "date": "", "ddg_snippet": "ABSTRACT Powder X-ray diffraction (XRD) patterns are highly effective for crystal identi-fication and play a pivotal role in materials discovery. Although machine learn-ing (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established bench-marks . To address this, we introduce SimXRD-4M , the largest open-source ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mkuB677eMM", "content": "ABSTRACT Powder X-ray diffraction (XRD) patterns are highly effective for crystal identi-fication and play a pivotal role in materials discovery. Although machine learn-ing (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established bench-marks . To address this, we introduce SimXRD-4M , the largest open-source ..."}
+{"idx": 3, "title": "PDF SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "2: Data analysis reveals that the symmetry labels follow a long-tailed distribution. 3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "2: Data analysis reveals that the symmetry labels follow a long-tailed distribution. 3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance."}
+{"idx": 4, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations. We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.15469", "content": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations. We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD ."}
+{"idx": 5, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381665624_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Accelerate_the_Crystalline_Symmetry_Classification", "content": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations."}
+{"idx": 6, "title": "Advancing Crystal Structure Analysis with SimXRD Dataset", "date": "", "ddg_snippet": "Title: SimXRD-4M : Big Simulated X-ray Diffraction Data Accelerate the Crystalline Symmetry Classification Abstract: Spectroscopic data , particularly diffraction data , contain detailed crystal and microstructure information and thus are crucial for materials discovery.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-advancing-crystal-structure-analysis-with-simxrd-dataset--ak4ex8o", "content": "Title: SimXRD-4M : Big Simulated X-ray Diffraction Data Accelerate the Crystalline Symmetry Classification Abstract: Spectroscopic data , particularly diffraction data , contain detailed crystal and microstructure information and thus are crucial for materials discovery."}
+{"idx": 7, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystalline ...", "date": "", "ddg_snippet": "Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established benchmarks . To address this, we introduce SimXRD , the largest open-source simulated XRD ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/mkuB677eMM@OpenReview", "content": "Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established benchmarks . To address this, we introduce SimXRD , the largest open-source simulated XRD ..."}
+{"idx": 8, "title": "ICLR Poster SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal ...", "date": "", "ddg_snippet": "Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established benchmarks . To address this, we introduce SimXRD , the largest open-source simulated XRD ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28452", "content": "Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established benchmarks . To address this, we introduce SimXRD , the largest open-source simulated XRD ..."}
+{"idx": 9, "title": "Publications - Ruifeng Tan", "date": "", "ddg_snippet": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper, we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ...", "subpage_snippet": "", "source": "ruifeng-tan.github.io", "link": "https://ruifeng-tan.github.io/publications/", "content": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper, we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ..."}
diff --git a/data/sampled_jsons/SimXRD_paper_appendix_A.3_Category_1_Category_2_space_group_classification_physical_difference.jsonl b/data/sampled_jsons/SimXRD_paper_appendix_A.3_Category_1_Category_2_space_group_classification_physical_difference.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cfd925f812cb99d9b57cdb3f9601ed40129c5e0c
--- /dev/null
+++ b/data/sampled_jsons/SimXRD_paper_appendix_A.3_Category_1_Category_2_space_group_classification_physical_difference.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "String theory - Wikipedia", "date": "", "ddg_snippet": "The starting point for string theory is the idea that the point-like particles of particle physics can also be modeled as one -dimensional objects called strings. String theory describes how strings propagate through space and interact with each other.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/String_theory", "content": "The starting point for string theory is the idea that the point-like particles of particle physics can also be modeled as one -dimensional objects called strings. String theory describes how strings propagate through space and interact with each other."}
+{"idx": 1, "title": "SimXRD -4M: Big Simulated X-ray Diffraction Data and Crystal...", "date": "", "ddg_snippet": "In this paper , we introduce SimXRD , the largest open-source XRD pattern dataset for symmetry identification. Data analysis reveals that the symmetry labels follow a long-tailed distribution. Space Group Classification . Metric. Category 1 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "In this paper , we introduce SimXRD , the largest open-source XRD pattern dataset for symmetry identification. Data analysis reveals that the symmetry labels follow a long-tailed distribution. Space Group Classification . Metric. Category 1 ."}
+{"idx": 2, "title": "(PDF) SimXRD -4M: Big Simulated X-ray Diffraction Data Accelerate...", "date": "", "ddg_snippet": "across various space groups (uniformly divided into 5 categories according to the frequenc y) and (B).T o further demonstrate the practical utility and applicability of SimXRD , we explore its use in two . critical tasks: crystal system classification and space group classification .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381665624_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Accelerate_the_Crystalline_Symmetry_Classification", "content": "across various space groups (uniformly divided into 5 categories according to the frequenc y) and (B).T o further demonstrate the practical utility and applicability of SimXRD , we explore its use in two . critical tasks: crystal system classification and space group classification ."}
+{"idx": 3, "title": "Advancing Crystal Structure Analysis with SimXRD Dataset", "date": "", "ddg_snippet": "# Physics # Materials Science. Advancing Crystal Structure Analysis with SimXRD Dataset. SimXRD offers millions of simulated XRD patterns for improved crystal classification .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-advancing-crystal-structure-analysis-with-simxrd-dataset--ak4ex8o", "content": "# Physics # Materials Science. Advancing Crystal Structure Analysis with SimXRD Dataset. SimXRD offers millions of simulated XRD patterns for improved crystal classification ."}
+{"idx": 4, "title": "GitHub - Bin-Cao/ SimXRD : [ICLR 2025] SimXRD -4M: Big Simulated...", "date": "", "ddg_snippet": "Open Source: SimXRD -4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups , each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Open Source: SimXRD -4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups , each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials."}
+{"idx": 5, "title": "machinelearningmastery.com/types-of- classification -in-machine...", "date": "", "ddg_snippet": "It involves predicting the class or category of a given example of input data.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/types-of-classification-in-machine-learning/", "content": "It involves predicting the class or category of a given example of input data."}
+{"idx": 6, "title": "What is Tabular Classification ? - Hugging Face", "date": "", "ddg_snippet": "Tabular classification is the task of classifying a target category (a group ) based on set of attributes.Binary variables: Variables that can take two values, like yes or no, open or closed. The task of predicting binary variables is called binary classification .", "subpage_snippet": "", "source": "huggingface.1319lm.top", "link": "https://huggingface.1319lm.top/tasks/tabular-classification", "content": "Tabular classification is the task of classifying a target category (a group ) based on set of attributes.Binary variables: Variables that can take two values, like yes or no, open or closed. The task of predicting binary variables is called binary classification ."}
+{"idx": 7, "title": "Schur Functors and Categorified Plethysm | The n- Category Café", "date": "", "ddg_snippet": "The n- Category Café. A group blog on math, physics and philosophy.. Our first main result, Theorem 3 . 1 , is that the category . Schur\\mathsf{Schur}. of abstract Schur functors is equivalent to the category .", "subpage_snippet": "", "source": "golem.ph.utexas.edu", "link": "https://golem.ph.utexas.edu/category/2021/06/schur_functors_and_categorifie.html", "content": "The n- Category Café. A group blog on math, physics and philosophy.. Our first main result, Theorem 3 . 1 , is that the category . Schur\\mathsf{Schur}. of abstract Schur functors is equivalent to the category ."}
+{"idx": 8, "title": "Intel Blast: Call of Duty: Black Ops 7 Multiplayer Deep Dive and New...", "date": "", "ddg_snippet": "Equip three Perks of the same category to activate the Enforcer (red), Recon (blue), or Strategist (green) Combat Specialty bonus. New to Black Ops 7 , players can now activate Hybrid Combat Specialties when pairing Perks of different categories .", "subpage_snippet": "", "source": "www.callofduty.com", "link": "https://www.callofduty.com/blog/2025/09/call-of-duty-black-ops-7-multiplayer-deep-dive", "content": "Equip three Perks of the same category to activate the Enforcer (red), Recon (blue), or Strategist (green) Combat Specialty bonus. New to Black Ops 7 , players can now activate Hybrid Combat Specialties when pairing Perks of different categories ."}
+{"idx": 9, "title": "Structure of the modulation in thiourea. II . Structure refinement", "date": "", "ddg_snippet": "The second harmonic (Fig. 4) rotates molecules 1 and 4 (respectively 2 and 3 ) in the same direction around b. However, the phase difference q. b between the two molecules of a pair leads to a net polarisation, albeit a reduced one compared to the preceding mechanism", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/jpa-00210718v1/document", "content": "The second harmonic (Fig. 4) rotates molecules 1 and 4 (respectively 2 and 3 ) in the same direction around b. However, the phase difference q. b between the two molecules of a pair leads to a net polarisation, albeit a reduced one compared to the preceding mechanism"}
diff --git a/data/sampled_jsons/Simone_Parisi_Alireza_Kazemipour_Michael_Bowling_Beyond_Optimism_Exploration_Partially_Observable_Re_year_2024.jsonl b/data/sampled_jsons/Simone_Parisi_Alireza_Kazemipour_Michael_Bowling_Beyond_Optimism_Exploration_Partially_Observable_Re_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1d19acba92f8118d1abf85b6a9f8d75b782e6328
--- /dev/null
+++ b/data/sampled_jsons/Simone_Parisi_Alireza_Kazemipour_Michael_Bowling_Beyond_Optimism_Exploration_Partially_Observable_Re_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.13909", "content": "Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits ..."}
+{"idx": 1, "title": "Simone Parisi", "date": "", "ddg_snippet": "Selected Papers Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi , Alireza Kazemipour , Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi , Montaser Mohammedalamen, Alireza Kazemipour , Matthew E. Taylor, Michael Bowling International Conference on Autonomous Agents and Multiagent Systems (AAMAS ...", "subpage_snippet": "", "source": "sparisi.github.io", "link": "https://sparisi.github.io/", "content": "Selected Papers Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi , Alireza Kazemipour , Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi , Montaser Mohammedalamen, Alireza Kazemipour , Matthew E. Taylor, Michael Bowling International Conference on Autonomous Agents and Multiagent Systems (AAMAS ..."}
+{"idx": 2, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Authors Simone Parisi , Alireza Kazemipour , Michael Bowling Abstract Exploration in reinforcement learning (RL) remains an open challenge.RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/784fd5a46dfe303e5b51c8621b84cf3f-Abstract-Conference.html", "content": "Authors Simone Parisi , Alireza Kazemipour , Michael Bowling Abstract Exploration in reinforcement learning (RL) remains an open challenge.RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards ..."}
+{"idx": 3, "title": "Beyond optimism | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "research-article Beyond optimism : exploration with partially observable rewards AUTHORs: Simone Parisi , Alireza Kazemipour , Michael Bowling", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740005", "content": "research-article Beyond optimism : exploration with partially observable rewards AUTHORs: Simone Parisi , Alireza Kazemipour , Michael Bowling"}
+{"idx": 4, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable ."}
+{"idx": 5, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=k6ZHvF1vkg", "content": "In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable ."}
+{"idx": 6, "title": "Beyond Optimism: Exploration With Partially Observable Rewards - arXiv.org", "date": "", "ddg_snippet": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi University of Alberta; Amii parisi@ualberta.ca & Alireza Kazemipour University of Alberta kazemipo@ualberta.ca & Michael Bowling University of Alberta; Amii mbowling@ualberta.ca", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v1", "content": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi University of Alberta; Amii parisi@ualberta.ca & Alireza Kazemipour University of Alberta kazemipo@ualberta.ca & Michael Bowling University of Alberta; Amii mbowling@ualberta.ca"}
+{"idx": 7, "title": "Research - Simone Parisi", "date": "", "ddg_snippet": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi , Alireza Kazemipour , Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi , Montaser Mohammedalamen, Alireza Kazemipour , Matthew E. Taylor, Michael Bowling", "subpage_snippet": "", "source": "sparisi.github.io", "link": "https://sparisi.github.io/assets/research.html", "content": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi , Alireza Kazemipour , Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi , Montaser Mohammedalamen, Alireza Kazemipour , Matthew E. Taylor, Michael Bowling"}
+{"idx": 8, "title": "PDF Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi Alireza Kazemipour Michael Bowling 38th Conference on Neural Information Processing Systems (NeurIPS 2024)", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/93919.pdf", "content": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi Alireza Kazemipour Michael Bowling 38th Conference on Neural Information Processing Systems (NeurIPS 2024)"}
+{"idx": 9, "title": "Papers by Simone Parisi - aimodels.fyi", "date": "", "ddg_snippet": "This paper explores exploration strategies in reinforcement learning (RL) problems with partially observable rewards . It introduces a new approach called \" Optimism Beyond Optimism \" (OBO) that aims to improve exploration in these challenging scenarios. The paper analyzes the theoretical guarantees of OBO and compares its performance to other stateoftheart exploration methods. Plain English ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/authors/arxiv/Simone+Parisi", "content": "This paper explores exploration strategies in reinforcement learning (RL) problems with partially observable rewards . It introduces a new approach called \" Optimism Beyond Optimism \" (OBO) that aims to improve exploration in these challenging scenarios. The paper analyzes the theoretical guarantees of OBO and compares its performance to other stateoftheart exploration methods. Plain English ..."}
diff --git a/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_SIIHPC_Algorithm_2_convergence_condition.jsonl b/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_SIIHPC_Algorithm_2_convergence_condition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3e91a7b09b3f20beae8b471c480bf5ea7d5a3b9
--- /dev/null
+++ b/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_SIIHPC_Algorithm_2_convergence_condition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "M Ulti - view", "date": "", "ddg_snippet": "Projective incomplete multi - view clustering .To demonstrate the effectiveness of our SIIHPC in clustering incomplete multi - view data, we conduct experiments under missing ratio = 30%, 50% and 70% respectively.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KijslFbfOL", "content": "Projective incomplete multi - view clustering .To demonstrate the effectiveness of our SIIHPC in clustering incomplete multi - view data, we conduct experiments under missing ratio = 30%, 50% and 70% respectively."}
+{"idx": 1, "title": "Simple yet Effective Incomplete Multi-view Clustering", "date": "", "ddg_snippet": "by S Yu · Cited by 1 — We present a simple yet effective IMVC approach, SIIHPC , in this work. It firstly transforms partial bipartition learning into original sample form.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KijslFbfOL", "content": "by S Yu · Cited by 1 — We present a simple yet effective IMVC approach, SIIHPC , in this work. It firstly transforms partial bipartition learning into original sample form."}
+{"idx": 2, "title": "ICLR 2025 - Bird's-eye views of conference proceedings", "date": "", "ddg_snippet": "Simple yet Effective Incomplete Multi-view Clustering : Similarity-level ... To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC, ...", "subpage_snippet": "", "source": "www.confviews.com", "link": "https://www.confviews.com/iclr2025/", "content": "Simple yet Effective Incomplete Multi-view Clustering : Similarity-level ... To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC, ..."}
+{"idx": 3, "title": "ICLR Poster Simple yet Effective Incomplete Multi - view Clustering ...", "date": "", "ddg_snippet": "To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC , in this work.Finally, the clustering results are obtained by implementing spectral grouping action on the eigenvectors of stacked multi -scale consensus similarity.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/30038", "content": "To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC , in this work.Finally, the clustering results are obtained by implementing spectral grouping action on the eigenvectors of stacked multi -scale consensus similarity."}
+{"idx": 4, "title": "Tensorized topological graph learning for generalized incomplete ...", "date": "", "ddg_snippet": "Clustering on such multi - view datasets is called incomplete multi - view clustering (IMC) and quite challenging.This paper proposes two simple yet effective algorithms to address this issue.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372352259_Tensorized_topological_graph_learning_for_generalized_incomplete_multi-view_clustering?_fam=1", "content": "Clustering on such multi - view datasets is called incomplete multi - view clustering (IMC) and quite challenging.This paper proposes two simple yet effective algorithms to address this issue."}
+{"idx": 5, "title": "Efficient and Effective Incomplete Multi - view Clustering", "date": "", "ddg_snippet": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specied incomplete views to improve cluster -ing performance.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/4350/4228", "content": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specied incomplete views to improve cluster -ing performance."}
+{"idx": 6, "title": "GitHub - Jeaninezpp/Awesome- Incomplete - multi - view - clustering ...", "date": "", "ddg_snippet": "Collections for incomplete multi - view clustering methods (papers and codes).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Jeaninezpp/Awesome-Incomplete-multi-view-clustering", "content": "Collections for incomplete multi - view clustering methods (papers and codes)."}
+{"idx": 7, "title": "Incomplete Multi - View Clustering With Reconstructed Views", "date": "", "ddg_snippet": "This paper proposes Incomplete Multi - view Clustering with Reconstructed Views (IMCRV), which utilizes the incomplete examples sufficiently.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tk/5555/01/09536450/1wRDve2jPpu", "content": "This paper proposes Incomplete Multi - view Clustering with Reconstructed Views (IMCRV), which utilizes the incomplete examples sufficiently."}
+{"idx": 8, "title": "Confidence Graph Learning for Incomplete Multi - View Clustering", "date": "", "ddg_snippet": "To achieve incomplete multi - view clustering , this paper proposes an effective confidence graph learning method, namely CGL-IMVC.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3653804.3654722", "content": "To achieve incomplete multi - view clustering , this paper proposes an effective confidence graph learning method, namely CGL-IMVC."}
+{"idx": 9, "title": "Complementary incomplete weighted concept factorization methods...", "date": "", "ddg_snippet": "Still, current incomplete multi - view clustering methods overlooked a number of important factors, such as providing a consensus representation across the kernel space, dealing with over-fitting issue from different views...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10115-024-02197-1", "content": "Still, current incomplete multi - view clustering methods overlooked a number of important factors, such as providing a consensus representation across the kernel space, dealing with over-fitting issue from different views..."}
diff --git a/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_Similarity-level_Imputation_ablation_study_Tab_year_2023.jsonl b/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_Similarity-level_Imputation_ablation_study_Tab_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..938e07c671be71b675f0d78336e57eb3b34b2cc1
--- /dev/null
+++ b/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_Similarity-level_Imputation_ablation_study_Tab_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Imputation -free and Alignment-free: Incomplete Multi - view ...", "date": "", "ddg_snippet": "Multi- level feature learning for contrastive multi - view clustering . In Proceedings of the IEEE/CVF Con-ference on Computer Vision and Pattern Recognition, pages 16051–16060, 2022. Imputation - and alignment-free study on YoutubeFace 10, NoisyMNIST, Yale and NUSWIDEOBJ10.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.11182", "content": "Multi- level feature learning for contrastive multi - view clustering . In Proceedings of the IEEE/CVF Con-ference on Computer Vision and Pattern Recognition, pages 16051–16060, 2022. Imputation - and alignment-free study on YoutubeFace 10, NoisyMNIST, Yale and NUSWIDEOBJ10."}
+{"idx": 1, "title": "Imputation -free and Alignment-free: Incomplete Multi - view ...", "date": "", "ddg_snippet": "In incomplete multi - view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsis-tencies across views. Table 3. Ablation study on Caltech-5V and ALOI-100. denotes FreeCSL with the component and the best results are ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_Imputation-free_and_Alignment-free_Incomplete_Multi-view_Clustering_Driven_by_Consensus_Semantic_CVPR_2025_paper.pdf", "content": "In incomplete multi - view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsis-tencies across views. Table 3. Ablation study on Caltech-5V and ALOI-100. denotes FreeCSL with the component and the best results are ..."}
+{"idx": 2, "title": "Simple yet Effective Incomplete Multi - view Clustering :...", "date": "", "ddg_snippet": "Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KijslFbfOL", "content": "Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity ."}
+{"idx": 3, "title": "(PDF) Fast Continual Multi - View Clustering with Incomplete Views", "date": "", "ddg_snippet": "View Clustering with Incomplete Views (FCMVC- IV ) to address. it. multiple views into corresponding undirected graphs based on. their pairwise similarities , then perform spectral clustering and. graph fusion simultaneously. In MKC [37], [38], one learns the.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371310872_Fast_Continual_Multi-View_Clustering_with_Incomplete_Views", "content": "View Clustering with Incomplete Views (FCMVC- IV ) to address. it. multiple views into corresponding undirected graphs based on. their pairwise similarities , then perform spectral clustering and. graph fusion simultaneously. In MKC [37], [38], one learns the."}
+{"idx": 4, "title": "Late Fusion Incomplete Multi - view Clustering - PMC", "date": "", "ddg_snippet": "Incomplete multi - view clustering optimally integrates a group of pre-specified incomplete views to improve clustering performance. Among various excellent solutions, multiple kernel k-means with incomplete kernels forms a benchmark, which redefines ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6494716/", "content": "Incomplete multi - view clustering optimally integrates a group of pre-specified incomplete views to improve clustering performance. Among various excellent solutions, multiple kernel k-means with incomplete kernels forms a benchmark, which redefines ..."}
+{"idx": 5, "title": "W ITH the rapid development of multimedia techniques", "date": "", "ddg_snippet": "NMF-Based Incomplete Multi - View Clustering . Fast Continual Multi - View Clustering with Incomplete Views. The Proposed Formulation.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/fast-continual-multi-view-clustering-with-incomplete-views-1fu9vnre.pdf", "content": "NMF-Based Incomplete Multi - View Clustering . Fast Continual Multi - View Clustering with Incomplete Views. The Proposed Formulation."}
+{"idx": 6, "title": "GitHub - dugzzuli/A-Survey-of- Multi - view - Clustering -Approaches...", "date": "", "ddg_snippet": "Imputation -based IMVC Incomplete Multi - view clustering . Incomplete multi - view clustering with cosine similarity [paper|[code]].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dugzzuli/A-Survey-of-Multi-view-Clustering-Approaches", "content": "Imputation -based IMVC Incomplete Multi - view clustering . Incomplete multi - view clustering with cosine similarity [paper|[code]]."}
+{"idx": 7, "title": "Efficient and Effective Incomplete Multi - View Clustering", "date": "", "ddg_snippet": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance.In this paper, we propose an Efficient and Effective Incomplete Multi - view Clustering (EE-IMVC) algorithm to address these issues.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/4350", "content": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance.In this paper, we propose an Efficient and Effective Incomplete Multi - view Clustering (EE-IMVC) algorithm to address these issues."}
+{"idx": 8, "title": "Semantic Invariant Multi - view Clustering with", "date": "", "ddg_snippet": "Index Terms— Multi - view Representation Learning, Multi - view Clustering , Incomplete Information, Semantic Invariance.", "subpage_snippet": "", "source": "pengxi.me", "link": "https://pengxi.me/wp-content/uploads/2025/02/Semantic-Invariant-Multi-view-Clustering-with-Fully-Incomplete-Information.pdf", "content": "Index Terms— Multi - view Representation Learning, Multi - view Clustering , Incomplete Information, Semantic Invariance."}
+{"idx": 9, "title": "Data Completion-Guided Unified Graph Learning for Incomplete ...", "date": "", "ddg_snippet": "2016. Incomplete multi - view clustering . In Proceedings of the Intelligent Information Processing VIII: 9th IFIP TC 12 International Conference (IIP ’16).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3664290", "content": "2016. Incomplete multi - view clustering . In Proceedings of the Intelligent Information Processing VIII: 9th IFIP TC 12 International Conference (IIP ’16)."}
diff --git a/data/sampled_jsons/Sinkhorn_1964_paper_author_year_1964.jsonl b/data/sampled_jsons/Sinkhorn_1964_paper_author_year_1964.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..28c118d79d148fe5ecc9ad5fe2eed37a9a4f53fc
--- /dev/null
+++ b/data/sampled_jsons/Sinkhorn_1964_paper_author_year_1964.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Overrelaxed Sinkhorn -Knopp Algorithm for Regularized Optimal...", "date": "", "ddg_snippet": "In this paper , we consider an overrelaxation scheme designed to accelerate the Sinkhorn –Knopp algorithm.[ Sinkhorn , 1964 ] Sinkhorn , R. ( 1964 ). A relationship between arbitrary positive matrices and dou-bly stochastic matrices.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-01629985/document", "content": "In this paper , we consider an overrelaxation scheme designed to accelerate the Sinkhorn –Knopp algorithm.[ Sinkhorn , 1964 ] Sinkhorn , R. ( 1964 ). A relationship between arbitrary positive matrices and dou-bly stochastic matrices."}
+{"idx": 1, "title": "Permanents, α -permanents and Sinkhorn balancing", "date": "", "ddg_snippet": "The notion of Sinkhorn balancing for non-negative square matrices was introduced in Sinkhorn ( 1964 ) and some of the theory was developed in Knopp and Sinkhorn (1967).", "subpage_snippet": "", "source": "tsapps.nist.gov", "link": "https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=913293", "content": "The notion of Sinkhorn balancing for non-negative square matrices was introduced in Sinkhorn ( 1964 ) and some of the theory was developed in Knopp and Sinkhorn (1967)."}
+{"idx": 2, "title": "Online Sinkhorn : Optimal Transport", "date": "", "ddg_snippet": "Online Sinkhorn : Optimal Transport distances from sample streams. Arthur Mensch PSL University CNRS, ENS, DMA Paris, France.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/123650dd0560587918b3d771cf0c0171-Paper.pdf", "content": "Online Sinkhorn : Optimal Transport distances from sample streams. Arthur Mensch PSL University CNRS, ENS, DMA Paris, France."}
+{"idx": 3, "title": "Sinkhorn AutoEncoders", "date": "", "ddg_snippet": "The sinkhorn autoencoder. Entropy regularized optimal transport.The Sinkhorn ( 1964 ) algorithm gained interest after Cuturi (2013) showed its application for fast compu-tation of Wasserstein distances.", "subpage_snippet": "", "source": "auai.org", "link": "https://auai.org/uai2019/proceedings/papers/253.pdf", "content": "The sinkhorn autoencoder. Entropy regularized optimal transport.The Sinkhorn ( 1964 ) algorithm gained interest after Cuturi (2013) showed its application for fast compu-tation of Wasserstein distances."}
+{"idx": 4, "title": "The entries of the sinkhorn", "date": "", "ddg_snippet": "In a 1964 paper , Sinkhorn [20] considered the following iterative scaling process.Decades before Sinkhorn ’s paper , the iterative scaling process was introduced by Kruithof [14] in the context of predicting telephone traffic.", "subpage_snippet": "", "source": "ericrowland.github.io", "link": "https://ericrowland.github.io/papers/The_entries_of_the_Sinkhorn_limit_of_an_m_by_n_matrix.pdf", "content": "In a 1964 paper , Sinkhorn [20] considered the following iterative scaling process.Decades before Sinkhorn ’s paper , the iterative scaling process was introduced by Kruithof [14] in the context of predicting telephone traffic."}
+{"idx": 5, "title": "S inkhorn n etworks", "date": "", "ddg_snippet": "vector of ones. Sinkhorn ( 1964 ) proved that S(X) must belong to the Birkhoff polytope, the set of doubly stochastic matrices, that we denote BN 2. Building on our analogy with categories, notice that choosing a category can always be cast as.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1802.08665", "content": "vector of ones. Sinkhorn ( 1964 ) proved that S(X) must belong to the Birkhoff polytope, the set of doubly stochastic matrices, that we denote BN 2. Building on our analogy with categories, notice that choosing a category can always be cast as."}
+{"idx": 6, "title": "S inkhorn a uto e ncoders", "date": "", "ddg_snippet": "NCODERS. Anonymous authors Paper under double-blind review.The Sinkhorn ( 1964 ) algorithm gained interest after Cuturi (2013) showed its application for fast computation of Wasserstein distances.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BygNqoR9tm", "content": "NCODERS. Anonymous authors Paper under double-blind review.The Sinkhorn ( 1964 ) algorithm gained interest after Cuturi (2013) showed its application for fast computation of Wasserstein distances."}
+{"idx": 7, "title": "Tensor Balancing on Statistical Manifold", "date": "", "ddg_snippet": "We evaluate the efciency of our algorithm compared to the two prominent balancing methods, the standard Sinkhorn -Knopp algorithm ( Sinkhorn , 1964 ) and the state-of-the-art. Number of iterations. Tensor Balancing on Statistical Manifold.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v70/sugiyama17a/sugiyama17a.pdf", "content": "We evaluate the efciency of our algorithm compared to the two prominent balancing methods, the standard Sinkhorn -Knopp algorithm ( Sinkhorn , 1964 ) and the state-of-the-art. Number of iterations. Tensor Balancing on Statistical Manifold."}
+{"idx": 8, "title": "Direct Measure Matching for Crowd Counting", "date": "", "ddg_snippet": "[ Sinkhorn , 1964 ] Richard Sinkhorn . A relationship between arbitrary positive matrices and doubly stochastic matrices. The annals of mathematical statistics, 1964 .", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2021/0116.pdf", "content": "[ Sinkhorn , 1964 ] Richard Sinkhorn . A relationship between arbitrary positive matrices and doubly stochastic matrices. The annals of mathematical statistics, 1964 ."}
+{"idx": 9, "title": "Permutation-Invariant Consensus over Crowdsourced Labels", "date": "", "ddg_snippet": "In this paper , we present a crowdsourcing consensus al-gorithm called PICA (Permutation-Invariant Crowdsourcing Aggregation), which is based on doubly-stochastic matri-ces (DSMs) and the Sinkhorn -Knopp algorithm (Adams and Zemel 2011; Sinkhorn 1964 ...", "subpage_snippet": "", "source": "users.wpi.edu", "link": "https://users.wpi.edu/~jrwhitehill/GiancolaPaffenrothWhitehill2018.pdf", "content": "In this paper , we present a crowdsourcing consensus al-gorithm called PICA (Permutation-Invariant Crowdsourcing Aggregation), which is based on doubly-stochastic matri-ces (DSMs) and the Sinkhorn -Knopp algorithm (Adams and Zemel 2011; Sinkhorn 1964 ..."}
diff --git a/data/sampled_jsons/Sinkhorn_algorithm_coordinate_descent_Luo_Tseng_generalization.jsonl b/data/sampled_jsons/Sinkhorn_algorithm_coordinate_descent_Luo_Tseng_generalization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..56c130648c216d1b5b7bc47205178eb157810b89
--- /dev/null
+++ b/data/sampled_jsons/Sinkhorn_algorithm_coordinate_descent_Luo_Tseng_generalization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear Convergence of Sinkhorn's Algorithm for Generalized ...", "date": "", "ddg_snippet": "Luo, Z.-Q. and Tseng, P. On the convergence of the coordi- nate descent method for convex differentiable minimiza- tion. Journal of Optimization Theory and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/a644c39247d5073d97fae0bbd2c5b921800516ea.pdf", "content": "Luo, Z.-Q. and Tseng, P. On the convergence of the coordi- nate descent method for convex differentiable minimiza- tion. Journal of Optimization Theory and ..."}
+{"idx": 1, "title": "On the Convergence of Block Coordinate Descent Type ...", "date": "", "ddg_snippet": "by A Beck · 2013 · Cited by 769 — The basic idea of this algorithm is to incorporate block coordinate decomposition and an incremental block averaging scheme into the classic (stochastic) mirror ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/120887679", "content": "by A Beck · 2013 · Cited by 769 — The basic idea of this algorithm is to incorporate block coordinate decomposition and an incremental block averaging scheme into the classic (stochastic) mirror ..."}
+{"idx": 2, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "16 Jul 2025 — We establish Kantorovich duality and linear convergence of Sinkhorn's algorithm for the generalized SSB problem under mild conditions. Our ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46671", "content": "16 Jul 2025 — We establish Kantorovich duality and linear convergence of Sinkhorn's algorithm for the generalized SSB problem under mild conditions. Our ..."}
+{"idx": 3, "title": "On Sinkhorn's Algorithm and Choice Modeling", "date": "", "ddg_snippet": "This paper is important because it established the global linear convergence of a broad class of block coordinate descent methods, which includes Sinkhorn's ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2310.00260v2", "content": "This paper is important because it established the global linear convergence of a broad class of block coordinate descent methods, which includes Sinkhorn's ..."}
+{"idx": 4, "title": "On the Linear Convergence of the Multimarginal Sinkhorn ...", "date": "", "ddg_snippet": "by G Carlier · 2022 · Cited by 74 — The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1137/21M1410634", "content": "by G Carlier · 2022 · Cited by 74 — The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal ..."}
+{"idx": 5, "title": "Riemannian Coordinate Descent Algorithms on Matrix Manifolds", "date": "", "ddg_snippet": "by A Han · Cited by 5 — In this work, we provide a general framework for developing computationally efficient coordinate descent (CD) algorithms on matrix manifolds that allows updat-.", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/han24c/han24c.pdf", "content": "by A Han · Cited by 5 — In this work, we provide a general framework for developing computationally efficient coordinate descent (CD) algorithms on matrix manifolds that allows updat-."}
+{"idx": 6, "title": "informs", "date": "", "ddg_snippet": "by Z Qu · 2023 · Cited by 6 — Connecting Sinkhorn' s algorithm to dual coordinate descent for entropy optimization, Luo and Tseng (1992) show that the dual optimality gap, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.00260", "content": "by Z Qu · 2023 · Cited by 6 — Connecting Sinkhorn' s algorithm to dual coordinate descent for entropy optimization, Luo and Tseng (1992) show that the dual optimality gap, ..."}
+{"idx": 7, "title": "1 Introduction", "date": "", "ddg_snippet": "Connecting Sinkhorn's algorithm to dual coordinate descent for entropy optimization, Luo and Tseng (1992) show that the dual optimality gap, defined in Equation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00260v2", "content": "Connecting Sinkhorn's algorithm to dual coordinate descent for entropy optimization, Luo and Tseng (1992) show that the dual optimality gap, defined in Equation ..."}
+{"idx": 8, "title": "Scalable Computation of Dynamic Flow Problems via ...", "date": "", "ddg_snippet": "by I Haasler · 2024 · Cited by 21 — As a result, global convergence of the Sinkhorn scheme (14) is guaranteed (Bauschke and Lewis [5], Luo and Tseng [47], Tseng [58]). The ...", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/10.1287/moor.2021.0148", "content": "by I Haasler · 2024 · Cited by 21 — As a result, global convergence of the Sinkhorn scheme (14) is guaranteed (Bauschke and Lewis [5], Luo and Tseng [47], Tseng [58]). The ..."}
+{"idx": 9, "title": "Efficient Computations of Multi-Species Mean Field Games ...", "date": "", "ddg_snippet": "by A Ringh · 2021 · Cited by 3 — Luo and P. Tseng . On the convergence of the coordinate descent method for convex differentiable minimization. Journal of. Optimization Theory and ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10316024", "content": "by A Ringh · 2021 · Cited by 3 — Luo and P. Tseng . On the convergence of the coordinate descent method for convex differentiable minimization. Journal of. Optimization Theory and ..."}
diff --git a/data/sampled_jsons/Sitan_Chen_github_blink_of_an_eye.jsonl b/data/sampled_jsons/Sitan_Chen_github_blink_of_an_eye.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..161f1de3bad121901e562b4b8b6961d5ef782982
--- /dev/null
+++ b/data/sampled_jsons/Sitan_Chen_github_blink_of_an_eye.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Katy Perry - Wikipedia", "date": "", "ddg_snippet": "Katheryn Elizabeth Hudson (born October 25, 1984), known professionally as Katy Perry , is an American singer, songwriter, and television personality. She is one of the best-selling music artists in history, having sold over 151 million records worldwide. Perry is known for her influence on pop music and her camp style, being dubbed the \"Queen of Camp\" by Vogue and Rolling Stone. The world's ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Katy_Perry", "content": "Katheryn Elizabeth Hudson (born October 25, 1984), known professionally as Katy Perry , is an American singer, songwriter, and television personality. She is one of the best-selling music artists in history, having sold over 151 million records worldwide. Perry is known for her influence on pop music and her camp style, being dubbed the \"Queen of Camp\" by Vogue and Rolling Stone. The world's ..."}
+{"idx": 1, "title": "Katy Perry | Official Site", "date": "", "ddg_snippet": "5 days ago · The official Katy Perry website.12/07/2025 Abu Dhabi Grand Prix Abu Dhabi BUY", "subpage_snippet": "", "source": "www.katyperry.com", "link": "https://www.katyperry.com/", "content": "5 days ago · The official Katy Perry website.12/07/2025 Abu Dhabi Grand Prix Abu Dhabi BUY"}
+{"idx": 2, "title": "Katy Perry | Songs, Husband, Space, Age, & Facts | Britannica", "date": "", "ddg_snippet": "Aug 26, 2025 · Katy Perry is an American pop singer who gained fame for a string of anthemic and often sexually suggestive hit songs, as well as for a playfully cartoonish sense of style. Her popular singles include ‘I Kissed a Girl,’ ‘Last Friday Night (T.G.I.F.),’ ‘Roar,’ and ‘Rise.’", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/biography/Katy-Perry", "content": "Aug 26, 2025 · Katy Perry is an American pop singer who gained fame for a string of anthemic and often sexually suggestive hit songs, as well as for a playfully cartoonish sense of style. Her popular singles include ‘I Kissed a Girl,’ ‘Last Friday Night (T.G.I.F.),’ ‘Roar,’ and ‘Rise.’"}
+{"idx": 3, "title": "Katy Perry Says She's 'Continuing to Move Forward' in Letter to...", "date": "", "ddg_snippet": "1 day ago · Katy Perry is reflecting on her past year. In a letter to her fans posted to Instagram on Monday, Sept. 22, Perry , 40, got personal while marking the anniversary of her 2024 album 143.", "subpage_snippet": "", "source": "people.com", "link": "https://people.com/katy-perry-reflects-on-past-year-in-letter-to-her-fans-11815083", "content": "1 day ago · Katy Perry is reflecting on her past year. In a letter to her fans posted to Instagram on Monday, Sept. 22, Perry , 40, got personal while marking the anniversary of her 2024 album 143."}
+{"idx": 4, "title": "Katy Perry says she's done \"forcing\" her career after ... - NME", "date": "", "ddg_snippet": "13 hours ago · Katy Perry has looked back at her \"rollercoaster year\", and said that she is done “forcing” her career in a new post.", "subpage_snippet": "", "source": "www.nme.com", "link": "https://www.nme.com/news/music/katy-perry-says-shes-done-forcing-her-career-after-rollercoaster-year-of-143-3894764", "content": "13 hours ago · Katy Perry has looked back at her \"rollercoaster year\", and said that she is done “forcing” her career in a new post."}
+{"idx": 5, "title": "Katy Perry on Rollercoaster Year After Orlando Bloom Break Up", "date": "", "ddg_snippet": "1 day ago · Katy Perry marked the anniversary of her album 143 by celebrating how the milestone has inspired her to let go, months after ending her engagement to Orlando Bloom.", "subpage_snippet": "", "source": "www.eonline.com", "link": "https://www.eonline.com/news/1422900/katy-perry-on-rollercoaster-year-after-orlando-bloom-break-up", "content": "1 day ago · Katy Perry marked the anniversary of her album 143 by celebrating how the milestone has inspired her to let go, months after ending her engagement to Orlando Bloom."}
+{"idx": 6, "title": "Katy Perry Tells Fans She's ‘Continuing to Move Forward’", "date": "", "ddg_snippet": "1 day ago · Katy Perry is marking the one-year anniversary of her album 143. The singer, 40, took to Instagram on Monday, September 22, to share several behind-the-scenes photos and videos, as well as a ...", "subpage_snippet": "", "source": "www.usmagazine.com", "link": "https://www.usmagazine.com/entertainment/news/katy-perry-tells-fans-shes-continuing-to-move-forward/", "content": "1 day ago · Katy Perry is marking the one-year anniversary of her album 143. The singer, 40, took to Instagram on Monday, September 22, to share several behind-the-scenes photos and videos, as well as a ..."}
+{"idx": 7, "title": "Katy Perry - YouTube", "date": "", "ddg_snippet": "Katy Perry - I'M HIS, HE'S MINE ft. Doechii (Official Video) Katy Perry 12M views11 months ago CC 3:46", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/c/@katyperry", "content": "Katy Perry - I'M HIS, HE'S MINE ft. Doechii (Official Video) Katy Perry 12M views11 months ago CC 3:46"}
+{"idx": 8, "title": "Katy Perry Shares Emotional Statement About the Future Two ... -...", "date": "", "ddg_snippet": "23 hours ago · Katy Perry shared a rare, heartfelt message on Instagram on September 22. In her message, she wrote that she was ready \"to move forward, and to dream.\" Her message comes after she reportedly ...", "subpage_snippet": "", "source": "www.yahoo.com", "link": "https://www.yahoo.com/entertainment/music/articles/katy-perry-shares-emotional-statement-142854834.html?fr=sycsrp_catchall", "content": "23 hours ago · Katy Perry shared a rare, heartfelt message on Instagram on September 22. In her message, she wrote that she was ready \"to move forward, and to dream.\" Her message comes after she reportedly ..."}
+{"idx": 9, "title": "Katy Perry Announces U.S. Leg Of The Lifetimes Tour", "date": "", "ddg_snippet": "Taking the stage as fireworks lit up the Rio sky, Perry had the 100,000-strong crowd going wild with dazzling visuals and pyrotechnics that transformed the City of Rock into a vibrant spectacle…” Katy Perry – The Lifetimes Tour 2025 New dates in bold 4/23 Mexico City, MX Arena CDMX 4/25 Mexico City, MX Arena CDMX 4/26 Mexico City, MX ...", "subpage_snippet": "", "source": "www.livenationentertainment.com", "link": "https://www.livenationentertainment.com/2025/01/katy-perry-announces-u-s-leg-of-the-lifetimes-tour/", "content": "Taking the stage as fireworks lit up the Rio sky, Perry had the 100,000-strong crowd going wild with dazzling visuals and pyrotechnics that transformed the City of Rock into a vibrant spectacle…” Katy Perry – The Lifetimes Tour 2025 New dates in bold 4/23 Mexico City, MX Arena CDMX 4/25 Mexico City, MX Arena CDMX 4/26 Mexico City, MX ..."}
diff --git a/data/sampled_jsons/Socialized_Coevolution_Advancing_a_Better_World_through_Cross-Task_Collaboration_ablation_study.jsonl b/data/sampled_jsons/Socialized_Coevolution_Advancing_a_Better_World_through_Cross-Task_Collaboration_ablation_study.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3568835ff6d5b0f6214ff66d07d5b08fe849fd6a
--- /dev/null
+++ b/data/sampled_jsons/Socialized_Coevolution_Advancing_a_Better_World_through_Cross-Task_Collaboration_ablation_study.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Socialized Coevolution : Advancing a Better World ...", "date": "", "ddg_snippet": "Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized in different downstream tasks.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46680", "content": "Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized in different downstream tasks."}
+{"idx": 1, "title": "Advancing a Better World through Cross-Task Collaboration", "date": "", "ddg_snippet": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration ... The ablation study (Table 3) is a good starting point, but it could ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0WQJ6DFSKp&referrer=[the+profile+of+Yu+Wang](/profile?id=~Yu_Wang33)", "content": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration ... The ablation study (Table 3) is a good starting point, but it could ..."}
+{"idx": 2, "title": "Socialized Coevolution: Advancing a Better World through Cross-Task ...", "date": "", "ddg_snippet": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0WQJ6DFSKp", "content": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu"}
+{"idx": 3, "title": "Socialized Coevolution: Advancing a Better World through Cross-Task ...", "date": "", "ddg_snippet": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration 1 2 Xinjie Yao 3 Yu Wang 1 2 3 Pengfei Zhu 1 2 3 Wanyu Lin 4 Ruipu Zhao 1 5 Zhoupeng Guo 1 2 Qinghua Hu 3", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0WQJ6DFSKp", "content": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration 1 2 Xinjie Yao 3 Yu Wang 1 2 3 Pengfei Zhu 1 2 3 Wanyu Lin 4 Ruipu Zhao 1 5 Zhoupeng Guo 1 2 Qinghua Hu 3"}
+{"idx": 4, "title": "两篇论文被icml 2025录用 - Visdrone", "date": "", "ddg_snippet": "论文题目： Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration 作者：姚鑫杰（博士研究生），王煜，朱鹏飞，林婉瑜，赵睿朴，郭周鹏，李维浩，胡清华 论文概述： 近期，团队提出了一个多智能体跨任务协同感知的社会化学习框架，已被2025国际机器学习大会（ICML）录用。在动态开放 ...", "subpage_snippet": "", "source": "aiskyeye.com", "link": "https://aiskyeye.com/论文《socialized-coevolution-advancing-a-better-world-through-cross-task-collaboration》和《task-gated-multi-expert-collaboration-network-for-degraded-multi/", "content": "论文题目： Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration 作者：姚鑫杰（博士研究生），王煜，朱鹏飞，林婉瑜，赵睿朴，郭周鹏，李维浩，胡清华 论文概述： 近期，团队提出了一个多智能体跨任务协同感知的社会化学习框架，已被2025国际机器学习大会（ICML）录用。在动态开放 ..."}
+{"idx": 5, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration · Analytical Construction on Geometric Architectures: Transitioning from ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration · Analytical Construction on Geometric Architectures: Transitioning from ..."}
+{"idx": 6, "title": "GitHub - yxjdarren/SC: The paper has been accepted to ICML 2025.", "date": "", "ddg_snippet": "The code repository for \" Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration \" (the paper has been accepted by ICML 2025) in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yxjdarren/SC", "content": "The code repository for \" Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration \" (the paper has been accepted by ICML 2025) in PyTorch."}
+{"idx": 7, "title": "Xinjie Yao | Vision Group", "date": "", "ddg_snippet": "[2025-05] \" Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration \" has been accepted by International Conference on Machine Learning (ICML, CCF-A).", "subpage_snippet": "", "source": "yxjdarren.github.io", "link": "https://yxjdarren.github.io/", "content": "[2025-05] \" Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration \" has been accepted by International Conference on Machine Learning (ICML, CCF-A)."}
+{"idx": 8, "title": "Zhoupeng Guo - OpenReview", "date": "", "ddg_snippet": "Publications Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 poster", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Zhoupeng_Guo2", "content": "Publications Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 poster"}
+{"idx": 9, "title": "openreview.net/profile?id=~Wanyu_Lin1", "date": "", "ddg_snippet": "Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration . Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Wanyu_Lin1", "content": "Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration . Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu."}
diff --git a/data/sampled_jsons/Socialized_Coevolution_Equation_19_scaling_factor_En_Et_Eloops.jsonl b/data/sampled_jsons/Socialized_Coevolution_Equation_19_scaling_factor_En_Et_Eloops.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/Socialized_Coevolution_Equation_19_scaling_factor_En_Et_Eloops.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Socialized_Coevolution_paper_Equation_19_scaling_factor_min_max.jsonl b/data/sampled_jsons/Socialized_Coevolution_paper_Equation_19_scaling_factor_min_max.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..18014a4682546cfafafd55b82c921c7a748c74c5
--- /dev/null
+++ b/data/sampled_jsons/Socialized_Coevolution_paper_Equation_19_scaling_factor_min_max.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Westermarck effect - Wikipedia", "date": "", "ddg_snippet": "The child-rearing practices of the kibbutz system are sometimes cited as an example of the Westermarck effect. Seen here are a group of children in Kibbutz Gan Shmuel, c. 1935–40. The Westermarck effect, also known as reverse sexual imprinting...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Westermarck_effect", "content": "The child-rearing practices of the kibbutz system are sometimes cited as an example of the Westermarck effect. Seen here are a group of children in Kibbutz Gan Shmuel, c. 1935–40. The Westermarck effect, also known as reverse sexual imprinting..."}
+{"idx": 1, "title": "[2006.12904] Min-Mid-Max Scaling, Limits of Agreement, and ... Images Socialized Coevolution: Advancing a Better World through ... GitHub - yxjdarren/SC: The paper has been accepted to ICML 2025. How to scale down a range of numbers with a known min and max ... Code sample 2020-09-19-About Feature Scaling and Normalization.ipynb - Colab Log Transformation, Min Max Scaling and Standard Scaling in ... Comprehensive Min - Max Scaling Guide for Enhanced Data Normalization Log Transformation, Min Max Scaling and Standard Scaling in ... - Medi… Comprehensive Min - Max Scaling Guide for Enhanced Data Normalization Comprehensive Min - Max Scaling Guide for Enhanced Data Normalization Comprehensive Min - Max Scaling Guide for Enhanced Data Normalization Comprehensive Min - Max Scaling Guide for Enhanced Data Normalization Comprehensive Min-Max Scaling Guide for Enhanced Data ...", "date": "", "ddg_snippet": "Jun 23, 2020 · In this paper , I solve a 60-year old question posed by Cohen's seminal paper (1960) and offer an agreement measure centered around the chance-expected agreement while isolating marginally forced agreement and disagreement. To achieve this, I formulate the minimum feasible agreement given row and column marginals by devising a new algorithm that minimizes the sum of diagonals in contingency ... View all May 1, 2025 · The reviewers broadly agree that this paper should be accepted, noting its novel and well-motivated concept of socialized coevolution , the practical framework it introduces for investigating socialized coevolution , that its contributions draw from multiple areas (e.g., distillation, MTL, and cognitive-inspired AI), and promising results on ... The code repository for \" Socialized Coevolution : Advancing a Better World through Cross-Task Collaboration\" (the paper has been accepted by ICML 2025) in PyTorch. You might also notice that (b-a)/( max - min ) is a scaling factor between the size of the new range and the size of the original range. So really we are first translating x by - min , scaling it to the correct factor , and then translating it back up to the new minimum value of a. See more on stackoverflow Sep 19 , 2020 · About Min - Max scaling An alternative approach to Z-Score normalization (or called standardization) is the so-called Min - Max Scaling (often also simply called Normalization - a common cause for ambiguities) In this approach, the data is scaled to a fixed range - usually [0, 1]. While Min - Max scaling can be sensitive to outliers, standard scaling is less affected because it relies on the mean and standard deviation of the data. How do you calculate min-max scaling? This is done by subtracting the minimum value of the feature and dividing by the range (i.e., the difference between the maximum and minimum values). The basic formula for Min-Max Scaling is given by: x ′ = x min (x) max (x) min (x) x′ = max(x) − min(x)x − min(x) where: x′ is the normalized value. How is standard scaling different from min-max scaling? For each data point’s feature value, subtract the mean and divide by the standard deviation. While Min-Max scaling can be sensitive to outliers, standard scaling is less affected because it relies on the mean and standard deviation of the data. Outliers can still influence the scaling, but not as much as with Min-Max scaling. Does min-max scaling distort the original distribution? Preservation of Relationships: Unlike other scaling techniques such as z-score normalization, Min-Max Scaling does not distort the original distribution . It preserves the original relationships in the data. What is min-max scaling in data preprocessing? Min-Max Scaling is mathematically straightforward , yet its implications in data preprocessing are profound. Take a dataset feature x x with minimum value m m and maximum value M M. The normalization for a given value x x is performed by: x ′ = x m M m x′ = M − mx −m When is min-max scaling most effective? Min - Max Scaling is most effective in scenarios where: The data distribution is known and the range is meaningful. Outliers are minimal or have been handled prior to scaling . The modeling process benefits from a bounded feature space, especially in geometric algorithms. What is min-max scaling in machine learning? Min - Max Scaling is widely applied in various machine learning algorithms, including: Neural Networks: Neural networks benefit from inputs that are normalized, as it accelerates the training process and often leads to more stable convergence when using activation functions like sigmoid, which are bounded between 0 and 1. Mar 13, 2025 · Explore the fundamentals of Min - Max Scaling , its application in data normalization, and learn step-by-step methods to implement this essential data preprocessing technique.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.12904", "content": "Jun 23, 2020 · In this paper , I solve a 60-year old question posed by Cohen's seminal paper (1960) and offer an agreement measure centered around the chance-expected agreement while isolating marginally forced agreement and disagreement. To achieve this, I formulate the minimum feasible agreement given row and column marginals by devising a new algorithm that minimizes the sum of diagonals in contingency ... View all May 1, 2025 · The reviewers broadly agree that this paper should be accepted, noting its novel and well-motivated concept of socialized coevolution , the practical framework it introduces for investigating socialized coevolution , that its contributions draw from multiple areas (e.g., distillation, MTL, and cognitive-inspired AI), and promising results on ... The code repository for \" Socialized Coevolution : Advancing a Better World through Cross-Task Collaboration\" (the paper has been accepted by ICML 2025) in PyTorch. You might also notice that (b-a)/( max - min ) is a scaling factor between the size of the new range and the size of the original range. So really we are first translating x by - min , scaling it to the correct factor , and then translating it back up to the new minimum value of a. See more on stackoverflow Sep 19 , 2020 · About Min - Max scaling An alternative approach to Z-Score normalization (or called standardization) is the so-called Min - Max Scaling (often also simply called Normalization - a common cause for ambiguities) In this approach, the data is scaled to a fixed range - usually [0, 1]. While Min - Max scaling can be sensitive to outliers, standard scaling is less affected because it relies on the mean and standard deviation of the data. How do you calculate min-max scaling? This is done by subtracting the minimum value of the feature and dividing by the range (i.e., the difference between the maximum and minimum values). The basic formula for Min-Max Scaling is given by: x ′ = x min (x) max (x) min (x) x′ = max(x) − min(x)x − min(x) where: x′ is the normalized value. How is standard scaling different from min-max scaling? For each data point’s feature value, subtract the mean and divide by the standard deviation. While Min-Max scaling can be sensitive to outliers, standard scaling is less affected because it relies on the mean and standard deviation of the data. Outliers can still influence the scaling, but not as much as with Min-Max scaling. Does min-max scaling distort the original distribution? Preservation of Relationships: Unlike other scaling techniques such as z-score normalization, Min-Max Scaling does not distort the original distribution . It preserves the original relationships in the data. What is min-max scaling in data preprocessing? Min-Max Scaling is mathematically straightforward , yet its implications in data preprocessing are profound. Take a dataset feature x x with minimum value m m and maximum value M M. The normalization for a given value x x is performed by: x ′ = x m M m x′ = M − mx −m When is min-max scaling most effective? Min - Max Scaling is most effective in scenarios where: The data distribution is known and the range is meaningful. Outliers are minimal or have been handled prior to scaling . The modeling process benefits from a bounded feature space, especially in geometric algorithms. What is min-max scaling in machine learning? Min - Max Scaling is widely applied in various machine learning algorithms, including: Neural Networks: Neural networks benefit from inputs that are normalized, as it accelerates the training process and often leads to more stable convergence when using activation functions like sigmoid, which are bounded between 0 and 1. Mar 13, 2025 · Explore the fundamentals of Min - Max Scaling , its application in data normalization, and learn step-by-step methods to implement this essential data preprocessing technique."}
+{"idx": 2, "title": "Socialized Coevolution: Advancing a Better World through ...", "date": "", "ddg_snippet": "May 1, 2025 · The reviewers broadly agree that this paper should be accepted, noting its novel and well-motivated concept of socialized coevolution , the practical framework it introduces for investigating socialized coevolution , that its contributions draw from multiple areas (e.g., distillation, MTL, and cognitive-inspired AI), and promising results on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0WQJ6DFSKp", "content": "May 1, 2025 · The reviewers broadly agree that this paper should be accepted, noting its novel and well-motivated concept of socialized coevolution , the practical framework it introduces for investigating socialized coevolution , that its contributions draw from multiple areas (e.g., distillation, MTL, and cognitive-inspired AI), and promising results on ..."}
+{"idx": 3, "title": "GitHub - yxjdarren/SC: The paper has been accepted to ICML 2025.", "date": "", "ddg_snippet": "The code repository for \" Socialized Coevolution : Advancing a Better World through Cross-Task Collaboration\" (the paper has been accepted by ICML 2025) in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yxjdarren/SC", "content": "The code repository for \" Socialized Coevolution : Advancing a Better World through Cross-Task Collaboration\" (the paper has been accepted by ICML 2025) in PyTorch."}
+{"idx": 4, "title": "How to scale down a range of numbers with a known min and max ... Code sample", "date": "", "ddg_snippet": "You might also notice that (b-a)/( max - min ) is a scaling factor between the size of the new range and the size of the original range. So really we are first translating x by - min , scaling it to the correct factor , and then translating it back up to the new minimum value of a. See more on stackoverflow", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/5294955/how-to-scale-down-a-range-of-numbers-with-a-known-min-and-max-value", "content": "You might also notice that (b-a)/( max - min ) is a scaling factor between the size of the new range and the size of the original range. So really we are first translating x by - min , scaling it to the correct factor , and then translating it back up to the new minimum value of a. See more on stackoverflow"}
+{"idx": 5, "title": "2020-09-19-About Feature Scaling and Normalization.ipynb - Colab", "date": "", "ddg_snippet": "Sep 19 , 2020 · About Min - Max scaling An alternative approach to Z-Score normalization (or called standardization) is the so-called Min - Max Scaling (often also simply called Normalization - a common cause for ambiguities) In this approach, the data is scaled to a fixed range - usually [0, 1].", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/JL1829/turbo-funicular/blob/master/_notebooks/2020-09-19-About+Feature+Scaling+and+Normalization.ipynb", "content": "Sep 19 , 2020 · About Min - Max scaling An alternative approach to Z-Score normalization (or called standardization) is the so-called Min - Max Scaling (often also simply called Normalization - a common cause for ambiguities) In this approach, the data is scaled to a fixed range - usually [0, 1]."}
+{"idx": 6, "title": "Log Transformation, Min Max Scaling and Standard Scaling in ...", "date": "", "ddg_snippet": "While Min - Max scaling can be sensitive to outliers, standard scaling is less affected because it relies on the mean and standard deviation of the data.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@datasciencejourney100_83560/feature-transformation-bb3db66fa4fe", "content": "While Min - Max scaling can be sensitive to outliers, standard scaling is less affected because it relies on the mean and standard deviation of the data."}
+{"idx": 7, "title": "Comprehensive Min-Max Scaling Guide for Enhanced Data ...", "date": "", "ddg_snippet": "Mar 13, 2025 · Explore the fundamentals of Min - Max Scaling , its application in data normalization, and learn step-by-step methods to implement this essential data preprocessing technique.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/comprehensive-min-max-scaling-guide", "content": "Mar 13, 2025 · Explore the fundamentals of Min - Max Scaling , its application in data normalization, and learn step-by-step methods to implement this essential data preprocessing technique."}
+{"idx": 8, "title": "ICML Poster Socialized Coevolution : Advancing a Better World...", "date": "", "ddg_snippet": "Motivated by Social Learning (SL), this paper introduces a practical paradigm of Socialized Coevolution (SC).The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46680", "content": "Motivated by Social Learning (SL), this paper introduces a practical paradigm of Socialized Coevolution (SC).The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale ."}
+{"idx": 9, "title": "(PDF) Social coevolution and Sine chaotic opposition learning Chimp...", "date": "", "ddg_snippet": "of existing algorithms and the proposal of new methods. erefore, this paper improves the CHoA algorithm. and proposes a Chimp optimization algorithm called Social coevolution and Sine Chaos oppositional Learn", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381998993_Social_coevolution_and_Sine_chaotic_opposition_learning_Chimp_Optimization_Algorithm_for_feature_selection", "content": "of existing algorithms and the proposal of new methods. erefore, this paper improves the CHoA algorithm. and proposes a Chimp optimization algorithm called Social coevolution and Sine Chaos oppositional Learn"}
diff --git a/data/sampled_jsons/Song_et_al._score-based_generative_models_training_instability_problems.jsonl b/data/sampled_jsons/Song_et_al._score-based_generative_models_training_instability_problems.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3923eba1c69a773bd71d6070d59b88c166646109
--- /dev/null
+++ b/data/sampled_jsons/Song_et_al._score-based_generative_models_training_instability_problems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improved Techniques for Training Score-Based Generative Models", "date": "", "ddg_snippet": "Jun 16, 2020 · Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization. However, existing training procedures are limited to images of low resolution (typically below 32x32), and can be unstable under some settings. We provide a new theoretical analysis of learning and sampling from score models in high dimensional spaces, explaining ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.09011", "content": "Jun 16, 2020 · Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization. However, existing training procedures are limited to images of low resolution (typically below 32x32), and can be unstable under some settings. We provide a new theoretical analysis of learning and sampling from score models in high dimensional spaces, explaining ..."}
+{"idx": 1, "title": "Improved Techniques for Training Score-Based Generative Models", "date": "", "ddg_snippet": "We propose a set of techniques to scale score-based generative models to high resolution images. Based on a new theoretical analysis on a simplified mixture model , we provide a method to analytically compute an effective set of Gaussian noise scales from training data.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2020/file/92c3b916311a5517d9290576e3ea37ad-Paper.pdf", "content": "We propose a set of techniques to scale score-based generative models to high resolution images. Based on a new theoretical analysis on a simplified mixture model , we provide a method to analytically compute an effective set of Gaussian noise scales from training data."}
+{"idx": 2, "title": "GitHub - yang-song/score_inverse_problems: Official repo for ... Consistency Models Generative Modeling by Estimating Gradients of ... - Yang Song Score-Based Generative Models - GitHub Pages Generative Modeling by Estimating Gradients of the Data Distribution Generative Modeling by Estimating Gradients of the Data Distribution Generative Modeling by Estimating Gradients of the Data Distribution Generative Modeling by Estimating Gradients of the Data Distribution Generative Modeling by Estimating Gradients of the Data Distribution Consistency Models - Proceedings of Machine Learning Research Score-based generative models are provably robust:", "date": "", "ddg_snippet": "This repo contains the JAX code for experiments in the paper Solving Inverse Problems in Medical Imaging with Score - Based Generative Models by Yang Song *, Liyue Shen*, Lei Xing, and Stefano Ermon. (*= joint first authors) See full list on github.com Train and evaluate our models through main.py. •config is the path to the config file. Our prescribed config files are provided in configs/. They are formatted according to ml_collections and should be mostly self-explanatory. sampling.cs_solver specifies which sampling method we use for solving the inverse problems . They have 4 possible values: •baseline: The \" Score SDE\" approach, as in the original paper Score - Based Generative Modeling through Stochastic Differential Equations •langevin: The \"Langevin\" approach, similar to the method in Robust Compressed Sensing MRI with Deep Generative Priors •langevin_projection: The \"ALD + Ours\" approach, used to demonstrate advantages of the projection- based conditioning method compared to prior work. •projection: Our full method. See full list on github.com Checkpoints and test data are provided in this Google drive. Please download the folder and move it to the same directory of this repo. See full list on github.com If you find the code useful for your research, please consider citing and its prior work See full list on github.com Here ∇ log ptpxq is the score function of ptpxq; hence dif-fusion models are also known as score-based generative models ( Song & Ermon, 2019; 2020; Song et al ., 2021). The resulting generative models , often called score-based generative models , has several important advantages over existing model families: GAN-level sample quality without adversarial training , flexible model architectures, exact log-likelihood computation, and inverse problem solving without re- training models . August 11, 2022 In 2021, I read a paper on denoising di usion models , which proposes a new type of generative models [HJA20]. Now, it turns out that there are parallel works by Yang Song and Stefano Ermon on the so-called \\ score-based models ,\" which is later discovered to be deeply connected (in other words, pretty much equivalent) to the former approach [SE19].1The approaches share the ... How can score-based generative models achieve high likelihood? Using this likelihood weighting function , we can train score-based generative models to achieve very high likelihoods, comparable or even superior to state-of-the-art autoregressive models By solving the estimated reverse SDE with numerical SDE solvers, we can simulate the reverse stochastic process for sample generation. Are score-based generative models suitable for solving inverse problems? Score-based generative models are particularly suitable for solving inverse problems. At its core, inverse problems are same as Bayesian inference problems. Let x and y be two random variables, and suppose we know the forward process of generating y from x, represented by the transition probability distribution p (y ∣ x). What are the different types of generative modeling techniques? Existing generative modeling techniques can largely be grouped into two categories based on how they represent probability distributions. likelihood-based models , which directly learn the distribution’s probability density (or mass) function via (approximate) maximum likelihood. Typical likelihood-based models include autoregressive models Are score-based generative modeling with multiple noise perturbations and diffusion probabilistic models different? Collectively, these latest developments seem to indicate that both score-based generative modeling with multiple noise perturbations and diffusion probabilistic models are different perspectives of the same model family , much like how wave mechanics and matrix mechanics are equivalent formulations of quantum mechanics in the history of physics 5 . What is the goal of generative modeling? Suppose we are given a dataset {x 1, x 2, ⋯, x N}, where each point is drawn independently from an underlying data distribution p (x). Given this dataset, the goal of generative modeling is to fit a model to the data distribution such that we can synthesize new data points at will by sampling from the distribution. How can we avoid a pre-trained score model in consistency distillation? Recall that in consistency distillation, we rely on a pre-trained score model sφpx, tq to approximate the ground truth score function ∇ log ptpxq. It turns out that we can avoid this pre-trained score model altogether by leveraging the following unbiased estimator (Lemma 1 in Appendix A): ́ x where x „ pdata and xt „ Npx; t2Iq. Jun 5, 2025 · We show how errors due to (a) finite sample approximation, (b) early stopping, (c) score -matching objective choice, (d) score function parametrization expressiveness, and (e) reference distribution choice, impact the quality of the generative model in terms of a d 1 bound of computable quantities.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yang-song/score_inverse_problems", "content": "This repo contains the JAX code for experiments in the paper Solving Inverse Problems in Medical Imaging with Score - Based Generative Models by Yang Song *, Liyue Shen*, Lei Xing, and Stefano Ermon. (*= joint first authors) See full list on github.com Train and evaluate our models through main.py. •config is the path to the config file. Our prescribed config files are provided in configs/. They are formatted according to ml_collections and should be mostly self-explanatory. sampling.cs_solver specifies which sampling method we use for solving the inverse problems . They have 4 possible values: •baseline: The \" Score SDE\" approach, as in the original paper Score - Based Generative Modeling through Stochastic Differential Equations •langevin: The \"Langevin\" approach, similar to the method in Robust Compressed Sensing MRI with Deep Generative Priors •langevin_projection: The \"ALD + Ours\" approach, used to demonstrate advantages of the projection- based conditioning method compared to prior work. •projection: Our full method. See full list on github.com Checkpoints and test data are provided in this Google drive. Please download the folder and move it to the same directory of this repo. See full list on github.com If you find the code useful for your research, please consider citing and its prior work See full list on github.com Here ∇ log ptpxq is the score function of ptpxq; hence dif-fusion models are also known as score-based generative models ( Song & Ermon, 2019; 2020; Song et al ., 2021). The resulting generative models , often called score-based generative models , has several important advantages over existing model families: GAN-level sample quality without adversarial training , flexible model architectures, exact log-likelihood computation, and inverse problem solving without re- training models . August 11, 2022 In 2021, I read a paper on denoising di usion models , which proposes a new type of generative models [HJA20]. Now, it turns out that there are parallel works by Yang Song and Stefano Ermon on the so-called \\ score-based models ,\" which is later discovered to be deeply connected (in other words, pretty much equivalent) to the former approach [SE19].1The approaches share the ... How can score-based generative models achieve high likelihood? Using this likelihood weighting function , we can train score-based generative models to achieve very high likelihoods, comparable or even superior to state-of-the-art autoregressive models By solving the estimated reverse SDE with numerical SDE solvers, we can simulate the reverse stochastic process for sample generation. Are score-based generative models suitable for solving inverse problems? Score-based generative models are particularly suitable for solving inverse problems. At its core, inverse problems are same as Bayesian inference problems. Let x and y be two random variables, and suppose we know the forward process of generating y from x, represented by the transition probability distribution p (y ∣ x). What are the different types of generative modeling techniques? Existing generative modeling techniques can largely be grouped into two categories based on how they represent probability distributions. likelihood-based models , which directly learn the distribution’s probability density (or mass) function via (approximate) maximum likelihood. Typical likelihood-based models include autoregressive models Are score-based generative modeling with multiple noise perturbations and diffusion probabilistic models different? Collectively, these latest developments seem to indicate that both score-based generative modeling with multiple noise perturbations and diffusion probabilistic models are different perspectives of the same model family , much like how wave mechanics and matrix mechanics are equivalent formulations of quantum mechanics in the history of physics 5 . What is the goal of generative modeling? Suppose we are given a dataset {x 1, x 2, ⋯, x N}, where each point is drawn independently from an underlying data distribution p (x). Given this dataset, the goal of generative modeling is to fit a model to the data distribution such that we can synthesize new data points at will by sampling from the distribution. How can we avoid a pre-trained score model in consistency distillation? Recall that in consistency distillation, we rely on a pre-trained score model sφpx, tq to approximate the ground truth score function ∇ log ptpxq. It turns out that we can avoid this pre-trained score model altogether by leveraging the following unbiased estimator (Lemma 1 in Appendix A): ́ x where x „ pdata and xt „ Npx; t2Iq. Jun 5, 2025 · We show how errors due to (a) finite sample approximation, (b) early stopping, (c) score -matching objective choice, (d) score function parametrization expressiveness, and (e) reference distribution choice, impact the quality of the generative model in terms of a d 1 bound of computable quantities."}
+{"idx": 3, "title": "Consistency Models", "date": "", "ddg_snippet": "Here ∇ log ptpxq is the score function of ptpxq; hence dif-fusion models are also known as score-based generative models ( Song & Ermon, 2019; 2020; Song et al ., 2021).", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/song23a/song23a.pdf", "content": "Here ∇ log ptpxq is the score function of ptpxq; hence dif-fusion models are also known as score-based generative models ( Song & Ermon, 2019; 2020; Song et al ., 2021)."}
+{"idx": 4, "title": "Generative Modeling by Estimating Gradients of ... - Yang Song", "date": "", "ddg_snippet": "The resulting generative models , often called score-based generative models , has several important advantages over existing model families: GAN-level sample quality without adversarial training , flexible model architectures, exact log-likelihood computation, and inverse problem solving without re- training models .", "subpage_snippet": "", "source": "yang-song.net", "link": "https://yang-song.net/blog/2021/score/", "content": "The resulting generative models , often called score-based generative models , has several important advantages over existing model families: GAN-level sample quality without adversarial training , flexible model architectures, exact log-likelihood computation, and inverse problem solving without re- training models ."}
+{"idx": 5, "title": "Score-Based Generative Models - GitHub Pages", "date": "", "ddg_snippet": "August 11, 2022 In 2021, I read a paper on denoising di usion models , which proposes a new type of generative models [HJA20]. Now, it turns out that there are parallel works by Yang Song and Stefano Ermon on the so-called \\ score-based models ,\" which is later discovered to be deeply connected (in other words, pretty much equivalent) to the former approach [SE19].1The approaches share the ...", "subpage_snippet": "", "source": "pkhungurn.github.io", "link": "https://pkhungurn.github.io/notes/notes/ml/score-based-generative-models/score-based-generative-models.pdf", "content": "August 11, 2022 In 2021, I read a paper on denoising di usion models , which proposes a new type of generative models [HJA20]. Now, it turns out that there are parallel works by Yang Song and Stefano Ermon on the so-called \\ score-based models ,\" which is later discovered to be deeply connected (in other words, pretty much equivalent) to the former approach [SE19].1The approaches share the ..."}
+{"idx": 6, "title": "Score-based generative models are provably robust:", "date": "", "ddg_snippet": "Jun 5, 2025 · We show how errors due to (a) finite sample approximation, (b) early stopping, (c) score -matching objective choice, (d) score function parametrization expressiveness, and (e) reference distribution choice, impact the quality of the generative model in terms of a d 1 bound of computable quantities.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739933", "content": "Jun 5, 2025 · We show how errors due to (a) finite sample approximation, (b) early stopping, (c) score -matching objective choice, (d) score function parametrization expressiveness, and (e) reference distribution choice, impact the quality of the generative model in terms of a d 1 bound of computable quantities."}
+{"idx": 7, "title": "Diffusion Models for Robotic Manipulation: A Survey", "date": "", "ddg_snippet": "This score - based generative modeling was first introduced in Song and Ermon, (2019) . Song et al ., 2021b also shows that once the score-network is trained , the reverse-time SDE can be replaced by an ordinary differential equation (ODE).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.08438v3", "content": "This score - based generative modeling was first introduced in Song and Ermon, (2019) . Song et al ., 2021b also shows that once the score-network is trained , the reverse-time SDE can be replaced by an ordinary differential equation (ODE)."}
+{"idx": 8, "title": "A New Look on Diffusion Times for Score - based Generative Models", "date": "", "ddg_snippet": "Score - based diffusion models map noise into data using stochastic differential equations.In practice, diffusion models are challenging to work with ( Song et al ., 2021c). First, a direct access to the true score function ∇ log p(xt, t) is required in the reverse diffusion is unavailable.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03889654v1/document", "content": "Score - based diffusion models map noise into data using stochastic differential equations.In practice, diffusion models are challenging to work with ( Song et al ., 2021c). First, a direct access to the true score function ∇ log p(xt, t) is required in the reverse diffusion is unavailable."}
+{"idx": 9, "title": "(PDF) Composition and Control with Distilled Energy Diffusion Models ...", "date": "", "ddg_snippet": "Diffusion models ( Song et al ., 2020, 2021; Ho et al ., 2020) generate new samples by simulating from the. stochastic process (2) with density denoted qλ. score - based generative modeling . Advances in Neu-. ral Information Processing Systems, 2021.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389129465_Composition_and_Control_with_Distilled_Energy_Diffusion_Models_and_Sequential_Monte_Carlo", "content": "Diffusion models ( Song et al ., 2020, 2021; Ho et al ., 2020) generate new samples by simulating from the. stochastic process (2) with density denoted qλ. score - based generative modeling . Advances in Neu-. ral Information Processing Systems, 2021."}
diff --git a/data/sampled_jsons/SpeechSSM_LM_initialization_language_model_pretraining_speech_generation_year_2024.jsonl b/data/sampled_jsons/SpeechSSM_LM_initialization_language_model_pretraining_speech_generation_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f491adb4f7e1a34dd7cbcd32aa6b4b6aa11c9ea
--- /dev/null
+++ b/data/sampled_jsons/SpeechSSM_LM_initialization_language_model_pretraining_speech_generation_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation ."}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Our work makes initial progress on naturalistic, audio-native, long-form speech generation : We introduce SpeechSSM , the first spoken language model for long-form speech . Our 2B and 9B models: produces speech textlessly and in constant memory, for unbounded real-time generation ; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ...", "subpage_snippet": "", "source": "google.github.io", "link": "https://google.github.io/tacotron/publications/speechssm/", "content": "Our work makes initial progress on naturalistic, audio-native, long-form speech generation : We introduce SpeechSSM , the first spoken language model for long-form speech . Our 2B and 9B models: produces speech textlessly and in constant memory, for unbounded real-time generation ; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ..."}
+{"idx": 2, "title": "Researcher develops 'SpeechSSM,' opening up possibilities for ...", "date": "", "ddg_snippet": "Jul 4, 2025 · Through these new evaluations, it was confirmed that speech generated by the SpeechSSM spoken language model consistently featured specific individuals mentioned in the initial prompt, and new characters and events unfolded naturally and contextually consistently, despite long-duration generation .", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-07-speechssm-possibilities-hour-ai-voice.html", "content": "Jul 4, 2025 · Through these new evaluations, it was confirmed that speech generated by the SpeechSSM spoken language model consistently featured specific individuals mentioned in the initial prompt, and new characters and events unfolded naturally and contextually consistently, despite long-duration generation ."}
+{"idx": 3, "title": "Long-Form Speech Generation with Spoken Language Models Long-Form Speech Generation with Spoken Language Models Researcher develops 'SpeechSSM,' opening up possibilities for ... Frontiers in generative speech modeling: binaural, long-form ... Speech Model Pre-training for End-to-End Spoken Language ... ICML Poster Long-Form Speech Generation with Spoken Language ... Long-Form Speech Generation with Spoken Language Models Long-Form Speech Generation with Spoken Language Models Long-Form Speech Generation with Spoken Language Models [2305.13009] Textually Pretrained Speech Language Models Long-Form Speech Generation with Spoken Language Models Long-Form Speech Generation with Spoken Language Models [2305.13009] Textually Pretrained Speech Language Models", "date": "", "ddg_snippet": "Dec 24, 2024 · From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates. Our work makes initial progress on naturalistic, audio-native, long-form speech generation : We introduce SpeechSSM , the first spoken language model for long-form speech . Our 2B and 9B models: produces speech textlessly and in constant memory, for unbounded real-time generation ; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ... Jul 4, 2025 · Through these new evaluations, it was confirmed that speech generated by the SpeechSSM spoken language model consistently featured specific individuals mentioned in the initial prompt, and new characters and events unfolded naturally and contextually consistently, despite long-duration generation . Feb 21, 2025 · I'll then present two recent works at Google built atop these principles---ZeroBAS [1], the first zero-shot neural method for mono-to-binaural synthesis; and SpeechSSM [2], the first \" language model \" for expressive long-form speech ---and motivate how these directions along with auditory perception tie into emerging topics in neural audio ... This repo contains the code for the paper \" Speech Model Pre-training for End-to-End Spoken Language Understanding\". If you have any questions about this code or have problems getting it to work, please send me an email at . See full list on github.com PyTorch, numpy, soundfile, pandas, tqdm, textgrid.py See full list on github.com First, change the asr_path and/or slu_path in the config file (like experiments/no_unfreezing.cfg, or whichever experiment you want to run) to point to where the LibriSpeech data and/or Fluent Speech Commands data are stored on your computer. SLU training: To train the model on Fluent Speech Commands, run the following command: See full list on github.com If you find this repo or our Fluent Speech Commands dataset useful, please cite our paper: •Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio, \" Speech Model Pre-training for End-to-End Spoken Language Understanding\", arXiv, 2019. See full list on github.com From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates. What is speechssm? From these considerations we derive SpeechSSM, the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech) in a single decoding session without text intermediates. What is speechssm X? We introduce SpeechSSM, the first spoken language model for long-form speech. Our 2B and 9B models: can be trained for either read speech or extemporaneous speech (SpeechSSM-X). We define evaluations for long-form speech continuation, from time-stratified evaluations to modern NLG metrics like LLM-as-judge. Why do textless spoken language models fail? However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation, and memory costs at inference time. Does twist outperform a cold-start speechlm? Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board . Does spirit LM use text during prompting or generation? No text is used during prompting or generation. Only Spirit LM uses text during finetuning. Automatic transcripts (Gemini 1.5 Pro-002) provided for convenience. He reached up among the branches and began to pick the sweet, insipid fruit. Long, ivory-colored berries tipped with faint pink, like white coral that Can librispeech-long be used for long-form speech processing? can be trained for either read speech or extemporaneous speech (SpeechSSM-X). We define evaluations for long-form speech continuation, from time-stratified evaluations to modern NLG metrics like LLM-as-judge. We release LibriSpeech-Long, a 3-4min reformat of LibriSpeech dev+test sets for future long-form speech processing research . May 22, 2023 · Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.18603", "content": "Dec 24, 2024 · From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates. Our work makes initial progress on naturalistic, audio-native, long-form speech generation : We introduce SpeechSSM , the first spoken language model for long-form speech . Our 2B and 9B models: produces speech textlessly and in constant memory, for unbounded real-time generation ; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ... Jul 4, 2025 · Through these new evaluations, it was confirmed that speech generated by the SpeechSSM spoken language model consistently featured specific individuals mentioned in the initial prompt, and new characters and events unfolded naturally and contextually consistently, despite long-duration generation . Feb 21, 2025 · I'll then present two recent works at Google built atop these principles---ZeroBAS [1], the first zero-shot neural method for mono-to-binaural synthesis; and SpeechSSM [2], the first \" language model \" for expressive long-form speech ---and motivate how these directions along with auditory perception tie into emerging topics in neural audio ... This repo contains the code for the paper \" Speech Model Pre-training for End-to-End Spoken Language Understanding\". If you have any questions about this code or have problems getting it to work, please send me an email at . See full list on github.com PyTorch, numpy, soundfile, pandas, tqdm, textgrid.py See full list on github.com First, change the asr_path and/or slu_path in the config file (like experiments/no_unfreezing.cfg, or whichever experiment you want to run) to point to where the LibriSpeech data and/or Fluent Speech Commands data are stored on your computer. SLU training: To train the model on Fluent Speech Commands, run the following command: See full list on github.com If you find this repo or our Fluent Speech Commands dataset useful, please cite our paper: •Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio, \" Speech Model Pre-training for End-to-End Spoken Language Understanding\", arXiv, 2019. See full list on github.com From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates. What is speechssm? From these considerations we derive SpeechSSM, the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech) in a single decoding session without text intermediates. What is speechssm X? We introduce SpeechSSM, the first spoken language model for long-form speech. Our 2B and 9B models: can be trained for either read speech or extemporaneous speech (SpeechSSM-X). We define evaluations for long-form speech continuation, from time-stratified evaluations to modern NLG metrics like LLM-as-judge. Why do textless spoken language models fail? However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation, and memory costs at inference time. Does twist outperform a cold-start speechlm? Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board . Does spirit LM use text during prompting or generation? No text is used during prompting or generation. Only Spirit LM uses text during finetuning. Automatic transcripts (Gemini 1.5 Pro-002) provided for convenience. He reached up among the branches and began to pick the sweet, insipid fruit. Long, ivory-colored berries tipped with faint pink, like white coral that Can librispeech-long be used for long-form speech processing? can be trained for either read speech or extemporaneous speech (SpeechSSM-X). We define evaluations for long-form speech continuation, from time-stratified evaluations to modern NLG metrics like LLM-as-judge. We release LibriSpeech-Long, a 3-4min reformat of LibriSpeech dev+test sets for future long-form speech processing research . May 22, 2023 · Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different ..."}
+{"idx": 4, "title": "Frontiers in generative speech modeling: binaural, long-form ...", "date": "", "ddg_snippet": "Feb 21, 2025 · I'll then present two recent works at Google built atop these principles---ZeroBAS [1], the first zero-shot neural method for mono-to-binaural synthesis; and SpeechSSM [2], the first \" language model \" for expressive long-form speech ---and motivate how these directions along with auditory perception tie into emerging topics in neural audio ...", "subpage_snippet": "", "source": "ccrma.stanford.edu", "link": "https://ccrma.stanford.edu/events/frontiers-in-generative-speech-modeling-binaural-long-form-and-beyond", "content": "Feb 21, 2025 · I'll then present two recent works at Google built atop these principles---ZeroBAS [1], the first zero-shot neural method for mono-to-binaural synthesis; and SpeechSSM [2], the first \" language model \" for expressive long-form speech ---and motivate how these directions along with auditory perception tie into emerging topics in neural audio ..."}
+{"idx": 5, "title": "Speech Model Pre-training for End-to-End Spoken Language ... ICML Poster Long-Form Speech Generation with Spoken Language ... Long-Form Speech Generation with Spoken Language Models Long-Form Speech Generation with Spoken Language Models Long-Form Speech Generation with Spoken Language Models [2305.13009] Textually Pretrained Speech Language Models Long-Form Speech Generation with Spoken Language Models Long-Form Speech Generation with Spoken Language Models [2305.13009] Textually Pretrained Speech Language Models", "date": "", "ddg_snippet": "This repo contains the code for the paper \" Speech Model Pre-training for End-to-End Spoken Language Understanding\". If you have any questions about this code or have problems getting it to work, please send me an email at . See full list on github.com PyTorch, numpy, soundfile, pandas, tqdm, textgrid.py See full list on github.com First, change the asr_path and/or slu_path in the config file (like experiments/no_unfreezing.cfg, or whichever experiment you want to run) to point to where the LibriSpeech data and/or Fluent Speech Commands data are stored on your computer. SLU training: To train the model on Fluent Speech Commands, run the following command: See full list on github.com If you find this repo or our Fluent Speech Commands dataset useful, please cite our paper: •Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio, \" Speech Model Pre-training for End-to-End Spoken Language Understanding\", arXiv, 2019. See full list on github.com From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates. What is speechssm? From these considerations we derive SpeechSSM, the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech) in a single decoding session without text intermediates. What is speechssm X? We introduce SpeechSSM, the first spoken language model for long-form speech. Our 2B and 9B models: can be trained for either read speech or extemporaneous speech (SpeechSSM-X). We define evaluations for long-form speech continuation, from time-stratified evaluations to modern NLG metrics like LLM-as-judge. Why do textless spoken language models fail? However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation, and memory costs at inference time. Does twist outperform a cold-start speechlm? Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board . Does spirit LM use text during prompting or generation? No text is used during prompting or generation. Only Spirit LM uses text during finetuning. Automatic transcripts (Gemini 1.5 Pro-002) provided for convenience. He reached up among the branches and began to pick the sweet, insipid fruit. Long, ivory-colored berries tipped with faint pink, like white coral that Can librispeech-long be used for long-form speech processing? can be trained for either read speech or extemporaneous speech (SpeechSSM-X). We define evaluations for long-form speech continuation, from time-stratified evaluations to modern NLG metrics like LLM-as-judge. We release LibriSpeech-Long, a 3-4min reformat of LibriSpeech dev+test sets for future long-form speech processing research . May 22, 2023 · Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mravanelli/pretrain_speech_model", "content": "This repo contains the code for the paper \" Speech Model Pre-training for End-to-End Spoken Language Understanding\". If you have any questions about this code or have problems getting it to work, please send me an email at . See full list on github.com PyTorch, numpy, soundfile, pandas, tqdm, textgrid.py See full list on github.com First, change the asr_path and/or slu_path in the config file (like experiments/no_unfreezing.cfg, or whichever experiment you want to run) to point to where the LibriSpeech data and/or Fluent Speech Commands data are stored on your computer. SLU training: To train the model on Fluent Speech Commands, run the following command: See full list on github.com If you find this repo or our Fluent Speech Commands dataset useful, please cite our paper: •Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio, \" Speech Model Pre-training for End-to-End Spoken Language Understanding\", arXiv, 2019. See full list on github.com From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates. What is speechssm? From these considerations we derive SpeechSSM, the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech) in a single decoding session without text intermediates. What is speechssm X? We introduce SpeechSSM, the first spoken language model for long-form speech. Our 2B and 9B models: can be trained for either read speech or extemporaneous speech (SpeechSSM-X). We define evaluations for long-form speech continuation, from time-stratified evaluations to modern NLG metrics like LLM-as-judge. Why do textless spoken language models fail? However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation, and memory costs at inference time. Does twist outperform a cold-start speechlm? Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board . Does spirit LM use text during prompting or generation? No text is used during prompting or generation. Only Spirit LM uses text during finetuning. Automatic transcripts (Gemini 1.5 Pro-002) provided for convenience. He reached up among the branches and began to pick the sweet, insipid fruit. Long, ivory-colored berries tipped with faint pink, like white coral that Can librispeech-long be used for long-form speech processing? can be trained for either read speech or extemporaneous speech (SpeechSSM-X). We define evaluations for long-form speech continuation, from time-stratified evaluations to modern NLG metrics like LLM-as-judge. We release LibriSpeech-Long, a 3-4min reformat of LibriSpeech dev+test sets for future long-form speech processing research . May 22, 2023 · Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different ..."}
+{"idx": 6, "title": "ICML Poster Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46499", "content": "From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates."}
+{"idx": 7, "title": "[2305.13009] Textually Pretrained Speech Language Models", "date": "", "ddg_snippet": "May 22, 2023 · Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.13009", "content": "May 22, 2023 · Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different ..."}
+{"idx": 8, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v2", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds..."}
+{"idx": 9, "title": "Textually Pretrained Speech Language Models", "date": "", "ddg_snippet": "Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/c859b99b5d717c9035e79d43dfd69435-Paper-Conference.pdf", "content": "Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models ."}
diff --git a/data/sampled_jsons/Spring_paper_Li_2024_blockchain_state_placement_account_migration_abstract.jsonl b/data/sampled_jsons/Spring_paper_Li_2024_blockchain_state_placement_account_migration_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e0e5c2457aca53777403d98cb1564754e047484f
--- /dev/null
+++ b/data/sampled_jsons/Spring_paper_Li_2024_blockchain_state_placement_account_migration_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SPRING: Improving the Throughput of Sharding Blockchain via ...", "date": "", "ddg_snippet": "May 13, 2024 · In this paper , we present SPRING , the first deep-reinforcement-learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3589334.3645386", "content": "May 13, 2024 · In this paper , we present SPRING , the first deep-reinforcement-learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy."}
+{"idx": 1, "title": "SPRING: Improving the Throughput of Sharding Blockchain via ...", "date": "", "ddg_snippet": "In this paper , we present Spring , the first deep-reinforcement-learning(DRL)-based sharding framework for state placement . Spring formulates the state placement as a Markov Decision Process which takes into consideration the cross-shard transaction ratio and workload balancing, and employs DRL to learn the efective state placement pol-icy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=8oczaP1YKD&name=pdf", "content": "In this paper , we present Spring , the first deep-reinforcement-learning(DRL)-based sharding framework for state placement . Spring formulates the state placement as a Markov Decision Process which takes into consideration the cross-shard transaction ratio and workload balancing, and employs DRL to learn the efective state placement pol-icy."}
+{"idx": 2, "title": "Account Migration across Blockchain Shards using Fine-tuned ...", "date": "", "ddg_snippet": "Abstract : Sharding is one of the most promising techniques for improving blockchain scalability. In blockchain state sharding, account migration across shards is crucial to the low ratio of cross-shard transactions and cross-shard workload balance. Through reviewing state -of-the-art protocols ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10621244", "content": "Abstract : Sharding is one of the most promising techniques for improving blockchain scalability. In blockchain state sharding, account migration across shards is crucial to the low ratio of cross-shard transactions and cross-shard workload balance. Through reviewing state -of-the-art protocols ..."}
+{"idx": 3, "title": "dblp: SPRING: Improving the Throughput of Sharding Blockchain ...", "date": "", "ddg_snippet": "Jan 19, 2025 · Pengze Li , Mingxuan Song, Mingzhe Xing, Zhen Xiao, Qiuyu Ding, Shengjie Guan, Jieyi Long: SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement . WWW 2024 : 2836-2846", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/www/LiSXXDGL24", "content": "Jan 19, 2025 · Pengze Li , Mingxuan Song, Mingzhe Xing, Zhen Xiao, Qiuyu Ding, Shengjie Guan, Jieyi Long: SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement . WWW 2024 : 2836-2846"}
+{"idx": 4, "title": "SPRING: Improving the Throughput of Sharding Blockchain via ...", "date": "", "ddg_snippet": "We formulate the Markov Decision Process for state placement in sharding blockchains, converting the state placement into a sequential decision-making task. Moreover, we propose a sharding blockchain system model and a decentralized agent deployment and training protocol to apply SPRING into sharding blockchains.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_Spring_camera_ready.pdf", "content": "We formulate the Markov Decision Process for state placement in sharding blockchains, converting the state placement into a sequential decision-making task. Moreover, we propose a sharding blockchain system model and a decentralized agent deployment and training protocol to apply SPRING into sharding blockchains."}
+{"idx": 5, "title": "MVSS: Blockchain Cross-shard Account Migration Based on Multi ...", "date": "", "ddg_snippet": "The cross-shard account migration mechanism is one of the most advanced solutions to the unbalanced load problem in blockchain sharding systems. Existing solutions employ locking schemes to ensure the atomicity of transactions and the consistency of account states. However, these methods suffer from high transaction locking rates, degraded system performance, and disordered transaction ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10944935", "content": "The cross-shard account migration mechanism is one of the most advanced solutions to the unbalanced load problem in blockchain sharding systems. Existing solutions employ locking schemes to ensure the atomicity of transactions and the consistency of account states. However, these methods suffer from high transaction locking rates, degraded system performance, and disordered transaction ..."}
+{"idx": 6, "title": "A Secure and Efficient Account Migration Protocol in ...", "date": "", "ddg_snippet": "Abstract —In sharding systems, account migration is viewed as an effective strategy to minimize the proportion of cross-shard transactions and optimize load balancing between shards. However, existing account migration schemes typically require locking the transactions of the target account , which leads to delayed transaction confirmations and reduced system efi-ciency. To address this ...", "subpage_snippet": "", "source": "lostnet.info", "link": "http://lostnet.info/cscwd2025/pdf/Paper_420.pdf", "content": "Abstract —In sharding systems, account migration is viewed as an effective strategy to minimize the proportion of cross-shard transactions and optimize load balancing between shards. However, existing account migration schemes typically require locking the transactions of the target account , which leads to delayed transaction confirmations and reduced system efi-ciency. To address this ..."}
+{"idx": 7, "title": "Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "13 May 2024 — In this paper , we present SPRING , the first deep-reinforcement-learning(DRL)-based sharding framework for state placement .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3589334.3645386", "content": "13 May 2024 — In this paper , we present SPRING , the first deep-reinforcement-learning(DRL)-based sharding framework for state placement ."}
+{"idx": 8, "title": "Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "by P Li · Cited by 21 — In this paper , we present Spring , the first deep- reinforcement-learning(DRL)-based sharding framework for state placement . Spring formulates the state ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=8oczaP1YKD", "content": "by P Li · Cited by 21 — In this paper , we present Spring , the first deep- reinforcement-learning(DRL)-based sharding framework for state placement . Spring formulates the state ..."}
+{"idx": 9, "title": "ContribChain: A Stress-Balanced Blockchain Sharding ...", "date": "", "ddg_snippet": "by X Huang · 2025 · Cited by 1 — In this paper , we propose ContribChain, a blockchain sharding protocol that can automatically be aware of node contributions to achieve stress ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.06899", "content": "by X Huang · 2025 · Cited by 1 — In this paper , we propose ContribChain, a blockchain sharding protocol that can automatically be aware of node contributions to achieve stress ..."}
diff --git a/data/sampled_jsons/State-space_models_(SSMs)_have_emerged_as_a_potential_alternative_finite_state_machines.jsonl b/data/sampled_jsons/State-space_models_(SSMs)_have_emerged_as_a_potential_alternative_finite_state_machines.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..333d4608c70286a0f363dd6d2848d1cbb142c165
--- /dev/null
+++ b/data/sampled_jsons/State-space_models_(SSMs)_have_emerged_as_a_potential_alternative_finite_state_machines.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "State - space models ( SSMs ) have emerged as a potential alternative to transformers. ... a different line of work, state space model ( SSM ) architectures ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v3", "content": "State - space models ( SSMs ) have emerged as a potential alternative to transformers. ... a different line of work, state space model ( SSM ) architectures ..."}
+{"idx": 1, "title": "Time-SSM: Simplifying and Unifying State Space Models for Time", "date": "", "ddg_snippet": "State Space Models ( SSMs ) [ 9 , 7 , 11 , 31 ] are recent deep learning models that have demonstrated significant potential in various sequence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.16312v2", "content": "State Space Models ( SSMs ) [ 9 , 7 , 11 , 31 ] are recent deep learning models that have demonstrated significant potential in various sequence ..."}
+{"idx": 2, "title": "On the Tradeoffs of SSMs and Transformers | Goomba Lab", "date": "", "ddg_snippet": "On the other hand, a state space model has always summarized its context $x_1, \\cdots, x_t$ into the hidden state $h_t$ (equation \\eqref{eq: ssm ...", "subpage_snippet": "", "source": "goombalab.github.io", "link": "https://goombalab.github.io/blog/2025/tradeoffs/", "content": "On the other hand, a state space model has always summarized its context $x_1, \\cdots, x_t$ into the hidden state $h_t$ (equation \\eqref{eq: ssm ..."}
+{"idx": 3, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "State - space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. State -offset Tuning: State -based Parameter-Efficient Fine-Tuning for State Space Models .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=transformer+states", "content": "State - space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. State -offset Tuning: State -based Parameter-Efficient Fine-Tuning for State Space Models ."}
+{"idx": 4, "title": "Ashish Sabharwal (@Ashish_S_AI) | Aguea", "date": "", "ddg_snippet": "The Illusion of State in State - Space Models . State - space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture.", "subpage_snippet": "", "source": "aguea.net", "link": "https://aguea.net/Ashish_S_AI", "content": "The Illusion of State in State - Space Models . State - space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture."}
+{"idx": 5, "title": "Kotlin coroutines 톺아보기 | PPT", "date": "", "ddg_snippet": "... converts coroutine code into recursive functions implementing a finite state machine to simulate sequential execution across asynchronous operations.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/kotlin-coroutines-250871163/250871163", "content": "... converts coroutine code into recursive functions implementing a finite state machine to simulate sequential execution across asynchronous operations."}
+{"idx": 6, "title": "Marcos TREVISO | Master of Science | University of São Paulo,", "date": "", "ddg_snippet": "State space models ( SSMs ), such as Mamba, have emerged as an efficient alternative to transformers for long-context sequence modeling .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Marcos-Treviso", "content": "State space models ( SSMs ), such as Mamba, have emerged as an efficient alternative to transformers for long-context sequence modeling ."}
+{"idx": 7, "title": "steve abreu", "date": "", "ddg_snippet": "In the quest for next-generation sequence modeling architectures, State Space Models ( SSMs ) have emerged as a potent alternative to transformers ...", "subpage_snippet": "", "source": "stevenabreu.com", "link": "https://stevenabreu.com/", "content": "In the quest for next-generation sequence modeling architectures, State Space Models ( SSMs ) have emerged as a potent alternative to transformers ..."}
+{"idx": 8, "title": "GPU Day", "date": "", "ddg_snippet": "In recent years, quantum machine learning (QML) has emerged as a rapidly expanding field within quantum algorithms and applications.", "subpage_snippet": "", "source": "gpuday.com", "link": "https://gpuday.com/gpu-day-2024", "content": "In recent years, quantum machine learning (QML) has emerged as a rapidly expanding field within quantum algorithms and applications."}
+{"idx": 9, "title": "ODSC West Schedule 2024 | Open Data Science Conference", "date": "", "ddg_snippet": "By drawing on the unique strengths of different models , and providing access to realtime databases and knowledge sources, we can overcome the finite ...", "subpage_snippet": "", "source": "staging6.odsc.com", "link": "https://staging6.odsc.com/california/odsc-west-schedule-2024/", "content": "By drawing on the unique strengths of different models , and providing access to realtime databases and knowledge sources, we can overcome the finite ..."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aafca78d4be54c0ade704b5bdfca36cbb7afc632
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46yLEXtav4", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated..."}
+{"idx": 2, "title": "\"Statistical Collusion by Collectives on Learning Platforms.\"", "date": "", "ddg_snippet": "Bibliographic details on Statistical Collusion by Collectives on Learning Platforms.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-04879", "content": "Bibliographic details on Statistical Collusion by Collectives on Learning Platforms."}
+{"idx": 3, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ..."}
+{"idx": 4, "title": "GitHub - GauthierE/statistical-collusion", "date": "", "ddg_snippet": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "The statistical inference capabilities of the collective serve two main purposes: first, to estimate the most effective strategies for influencing the platform, and second, to infer key parameters that determine the collective's success. This dual approach allows the collective to predict its potential impact on the platform with high ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "The statistical inference capabilities of the collective serve two main purposes: first, to estimate the most effective strategies for influencing the platform, and second, to infer key parameters that determine the collective's success. This dual approach allows the collective to predict its potential impact on the platform with high ..."}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "The study focuses on statistical collusion - where groups create patterns in training data that wouldn't occur naturally. These coordinated actions can make AI systems behave in ways that serve the group's interests rather than working as intended.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/statistical-collusion-by-collectives-learning-platforms", "content": "The study focuses on statistical collusion - where groups create patterns in training data that wouldn't occur naturally. These coordinated actions can make AI systems behave in ways that serve the group's interests rather than working as intended."}
+{"idx": 7, "title": "PDF Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier, Francis Bach, Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/47263.pdf", "content": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier, Francis Bach, Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices"}
+{"idx": 8, "title": "Statistical Collusion by Collectives on Learning Platfor...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 9, "title": "Publications - University of California, Berkeley", "date": "", "ddg_snippet": "Statistical collusion by collectives on learning platforms. E. Gauthier, F. Bach, and M. I. Jordan. International Conference on Machine Learning (ICML), 2025. Prediction-aware learning in multi-agent systems. A. Capitaine, E. Boursier, E. Moulines, M. I. Jordan, and A. Durmus. International Conference on Machine Learning (ICML), 2025.", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jordan/publications.html", "content": "Statistical collusion by collectives on learning platforms. E. Gauthier, F. Bach, and M. I. Jordan. International Conference on Machine Learning (ICML), 2025. Prediction-aware learning in multi-agent systems. A. Capitaine, E. Boursier, E. Moulines, M. I. Jordan, and A. Durmus. International Conference on Machine Learning (ICML), 2025."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_Rs(k)_definition_formula_page_3.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_Rs(k)_definition_formula_page_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..38343d0eed65c1ace12475e7ef1a07672ef14c20
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_Rs(k)_definition_formula_page_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Target rating point - Wikipedia", "date": "", "ddg_snippet": "For example, if an advertisement appears more than once, the entire gross audience, the TRP figure is the sum of each individual GRP, multiplied by ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Target_rating_point", "content": "For example, if an advertisement appears more than once, the entire gross audience, the TRP figure is the sum of each individual GRP, multiplied by ..."}
+{"idx": 1, "title": "CA2737610C - Collusion resistant watermarking generation method", "date": "", "ddg_snippet": "H04N1/32 — Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/CA2737610C/en", "content": "H04N1/32 — Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device ..."}
+{"idx": 2, "title": "(PDF) Mafia and politics as concurrent governance actors.", "date": "", "ddg_snippet": "... in the territory where the ‘ndrangheta clans are most powerful, infiltration by criminal clans in the political process is rather through collusion ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/275961289_Mafia_and_politics_as_concurrent_governance_actors_Revisiting_political_power_and_crime_in_Southern_Italy", "content": "... in the territory where the ‘ndrangheta clans are most powerful, infiltration by criminal clans in the political process is rather through collusion ..."}
+{"idx": 3, "title": "CA CPT : Volume 1 - Model Test Paper 14 (with Answers) -", "date": "", "ddg_snippet": "... account ( b ) Real account (c) Nominal account (d) None of the above On March 31, 2010 Narain Enterprises purchased a loader from Laxmi Motors for Rs ...", "subpage_snippet": "", "source": "www.respaper.com", "link": "https://www.respaper.com/ca_cpt/285/1287-pdf.html", "content": "... account ( b ) Real account (c) Nominal account (d) None of the above On March 31, 2010 Narain Enterprises purchased a loader from Laxmi Motors for Rs ..."}
+{"idx": 4, "title": "Jäger\\xspace: Automated Telephone Call Traceback", "date": "", "ddg_snippet": "... of a call, an investigator must start at the destination with knowledge of the call time, call destination, and claimed call source, and go hop- by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.02839v4", "content": "... of a call, an investigator must start at the destination with knowledge of the call time, call destination, and claimed call source, and go hop- by ..."}
+{"idx": 5, "title": "Insulin Resistance and Truncal Obesity as Important", "date": "", "ddg_snippet": "We have reported a threefold prevalence of diabetes in men and women aged 40–70 years of Indian Asian and African Caribbean origin compared with ...", "subpage_snippet": "", "source": "diabetesjournals.org", "link": "https://diabetesjournals.org/care/article/36/2/383/38145/Insulin-Resistance-and-Truncal-Obesity-as", "content": "We have reported a threefold prevalence of diabetes in men and women aged 40–70 years of Indian Asian and African Caribbean origin compared with ..."}
+{"idx": 6, "title": "IACR News", "date": "", "ddg_snippet": "We analyze Kaneko's bound to prove that, away from the $j$-invariant $0$, edges of multiplicity at least three can occur in the supersingular $\\ell ...", "subpage_snippet": "", "source": "www.iacr.org", "link": "https://www.iacr.org/news/index.php?page=13", "content": "We analyze Kaneko's bound to prove that, away from the $j$-invariant $0$, edges of multiplicity at least three can occur in the supersingular $\\ell ..."}
+{"idx": 7, "title": "the practice: Latest News & Videos, Photos about the", "date": "", "ddg_snippet": "... chased by a recruiter with over 60 messages and calls in three days—prompting them to block the number and cancel an interview with a major company.", "subpage_snippet": "", "source": "economictimes.indiatimes.com", "link": "https://economictimes.indiatimes.com/topic/the-practice", "content": "... chased by a recruiter with over 60 messages and calls in three days—prompting them to block the number and cancel an interview with a major company."}
+{"idx": 8, "title": "Dig 'em Up and Move 'em Out: Confederate Fire Sale - The", "date": "", "ddg_snippet": "Keep them movin', movin', movin', Though they' re disapprovin', Keep them dawwgies rollin', Bigtime! Don't try to understand them, Just grope and throw ...", "subpage_snippet": "", "source": "thepeoplescube.com", "link": "https://thepeoplescube.com/peoples-blog/dig-em-up-and-move-em-out-t16639.html", "content": "Keep them movin', movin', movin', Though they' re disapprovin', Keep them dawwgies rollin', Bigtime! Don't try to understand them, Just grope and throw ..."}
+{"idx": 9, "title": "Motor Vehicles Act 1988 - Complete Bare Act - B&B", "date": "", "ddg_snippet": "3 ) It shall come into force on such date [1] as the Central Government may, by notification in the Official Gazette, appoint; and different dates ...", "subpage_snippet": "", "source": "bnblegal.com", "link": "https://bnblegal.com/bareact/motor-vehicles-act-1988/", "content": "3 ) It shall come into force on such date [1] as the Central Government may, by notification in the Official Gazette, appoint; and different dates ..."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..50fba30d1d9dff8b816dcc83aa6af39452c661b1
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms | Read Paper ...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 2, "title": "GitHub - GauthierE/statistical-collusion", "date": "", "ddg_snippet": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 3, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46yLEXtav4", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated..."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ..."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v1", "content": "Abstract As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In ..."}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47263", "content": "Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT"}
+{"idx": 7, "title": "[论文审查] Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "这篇论文由Etienne Gauthier、Francis Bach和Michael I. Jordan撰写，主要探讨了在学习平台上，集体如何通过协调行为影响系统以符合其自身利益的问题。随着各种平台越来越依赖于学习算法，可能会形成由多个个体组成的集体，寻求共同的策略来影响这些平台，该过程包括修改数据以实现他们的目标 ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/zh/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "这篇论文由Etienne Gauthier、Francis Bach和Michael I. Jordan撰写，主要探讨了在学习平台上，集体如何通过协调行为影响系统以符合其自身利益的问题。随着各种平台越来越依赖于学习算法，可能会形成由多个个体组成的集体，寻求共同的策略来影响这些平台，该过程包括修改数据以实现他们的目标 ..."}
+{"idx": 8, "title": "Learn Statistics Online - Massive Open Online Courses Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Join millions of learners from around the world already learning on Udemy. Learn how to apply probability & statistics to real data science & business applications.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=j9cAth2UcCExC8gQyLRmsFech7Z0_JrOgqFsf3R8-1iiet6mQ_SNbwZXZC2lxgvM-zdHlnuE5a0NeR8z0vUcZR5hHIpleOUPQouTTKAgbEChE2JtZDMGmNNpMR00qaRj.tjC-vwpa-Jq3uiE-dvqqdg&rut=898d7139e9e6afecb2049ddadc385c76ceec5f0e477de88d9ff2624def0d7892&u3=https://www.bing.com/aclick?ld=e8a3o4iMbTWoBGD177XZoB5jVUCUxh-n0gJTNZFzI3jgLi1RebcEQRCFpM51lDqKRByU3XkRFo0iBHI3dVDVaEAu92ye11EJsDkoRI0G2luvuCUPAgJeGAA_GS-s6kTB24agOVJJaeEhkMz3i76DYBSnEpF4Ewt0bxiQvOGxxNKGHaHaPTmU69w6sbfrfYxQvOrdmgNw&u=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&rlid=66c120f4c8e11b9bebba48259aa82c36&vqd=4-284565223617560550123442036179526464606&iurl={1}IG=68DF4002EF57459BA99283A9D25D2CBE&CID=063D130E2E5069CB0E1A05612F506807&ID=DevEx,5046.1", "content": "Join millions of learners from around the world already learning on Udemy. Learn how to apply probability & statistics to real data science & business applications."}
+{"idx": 9, "title": "Publications - University of California, Berkeley", "date": "", "ddg_snippet": "Statistical collusion by collectives on learning platforms . E. Gauthier, F. Bach, and M. I. Jordan. International Conference on Machine Learning (ICML), 2025. Prediction-aware learning in multi-agent systems. A. Capitaine, E. Boursier, E. Moulines, M. I. Jordan, and A. Durmus. International Conference on Machine Learning (ICML), 2025.", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jordan/publications.html", "content": "Statistical collusion by collectives on learning platforms . E. Gauthier, F. Bach, and M. I. Jordan. International Conference on Machine Learning (ICML), 2025. Prediction-aware learning in multi-agent systems. A. Capitaine, E. Boursier, E. Moulines, M. I. Jordan, and A. Durmus. International Conference on Machine Learning (ICML), 2025."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Algorithm_1_delta_tilde_definition_year_2023.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Algorithm_1_delta_tilde_definition_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b6a7abd9ea245ecf7f5312fe616f6cf161d26652
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Algorithm_1_delta_tilde_definition_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Collective Action in Machine Learning . We draw on the work of Hardt et al. (2023), who investigate the follow-ing problem: a population of individuals (x, y) ∈ X × Y, drawn i . i .d. from a distribution D, interacts with a plat - form .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=46yLEXtav4", "content": "Collective Action in Machine Learning . We draw on the work of Hardt et al. (2023), who investigate the follow-ing problem: a population of individuals (x, y) ∈ X × Y, drawn i . i .d. from a distribution D, interacts with a plat - form ."}
+{"idx": 2, "title": "(PDF) Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests.on online platforms . 3 Statistical Algorithmic Collective Action in Classification.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848421_Statistical_Collusion_by_Collectives_on_Learning_Platforms", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests.on online platforms . 3 Statistical Algorithmic Collective Action in Classification."}
+{"idx": 3, "title": "[Literature Review] Statistical Collusion by Collectives on Learning ...", "date": "", "ddg_snippet": "Statistical Collusion . Collective Action. Learning Platforms . Signal Planting.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "Statistical Collusion . Collective Action. Learning Platforms . Signal Planting."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Statistical-Collusion-by-Collectives-on-Learning-Platforms-deaeb81c-5365-4cec-9af2-e4d286dfd04a", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 5, "title": "GauthierE/ statistical - collusion : Statistical Collusion by Collectives ...", "date": "", "ddg_snippet": "About. Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879. Resources.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "About. Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879. Resources."}
+{"idx": 6, "title": "Papers by Francis Bach with links to code and results.", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms .In many areas of applied statistics and machine learning , generating an arbitrary number of independent and identically distributed ( i . i . d.)", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Francis+Bach", "content": "Statistical Collusion by Collectives on Learning Platforms .In many areas of applied statistics and machine learning , generating an arbitrary number of independent and identically distributed ( i . i . d.)"}
+{"idx": 7, "title": "Etienne Gauthier - Google Scholar", "date": "", "ddg_snippet": "Verified email at inria.fr - Homepage. machine learning multi-agent systems uncertainty quantification. Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Tiwn0RMAAAAJ&hl=en", "content": "Verified email at inria.fr - Homepage. machine learning multi-agent systems uncertainty quantification. Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 8, "title": "Publications", "date": "", "ddg_snippet": "Statistical collusion by collectives on learning platforms . First -order algorithms for nonlinear generalized Nash equilibrium problems.", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jordan/publications.html", "content": "Statistical collusion by collectives on learning platforms . First -order algorithms for nonlinear generalized Nash equilibrium problems."}
+{"idx": 9, "title": "ICML 2025 Sneak Peek: How Generative AI + RL Will Act... | Medium", "date": "", "ddg_snippet": "The single biggest story from ICML 2025 is not a single model or a new benchmark; it is a grand unification of two previously distinct fields of AI: Generative AI (GenAI) and Reinforcement Learning (RL).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/foundation-models-deep-dive/icml-2025-sneak-peek-how-generative-ai-rl-will-act-not-just-react-b3dcb588c926", "content": "The single biggest story from ICML 2025 is not a single model or a new benchmark; it is a grand unification of two previously distinct fields of AI: Generative AI (GenAI) and Reinforcement Learning (RL)."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Hoeffding_error_Rs(k).jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Hoeffding_error_Rs(k).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06a238f57e93116f08bfa882e8d404b18e3648ab
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Hoeffding_error_Rs(k).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GauthierE/ statistical - collusion : Statistical Collusion by Collectives ...", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms .About. Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879. Resources. Readme. Uh oh! There was an error while loading.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "Statistical Collusion by Collectives on Learning Platforms .About. Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879. Resources. Readme. Uh oh! There was an error while loading."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "Abstract As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 2, "title": "Statistical Collusion by Collectives on Learning Platforms ...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 3, "title": "Statistical Learning Theory Part 1: Hoeffding’s Inequality ...", "date": "", "ddg_snippet": "Oct 20, 2023 · We now perform a computational simulation in Python showing the conservative (but probabilistically correct) bounds Hoeffding ’ s Inequality can provide around Random Variables and empirical...", "subpage_snippet": "", "source": "anr248.medium.com", "link": "https://anr248.medium.com/statistical-learning-theory-hoeffdings-inequality-derivation-simulation-e3a97100d147", "content": "Oct 20, 2023 · We now perform a computational simulation in Python showing the conservative (but probabilistically correct) bounds Hoeffding ’ s Inequality can provide around Random Variables and empirical..."}
+{"idx": 4, "title": "lecture07.pdf | Topics in Statistics: Statistical Learning ...", "date": "", "ddg_snippet": "This file covers lecture explaining Hoeffding ' s inequality, Hoeffding -Chernoff inequality based on theorems.", "subpage_snippet": "", "source": "ocw.mit.edu", "link": "https://ocw.mit.edu/courses/18-465-topics-in-statistics-statistical-learning-theory-spring-2007/resources/lecture07/", "content": "This file covers lecture explaining Hoeffding ' s inequality, Hoeffding -Chernoff inequality based on theorems."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Dec 31, 2024 · Abstract: As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=68f74mHCTf", "content": "Dec 31, 2024 · Abstract: As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 6, "title": "Hoeffding’s inequality for general Markov chains with its ...", "date": "", "ddg_snippet": "In this paper, we generalize the classical Hoeffding ’ s lemma and inequality at their full strength for functions of general (general-state-space and not necessarily reversible) Markov chains.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8457514/", "content": "In this paper, we generalize the classical Hoeffding ’ s lemma and inequality at their full strength for functions of general (general-state-space and not necessarily reversible) Markov chains."}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Collective Action in Machine Learning .This statistical framework allows for the derivation of three key results, each tied to a distinct objective that a collective may pursue to influence a platform : signal planting, signal erasing, and a new objective we introduce, signal unplanting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v2", "content": "Collective Action in Machine Learning .This statistical framework allows for the derivation of three key results, each tied to a distinct objective that a collective may pursue to influence a platform : signal planting, signal erasing, and a new objective we introduce, signal unplanting."}
+{"idx": 8, "title": "(PDF) Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.We will denote Hoeffding error terms as follows", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848421_Statistical_Collusion_by_Collectives_on_Learning_Platforms", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.We will denote Hoeffding error terms as follows"}
+{"idx": 9, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Statistical-Collusion-by-Collectives-on-Learning-Platforms-deaeb81c-5365-4cec-9af2-e4d286dfd04a", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
diff --git "a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Theorem_3.3_concentration_bound_R\316\264(n).jsonl" "b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Theorem_3.3_concentration_bound_R\316\264(n).jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..3738cfb46e1c404bd8779224c4682d9de2ee3f12
--- /dev/null
+++ "b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Theorem_3.3_concentration_bound_R\316\264(n).jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "Abstract As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 1, "title": "PDF Concentration of Measure", "date": "", "ddg_snippet": "Concentration of measure is a vast and still growing area. Some good references are De-vroye, Gyorfi and Lugosi (1996), van der Vaart and Wellner (1996), Chapter 19 of van der Vaart (1998), Dubhashi and Panconesi (2009), and Ledoux (2005).", "subpage_snippet": "", "source": "www.stat.cmu.edu", "link": "https://www.stat.cmu.edu/~larry/=sml/Concentration.pdf", "content": "Concentration of measure is a vast and still growing area. Some good references are De-vroye, Gyorfi and Lugosi (1996), van der Vaart and Wellner (1996), Chapter 19 of van der Vaart (1998), Dubhashi and Panconesi (2009), and Ledoux (2005)."}
+{"idx": 2, "title": "Statistical Collusion by Collectives on Learning Platforms | Read Paper ...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 3, "title": "PDF ECE 543: Statistical Learning Theory", "date": "", "ddg_snippet": "Statistical learning theory [Vap98, Vid03] primarily concerns itself with the rst of these questions, while the second question is within the purview of computational learning theory [Val84, KV94].", "subpage_snippet": "", "source": "courses.grainger.illinois.edu", "link": "https://courses.grainger.illinois.edu/ece543/sp2019/SLT.pdf", "content": "Statistical learning theory [Vap98, Vid03] primarily concerns itself with the rst of these questions, while the second question is within the purview of computational learning theory [Val84, KV94]."}
+{"idx": 4, "title": "PDF Statistical Learning Theory - University of British Columbia", "date": "", "ddg_snippet": "A learning algorithm A is a function that takes in a sample S and returns hypothesis. Ideally, one with low risk. The most common learning algorithm we'll think about is empirical risk minimization: ERM(S) ∈ arg minh∈H LS(h). (If there are ties, by default we think of the algorithm returning an arbitrary choice.) The returned hypothesis, ERM(S) which we will also often denote ˆhS, is ...", "subpage_snippet": "", "source": "www.cs.ubc.ca", "link": "https://www.cs.ubc.ca/~dsuth/532D/22w1/notes.pdf", "content": "A learning algorithm A is a function that takes in a sample S and returns hypothesis. Ideally, one with low risk. The most common learning algorithm we'll think about is empirical risk minimization: ERM(S) ∈ arg minh∈H LS(h). (If there are ties, by default we think of the algorithm returning an arbitrary choice.) The returned hypothesis, ERM(S) which we will also often denote ˆhS, is ..."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "The paper explores the statistical inference for collective action in learning platforms , in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining the optimal strategy h. In the end, the authors develop a framework that provides a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46yLEXtav4", "content": "The paper explores the statistical inference for collective action in learning platforms , in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining the optimal strategy h. In the end, the authors develop a framework that provides a ..."}
+{"idx": 6, "title": "PDF Lecture 12{13: Statistical Learning Theory - University of Wisconsin ...", "date": "", "ddg_snippet": "1 Statistical Learning Theory We denote by f : X ! [0; 1] the unknown true regression function. We observe n data points (Xi; f (Xi); i = 1; : : : ; n, where Xi is drawn i.i.d. from some unknown distribution . We would like to use the data to compute an estimator ^f of f .", "subpage_snippet": "", "source": "pages.cs.wisc.edu", "link": "https://pages.cs.wisc.edu/~yudongchen/orie7790_sp20/Lecture12-13_statistical_learning.pdf", "content": "1 Statistical Learning Theory We denote by f : X ! [0; 1] the unknown true regression function. We observe n data points (Xi; f (Xi); i = 1; : : : ; n, where Xi is drawn i.i.d. from some unknown distribution . We would like to use the data to compute an estimator ^f of f ."}
+{"idx": 7, "title": "PDF Lecture Notes for Machine Learning Theory (CS229M/STATS214)", "date": "", "ddg_snippet": "Whereas asymptotic analysis uses large-sample theorems such as the central limit theorem and the law of large numbers to provide convergence guarantees, non-asymptotic analysis relies on concentration inequalities to develop alternative techniques for reasoning about the performance of learning algorithms.", "subpage_snippet": "", "source": "tselilschramm.org", "link": "https://tselilschramm.org/mltheory/ma.pdf", "content": "Whereas asymptotic analysis uses large-sample theorems such as the central limit theorem and the law of large numbers to provide convergence guarantees, non-asymptotic analysis relies on concentration inequalities to develop alternative techniques for reasoning about the performance of learning algorithms."}
+{"idx": 8, "title": "PDF Concentration of measure: fundamentals and tools", "date": "", "ddg_snippet": "The follow lecture notes, available on -line, are an excellent introduction to the subject, and covers all the results given here (Markov, Cherno , Hoe ding, Efron-Stein and Sobolev inequalities) in good detail, along with many examples from statistical learning theory.", "subpage_snippet": "", "source": "www.stat.rice.edu", "link": "https://www.stat.rice.edu/~jrojo/PASI/lectures/TyronCMarticle.pdf", "content": "The follow lecture notes, available on -line, are an excellent introduction to the subject, and covers all the results given here (Markov, Cherno , Hoe ding, Efron-Stein and Sobolev inequalities) in good detail, along with many examples from statistical learning theory."}
+{"idx": 9, "title": "arXiv:2502.04879v1 [stat.ML] 7 Feb 2025", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879v1", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_arXiv.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..caaca65f463c8534f268ebc0eee027385d002d94
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms -", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms ... platforms increasingly rely on learning algorithms, collectives may formand seek ways ...", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/608798/statistical-collusion-by-collectives-on-learning-platforms", "content": "Statistical Collusion by Collectives on Learning Platforms ... platforms increasingly rely on learning algorithms, collectives may formand seek ways ..."}
+{"idx": 1, "title": "Algorithmic Collusion by Large Language ModelsTo Krangy, a", "date": "", "ddg_snippet": "2023 ) study platform design to reduce the harm to consumers from autonomous collusion by pricing algorithms based on Q Q - learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.00806v4", "content": "2023 ) study platform design to reduce the harm to consumers from autonomous collusion by pricing algorithms based on Q Q - learning ."}
+{"idx": 2, "title": "Enhancing Model Privacy in Federated Learning with Random", "date": "", "ddg_snippet": "Experimental results across various models and tasks demonstrate that our approach not only maintains strong model performance in federated learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18911v1", "content": "Experimental results across various models and tasks demonstrate that our approach not only maintains strong model performance in federated learning ..."}
+{"idx": 3, "title": "Papers, arXiv.org | IDEAS/RePEc", "date": "", "ddg_snippet": "2508.14762 Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions by Yoonsik Hong & Diego Klabjan", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/s/arx/papers.html", "content": "2508.14762 Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions by Yoonsik Hong & Diego Klabjan"}
+{"idx": 4, "title": "Most Influential ArXiv (Information Theory) Papers (2025-03", "date": "", "ddg_snippet": "... only about adding more antennas but will also uncover new propagation and hardware phenomena that can only be treated by jointly utilizing insights ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/03/most-influential-arxiv-information-theory-papers-2025-03-version/", "content": "... only about adding more antennas but will also uncover new propagation and hardware phenomena that can only be treated by jointly utilizing insights ..."}
+{"idx": 5, "title": "Most Influential ArXiv (Information Theory) Papers (2025-03", "date": "", "ddg_snippet": "... only about adding more antennas but will also uncover new propagation and hardware phenomena that can only be treated by jointly utilizing insights ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2025/03/most-influential-arxiv-information-theory-papers-2025-03-version/", "content": "... only about adding more antennas but will also uncover new propagation and hardware phenomena that can only be treated by jointly utilizing insights ..."}
+{"idx": 6, "title": "Zach Y. Brown | IDEAS/RePEc", "date": "", "ddg_snippet": "Naive Algorithmic Collusion : When Do Bandit Learners Cooperate and When Do They Compete? ,\" Papers 2411.16574, arXiv .org.", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/f/pbr897.html", "content": "Naive Algorithmic Collusion : When Do Bandit Learners Cooperate and When Do They Compete? ,\" Papers 2411.16574, arXiv .org."}
+{"idx": 7, "title": "Vers un virage algorithmique de la lutte anticartels ?", "date": "", "ddg_snippet": "Since the seminal works of Ezrachi and Stucke (2015, 2016) on algorithmic collusion , the question of cartels supported or even prompted by pricing ...", "subpage_snippet": "", "source": "journals.openedition.org", "link": "https://journals.openedition.org/ethiquepublique/6566", "content": "Since the seminal works of Ezrachi and Stucke (2015, 2016) on algorithmic collusion , the question of cartels supported or even prompted by pricing ..."}
+{"idx": 8, "title": "Analysis of the communication between colluding applications on", "date": "", "ddg_snippet": "... of possible implications of application collusion attacks---quite the contrary--- on existing platforms , users are implicitly led to believe that by ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/261851480_Analysis_of_the_communication_between_colluding_applications_on_modern_Smartphones", "content": "... of possible implications of application collusion attacks---quite the contrary--- on existing platforms , users are implicitly led to believe that by ..."}
+{"idx": 9, "title": "(PDF) A Review on Decentralized Artificial Intelligence in the", "date": "", "ddg_snippet": "Furthermore, we shed light on the emergent challenges stemming from large-scale models, particularly in the realms of cryptography, privacy ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380564678_A_Review_on_Decentralized_Artificial_Intelligence_in_the_Era_of_Large_Models", "content": "Furthermore, we shed light on the emergent challenges stemming from large-scale models, particularly in the realms of cryptography, privacy ..."}
diff --git a/data/sampled_jsons/Step_1_Step_2_Step_3_Step_4_self-supervised_learning_emotion_behavior_mapping_DIKE.jsonl b/data/sampled_jsons/Step_1_Step_2_Step_3_Step_4_self-supervised_learning_emotion_behavior_mapping_DIKE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9197688f6e0ac9e262bec5e439090a4ed0c19008
--- /dev/null
+++ b/data/sampled_jsons/Step_1_Step_2_Step_3_Step_4_self-supervised_learning_emotion_behavior_mapping_DIKE.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "Multi-LLM Agent Collaborative Intelligence: The Path to AGI", "date": "", "ddg_snippet": "... self-supervised learning to predict masked words within a context, along ... step #1 , asking GPT-3 to identify the conclusion of a document. To avoid ... 589 pages", "subpage_snippet": "", "source": "shuyuej.com", "link": "http://shuyuej.com/books/The-Path-to-Artificial-General-Intelligence.pdf", "content": "... self-supervised learning to predict masked words within a context, along ... step #1 , asking GPT-3 to identify the conclusion of a document. To avoid ... 589 pages"}
diff --git a/data/sampled_jsons/Stochastic_Gradient_Langevin_Dynamics_SGLD.jsonl b/data/sampled_jsons/Stochastic_Gradient_Langevin_Dynamics_SGLD.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4951ac6d879cdbd3ec2fae7f193bd31f16701b6f
--- /dev/null
+++ b/data/sampled_jsons/Stochastic_Gradient_Langevin_Dynamics_SGLD.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stochastic gradient Langevin dynamics - Wikipedia", "date": "", "ddg_snippet": "Stochastic gradient Langevin dynamics ( SGLD ) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a Robbins-Monro optimization algorithm, and Langevin dynamics , a mathematical extension of molecular dynamics models.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic_Gradient_Langevin_Dynamics", "content": "Stochastic gradient Langevin dynamics ( SGLD ) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a Robbins-Monro optimization algorithm, and Langevin dynamics , a mathematical extension of molecular dynamics models."}
+{"idx": 1, "title": "PDF Bayesian Learning via Stochastic Gradient Langevin Dynamics", "date": "", "ddg_snippet": "In this section we consider the use of our stochastic gradient Langevin dynamics algorithm as one which produces samples from the posterior distribution. We first derive an estimate of when the algorithm will transition from stochastic optimization to Langevin dynamics .", "subpage_snippet": "", "source": "www.stats.ox.ac.uk", "link": "https://www.stats.ox.ac.uk/~teh/research/compstats/WelTeh2011a.pdf", "content": "In this section we consider the use of our stochastic gradient Langevin dynamics algorithm as one which produces samples from the posterior distribution. We first derive an estimate of when the algorithm will transition from stochastic optimization to Langevin dynamics ."}
+{"idx": 2, "title": "A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics", "date": "", "ddg_snippet": "We study the Stochastic Gradient Langevin Dynamics ( SGLD ) algorithm for non-convex optimization. The algorithm performs stochastic gradient descent, where in each step it injects appropriately scaled Gaussian noise to the update. We analyze the algorithm's hitting time to an arbitrary subset of the parameter space. Two results follow from our general theory: First, we prove that for empirical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1702.05575", "content": "We study the Stochastic Gradient Langevin Dynamics ( SGLD ) algorithm for non-convex optimization. The algorithm performs stochastic gradient descent, where in each step it injects appropriately scaled Gaussian noise to the update. We analyze the algorithm's hitting time to an arbitrary subset of the parameter space. Two results follow from our general theory: First, we prove that for empirical ..."}
+{"idx": 3, "title": "[2407.04264] Langevin Dynamics: A Unified Perspective on Optimization ...", "date": "", "ddg_snippet": "We study the problem of non-convex optimization using Stochastic Gradient Langevin Dynamics ( SGLD ). SGLD is a natural and popular variation of stochastic gradient descent where at each step, appropriately scaled Gaussian noise is added. To our knowledge, the only strategy for showing global convergence of SGLD on the loss function is to show that SGLD can sample from a stationary distribution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.04264", "content": "We study the problem of non-convex optimization using Stochastic Gradient Langevin Dynamics ( SGLD ). SGLD is a natural and popular variation of stochastic gradient descent where at each step, appropriately scaled Gaussian noise is added. To our knowledge, the only strategy for showing global convergence of SGLD on the loss function is to show that SGLD can sample from a stationary distribution ..."}
+{"idx": 4, "title": "The promises and pitfalls of Stochastic Gradient Langevin Dynamics", "date": "", "ddg_snippet": "Stochastic Gradient Langevin Dynamics ( SGLD ) has emerged as a key MCMC algorithm for Bayesian learning from large scale datasets. While SGLD with decreasing step sizes converges weakly to the posterior distribution, the algorithm is often used with a constant step size in practice and has demonstrated successes in machine learning tasks. The current practice is to set the step size inversely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1811.10072", "content": "Stochastic Gradient Langevin Dynamics ( SGLD ) has emerged as a key MCMC algorithm for Bayesian learning from large scale datasets. While SGLD with decreasing step sizes converges weakly to the posterior distribution, the algorithm is often used with a constant step size in practice and has demonstrated successes in machine learning tasks. The current practice is to set the step size inversely ..."}
+{"idx": 5, "title": "PDF Non-Convex Learning via Stochastic Gradient Langevin Dynamics: A ...", "date": "", "ddg_snippet": "Abstract Stochastic Gradient Langevin Dynamics ( SGLD ) is a popular variant of Stochastic Gradient De-scent, where properly scaled isotropic Gaussian noise is added to an unbiased estimate of the gra-dient at each iteration. This modest change allows SGLD to escape local minima and suffices to guarantee asymptotic convergence to global minimizers for sufficiently regular non-convex objec-tives ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v65/raginsky17a/raginsky17a.pdf", "content": "Abstract Stochastic Gradient Langevin Dynamics ( SGLD ) is a popular variant of Stochastic Gradient De-scent, where properly scaled isotropic Gaussian noise is added to an unbiased estimate of the gra-dient at each iteration. This modest change allows SGLD to escape local minima and suffices to guarantee asymptotic convergence to global minimizers for sufficiently regular non-convex objec-tives ..."}
+{"idx": 6, "title": "PDF Federated Stochastic Gradient Langevin Dynamics", "date": "", "ddg_snippet": "Abstract Stochastic gradient MCMC methods, such as stochastic gradient Langevin dynamics ( SGLD ), employ fast but noisy gradient estimates to enable large-scale posterior sampling. Although we can easily extend SGLD to distributed settings, it suf-fers from two issues when applied to federated non-IID data. First, the variance of these estimates increases significantly. Second, delaying commu ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v161/mekkaoui21a/mekkaoui21a.pdf", "content": "Abstract Stochastic gradient MCMC methods, such as stochastic gradient Langevin dynamics ( SGLD ), employ fast but noisy gradient estimates to enable large-scale posterior sampling. Although we can easily extend SGLD to distributed settings, it suf-fers from two issues when applied to federated non-IID data. First, the variance of these estimates increases significantly. Second, delaying commu ..."}
+{"idx": 7, "title": "Stochastic Gradient Langevin Dynamics — sgld • sgmcmc", "date": "", "ddg_snippet": "Stochastic Gradient Langevin Dynamics Simulates from the posterior defined by the functions logLik and logPrior using stochastic gradient Langevin Dynamics . The function uses TensorFlow, so needs TensorFlow for python installed. sgld (logLik, dataset, params, stepsize, logPrior = NULL,", "subpage_snippet": "", "source": "stor-i.github.io", "link": "https://stor-i.github.io/sgmcmc/reference/sgld.html", "content": "Stochastic Gradient Langevin Dynamics Simulates from the posterior defined by the functions logLik and logPrior using stochastic gradient Langevin Dynamics . The function uses TensorFlow, so needs TensorFlow for python installed. sgld (logLik, dataset, params, stepsize, logPrior = NULL,"}
+{"idx": 8, "title": "PDF Stochastic Gradient Riemannian Langevin Dynamics on the ... - NeurIPS", "date": "", "ddg_snippet": "The stochastic gradient Langevin dynamics ( SGLD ) algorithm [WT11] replaces the calculation of the gradient over the full data set, with a stochastic approximation based on a subset of data.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2013/file/309928d4b100a5d75adff48a9bfc1ddb-Paper.pdf", "content": "The stochastic gradient Langevin dynamics ( SGLD ) algorithm [WT11] replaces the calculation of the gradient over the full data set, with a stochastic approximation based on a subset of data."}
+{"idx": 9, "title": "Non-convex learning via Stochastic Gradient Langevin Dynamics: a ...", "date": "", "ddg_snippet": "Stochastic Gradient Langevin Dynamics ( SGLD ) is a popular variant of Stochastic Gradient Descent, where properly scaled isotropic Gaussian noise is added to an unbiased estimate of the gradient at each iteration. This modest change allows SGLD to escape local minima and suffices to guarantee asymptotic convergence to global minimizers for sufficiently regular non-convex objectives (Gelfand and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1702.03849", "content": "Stochastic Gradient Langevin Dynamics ( SGLD ) is a popular variant of Stochastic Gradient Descent, where properly scaled isotropic Gaussian noise is added to an unbiased estimate of the gradient at each iteration. This modest change allows SGLD to escape local minima and suffices to guarantee asymptotic convergence to global minimizers for sufficiently regular non-convex objectives (Gelfand and ..."}
diff --git a/data/sampled_jsons/Stochastic_Gradient_Langevin_Dynamics_SGLD_noise_tensor_shape_parameters.jsonl b/data/sampled_jsons/Stochastic_Gradient_Langevin_Dynamics_SGLD_noise_tensor_shape_parameters.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a5a1ce7bdb3765d9d0f78cc6c2ddc295ff0a7692
--- /dev/null
+++ b/data/sampled_jsons/Stochastic_Gradient_Langevin_Dynamics_SGLD_noise_tensor_shape_parameters.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stochastic gradient Markov chain Monte Carlo - lyndonduong.com", "date": "", "ddg_snippet": "... SGLD : def __init__ ( self , params , eta , log_density ): \"\"\" Stochastic gradilog_densityent monte carlo sampler via Langevin Dynamics Parameters ...", "subpage_snippet": "", "source": "www.lyndonduong.com", "link": "https://www.lyndonduong.com/sgmcmc/", "content": "... SGLD : def __init__ ( self , params , eta , log_density ): \"\"\" Stochastic gradilog_densityent monte carlo sampler via Langevin Dynamics Parameters ..."}
+{"idx": 1, "title": "NeurIPS 2021 Papers", "date": "", "ddg_snippet": "Spherical Motion Dynamics : Learning Dynamics of Normalized Neural Network using SGD and Weight Decay ... Towards Gradient -based Bilevel Optimization ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2021/papers.html", "content": "Spherical Motion Dynamics : Learning Dynamics of Normalized Neural Network using SGD and Weight Decay ... Towards Gradient -based Bilevel Optimization ..."}
+{"idx": 2, "title": "Object Insertion in Gaussian Splatting - MCMC Paper Explanation", "date": "", "ddg_snippet": "... in Bayesian statistics, calculating the posterior distribution requires evaluating an integral over all possible values of the model parameters ...", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/object-insertion-in-gaussian-splatting/", "content": "... in Bayesian statistics, calculating the posterior distribution requires evaluating an integral over all possible values of the model parameters ..."}
+{"idx": 3, "title": "COLT18/For authors", "date": "", "ddg_snippet": "We show that the ( stochastic ) gradient descent algorithm provides an implicit regularization effect in the learning of over-parameterized matrix ...", "subpage_snippet": "", "source": "www.learningtheory.org", "link": "https://www.learningtheory.org/colt2018/call.html", "content": "We show that the ( stochastic ) gradient descent algorithm provides an implicit regularization effect in the learning of over-parameterized matrix ..."}
+{"idx": 4, "title": "Laplace Sample Information: Data Informativeness Through a", "date": "", "ddg_snippet": "Langevin dynamics (Negrea et al., 2019 ) or Gaussian processes (Ye et al., 2023 ) , result in a distribution of parameters but come with a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.15303v1", "content": "Langevin dynamics (Negrea et al., 2019 ) or Gaussian processes (Ye et al., 2023 ) , result in a distribution of parameters but come with a ..."}
+{"idx": 5, "title": "System Identification of Fokker-Planck Equations via Transport", "date": "", "ddg_snippet": "For example, Sato and Nakagawa analyzed the stochastic gradient Langevin dynamics ( SGLD ) algorithm using the Fokker-Planck equation and demonstrated ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15091v1", "content": "For example, Sato and Nakagawa analyzed the stochastic gradient Langevin dynamics ( SGLD ) algorithm using the Fokker-Planck equation and demonstrated ..."}
+{"idx": 6, "title": "UAI 2018", "date": "", "ddg_snippet": "Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points ...", "subpage_snippet": "", "source": "www.auai.org", "link": "http://www.auai.org/uai2018/accepted.php", "content": "Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points ..."}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "... Stochastic Multi-Block Alternating ... A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "... Stochastic Multi-Block Alternating ... A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI"}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems"}
+{"idx": 9, "title": "MATH4UQ Forschungseminar - RWTH AACHEN UNIVERSITY UQ - Deutsch", "date": "", "ddg_snippet": "... Bayesian optimal experimental design (OED) problems governed by partial differential equations (PDEs) with infinite-dimensional parameter fields.", "subpage_snippet": "", "source": "www.uq.rwth-aachen.de", "link": "https://www.uq.rwth-aachen.de/cms/UQ/Forschung/~gbopj/MATH4UQ-Forschungseminar/", "content": "... Bayesian optimal experimental design (OED) problems governed by partial differential equations (PDEs) with infinite-dimensional parameter fields."}
diff --git a/data/sampled_jsons/StreamRF_streaming_dynamic_scene_reconstruction_abstract.jsonl b/data/sampled_jsons/StreamRF_streaming_dynamic_scene_reconstruction_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7442eadd0a556138b2f85c46e82581ae6e931ff7
--- /dev/null
+++ b/data/sampled_jsons/StreamRF_streaming_dynamic_scene_reconstruction_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fast and Generalizable Streaming of Dynamic Scene ...", "date": "", "ddg_snippet": "14 Mar 2025 — Our goal is to model dynamic scenes in a streaming manner with minimal per-frame reconstruction time. To achieve this, we adopt a generalized ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "14 Mar 2025 — Our goal is to model dynamic scenes in a streaming manner with minimal per-frame reconstruction time. To achieve this, we adopt a generalized ..."}
+{"idx": 1, "title": "Fast and Generalizable Streaming of Dynamic Scene ...", "date": "", "ddg_snippet": "by J Yan · 2025 · Cited by 6 — To promote the streaming framework to be more prac- tical, we introduce Instant Gaussian Stream (IGS), a streaming approach for dynamic scene reconstruction ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "by J Yan · 2025 · Cited by 6 — To promote the streaming framework to be more prac- tical, we introduce Instant Gaussian Stream (IGS), a streaming approach for dynamic scene reconstruction ..."}
+{"idx": 2, "title": "Streaming Radiance Fields for 3D Video Synthesis", "date": "", "ddg_snippet": "by L LI · 2022 · Cited by 98 — We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes . 14 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/57c2cc952f388f6185db98f441351c96-Paper-Conference.pdf", "content": "by L LI · 2022 · Cited by 98 — We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes . 14 pages"}
+{"idx": 3, "title": "Dynamics-Aware Gaussian Splatting Streaming Towards ...", "date": "", "ddg_snippet": "22 Nov 2024 — The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction from multi-view visual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.14847v1", "content": "22 Nov 2024 — The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction from multi-view visual ..."}
+{"idx": 4, "title": "Streaming Radiance Fields for 3D Video Synthesis", "date": "", "ddg_snippet": "by L Li · 2022 · Cited by 98 — This paper presents a grid-based method to reconstruct a streaming radiance field for dynamic scenes . More precisely, this paper formulates the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oMhmv3hLOF2", "content": "by L Li · 2022 · Cited by 98 — This paper presents a grid-based method to reconstruct a streaming radiance field for dynamic scenes . More precisely, this paper formulates the ..."}
+{"idx": 5, "title": "Motion-aware 3D Gaussian for Streamable Dynamic Scene ...", "date": "", "ddg_snippet": "MGStream is a method for streamable dynamic novel view synthesis (DNVS) that utilizes 3D Gaussian Splatting (3DGS).", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/mgstream-motion-aware-3d-gaussian-for-streamable-dynamic-scene-reconstruction", "content": "MGStream is a method for streamable dynamic novel view synthesis (DNVS) that utilizes 3D Gaussian Splatting (3DGS)."}
+{"idx": 6, "title": "CVPR Poster Instant Gaussian Stream", "date": "", "ddg_snippet": "Our goal is to model dynamic scenes in a streaming manner with minimal per-frame reconstruction time. To achieve this, we adopt a generalized AGM-Net that ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33775", "content": "Our goal is to model dynamic scenes in a streaming manner with minimal per-frame reconstruction time. To achieve this, we adopt a generalized AGM-Net that ..."}
+{"idx": 7, "title": "Compensating Spatiotemporally Inconsistent Observations ...", "date": "", "ddg_snippet": "by Y Yun · 2025 — Abstract . Online reconstruction of dynamic scenes is significant as it enables learning scenes from live- streaming video inputs, while ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3721238.3730678", "content": "by Y Yun · 2025 — Abstract . Online reconstruction of dynamic scenes is significant as it enables learning scenes from live- streaming video inputs, while ..."}
+{"idx": 8, "title": "HiCoM: Hierarchical Coherent Motion for Streamable ...", "date": "", "ddg_snippet": "by Q Gao · 2024 · Cited by 11 — Abstract . The online reconstruction of dynamic scenes from multi-view streaming videos faces significant challenges in training, rendering and storage ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/9370cc8d438b9ed8cb4344818a251d9a-Paper-Conference.pdf", "content": "by Q Gao · 2024 · Cited by 11 — Abstract . The online reconstruction of dynamic scenes from multi-view streaming videos faces significant challenges in training, rendering and storage ..."}
+{"idx": 9, "title": "Instant Gaussian Stream", "date": "", "ddg_snippet": "Instant Gaussian Stream (IGS) is a framework designed for fast and generalizable streaming reconstruction of dynamic scenes for Free-Viewpoint Video.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.16979", "content": "Instant Gaussian Stream (IGS) is a framework designed for fast and generalizable streaming reconstruction of dynamic scenes for Free-Viewpoint Video."}
diff --git a/data/sampled_jsons/Streaming_Radiance_Fields_for_3D_Video_Synthesis_abstract.jsonl b/data/sampled_jsons/Streaming_Radiance_Fields_for_3D_Video_Synthesis_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9387df7138910c5a18cb155e089a4c663ace0c3c
--- /dev/null
+++ b/data/sampled_jsons/Streaming_Radiance_Fields_for_3D_Video_Synthesis_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Streaming Radiance Fields for 3D Video Synthesis", "date": "", "ddg_snippet": "by L Li · 2022 · Cited by 98 — Abstract :We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2210.14831", "content": "by L Li · 2022 · Cited by 98 — Abstract :We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real ..."}
+{"idx": 1, "title": "Streaming Radiance Fields for 3D Video Synthesis", "date": "", "ddg_snippet": "by L LI · 2022 · Cited by 98 — Abstract . We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic ... 14 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/57c2cc952f388f6185db98f441351c96-Paper-Conference.pdf", "content": "by L LI · 2022 · Cited by 98 — Abstract . We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic ... 14 pages"}
+{"idx": 2, "title": "Streaming radiance fields for 3D video synthesis", "date": "", "ddg_snippet": "by L Li · 2022 · Cited by 98 — Abstract . We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3600270.3601250", "content": "by L Li · 2022 · Cited by 98 — Abstract . We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real ..."}
+{"idx": 3, "title": "Streaming Radiance Fields for 3D Video Synthesis", "date": "", "ddg_snippet": "by L Li · 2022 · Cited by 98 — Abstract : We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oMhmv3hLOF2", "content": "by L Li · 2022 · Cited by 98 — Abstract : We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real ..."}
+{"idx": 4, "title": "Streaming Radiance Fields for 3D Video Synthesis", "date": "", "ddg_snippet": "26 Oct 2022 — We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2210.14831", "content": "26 Oct 2022 — We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world ..."}
+{"idx": 5, "title": "Streaming Radiance Fields for 3D Video Synthesis ...", "date": "", "ddg_snippet": "We capture the Meet Room dataset by using 13 Azure Kinect DK [1] cameras. We use the view in centre for test and the rest views of 12 cameras for training.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/57c2cc952f388f6185db98f441351c96-Supplemental-Conference.pdf", "content": "We capture the Meet Room dataset by using 13 Azure Kinect DK [1] cameras. We use the view in centre for test and the rest views of 12 cameras for training."}
+{"idx": 6, "title": "visonpon/New-View-Synthesis", "date": "", "ddg_snippet": "Streaming Radiance Fields for 3D Video Synthesis [ pdf ]. NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields [pdf]. NeRFPlayer A Streamable ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/visonpon/New-View-Synthesis", "content": "Streaming Radiance Fields for 3D Video Synthesis [ pdf ]. NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields [pdf]. NeRFPlayer A Streamable ..."}
+{"idx": 7, "title": "Neural Residual Radiance Fields for Streamably Free ...", "date": "", "ddg_snippet": "by L Wang · Cited by 76 — In this paper, we present a novel neural modeling tech- nique that we call the Residual Radiance Field or ReRF as a highly compact representation of dynamic ...", "subpage_snippet": "", "source": "aoliao12138.github.io", "link": "https://aoliao12138.github.io/files/Paper/ReRF/ReRF.pdf", "content": "by L Wang · Cited by 76 — In this paper, we present a novel neural modeling tech- nique that we call the Residual Radiance Field or ReRF as a highly compact representation of dynamic ..."}
+{"idx": 8, "title": "arXiv:2301.02238v2 [cs.CV] 29 May 2023", "date": "", "ddg_snippet": "by B Attal · 2023 · Cited by 160 — Abstract . Volumetric scene representations enable photorealistic ... Streaming radiance fields for 3D video synthesis . In. NeurIPS, 2022 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2301.02238", "content": "by B Attal · 2023 · Cited by 160 — Abstract . Volumetric scene representations enable photorealistic ... Streaming radiance fields for 3D video synthesis . In. NeurIPS, 2022 ..."}
+{"idx": 9, "title": "PDF", "date": "", "ddg_snippet": "JavaScript is disabled. In order to continue, we need to verify that you're not a robot. This requires JavaScript. Enable JavaScript and then reload the page.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/5974066e3b20b2ea52b980238849c2acfe40613a", "content": "JavaScript is disabled. In order to continue, we need to verify that you're not a robot. This requires JavaScript. Enable JavaScript and then reload the page."}
diff --git a/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_D_lock_D_weak_D_strong_dataset.jsonl b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_D_lock_D_weak_D_strong_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d7df17f7b4d76ae3caa1ab4a0e55a37230b5ba8c
--- /dev/null
+++ b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_D_lock_D_weak_D_strong_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · Building password - locked models robust against the kind of elicitation studied in the weak -to- strong generalization paper; Using unsupervised behavior discovery +RL on password - locked models (we bet it works well) and trying to build password - locked models that are robust to this method.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · Building password - locked models robust against the kind of elicitation studied in the weak -to- strong generalization paper; Using unsupervised behavior discovery +RL on password - locked models (we bet it works well) and trying to build password - locked models that are robust to this method."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked Models Stress-testing capability elicitation with password-locked models [Paper] Stress-testing capability elicitation with password ... Stress-testing capability elicitation with password-locked models The Elicitation Game: Stress-Testing Capability Elicitation ... Images NeurIPS Poster Stress-Testing Capability Elicitation With ... Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "May 29, 2024 · In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden. Jun 5, 2025 · In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden. Jun 4, 2024 · Building password - locked models robust against the kind of elicitation studied in the weak -to- strong generalization paper; Using unsupervised behavior discovery +RL on password - locked models (we bet it works well) and trying to build password - locked models that are robust to this method. Internal computations that result in locking may differ from a simple conditional policy in important ways. Hidden capabilities may be less “salient” in realistic models . Real models might not exhibit the hidden capability at all, making RL-based elicitation even less effective. Oct 9, 2024 · We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password - locked model . We focus on elicitation techniques based on prompting and activation steering, and compare these to supervised fine-tuning. View all In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden. Sep 26, 2024 · This table summarizes the specific large language models (LLMs) used as the base models ( strong and weak ) for each of the five tasks, as well as the high-quality demonstrations used for supervised fine-tuning (SFT) in the elicitation experiments.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.19550", "content": "May 29, 2024 · In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden. Jun 5, 2025 · In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden. Jun 4, 2024 · Building password - locked models robust against the kind of elicitation studied in the weak -to- strong generalization paper; Using unsupervised behavior discovery +RL on password - locked models (we bet it works well) and trying to build password - locked models that are robust to this method. Internal computations that result in locking may differ from a simple conditional policy in important ways. Hidden capabilities may be less “salient” in realistic models . Real models might not exhibit the hidden capability at all, making RL-based elicitation even less effective. Oct 9, 2024 · We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password - locked model . We focus on elicitation techniques based on prompting and activation steering, and compare these to supervised fine-tuning. View all In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden. Sep 26, 2024 · This table summarizes the specific large language models (LLMs) used as the base models ( strong and weak ) for each of the five tasks, as well as the high-quality demonstrations used for supervised fine-tuning (SFT) in the elicitation experiments."}
+{"idx": 2, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Jun 5, 2025 · In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740125", "content": "Jun 5, 2025 · In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden."}
+{"idx": 3, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Internal computations that result in locking may differ from a simple conditional policy in important ways. Hidden capabilities may be less “salient” in realistic models . Real models might not exhibit the hidden capability at all, making RL-based elicitation even less effective.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/92923.pdf", "content": "Internal computations that result in locking may differ from a simple conditional policy in important ways. Hidden capabilities may be less “salient” in realistic models . Real models might not exhibit the hidden capability at all, making RL-based elicitation even less effective."}
+{"idx": 4, "title": "The Elicitation Game: Stress-Testing Capability Elicitation ...", "date": "", "ddg_snippet": "Oct 9, 2024 · We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password - locked model . We focus on elicitation techniques based on prompting and activation steering, and compare these to supervised fine-tuning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zy6LB5t62f", "content": "Oct 9, 2024 · We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password - locked model . We focus on elicitation techniques based on prompting and activation steering, and compare these to supervised fine-tuning."}
+{"idx": 5, "title": "NeurIPS Poster Stress-Testing Capability Elicitation With ...", "date": "", "ddg_snippet": "In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden."}
+{"idx": 6, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Sep 26, 2024 · This table summarizes the specific large language models (LLMs) used as the base models ( strong and weak ) for each of the five tasks, as well as the high-quality demonstrations used for supervised fine-tuning (SFT) in the elicitation experiments.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "Sep 26, 2024 · This table summarizes the specific large language models (LLMs) used as the base models ( strong and weak ) for each of the five tasks, as well as the high-quality demonstrations used for supervised fine-tuning (SFT) in the elicitation experiments."}
+{"idx": 7, "title": "[Paper] Stress-testing capability elicitation with", "date": "", "ddg_snippet": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought ..."}
+{"idx": 8, "title": "Password-locked models: a stress case for capabilities", "date": "", "ddg_snippet": "... password - locked models are built, there remain a large difference with deceptive models : the search for the correct password might have very different ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "... password - locked models are built, there remain a large difference with deceptive models : the search for the correct password might have very different ..."}
+{"idx": 9, "title": "Password-locked models: a stress case for capabilities", "date": "", "ddg_snippet": "... password - locked models are built, there remain a large difference with deceptive models : the search for the correct password might have very different ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "... password - locked models are built, there remain a large difference with deceptive models : the search for the correct password might have very different ..."}
diff --git a/data/sampled_jsons/Stress-Testing_Capability_Elicitation_weak_policy_poor_MATH_Pythia-1B_RL_methods_struggle.jsonl b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_weak_policy_poor_MATH_Pythia-1B_RL_methods_struggle.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e183a4078f9f1452f8e9db033b4453d16df6d67
--- /dev/null
+++ b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_weak_policy_poor_MATH_Pythia-1B_RL_methods_struggle.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Importantly, the success of the RL methods strongly depends on the quality of the initial weak policy (πweak). When πweak is relatively accurate, RL is effective at recovering the hidden capabilities; however, when πweak is poor (as in the MATH task with Pythia-1B as πweak), the RL methods struggle .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "Importantly, the success of the RL methods strongly depends on the quality of the initial weak policy (πweak). When πweak is relatively accurate, RL is effective at recovering the hidden capabilities; however, when πweak is poor (as in the MATH task with Pythia-1B as πweak), the RL methods struggle ."}
+{"idx": 1, "title": "Findings of the Association for Computational Linguistics", "date": "", "ddg_snippet": "by W Che · 2025 — Despite current advancements, truncation methods struggle with limited capacity , unstable training and inconsistency of selected threshold. To address these ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2025.findings-acl/", "content": "by W Che · 2025 — Despite current advancements, truncation methods struggle with limited capacity , unstable training and inconsistency of selected threshold. To address these ..."}
+{"idx": 2, "title": "ICLR 2025 Orals", "date": "", "ddg_snippet": "Contemporary learning-based planning approaches such as imitation learning methods often struggle to balance competing objectives and lack of safety ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/events/oral", "content": "Contemporary learning-based planning approaches such as imitation learning methods often struggle to balance competing objectives and lack of safety ..."}
+{"idx": 3, "title": "dair-ai/ML-Papers-of-the-Week", "date": "", "ddg_snippet": "Despite using no reinforcement learning, OpenThinker3-7B outperforms all other open-data 7B and 8B models on standard math , code, and science reasoning ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dair-ai/ML-Papers-of-the-Week", "content": "Despite using no reinforcement learning, OpenThinker3-7B outperforms all other open-data 7B and 8B models on standard math , code, and science reasoning ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 5, "title": "Surveying Techniques from Alignment to Reasoning", "date": "", "ddg_snippet": "21 May 2025 — This paper presents the first comprehensive survey of PoLMs, systematically tracing their evolution across five core paradigms: Fine-tuning, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06072v2", "content": "21 May 2025 — This paper presents the first comprehensive survey of PoLMs, systematically tracing their evolution across five core paradigms: Fine-tuning, ..."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "This paper offers a cross-disciplinary synthesis of AGI development, spanning artificial intelligence, cognitive neuroscience, psychology, generative models, ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=continue-thinking+token", "content": "This paper offers a cross-disciplinary synthesis of AGI development, spanning artificial intelligence, cognitive neuroscience, psychology, generative models, ..."}
+{"idx": 7, "title": "RewardBench: Evaluating Reward Models for Language ...", "date": "", "ddg_snippet": "8 Jun 2024 — The RewardBench dataset is a collection of prompt-chosen-rejected trios spanning chat, reasoning, and safety, to benchmark how reward models perform.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.13787v2", "content": "8 Jun 2024 — The RewardBench dataset is a collection of prompt-chosen-rejected trios spanning chat, reasoning, and safety, to benchmark how reward models perform."}
+{"idx": 8, "title": "The Journey of Software Engineering in the AI Era", "date": "", "ddg_snippet": "by A Mastropaolo · 2025 · Cited by 1 — In this article, we aim at outlining the key elements that, based on our expertise, are vital for the smooth integration of AI into SE.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3709360", "content": "by A Mastropaolo · 2025 · Cited by 1 — In this article, we aim at outlining the key elements that, based on our expertise, are vital for the smooth integration of AI into SE."}
+{"idx": 9, "title": "Paper Digest: ICLR 2025 Papers & Highlights", "date": "", "ddg_snippet": "17 Mar 2025 — The International Conference on Learning Representations (ICLR) is one of the top machine learning conferences in the world.", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2025/03/iclr-2025-papers-highlights/", "content": "17 Mar 2025 — The International Conference on Learning Representations (ICLR) is one of the top machine learning conferences in the world."}
diff --git a/data/sampled_jsons/Swinir_Image_restoration_using_swin_transformer_Liang_et_al.,_2021_abstract.jsonl b/data/sampled_jsons/Swinir_Image_restoration_using_swin_transformer_Liang_et_al.,_2021_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..33ae51d07de3f7b2efdc057d20d868c0d1731651
--- /dev/null
+++ b/data/sampled_jsons/Swinir_Image_restoration_using_swin_transformer_Liang_et_al.,_2021_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Practical Blind Image Denoising via Swin-Conv-UNet and Data", "date": "", "ddg_snippet": "... swin -conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block, ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11633-023-1466-0", "content": "... swin -conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block, ..."}
+{"idx": 1, "title": "Rethinking LayerNorm in Image Restoration Transformers", "date": "", "ddg_snippet": "Additionally, we can achieve substantial performance improvements across various image restoration tasks and also obtain an IR Transformer that is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.06629v1", "content": "Additionally, we can achieve substantial performance improvements across various image restoration tasks and also obtain an IR Transformer that is ..."}
+{"idx": 2, "title": "Fourier-Guided Attention Upsampling for Image Super-Resolution", "date": "", "ddg_snippet": "SwinIR [ 30 ] , building on the Swin Transformer [ 36 ] , adopts self-attention within fixed windows and shifted window strategies to enlarge ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10616v1", "content": "SwinIR [ 30 ] , building on the Swin Transformer [ 36 ] , adopts self-attention within fixed windows and shifted window strategies to enlarge ..."}
+{"idx": 3, "title": "Efficient Image Super-Resolution With Multi-Branch Mixer", "date": "", "ddg_snippet": "N-gram in swin transformers for efficient lightweight image super-resolution. ... Swinir : Image restoration using swin transformer .", "subpage_snippet": "", "source": "www.ias-iss.org", "link": "https://www.ias-iss.org/ojs/IAS/article/view/3399", "content": "N-gram in swin transformers for efficient lightweight image super-resolution. ... Swinir : Image restoration using swin transformer ."}
+{"idx": 4, "title": "Blind Face Restoration Survey | Sharipov | International", "date": "", "ddg_snippet": "Progressive semantic-aware style transformation for blind face restoration / Chaofeng Chen, Xiaoming Li, Lingbo Yang et al .", "subpage_snippet": "", "source": "injoit.org", "link": "http://injoit.org/index.php/j1/article/view/1589/0", "content": "Progressive semantic-aware style transformation for blind face restoration / Chaofeng Chen, Xiaoming Li, Lingbo Yang et al ."}
+{"idx": 5, "title": "Blind Face Restoration Survey | Sharipov | International", "date": "", "ddg_snippet": "Progressive semantic-aware style transformation for blind face restoration / Chaofeng Chen, Xiaoming Li, Lingbo Yang et al .", "subpage_snippet": "", "source": "injoit.org", "link": "http://injoit.org/index.php/j1/article/view/1589", "content": "Progressive semantic-aware style transformation for blind face restoration / Chaofeng Chen, Xiaoming Li, Lingbo Yang et al ."}
+{"idx": 6, "title": "NTIRE2023: New Trends in Image Restoration and Enhancement", "date": "", "ddg_snippet": "This workshop builds upon the success of New Trends in Image Restoration and Enhancement (NTIRE) editions: at CVPR 2022 , 2021 , 2020 , 2019 , 2018 ...", "subpage_snippet": "", "source": "cvlai.net", "link": "http://cvlai.net/ntire/2023/", "content": "This workshop builds upon the success of New Trends in Image Restoration and Enhancement (NTIRE) editions: at CVPR 2022 , 2021 , 2020 , 2019 , 2018 ..."}
+{"idx": 7, "title": "Transformers in Vision: A Survey | ACM Computing Surveys", "date": "", "ddg_snippet": "Furthermore, the straightforward design of Transformers allows processing multiple modalities ( e .g., images , videos, text, and speech) using similar ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3505244", "content": "Furthermore, the straightforward design of Transformers allows processing multiple modalities ( e .g., images , videos, text, and speech) using similar ..."}
+{"idx": 8, "title": "ESSD - MDG625: a daily high-resolution meteorological dataset", "date": "", "ddg_snippet": "... resolution reanalysis dataset, downscaling is widely used in geoprocessing ( Atkinson , 2013 ) , especially in climate-related fields ( Wang et al ...", "subpage_snippet": "", "source": "essd.copernicus.org", "link": "https://essd.copernicus.org/articles/17/1501/2025/", "content": "... resolution reanalysis dataset, downscaling is widely used in geoprocessing ( Atkinson , 2013 ) , especially in climate-related fields ( Wang et al ..."}
+{"idx": 9, "title": "3D reconstruction from structured-light profilometry with", "date": "", "ddg_snippet": "Compared with classical methods such as Fourier transform profilometry, many deep neural networks are utilized to restore 3D shape from single-shot ...", "subpage_snippet": "", "source": "asp-eurasipjournals.springeropen.com", "link": "https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-022-00848-5", "content": "Compared with classical methods such as Fourier transform profilometry, many deep neural networks are utilized to restore 3D shape from single-shot ..."}
diff --git a/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_for_Robust_Learning_on_Diverse_Tasks_and_Model_Scales_Equation.jsonl b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_for_Robust_Learning_on_Diverse_Tasks_and_Model_Scales_Equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a0df571cea9b3bb45584fdbf6413ce3bd913078
--- /dev/null
+++ b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_for_Robust_Learning_on_Diverse_Tasks_and_Model_Scales_Equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · Cited by 1 — In this work, we enhance the stability of the RL training procedure by adapting reverse cross-entropy (RCE) from supervised learning for noisy ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9oq0iY2Jxx", "content": "by JS Byun · Cited by 1 — In this work, we enhance the stability of the RL training procedure by adapting reverse cross-entropy (RCE) from supervised learning for noisy ..."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "27 May 2024 — In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17618v1", "content": "27 May 2024 — In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to ..."}
+{"idx": 2, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales ... (Equation 6): the original actor loss Lrl (A2C or PPO) and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44897", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales ... (Equation 6): the original actor loss Lrl (A2C or PPO) and ..."}
+{"idx": 3, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · 2024 · Cited by 1 — In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618?", "content": "by JS Byun · 2024 · Cited by 1 — In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to ..."}
+{"idx": 4, "title": "Mastering diverse control tasks through world models", "date": "", "ddg_snippet": "by D Hafner · 2025 · Cited by 82 — Here we present the third generation of Dreamer, a general algorithm that outperforms specialized methods across over 150 diverse tasks , with a single ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-025-08744-2", "content": "by D Hafner · 2025 · Cited by 82 — Here we present the third generation of Dreamer, a general algorithm that outperforms specialized methods across over 150 diverse tasks , with a single ..."}
+{"idx": 5, "title": "A novel symmetry-learning actor-critic extension", "date": "", "ddg_snippet": "by M Abreu · 2025 · Cited by 11 — Symmetry, a fundamental concept to understand our environment, often oversimplifies reality from a mathematical perspective.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S092523122401542X", "content": "by M Abreu · 2025 · Cited by 11 — Symmetry, a fundamental concept to understand our environment, often oversimplifies reality from a mathematical perspective."}
+{"idx": 6, "title": "Mastering diverse control tasks through world models - PMC", "date": "", "ddg_snippet": "by D Hafner · 2025 · Cited by 73 — A general reinforcement - learning algorithm, called Dreamer, outperforms specialized expert algorithms across diverse tasks by learning a model of the ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12003158/", "content": "by D Hafner · 2025 · Cited by 73 — A general reinforcement - learning algorithm, called Dreamer, outperforms specialized expert algorithms across diverse tasks by learning a model of the ..."}
+{"idx": 7, "title": "USN: : A Robust Imitation Learning Method against Diverse ...", "date": "", "ddg_snippet": "by X Yu · 2024 · Cited by 2 — To study the correlations, we pre-train imitation learning models with the expert demonstrations, and then estimate the corresponding loss and uncertainty ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1613/jair.1.15819", "content": "by X Yu · 2024 · Cited by 2 — To study the correlations, we pre-train imitation learning models with the expert demonstrations, and then estimate the corresponding loss and uncertainty ..."}
+{"idx": 8, "title": "Normality-Guided Distributional Reinforcement Learning ...", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales ... equation to the learning of approximate value distributions.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/c28cca3538c949b223194a00e14c3575b323ef2f", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales ... equation to the learning of approximate value distributions."}
+{"idx": 9, "title": "Loss Learning for Noisy Classification Tasks", "date": "", "ddg_snippet": "by B Gao · 2021 · Cited by 27 — We present a “ learning to learn ” approach for discover- ing white-box classification loss functions that are robust to label noise in the training data.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2021/papers/Gao_Searching_for_Robustness_Loss_Learning_for_Noisy_Classification_Tasks_ICCV_2021_paper.pdf", "content": "by B Gao · 2021 · Cited by 27 — We present a “ learning to learn ” approach for discover- ing white-box classification loss functions that are robust to label noise in the training data."}
diff --git a/data/sampled_jsons/T-Agent_sqlmap_integration_CVE-Bench_AI_agent_SQL_injection_year_2024.jsonl b/data/sampled_jsons/T-Agent_sqlmap_integration_CVE-Bench_AI_agent_SQL_injection_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b6b0ef3ac7575e44d17323fc9c26f8b48b054bd6
--- /dev/null
+++ b/data/sampled_jsons/T-Agent_sqlmap_integration_CVE-Bench_AI_agent_SQL_injection_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real-World Web Application Vulnerabilities ... CVE - Bench , we design a sandbox framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real-World Web Application Vulnerabilities ... CVE - Bench , we design a sandbox framework ..."}
+{"idx": 1, "title": "xOffense: An AI-driven autonomous penetration testing framework", "date": "", "ddg_snippet": "To overcome this, recent works have turned toward AI agent -based paradigms, in which multiple specialized agents collaborate to emulate the workflow ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13021v1", "content": "To overcome this, recent works have turned toward AI agent -based paradigms, in which multiple specialized agents collaborate to emulate the workflow ..."}
+{"idx": 2, "title": "Frontier AI Risk Management Framework in Practice: A Risk", "date": "", "ddg_snippet": "While a Few Frontier Models Can Autonomously bypass Simple, Misconfigured Defenses, Current AI Agents Universally Fail to Penetrate State-of-the-Art ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16534v1", "content": "While a Few Frontier Models Can Autonomously bypass Simple, Misconfigured Defenses, Current AI Agents Universally Fail to Penetrate State-of-the-Art ..."}
+{"idx": 3, "title": "Track Awesome Security (sbilly/awesome-security) Updates Daily", "date": "", "ddg_snippet": "Awesome Security Hardening (⭐5k) - A collection of awesome security hardening guides, best practices, checklists, benchmarks, tools and other ...", "subpage_snippet": "", "source": "www.trackawesomelist.com", "link": "https://www.trackawesomelist.com/sbilly/awesome-security/", "content": "Awesome Security Hardening (⭐5k) - A collection of awesome security hardening guides, best practices, checklists, benchmarks, tools and other ..."}
+{"idx": 4, "title": "Track Awesome Security (sbilly/awesome-security) Updates Weekly", "date": "", "ddg_snippet": "Awesome Security Hardening (⭐5k) - A collection of awesome security hardening guides, best practices, checklists, benchmarks, tools and other ...", "subpage_snippet": "", "source": "www.trackawesomelist.com", "link": "https://www.trackawesomelist.com/sbilly/awesome-security/week/", "content": "Awesome Security Hardening (⭐5k) - A collection of awesome security hardening guides, best practices, checklists, benchmarks, tools and other ..."}
+{"idx": 5, "title": "GitHub - sbilly/awesome-security: A collection of awesome", "date": "", "ddg_snippet": "... testing programs, including nmap (a port scanner), Wireshark (a packet analyzer), John the Ripper (a password cracker), and Aircrack-ng (a software ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sbilly/awesome-security", "content": "... testing programs, including nmap (a port scanner), Wireshark (a packet analyzer), John the Ripper (a password cracker), and Aircrack-ng (a software ..."}
+{"idx": 6, "title": "GitHub - paralax/Awesome-Pentest-1: Awesome Penetration Testing", "date": "", "ddg_snippet": "To see all available qualifiers, see ... Mass Scan - TCP port scanner, spews SYN packets asynchronously, scanning entire Internet in under 5 minutes.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/paralax/Awesome-Pentest-1", "content": "To see all available qualifiers, see ... Mass Scan - TCP port scanner, spews SYN packets asynchronously, scanning entire Internet in under 5 minutes."}
+{"idx": 7, "title": "GitHub - wtsxDev/Penetration-Testing: List of awesome", "date": "", "ddg_snippet": "The Pentesters Framework - PTF attempts to install all of your penetration testing tools (latest and greatest), compile them, build them, and make it ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wtsxDev/Penetration-Testing", "content": "The Pentesters Framework - PTF attempts to install all of your penetration testing tools (latest and greatest), compile them, build them, and make it ..."}
+{"idx": 8, "title": "GitHub - paulveillard/cybersecurity: Welcome", "date": "", "ddg_snippet": "... is the art of protecting networks, devices, and data from unauthorized access or criminal use and the practice of ensuring confidentiality, integrity ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/paulveillard/cybersecurity", "content": "... is the art of protecting networks, devices, and data from unauthorized access or criminal use and the practice of ensuring confidentiality, integrity ..."}
+{"idx": 9, "title": "Awesome Security (sbilly/awesome-security) Overview - Track", "date": "", "ddg_snippet": "Performs log analysis, file integrity checking, policy monitoring, rootkit detection, real-time alerting and active response.", "subpage_snippet": "", "source": "www.trackawesomelist.com", "link": "https://www.trackawesomelist.com/sbilly/awesome-security/readme/", "content": "Performs log analysis, file integrity checking, policy monitoring, rootkit detection, real-time alerting and active response."}
diff --git a/data/sampled_jsons/TARFLOW_vs_MAF_Masked_Autoregressive_Flow_Transformer_MLP_architecture_comparison.jsonl b/data/sampled_jsons/TARFLOW_vs_MAF_Masked_Autoregressive_Flow_Transformer_MLP_architecture_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9bccbd25b9db42e09cb4862045d747c050a72fb8
--- /dev/null
+++ b/data/sampled_jsons/TARFLOW_vs_MAF_Masked_Autoregressive_Flow_Transformer_MLP_architecture_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "D'Onta Foreman - Wikipedia", "date": "", "ddg_snippet": "D'Onta Foreman (/ diˈɒnteɪ / dee-ON-tay; born April 24, 1996) is an American professional football running back . He played college football for the Texas Longhorns, and was selected by the Houston Texans in the third round of the 2017 NFL draft.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/D'Onta_Foreman", "content": "D'Onta Foreman (/ diˈɒnteɪ / dee-ON-tay; born April 24, 1996) is an American professional football running back . He played college football for the Texas Longhorns, and was selected by the Houston Texans in the third round of the 2017 NFL draft."}
+{"idx": 1, "title": "Transformer (deep learning architecture ) - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A standard Transformer architecture , showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 T...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "Machine learningand data mining. v. t. e. A standard Transformer architecture , showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 T..."}
+{"idx": 2, "title": "[2412.06329] Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "TarFlow can be thought of as a Transformer -based variant of Masked Autoregressive Flows ( MAFs ): it consists of a stack of autoregressive Transformer blocks on image patches, alternating the autoregression direction between layers.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.06329", "content": "TarFlow can be thought of as a Transformer -based variant of Masked Autoregressive Flows ( MAFs ): it consists of a stack of autoregressive Transformer blocks on image patches, alternating the autoregression direction between layers."}
+{"idx": 3, "title": "Unlocking the Power of Normalizing Flows in Generative... - Glcnd.io", "date": "", "ddg_snippet": "Table of contents [hide]Unveiling TarFlow : Advancing Normalizing Flows in Generative ModelingThe Importance of Normalizing Flows", "subpage_snippet": "", "source": "glcnd.io", "link": "https://glcnd.io/unlocking-the-power-of-normalizing-flows-in-generative-modeling/", "content": "Table of contents [hide]Unveiling TarFlow : Advancing Normalizing Flows in Generative ModelingThe Importance of Normalizing Flows"}
+{"idx": 4, "title": "Transformer Autoregressive Flow ( TARFlow )", "date": "", "ddg_snippet": "Transformer Autoregressive Flow ( TARFlow ). Updated 30 June 2025. TARFlow is a model class that merges autoregressive Transformers with normalizing flows to enable tractable likelihood computation and expressive density modeling.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/transformer-autoregressive-flow-tarflow", "content": "Transformer Autoregressive Flow ( TARFlow ). Updated 30 June 2025. TARFlow is a model class that merges autoregressive Transformers with normalizing flows to enable tractable likelihood computation and expressive density modeling."}
+{"idx": 5, "title": "Flow -based Deep Generative Models | Lil'Log", "date": "", "ddg_snippet": "Masked Autoregressive Flow . Masked Autoregressive Flow ( MAF ; Papamakarios et al., 2017) is a type of normalizing flows , where the transformation layer is built as an autoregressive neural network. MAF is very similar to Inverse Autoregressive Flow (IAF) introduced later.", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2018-10-13-flow-models/", "content": "Masked Autoregressive Flow . Masked Autoregressive Flow ( MAF ; Papamakarios et al., 2017) is a type of normalizing flows , where the transformation layer is built as an autoregressive neural network. MAF is very similar to Inverse Autoregressive Flow (IAF) introduced later."}
+{"idx": 6, "title": "(PDF) Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "to Masked Autoregressive Flows ( MAFs ) (Papamakarios. et al.,2017), where we compose a deep transformation by. iteratively stacking multiple blocks of autoregressive trans-. formations with alternating directions.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386577118_Normalizing_Flows_are_Capable_Generative_Models", "content": "to Masked Autoregressive Flows ( MAFs ) (Papamakarios. et al.,2017), where we compose a deep transformation by. iteratively stacking multiple blocks of autoregressive trans-. formations with alternating directions."}
+{"idx": 7, "title": "encoreus/ Transformer _ Autoregressive _ Flow · Hugging Face", "date": "", "ddg_snippet": "/ Transformer _ Autoregressive _ Flow . like. 2.Normalizing flows are capable generative models[J]. arXiv preprint arXiv:2412.06329, 2024.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/encoreus/Transformer_Autoregressive_Flow", "content": "/ Transformer _ Autoregressive _ Flow . like. 2.Normalizing flows are capable generative models[J]. arXiv preprint arXiv:2412.06329, 2024."}
+{"idx": 8, "title": "GitHub - ZackNay/KDE- VS - MAF : A comparison between the kernel...", "date": "", "ddg_snippet": "A comparison between the kernel density estimator and masked autoregressive flows .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ZackNay/KDE-VS-MAF", "content": "A comparison between the kernel density estimator and masked autoregressive flows ."}
+{"idx": 9, "title": "Normalizing Flows - Part 2 | Skit Tech", "date": "", "ddg_snippet": "Masked Autoregressive Flows . Masked Autoregressive Flows ( MAFs ). The time taken to train autoregressive flow models is very high because of the need to sequentially generate outputs. Using MAFs one can parallelize the training process.", "subpage_snippet": "", "source": "tech.skit.ai", "link": "https://tech.skit.ai/normalizing-flows-part-2/", "content": "Masked Autoregressive Flows . Masked Autoregressive Flows ( MAFs ). The time taken to train autoregressive flow models is very high because of the need to sequentially generate outputs. Using MAFs one can parallelize the training process."}
diff --git a/data/sampled_jsons/TCE_Li_et_al._2024_reinforcement_learning_abstract_year_2024.jsonl b/data/sampled_jsons/TCE_Li_et_al._2024_reinforcement_learning_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0d59b26c59a37052f2294ca02b7e3e0556c49a4c
--- /dev/null
+++ b/data/sampled_jsons/TCE_Li_et_al._2024_reinforcement_learning_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "transformer-based off-policy episodic reinforcement learning", "date": "", "ddg_snippet": "by G Li · 2024 · Cited by 7 — To address this issue, a most recently proposed method, Temporally-Correlated ERL ( TCE ). ( Li et al ., 2024 ) introduced a more efficient update ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "by G Li · 2024 · Cited by 7 — To address this issue, a most recently proposed method, Temporally-Correlated ERL ( TCE ). ( Li et al ., 2024 ) introduced a more efficient update ..."}
+{"idx": 1, "title": "Cooperative Multi-Agent Reinforcement Learning ...", "date": "", "ddg_snippet": "by H Shi · 2024 · Cited by 4 — Abstract . Edge Intelligence (EI) technologies obtain an advance with promotion by Consumer Electronics (CE) and spread to the Intelligent ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1109/TCE.2024.3416822", "content": "by H Shi · 2024 · Cited by 4 — Abstract . Edge Intelligence (EI) technologies obtain an advance with promotion by Consumer Electronics (CE) and spread to the Intelligent ..."}
+{"idx": 2, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement ...", "date": "", "ddg_snippet": "Abstract —This work introduces Transformer-based Off-Policy. Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in ...", "subpage_snippet": "", "source": "rss25-roboreps.github.io", "link": "https://rss25-roboreps.github.io/papers/1_TOP_ERL_Transformer_based_Of.pdf", "content": "Abstract —This work introduces Transformer-based Off-Policy. Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in ..."}
+{"idx": 3, "title": "Integrated reinforcement learning of automated guided ...", "date": "", "ddg_snippet": "by GTS Ho · 2025 · Cited by 7 — This study presents a reinforcement learning (RL) approach for real-time path planning in automated guided vehicles within smart warehouses or smart factories.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1366554525000493", "content": "by GTS Ho · 2025 · Cited by 7 — This study presents a reinforcement learning (RL) approach for real-time path planning in automated guided vehicles within smart warehouses or smart factories."}
+{"idx": 4, "title": "Reinforcement-Learning-Based Intrusion Detection in ...", "date": "", "ddg_snippet": "by H Kheddar · 2024 · Cited by 37 — Abstract —Modern communication networks have to meet the performance requirements of contemporary industrial control. 50 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/9739/11123909/10729241.pdf", "content": "by H Kheddar · 2024 · Cited by 37 — Abstract —Modern communication networks have to meet the performance requirements of contemporary industrial control. 50 pages"}
+{"idx": 5, "title": "Zero-Shot Context Generalization in Reinforcement ...", "date": "", "ddg_snippet": "by J Chapman · 2025 — Deep reinforcement learning (DRL) has achieved remarkable success across mul- tiple domains, including competitive games, natural language ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.07348?", "content": "by J Chapman · 2025 — Deep reinforcement learning (DRL) has achieved remarkable success across mul- tiple domains, including competitive games, natural language ..."}
+{"idx": 6, "title": "Reinforcement Learning Based Acceptance Criteria for ...", "date": "", "ddg_snippet": "by OA Arık · 2025 — This paper proposes novel reinforcement learning -based acceptance criteria for metaheuristic algorithms. We develop Q-learning and Deep ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s44196-025-00924-2", "content": "by OA Arık · 2025 — This paper proposes novel reinforcement learning -based acceptance criteria for metaheuristic algorithms. We develop Q-learning and Deep ..."}
+{"idx": 7, "title": "Multi-agent deep reinforcement learning-based truck-drone ...", "date": "", "ddg_snippet": "by W Peng · 2025 · Cited by 5 — A vehicle routing problem with drones under dynamic demands is studied. A Markov game model is developed to coordinate the actions of trucks and drones under ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1366554525000158", "content": "by W Peng · 2025 · Cited by 5 — A vehicle routing problem with drones under dynamic demands is studied. A Markov game model is developed to coordinate the actions of trucks and drones under ..."}
+{"idx": 8, "title": "AdaFedProx: A Heterogeneity-Aware Federated Deep ...", "date": "", "ddg_snippet": "Abstract —In the realm of smart healthcare, vast amounts of valuable patient data are generated worldwide. However, healthcare providers face challenges in ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/30/11038962/10681112.pdf", "content": "Abstract —In the realm of smart healthcare, vast amounts of valuable patient data are generated worldwide. However, healthcare providers face challenges in ..."}
+{"idx": 9, "title": "Advancing Security and Trust in WSNs: A Federated Multi ...", "date": "", "ddg_snippet": "1 Nov 2024 — Recently, Multi-Agent Deep Reinforcement Learning (MARL) has gained significant attention for enhancing Intrusion Detection Systems (IDS) ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/TCE.2024.3440178", "content": "1 Nov 2024 — Recently, Multi-Agent Deep Reinforcement Learning (MARL) has gained significant attention for enhancing Intrusion Detection Systems (IDS) ..."}
diff --git a/data/sampled_jsons/TCE_Li_et_al_2024_REINFORCE_policy_gradient_on-policy_method_year_2024.jsonl b/data/sampled_jsons/TCE_Li_et_al_2024_REINFORCE_policy_gradient_on-policy_method_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a97df2cbdebb07a77dc74cc616752cd4339ebe1
--- /dev/null
+++ b/data/sampled_jsons/TCE_Li_et_al_2024_REINFORCE_policy_gradient_on-policy_method_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2401.13662] The Definitive Guide to Policy Gradients in Deep ... Policy Gradient Methods in Reinforcement Learning TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT ... The Reinforce Policy Gradient Algorithm Revisited On the Design of KL-Regularized Policy Gradient Algorithms ... Analysis and Improvement of Policy Gradient Estimation Policy Optimization with REINFORCE : A Deep Dive into Policy Gradients The Reinforce Policy Gradient Algorithm Revisited - arXiv.org The Reinforce Policy Gradient Algorithm Revisited - arXiv.org TOP-ERL: TRANSFORMER BASED OFF- POLICY E REINFORCEMENT LEA… TOP-ERL: TRANSFORMER BASED OFF- POLICY E REINFORCEMENT LEA… Policy Optimization with REINFORCE: A Deep Dive into Policy ...", "date": "", "ddg_snippet": "Jan 24, 2024 · In recent years, various powerful policy gradient algorithms have been proposed in deep reinforcement learning. While all these algorithms build on the Policy Gradient Theorem, the specific design choices differ significantly across algorithms. We provide a holistic overview of on-policy policy gradient algorithms to facilitate the understanding of both their theoretical foundations and their ... Jul 23, 2025 · Policy Gradient methods in Reinforcement Learning (RL) to directly optimize the policy , unlike value-based methods that estimate the value of states. These methods are particularly useful in environments with continuous action spaces or complex tasks where value-based approaches struggle. tep-based methods , especially in settings with dense re-wards. To address this issue, a most recently proposed method , Temporally-Correlated ERL ( TCE ) ( Li et al., 2024 ) introduced a more eficient update scheme that ”opens the black-box” and utilizes sub-segment information for pol I. INTRODUCTION Policy gradient methods [25], [26] are a popular class of approaches in reinforcement learning. The policy in these approaches is considered parameterized and one updates the policy parameter along a gradient search direction where the gradient is of a performance objective, normally the value function. The policy gradient theorem [26], [20], [10] which is a fundamental result ... Reinforcement learning (RL), particularly policy gradient (PG) methods , provides a powerful framework for solving sequential decision-making problems in complex environments. These methods have been successfully applied in diverse domains, ranging from robotics to game playing, and have recently become instrumental in fine-tuning large language models (LLMs) to align with human preferences and ... Abstract Policy gradient is a useful model-free reinforcement learning approach, but it tends to suffer from instability of gradient estimates. In this paper, we analyze and improve the stability of policy gradient methods . What is reinforce policy gradient theorem? The development of the REINFORCE algorithm and the associated policy gradient theorem was driven by a need to optimize policies in reinforcement learning (RL) environments where the outcome (reward) is uncertain and depends on a sequence of actions. How does the reinforce policy gradient algorithm work? We revisit the Reinforce policy gradient algorithm from the literature. Note that this algorithm typically works with cost returns obtained over random length episodes obtained from either termination upon reaching a goal state (as with episodic tasks) or from instants of visit to a prescribed recurrent state (in the case of continuing tasks). Which algorithm uses the policy gradient theorem? The Reinforce algorithm , makes use of the policy gradient theorem as the latter indicates that the gradient of the value function is the expectation of the gradient of a function of the noisy returns obtained from episodes. Is Tic linear policies competitive for reinforcement Learnin? tic linear policies is competitive for reinforcement learnin . Advances in Neural Information Processing Systems, 31, 2018.Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Belle-mare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through Do ERL methods have a low update eficiency? dvantages, ERL methods often suffer from low update eficiency . Nearly all ERL approaches to date remain constrained to an on-policy training paradigm, limiting their ability to exploit more eficient off-policy update rules, where an action-value function, or critic, is ex Aug 30, 2024 · Before policy gradient methods like REINFORCE were developed, many reinforcement learning approaches focused on value-based methods , like Q-learning, where the objective is to learn a value function that estimates the expected reward for a given state-action pair. However, these methods don’t directly optimize the policy itself.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.13662", "content": "Jan 24, 2024 · In recent years, various powerful policy gradient algorithms have been proposed in deep reinforcement learning. While all these algorithms build on the Policy Gradient Theorem, the specific design choices differ significantly across algorithms. We provide a holistic overview of on-policy policy gradient algorithms to facilitate the understanding of both their theoretical foundations and their ... Jul 23, 2025 · Policy Gradient methods in Reinforcement Learning (RL) to directly optimize the policy , unlike value-based methods that estimate the value of states. These methods are particularly useful in environments with continuous action spaces or complex tasks where value-based approaches struggle. tep-based methods , especially in settings with dense re-wards. To address this issue, a most recently proposed method , Temporally-Correlated ERL ( TCE ) ( Li et al., 2024 ) introduced a more eficient update scheme that ”opens the black-box” and utilizes sub-segment information for pol I. INTRODUCTION Policy gradient methods [25], [26] are a popular class of approaches in reinforcement learning. The policy in these approaches is considered parameterized and one updates the policy parameter along a gradient search direction where the gradient is of a performance objective, normally the value function. The policy gradient theorem [26], [20], [10] which is a fundamental result ... Reinforcement learning (RL), particularly policy gradient (PG) methods , provides a powerful framework for solving sequential decision-making problems in complex environments. These methods have been successfully applied in diverse domains, ranging from robotics to game playing, and have recently become instrumental in fine-tuning large language models (LLMs) to align with human preferences and ... Abstract Policy gradient is a useful model-free reinforcement learning approach, but it tends to suffer from instability of gradient estimates. In this paper, we analyze and improve the stability of policy gradient methods . What is reinforce policy gradient theorem? The development of the REINFORCE algorithm and the associated policy gradient theorem was driven by a need to optimize policies in reinforcement learning (RL) environments where the outcome (reward) is uncertain and depends on a sequence of actions. How does the reinforce policy gradient algorithm work? We revisit the Reinforce policy gradient algorithm from the literature. Note that this algorithm typically works with cost returns obtained over random length episodes obtained from either termination upon reaching a goal state (as with episodic tasks) or from instants of visit to a prescribed recurrent state (in the case of continuing tasks). Which algorithm uses the policy gradient theorem? The Reinforce algorithm , makes use of the policy gradient theorem as the latter indicates that the gradient of the value function is the expectation of the gradient of a function of the noisy returns obtained from episodes. Is Tic linear policies competitive for reinforcement Learnin? tic linear policies is competitive for reinforcement learnin . Advances in Neural Information Processing Systems, 31, 2018.Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Belle-mare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through Do ERL methods have a low update eficiency? dvantages, ERL methods often suffer from low update eficiency . Nearly all ERL approaches to date remain constrained to an on-policy training paradigm, limiting their ability to exploit more eficient off-policy update rules, where an action-value function, or critic, is ex Aug 30, 2024 · Before policy gradient methods like REINFORCE were developed, many reinforcement learning approaches focused on value-based methods , like Q-learning, where the objective is to learn a value function that estimates the expected reward for a given state-action pair. However, these methods don’t directly optimize the policy itself."}
+{"idx": 1, "title": "Policy Gradient Methods in Reinforcement Learning", "date": "", "ddg_snippet": "Jul 23, 2025 · Policy Gradient methods in Reinforcement Learning (RL) to directly optimize the policy , unlike value-based methods that estimate the value of states. These methods are particularly useful in environments with continuous action spaces or complex tasks where value-based approaches struggle.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/policy-gradient-methods-in-reinforcement-learning/", "content": "Jul 23, 2025 · Policy Gradient methods in Reinforcement Learning (RL) to directly optimize the policy , unlike value-based methods that estimate the value of states. These methods are particularly useful in environments with continuous action spaces or complex tasks where value-based approaches struggle."}
+{"idx": 2, "title": "TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT ...", "date": "", "ddg_snippet": "tep-based methods , especially in settings with dense re-wards. To address this issue, a most recently proposed method , Temporally-Correlated ERL ( TCE ) ( Li et al., 2024 ) introduced a more eficient update scheme that ”opens the black-box” and utilizes sub-segment information for pol", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fPHT0z4cS5", "content": "tep-based methods , especially in settings with dense re-wards. To address this issue, a most recently proposed method , Temporally-Correlated ERL ( TCE ) ( Li et al., 2024 ) introduced a more eficient update scheme that ”opens the black-box” and utilizes sub-segment information for pol"}
+{"idx": 3, "title": "The Reinforce Policy Gradient Algorithm Revisited", "date": "", "ddg_snippet": "I. INTRODUCTION Policy gradient methods [25], [26] are a popular class of approaches in reinforcement learning. The policy in these approaches is considered parameterized and one updates the policy parameter along a gradient search direction where the gradient is of a performance objective, normally the value function. The policy gradient theorem [26], [20], [10] which is a fundamental result ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.05000", "content": "I. INTRODUCTION Policy gradient methods [25], [26] are a popular class of approaches in reinforcement learning. The policy in these approaches is considered parameterized and one updates the policy parameter along a gradient search direction where the gradient is of a performance objective, normally the value function. The policy gradient theorem [26], [20], [10] which is a fundamental result ..."}
+{"idx": 4, "title": "On the Design of KL-Regularized Policy Gradient Algorithms ...", "date": "", "ddg_snippet": "Reinforcement learning (RL), particularly policy gradient (PG) methods , provides a powerful framework for solving sequential decision-making problems in complex environments. These methods have been successfully applied in diverse domains, ranging from robotics to game playing, and have recently become instrumental in fine-tuning large language models (LLMs) to align with human preferences and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.17508", "content": "Reinforcement learning (RL), particularly policy gradient (PG) methods , provides a powerful framework for solving sequential decision-making problems in complex environments. These methods have been successfully applied in diverse domains, ranging from robotics to game playing, and have recently become instrumental in fine-tuning large language models (LLMs) to align with human preferences and ..."}
+{"idx": 5, "title": "Analysis and Improvement of Policy Gradient Estimation", "date": "", "ddg_snippet": "Abstract Policy gradient is a useful model-free reinforcement learning approach, but it tends to suffer from instability of gradient estimates. In this paper, we analyze and improve the stability of policy gradient methods .", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/4264-analysis-and-improvement-of-policy-gradient-estimation.pdf", "content": "Abstract Policy gradient is a useful model-free reinforcement learning approach, but it tends to suffer from instability of gradient estimates. In this paper, we analyze and improve the stability of policy gradient methods ."}
+{"idx": 6, "title": "Policy Optimization with REINFORCE: A Deep Dive into Policy ...", "date": "", "ddg_snippet": "Aug 30, 2024 · Before policy gradient methods like REINFORCE were developed, many reinforcement learning approaches focused on value-based methods , like Q-learning, where the objective is to learn a value function that estimates the expected reward for a given state-action pair. However, these methods don’t directly optimize the policy itself.", "subpage_snippet": "", "source": "snawarhussain.com", "link": "https://snawarhussain.com/educational/reinforcement+learning/Policy-Gradient-Methods-in-ReInforcement-Learning-Log-trick-for-gradient-computation/", "content": "Aug 30, 2024 · Before policy gradient methods like REINFORCE were developed, many reinforcement learning approaches focused on value-based methods , like Q-learning, where the objective is to learn a value function that estimates the expected reward for a given state-action pair. However, these methods don’t directly optimize the policy itself."}
+{"idx": 7, "title": "JBShield: Defending Large Language Models from Jailbreak", "date": "", "ddg_snippet": "A few approaches [ 21 , 50 , 52 ] have sought to design defensive methods by understanding the effects of jailbreak prompts on LLMs, such as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.07557v1", "content": "A few approaches [ 21 , 50 , 52 ] have sought to design defensive methods by understanding the effects of jailbreak prompts on LLMs, such as ..."}
+{"idx": 8, "title": "Highly Thermal Conductive and Electromagnetic Shielding Polymer", "date": "", "ddg_snippet": "... life cycle assessment (LCA) and techno-economic assessment (TEA) are also performed to evaluate the advantages of this upcycling strategy over ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40820-025-01796-z", "content": "... life cycle assessment (LCA) and techno-economic assessment (TEA) are also performed to evaluate the advantages of this upcycling strategy over ..."}
+{"idx": 9, "title": "A comprehensive survey on deep learning techniques in", "date": "", "ddg_snippet": "... fields are typically interdisciplinary, including but not limited to information retrieval, data analysis, psycho-pedagogy, cognitive psychology, etc ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2309.04761v4", "content": "... fields are typically interdisciplinary, including but not limited to information retrieval, data analysis, psycho-pedagogy, cognitive psychology, etc ..."}
diff --git a/data/sampled_jsons/TOP-ERL_Meta-World_dims_per_head_table_4.jsonl b/data/sampled_jsons/TOP-ERL_Meta-World_dims_per_head_table_4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b8ed2d5f428c87f4abb9b9ba71a0fa929e06525b
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_Meta-World_dims_per_head_table_4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Top Hat | Interactive Learning Platform", "date": "", "ddg_snippet": "Experience a seamless connection between Top Hat and your LMS. Enjoy easy navigation, direct links to course materials, and synced grades for better teaching and learning.", "subpage_snippet": "", "source": "tophat.com", "link": "https://tophat.com/", "content": "Experience a seamless connection between Top Hat and your LMS. Enjoy easy navigation, direct links to course materials, and synced grades for better teaching and learning."}
+{"idx": 1, "title": "Login to Your Course | Top Hat", "date": "", "ddg_snippet": "Welcome back to Top Hat. Students and Professors log in here to access your course.", "subpage_snippet": "", "source": "app.tophat.com", "link": "https://app.tophat.com/login", "content": "Welcome back to Top Hat. Students and Professors log in here to access your course."}
+{"idx": 2, "title": "Student Log In | Top Hat", "date": "", "ddg_snippet": "Top Hat was created by students for students, with the goal of helping everyone succeed in higher education. Whether you're logging in for the first time or looking to get the most out of our platform, we’re here to help.", "subpage_snippet": "", "source": "tophat.com", "link": "https://tophat.com/students/", "content": "Top Hat was created by students for students, with the goal of helping everyone succeed in higher education. Whether you're logging in for the first time or looking to get the most out of our platform, we’re here to help."}
+{"idx": 3, "title": "Sign Up | Top Hat", "date": "", "ddg_snippet": "Sign up for a Top Hat account. Professors and students can quickly and easily register online by following these steps. Experience everything Top Hat has to offer.", "subpage_snippet": "", "source": "app.tophat.com", "link": "https://app.tophat.com/register/", "content": "Sign up for a Top Hat account. Professors and students can quickly and easily register online by following these steps. Experience everything Top Hat has to offer."}
+{"idx": 4, "title": "Top Hat Ace | Top Hat", "date": "", "ddg_snippet": "Ace, Top Hat’s AI-powered assistant, is designed to foster more impactful learning where it matters most: one-on-one. Educators can save valuable time on course prep and implement evidence-based learning with a single click, while students get 24/7 personalized study support.", "subpage_snippet": "", "source": "tophat.com", "link": "https://tophat.com/features/ace-ai/", "content": "Ace, Top Hat’s AI-powered assistant, is designed to foster more impactful learning where it matters most: one-on-one. Educators can save valuable time on course prep and implement evidence-based learning with a single click, while students get 24/7 personalized study support."}
+{"idx": 5, "title": "Login - Top Hat", "date": "", "ddg_snippet": "Product Overview Pricing Catalog Support Accessibility Resources Teaching Resources Blog Educator Stories Glossary of Higher Ed Company The Top Hat Story Meet the Team Newsroom Terms and Conditions Security Privacy Policy Contact Us Careers More Publisher Partnership Bookstore Services Compare Top Hat ©2024 Tophatmonocle Corp.", "subpage_snippet": "", "source": "support.tophat.com", "link": "https://support.tophat.com/login", "content": "Product Overview Pricing Catalog Support Accessibility Resources Teaching Resources Blog Educator Stories Glossary of Higher Ed Company The Top Hat Story Meet the Team Newsroom Terms and Conditions Security Privacy Policy Contact Us Careers More Publisher Partnership Bookstore Services Compare Top Hat ©2024 Tophatmonocle Corp."}
+{"idx": 6, "title": "Build Your Ultimate Course | Top Hat", "date": "", "ddg_snippet": "Choose from thousands of textbooks and course materials in the Top Hat Catalog, and collaborate in a unique community of educators", "subpage_snippet": "", "source": "tophat.com", "link": "https://tophat.com/catalog/", "content": "Choose from thousands of textbooks and course materials in the Top Hat Catalog, and collaborate in a unique community of educators"}
+{"idx": 7, "title": "Top Hat: Pricing", "date": "", "ddg_snippet": "Whether you're a student looking to buy a Top Hat subscription for a semester or an institutional leader looking to bring Top Hat to a whole campus, we have a plan to fit your needs.", "subpage_snippet": "", "source": "tophat.com", "link": "https://tophat.com/pricing/", "content": "Whether you're a student looking to buy a Top Hat subscription for a semester or an institutional leader looking to bring Top Hat to a whole campus, we have a plan to fit your needs."}
+{"idx": 8, "title": "Explore Top Hat: Interactive Tour | Top Hat", "date": "", "ddg_snippet": "Discover the power of Top Hat with our self-guided walkthroughs. Dive into different features and learn how Top Hat can transform your course.", "subpage_snippet": "", "source": "tophat.com", "link": "https://tophat.com/explore/", "content": "Discover the power of Top Hat with our self-guided walkthroughs. Dive into different features and learn how Top Hat can transform your course."}
+{"idx": 9, "title": "Getting Started for Bookstore Redemptions - Top Hat", "date": "", "ddg_snippet": "Enrolling in your class with Top Hat takes a few minutes. Here are a few steps to get you on your way.", "subpage_snippet": "", "source": "tophat.com", "link": "https://tophat.com/getting-started-for-bookstore-redemptions/", "content": "Enrolling in your class with Top Hat takes a few minutes. Here are a few steps to get you on your way."}
diff --git a/data/sampled_jsons/TOP-ERL_Section_5.3_random_segment_length_fixed_segment_performance.jsonl b/data/sampled_jsons/TOP-ERL_Section_5.3_random_segment_length_fixed_segment_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b027336060501f1dde2b04c150154448663afcde
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_Section_5.3_random_segment_length_fixed_segment_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Erlang -- erl_driver", "date": "", "ddg_snippet": "... Erlang Run-Time System Application (ERTS) ... length /1 ... erl _driver", "subpage_snippet": "", "source": "erlang.org", "link": "http://erlang.org/documentation/doc-12.0-rc3/erts-12.0/doc/html/erl_driver.html", "content": "... Erlang Run-Time System Application (ERTS) ... length /1 ... erl _driver"}
+{"idx": 1, "title": "Erlang -- erl_driver", "date": "", "ddg_snippet": "User's Guide Reference Manual Release Notes PDF Top ... Erlang Run-Time System Application (ERTS) Reference Manual Version 8.2", "subpage_snippet": "", "source": "erlang.org", "link": "http://erlang.org/documentation/doc-8.2/erts-8.2/doc/html/erl_driver.html", "content": "User's Guide Reference Manual Release Notes PDF Top ... Erlang Run-Time System Application (ERTS) Reference Manual Version 8.2"}
+{"idx": 2, "title": "Erlang -- ERTS Release Notes", "date": "", "ddg_snippet": "Fixed a bug where starting Erlang ... Fix dtrace/systemtap bug where the probe arguments would be concatenated due to faulty length calculation.", "subpage_snippet": "", "source": "erlang.org", "link": "http://erlang.org/documentation/doc-6.0/erts-6.0/doc/html/notes.html", "content": "Fixed a bug where starting Erlang ... Fix dtrace/systemtap bug where the probe arguments would be concatenated due to faulty length calculation."}
+{"idx": 3, "title": "Erlang -- erl_driver", "date": "", "ddg_snippet": "User's Guide Reference Manual Release Notes PDF Top ... Erlang Run-Time System Application (ERTS) Reference Manual Version 8.1", "subpage_snippet": "", "source": "erlang.org", "link": "http://erlang.org/documentation/doc-8.1/erts-8.1/doc/html/erl_driver.html", "content": "User's Guide Reference Manual Release Notes PDF Top ... Erlang Run-Time System Application (ERTS) Reference Manual Version 8.1"}
+{"idx": 4, "title": "Erlang -- Kernel Release Notes", "date": "", "ddg_snippet": "This means that writing a binary > 4 Gib to file now works but on e.g 64-bit Windows (that has scatter/gather I/O buffer segment lengths of 32 ...", "subpage_snippet": "", "source": "erlang.org", "link": "http://erlang.org/documentation/doc-5.9.3/lib/kernel-2.15.3/doc/html/notes.html", "content": "This means that writing a binary > 4 Gib to file now works but on e.g 64-bit Windows (that has scatter/gather I/O buffer segment lengths of 32 ..."}
+{"idx": 5, "title": "SyncMapV2: Robust and Adaptive Unsupervised Segmentation", "date": "", "ddg_snippet": "... without any self-training or robust training, unlike other segmentation tasks such as instance or semantic segmentation [ 13 , 26 , 27 , 28 , 14 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.16297v3", "content": "... without any self-training or robust training, unlike other segmentation tasks such as instance or semantic segmentation [ 13 , 26 , 27 , 28 , 14 ] ."}
+{"idx": 6, "title": "Erlang -- ERTS Release Notes", "date": "", "ddg_snippet": "Fix a bug where disabling tracing from a process that had return_to tracing enabled and was tracing on erlang:trace/ 3 would cause a segmentation ...", "subpage_snippet": "", "source": "zxq9.com", "link": "http://zxq9.com/erlang/docs/reg/19.1/erts-8.1/doc/html/notes.html", "content": "Fix a bug where disabling tracing from a process that had return_to tracing enabled and was tracing on erlang:trace/ 3 would cause a segmentation ..."}
+{"idx": 7, "title": "Erlang -- ERTS Release Notes", "date": "", "ddg_snippet": "This bug fix can make previous configuration options to erl passed through ERL _FLAGS , ERL _AFLAGS , ERL _ZFLAGS or -args_file not be interpreted in ...", "subpage_snippet": "", "source": "zxq9.com", "link": "https://zxq9.com/erlang/docs/reg/23.0/erts-11.0/doc/html/notes.html", "content": "This bug fix can make previous configuration options to erl passed through ERL _FLAGS , ERL _AFLAGS , ERL _ZFLAGS or -args_file not be interpreted in ..."}
+{"idx": 8, "title": "WO2015048400A1 - Estimation of effective channel length for", "date": "", "ddg_snippet": "Aspects of this approach can be applied to any three -dimensional transistor with tight gate control, including transistor bodies made up of 2 ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2015048400A1/en", "content": "Aspects of this approach can be applied to any three -dimensional transistor with tight gate control, including transistor bodies made up of 2 ..."}
+{"idx": 9, "title": "n64docs latest documentation", "date": "", "ddg_snippet": "... counter and can be configured to use 32 ... There are segments of the address space that use fixed translation, and segments that are configurable.", "subpage_snippet": "", "source": "n64.readthedocs.io", "link": "https://n64.readthedocs.io/", "content": "... counter and can be configured to use 32 ... There are segments of the address space that use fixed translation, and segments that are configurable."}
diff --git a/data/sampled_jsons/TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning_ablation_study_Figure_5.jsonl b/data/sampled_jsons/TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning_ablation_study_Figure_5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d20565118b84bddd1517ae30ef030fd4982de866
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning_ablation_study_Figure_5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TOP-ERL: Transformer-based Off-Policy Episodic ...", "date": "", "ddg_snippet": "by G Li · 2024 · Cited by 7 — This work introduces Transformer - based Off - Policy Episodic Reinforcement Learning ( TOP - ERL ), a novel algorithm that enables off - policy updates in the ERL ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09536", "content": "by G Li · 2024 · Cited by 7 — This work introduces Transformer - based Off - Policy Episodic Reinforcement Learning ( TOP - ERL ), a novel algorithm that enables off - policy updates in the ERL ..."}
+{"idx": 1, "title": "TOP-ERL: Transformer-based Off-Policy Episodic ...", "date": "", "ddg_snippet": "by G Li · Cited by 7 — The results demonstrate that the transformer critic in TOP - ERL predicts values with less bias, whereas the ablated model shows clear overestimation in its ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=N4NhVN30ph", "content": "by G Li · Cited by 7 — The results demonstrate that the transformer critic in TOP - ERL predicts values with less bias, whereas the ablated model shows clear overestimation in its ..."}
+{"idx": 2, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement ...", "date": "", "ddg_snippet": "Abstract—This work introduces Transformer - based Off - Policy . Episodic Reinforcement Learning ( TOP - ERL ), a novel algorithm that enables off - policy updates in ...", "subpage_snippet": "", "source": "rss25-roboreps.github.io", "link": "https://rss25-roboreps.github.io/papers/1_TOP_ERL_Transformer_based_Of.pdf", "content": "Abstract—This work introduces Transformer - based Off - Policy . Episodic Reinforcement Learning ( TOP - ERL ), a novel algorithm that enables off - policy updates in ..."}
+{"idx": 3, "title": "TOP-ERL: TRANSFORMER-BASED OFF-POLICY", "date": "", "ddg_snippet": "This work introduces Transformer - based Off - Policy Episodic Reinforcement . Learning ( TOP - ERL ), a novel algorithm that enables off - policy updates in the.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/644d145de2ed5780672bcf3003d4441a17be6e89.pdf", "content": "This work introduces Transformer - based Off - Policy Episodic Reinforcement . Learning ( TOP - ERL ), a novel algorithm that enables off - policy updates in the."}
+{"idx": 4, "title": "transformer-based off-policy episodic reinforcement learning", "date": "", "ddg_snippet": "by G Li · 2024 · Cited by 7 — TOP-ERL significantly outperforms state-of-the-art RL methods . Thor- ough ablation studies additionally show the impact of key design choices on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "by G Li · 2024 · Cited by 7 — TOP-ERL significantly outperforms state-of-the-art RL methods . Thor- ough ablation studies additionally show the impact of key design choices on ..."}
+{"idx": 5, "title": "Efficient Recurrent Off-Policy RL Requires a Context-Encoder ...", "date": "", "ddg_snippet": "24 May 2024 — This paper demonstrates that efficient recurrent off - policy reinforcement learning necessitates a context-encoder-specific learning rate for ...", "subpage_snippet": "", "source": "liner.com", "link": "https://liner.com/review/efficient-recurrent-offpolicy-rl-requires-a-contextencoderspecific-learning-rate", "content": "24 May 2024 — This paper demonstrates that efficient recurrent off - policy reinforcement learning necessitates a context-encoder-specific learning rate for ..."}
+{"idx": 6, "title": "Dong Tian", "date": "", "ddg_snippet": "This work introduces Transformer - based Off - Policy Episodic Reinforcement Learning ( TOP - ERL ), a novel algorithm that enables off - policy updates in the ERL ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Dong+Tian", "content": "This work introduces Transformer - based Off - Policy Episodic Reinforcement Learning ( TOP - ERL ), a novel algorithm that enables off - policy updates in the ERL ..."}
+{"idx": 7, "title": "Book", "date": "", "ddg_snippet": "TOP - ERL : Transformer - based Off - Policy Episodic Reinforcement Learning Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann; ODE ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025", "content": "TOP - ERL : Transformer - based Off - Policy Episodic Reinforcement Learning Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann; ODE ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "3 days ago — In this study , we propose an offline reinforcement learning from human feedback (RLHF) framework to align LMs using pre-generated samples ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Offline-to-online+reinforcement+learning", "content": "3 days ago — In this study , we propose an offline reinforcement learning from human feedback (RLHF) framework to align LMs using pre-generated samples ..."}
+{"idx": 9, "title": "Track: Poster Session 4", "date": "", "ddg_snippet": "25 Apr 2025 — TOP - ERL : Transformer - based Off - Policy Episodic Reinforcement Learning ... We study online reinforcement learning (RL) in non-stationary ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31974", "content": "25 Apr 2025 — TOP - ERL : Transformer - based Off - Policy Episodic Reinforcement Learning ... We study online reinforcement learning (RL) in non-stationary ..."}
diff --git a/data/sampled_jsons/TOP-ERL_ablation_study_trust_region_constraints_initial_condition_enforcement_layer_normalization_ra.jsonl b/data/sampled_jsons/TOP-ERL_ablation_study_trust_region_constraints_initial_condition_enforcement_layer_normalization_ra.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1d27667850b4a57ee6899320f90c0053072bdab0
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_ablation_study_trust_region_constraints_initial_condition_enforcement_layer_normalization_ra.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TOP-ERL: Transformer-based Off-policy Episodic Reinforcement ...", "date": "", "ddg_snippet": "TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step return estimation.", "subpage_snippet": "", "source": "brucegeli.github.io", "link": "https://brucegeli.github.io/TOP_ERL_ICLR25/", "content": "TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step return estimation."}
+{"idx": 1, "title": "[2410.09536] TOP-ERL: Transformer-based Off-Policy Episodic ... TRC: Trust Region Conditional Value at Risk for Safe ... GitHub - toperliclr2025/TOP_ERL TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement ... (PDF) TOP-ERL: Transformer-based Off-Policy Episodic ... TRC: Trust Region Conditional Value at Risk for Safe Reinforcement TOP - ERL : Transformer-based Off-Policy Episodic Reinforcement Lea… GitHub - toperliclr2025/TOP_ERL TOP - ERL : Transformer-based Off-policy Episodic Reinforcement Lear… The trust region filter strategy: Survey of a rigorous ...", "date": "", "ddg_snippet": "Oct 12, 2024 · This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ... There are various types of constraints , among which constraints on conditional value at risk (CVaR) effectively lower the probability of failures caused by high costs since CVaR is a conditional expectation obtained above a certain percentile. In this paper, we propose a trust region -based safe RL method with CVaR constraints , called TRC. In TOP-ERL , we utilize Transformers as an action sequence value predictor, training it via the N-step future returns. To ensure stable critic learning, we adapt the trajectory segmentation strategy in [9] by splitting the long trajectory into sub-sequences of varying lengths . Jan 22, 2025 · Layer normalization and trust region constraints is important for TOP-ERL achieving good performance . But the paper lacks a detailed discussion about the importance of these two components. Oct 12, 2024 · TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step ... What is a trust region-based safe RL method? There are various types of constraints, among which constraints on conditional value at risk (CVaR) effectively lower the probability of failures caused by high costs since CVaR is a conditional expectation obtained above a certain percentile. In this paper, we propose a trust region-based safe RL method with CVaR constraints, called TRC . Why are ERL methods constrained to on-policy frameworks? However, ERL methods are often constrained to on-policy frameworks due to the difficulty of evaluating state-action values for entire action sequences , limiting their sample efficiency and preventing the use of more efficient off-policy architectures. What is the difference between step-based RL and Erl? Unlike Step-based RL (SRL) [2, 3], ERL shifts the solution search from per-step actions to a parameterized trajectory space , leveraging techniques like Movement Primitives (MPs) [6, 7, 8] for generating action sequences. Does top-ERL outperform RL methods? TOP-ERL significantly outperforms state-of-the-art RL methods. Thorough ablation studies additionally show the impact of key design choices on the model performance. In TOP-ERL, we utilize Transformers as an action sequence value estimator, to evaluate the value of executing a sequence of actions from an intermediate state in the episode. Dec 1, 2024 · The trust region filter framework, sketched in Fig. 2b and introduced in the next section, allows us to answer this question through the elements of the TRF algorithm. Section 3 then presents a summary of the convergence properties as well as additional features that allow simplifications of the TRF algorithm.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09536", "content": "Oct 12, 2024 · This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ... There are various types of constraints , among which constraints on conditional value at risk (CVaR) effectively lower the probability of failures caused by high costs since CVaR is a conditional expectation obtained above a certain percentile. In this paper, we propose a trust region -based safe RL method with CVaR constraints , called TRC. In TOP-ERL , we utilize Transformers as an action sequence value predictor, training it via the N-step future returns. To ensure stable critic learning, we adapt the trajectory segmentation strategy in [9] by splitting the long trajectory into sub-sequences of varying lengths . Jan 22, 2025 · Layer normalization and trust region constraints is important for TOP-ERL achieving good performance . But the paper lacks a detailed discussion about the importance of these two components. Oct 12, 2024 · TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step ... What is a trust region-based safe RL method? There are various types of constraints, among which constraints on conditional value at risk (CVaR) effectively lower the probability of failures caused by high costs since CVaR is a conditional expectation obtained above a certain percentile. In this paper, we propose a trust region-based safe RL method with CVaR constraints, called TRC . Why are ERL methods constrained to on-policy frameworks? However, ERL methods are often constrained to on-policy frameworks due to the difficulty of evaluating state-action values for entire action sequences , limiting their sample efficiency and preventing the use of more efficient off-policy architectures. What is the difference between step-based RL and Erl? Unlike Step-based RL (SRL) [2, 3], ERL shifts the solution search from per-step actions to a parameterized trajectory space , leveraging techniques like Movement Primitives (MPs) [6, 7, 8] for generating action sequences. Does top-ERL outperform RL methods? TOP-ERL significantly outperforms state-of-the-art RL methods. Thorough ablation studies additionally show the impact of key design choices on the model performance. In TOP-ERL, we utilize Transformers as an action sequence value estimator, to evaluate the value of executing a sequence of actions from an intermediate state in the episode. Dec 1, 2024 · The trust region filter framework, sketched in Fig. 2b and introduced in the next section, allows us to answer this question through the elements of the TRF algorithm. Section 3 then presents a summary of the convergence properties as well as additional features that allow simplifications of the TRF algorithm."}
+{"idx": 2, "title": "TRC: Trust Region Conditional Value at Risk for Safe ...", "date": "", "ddg_snippet": "There are various types of constraints , among which constraints on conditional value at risk (CVaR) effectively lower the probability of failures caused by high costs since CVaR is a conditional expectation obtained above a certain percentile. In this paper, we propose a trust region -based safe RL method with CVaR constraints , called TRC.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9677982", "content": "There are various types of constraints , among which constraints on conditional value at risk (CVaR) effectively lower the probability of failures caused by high costs since CVaR is a conditional expectation obtained above a certain percentile. In this paper, we propose a trust region -based safe RL method with CVaR constraints , called TRC."}
+{"idx": 3, "title": "GitHub - toperliclr2025/TOP_ERL", "date": "", "ddg_snippet": "In TOP-ERL , we utilize Transformers as an action sequence value predictor, training it via the N-step future returns. To ensure stable critic learning, we adapt the trajectory segmentation strategy in [9] by splitting the long trajectory into sub-sequences of varying lengths .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/toperliclr2025/TOP_ERL", "content": "In TOP-ERL , we utilize Transformers as an action sequence value predictor, training it via the N-step future returns. To ensure stable critic learning, we adapt the trajectory segmentation strategy in [9] by splitting the long trajectory into sub-sequences of varying lengths ."}
+{"idx": 4, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement ...", "date": "", "ddg_snippet": "Jan 22, 2025 · Layer normalization and trust region constraints is important for TOP-ERL achieving good performance . But the paper lacks a detailed discussion about the importance of these two components.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=N4NhVN30ph", "content": "Jan 22, 2025 · Layer normalization and trust region constraints is important for TOP-ERL achieving good performance . But the paper lacks a detailed discussion about the importance of these two components."}
+{"idx": 5, "title": "(PDF) TOP-ERL: Transformer-based Off-Policy Episodic ...", "date": "", "ddg_snippet": "Oct 12, 2024 · TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929465_TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning", "content": "Oct 12, 2024 · TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step ..."}
+{"idx": 6, "title": "The trust region filter strategy: Survey of a rigorous ...", "date": "", "ddg_snippet": "Dec 1, 2024 · The trust region filter framework, sketched in Fig. 2b and introduced in the next section, allows us to answer this question through the elements of the TRF algorithm. Section 3 then presents a summary of the convergence properties as well as additional features that allow simplifications of the TRF algorithm.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772508124000590", "content": "Dec 1, 2024 · The trust region filter framework, sketched in Fig. 2b and introduced in the next section, allows us to answer this question through the elements of the TRF algorithm. Section 3 then presents a summary of the convergence properties as well as additional features that allow simplifications of the TRF algorithm."}
+{"idx": 7, "title": "Data Normalization Techniques - FasterCapital", "date": "", "ddg_snippet": "When implementing data normalization techniques, several technical considerations must be taken into account. For instance, the choice of normalization level can significantly impact database performance .", "subpage_snippet": "", "source": "fastercapital.com", "link": "https://fastercapital.com/term/data-normalization-techniques.html", "content": "When implementing data normalization techniques, several technical considerations must be taken into account. For instance, the choice of normalization level can significantly impact database performance ."}
+{"idx": 8, "title": "VP-SAM: Taming Segment Anything Model for", "date": "", "ddg_snippet": "11: (a-b) Ablation studies of clip length L. (c) Failure cases, where red, green and yellow represent the GT, prediction and their overlapping regions , respectively.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/03403.pdf", "content": "11: (a-b) Ablation studies of clip length L. (c) Failure cases, where red, green and yellow represent the GT, prediction and their overlapping regions , respectively."}
+{"idx": 9, "title": "Conditions from ECG Images with Deep Learning", "date": "", "ddg_snippet": "This study proposes a deep learning-based framework for the detection of cardiovascular conditions from electrocardiogram (ECG) images.", "subpage_snippet": "", "source": "www.ijisrt.com", "link": "https://www.ijisrt.com/assets/upload/files/IJISRT25MAY175.pdf", "content": "This study proposes a deep learning-based framework for the detection of cardiovascular conditions from electrocardiogram (ECG) images."}
diff --git a/data/sampled_jsons/TRSSL_Rizve_et_al._2022_abstract.jsonl b/data/sampled_jsons/TRSSL_Rizve_et_al._2022_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d89581ca2898b32746dfc604fcaddb467d4440a7
--- /dev/null
+++ b/data/sampled_jsons/TRSSL_Rizve_et_al._2022_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Proceedings of the International Conference on Machine Learning 2022", "date": "", "ddg_snippet": "SSL algorithms ( Rizve et al ., 2021; Sohn et al ., 2020) that utilize pseudo-label techniques often employ a xed threshold to select relevant imputations from unlabeled data.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03982898/document", "content": "SSL algorithms ( Rizve et al ., 2021; Sohn et al ., 2020) that utilize pseudo-label techniques often employ a xed threshold to select relevant imputations from unlabeled data."}
+{"idx": 1, "title": "Computer Vision and Image Understanding, 219 ( 2022 ) 103406.", "date": "", "ddg_snippet": "I. Dave, R. Gupta, M.N. Rizve et al . complex augmentations like Gaussian blurring, shearing and rotation, however these are not used in the results reported in this paper. We provide results with more complex augmentation in Section D of the supplementary material.", "subpage_snippet": "", "source": "www.crcv.ucf.edu", "link": "https://www.crcv.ucf.edu/wp-content/uploads/2018/11/1-s2.0-S1077314222000376-main.pdf", "content": "I. Dave, R. Gupta, M.N. Rizve et al . complex augmentations like Gaussian blurring, shearing and rotation, however these are not used in the results reported in this paper. We provide results with more complex augmentation in Section D of the supplementary material."}
+{"idx": 2, "title": "Exploring Complementary Strengths of Invariant and Equivariant...", "date": "", "ddg_snippet": "Rizve et al . (2021) showed that the complementary of invariance and equivariance promotes a general base feature extractor for FSL tasks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/355875837_Exploring_Complementary_Strengths_of_Invariant_and_Equivariant_Representations_for_Few-Shot_Learning", "content": "Rizve et al . (2021) showed that the complementary of invariance and equivariance promotes a general base feature extractor for FSL tasks."}
+{"idx": 3, "title": "Fer for o pen -w orld s emi -s upervised L earning", "date": "", "ddg_snippet": "Concurrently, Rizve et al . ( 2022 a;b) propose more effective pseudo labeling strategies for unlabeled data.Following Cao et al . ( 2022 ), we first divide all classes into 50% seen and 50% unseen classes, then select 50% of the seen classes data as labeled dataset.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=7IMneQViz6h", "content": "Concurrently, Rizve et al . ( 2022 a;b) propose more effective pseudo labeling strategies for unlabeled data.Following Cao et al . ( 2022 ), we first divide all classes into 50% seen and 50% unseen classes, then select 50% of the seen classes data as labeled dataset."}
+{"idx": 4, "title": "Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised...", "date": "", "ddg_snippet": "Temporal Action Localization (TAL) (Xu et al ., 2017; Sridhar et al ., 2021; Xu et al ., 2020; Zhang et al ., 2022 ; Shi et al ., 2023) aims at predicting the start and end of the actions within an arbitrarily long untrimmed video, and classify the snippets simultaneously.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.08971v1", "content": "Temporal Action Localization (TAL) (Xu et al ., 2017; Sridhar et al ., 2021; Xu et al ., 2020; Zhang et al ., 2022 ; Shi et al ., 2023) aims at predicting the start and end of the actions within an arbitrarily long untrimmed video, and classify the snippets simultaneously."}
+{"idx": 5, "title": "Rethinking Confidence Scores and Thresholds in...", "date": "", "ddg_snippet": "Rizve et al . (2021) utilize negative labels and an uncertainty-aware pseudola-bel selection technique.", "subpage_snippet": "", "source": "pages.cs.wisc.edu", "link": "https://pages.cs.wisc.edu/~fredsala/ssl_icml25.pdf", "content": "Rizve et al . (2021) utilize negative labels and an uncertainty-aware pseudola-bel selection technique."}
+{"idx": 6, "title": "Let’s Start Over: Retraining with Selective Samples for Generalized...", "date": "", "ddg_snippet": "[ Rizve et al ., 2022 ] Mamshad Nayeem Rizve , Navid Kar-dan, and Mubarak Shah. Towards realistic semi-supervised learning. In European Conference on Com-puter Vision, pages 437–455.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0532.pdf", "content": "[ Rizve et al ., 2022 ] Mamshad Nayeem Rizve , Navid Kar-dan, and Mubarak Shah. Towards realistic semi-supervised learning. In European Conference on Com-puter Vision, pages 437–455."}
+{"idx": 7, "title": "Neighborhood-Regularized Self-Training for Learning with Few Labels", "date": "", "ddg_snippet": "(2020); Kim et al . ( 2022 b) le verage model predic-. tiv e confidence with a thresholding function, Mukherjee. and A wadallah (2020); Tsai, Lin, and Fu ( 2022 ) propose to.", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/neighborhood-regularized-self-training-for-learning-with-5173258", "content": "(2020); Kim et al . ( 2022 b) le verage model predic-. tiv e confidence with a thresholding function, Mukherjee. and A wadallah (2020); Tsai, Lin, and Fu ( 2022 ) propose to."}
+{"idx": 8, "title": "Uncertainty-Aware Self-Training for Low-Resource Neural Sequence...", "date": "", "ddg_snippet": "2022 ) and slot filling (Liu et al . 2022 ), which aims at classifying language tokens into a pre-defined set of classes (Shen et al .Self-training is one of the typical SSL techniques which can be viewed as teacher-student training (Grandvalet and Bengio 2004; Amini et al .", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/26603/26375", "content": "2022 ) and slot filling (Liu et al . 2022 ), which aims at classifying language tokens into a pre-defined set of classes (Shen et al .Self-training is one of the typical SSL techniques which can be viewed as teacher-student training (Grandvalet and Bengio 2004; Amini et al ."}
+{"idx": 9, "title": "ahajournals.org/journal/circ", "date": "", "ddg_snippet": "American Heart Association, Circulation, 2022 .", "subpage_snippet": "", "source": "www.ahajournals.org", "link": "https://www.ahajournals.org/journal/circ", "content": "American Heart Association, Circulation, 2022 ."}
diff --git a/data/sampled_jsons/TRSSL_Rizve_et_al_2022_semi-supervised_learning_abstract_year_2022.jsonl b/data/sampled_jsons/TRSSL_Rizve_et_al_2022_semi-supervised_learning_abstract_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..208387c396d5eb2a840b9c0c2722e463e7ad98a5
--- /dev/null
+++ b/data/sampled_jsons/TRSSL_Rizve_et_al_2022_semi-supervised_learning_abstract_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "nayeemrizve/ TRSSL : \"Towards Realistic Semi - Supervised Learning ...\"", "date": "", "ddg_snippet": "\"Towards Realistic Semi - Supervised Learning \" by Mamshad Nayeem Rizve , Navid Kardan, Mubarak Shah (ECCV 2022 ). Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nayeemrizve/TRSSL", "content": "\"Towards Realistic Semi - Supervised Learning \" by Mamshad Nayeem Rizve , Navid Kardan, Mubarak Shah (ECCV 2022 ). Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost."}
+{"idx": 1, "title": "(PDF) Towards Realistic Semi - Supervised Learning", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost.Towards Realistic Semi - Supervised Learning . Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361807923_Towards_Realistic_Semi-Supervised_Learning", "content": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost.Towards Realistic Semi - Supervised Learning . Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah."}
+{"idx": 2, "title": "Stable Mean Teacher for Semi - supervised Video Action Detection", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ) is an active research area with two prominent approaches: iterative proxy-label ( Rizve et al . 2020) and consistency based methods (Tarvainen and Valpola 2017) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.07072v1", "content": "Semi - supervised learning ( SSL ) is an active research area with two prominent approaches: iterative proxy-label ( Rizve et al . 2020) and consistency based methods (Tarvainen and Valpola 2017) ."}
+{"idx": 3, "title": "Complementing Semi - Supervised Learning with Uncertainty... | DeepAI", "date": "", "ddg_snippet": "Combating Noise: Semi - supervised Learning by Region Uncertainty Quantification. Semi - supervised learning aims to leverage a large amount of unlabeled da... 0 Zhenyu Wang, et al . ∙.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/complementing-semi-supervised-learning-with-uncertainty-quantification", "content": "Combating Noise: Semi - supervised Learning by Region Uncertainty Quantification. Semi - supervised learning aims to leverage a large amount of unlabeled da... 0 Zhenyu Wang, et al . ∙."}
+{"idx": 4, "title": "Proceedings of the International Conference on Machine Learning 2022", "date": "", "ddg_snippet": "Abstract . Semi - supervised learning ( SSL ) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/wang22s/wang22s.pdf", "content": "Abstract . Semi - supervised learning ( SSL ) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data."}
+{"idx": 5, "title": "LION: Label Disambiguation for Semi - supervised Facial Expression...", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ) appropriately frees up the stringent requirement of fully supervised learning for com-pletely labeled dataset, while at the same time guaranteeing the performance of the model.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2023/0078.pdf", "content": "Semi - supervised learning ( SSL ) appropriately frees up the stringent requirement of fully supervised learning for com-pletely labeled dataset, while at the same time guaranteeing the performance of the model."}
+{"idx": 6, "title": "Open-world barely- supervised learning via augmented pseudo labels", "date": "", "ddg_snippet": "Open-world semi - supervised learning (OWSSL) has received significant attention since it addresses the issue of unlabeled data containing classes not present in the labeled data.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/id/671b8a5dba35de7eae907c38", "content": "Open-world semi - supervised learning (OWSSL) has received significant attention since it addresses the issue of unlabeled data containing classes not present in the labeled data."}
+{"idx": 7, "title": "nayeemrizve/ TRSSL : \"Towards Realistic Semi - Supervised Learning ...\"", "date": "", "ddg_snippet": "\"Towards Realistic Semi - Supervised Learning \" by Mamshad Nayeem Rizve , Navid Kardan, Mubarak Shah (ECCV 2022 ).", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/nayeemrizve/TRSSL", "content": "\"Towards Realistic Semi - Supervised Learning \" by Mamshad Nayeem Rizve , Navid Kardan, Mubarak Shah (ECCV 2022 )."}
+{"idx": 8, "title": "Towards Realistic Semi - Supervised Learning", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136910423.pdf", "content": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data."}
+{"idx": 9, "title": "Towards Realistic Semi - supervised Learning | SpringerLink", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost.Berthelot, D., et al .: ReMixMatch: semi - supervised learning with distribution matching and augmentation anchoring.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-19821-2_25", "content": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost.Berthelot, D., et al .: ReMixMatch: semi - supervised learning with distribution matching and augmentation anchoring."}
diff --git a/data/sampled_jsons/TRSSL_semi-supervised_learning_Sinkhorn-Knopp_self-label_assignment.jsonl b/data/sampled_jsons/TRSSL_semi-supervised_learning_Sinkhorn-Knopp_self-label_assignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ff2526086bf0248f39048d15dc1d4a62c9d20c8
--- /dev/null
+++ b/data/sampled_jsons/TRSSL_semi-supervised_learning_Sinkhorn-Knopp_self-label_assignment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed ...", "date": "", "ddg_snippet": "Self -training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the classifier ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2102.08622", "content": "Self -training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the classifier ..."}
+{"idx": 1, "title": "PDF Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed ...", "date": "", "ddg_snippet": "Abstract Self -training is a standard approach to semi-supervised learning where the learner's own pre-dictions on unlabeled data are used as supervi-sion during training. In this paper, we reinterpret this label assignment process as an optimal trans-portation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/tai21a/tai21a.pdf", "content": "Abstract Self -training is a standard approach to semi-supervised learning where the learner's own pre-dictions on unlabeled data are used as supervi-sion during training. In this paper, we reinterpret this label assignment process as an optimal trans-portation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of ..."}
+{"idx": 2, "title": "GitHub - stanford-futuredata/sinkhorn-label-allocation: Sinkhorn Label ...", "date": "", "ddg_snippet": "We call the resulting label assignment process Sinkhorn Label Allocation, or SLA for short. When combined with consistency regularization, SLA yields a self -training algorithm that achieves strong performance on semi-supervised versions of CIFAR-10, CIFAR-100 and SVHN.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/stanford-futuredata/sinkhorn-label-allocation", "content": "We call the resulting label assignment process Sinkhorn Label Allocation, or SLA for short. When combined with consistency regularization, SLA yields a self -training algorithm that achieves strong performance on semi-supervised versions of CIFAR-10, CIFAR-100 and SVHN."}
+{"idx": 3, "title": "[2102.08622] Sinkhorn Label Allocation: Semi-Supervised Classification ...", "date": "", "ddg_snippet": "Abstract Self -training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2102.08622", "content": "Abstract Self -training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the ..."}
+{"idx": 4, "title": "GitHub - nayeemrizve/TRSSL: \"Towards Realistic Semi-Supervised Learning ...", "date": "", "ddg_snippet": "Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nayeemrizve/TRSSL", "content": "Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved."}
+{"idx": 5, "title": "Promote knowledge mining towards open-world semi-supervised learning ...", "date": "", "ddg_snippet": "We propose a unified learning paradigm based on the Sinkhorn-Knopp algorithm that can handle both labeled and unlabeled data of both seen and novel classes in a consistent way for open-world semi-supervised learning .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320324000104", "content": "We propose a unified learning paradigm based on the Sinkhorn-Knopp algorithm that can handle both labeled and unlabeled data of both seen and novel classes in a consistent way for open-world semi-supervised learning ."}
+{"idx": 6, "title": "Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed ...", "date": "", "ddg_snippet": "Self -training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/tai21a.html", "content": "Self -training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the ..."}
+{"idx": 7, "title": "ICML 2021 Sinkhorn Label Allocation: Semi-Supervised Classification via ...", "date": "", "ddg_snippet": "Self -training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the classifier ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/spotlight/9222", "content": "Self -training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the classifier ..."}
+{"idx": 8, "title": "sinkhorn-label-allocation/README.md at master - GitHub", "date": "", "ddg_snippet": "We call the resulting label assignment process Sinkhorn Label Allocation, or SLA for short. When combined with consistency regularization, SLA yields a self -training algorithm that achieves strong performance on semi-supervised versions of CIFAR-10, CIFAR-100 and SVHN.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/stanford-futuredata/sinkhorn-label-allocation/blob/master/README.md", "content": "We call the resulting label assignment process Sinkhorn Label Allocation, or SLA for short. When combined with consistency regularization, SLA yields a self -training algorithm that achieves strong performance on semi-supervised versions of CIFAR-10, CIFAR-100 and SVHN."}
+{"idx": 9, "title": "PDF Kai Sheng Tai Peter Bailis Gregory Valiant fkst,pbailis,valiantg@cs ...", "date": "", "ddg_snippet": "Abstract Self -training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/349391805_Sinkhorn_Label_Allocation_Semi-Supervised_Classification_via_Annealed_Self-Training/fulltext/602dd9d692851c4ed57bfe3c/Sinkhorn-Label-Allocation-Semi-Supervised-Classification-via-Annealed-Self-Training.pdf", "content": "Abstract Self -training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training."}
diff --git a/data/sampled_jsons/TRSSL_semi-supervised_learning_review_comparison_survey.jsonl b/data/sampled_jsons/TRSSL_semi-supervised_learning_review_comparison_survey.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d39a861e716f3634340e5db93f4e36bbe8c8b407
--- /dev/null
+++ b/data/sampled_jsons/TRSSL_semi-supervised_learning_review_comparison_survey.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Weak supervision - Wikipedia", "date": "", "ddg_snippet": "Semi - supervised learning may refer to either transductive learning or inductive learning .[1] The goal of transductive learning is to infer the correct labels for the given unlabeled data.\"Weak Supervision: A Survey on Predictive Maintenance\".", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Weak_supervision", "content": "Semi - supervised learning may refer to either transductive learning or inductive learning .[1] The goal of transductive learning is to infer the correct labels for the given unlabeled data.\"Weak Supervision: A Survey on Predictive Maintenance\"."}
+{"idx": 1, "title": "nayeemrizve/ TRSSL : \"Towards Realistic Semi - Supervised Learning ...\"", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nayeemrizve/TRSSL", "content": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data."}
+{"idx": 2, "title": "(PDF) Semi - supervised learning : a brief review", "date": "", "ddg_snippet": "Semi - supervised learning : a brief review . February 2018. International Journal of Engineering & Technology 7(1-8):81.niques of Semi - supervised learning ( SSL ) such as self-training, co-training, multi-view learning , TSVMs methods. Traditionally SSL is.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/324050146_Semi-supervised_learning_a_brief_review", "content": "Semi - supervised learning : a brief review . February 2018. International Journal of Engineering & Technology 7(1-8):81.niques of Semi - supervised learning ( SSL ) such as self-training, co-training, multi-view learning , TSVMs methods. Traditionally SSL is."}
+{"idx": 3, "title": "Semi - Supervised Learning : Techniques & Examples [2024]", "date": "", "ddg_snippet": "Learn what Semi - Supervised Learning is, how it works, and what its limitations are. Explore the most exciting research papers and datasets, and start labeling your data on V7.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/semi-supervised-learning-guide", "content": "Learn what Semi - Supervised Learning is, how it works, and what its limitations are. Explore the most exciting research papers and datasets, and start labeling your data on V7."}
+{"idx": 4, "title": "What Is Semi - Supervised Learning ? | IBM", "date": "", "ddg_snippet": "Semi - supervised learning is a branch of machine learning that combines supervised and unsupervised learning by using both labeled and unlabeled data to train artificial intelligence (AI) models for classification and regression tasks.", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/topics/semi-supervised-learning", "content": "Semi - supervised learning is a branch of machine learning that combines supervised and unsupervised learning by using both labeled and unlabeled data to train artificial intelligence (AI) models for classification and regression tasks."}
+{"idx": 5, "title": "Track: Semisupervised Learning 3", "date": "", "ddg_snippet": "While semi - supervised learning ( SSL ) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use either all unlabeled examples or the unlabeled examples with a fixed...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/session/12092", "content": "While semi - supervised learning ( SSL ) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use either all unlabeled examples or the unlabeled examples with a fixed..."}
+{"idx": 6, "title": "A systematic literature review of cyber-security data repositories and...", "date": "", "ddg_snippet": "The keywords (“ semi - supervised learning ” AND “cyber-security”) have been chosen because SSL has been increasingly used in cyber-security to improve the accuracy of detection and classification systems [90].", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s44248-023-00003-x", "content": "The keywords (“ semi - supervised learning ” AND “cyber-security”) have been chosen because SSL has been increasingly used in cyber-security to improve the accuracy of detection and classification systems [90]."}
+{"idx": 7, "title": "Review for NeurIPS paper: Unsupervised Semantic Aggregation and...", "date": "", "ddg_snippet": "Specifically, the authors combine both unsupervised learning and semi - supervised learning ( SSL ) to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/71a58e8cb75904f24cde464161c3e766-Review.html", "content": "Specifically, the authors combine both unsupervised learning and semi - supervised learning ( SSL ) to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL ."}
+{"idx": 8, "title": "Comprehensive study of semi - supervised learning for DNA...", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ), an intermediate approach between unsupervised (with no labeled training data) and supervised (with only labeled training data) learning , is often used when labeling data is not feasible or requires substantial resources [10, 11].", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9178802/", "content": "Semi - supervised learning ( SSL ), an intermediate approach between unsupervised (with no labeled training data) and supervised (with only labeled training data) learning , is often used when labeling data is not feasible or requires substantial resources [10, 11]."}
+{"idx": 9, "title": "Towards Realistic Semi - Supervised Learning", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136910423.pdf", "content": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data."}
diff --git a/data/sampled_jsons/Table_1_MCC_Contrastive_synthetic_real_ablation_results_sitearxiv.org.jsonl b/data/sampled_jsons/Table_1_MCC_Contrastive_synthetic_real_ablation_results_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..95a0e491f77853d5094f5b6cb8214fc3fb909846
--- /dev/null
+++ b/data/sampled_jsons/Table_1_MCC_Contrastive_synthetic_real_ablation_results_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2305.08164v1 [cs.LG] 14 May 2023", "date": "", "ddg_snippet": "by Z Huang · 2023 · Cited by 5 — More empirical results can be found in Appendix D. 4.1 Datasets. We use synthetic and real -world datasets for evaluating the ... Table 3: Ablation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.08164", "content": "by Z Huang · 2023 · Cited by 5 — More empirical results can be found in Appendix D. 4.1 Datasets. We use synthetic and real -world datasets for evaluating the ... Table 3: Ablation ..."}
+{"idx": 1, "title": "arXiv:2306.06510v2 [cs.LG] 5 Jan 2025", "date": "", "ddg_snippet": "by L Kong · 2023 · Cited by 21 — From Table 1 , we can observe that both R2 and MCC of our ... Table 4 and Table 5 contain the ablation results for individual loss terms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.06510", "content": "by L Kong · 2023 · Cited by 21 — From Table 1 , we can observe that both R2 and MCC of our ... Table 4 and Table 5 contain the ablation results for individual loss terms."}
+{"idx": 2, "title": "Self-supervised contrastive learning performs non-linear ...", "date": "", "ddg_snippet": "Similar to our results for the Euclidean case ( Table 1 ), in Figure 9 we show ... Table 6: SLDS and Lorenz dataset from Table 1 with the addition of the MCC metric ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.14673v2", "content": "Similar to our results for the Euclidean case ( Table 1 ), in Figure 9 we show ... Table 6: SLDS and Lorenz dataset from Table 1 with the addition of the MCC metric ..."}
+{"idx": 3, "title": "F2RVLM: Boosting Fine-grained Fragment Retrieval for Multi-Modal...", "date": "", "ddg_snippet": "Overall Retrieval Performance. Table 2 summarizes fragment-level retrieval results on the in-domain MLDR validation set and the real -domain WeChat test set.Multimodal Contrastive Learning for Dialogue Embeddings with Global and Local Views.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.17714v1", "content": "Overall Retrieval Performance. Table 2 summarizes fragment-level retrieval results on the in-domain MLDR validation set and the real -domain WeChat test set.Multimodal Contrastive Learning for Dialogue Embeddings with Global and Local Views."}
+{"idx": 4, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "27 Feb 2025 — Identifiability results for multimodal contrastive ... Right: Average Spearman's rank correlation matrices for real and synthetic ablation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v1", "content": "27 Feb 2025 — Identifiability results for multimodal contrastive ... Right: Average Spearman's rank correlation matrices for real and synthetic ablation ..."}
+{"idx": 5, "title": "On the Value of Cross-Modal Misalignment in Multimodal ...", "date": "", "ddg_snippet": "7 Aug 2025 — ... synthetic data and real image-text datasets, shedding light ... Figure˜6 presents the nonlinear MCC results with learned representations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.10143v5", "content": "7 Aug 2025 — ... synthetic data and real image-text datasets, shedding light ... Figure˜6 presents the nonlinear MCC results with learned representations."}
+{"idx": 6, "title": "Causal Representation Learning from Multimodal ...", "date": "", "ddg_snippet": "10 Nov 2024 — Results and ablation . Report issue for preceding element. Figure ... Extensive experimental results on synthetic and real -world datasets ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.06518v1", "content": "10 Nov 2024 — Results and ablation . Report issue for preceding element. Figure ... Extensive experimental results on synthetic and real -world datasets ..."}
+{"idx": 7, "title": "Periodicity-aware Latent-state Representation Learning for ...", "date": "", "ddg_snippet": "by J Wang · 2025 — The evaluation results are summarized in Table 1 and full results are shown in Appendix G. ... synthetic and real - valued univariate time ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.05478", "content": "by J Wang · 2025 — The evaluation results are summarized in Table 1 and full results are shown in Appendix G. ... synthetic and real - valued univariate time ..."}
+{"idx": 8, "title": "causal representation learning from multi", "date": "", "ddg_snippet": "by Y Sun · 2024 · Cited by 6 — Results and ablation . Figure 5 shows the identifiability results in ... Extensive experimental results on synthetic and real -world datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.06518?", "content": "by Y Sun · 2024 · Cited by 6 — Results and ablation . Figure 5 shows the identifiability results in ... Extensive experimental results on synthetic and real -world datasets."}
+{"idx": 9, "title": "Causal Representation Learning from Multimodal ...", "date": "", "ddg_snippet": "Results and ablation . Report issue for preceding element. Figure 5 shows the ... Extensive experimental results on synthetic and real -world datasets demonstrate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.06518v2", "content": "Results and ablation . Report issue for preceding element. Figure 5 shows the ... Extensive experimental results on synthetic and real -world datasets demonstrate ..."}
diff --git a/data/sampled_jsons/Table_1_MCC_scores_Contrastive_CRL_synthetic_real_sitearxiv.org2502.20099.jsonl b/data/sampled_jsons/Table_1_MCC_scores_Contrastive_CRL_synthetic_real_sitearxiv.org2502.20099.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a9b1335d62d52d95bdeaa1e670857c505b398dba
--- /dev/null
+++ b/data/sampled_jsons/Table_1_MCC_scores_Contrastive_CRL_synthetic_real_sitearxiv.org2502.20099.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "Synthetic ablation. 3 Results. 3. 1 Contrastive CRL .We apply our sanity check to methods representative of three different families of approaches: contrastive CRL (Section 3. 1 ), multiview CRL (Section 3.2), and CRL from temporal intervened sequences (Section 3.3).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v1", "content": "Synthetic ablation. 3 Results. 3. 1 Contrastive CRL .We apply our sanity check to methods representative of three different families of approaches: contrastive CRL (Section 3. 1 ), multiview CRL (Section 3.2), and CRL from temporal intervened sequences (Section 3.3)."}
+{"idx": 1, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "Table 2: Contrastive CRL hyperparameters. Table 5: Test score of a supervised model trained to predict latents from images for the respective datasets. CCRL & Multiview CRL Dataset CITRIS Dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20099", "content": "Table 2: Contrastive CRL hyperparameters. Table 5: Test score of a supervised model trained to predict latents from images for the respective datasets. CCRL & Multiview CRL Dataset CITRIS Dataset."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Table_1_Sieve_MLE_FD3_SD_sitearxiv.org.jsonl b/data/sampled_jsons/Table_1_Sieve_MLE_FD3_SD_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..19343b92817a3eea6bb9ef9b73d4dcdc73d9f639
--- /dev/null
+++ b/data/sampled_jsons/Table_1_Sieve_MLE_FD3_SD_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multivariate Density Estimation via Adaptive Partitioning ( I ): Sieve MLE", "date": "", "ddg_snippet": "This is to say, ΘI is the collection of piecewise constant density functions supported by the binary partitions of size I . Then these ΘI constitute a sequence of approximating spaces to F ( i .e. a sieve , see [10] and [26] for. background on sieve theory).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1401.2597", "content": "This is to say, ΘI is the collection of piecewise constant density functions supported by the binary partitions of size I . Then these ΘI constitute a sequence of approximating spaces to F ( i .e. a sieve , see [10] and [26] for. background on sieve theory)."}
+{"idx": 1, "title": "[1401.2597] Multivariate Density Estimation via Adaptive ... Multivariate Density Estimation via Adaptive Partitioning (I ... arXiv:2410.02025v1 [math.ST] 2 Oct 2024 Efficient Estimation of Structural Models via Sieves [1401.2597] Multivariate Density Estimation via Adaptive ... Multivariate Density Estimation via Adaptive Partitioning (II ... Efficient estimation of copula-based semiparametric Markov models", "date": "", "ddg_snippet": "Jan 12, 2014 · We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that for a ... In this section, we apply the above large-deviation type inequality and the bound of the Kullback-Leibler divergence to derive convergence rate of the sieve MLE for f0 2 F0. pt for the MSE( SD ) for the FD3 dataset. However, for the FD3 dataset, we found that as the training sample size increases further, the MSE( SD ) of the sieve MLE achieves perf where θ is the parameter of interest and Ψ is a representation of the structural model. 1 While Ψ is explicit, solving for p couldbediஂቶcultorcostly.Suchcomputational burden limits the use of standard estimators. For instance, the maximum likelihood estimator ( MLE ) repeatedly guesses θ and evaluates data likelihood using the solution of ( 1 ), p∗(θ). However, finding the solution can ... Abstract We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that ... Compared to the sieve MLE , the main advantage of the Bayesian method is that it can adapt to the unknown complexity of the true density function, thus achieving the optimal convergence rate without arti cial condi-tions on the density. 1 . Introduction. It is interesting to note that although the conditional distribu-tion of a copula-based semiparametric stationary Markov model depends on the unknown invariant distribution, the plug-in sieve MLE estimators of the nonlinear conditional quantiles (VaR) are still √n-consistent, asymptotically normal and efficient.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1401.2597", "content": "Jan 12, 2014 · We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that for a ... In this section, we apply the above large-deviation type inequality and the bound of the Kullback-Leibler divergence to derive convergence rate of the sieve MLE for f0 2 F0. pt for the MSE( SD ) for the FD3 dataset. However, for the FD3 dataset, we found that as the training sample size increases further, the MSE( SD ) of the sieve MLE achieves perf where θ is the parameter of interest and Ψ is a representation of the structural model. 1 While Ψ is explicit, solving for p couldbediஂቶcultorcostly.Suchcomputational burden limits the use of standard estimators. For instance, the maximum likelihood estimator ( MLE ) repeatedly guesses θ and evaluates data likelihood using the solution of ( 1 ), p∗(θ). However, finding the solution can ... Abstract We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that ... Compared to the sieve MLE , the main advantage of the Bayesian method is that it can adapt to the unknown complexity of the true density function, thus achieving the optimal convergence rate without arti cial condi-tions on the density. 1 . Introduction. It is interesting to note that although the conditional distribu-tion of a copula-based semiparametric stationary Markov model depends on the unknown invariant distribution, the plug-in sieve MLE estimators of the nonlinear conditional quantiles (VaR) are still √n-consistent, asymptotically normal and efficient."}
+{"idx": 2, "title": "Efficient Estimation of Structural Models via Sieves", "date": "", "ddg_snippet": "where θ is the parameter of interest and Ψ is a representation of the structural model. 1 While Ψ is explicit, solving for p couldbediஂቶcultorcostly.Suchcomputational burden limits the use of standard estimators. For instance, the maximum likelihood estimator ( MLE ) repeatedly guesses θ and evaluates data likelihood using the solution of ( 1 ), p∗(θ). However, finding the solution can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2204.13488v2", "content": "where θ is the parameter of interest and Ψ is a representation of the structural model. 1 While Ψ is explicit, solving for p couldbediஂቶcultorcostly.Suchcomputational burden limits the use of standard estimators. For instance, the maximum likelihood estimator ( MLE ) repeatedly guesses θ and evaluates data likelihood using the solution of ( 1 ), p∗(θ). However, finding the solution can ..."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using", "date": "", "ddg_snippet": "Sieve MLE . CKDE. FlexCode. FD 1 . MEAN SD .However, for the FD 3 dataset, we found that as the training sample size increases further, the MSE( SD ) of the sieve MLE achieves performance increasingly comparable to CKDE. Simulation from distributions on manifolds.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "Sieve MLE . CKDE. FlexCode. FD 1 . MEAN SD .However, for the FD 3 dataset, we found that as the training sample size increases further, the MSE( SD ) of the sieve MLE achieves performance increasingly comparable to CKDE. Simulation from distributions on manifolds."}
+{"idx": 4, "title": "[1401.2597] Multivariate Density Estimation via Adaptive ...", "date": "", "ddg_snippet": "Abstract We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1401.2597", "content": "Abstract We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that ..."}
+{"idx": 5, "title": "Multivariate Density Estimation via Adaptive Partitioning (II ...", "date": "", "ddg_snippet": "Compared to the sieve MLE , the main advantage of the Bayesian method is that it can adapt to the unknown complexity of the true density function, thus achieving the optimal convergence rate without arti cial condi-tions on the density. 1 . Introduction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1508.04812.pdf", "content": "Compared to the sieve MLE , the main advantage of the Bayesian method is that it can adapt to the unknown complexity of the true density function, thus achieving the optimal convergence rate without arti cial condi-tions on the density. 1 . Introduction."}
+{"idx": 6, "title": "Efficient estimation of copula-based semiparametric Markov models", "date": "", "ddg_snippet": "It is interesting to note that although the conditional distribu-tion of a copula-based semiparametric stationary Markov model depends on the unknown invariant distribution, the plug-in sieve MLE estimators of the nonlinear conditional quantiles (VaR) are still √n-consistent, asymptotically normal and efficient.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/0901.0751v4", "content": "It is interesting to note that although the conditional distribu-tion of a copula-based semiparametric stationary Markov model depends on the unknown invariant distribution, the plug-in sieve MLE estimators of the nonlinear conditional quantiles (VaR) are still √n-consistent, asymptotically normal and efficient."}
+{"idx": 7, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE .Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE( SD ) for the FD 3 dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE .Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE( SD ) for the FD 3 dataset."}
+{"idx": 8, "title": "A Likelihood Approach to Nonparametric Estimation of a", "date": "", "ddg_snippet": "3 Convergence Rate of a Sieve MLE . 4 Class of True Distributions.That is, we obtain a sieve MLE of f∗ based on the perturbed observation Xi = Xi + i , where i is an articial noise vector following the distribution N (0D, σ2ID).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2105.04046", "content": "3 Convergence Rate of a Sieve MLE . 4 Class of True Distributions.That is, we obtain a sieve MLE of f∗ based on the perturbed observation Xi = Xi + i , where i is an articial noise vector following the distribution N (0D, σ2ID)."}
+{"idx": 9, "title": "Why Machine Learning Cannot Ignore", "date": "", "ddg_snippet": "We now introduce a special sieve MLE , the HAL- MLE . The HAL- MLE is generally theoretically superior to other types of sieve . 6 Why Machine Learning Cannot Ignore Maximum Likelihood Estimation. MLEs , with its statistical properties presented in the subsequent section.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2110.12112", "content": "We now introduce a special sieve MLE , the HAL- MLE . The HAL- MLE is generally theoretically superior to other types of sieve . 6 Why Machine Learning Cannot Ignore Maximum Likelihood Estimation. MLEs , with its statistical properties presented in the subsequent section."}
diff --git a/data/sampled_jsons/Table_1_dataset_statistics_ML-1M_Amazon_Beauty_Amazon_Toys_Games_train_density_27tMzmzDjO.jsonl b/data/sampled_jsons/Table_1_dataset_statistics_ML-1M_Amazon_Beauty_Amazon_Toys_Games_train_density_27tMzmzDjO.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..537783572a04ad220843a5197a520017eb9f3530
--- /dev/null
+++ b/data/sampled_jsons/Table_1_dataset_statistics_ML-1M_Amazon_Beauty_Amazon_Toys_Games_train_density_27tMzmzDjO.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "kaiboon0216/Amazon-beauty-products-analysis - GitHub", "date": "", "ddg_snippet": "Amazon - beauty -products-analysis Using data science process to gain business insights from the Amazon beauty datasets and determine the factors that contribute to the high sales of a beauty product Our anaylsis are focused on answering the following questions:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kaiboon0216/Amazon-beauty-products-analysis", "content": "Amazon - beauty -products-analysis Using data science process to gain business insights from the Amazon beauty datasets and determine the factors that contribute to the high sales of a beauty product Our anaylsis are focused on answering the following questions:"}
+{"idx": 1, "title": "Project Part 2: Amazon Beauty Products - GitHub Pages", "date": "", "ddg_snippet": "2.1 meta_All_Beauty.json This dataset contains all the product details", "subpage_snippet": "", "source": "liewkuanyung.github.io", "link": "https://liewkuanyung.github.io/Amazon-Beauty-Product/", "content": "2.1 meta_All_Beauty.json This dataset contains all the product details"}
+{"idx": 2, "title": "Amazon Beauty Dataset - brightdata.com", "date": "", "ddg_snippet": "Access an Amazon beauty products dataset with reviews, ratings, and insights. Analyze trends, customer feedback, and improve marketing strategies in the beauty industry.", "subpage_snippet": "", "source": "brightdata.com", "link": "https://brightdata.com/products/datasets/amazon/beauty", "content": "Access an Amazon beauty products dataset with reviews, ratings, and insights. Analyze trends, customer feedback, and improve marketing strategies in the beauty industry."}
+{"idx": 3, "title": "luminati-io/Amazon-dataset-samples - GitHub", "date": "", "ddg_snippet": "A sample dataset of over 1,000 Amazon product listings, extracted using the Bright Data API, perfect for competitive analysis, market trends, and eCommerce insights.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/luminati-io/Amazon-dataset-samples", "content": "A sample dataset of over 1,000 Amazon product listings, extracted using the Bright Data API, perfect for competitive analysis, market trends, and eCommerce insights."}
+{"idx": 4, "title": "Amazon Beauty Products - Kaggle", "date": "", "ddg_snippet": "2+ Million records Amazon Beauty Products Dataset along with URL.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/satrapankti/amazon-beauty-product-recommendation", "content": "2+ Million records Amazon Beauty Products Dataset along with URL."}
+{"idx": 5, "title": "Amazon-Beauty dataset - LDM", "date": "", "ddg_snippet": "The Amazon-Beauty dataset is a sub- dataset from Amazon Product Reviews 3, which records large crawls of user reviews and product metadata.", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/amazon-beauty-dataset", "content": "The Amazon-Beauty dataset is a sub- dataset from Amazon Product Reviews 3, which records large crawls of user reviews and product metadata."}
+{"idx": 6, "title": "Postgres Amazon Beauty Ratings Tutorial.ipynb - Colab", "date": "", "ddg_snippet": "This notebook will walk you through an example of: *Downloading the Amazon Beauty dataset into jsonl files and storing it in a Postgres instance. Setting up a model for the Amazon Beauty dataset from the Postgres instance. Fetching the products that are most likely to be purcahsed for a specific user. *This step has nothing to do with Shaped and is all about prepping the test dataset . If you ...", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/shaped-ai/shaped-tutorials/blob/main/tutorials/Postgres+Amazon+Beauty+Ratings+Tutorial.ipynb", "content": "This notebook will walk you through an example of: *Downloading the Amazon Beauty dataset into jsonl files and storing it in a Postgres instance. Setting up a model for the Amazon Beauty dataset from the Postgres instance. Fetching the products that are most likely to be purcahsed for a specific user. *This step has nothing to do with Shaped and is all about prepping the test dataset . If you ..."}
+{"idx": 7, "title": "vessl/amazon-beauty-dataset · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/vessl/amazon-beauty-dataset", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 8, "title": "Amazon Beauty Dataset | Kaggle", "date": "", "ddg_snippet": "Kaggle is the world's largest data science community with powerful tools and resources to help you achieve your data science goals.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/smritimadangarli/amazon-beauty-dataset", "content": "Kaggle is the world's largest data science community with powerful tools and resources to help you achieve your data science goals."}
+{"idx": 9, "title": "reczoo/AmazonBeauty_m1 · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/reczoo/AmazonBeauty_m1", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
diff --git a/data/sampled_jsons/Table_1_stability_RA-SAE_TopK_SAE_sitearxiv.org.jsonl b/data/sampled_jsons/Table_1_stability_RA-SAE_TopK_SAE_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..df698947f1a3f50766bcc026846debcb68515d31
--- /dev/null
+++ b/data/sampled_jsons/Table_1_stability_RA-SAE_TopK_SAE_sitearxiv.org.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept...", "date": "", "ddg_snippet": "A relaxed variant ( RA - SAE ) allows mild relaxation, matching standard SAEs in reconstruction while maintaining stability . Both integrate with any SAE variant (e.g., TopK , JumpReLU).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v1", "content": "A relaxed variant ( RA - SAE ) allows mild relaxation, matching standard SAEs in reconstruction while maintaining stability . Both integrate with any SAE variant (e.g., TopK , JumpReLU)."}
+{"idx": 1, "title": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept...", "date": "", "ddg_snippet": "A relaxed variant ( RA - SAE ) allows mild relaxation, matching standard SAEs in reconstruction while maintaining stability . Both integrate with any SAE variant (e.g., TopK , JumpReLU).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v2", "content": "A relaxed variant ( RA - SAE ) allows mild relaxation, matching standard SAEs in reconstruction while maintaining stability . Both integrate with any SAE variant (e.g., TopK , JumpReLU)."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Table_1_stability_score_RA-SAE_TopK_SAE_0.95_0.85_Archetypal_SAE.jsonl b/data/sampled_jsons/Table_1_stability_score_RA-SAE_TopK_SAE_0.95_0.85_Archetypal_SAE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a0358915d5acf9a91264989820dcbe3ee60bd40
--- /dev/null
+++ b/data/sampled_jsons/Table_1_stability_score_RA-SAE_TopK_SAE_0.95_0.85_Archetypal_SAE.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ensembling Sparse Autoencoders", "date": "", "ddg_snippet": "by S Gadgil · 2025 — Table 1 : Intrinsic evaluation metrics for a single SAE , naive bagging, and boosting (ensembling 8. SAEs). Means along with 95% confidence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.16077", "content": "by S Gadgil · 2025 — Table 1 : Intrinsic evaluation metrics for a single SAE , naive bagging, and boosting (ensembling 8. SAEs). Means along with 95% confidence ..."}
+{"idx": 1, "title": "applying-patterns-in-embedded-systems-design-for-managing- ...", "date": "", "ddg_snippet": "the best result has score 1 , and the other two scores are equal or less than 1 . We note that for New and Total violations, the ratio is inverse, i.e., more ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/applying-patterns-in-embedded-systems-design-for-managing-4d9nvr322h.pdf", "content": "the best result has score 1 , and the other two scores are equal or less than 1 . We note that for New and Total violations, the ratio is inverse, i.e., more ..."}
+{"idx": 2, "title": "Non-Ideal Phenomena in Rotating Detonation Combustors", "date": "", "ddg_snippet": "Table A. 1 : Summary of quantities used to estimate the response time of injectors according to the schematic of figure A. 1 . assumption that the expansion ...", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "https://deepblue.lib.umich.edu/bitstream/handle/2027.42/163298/fchacon_1.pdf?sequence=1&isAllowed=y", "content": "Table A. 1 : Summary of quantities used to estimate the response time of injectors according to the schematic of figure A. 1 . assumption that the expansion ..."}
+{"idx": 3, "title": "Artificial Intelligence in Medicine", "date": "", "ddg_snippet": "23 Jun 2025 — Table 1 . Link prediction metric obtained on the PrimeKG triples test set for each of the optimized. KGE models. The best result for each ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-95838-0.pdf", "content": "23 Jun 2025 — Table 1 . Link prediction metric obtained on the PrimeKG triples test set for each of the optimized. KGE models. The best result for each ..."}
+{"idx": 4, "title": "Real-Time Systems: the past, the present, and the future", "date": "", "ddg_snippet": "Table 1 . Test (2) performed best in this case, finding 75% of the sets considered schedulable by the reference test. Test ( 1 ) performed very ...", "subpage_snippet": "", "source": "www.cs.unc.edu", "link": "http://www.cs.unc.edu/~baruah/AlanFest/Procs.pdf", "content": "Table 1 . Test (2) performed best in this case, finding 75% of the sets considered schedulable by the reference test. Test ( 1 ) performed very ..."}
+{"idx": 5, "title": "Integrated Vehicle-Based Safety Systems (IVBSS)", "date": "", "ddg_snippet": "by P Green · 2008 · Cited by 41 — Table 1 shows the crash warning subsystems integrated into the IVBSS program. Table 1 . IVBSS warning subsystems. Warning. Abbrev. Platform. Description. Forward ...", "subpage_snippet": "", "source": "www.nhtsa.gov", "link": "https://www.nhtsa.gov/sites/nhtsa.gov/files/dot-hs-810-905.pdf", "content": "by P Green · 2008 · Cited by 41 — Table 1 shows the crash warning subsystems integrated into the IVBSS program. Table 1 . IVBSS warning subsystems. Warning. Abbrev. Platform. Description. Forward ..."}
+{"idx": 6, "title": "12th European Headache Federation Congress jointly with ...", "date": "", "ddg_snippet": "2018 · Cited by 4 — The observed differences between treatment groups exceeded the respective MIDs for the MSQ-RFR (≥3.2) and MSQ-EF (≥7.5) domain scores ( Table 1 ).", "subpage_snippet": "", "source": "thejournalofheadacheandpain.biomedcentral.com", "link": "https://thejournalofheadacheandpain.biomedcentral.com/articles/10.1186/s10194-018-0900-0", "content": "2018 · Cited by 4 — The observed differences between treatment groups exceeded the respective MIDs for the MSQ-RFR (≥3.2) and MSQ-EF (≥7.5) domain scores ( Table 1 )."}
+{"idx": 7, "title": "GAP Future Direction", "date": "", "ddg_snippet": "14 Aug 2025 — Abstract. Wurts (2024 GAP) proposed directions for future research within Section 3.3. This publication responds by providing.", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5392095.pdf?abstractid=5392095&mirid=1", "content": "14 Aug 2025 — Abstract. Wurts (2024 GAP) proposed directions for future research within Section 3.3. This publication responds by providing."}
+{"idx": 8, "title": "Helicopter Acoustics", "date": "", "ddg_snippet": "The papers included herein were presented at the International. Specialists. Symposium on Helicopter Acoustics which was held at the NASA Langley Research.", "subpage_snippet": "", "source": "ntrs.nasa.gov", "link": "https://ntrs.nasa.gov/api/citations/19780024873/downloads/19780024873.pdf", "content": "The papers included herein were presented at the International. Specialists. Symposium on Helicopter Acoustics which was held at the NASA Langley Research."}
+{"idx": 9, "title": "Energy Efficient Mobility Systems 2017 Annual Progress ...", "date": "", "ddg_snippet": "5 Apr 2017 — This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government ...", "subpage_snippet": "", "source": "www.energy.gov", "link": "https://www.energy.gov/sites/prod/files/2018/05/f51/EEMS_FY2017_APR_Final.pdf", "content": "5 Apr 2017 — This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government ..."}
diff --git a/data/sampled_jsons/Table_3_CIFAR-100_novel_accuracy_OwMatch_Conditional_Self-Labeling_ConSL_year_2024.jsonl b/data/sampled_jsons/Table_3_CIFAR-100_novel_accuracy_OwMatch_Conditional_Self-Labeling_ConSL_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f0884bd1d9a34d34c517f8bda18fe04e5b84431
--- /dev/null
+++ b/data/sampled_jsons/Table_3_CIFAR-100_novel_accuracy_OwMatch_Conditional_Self-Labeling_ConSL_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CIFAR -10 - Wikipedia", "date": "", "ddg_snippet": "CIFAR -10 is a labeled subset of the 80 Million Tiny Images dataset from 2008, published in 2009.This is a table of some of the research papers that claim to have achieved state-of-the-art results on the CIFAR -10 dataset.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/CIFAR-10", "content": "CIFAR -10 is a labeled subset of the 80 Million Tiny Images dataset from 2008, published in 2009.This is a table of some of the research papers that claim to have achieved state-of-the-art results on the CIFAR -10 dataset."}
+{"idx": 1, "title": "OwMatch : Conditional Self - Labeling with Consistency", "date": "", "ddg_snippet": "conditional self - labeling method to refine the self - label assignment under partial supervision. Table 1: Average accuracy on the CIFAR -10/ 100 and ImageNet- 100 with both novel class ratio and label ratio of 50%.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/b4fd162d3e2d015233486a2e313828a7-Paper-Conference.pdf", "content": "conditional self - labeling method to refine the self - label assignment under partial supervision. Table 1: Average accuracy on the CIFAR -10/ 100 and ImageNet- 100 with both novel class ratio and label ratio of 50%."}
+{"idx": 2, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "4 Nov 2024 — Table 3 : Ablation studies on each component with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self - labeling , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.01833v1", "content": "4 Nov 2024 — Table 3 : Ablation studies on each component with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self - labeling , ..."}
+{"idx": 3, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "9 Dec 2024 — Table 1: Average accuracy on the CIFAR -10/ 100 and ImageNet- 100 with both novel class ratio and label ratio of 50%. We compare OwMatch with ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93416", "content": "9 Dec 2024 — Table 1: Average accuracy on the CIFAR -10/ 100 and ImageNet- 100 with both novel class ratio and label ratio of 50%. We compare OwMatch with ..."}
+{"idx": 4, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "This harmonious approach yields the best all-classes accuracy while roughly maintaining both seen-class accuracy and novel -class clustering accuracy .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rle9X7DQuH&referrer=[the+profile+of+Jian+Huang](/profile?id=~Jian_Huang5)", "content": "This harmonious approach yields the best all-classes accuracy while roughly maintaining both seen-class accuracy and novel -class clustering accuracy ."}
+{"idx": 5, "title": "OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "2 Methodology Conditional Self - labeling Open-world Hierarchical Thresholding. Table 1: Average accuracy on the CIFAR -10/ 100 and ImageNet 100 with 50% novel classes and 50% labeled data within seen classes.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/93416.pdf", "content": "2 Methodology Conditional Self - labeling Open-world Hierarchical Thresholding. Table 1: Average accuracy on the CIFAR -10/ 100 and ImageNet 100 with 50% novel classes and 50% labeled data within seen classes."}
+{"idx": 6, "title": "(PDF) OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "Table 1: Average accuracy on the CIFAR -10/ 100 and ImageNet- 100 with both novel class ratio and label ratio of 50%. We compare OwMatch with existing literature on OwSSL.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/owmatch-conditional-self-labeling-with-consistency-for-open-2qmo1h06mr3a", "content": "Table 1: Average accuracy on the CIFAR -10/ 100 and ImageNet- 100 with both novel class ratio and label ratio of 50%. We compare OwMatch with existing literature on OwSSL."}
+{"idx": 7, "title": "CIFAR-10 and CIFAR - 100 datasets", "date": "", "ddg_snippet": "The CIFAR-10 and CIFAR - 100 datasets are labeled subsets of the 80 million tiny images dataset. CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.household furniture. bed, chair, couch, table , wardrobe.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~kriz/cifar.html", "content": "The CIFAR-10 and CIFAR - 100 datasets are labeled subsets of the 80 million tiny images dataset. CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.household furniture. bed, chair, couch, table , wardrobe."}
+{"idx": 8, "title": "[2411.01833] OwMatch : Conditional Self - Labeling with Consistency...", "date": "", "ddg_snippet": "View a PDF of the paper titled OwMatch : Conditional Self - Labeling with Consistency for Open-World Semi-Supervised Learning, by Shengjie Niu and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.01833", "content": "View a PDF of the paper titled OwMatch : Conditional Self - Labeling with Consistency for Open-World Semi-Supervised Learning, by Shengjie Niu and 3 other authors."}
+{"idx": 9, "title": "huggingface- cifar 100 .ipynb - Colab", "date": "", "ddg_snippet": "Hugging Face CIFAR - 100 Embeddings Example. subdirectory_arrow_right 24 cells hidden. spark Gemini.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/3lc-ai/3lc-examples/blob/main/example-notebooks/huggingface-cifar100.ipynb", "content": "Hugging Face CIFAR - 100 Embeddings Example. subdirectory_arrow_right 24 cells hidden. spark Gemini."}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_Table_3_Llama2_World_Capital.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_Table_3_Llama2_World_Capital.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eb844f38fb63852866a092d6789af86afad13ce2
--- /dev/null
+++ b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_Table_3_Llama2_World_Capital.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models - arXiv.org", "date": "", "ddg_snippet": "Abstract Language Models (LMs) often encounter knowl-edge conflicts when parametric memory con-tradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or con-text exclusively. In this study, we go beyond this fundamental assumption by uncovering a criti-cal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10996v2", "content": "Abstract Language Models (LMs) often encounter knowl-edge conflicts when parametric memory con-tradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or con-text exclusively. In this study, we go beyond this fundamental assumption by uncovering a criti-cal ..."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models - GitHub", "date": "", "ddg_snippet": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GaotangLi/JUICE", "content": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated."}
+{"idx": 2, "title": "Taming Knowledge Conflicts in Language Models - Semantic Scholar", "date": "", "ddg_snippet": "This work proposes Just Run Twice (JuICE), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Taming-Knowledge-Conflicts-in-Language-Models-Li-Chen/b7ba9df4eb239708cf48f25be87b5bceeca010e3", "content": "This work proposes Just Run Twice (JuICE), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ..."}
+{"idx": 3, "title": "ICML Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Poster in Workshop: Actionable Interpretability Taming Knowledge Conflicts in Language Models Gaotang Li · Yuzhong Chen · Hanghang Tong [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/49596", "content": "Poster in Workshop: Actionable Interpretability Taming Knowledge Conflicts in Language Models Gaotang Li · Yuzhong Chen · Hanghang Tong [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT"}
+{"idx": 4, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ..."}
+{"idx": 5, "title": "Taming Knowledge Conflicts in Language Models | alphaXiv", "date": "", "ddg_snippet": "View recent discussion. Abstract: Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.10996v2", "content": "View recent discussion. Abstract: Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a ..."}
+{"idx": 6, "title": "Taming Knowledge Conflicts in Language Models | AI Research Paper Details", "date": "", "ddg_snippet": "The researchers developed a comprehensive framework for addressing knowledge conflicts in language models . They start by distinguishing between parametric conflicts (contradictions within the model's parameters) and contextual conflicts (contradictions between the model's parameters and provided context).", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/taming-knowledge-conflicts-language-models", "content": "The researchers developed a comprehensive framework for addressing knowledge conflicts in language models . They start by distinguishing between parametric conflicts (contradictions within the model's parameters) and contextual conflicts (contradictions between the model's parameters and provided context)."}
+{"idx": 7, "title": "Resolving Knowledge Conflicts in Large Language Models", "date": "", "ddg_snippet": "Large language models (LLMs) often encounter knowledge conflicts , scenarios where discrepancy arises between the internal parametric knowledge of LLMs and non-parametric information provided in the prompt context. In this work we ask what are the desiderata for LLMs when a knowledge conflict arises and whether existing LLMs fulfill them.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ptvV5HGTNN", "content": "Large language models (LLMs) often encounter knowledge conflicts , scenarios where discrepancy arises between the internal parametric knowledge of LLMs and non-parametric information provided in the prompt context. In this work we ask what are the desiderata for LLMs when a knowledge conflict arises and whether existing LLMs fulfill them."}
+{"idx": 8, "title": "ICML Poster Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Language models frequently encounter \" knowledge conflicts ,\" where their pre-trained knowledge contradicts the information provided by specific contexts. These conflicts often arise in context-dependent systems, such as retrieval-augmented generation and tools integrated with language models .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46677", "content": "Language models frequently encounter \" knowledge conflicts ,\" where their pre-trained knowledge contradicts the information provided by specific contexts. These conflicts often arise in context-dependent systems, such as retrieval-augmented generation and tools integrated with language models ."}
+{"idx": 9, "title": "Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting ...", "date": "", "ddg_snippet": "Cutting Off the Head Ends the Conflict : A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models . In Findings of the Association for Computational Linguistics: ACL 2024, pages 1193-1215, Bangkok, Thailand. Association for Computational Linguistics. Cite (Informal):", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.70/", "content": "Cutting Off the Head Ends the Conflict : A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models . In Findings of the Association for Computational Linguistics: ACL 2024, pages 1193-1215, Bangkok, Thailand. Association for Computational Linguistics. Cite (Informal):"}
diff --git a/data/sampled_jsons/Task-Aware_Parameter_Initialization_at_Flexible_Scales_ICML_2025_year_2025.jsonl b/data/sampled_jsons/Task-Aware_Parameter_Initialization_at_Flexible_Scales_ICML_2025_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e579a91a61b686fb9419bf2a15adea3f86a9ff48
--- /dev/null
+++ b/data/sampled_jsons/Task-Aware_Parameter_Initialization_at_Flexible_Scales_ICML_2025_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Learngene Tells You How to Customize: Task - Aware ...", "date": "", "ddg_snippet": "Graph HyperNetwork (GHN), a parameter initialization method, has recently demonstrated strong performance in initializing models.However, GHN still faces several challenges, including limited effectiveness in initializing larger models, poor performance on smaller datasets, and the...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45736", "content": "Graph HyperNetwork (GHN), a parameter initialization method, has recently demonstrated strong performance in initializing models.However, GHN still faces several challenges, including limited effectiveness in initializing larger models, poor performance on smaller datasets, and the..."}
+{"idx": 1, "title": "Learngene Tells You How to Customize: Task-Aware Parameter...", "date": "", "ddg_snippet": "back arrow Go to ICML 2025 Conference ... 2025ICML 2025 posterEveryoneRevisionsBibTeXCC BY 4.0. TL;DR: Task-Aware Parameter Initialization at Flexible Scales .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=IRQ0n961nn", "content": "back arrow Go to ICML 2025 Conference ... 2025ICML 2025 posterEveryoneRevisionsBibTeXCC BY 4.0. TL;DR: Task-Aware Parameter Initialization at Flexible Scales ."}
+{"idx": 2, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales · SADA: Stability-guided Adaptive Diffusion Acceleration · Two ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales · SADA: Stability-guided Adaptive Diffusion Acceleration · Two ..."}
+{"idx": 3, "title": "ICML 2025 Conference Submissions", "date": "", "ddg_snippet": "ICML 2025 Conference Submissions. Foundation Molecular ... Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/submissions?page=60&venue=ICML.cc/2025/Conference", "content": "ICML 2025 Conference Submissions. Foundation Molecular ... Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales ."}
+{"idx": 4, "title": "Task-Aware Parameter Initialization at Flexible Scales", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales . ICML 2025 18 Jun 2025 . Jiaze Xu, Shiyu Xia, Xu Yang, Jiaqi Lv ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/zh-CN/chatpaper/paper/166550", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales . ICML 2025 18 Jun 2025 . Jiaze Xu, Shiyu Xia, Xu Yang, Jiaqi Lv ..."}
+{"idx": 5, "title": "Task-Aware Parameter Initialization at Flexible Scales", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales . ICML 2025 18 Jun 2025 . Jiaze Xu, Shiyu Xia, Xu Yang, Jiaqi Lv ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/fr/paper/166550", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales . ICML 2025 18 Jun 2025 . Jiaze Xu, Shiyu Xia, Xu Yang, Jiaqi Lv ..."}
+{"idx": 6, "title": "GitHub - mathieuxu/ Task - Aware -Learngene: Task - Aware Parameter ...", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task - Aware Parameter Initialization at Flexible Scales .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mathieuxu/Task-Aware-Learngene", "content": "Learngene Tells You How to Customize: Task - Aware Parameter Initialization at Flexible Scales ."}
+{"idx": 7, "title": "Downloads 2025 - icml.cc", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Learning Adaptive Lighting via Channel-Aware Guidance Learning Adversarial MDPs with Stochastic Hard Constraints Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Learning Adaptive Lighting via Channel-Aware Guidance Learning Adversarial MDPs with Stochastic Hard Constraints Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning"}
+{"idx": 8, "title": "Learngene Tells You How to Customize: Task-Aware Parameter ...", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Jiaze Xu 1 2 Shiyu Xia 1 2 Xu Yang 1 2", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=IRQ0n961nn", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Jiaze Xu 1 2 Shiyu Xia 1 2 Xu Yang 1 2"}
+{"idx": 9, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales SADA: Stability-guided Adaptive Diffusion Acceleration Two Tickets are Better than One: Fair and Accurate Hiring Under Strategic LLM Manipulations Beyond Confidence: Exploiting Homogeneous Pattern for Semi-Supervised Semantic Segmentation", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html?search=agent", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales SADA: Stability-guided Adaptive Diffusion Acceleration Two Tickets are Better than One: Fair and Accurate Hiring Under Strategic LLM Manipulations Beyond Confidence: Exploiting Homogeneous Pattern for Semi-Supervised Semantic Segmentation"}
diff --git a/data/sampled_jsons/Temporally-Correlated_Episodic_RL_TCE_arXiv2401.11437_year_2024.jsonl b/data/sampled_jsons/Temporally-Correlated_Episodic_RL_TCE_arXiv2401.11437_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a8e7203cd689b5c6687a2363d347883f8ce60397
--- /dev/null
+++ b/data/sampled_jsons/Temporally-Correlated_Episodic_RL_TCE_arXiv2401.11437_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2401.11437] Open the Black Box: Step-based Policy Updates ... GitHub - BruceGeLi/TCE_RL: Temporally Correlated Episodic ... ALR - Publications - New ICLR paper: Open the Black Box ... - KIT (PDF) TOP-ERL: Transformer-based Off-Policy Episodic ... Paper page - Open the Black Box: Step-based Policy Updates ... arXiv:2401.11437v1 [cs.LG] 21 Jan 2024 Open the Black Box: Step-based Policy Updates for Temporally ...", "date": "", "ddg_snippet": "Jan 21, 2024 · In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space. In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space. In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space. Oct 12, 2024 · TCE Temporally-Correlated Episodic RL ( TCE ) (Li et al., 2024) is an innovative ERL algorithm that leverages step-lev el information in episodic policy updates, shedding light on the ’black box’ In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space. into the policy update process of ERL. Our proposed method, Temporally-Correlated Episodic RL ( TCE ), moves beyond the traditional approach of treating an en ire trajectory as a single data point. Instead, we transform trajectory-wide elements, such as reproducing likelihood and advantage In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.11437", "content": "Jan 21, 2024 · In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space. In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space. In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space. Oct 12, 2024 · TCE Temporally-Correlated Episodic RL ( TCE ) (Li et al., 2024) is an innovative ERL algorithm that leverages step-lev el information in episodic policy updates, shedding light on the ’black box’ In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space. into the policy update process of ERL. Our proposed method, Temporally-Correlated Episodic RL ( TCE ), moves beyond the traditional approach of treating an en ire trajectory as a single data point. Instead, we transform trajectory-wide elements, such as reproducing likelihood and advantage In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}
+{"idx": 1, "title": "GitHub - BruceGeLi/TCE_RL: Temporally Correlated Episodic ...", "date": "", "ddg_snippet": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BruceGeLi/TCE_RL", "content": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}
+{"idx": 2, "title": "(PDF) TOP-ERL: Transformer-based Off-Policy Episodic ...", "date": "", "ddg_snippet": "Oct 12, 2024 · TCE Temporally-Correlated Episodic RL ( TCE ) (Li et al., 2024) is an innovative ERL algorithm that leverages step-lev el information in episodic policy updates, shedding light on the ’black box’", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929465_TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning", "content": "Oct 12, 2024 · TCE Temporally-Correlated Episodic RL ( TCE ) (Li et al., 2024) is an innovative ERL algorithm that leverages step-lev el information in episodic policy updates, shedding light on the ’black box’"}
+{"idx": 3, "title": "arXiv:2401.11437v1 [cs.LG] 21 Jan 2024", "date": "", "ddg_snippet": "into the policy update process of ERL. Our proposed method, Temporally-Correlated Episodic RL ( TCE ), moves beyond the traditional approach of treating an en ire trajectory as a single data point. Instead, we transform trajectory-wide elements, such as reproducing likelihood and advantage", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.11437.pdf", "content": "into the policy update process of ERL. Our proposed method, Temporally-Correlated Episodic RL ( TCE ), moves beyond the traditional approach of treating an en ire trajectory as a single data point. Instead, we transform trajectory-wide elements, such as reproducing likelihood and advantage"}
+{"idx": 4, "title": "Open the Black Box: Step-based Policy Updates for Temporally ...", "date": "", "ddg_snippet": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240111437L/abstract", "content": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}
+{"idx": 5, "title": "Paper page - Open the Black Box: Step-based Policy Updates ...", "date": "", "ddg_snippet": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2401.11437", "content": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}
+{"idx": 6, "title": "ALR - Publications - New ICLR paper: Open the Black Box: Step-based...", "date": "", "ddg_snippet": "In this work, we introduce a novel ERL algorithm, Temporally - Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "alr.iar.kit.edu", "link": "https://alr.iar.kit.edu/603.php", "content": "In this work, we introduce a novel ERL algorithm, Temporally - Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}
+{"idx": 7, "title": "U pdates for t emporally -c orrelated e pisodic", "date": "", "ddg_snippet": "Algorithm 1 Temporally - Correlated Episodic RL ( TCE ) 1: Initialize policy parameters θ and value function parameters ϕ 2: for iteration = 1, 2, ... do 3: Get the initial state s0 4: Predict the mean µw, covariance Σw, and sample w∗ 5: Generate the trajectory y∗ using Eq.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.11437", "content": "Algorithm 1 Temporally - Correlated Episodic RL ( TCE ) 1: Initialize policy parameters θ and value function parameters ϕ 2: for iteration = 1, 2, ... do 3: Get the initial state s0 4: Predict the mean µw, covariance Σw, and sample w∗ 5: Generate the trajectory y∗ using Eq."}
+{"idx": 8, "title": "Episode-Based Reinforcement Learning using Movement Primitives...", "date": "", "ddg_snippet": "Temporally Correlated Episodic RL [Li et al., 2024]. • Similar setup to previous BBRL approach • Trajectory segments as data points • Policy update based on segment likelihood. TCE . J(θ). = Eπold. Temporally Correlated Episodic RL .", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/seminar/2024/episode-based-rl-with-movement-primitive/2024-02-08+-+Maximilian+Hüttenrauch+-+Episode-based+RL+with+Movement+Primitive.pdf", "content": "Temporally Correlated Episodic RL [Li et al., 2024]. • Similar setup to previous BBRL approach • Trajectory segments as data points • Policy update based on segment likelihood. TCE . J(θ). = Eπold. Temporally Correlated Episodic RL ."}
+{"idx": 9, "title": "Open the Black Box: Step-based Policy Updates for...", "date": "", "ddg_snippet": "We introduce the Temporally - Correlated Episodic RL ( TCE ) method to address these shortcomings.We validate the effectiveness of TCE through experiments on various robotic manipulation tasks, showing its advantages over step-based and episodic RL approaches.", "subpage_snippet": "", "source": "temporallycorrelatedexploration.github.io", "link": "https://temporallycorrelatedexploration.github.io/", "content": "We introduce the Temporally - Correlated Episodic RL ( TCE ) method to address these shortcomings.We validate the effectiveness of TCE through experiments on various robotic manipulation tasks, showing its advantages over step-based and episodic RL approaches."}
diff --git a/data/sampled_jsons/Teng_Xiao_Pennsylvania_State_University_On_a_Connection_Between_Imitation_Learning_and_RLHF.jsonl b/data/sampled_jsons/Teng_Xiao_Pennsylvania_State_University_On_a_Connection_Between_Imitation_Learning_and_RLHF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..778495b27359e6c53817698cef6090215ff4d56f
--- /dev/null
+++ b/data/sampled_jsons/Teng_Xiao_Pennsylvania_State_University_On_a_Connection_Between_Imitation_Learning_and_RLHF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Teng Xiao", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF Teng Xiao , Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G Honavar ICLR , 2025.", "subpage_snippet": "", "source": "tengxiao1.github.io", "link": "https://tengxiao1.github.io/", "content": "On a Connection Between Imitation Learning and RLHF Teng Xiao , Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G Honavar ICLR , 2025."}
+{"idx": 1, "title": "tengxiao1 (Teng Xiao) · GitHub", "date": "", "ddg_snippet": "I work on machine learning , reinforcement learning , and their applications. ... On a Connection Between Imitation Learning and RLHF (ICLR 2025)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1", "content": "I work on machine learning , reinforcement learning , and their applications. ... On a Connection Between Imitation Learning and RLHF (ICLR 2025)"}
+{"idx": 2, "title": "Sub-Scaling Laws: On the Role of Data Density and Training", "date": "", "ddg_snippet": "New Density Computation Method: We introduce a new density computation method that considers both intra-cluster concentration and inter-cluster ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10613v1", "content": "New Density Computation Method: We introduce a new density computation method that considers both intra-cluster concentration and inter-cluster ..."}
+{"idx": 3, "title": "[2503.05079] On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.05079", "content": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 4, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2QdsjiNXgj", "content": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 5, "title": "[Literature Review] On a Connection Between Imitation Learning ...", "date": "", "ddg_snippet": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-a-connection-between-imitation-learning-and-rlhf", "content": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning ."}
+{"idx": 6, "title": "On a Connection Between Imitation Learning and RLHF | alphaXiv", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.05079v1", "content": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 7, "title": "tengxiao1/DIL: On a Connection Between Imitation Learning and ...", "date": "", "ddg_snippet": "This repository contains the code for our ICLR 2025 paper On a Connection Between Imitation Learning and RLHF .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL", "content": "This repository contains the code for our ICLR 2025 paper On a Connection Between Imitation Learning and RLHF ."}
+{"idx": 8, "title": "dblp: List of computer science publications by Teng Xiao", "date": "", "ddg_snippet": "Teng Xiao , Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G. Honavar: On a Connection Between Imitation Learning and RLHF .Zhimeng Guo, Teng Xiao , Charu Aggarwal, Hui Liu, Suhang Wang : Counterfactual Learning on Graphs: A Survey.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/228/7977.html", "content": "Teng Xiao , Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G. Honavar: On a Connection Between Imitation Learning and RLHF .Zhimeng Guo, Teng Xiao , Charu Aggarwal, Hui Liu, Suhang Wang : Counterfactual Learning on Graphs: A Survey."}
+{"idx": 9, "title": "(PDF) ALIGN: Word Association Learning for Cross-Cultural...", "date": "", "ddg_snippet": "Teng Xiao , Yige Yuan, Mingxiao Li, Zhengyu Chen, and Vasant G Honavar. 2025. On a connection between imitation learning and RLHF . In The Thirteenth International Conference on Learning Representations.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/143590844/ALIGN_Word_Association_Learning_for_Cross_Cultural_Generalization_in_Large_Language_Models", "content": "Teng Xiao , Yige Yuan, Mingxiao Li, Zhengyu Chen, and Vasant G Honavar. 2025. On a connection between imitation learning and RLHF . In The Thirteenth International Conference on Learning Representations."}
diff --git a/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_equation_2_Gaussian_bivariate_normal_CDF.jsonl b/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_equation_2_Gaussian_bivariate_normal_CDF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fddc045814c32c058adff6ddf2da35e120372b1e
--- /dev/null
+++ b/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_equation_2_Gaussian_bivariate_normal_CDF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "the relative effectiveness of prediction in surfacing the worst - off . Our findings provide clear. The value of a risk. predictor is ultimately determined by its impact on bottom-line welfare (e.g., graduation rates).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388634300_The_Value_of_Prediction_in_Identifying_the_Worst-Off", "content": "the relative effectiveness of prediction in surfacing the worst - off . Our findings provide clear. The value of a risk. predictor is ultimately determined by its impact on bottom-line welfare (e.g., graduation rates)."}
+{"idx": 1, "title": "The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "This paper develops a framework for quantifying the relative value of prediction in identifying the worst - off . We formal-ize tradeoffs between expanding screening capacity and improving predictive models, and show through both mathe", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=26JsumCG0z", "content": "This paper develops a framework for quantifying the relative value of prediction in identifying the worst - off . We formal-ize tradeoffs between expanding screening capacity and improving predictive models, and show through both mathe"}
+{"idx": 2, "title": "[2501.19334] The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst - off .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.19334", "content": "Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst - off ."}
+{"idx": 3, "title": "ICML Poster The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst - off .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46605", "content": "Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst - off ."}
+{"idx": 4, "title": "[Paper Note] The Value of Prediction in Identifying the Worst - Off ...", "date": "", "ddg_snippet": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/AkihikoWatanabe/paper_notes/2220", "content": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity."}
+{"idx": 5, "title": "Helping the Worst - Off : When Hiring More Case Workers... | Medium", "date": "", "ddg_snippet": "The findings, detailed in “ The Value of Prediction in Identifying the Worst - Off ,” challenge the current policy focus on perfecting prediction accuracy.", "subpage_snippet": "", "source": "nyudatascience.medium.com", "link": "https://nyudatascience.medium.com/helping-the-worst-off-when-hiring-more-case-workers-beats-building-better-ai-12e6f968de0b", "content": "The findings, detailed in “ The Value of Prediction in Identifying the Worst - Off ,” challenge the current policy focus on perfecting prediction accuracy."}
+{"idx": 6, "title": "A new faculty member explains performative prediction and how it...", "date": "", "ddg_snippet": "His most recent paper in this line of work, “ The Value of Prediction in Identifying the Worst - Off ”, was recently recognized with an Outstanding Paper honor at the 2025 International Conference on Machine Learning, awarded to the top six papers at the conference out of over 10...", "subpage_snippet": "", "source": "engineering.nyu.edu", "link": "https://engineering.nyu.edu/news/new-faculty-member-explains-performative-prediction-and-how-it-affects-us-all", "content": "His most recent paper in this line of work, “ The Value of Prediction in Identifying the Worst - Off ”, was recently recognized with an Outstanding Paper honor at the 2025 International Conference on Machine Learning, awarded to the top six papers at the conference out of over 10..."}
+{"idx": 7, "title": "The Value of Prediction in Identifying the Worst - Off Authors: Unai...", "date": "", "ddg_snippet": "Краеугольным камнем методологии статьи является коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR) — метрика, предназначенная для количественной оценки компромисса между двумя ключевыми инструментами политики: 1...", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/wall49591166_67792", "content": "Краеугольным камнем методологии статьи является коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR) — метрика, предназначенная для количественной оценки компромисса между двумя ключевыми инструментами политики: 1..."}
+{"idx": 8, "title": "Fisch Devstring Rod Skin – How to Get the Secret Devstring...", "date": "", "ddg_snippet": "The value of prediction in identifying the worst - off : Interview with Unai Fischer Abaigar.", "subpage_snippet": "", "source": "786hz.com", "link": "https://786hz.com/fisch-devstring-rod-skin/", "content": "The value of prediction in identifying the worst - off : Interview with Unai Fischer Abaigar."}
+{"idx": 9, "title": "Unai Fischer Abaigar on ICML2025 paper award | LinkedIn", "date": "", "ddg_snippet": "The value of prediction in identifying the worst - off : Interview with Unai Fischer Abaigar.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/aihuborg_the-value-of-prediction-in-identifying-the-activity-7366838647934959618-nO6p", "content": "The value of prediction in identifying the worst - off : Interview with Unai Fischer Abaigar."}
diff --git a/data/sampled_jsons/Theorem_1_epistemic_limits_passive_data_collection_Nickel_year_2024.jsonl b/data/sampled_jsons/Theorem_1_epistemic_limits_passive_data_collection_Nickel_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..610c863d1140586e6261a4e4d9ea16ca624b61de
--- /dev/null
+++ b/data/sampled_jsons/Theorem_1_epistemic_limits_passive_data_collection_Nickel_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "Authors Maximilian Nickel Abstract Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity. Yet, without rigorous model validation we cannot ensure the intended outcomes of ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/hash/b97fc02c9e536d68300d82be05c23aa2-Abstract-Conference.html", "content": "Authors Maximilian Nickel Abstract Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity. Yet, without rigorous model validation we cannot ensure the intended outcomes of ..."}
+{"idx": 1, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "View a PDF of the paper titled No Free Delivery Service: Epistemic limits of passive data collection in complex social systems, by Maximilian Nickel", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.13653", "content": "View a PDF of the paper titled No Free Delivery Service: Epistemic limits of passive data collection in complex social systems, by Maximilian Nickel"}
+{"idx": 2, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=XZ0fpoAKEB", "content": "These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices."}
+{"idx": 3, "title": "Lectures - Applied Data Science - Decision-making beyond Prediction ...", "date": "", "ddg_snippet": "Maximilian Nickel : No free delivery service - Epistemic limits of passive data collection in complex social systems (NeurIPS 2024) Maximilian Nickel , Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich: A Review of Relational Machine Learning for Knowledge Graphs, until Section IV", "subpage_snippet": "", "source": "orie5355.github.io", "link": "https://orie5355.github.io/Fall_2024/lectures/", "content": "Maximilian Nickel : No free delivery service - Epistemic limits of passive data collection in complex social systems (NeurIPS 2024) Maximilian Nickel , Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich: A Review of Relational Machine Learning for Knowledge Graphs, until Section IV"}
+{"idx": 4, "title": "No free delivery service Epistemic limits of passive data collection in ...", "date": "", "ddg_snippet": "This paper proposes two conditions, isomeric condition and relative well-conditionedness, for guaranteeing an arbitrary matrix to be recoverable from a sampling of the matrix entries and proves a collection of theorems for missing data recovery as well as convex/nonconvex matrix completion.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/No-free-delivery-service-Epistemic-limits-of-data-Nickel/fee71b955b372bea53199c627346ccccc6425570/figure/8", "content": "This paper proposes two conditions, isomeric condition and relative well-conditionedness, for guaranteeing an arbitrary matrix to be recoverable from a sampling of the matrix entries and proves a collection of theorems for missing data recovery as well as convex/nonconvex matrix completion."}
+{"idx": 5, "title": "No Free Delivery Service: Epistemic Limits of Passive Data Collection ...", "date": "", "ddg_snippet": "No Free Delivery Service Epistemic limits of passive data collection in complex social systems Maximilian Nickel FAIR at Meta Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/795949995/2411-13653v1", "content": "No Free Delivery Service Epistemic limits of passive data collection in complex social systems Maximilian Nickel FAIR at Meta Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test ..."}
+{"idx": 6, "title": "Maximilian Nickel - catalyzex.com", "date": "", "ddg_snippet": "No Free Delivery Service: Epistemic limits of passive data collection in complex social systems Request Code Nov 20, 2024 Maximilian Nickel", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Maximilian+Nickel", "content": "No Free Delivery Service: Epistemic limits of passive data collection in complex social systems Request Code Nov 20, 2024 Maximilian Nickel"}
+{"idx": 7, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "Maximilian Nickel Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity. Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI ...", "subpage_snippet": "", "source": "www.deepnlp.org", "link": "http://www.deepnlp.org/content/articles/no-free-delivery-service:-epistemic-limits-of-passive-data-collection-in-complex-social-systems", "content": "Maximilian Nickel Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity. Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI ..."}
+{"idx": 8, "title": "PDF No free delivery service - proceedings.nips.cc", "date": "", "ddg_snippet": "No free delivery service Epistemic limits of passive data collection in complex social systems Maximilian Nickel FAIR, Meta New York, NY maxn@meta.com", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2024/file/b97fc02c9e536d68300d82be05c23aa2-Paper-Conference.pdf", "content": "No free delivery service Epistemic limits of passive data collection in complex social systems Maximilian Nickel FAIR, Meta New York, NY maxn@meta.com"}
+{"idx": 9, "title": "NoFreeDeliveryService No - arXiv.org", "date": "", "ddg_snippet": "Epistemic limits of passive data collection in complex social systems Maximilian Nickel FAIR at Meta hine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions e", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.13653v1", "content": "Epistemic limits of passive data collection in complex social systems Maximilian Nickel FAIR at Meta hine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions e"}
diff --git a/data/sampled_jsons/Theorem_3.2_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_year_2024.jsonl b/data/sampled_jsons/Theorem_3.2_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a32b520742bfed2c5b39de8da8d52e15dd77930
--- /dev/null
+++ b/data/sampled_jsons/Theorem_3.2_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "May 1, 2025 · The paper introduces LUNO, a framework for uncertainty quantification in neural operators using function-valued Gaussian processes . By leveraging linearization , the method propagates Gaussian weight-space uncertainty to the operator ’s predictions, effectively converting trained neural operators into Gaussian random processes .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "May 1, 2025 · The paper introduces LUNO, a framework for uncertainty quantification in neural operators using function-valued Gaussian processes . By leveraging linearization , the method propagates Gaussian weight-space uncertainty to the operator ’s predictions, effectively converting trained neural operators into Gaussian random processes ."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "Theorem 3.2 reveals an insight into the abstract concept of function-valued Gaussian processes : Function-valued Gaus-sian processes are equivalent to (multi-output) Gaussian processes with augmented input spaces.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.05072", "content": "Theorem 3.2 reveals an insight into the abstract concept of function-valued Gaussian processes : Function-valued Gaus-sian processes are equivalent to (multi-output) Gaussian processes with augmented input spaces."}
+{"idx": 2, "title": "LUNO: Linearized Predictive Uncertainty in Neural Operators", "date": "", "ddg_snippet": "This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes \" by Magnani et al. (2025).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno", "content": "This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes \" by Magnani et al. (2025)."}
+{"idx": 3, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "The paper introduces NOLA, a novel framework that linearizes neural operators with a Laplace approximation to produce function-valued Gaussian processes . It converts neural operators into probabilistic models via currying and linearization , enabling post-hoc uncertainty quantification without retraining.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.05072", "content": "The paper introduces NOLA, a novel framework that linearizes neural operators with a Laplace approximation to produce function-valued Gaussian processes . It converts neural operators into probabilistic models via currying and linearization , enabling post-hoc uncertainty quantification without retraining."}
+{"idx": 4, "title": "Operator learning with Gaussian processes - ScienceDirect", "date": "", "ddg_snippet": "Feb 1, 2025 · To build more flexible neural operators that accommodate irregular geometries and sampling grids, [34] introduces a coordinate-based model that leverages implicit neural representations to encode functions into low-dimensional latent spaces and, in turn , infer mappings between function representations in the latent space.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782524008351", "content": "Feb 1, 2025 · To build more flexible neural operators that accommodate irregular geometries and sampling grids, [34] introduces a coordinate-based model that leverages implicit neural representations to encode functions into low-dimensional latent spaces and, in turn , infer mappings between function representations in the latent space."}
+{"idx": 5, "title": "Abstract Linearization Turns Neural Operators into Function ...", "date": "", "ddg_snippet": "ocesses. We then use Gaussian currying to construct function-valued Gaussian processes from neural operators with Gaussian weight po teriors. We discuss related work in Section 4 and showcase the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes/fulltext/6666938685a4ee7261b375f0/Linearization-Turns-Neural-Operators-into-Function-Valued-Gaussian-Processes.pdf", "content": "ocesses. We then use Gaussian currying to construct function-valued Gaussian processes from neural operators with Gaussian weight po teriors. We discuss related work in Section 4 and showcase the ..."}
+{"idx": 6, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "Jun 7, 2024 · As for all statistical models, the predictions of these models are imperfect and exhibit errors. Such errors are particularly difficult to spot in the complex nonlinear behaviour of dynamical systems. We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.05072v1", "content": "Jun 7, 2024 · As for all statistical models, the predictions of these models are imperfect and exhibit errors. Such errors are particularly difficult to spot in the complex nonlinear behaviour of dynamical systems. We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes ."}
+{"idx": 7, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "Theorem 3.2 reveals an insight into the abstract concept of function-valued Gaussian processes : Function-valued Gaussian processes are equivalent to (multi- ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Theorem 3.2 reveals an insight into the abstract concept of function-valued Gaussian processes : Function-valued Gaussian processes are equivalent to (multi- ..."}
+{"idx": 8, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — Theorem 3.2 reveals an insight into the abstract concept of function-valued Gaussian processes : Function-valued Gaus- sian processes are equivalent to (multi- ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — Theorem 3.2 reveals an insight into the abstract concept of function-valued Gaussian processes : Function-valued Gaus- sian processes are equivalent to (multi- ..."}
+{"idx": 9, "title": "probabilistic neural operators for functional", "date": "", "ddg_snippet": "by C Bülte · 2025 · Cited by 3 — In the following theorem we prove that the ... Linearization Turns Neural Operators into . Function - Valued Gaussian Processes , June 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.12902", "content": "by C Bülte · 2025 · Cited by 3 — In the following theorem we prove that the ... Linearization Turns Neural Operators into . Function - Valued Gaussian Processes , June 2024."}
diff --git a/data/sampled_jsons/Theorem_4.5_Chen_Human-in-the-loop_preference-based_reinforcement_learning_2022_year_2022.jsonl b/data/sampled_jsons/Theorem_4.5_Chen_Human-in-the-loop_preference-based_reinforcement_learning_2022_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..68a26d85d9d97a5678c4de0922cc0c99157e2281
--- /dev/null
+++ b/data/sampled_jsons/Theorem_4.5_Chen_Human-in-the-loop_preference-based_reinforcement_learning_2022_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Provable Reward-Agnostic Preference-Based Reinforcement Learning", "date": "", "ddg_snippet": "To overcome this challenge, there has been a recent surge of interest in Preference - based Reinforcement Learning (PbRL) with human feedback.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18505v3", "content": "To overcome this challenge, there has been a recent surge of interest in Preference - based Reinforcement Learning (PbRL) with human feedback."}
+{"idx": 1, "title": "Human-in-the-loop Learning Through Decentralized Communication", "date": "", "ddg_snippet": "Over the past decade, human - in - the - loop learning (HILL), which involves human agents as both information producers and consumers, has been widely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09574v1", "content": "Over the past decade, human - in - the - loop learning (HILL), which involves human agents as both information producers and consumers, has been widely ..."}
+{"idx": 2, "title": "Single Agent Robust Deep Reinforcement Learning for Bus Fleet", "date": "", "ddg_snippet": "MARL allows each bus to learn its own policy based on local observations, and has shown promise in loop -line or circular route simulations where the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20784v1", "content": "MARL allows each bus to learn its own policy based on local observations, and has shown promise in loop -line or circular route simulations where the ..."}
+{"idx": 3, "title": "Learning Guarantee of Reward Modeling Using Deep Neural Networks", "date": "", "ddg_snippet": "... bound, which substantiates the empirical efficiency of Reinforcement Learning from Human Feedback and highlights clear human beliefs in its success.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06601v1", "content": "... bound, which substantiates the empirical efficiency of Reinforcement Learning from Human Feedback and highlights clear human beliefs in its success."}
+{"idx": 4, "title": "Xiaoyu Chen | DeepAI", "date": "", "ddg_snippet": "Human - in - the - loop : Provably Efficient Preference - based Reinforcement Learning with General Function Approximation ... human - in - the - loop reinforcement ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/xiaoyu-chen", "content": "Human - in - the - loop : Provably Efficient Preference - based Reinforcement Learning with General Function Approximation ... human - in - the - loop reinforcement ..."}
+{"idx": 5, "title": "Machine Learning and Algorithms | Sugaku", "date": "", "ddg_snippet": "... learning , deep learning , Gaussian processes, image classification, text categorization, batch mode active learning , statistical guarantees, and ...", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/T12072/", "content": "... learning , deep learning , Gaussian processes, image classification, text categorization, batch mode active learning , statistical guarantees, and ..."}
+{"idx": 6, "title": "(PDF) Memory-Aware Active Learning in Mobile Sensing Systems", "date": "", "ddg_snippet": "Our approach is inspired by human - beings’ limited capacity to respond to prompts on their mobile device. ... introduce the notion of mindful active ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/342976852_Memory-Aware_Active_Learning_in_Mobile_Sensing_Systems", "content": "Our approach is inspired by human - beings’ limited capacity to respond to prompts on their mobile device. ... introduce the notion of mindful active ..."}
+{"idx": 7, "title": "Vol. 37 No. 5: AAAI-23 Technical Tracks 5 | Proceedings of the", "date": "", "ddg_snippet": "The conference scope included machine learning (deep learning , statistical learning , etc), natural language processing, computer vision, data mining, ...", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/issue/view/552", "content": "The conference scope included machine learning (deep learning , statistical learning , etc), natural language processing, computer vision, data mining, ..."}
+{"idx": 8, "title": "Bingo: Boosting Efficient Reasoning of LLMs via Dynamic and", "date": "", "ddg_snippet": "On the more challenging TheoremQA dataset, it achieves a 4 . 5 -point accuracy improvement and a 60% reduction in response length.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08125v1", "content": "On the more challenging TheoremQA dataset, it achieves a 4 . 5 -point accuracy improvement and a 60% reduction in response length."}
+{"idx": 9, "title": "AFMR: Model Advancement - Microsoft Research", "date": "", "ddg_snippet": "In terms of techniques, reinforcement learning , human feedback, retrieval- based methods, fine-tuning, model compression, task-oriented dialogue, and ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/project/afmr-model-advancement/", "content": "In terms of techniques, reinforcement learning , human feedback, retrieval- based methods, fine-tuning, model compression, task-oriented dialogue, and ..."}
diff --git a/data/sampled_jsons/Theorem_4.5_regret_lower_bound_preference-based_reinforcement_learning_Chen_et_al_2022.jsonl b/data/sampled_jsons/Theorem_4.5_regret_lower_bound_preference-based_reinforcement_learning_Chen_et_al_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..12f01d30941a5d5fb5ab1068b9356f2b05e11037
--- /dev/null
+++ b/data/sampled_jsons/Theorem_4.5_regret_lower_bound_preference-based_reinforcement_learning_Chen_et_al_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF RA-PbRL: Provably Eficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Among these, trajectory preference is identified as the most general and widely studied form of preference-based feedback, as evidenced by the rich literature on the topic Chen et al . [ 2022 ], Xu et al . [2020a], Wu and Sun [2023].", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "Among these, trajectory preference is identified as the most general and widely studied form of preference-based feedback, as evidenced by the rich literature on the topic Chen et al . [ 2022 ], Xu et al . [2020a], Wu and Sun [2023]."}
+{"idx": 1, "title": "PDF Near-optimal Regret Bounds for Reinforcement Learning∗", "date": "", "ddg_snippet": "We present a reinforcement learning algorithm with total regret AT) after T steps for any unknown W(√ MDP with S states, A actions per state, and diameter D. A corresponding lower bound of DSAT) on the total regret of any learning algorithm is given as well.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume11/jaksch10a/jaksch10a.pdf", "content": "We present a reinforcement learning algorithm with total regret AT) after T steps for any unknown W(√ MDP with S states, A actions per state, and diameter D. A corresponding lower bound of DSAT) on the total regret of any learning algorithm is given as well."}
+{"idx": 2, "title": "PDF arXiv:2112.03432v4 [cs.LG] 21 Oct 2022", "date": "", "ddg_snippet": "Abstract Obtaining rst-order regret bounds|regret bounds scaling not as the worst-case but with some measure of the performance of the optimal policy on a given instance|is a core question in sequential decision-making. While such bounds exist in many settings, they have proven elusive in reinforcement learning with large state spaces. In this work we address this gap, and show that it is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2112.03432.pdf", "content": "Abstract Obtaining rst-order regret bounds|regret bounds scaling not as the worst-case but with some measure of the performance of the optimal policy on a given instance|is a core question in sequential decision-making. While such bounds exist in many settings, they have proven elusive in reinforcement learning with large state spaces. In this work we address this gap, and show that it is ..."}
+{"idx": 3, "title": "PDF Learning, Regret minimization, and Equilibria", "date": "", "ddg_snippet": "In a zero-sum game, such algorithms are guaranteed to approach or exceed the minimax value of the game, and even provide a simple proof of the minimax theorem . We then turn to algorithms that minimize an even stronger form of regret , known as internal or swap regret .", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~avrim/Papers/regret-chapter.pdf", "content": "In a zero-sum game, such algorithms are guaranteed to approach or exceed the minimax value of the game, and even provide a simple proof of the minimax theorem . We then turn to algorithms that minimize an even stronger form of regret , known as internal or swap regret ."}
+{"idx": 4, "title": "PDF Human-in-the-loop: Provably Efficient Preference-based Reinforcement ...", "date": "", "ddg_snippet": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ..."}
+{"idx": 5, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Among these, trajectory preference is identified as the most general and widely studied form of preference-based feedback, as evidenced by the rich literature on the topic Chen et al . ( 2022 ); Xu et al . (2020a); Wu & Sun (2023).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v3", "content": "Among these, trajectory preference is identified as the most general and widely studied form of preference-based feedback, as evidenced by the rich literature on the topic Chen et al . ( 2022 ); Xu et al . (2020a); Wu & Sun (2023)."}
+{"idx": 6, "title": "PDF First-Order Regret in Reinforcement Learning with Linear Function ...", "date": "", "ddg_snippet": "Abstract Obtaining first-order regret bounds—regret bounds scaling not as the worst-case but with some measure of the performance of the opti-mal policy on a given instance—is a core ques-tion in sequential decision-making. While such bounds exist in many settings, they have proven elusive in reinforcement learning with large state spaces.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/wagenmaker22a/wagenmaker22a.pdf", "content": "Abstract Obtaining first-order regret bounds—regret bounds scaling not as the worst-case but with some measure of the performance of the opti-mal policy on a given instance—is a core ques-tion in sequential decision-making. While such bounds exist in many settings, they have proven elusive in reinforcement learning with large state spaces."}
+{"idx": 7, "title": "Human-in-the-loop: Provably Efficient Preference-based Reinforcement ...", "date": "", "ddg_snippet": "Abstract We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the agent only receives preferences over trajectory pairs from a human overseer. The goal of the agent is to learn the optimal policy which is most preferred by the human overseer.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2205.11140", "content": "Abstract We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the agent only receives preferences over trajectory pairs from a human overseer. The goal of the agent is to learn the optimal policy which is most preferred by the human overseer."}
+{"idx": 8, "title": "Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds", "date": "", "ddg_snippet": "The theoretical framework for learning from preference-based feedback (see, e.g., Pásztor et al ., 2024; Xu et al ., 2024) can be modeled as Bayesian Optimization from Human Feedback (BOHF). Similarly to conventional BO (Frazier, 2018; Shahriari et al ., 2015; Srinivas et al ., 2010), the learner leverages previously collected samples through kernel- based regression to learn an unknown black-box ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23673v1", "content": "The theoretical framework for learning from preference-based feedback (see, e.g., Pásztor et al ., 2024; Xu et al ., 2024) can be modeled as Bayesian Optimization from Human Feedback (BOHF). Similarly to conventional BO (Frazier, 2018; Shahriari et al ., 2015; Srinivas et al ., 2010), the learner leverages previously collected samples through kernel- based regression to learn an unknown black-box ..."}
+{"idx": 9, "title": "PDF Reinforcement Learning: Theory and Algorithms - GitHub Pages", "date": "", "ddg_snippet": "This chapter follows the results due to [Azar et al ., 2013], along with some improved rates due to [Agarwal et al ., 2020c]. One of the key observations in this chapter is that we can find a near optimal policy using a number of observed transitions that is sublinear in the model size, i.e. use a number of samples that is smaller than O(jSj2jAj).", "subpage_snippet": "", "source": "rltheorybook.github.io", "link": "https://rltheorybook.github.io/rltheorybook_AJKS.pdf", "content": "This chapter follows the results due to [Azar et al ., 2013], along with some improved rates due to [Agarwal et al ., 2020c]. One of the key observations in this chapter is that we can find a near optimal policy using a number of observed transitions that is sublinear in the model size, i.e. use a number of samples that is smaller than O(jSj2jAj)."}
diff --git a/data/sampled_jsons/This_study_examined_whether_people_living_in_the_US_connect_their_sensory_experiences_Kirilenko_2015.jsonl b/data/sampled_jsons/This_study_examined_whether_people_living_in_the_US_connect_their_sensory_experiences_Kirilenko_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c9bd69a4931490c814dd3fe74999b082ee22a33b
--- /dev/null
+++ b/data/sampled_jsons/This_study_examined_whether_people_living_in_the_US_connect_their_sensory_experiences_Kirilenko_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Andrei KIRILENKO | Professor (Associate) | Ph.D. | University of...", "date": "", "ddg_snippet": "Andrei P Kirilenko . This experimental study addresses the effect of a destination promotional video on tourists' perceptions, attitudes, and intent to visit that destination depending on the viewing mode of the video: traditional versus semi-immersive.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Andrei-Kirilenko", "content": "Andrei P Kirilenko . This experimental study addresses the effect of a destination promotional video on tourists' perceptions, attitudes, and intent to visit that destination depending on the viewing mode of the video: traditional versus semi-immersive."}
+{"idx": 1, "title": "People as sensors: Mass media and local temperature influence...", "date": "", "ddg_snippet": "This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media inuences the process.", "subpage_snippet": "", "source": "research.fit.edu", "link": "https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/climate-communications/youth-climate-amp-social-media/Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect-CC-Discussion-On-Twitter.pdf", "content": "This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media inuences the process."}
+{"idx": 2, "title": "Andrei Kirilenko - Independent Researcher", "date": "", "ddg_snippet": "This study examined whether people living in the US connect their sensory experiences with 5 loca... more This study Significance of surface water in the terrestrial water budget: A case study in the Prairie Coteau using GRACE, GLDAS, Landsat, and groundwater well data.", "subpage_snippet": "", "source": "independent.academia.edu", "link": "https://independent.academia.edu/AndreiKirilenko", "content": "This study examined whether people living in the US connect their sensory experiences with 5 loca... more This study Significance of surface water in the terrestrial water budget: A case study in the Prairie Coteau using GRACE, GLDAS, Landsat, and groundwater well data."}
+{"idx": 3, "title": "People as sensors: Mass media and local temperature influence ...", "date": "", "ddg_snippet": "Jan 1, 2015 · This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process. We used the volume of Twitter messages containing words “climate change” and “global warming” as the indicator of attention that public pays to the issue.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0959378014001952", "content": "Jan 1, 2015 · This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process. We used the volume of Twitter messages containing words “climate change” and “global warming” as the indicator of attention that public pays to the issue."}
+{"idx": 4, "title": "People as sensors: Mass media and local temperature influence ...", "date": "", "ddg_snippet": "Jan 1, 2015 · This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0959378014001952", "content": "Jan 1, 2015 · This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process."}
+{"idx": 5, "title": "(PDF) Using Social Media to Detect and Locate Wildfires", "date": "", "ddg_snippet": "Heat map showing Twitter activity (contour plots) in relation to observed fire events (black points) for the California-Nevada region, using MODIS ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/311133785_Using_Social_Media_to_Detect_and_Locate_Wildfires", "content": "Heat map showing Twitter activity (contour plots) in relation to observed fire events (black points) for the California-Nevada region, using MODIS ..."}
+{"idx": 6, "title": "Catalyst Cohort Archive | Vancouver International Film Festival", "date": "", "ddg_snippet": "... using these anthropological practices as a basis for her films, she hopes to create a personal and engaging narrative and bring women’s experiences ...", "subpage_snippet": "", "source": "viff.org", "link": "https://viff.org/catalyst/cohort-archive/", "content": "... using these anthropological practices as a basis for her films, she hopes to create a personal and engaging narrative and bring women’s experiences ..."}
+{"idx": 7, "title": "View article - xs.gupiaoq.com", "date": "", "ddg_snippet": "This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process.", "subpage_snippet": "", "source": "xs.gupiaoq.com", "link": "https://xs.gupiaoq.com/citations?view_op=view_citation&hl=en&user=X1rFNi0AAAAJ&citation_for_view=X1rFNi0AAAAJ:mVmsd5A6BfQC", "content": "This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process."}
+{"idx": 8, "title": "Svetlana STEPCHENKOVA | Professor (Associate) | PhD ...", "date": "", "ddg_snippet": "This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Svetlana-Stepchenkova", "content": "This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process."}
+{"idx": 9, "title": "(PDF) Social Sensing of Heatwaves - Academia.edu", "date": "", "ddg_snippet": "This study examined whether people living in the US connect their sensory experiences with 5 local temperature to climate change and whether mass media influences the process.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/95211612/Social_Sensing_of_Heatwaves", "content": "This study examined whether people living in the US connect their sensory experiences with 5 local temperature to climate change and whether mass media influences the process."}
diff --git a/data/sampled_jsons/To_comprehensively_evaluate_the_impact_of_retriever_choice_on_RAG_performance,_we_select_three_disti.jsonl b/data/sampled_jsons/To_comprehensively_evaluate_the_impact_of_retriever_choice_on_RAG_performance,_we_select_three_disti.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f45959504dc3dead4fcae202bab47a30571b60a
--- /dev/null
+++ b/data/sampled_jsons/To_comprehensively_evaluate_the_impact_of_retriever_choice_on_RAG_performance,_we_select_three_disti.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGtifier: Evaluating RAG Generation Approaches of", "date": "", "ddg_snippet": "We consider recent advances in RAG and cross- evaluate various answer generation approaches with distinct retriever and reranker settings to compare ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.14412v1", "content": "We consider recent advances in RAG and cross- evaluate various answer generation approaches with distinct retriever and reranker settings to compare ..."}
+{"idx": 1, "title": "Shifting from Ranking to Set Selection for Retrieval Augmented", "date": "", "ddg_snippet": "Comprehensive Evaluation of Set Retrieval and Generation: To validate the effectiveness of our approach, we conduct extensive evaluations on both the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.06838v2", "content": "Comprehensive Evaluation of Set Retrieval and Generation: To validate the effectiveness of our approach, we conduct extensive evaluations on both the ..."}
+{"idx": 2, "title": "RAG Without the Lag: Interactive Debugging for", "date": "", "ddg_snippet": "To address these challenges, we present raggy , a developer tool that combines a Python library of composable RAG primitives with an interactive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.13587v1", "content": "To address these challenges, we present raggy , a developer tool that combines a Python library of composable RAG primitives with an interactive ..."}
+{"idx": 3, "title": "A Systematic Review of Key Retrieval-Augmented Generation (RAG)", "date": "", "ddg_snippet": "The objective of this systematic review is to provide a comprehensive overview of the development of RAG and its expanding role in information access.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.18910v1", "content": "The objective of this systematic review is to provide a comprehensive overview of the development of RAG and its expanding role in information access."}
+{"idx": 4, "title": "Retrieval-Augmented Generation (RAG): Deep Dive into 25", "date": "", "ddg_snippet": "It evaluates the accuracy and relevance of its responses, iteratively refining its retrieval methods. ... tool can use Attention-Based RAG to identify ...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/11/25/retrieval-augmented-generation-rag-deep-dive-into-25-different-types-of-rag/", "content": "It evaluates the accuracy and relevance of its responses, iteratively refining its retrieval methods. ... tool can use Attention-Based RAG to identify ..."}
+{"idx": 5, "title": "Evaluate the reliability of Retrieval Augmented Generation", "date": "", "ddg_snippet": "In this post, we show you how to evaluate the performance , trustworthiness, and potential biases of your RAG pipelines and applications on Amazon ...", "subpage_snippet": "", "source": "aws.amazon.com", "link": "https://aws.amazon.com/blogs/machine-learning/evaluate-the-reliability-of-retrieval-augmented-generation-applications-using-amazon-bedrock/", "content": "In this post, we show you how to evaluate the performance , trustworthiness, and potential biases of your RAG pipelines and applications on Amazon ..."}
+{"idx": 6, "title": "Retrieval-Augmented Generation (RAG): 2025 Definitive Guide", "date": "", "ddg_snippet": "One of the most fascinating aspects of Retrieval -Augmented Generation ( RAG ) is its ability to dynamically integrate external knowledge into the ...", "subpage_snippet": "", "source": "www.chitika.com", "link": "https://www.chitika.com/retrieval-augmented-generation-rag-the-definitive-guide-2025/", "content": "One of the most fascinating aspects of Retrieval -Augmented Generation ( RAG ) is its ability to dynamically integrate external knowledge into the ..."}
+{"idx": 7, "title": "Evaluate the reliability of Retrieval Augmented Generation", "date": "", "ddg_snippet": "In this post, we show you how to evaluate the performance , trustworthiness, and potential biases of your RAG pipelines and applications on Amazon ...", "subpage_snippet": "", "source": "cybercm.tech", "link": "https://cybercm.tech/blog/2024/06/20/evaluate-the-reliability-of-retrieval-augmented-generation-applications-using-amazon-bedrock/", "content": "In this post, we show you how to evaluate the performance , trustworthiness, and potential biases of your RAG pipelines and applications on Amazon ..."}
+{"idx": 8, "title": "What is RAG? (Retrieval Augmented Generation) | Sendbird", "date": "", "ddg_snippet": "This blog provides a comprehensive overview of Retrieval Augmented Generation ( RAG ), walking through the process of building a high-performing RAG ...", "subpage_snippet": "", "source": "sendbird.com", "link": "https://sendbird.com/blog/what-is-an-ai-agent/rag", "content": "This blog provides a comprehensive overview of Retrieval Augmented Generation ( RAG ), walking through the process of building a high-performing RAG ..."}
+{"idx": 9, "title": "Advanced RAG Techniques: What They Are & How to Use Them", "date": "", "ddg_snippet": "... the most basic form of RAG , where the system directly uses the retrieved data as input for the generation model without applying advanced techniques ...", "subpage_snippet": "", "source": "www.falkordb.com", "link": "https://www.falkordb.com/blog/advanced-rag/", "content": "... the most basic form of RAG , where the system directly uses the retrieved data as input for the generation model without applying advanced techniques ..."}
diff --git a/data/sampled_jsons/Tor_clustering_null_family_ID_Abnormal_Behavioral_Pattern_nodes_grouping_methodology.jsonl b/data/sampled_jsons/Tor_clustering_null_family_ID_Abnormal_Behavioral_Pattern_nodes_grouping_methodology.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6df3d48765b3081fb9b462d9519c276d1194b5fa
--- /dev/null
+++ b/data/sampled_jsons/Tor_clustering_null_family_ID_Abnormal_Behavioral_Pattern_nodes_grouping_methodology.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via ...", "date": "", "ddg_snippet": "Specifically, anomalous links of Discrete nodes in cluster 3 to Family Group 3 are exposed mainly due to their similar be-havioral patterns , demonstrating the need to include the con-nectivity metric we proposed in assessing node associations.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "Specifically, anomalous links of Discrete nodes in cluster 3 to Family Group 3 are exposed mainly due to their similar be-havioral patterns , demonstrating the need to include the con-nectivity metric we proposed in assessing node associations."}
+{"idx": 1, "title": "How to exclude specific guard nodes - Tor Project Forum", "date": "", "ddg_snippet": "Hi there. Is there a way to exclude specific IP addresses from being selected by Tor Browser as guard node for Tor circuit? Some node IP address had been reported as malicious hosts to IPS database of my local network (although they may not be actually malicious). So every time when my Tor Browser tries to make connection to these nodes will always fail and also raises alert to network managers.", "subpage_snippet": "", "source": "forum.torproject.org", "link": "https://forum.torproject.org/t/how-to-exclude-specific-guard-nodes/6552", "content": "Hi there. Is there a way to exclude specific IP addresses from being selected by Tor Browser as guard node for Tor circuit? Some node IP address had been reported as malicious hosts to IPS database of my local network (although they may not be actually malicious). So every time when my Tor Browser tries to make connection to these nodes will always fail and also raises alert to network managers."}
+{"idx": 2, "title": "PDF Defending Against Malicious Cyber Activity Originating from Tor - CISA", "date": "", "ddg_snippet": "Using a behavior-based approach, network defenders can uncover suspicious Tor activity by searching for the operational patterns of Tor client software and protocols.", "subpage_snippet": "", "source": "www.cisa.gov", "link": "https://www.cisa.gov/sites/default/files/publications/AA20-183A_Defending_Against_Malicious_Cyber_Activity_Originating_from_Tor_S508C.pdf", "content": "Using a behavior-based approach, network defenders can uncover suspicious Tor activity by searching for the operational patterns of Tor client software and protocols."}
+{"idx": 3, "title": "Tor Relay/Router alerts, false positives? : r/Ubiquiti - Reddit", "date": "", "ddg_snippet": "I also run a UDM Pro with IPS/ IDS enabled. I've been getting these alerts on from the UDM - \"ET TOR Known Tor Relay/Router (Not Exit) Node Traffic group \" Source changes (worldwide), destination is always the same, the adguard node .", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/Ubiquiti/comments/vcv4vx/tor_relayrouter_alerts_false_positives/", "content": "I also run a UDM Pro with IPS/ IDS enabled. I've been getting these alerts on from the UDM - \"ET TOR Known Tor Relay/Router (Not Exit) Node Traffic group \" Source changes (worldwide), destination is always the same, the adguard node ."}
+{"idx": 4, "title": "Multi-homed abnormal behavior detection algorithm based on fuzzy ...", "date": "", "ddg_snippet": "In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering .", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-022-26142-w", "content": "In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering ."}
+{"idx": 5, "title": "Network traffic analysis over clustering-based collective anomaly ...", "date": "", "ddg_snippet": "Clustering refers to unsupervised learning algorithms which do not need pre-labeled data to extract rules for grouping similar data instances. The clustering process results in a given dataset being partitioned based on the different criteria used.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1389128622000019", "content": "Clustering refers to unsupervised learning algorithms which do not need pre-labeled data to extract rules for grouping similar data instances. The clustering process results in a given dataset being partitioned based on the different criteria used."}
+{"idx": 6, "title": "Towards Analyzing Family Misconfiguration in Tor Network", "date": "", "ddg_snippet": "This paper gives an analysis of family misconfiguration in Tor network. Two types of family misconfigurations together with the corresponding methods that discover and correct them are presented. The experiment over live Tor network data confirms the pervasiveness of family misconfigurations in Tor network.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-94-007-5699-1_51", "content": "This paper gives an analysis of family misconfiguration in Tor network. Two types of family misconfigurations together with the corresponding methods that discover and correct them are presented. The experiment over live Tor network data confirms the pervasiveness of family misconfigurations in Tor network."}
+{"idx": 7, "title": "PDF Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via ...", "date": "", "ddg_snippet": "In contrast, cluster 3 and 5 contain members from family group(s) along with isolated nodes while the major-ity of cluster 2 and 4 are isolated nodes . These nodes in the same cluster which do not belong to the same family , are gathered based on features other than strict family relationship.", "subpage_snippet": "", "source": "cse.seu.edu.cn", "link": "https://cse.seu.edu.cn/_upload/article/files/82/36/84188a1a47a79fe2b6dc589a1c39/a6db7ad8-3fef-4518-8046-480a6ac64280.pdf", "content": "In contrast, cluster 3 and 5 contain members from family group(s) along with isolated nodes while the major-ity of cluster 2 and 4 are isolated nodes . These nodes in the same cluster which do not belong to the same family , are gathered based on features other than strict family relationship."}
+{"idx": 8, "title": "A novel approach for classification of Tor and non-Tor traffic using ...", "date": "", "ddg_snippet": "To overcome Tor's limitations not only in terms of achieving better accuracy but also in performing classification in time-constrained scenarios, we propose a classification approach for Tor and non- Tor traffic classification, utilizing multiple models to enhance categorization and application identification.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424004093", "content": "To overcome Tor's limitations not only in terms of achieving better accuracy but also in performing classification in time-constrained scenarios, we propose a classification approach for Tor and non- Tor traffic classification, utilizing multiple models to enhance categorization and application identification."}
+{"idx": 9, "title": "Behavior Anomaly Detection: Techniques & Best Practices", "date": "", "ddg_snippet": "Cluster-Based Methods Cluster-based anomaly detection involves grouping data points and identifying any data that deviates significantly from these clusters. Techniques like k-means or DBSCAN dynamically define clusters based on similarity and isolate points that don't fit these groups as abnormal .", "subpage_snippet": "", "source": "www.exabeam.com", "link": "https://www.exabeam.com/explainers/ueba/behavior-anomaly-detection-techniques-and-best-practices/", "content": "Cluster-Based Methods Cluster-based anomaly detection involves grouping data points and identifying any data that deviates significantly from these clusters. Techniques like k-means or DBSCAN dynamically define clusters based on similarity and isolate points that don't fit these groups as abnormal ."}
diff --git a/data/sampled_jsons/Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Figure_3_peak-then-decline.jsonl b/data/sampled_jsons/Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Figure_3_peak-then-decline.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b0e8e81d6bda77330f118e2a42399baaf7c296a1
--- /dev/null
+++ b/data/sampled_jsons/Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Figure_3_peak-then-decline.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Engineering RAG Systems for Real-World Applications: Design,", "date": "", "ddg_snippet": "This study investigates how RAG systems can be engineered and evaluated in real-world contexts, focusing on system design , domain-specific ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.20869v2", "content": "This study investigates how RAG systems can be engineered and evaluated in real-world contexts, focusing on system design , domain-specific ..."}
+{"idx": 1, "title": "Real-Time RAG for the Identification of Supply Chain", "date": "", "ddg_snippet": "... and τ \\tau we can get closer to achieving a real-time system , capable of reasoning against information and events without the knowledge gap inherent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10469v1", "content": "... and τ \\tau we can get closer to achieving a real-time system , capable of reasoning against information and events without the knowledge gap inherent ..."}
+{"idx": 2, "title": "LangChain vs LlamaIndex: In-Depth Comparison and Use", "date": "", "ddg_snippet": "... of indexing and retrieving the relevant information with the advanced search. LlamaIndex primarily focuses on storing, structuring, and accessing ...", "subpage_snippet": "", "source": "www.deepchecks.com", "link": "https://www.deepchecks.com/langchain-vs-llamaindex-depth-comparison-use/", "content": "... of indexing and retrieving the relevant information with the advanced search. LlamaIndex primarily focuses on storing, structuring, and accessing ..."}
+{"idx": 3, "title": "Explainable Knowledge Graph Retrieval-Augmented Generation", "date": "", "ddg_snippet": "... RAG is still essentially a black box; users cannot ... The goal of this study is to improve the transparency and accountability of RAG -based systems .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03626v1", "content": "... RAG is still essentially a black box; users cannot ... The goal of this study is to improve the transparency and accountability of RAG -based systems ."}
+{"idx": 4, "title": "A Dynamic Fusion Model for Consistent Crisis Response", "date": "", "ddg_snippet": "People in crisis often turn to social networks for information, support, and assistance, especially when other sources cannot be relied upon Bukar ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.01053v2", "content": "People in crisis often turn to social networks for information, support, and assistance, especially when other sources cannot be relied upon Bukar ..."}
+{"idx": 5, "title": "Mobile-Agent-v3: Foundamental Agents for GUI Automation", "date": "", "ddg_snippet": "... agent framework, Mobile-Agent-v3, which further enhances GUI-Owl’s performance (73. 3 on AndroidWorld and 37.7 on OSWorld), achieving a new state- of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15144v1", "content": "... agent framework, Mobile-Agent-v3, which further enhances GUI-Owl’s performance (73. 3 on AndroidWorld and 37.7 on OSWorld), achieving a new state- of ..."}
+{"idx": 6, "title": "Extending the NVIDIA NeMo Agent Toolkit to Support New Agentic", "date": "", "ddg_snippet": "... of the toolkit, allowing it to integrate with ... The Agent toolkit plugin system is designed around two main concepts: entry points and decorators.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/extending-the-nvidia-nemo-agent-toolkit-to-support-new-agentic-frameworks/", "content": "... of the toolkit, allowing it to integrate with ... The Agent toolkit plugin system is designed around two main concepts: entry points and decorators."}
+{"idx": 7, "title": "All | MongoDB Blog", "date": "", "ddg_snippet": "... of AI, embedding models are the bedrock of advanced applications like retrieval augmented generation ( RAG ), semantic search , and recommendation ...", "subpage_snippet": "", "source": "www.mongodb.com", "link": "https://www.mongodb.com/company/blog/all/263", "content": "... of AI, embedding models are the bedrock of advanced applications like retrieval augmented generation ( RAG ), semantic search , and recommendation ..."}
+{"idx": 8, "title": "MongoDB Blog", "date": "", "ddg_snippet": "This level of tailored experience has long been difficult to achieve in brick- and -mortar retail, but that’s changing thanks to digital receipts.", "subpage_snippet": "", "source": "www.mongodb.com", "link": "https://www.mongodb.com/blog/authors/richard-guo-(nomic)/2", "content": "This level of tailored experience has long been difficult to achieve in brick- and -mortar retail, but that’s changing thanks to digital receipts."}
+{"idx": 9, "title": "Improving automated deep phenotyping through large language", "date": "", "ddg_snippet": "In evaluations, RAG -HPO + LLaMa- 3 .1 70B achieved a mean precision of 0.81, recall of 0.76, and an F1 score of 0.78—significantly surpassing ...", "subpage_snippet": "", "source": "genomemedicine.biomedcentral.com", "link": "https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-025-01521-w", "content": "In evaluations, RAG -HPO + LLaMa- 3 .1 70B achieved a mean precision of 0.81, recall of 0.76, and an F1 score of 0.78—significantly surpassing ..."}
diff --git a/data/sampled_jsons/Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_arXiv.jsonl b/data/sampled_jsons/Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..daf1955663fafbc225569b3b58487a93612160b7
--- /dev/null
+++ b/data/sampled_jsons/Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "View a PDF of the paper titled Training - free Diffusion Model Alignment with Sampling Demons , by Po-Hung Yeh and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.05760", "content": "View a PDF of the paper titled Training - free Diffusion Model Alignment with Sampling Demons , by Po-Hung Yeh and 2 other authors."}
+{"idx": 1, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Training diffusion models with reinforcement learning. In International Conference on Machine Learning, 2023. Clark et al.Classifier- free diffusion guidance. In NeurIPS Workshop on Deep Generative Models and Downstream Applications, 2021. Ho et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v1", "content": "Training diffusion models with reinforcement learning. In International Conference on Machine Learning, 2023. Clark et al.Classifier- free diffusion guidance. In NeurIPS Workshop on Deep Generative Models and Downstream Applications, 2021. Ho et al."}
+{"idx": 2, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "This paper introduces a training - free technique called \" Sampling Demons \" for aligning diffusion models with target objectives. The method works by modifying the diffusion sampling process to match the desired output during inference, without requiring any additional training.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/training-free-diffusion-model-alignment-sampling-demons", "content": "This paper introduces a training - free technique called \" Sampling Demons \" for aligning diffusion models with target objectives. The method works by modifying the diffusion sampling process to match the desired output during inference, without requiring any additional training."}
+{"idx": 3, "title": "GitHub - aiiu-lab/DemonSampling: [ICLR'25] Official implementation of...", "date": "", "ddg_snippet": "[ICLR'25] Official implementation of \" Training - free Diffusion Model Alignment with Sampling Demons \".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling", "content": "[ICLR'25] Official implementation of \" Training - free Diffusion Model Alignment with Sampling Demons \"."}
+{"idx": 4, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2410.05760v2", "content": "Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation..."}
+{"idx": 5, "title": "aiiu-lab/DemonSampling - Githubissues", "date": "", "ddg_snippet": "Sampling Demon Official implementation of ICLR 2025 \" Sampling Demon \" ( arXiv :2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/aiiu-lab/DemonSampling/readme", "content": "Sampling Demon Official implementation of ICLR 2025 \" Sampling Demon \" ( arXiv :2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models ."}
+{"idx": 6, "title": "aiiu-lab/DemonSampling | DeepWiki", "date": "", "ddg_snippet": "This document introduces the DemonSampling system, a training - free diffusion model alignment technique that enables preference alignment during inference without requiring backpropagation. For installation instructions, see Installation.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/aiiu-lab/DemonSampling", "content": "This document introduces the DemonSampling system, a training - free diffusion model alignment technique that enables preference alignment during inference without requiring backpropagation. For installation instructions, see Installation."}
+{"idx": 7, "title": "ICLR Poster Training - Free Diffusion Model Alignment with ...", "date": "", "ddg_snippet": "alignment method for diffusion models .Our method can be easily integrated with existing diffusion models without further training .Our experiments show that the proposed approach significantly improves the average aesthetics scores for text-to-image generation.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28034", "content": "alignment method for diffusion models .Our method can be easily integrated with existing diffusion models without further training .Our experiments show that the proposed approach significantly improves the average aesthetics scores for text-to-image generation."}
+{"idx": 8, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge.To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Training-free-Diffusion-Model-Alignment-with-Sampling-Demons-7d88de32-4c7d-456a-8886-7029b4030b48", "content": "Aligning diffusion models with user preferences has been a key challenge.To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models ."}
+{"idx": 9, "title": "\" Alignment without Over-optimization: Training - Free Solution for...\"", "date": "", "ddg_snippet": "Sequential Monte Carlo sampling brings training - free precision to diffusion model alignment . A training - free method to align diffusion models with specific objectives while preserving their versatility and avoiding over-optimization through Sequential Monte Carlo sampling .", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/alignment-without-over-optimization", "content": "Sequential Monte Carlo sampling brings training - free precision to diffusion model alignment . A training - free method to align diffusion models with specific objectives while preserving their versatility and avoiding over-optimization through Sequential Monte Carlo sampling ."}
diff --git a/data/sampled_jsons/Training_Diffusion_Models_with_Reinforcement_Learning_Black_et_al..jsonl b/data/sampled_jsons/Training_Diffusion_Models_with_Reinforcement_Learning_Black_et_al..jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a5ac8a43ea71142f89690e67e5748f45a3dd9c3a
--- /dev/null
+++ b/data/sampled_jsons/Training_Diffusion_Models_with_Reinforcement_Learning_Black_et_al..jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "by K Black · 2023 · Cited by 451 — Access Paper: View a PDF of the paper titled Training Diffusion Models with Reinforcement Learning , by Kevin Black and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.13301", "content": "by K Black · 2023 · Cited by 451 — Access Paper: View a PDF of the paper titled Training Diffusion Models with Reinforcement Learning , by Kevin Black and 4 other authors."}
+{"idx": 1, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "by K Black · Cited by 451 — Training Diffusion Models with Reinforcement Learning . Download PDF. Kevin Black , Michael Janner, Yilun Du, Ilya Kostrikov, Sergey Levine.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YCWjhGrJFD", "content": "by K Black · Cited by 451 — Training Diffusion Models with Reinforcement Learning . Download PDF. Kevin Black , Michael Janner, Yilun Du, Ilya Kostrikov, Sergey Levine."}
+{"idx": 2, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "14 Jul 2023 — Training Diffusion Models with Reinforcement Learning · Kevin Black *, Michael Janner*, Yilun Du, Ilya Kostrikov, and Sergey Levine · arXiv ...", "subpage_snippet": "", "source": "bair.berkeley.edu", "link": "http://bair.berkeley.edu/blog/2023/07/14/ddpo/", "content": "14 Jul 2023 — Training Diffusion Models with Reinforcement Learning · Kevin Black *, Michael Janner*, Yilun Du, Ilya Kostrikov, and Sergey Levine · arXiv ..."}
+{"idx": 3, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Training Diffusion Models with Reinforcement Learning . Paper | Code & Weights | Blog · Kevin Black *, Michael Janner*, Yilun Du, Ilya Kostrikov, and Sergey ...", "subpage_snippet": "", "source": "rl-diffusion.github.io", "link": "https://rl-diffusion.github.io/", "content": "Training Diffusion Models with Reinforcement Learning . Paper | Code & Weights | Blog · Kevin Black *, Michael Janner*, Yilun Du, Ilya Kostrikov, and Sergey ..."}
+{"idx": 4, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "In this paper, we investigate reinforcement learning methods for directly optimizing diffusion models for such objectives.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.13301v4", "content": "In this paper, we investigate reinforcement learning methods for directly optimizing diffusion models for such objectives."}
+{"idx": 5, "title": "[PDF] Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Training Diffusion Models with Reinforcement Learning · Kevin Black , Michael Janner, +2 authors. S. Levine · Published in International Conference on… 22 May 2023 ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Training-Diffusion-Models-with-Reinforcement-Black-Janner/d8c78221e4366d6a72a6b3e41e35b706cc45c01d", "content": "Training Diffusion Models with Reinforcement Learning · Kevin Black , Michael Janner, +2 authors. S. Levine · Published in International Conference on… 22 May 2023 ..."}
+{"idx": 6, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Diffusion models are a class of flexible generative models trained with an approximation to the log-likelihood objective. However, most use.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/11499e3101e2bbbc27cb4147e7205ac75ad7045a.pdf", "content": "Diffusion models are a class of flexible generative models trained with an approximation to the log-likelihood objective. However, most use."}
+{"idx": 7, "title": "Towards Better Alignment: Training Diffusion Models with ...", "date": "", "ddg_snippet": "by Z Hu · 2025 · Cited by 7 — Diffusion models have achieved remarkable success in text-to-image generation. However, their practical applica- tions are hindered by the misalignment ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_Towards_Better_Alignment_Training_Diffusion_Models_with_Reinforcement_Learning_Against_CVPR_2025_paper.pdf", "content": "by Z Hu · 2025 · Cited by 7 — Diffusion models have achieved remarkable success in text-to-image generation. However, their practical applica- tions are hindered by the misalignment ... 11 pages"}
+{"idx": 8, "title": "Reinforcement learning training with DDPO", "date": "", "ddg_snippet": "... Black et al . in Training Diffusion Models with Reinforcement Learning , which is implemented in TRL with the ~trl.DDPOTrainer . For more information ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/diffusers/v0.25.0/en/training/ddpo", "content": "... Black et al . in Training Diffusion Models with Reinforcement Learning , which is implemented in TRL with the ~trl.DDPOTrainer . For more information ..."}
+{"idx": 9, "title": "Training Diffusion Models With Reinforcement Learning", "date": "", "ddg_snippet": "Training Diffusion Models With Reinforcement Learning . This document discusses using reinforcement learning to directly optimize diffusion models for downstream ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/682018128/Training-Diffusion-Models-with-Reinforcement-Learning", "content": "Training Diffusion Models With Reinforcement Learning . This document discusses using reinforcement learning to directly optimize diffusion models for downstream ..."}
diff --git a/data/sampled_jsons/Trockman_Kolter_2023_paper_identity_matrix.jsonl b/data/sampled_jsons/Trockman_Kolter_2023_paper_identity_matrix.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b1ea6391e18898557ed835b97d49c75fed26c1e7
--- /dev/null
+++ b/data/sampled_jsons/Trockman_Kolter_2023_paper_identity_matrix.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2311.16833v1 [cs.LG] 28 Nov 2023", "date": "", "ddg_snippet": "by B Prach · 2023 · Cited by 10 — A matrix M is orthogonal if MM⊤ = I, where I is the identity matrix . For layers with an orthogonal Jacobian, Equation (1) always holds with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.16833", "content": "by B Prach · 2023 · Cited by 10 — A matrix M is orthogonal if MM⊤ = I, where I is the identity matrix . For layers with an orthogonal Jacobian, Equation (1) always holds with ..."}
+{"idx": 1, "title": "Exploiting Connections between Lipschitz Structures for ...", "date": "", "ddg_snippet": "by AJ Havens · Cited by 11 — In addition, we denote the n × n identity matrix and the n × n zero matrix as In and 0n, respectively. The subscripts will be omitted when the dimension is ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2023/file/4462db5eee6823b2abad0d1f955e187a-Paper-Conference.pdf", "content": "by AJ Havens · Cited by 11 — In addition, we denote the n × n identity matrix and the n × n zero matrix as In and 0n, respectively. The subscripts will be omitted when the dimension is ..."}
+{"idx": 2, "title": "Mimetic Initialization Helps State Space Models Learn to ...", "date": "", "ddg_snippet": "14 Oct 2024 — Initializing the queries and keys to be correlated was also noted by Trockman & Kolter ( 2023 ) , who suggest these weights should not be strictly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.11135v1", "content": "14 Oct 2024 — Initializing the queries and keys to be correlated was also noted by Trockman & Kolter ( 2023 ) , who suggest these weights should not be strictly ..."}
+{"idx": 3, "title": "Structured Initialization for Attention in Vision Transformers", "date": "", "ddg_snippet": "by J Zheng · Cited by 3 — In this paper , we propose that the architectural bias inherent to CNNs can be reinterpreted as an initialization bias within ViT.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=z9UBpl4pv5", "content": "by J Zheng · Cited by 3 — In this paper , we propose that the architectural bias inherent to CNNs can be reinterpreted as an initialization bias within ViT."}
+{"idx": 4, "title": "Asymmetric Certified Robustness via Feature-Convex ...", "date": "", "ddg_snippet": "by S Pfrommer · 2023 · Cited by 12 — The d × d identity matrix is written as Id ∈ Rd×d, and the identity map on Rd is denoted by Id: x 7→ x. For A ∈ Rn×d, we define |A| ∈ Rn×d by |A|ij ... 36 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/a45b205c10ef082515cacae80555bbef-Paper-Conference.pdf", "content": "by S Pfrommer · 2023 · Cited by 12 — The d × d identity matrix is written as Id ∈ Rd×d, and the identity map on Rd is denoted by Id: x 7→ x. For A ∈ Rn×d, we define |A| ∈ Rn×d by |A|ij ... 36 pages"}
+{"idx": 5, "title": "Mimetic Initialization for Deep Neural Networks", "date": "", "ddg_snippet": "taking inspiration from mimetic initialization ( Trockman and Kolter , 2023 ; Trockman et al., ... scalar-times- identity matrices , resulting in just H parameters.", "subpage_snippet": "", "source": "csd.cmu.edu", "link": "https://csd.cmu.edu/sites/default/files/phd-thesis/CMU-CS-25-114.pdf", "content": "taking inspiration from mimetic initialization ( Trockman and Kolter , 2023 ; Trockman et al., ... scalar-times- identity matrices , resulting in just H parameters."}
+{"idx": 6, "title": "Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "23 Aug 2023 — To address this problem, a paper written by Asher Trockman , J. Zico Kolter proposes mimetic initialization. As its name suggests, the self ...", "subpage_snippet": "", "source": "lovo.ai", "link": "https://lovo.ai/post/mimetic-initialization-of-self-attention-layers", "content": "23 Aug 2023 — To address this problem, a paper written by Asher Trockman , J. Zico Kolter proposes mimetic initialization. As its name suggests, the self ..."}
+{"idx": 7, "title": "The underlying structures of self-attention: symmetry...", "date": "", "ddg_snippet": "by M Saponati · Cited by 1 — This work generalizes prior results (e.g. Trockman & Kolter ( 2023 )) to initialize query-key matrices as identities based on uncovered diagonal ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gpizm0I3lp", "content": "by M Saponati · Cited by 1 — This work generalizes prior results (e.g. Trockman & Kolter ( 2023 )) to initialize query-key matrices as identities based on uncovered diagonal ..."}
+{"idx": 8, "title": "SIMPLIFYING TRANSFORMER BLOCKS", "date": "", "ddg_snippet": "by B He · Cited by 62 — Trockman & Kolter ( 2023 ) observe that the product of value and projection parameters often has a large identity component in trained transformers, and design an ... 29 pages", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/24fd58f52ff8d0496add8da3991644e9-Paper-Conference.pdf", "content": "by B He · Cited by 62 — Trockman & Kolter ( 2023 ) observe that the product of value and projection parameters often has a large identity component in trained transformers, and design an ... 29 pages"}
+{"idx": 9, "title": "1-Lipschitz Layers Compared: Memory Speed and Certifiable ...", "date": "", "ddg_snippet": "by B Prach · 2024 · Cited by 10 — A matrix M is orthogonal if MM⊤ = I, where I is the identity matrix . For layers with an orthogonal Jacobian, Equation (1) always holds with equality and ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Prach_1-Lipschitz_Layers_Compared_Memory_Speed_and_Certifiable_Robustness_CVPR_2024_paper.pdf", "content": "by B Prach · 2024 · Cited by 10 — A matrix M is orthogonal if MM⊤ = I, where I is the identity matrix . For layers with an orthogonal Jacobian, Equation (1) always holds with equality and ... 10 pages"}
diff --git a/data/sampled_jsons/Trockman_Kolter_arXiv_2023_mimetic_initialization_self-attention.jsonl b/data/sampled_jsons/Trockman_Kolter_arXiv_2023_mimetic_initialization_self-attention.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..43f4054da3f34040a0ec6a6d59acf8494c0a31b3
--- /dev/null
+++ b/data/sampled_jsons/Trockman_Kolter_arXiv_2023_mimetic_initialization_self-attention.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.09828] Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "by A Trockman · 2023 · Cited by 51 — We find that simply initializing the weights of self-attention layers so that they look more like their pre-trained counterparts allows us to train vanilla ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.09828", "content": "by A Trockman · 2023 · Cited by 51 — We find that simply initializing the weights of self-attention layers so that they look more like their pre-trained counterparts allows us to train vanilla ..."}
+{"idx": 1, "title": "Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "by A Trockman · Cited by 51 — Abstract. It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models are instead used as the starting point.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/trockman23a/trockman23a.pdf?utm_source=ainews&utm_medium=email&utm_campaign=ainews-google-io-in-60-seconds", "content": "by A Trockman · Cited by 51 — Abstract. It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models are instead used as the starting point."}
+{"idx": 2, "title": "Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "by A Trockman · 2023 · Cited by 51 — However, this technique actually trains self - attention layers to mimick the behavior of a handcrafted, convolution-like target similar to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/pdf/2305.09828", "content": "by A Trockman · 2023 · Cited by 51 — However, this technique actually trains self - attention layers to mimick the behavior of a handcrafted, convolution-like target similar to ..."}
+{"idx": 3, "title": "[PDF] Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "Surprisingly, it is found that simply initializing the weights of self - attention layers so that they \"look\" more like their pre-trained counterparts allows ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/26d7a95a029f91fd36d32bd5520a8cde691ffd0c", "content": "Surprisingly, it is found that simply initializing the weights of self - attention layers so that they \"look\" more like their pre-trained counterparts allows ..."}
+{"idx": 4, "title": "Mimetic Initialization Helps State Space Models Learn to ...", "date": "", "ddg_snippet": "14 Oct 2024 — Trockman & Kolter (2023) Asher Trockman and J Zico Kolter. Mimetic initialization of self-attention layers . In International Conference on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.11135v1", "content": "14 Oct 2024 — Trockman & Kolter (2023) Asher Trockman and J Zico Kolter. Mimetic initialization of self-attention layers . In International Conference on ..."}
+{"idx": 5, "title": "Mimetic Initialization Helps State Space Models Learn to ...", "date": "", "ddg_snippet": "by A Trockman · Cited by 5 — The idea is not particularly novel compared to \" Mimetic Initialization of Self - Attention Layers\" ( Trockman 2023 ), which is cited throughout the paper.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=iVy7aRMb0K", "content": "by A Trockman · Cited by 5 — The idea is not particularly novel compared to \" Mimetic Initialization of Self - Attention Layers\" ( Trockman 2023 ), which is cited throughout the paper."}
+{"idx": 6, "title": "Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "23 Aug 2023 — The self - attention weights of the transformers are initialized in a way that mimics those of their trained counterparts, allowing for a faster convergence.", "subpage_snippet": "", "source": "lovo.ai", "link": "https://lovo.ai/post/mimetic-initialization-of-self-attention-layers", "content": "23 Aug 2023 — The self - attention weights of the transformers are initialized in a way that mimics those of their trained counterparts, allowing for a faster convergence."}
+{"idx": 7, "title": "Asher Trockman", "date": "", "ddg_snippet": "Mimetic initialization of self-attention layers . A Trockman, JZ Kolter. International Conference on Machine Learning, 34456-34468, 2023. 51, 2023. Heard It ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=xd3LCGcAAAAJ&hl=en", "content": "Mimetic initialization of self-attention layers . A Trockman, JZ Kolter. International Conference on Machine Learning, 34456-34468, 2023. 51, 2023. Heard It ..."}
+{"idx": 8, "title": "Mimetic Initialization Helps State Space Models Learn to Recall", "date": "", "ddg_snippet": "This work proposes a structured initialization technique that allows state space layers to more readily mimic attention and makes it substantially easier ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Mimetic-Initialization-Helps-State-Space-Models-to-Trockman-Harutyunyan/d047f704fb38c031b548fdf59646096162bdbed3", "content": "This work proposes a structured initialization technique that allows state space layers to more readily mimic attention and makes it substantially easier ..."}
+{"idx": 9, "title": "Mimetic initialization of self-attention layers | Proceedings of the 40th ...", "date": "", "ddg_snippet": "It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models are instead used as the starting point.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3619843", "content": "It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models are instead used as the starting point."}
diff --git a/data/sampled_jsons/Trust-region-based_semi-supervised_learning_Rizve_Roy_Bakar_2022.jsonl b/data/sampled_jsons/Trust-region-based_semi-supervised_learning_Rizve_Roy_Bakar_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..533beff96c1f16669b8832c27326cadef3e5d806
--- /dev/null
+++ b/data/sampled_jsons/Trust-region-based_semi-supervised_learning_Rizve_Roy_Bakar_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2022 Index IEEE Transactions on Neural Networks and ...", "date": "", "ddg_snippet": "by J Abbate · 2022 — This index covers all technical items—papers, correspondence, reviews, etc.—that appeared in this periodical during 2022 , and items from previous. 109 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/5962385/9966944/09975174.pdf", "content": "by J Abbate · 2022 — This index covers all technical items—papers, correspondence, reviews, etc.—that appeared in this periodical during 2022 , and items from previous. 109 pages"}
+{"idx": 1, "title": "Computer Science Sep 2022", "date": "", "ddg_snippet": "12 Sept 2022 — Title: TRBoost: A Generic Gradient Boosting Machine based on Trust - region Method ... Title: Prompt-driven efficient Open-set Semi - supervised ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2022-09?skip=4750&show=2000", "content": "12 Sept 2022 — Title: TRBoost: A Generic Gradient Boosting Machine based on Trust - region Method ... Title: Prompt-driven efficient Open-set Semi - supervised ..."}
+{"idx": 2, "title": "A Catalogue of Machine Learning Algorithms for ...", "date": "", "ddg_snippet": "by A Mavrogiorgou · 2022 · Cited by 42 — This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9695983/", "content": "by A Mavrogiorgou · 2022 · Cited by 42 — This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML ..."}
+{"idx": 3, "title": "New Insights in Machine Learning and Deep Neural ...", "date": "", "ddg_snippet": "our gathered knowledge, no other research has attempted a 3D deep learning approach using the entire lung volumetric region in a semi - supervised fashion.", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/8315/New_Insights_in_Machine_Learning_and_Deep_Neural_Networks.pdf?v=1743728937", "content": "our gathered knowledge, no other research has attempted a 3D deep learning approach using the entire lung volumetric region in a semi - supervised fashion."}
+{"idx": 4, "title": "Multimedia learning: current themes, trends and future ...", "date": "", "ddg_snippet": "Multimedia learning (ML) materials are instructional materials that combine words (written or spoken) and visuals to present information (Mayer, 2014).", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/13614568.2025.2522104?scroll=top&needAccess=true", "content": "Multimedia learning (ML) materials are instructional materials that combine words (written or spoken) and visuals to present information (Mayer, 2014)."}
+{"idx": 5, "title": "Human Behavior Analysis: A Comprehensive Survey on ...", "date": "", "ddg_snippet": "by S Essahraui · 2025 — and semi - supervised learning approaches. The survey serves as a valuable resource for understanding the state-of-the-art methods and future ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/11082162.pdf", "content": "by S Essahraui · 2025 — and semi - supervised learning approaches. The survey serves as a valuable resource for understanding the state-of-the-art methods and future ..."}
+{"idx": 6, "title": "Computer Vision and Pattern Recognition May 2025", "date": "", "ddg_snippet": "Title: Cross-Sequence Semi - Supervised Learning for Multi-Parametric MRI- Based Visual Pathway Delineation. Alou Diakite, Cheng Li, Lei Xie, Yuanjing Feng ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CV/2025-05?skip=1575&show=2000", "content": "Title: Cross-Sequence Semi - Supervised Learning for Multi-Parametric MRI- Based Visual Pathway Delineation. Alou Diakite, Cheng Li, Lei Xie, Yuanjing Feng ..."}
+{"idx": 7, "title": "A review and critical analysis of multimodal datasets for ...", "date": "", "ddg_snippet": "by S Al-Azani · 2025 — In addition, datasets primarily focused on psychological research related to emotional stimuli and cognitive reactions, such as (Diconne et al.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11271-1", "content": "by S Al-Azani · 2025 — In addition, datasets primarily focused on psychological research related to emotional stimuli and cognitive reactions, such as (Diconne et al."}
+{"idx": 8, "title": "Exploring the Landscape of Ubiquitous In-home Health ...", "date": "", "ddg_snippet": "by F Pourpanah · 2024 · Cited by 5 — In this study , we perform a comprehensive survey on this topic by reviewing a large number of literature in the area . We investigate these systems from various ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3670854", "content": "by F Pourpanah · 2024 · Cited by 5 — In this study , we perform a comprehensive survey on this topic by reviewing a large number of literature in the area . We investigate these systems from various ..."}
+{"idx": 9, "title": "k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY", "date": "", "ddg_snippet": "The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k ...", "subpage_snippet": "", "source": "www.worldscientific.com", "link": "https://www.worldscientific.com/doi/abs/10.1142/s0218488502001648?src=recsys&srsltid=AfmBOooXm6dgwyu-De2oSNpKzGGZpzTjvUkti-yJica73Vtdj7iSN7lK", "content": "The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k ..."}
diff --git a/data/sampled_jsons/Tsamardinos_max-min_hill-climbing_Bayesian_network_structure_learning_algorithm_2006_year_2006.jsonl b/data/sampled_jsons/Tsamardinos_max-min_hill-climbing_Bayesian_network_structure_learning_algorithm_2006_year_2006.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9b84af4c5e1ddecf80f560b74c2486e57b1b1d19
--- /dev/null
+++ b/data/sampled_jsons/Tsamardinos_max-min_hill-climbing_Bayesian_network_structure_learning_algorithm_2006_year_2006.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bayesian networks | bayesian-nets", "date": "", "ddg_snippet": "Learning Bayesian Network Structure ... The max - min hill - climbing Bayesian network structure learning algorithm ( 2006 ) Ioannis Tsamardinos , Laura E.", "subpage_snippet": "", "source": "mlpapers.org", "link": "https://mlpapers.org/bayesian-nets/", "content": "Learning Bayesian Network Structure ... The max - min hill - climbing Bayesian network structure learning algorithm ( 2006 ) Ioannis Tsamardinos , Laura E."}
+{"idx": 1, "title": "GitHub - mlpapers/bayesian-nets: Awesome papers on Bayesian", "date": "", "ddg_snippet": "Learning Bayesian Network Structure ... The max - min hill - climbing Bayesian network structure learning algorithm ( 2006 ) Ioannis Tsamardinos , Laura E.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mlpapers/bayesian-nets", "content": "Learning Bayesian Network Structure ... The max - min hill - climbing Bayesian network structure learning algorithm ( 2006 ) Ioannis Tsamardinos , Laura E."}
+{"idx": 2, "title": "Finding the optimal Bayesian network given a constraint graph", "date": "", "ddg_snippet": "Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-122/", "content": "Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen ..."}
+{"idx": 3, "title": "NHESS - BN-FLEMOΔ: a Bayesian-network-based flood loss", "date": "", "ddg_snippet": "BN-FLEMO Δ : a Bayesian - network -based flood loss estimation model for adaptation planning in Ho Chi Minh City, Vietnam BN-FLEMO Δ : a Bayesian ...", "subpage_snippet": "", "source": "nhess.copernicus.org", "link": "https://nhess.copernicus.org/articles/25/2845/2025/", "content": "BN-FLEMO Δ : a Bayesian - network -based flood loss estimation model for adaptation planning in Ho Chi Minh City, Vietnam BN-FLEMO Δ : a Bayesian ..."}
+{"idx": 4, "title": "Application of the Bayesian network theory in clinical trial", "date": "", "ddg_snippet": "... of using the Bayesian framework comes from the possibility of capturing a priori knowledge about a specific pattern by deriving the network structure ...", "subpage_snippet": "", "source": "www.msard-journal.com", "link": "https://www.msard-journal.com/article/S2211-0348(24)00045-2/fulltext", "content": "... of using the Bayesian framework comes from the possibility of capturing a priori knowledge about a specific pattern by deriving the network structure ..."}
+{"idx": 5, "title": "A Bayesian incorporated linear non-Gaussian acyclic model for", "date": "", "ddg_snippet": "Calhoun , Yu-Ping Wang; A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/netn/article/8/3/791/121183/A-Bayesian-incorporated-linear-non-Gaussian", "content": "Calhoun , Yu-Ping Wang; A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion ..."}
+{"idx": 6, "title": "GitHub - FenTechSolutions/CausalDiscoveryToolbox: Package for", "date": "", "ddg_snippet": "It implements lots of algorithms for graph structure recovery (including algorithms from the bnlearn , pcalg packages), mainly based out of ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FenTechSolutions/CausalDiscoveryToolbox", "content": "It implements lots of algorithms for graph structure recovery (including algorithms from the bnlearn , pcalg packages), mainly based out of ..."}
+{"idx": 7, "title": "(Conditional) independence tests — causal-learn 0.1.3.6", "date": "", "ddg_snippet": "... would like to introduce (conditional) independence tests in causal- learn ... The max - min hill - climbing Bayesian network structure learning algorithm .", "subpage_snippet": "", "source": "causal-learn.readthedocs.io", "link": "https://causal-learn.readthedocs.io/en/latest/independence_tests_index/index.html", "content": "... would like to introduce (conditional) independence tests in causal- learn ... The max - min hill - climbing Bayesian network structure learning algorithm ."}
+{"idx": 8, "title": "Utilizing RNA-seq data in monotone iterative generalized linear", "date": "", "ddg_snippet": "The algorithms designed for inferring the structure of general Bayesian networks were initially developed to address problems with unknown ...", "subpage_snippet": "", "source": "bmcbioinformatics.biomedcentral.com", "link": "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-025-06161-w", "content": "The algorithms designed for inferring the structure of general Bayesian networks were initially developed to address problems with unknown ..."}
+{"idx": 9, "title": "becker > bayes | BibSonomy", "date": "", "ddg_snippet": "... deep learning auto encoder autoencoder neural network bayes bayesian ... 6 The max - min hill - climbing Bayesian network structure learning algorithm", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/user/becker/bayes", "content": "... deep learning auto encoder autoencoder neural network bayes bayesian ... 6 The max - min hill - climbing Bayesian network structure learning algorithm"}
diff --git "a/data/sampled_jsons/Tyurin_Richt\303\241rik_2024_paper_abstract_year_2024.jsonl" "b/data/sampled_jsons/Tyurin_Richt\303\241rik_2024_paper_abstract_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..1f2af95c02b39365bee4e0881ee9b75828c1d45c
--- /dev/null
+++ "b/data/sampled_jsons/Tyurin_Richt\303\241rik_2024_paper_abstract_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "Tyurin , A., Pozzi, M., Ilin, I., and Richtárik , P. ( 2024 ). Shadowheart SGD: Distributed asynchronous SGD with optimal time complexity under arbitrary computation and communication heterogeneity. arXiv preprint arXiv:2402.04785.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.16218v1", "content": "Tyurin , A., Pozzi, M., Ilin, I., and Richtárik , P. ( 2024 ). Shadowheart SGD: Distributed asynchronous SGD with optimal time complexity under arbitrary computation and communication heterogeneity. arXiv preprint arXiv:2402.04785."}
+{"idx": 1, "title": "On the Optimal Time Complexities in Decentralized Stochastic ...", "date": "", "ddg_snippet": "Authors Alexander Tyurin , Peter Richtárik Abstract We consider the decentralized stochastic asynchronous optimization setup, where many workers asynchronously calculate stochastic gradients and asynchronously communicate with each other using edges in a multigraph. For both homogeneous and heterogeneous setups, we prove new time complexity lower bounds under the assumption that computation ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/dd850be1e74582b68fa82078f3fc6d4d-Abstract-Conference.html", "content": "Authors Alexander Tyurin , Peter Richtárik Abstract We consider the decentralized stochastic asynchronous optimization setup, where many workers asynchronously calculate stochastic gradients and asynchronously communicate with each other using edges in a multigraph. For both homogeneous and heterogeneous setups, we prove new time complexity lower bounds under the assumption that computation ..."}
+{"idx": 2, "title": "[2405.16218] On the Optimal Time Complexities in Decentralized ...", "date": "", "ddg_snippet": "View a PDF of the paper titled On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization, by Alexander Tyurin and 1 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.16218", "content": "View a PDF of the paper titled On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization, by Alexander Tyurin and 1 other authors"}
+{"idx": 3, "title": "Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time ...", "date": "", "ddg_snippet": "Authors Alexander Tyurin , Marta Pozzi, Ivan Ilin, Peter Richtárik Abstract We consider nonconvex stochastic optimization problems in the asynchronous centralized distributed setup where the communication times from workers to a server can not be ignored, and the computation and communication times are potentially different for all workers. Using an unbiassed compression technique, we develop ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2024/hash/06e2dd57e90a736a5a1fd3bb2bf95c6c-Abstract-Conference.html", "content": "Authors Alexander Tyurin , Marta Pozzi, Ivan Ilin, Peter Richtárik Abstract We consider nonconvex stochastic optimization problems in the asynchronous centralized distributed setup where the communication times from workers to a server can not be ignored, and the computation and communication times are potentially different for all workers. Using an unbiassed compression technique, we develop ..."}
+{"idx": 4, "title": "Tight Time Complexities in Parallel Stochastic Optimization with ...", "date": "", "ddg_snippet": "On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization Alexander Tyurin,Peter Richtárik 2024 -11-02", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2408.04929", "content": "On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization Alexander Tyurin,Peter Richtárik 2024 -11-02"}
+{"idx": 5, "title": "Improving the Worst-Case Bidirectional Communication Complexity for ...", "date": "", "ddg_snippet": "Authors Kaja Gruntkowska, Alexander Tyurin , Peter Richtárik Abstract Effective communication between the server and workers plays a key role in distributed optimization. In this paper , we focus on optimizing communication, uncovering inefficiencies in prevalent downlink compression approaches. Considering first the pure setup where the uplink communication costs are negligible, we introduce ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a1d20cc72a21ef971d7e49a90d8fa56f-Abstract-Conference.html", "content": "Authors Kaja Gruntkowska, Alexander Tyurin , Peter Richtárik Abstract Effective communication between the server and workers plays a key role in distributed optimization. In this paper , we focus on optimizing communication, uncovering inefficiencies in prevalent downlink compression approaches. Considering first the pure setup where the uplink communication costs are negligible, we introduce ..."}
+{"idx": 6, "title": "On the Optimal Time Complexities in Decentralized Stochastic ...", "date": "", "ddg_snippet": "Alexander Tyurin , Peter Richtárik Published: 25 Sept 2024 , Last Modified: 05 Nov 2024 NeurIPS 2024 poster Everyone Revisions BibTeX CC BY 4.0 Keywords: nonconvex and convex optimization, stochastic optimization, asynchronous methods, decentralized optimization, lower bounds", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=IXRa8adMHX", "content": "Alexander Tyurin , Peter Richtárik Published: 25 Sept 2024 , Last Modified: 05 Nov 2024 NeurIPS 2024 poster Everyone Revisions BibTeX CC BY 4.0 Keywords: nonconvex and convex optimization, stochastic optimization, asynchronous methods, decentralized optimization, lower bounds"}
+{"idx": 7, "title": "PDF The First Optimal Parallel SGD - richtarik.org", "date": "", "ddg_snippet": "2 ⇤ 2 Our Papers on Optimal Parallel SGD 5/2023 2/2024 5/2024 5/2024 Alexander Tyurin and P.R. On the optimal time complexities in decentralized stochastic asynchronous optimization arXiv:2405.16218, 2024", "subpage_snippet": "", "source": "richtarik.org", "link": "https://richtarik.org/talks/TALK-2024-06-17-FedVision@CVPR2024-ASYNC.pdf", "content": "2 ⇤ 2 Our Papers on Optimal Parallel SGD 5/2023 2/2024 5/2024 5/2024 Alexander Tyurin and P.R. On the optimal time complexities in decentralized stochastic asynchronous optimization arXiv:2405.16218, 2024"}
+{"idx": 8, "title": "Tight Time Complexities in Parallel Stochastic Optimization with ...", "date": "", "ddg_snippet": "We show that these lower bounds, up to constant factors, are matched by the optimal Rennala SGD and Malenia SGD methods ( Tyurin & Richtárik , 2023).", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240804929T/abstract", "content": "We show that these lower bounds, up to constant factors, are matched by the optimal Rennala SGD and Malenia SGD methods ( Tyurin & Richtárik , 2023)."}
+{"idx": 9, "title": "Optimal Time Complexities of Parallel Stochastic Optimization Methods ...", "date": "", "ddg_snippet": "On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization Alexander Tyurin,Peter Richtárik 2024 -11-02", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2305.12387", "content": "On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization Alexander Tyurin,Peter Richtárik 2024 -11-02"}
diff --git a/data/sampled_jsons/UBPR_inverse_propensity_score_Saito_2020.jsonl b/data/sampled_jsons/UBPR_inverse_propensity_score_Saito_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..557b17cab8faa4e517b56a9a0124bce1bcec2d62
--- /dev/null
+++ b/data/sampled_jsons/UBPR_inverse_propensity_score_Saito_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Implicit Recommendation and Propensity ...", "date": "", "ddg_snippet": "by Z Zhu · 2020 · Cited by 61 — This paper focuses on how to generate unbiased recommendations based on biased implicit user-item interactions.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/fullHtml/10.1145/3383313.3412210", "content": "by Z Zhu · 2020 · Cited by 61 — This paper focuses on how to generate unbiased recommendations based on biased implicit user-item interactions."}
+{"idx": 1, "title": "Unbiased Implicit Recommendation and Propensity ...", "date": "", "ddg_snippet": "by Z ZHU · Cited by 61 — This paper focuses on how to generate unbiased recommendations based on biased implicit user-item interactions. We propose a combinational joint learning ...", "subpage_snippet": "", "source": "zziwei.github.io", "link": "https://zziwei.github.io/pubs/Ziwei_RecSys_2020_Unbias.pdf", "content": "by Z ZHU · Cited by 61 — This paper focuses on how to generate unbiased recommendations based on biased implicit user-item interactions. We propose a combinational joint learning ..."}
+{"idx": 2, "title": "dually-enhanced-propensity-score-estimation-in-sequential- ...", "date": "", "ddg_snippet": "ABSTRACT. Sequential recommender systems train their models based on a large amount of implicit user feedback data and may be subject to.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/dually-enhanced-propensity-score-estimation-in-sequential-s5nvno4x.pdf", "content": "ABSTRACT. Sequential recommender systems train their models based on a large amount of implicit user feedback data and may be subject to."}
+{"idx": 3, "title": "Unbiased Collaborative Filtering with Fair Sampling", "date": "", "ddg_snippet": "18 Apr 2025 — Several methods based on inverse propensity scores (IPS) ( Saito et al., 2020 ; Saito , 2020 ) have been proposed to mitigate popularity bias by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.13840v2", "content": "18 Apr 2025 — Several methods based on inverse propensity scores (IPS) ( Saito et al., 2020 ; Saito , 2020 ) have been proposed to mitigate popularity bias by ..."}
+{"idx": 4, "title": "Unbiased Pairwise Learning from Implicit Feedback for ...", "date": "", "ddg_snippet": "by Y Ren · 2023 · Cited by 7 — The central role of the propensity score in observational studies for causal effects. Biometrika 70, 1 (1983), 41–55. [21] Yuta Saito . 2020 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.05066", "content": "by Y Ren · 2023 · Cited by 7 — The central role of the propensity score in observational studies for causal effects. Biometrika 70, 1 (1983), 41–55. [21] Yuta Saito . 2020 ."}
+{"idx": 5, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "Current efforts mainly employ inverse propensity weighting (IPW) to correct such selection bias. It re-weights each observed sample using a propensity score , ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "Current efforts mainly employ inverse propensity weighting (IPW) to correct such selection bias. It re-weights each observed sample using a propensity score , ..."}
+{"idx": 6, "title": "A framework for unbiased explainable pairwise ranking ...", "date": "", "ddg_snippet": "by K Damak · 2022 — This approach relies on Inverse Propensity Scoring (IPS) [9] to theoretically eliminate the exposure bias in the BPR loss.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2665963821000920", "content": "by K Damak · 2022 — This approach relies on Inverse Propensity Scoring (IPS) [9] to theoretically eliminate the exposure bias in the BPR loss."}
+{"idx": 7, "title": "Practically Unbiased Pairwise Loss for Recommendation ...", "date": "", "ddg_snippet": "by T Cao · 2025 · Cited by 2 — In this paper, we focus on unbiased ranking loss weighted by inversed propensity scores (IPS), which are widely used in recommendations with implicit feedback ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/04/10810273/22O7O9fFhOo", "content": "by T Cao · 2025 · Cited by 2 — In this paper, we focus on unbiased ranking loss weighted by inversed propensity scores (IPS), which are widely used in recommendations with implicit feedback ..."}
+{"idx": 8, "title": "New accurate, explainable, and unbiased machine learning ...", "date": "", "ddg_snippet": "by K Damak · 2022 — This approach, called Inverse Propensity Scoring (IPS), showed success in recommendation settings with user profiles including our proposed approach which was ...", "subpage_snippet": "", "source": "ir.library.louisville.edu", "link": "https://ir.library.louisville.edu/cgi/viewcontent.cgi?article=5128&context=etd", "content": "by K Damak · 2022 — This approach, called Inverse Propensity Scoring (IPS), showed success in recommendation settings with user profiles including our proposed approach which was ..."}
+{"idx": 9, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — This paper focuses on addressing the lack of negative feedback in recommendations with implicit feedback and points out the limitations of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — This paper focuses on addressing the lack of negative feedback in recommendations with implicit feedback and points out the limitations of ..."}
diff --git "a/data/sampled_jsons/UPGNET_NFR_layer_Equation_11_L1_regularization_feature_selection_\316\2741.jsonl" "b/data/sampled_jsons/UPGNET_NFR_layer_Equation_11_L1_regularization_feature_selection_\316\2741.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..5835504c304e36bdc12352118944cec1e387093f
--- /dev/null
+++ "b/data/sampled_jsons/UPGNET_NFR_layer_Equation_11_L1_regularization_feature_selection_\316\2741.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regularization in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "A regression model which uses the L 1 Regularization technique is called LASSO (Least Absolute Shrinkage and Selection Operator) regression.The coefficients reflect the regularized feature weights.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/regularization-in-machine-learning/", "content": "A regression model which uses the L 1 Regularization technique is called LASSO (Least Absolute Shrinkage and Selection Operator) regression.The coefficients reflect the regularized feature weights."}
+{"idx": 1, "title": "Unlocking the power of L 1 regularization : A novel approach to taming...", "date": "", "ddg_snippet": "This regularization method, ideal for scenarios involving many features , promotes sparsity and computational efficiency, aiding in feature selection . It is applied to various components of the dense layer , including kernel weights, biases, and activity.", "subpage_snippet": "", "source": "ueaeprints.uea.ac.uk", "link": "https://ueaeprints.uea.ac.uk/id/eprint/100427/1/journal.pone.0327985.pdf", "content": "This regularization method, ideal for scenarios involving many features , promotes sparsity and computational efficiency, aiding in feature selection . It is applied to various components of the dense layer , including kernel weights, biases, and activity."}
+{"idx": 2, "title": "Feature Grouping Using Weighted L 1 Norm For High-Dimensional Data", "date": "", "ddg_snippet": "1. Non-convex Feature Grouping and Selection (ncFGS): NcFGS [20] uses the 1 norm for feature selection and the dierence between absolute values of the coecients of features connected in graph to perform feature grouping. The formulation is given below. arg min.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/56686379.pdf", "content": "1. Non-convex Feature Grouping and Selection (ncFGS): NcFGS [20] uses the 1 norm for feature selection and the dierence between absolute values of the coecients of features connected in graph to perform feature grouping. The formulation is given below. arg min."}
+{"idx": 3, "title": "Regularization Techniques in Machine Learning: Balancing... | Medium", "date": "", "ddg_snippet": "How it works: L 1 regularization enforces a penalty that is equal to the absolute values of the model weights.Suitable for feature selection in high-dimensional data. Helps interpretability since only the most significant variables remain.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@mr.zamin40/regularization-techniques-in-machine-learning-balancing-performance-and-generalization-6b52020b9b49", "content": "How it works: L 1 regularization enforces a penalty that is equal to the absolute values of the model weights.Suitable for feature selection in high-dimensional data. Helps interpretability since only the most significant variables remain."}
+{"idx": 4, "title": "towardsdatascience.com/understanding- l 1 -and-l2- regularization ...", "date": "", "ddg_snippet": "L 1 and L2 Regularization Methods Anuja Nagpal 4.96K 25.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/understanding-l1-and-l2-regularization-93918a5ac8d0/", "content": "L 1 and L2 Regularization Methods Anuja Nagpal 4.96K 25."}
+{"idx": 5, "title": "ICML Poster Going Deeper into Locally Differentially Private Graph...", "date": "", "ddg_snippet": "In this paper, we present UPGNet , an LDP-based privacy-preserving graph learning framework that enhances utility while safeguarding user privacy. Specifically, we propose a three-stage pipeline that generalizes the LDP protocols for node features , targeting privacy-sensitive scenarios.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46579", "content": "In this paper, we present UPGNet , an LDP-based privacy-preserving graph learning framework that enhances utility while safeguarding user privacy. Specifically, we propose a three-stage pipeline that generalizes the LDP protocols for node features , targeting privacy-sensitive scenarios."}
+{"idx": 6, "title": "Going Deeper into Locally Differentially Private Graph ...", "date": "", "ddg_snippet": "by L He — Under the N-H architecture, the NFR layer aims to enhance utility through efficient feature selection of the perturbed node features x′ using L1 - regularization .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2aKHuXdr7Q", "content": "by L He — Under the N-H architecture, the NFR layer aims to enhance utility through efficient feature selection of the perturbed node features x′ using L1 - regularization ."}
+{"idx": 7, "title": "L 1 и L2 регуляризация в машинном обучении: эффективные методы", "date": "", "ddg_snippet": "L 1 регуляризация: особенности и преимущества метода. L 1 регуляризация, также известная как Lasso (Least Absolute Shrinkage and Selection Operator), добавляет к функции потерь штраф, пропорциональный абсолютной величине коэффициентов модели.", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/wiki/analytics/l1-i-l2-regulyarizatsiya-v-mashinnom-obuchenii-effektivnye-metody/", "content": "L 1 регуляризация: особенности и преимущества метода. L 1 регуляризация, также известная как Lasso (Least Absolute Shrinkage and Selection Operator), добавляет к функции потерь штраф, пропорциональный абсолютной величине коэффициентов модели."}
+{"idx": 8, "title": "LogisticRegression — scikit-learn 1.7.2 documentation", "date": "", "ddg_snippet": "Added in version 0.19: l 1 penalty with SAGA solver (allowing ‘multinomial’ + L 1 ). dualbool, default=False. Dual (constrained) or primal ( regularized , see also this equation ) formulation.Model-based and sequential feature selection . Examples of Using FrozenEstimator.", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html", "content": "Added in version 0.19: l 1 penalty with SAGA solver (allowing ‘multinomial’ + L 1 ). dualbool, default=False. Dual (constrained) or primal ( regularized , see also this equation ) formulation.Model-based and sequential feature selection . Examples of Using FrozenEstimator."}
+{"idx": 9, "title": "SGDClassifier — scikit-learn 1.7.0 documentation - sklearn", "date": "", "ddg_snippet": "BayesianGaussianMixture. GaussianMixture. sklearn.model_ selection .If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection .", "subpage_snippet": "", "source": "sklearn.org", "link": "https://sklearn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html", "content": "BayesianGaussianMixture. GaussianMixture. sklearn.model_ selection .If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection ."}
diff --git a/data/sampled_jsons/UPGNET_locally_differentially_private_graph_neural_networks_HOA_oversmoothing_year_2024.jsonl b/data/sampled_jsons/UPGNET_locally_differentially_private_graph_neural_networks_HOA_oversmoothing_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8acd23a30cd264111110a23c6863b435196927b6
--- /dev/null
+++ b/data/sampled_jsons/UPGNET_locally_differentially_private_graph_neural_networks_HOA_oversmoothing_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Going Deeper into Locally Differentially Private Graph ...", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks.In this paper, we present UPGNet , an LDP-based privacy -preserving graph learning framework that enhances utility while safeguarding user privacy .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46579", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks.In this paper, we present UPGNet , an LDP-based privacy -preserving graph learning framework that enhances utility while safeguarding user privacy ."}
+{"idx": 1, "title": "Blink: Link Local Differential Privacy in Graph Neural Networks via...", "date": "", "ddg_snippet": "Graph neural networks (GNNs) have gained an increasing amount of popularity due to their superior capability in learning node em-beddings for various graph inference tasks, but training them can raise privacy concerns. To address this, we propose using link local differential privacy over ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2309.03190", "content": "Graph neural networks (GNNs) have gained an increasing amount of popularity due to their superior capability in learning node em-beddings for various graph inference tasks, but training them can raise privacy concerns. To address this, we propose using link local differential privacy over ..."}
+{"idx": 2, "title": "(PDF) Degree-Preserving Randomized Response for Graph Neural ...", "date": "", "ddg_snippet": "Abstract: Differentially private GNNs ( Graph Neural Networks ) have been recently studied to provide high accuracy in various tasks on graph data while strongly protecting user privacy .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/degree-preserving-randomized-response-for-graph-neural-16q231u6", "content": "Abstract: Differentially private GNNs ( Graph Neural Networks ) have been recently studied to provide high accuracy in various tasks on graph data while strongly protecting user privacy ."}
+{"idx": 3, "title": "GAP: Differentially Private Graph Neural Networks with... | USENIX", "date": "", "ddg_snippet": "In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation function to statistically...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity23/presentation/sajadmanesh", "content": "In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation function to statistically..."}
+{"idx": 4, "title": "Local Differential Privacy in Graph Neural Networks", "date": "", "ddg_snippet": "Node-level differentially private graph neural networks , 2022. [10] Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. Collecting telemetry data privately .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tHQdZ74NLe", "content": "Node-level differentially private graph neural networks , 2022. [10] Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. Collecting telemetry data privately ."}
+{"idx": 5, "title": "Differentially Private Graph Convolutional Networks with Privacy ...", "date": "", "ddg_snippet": "Locally Private Graph Neural Networks . Conference Paper. Nov 2021.Node-Level Differentially Private Graph Neural Networks . A Daigavane. G Madan.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390509968_Differentially_Private_Graph_Convolutional_Networks_with_Privacy_Amplification", "content": "Locally Private Graph Neural Networks . Conference Paper. Nov 2021.Node-Level Differentially Private Graph Neural Networks . A Daigavane. G Madan."}
+{"idx": 6, "title": "Locally Private Graph Neural Networks | Proceedings of the 2021...", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460120.3484565?cookieSet=1", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private ."}
+{"idx": 7, "title": "Local Differential Privacy in Graph Neural Networks ...", "date": "", "ddg_snippet": "Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/local-differential-privacy-graph-neural-networks-reconstruction", "content": "Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs."}
+{"idx": 8, "title": "Differentially Private Graph Neural Network with... | DeepAI", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) with differential privacy have been proposed to preserve graph privacy when nodes represent personal and sensitive information.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/differentially-private-graph-neural-network-with-importance-grained-noise-adaption", "content": "Graph Neural Networks (GNNs) with differential privacy have been proposed to preserve graph privacy when nodes represent personal and sensitive information."}
+{"idx": 9, "title": "Understanding Oversmoothing in Graph Neural Networks ... - YouTube", "date": "", "ddg_snippet": "A Google TechTalk, presented by Xinyi Wu, 2024-01-18A Google Algorithm Seminar. ABSTRACT: Oversmoothing in Graph Neural Networks (GNNs) refers to the phenome...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=MLiEoJOhXJA", "content": "A Google TechTalk, presented by Xinyi Wu, 2024-01-18A Google Algorithm Seminar. ABSTRACT: Oversmoothing in Graph Neural Networks (GNNs) refers to the phenome..."}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_UV_Mapping_36MB_compression.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_UV_Mapping_36MB_compression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bae2e2a4e0f6e58a8c56721806d5ba1bd2ac8bb0
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_UV_Mapping_36MB_compression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.01846] UVGS : Reimagining Unstructured 3 D Gaussian ...", "date": "", "ddg_snippet": "Title: UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .Abstract: 3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "Title: UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .Abstract: 3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes."}
+{"idx": 1, "title": "Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33266", "content": "3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation."}
+{"idx": 2, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "OverviewIntroduces UV mapping techniques to enhance 3 D Gaussian Splatting Introduces spherical and cylindrical UV mapping for different object types", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/uvgs-reimagining-unstructured-3d-gaussian-splatting-using", "content": "OverviewIntroduces UV mapping techniques to enhance 3 D Gaussian Splatting Introduces spherical and cylindrical UV mapping for different object types"}
+{"idx": 3, "title": "Free 3 D Gaussian Splatting Tool | Polycam", "date": "", "ddg_snippet": "Gaussian splatting is an exciting new rendering technique that excels at creating lifelike, natural-looking 3 D scenes and subjects - think of a 3 D photograph. Learn more about splatting and try it out for yourself!", "subpage_snippet": "", "source": "poly.cam", "link": "https://poly.cam/tools/gaussian-splatting", "content": "Gaussian splatting is an exciting new rendering technique that excels at creating lifelike, natural-looking 3 D scenes and subjects - think of a 3 D photograph. Learn more about splatting and try it out for yourself!"}
+{"idx": 4, "title": "PlayCanvas Open Sources SOG: The WebP of Gaussian Splatting", "date": "", "ddg_snippet": "SuperSplat, the #1 platform publishing 3 D Gaussian Splats, has been updated to compress your scans with SOG. Since it provides around 2-3x the compression of Compressed PLY, your creations will load much faster and will load on more memory constrained devices.", "subpage_snippet": "", "source": "blog.playcanvas.com", "link": "https://blog.playcanvas.com/playcanvas-open-sources-sog-format-for-gaussian-splatting/", "content": "SuperSplat, the #1 platform publishing 3 D Gaussian Splats, has been updated to compress your scans with SOG. Since it provides around 2-3x the compression of Compressed PLY, your creations will load much faster and will load on more memory constrained devices."}
+{"idx": 5, "title": "UVGS Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=1uMuXsJScvY", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 6, "title": "Figure 4. Complex object reconstructions (K=4) using pretrained...", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .To show the effectiveness of proposed UVGS maps in capturing the intricacies of a complex objects, we used a pretrained image based autoencoder to reconstruct a 4 layer UVGS as shown in Fig. ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Complex-object-reconstructions-K4-using-pretrained-image-based-autoencoder_fig1_388685707", "content": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .To show the effectiveness of proposed UVGS maps in capturing the intricacies of a complex objects, we used a pretrained image based autoencoder to reconstruct a 4 layer UVGS as shown in Fig. ..."}
+{"idx": 7, "title": "Latest 10 Papers - February 14, 2025 · Issue #28...", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .HAC++: Towards 100X Compression of 3 D Gaussian Splatting .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/luohongk/Awesome-Localization-And-3D-Reconstruction-From-Arxiv/issues/28", "content": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .HAC++: Towards 100X Compression of 3 D Gaussian Splatting ."}
+{"idx": 8, "title": "Monocular Online Reconstruction with Enhanced Detail...", "date": "", "ddg_snippet": "[3] UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping . 3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. AAashish RaiDDilin Wang.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/monocular-online-reconstruction-with-enhanced-detail-preservation/1129360498676990011-108597", "content": "[3] UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping . 3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. AAashish RaiDDilin Wang."}
+{"idx": 9, "title": "Workflow 3 D -художника. Как не утонуть в тонне информации. / Хабр", "date": "", "ddg_snippet": "8 мин. 36K. UV mapping текстурирование PBR", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/455319/", "content": "8 мин. 36K. UV mapping текстурирование PBR"}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_UV_Mapping_Objaverse_dataset_objects_number_year_2024.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_UV_Mapping_Objaverse_dataset_objects_number_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ef402246db0fbaa650d4e63540b8c55348aaa383
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_UV_Mapping_Objaverse_dataset_objects_number_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Comparison with Baselines. UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .3DGS Dataset and UV maps To train the mapping net-works and learn a latent space for unconditional and con-ditional sampling, we need a large amount of 3DGS as- sets .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846", "content": "Comparison with Baselines. UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .3DGS Dataset and UV maps To train the mapping net-works and learn a latent space for unconditional and con-ditional sampling, we need a large amount of 3DGS as- sets ."}
+{"idx": 1, "title": "(PDF) UVGS : Reimagining Unstructured 3 D Gaussian Splatting ...", "date": "", "ddg_snippet": "3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to..."}
+{"idx": 2, "title": "Free 3 D Gaussian Splatting Tool | Polycam", "date": "", "ddg_snippet": "Gaussian splatting can effectively render shiny, reflective objects as well as long and thin details. Splatting also excels at capturing large, expansive spaces without sacrificing smaller details.", "subpage_snippet": "", "source": "poly.cam", "link": "https://poly.cam/tools/gaussian-splatting", "content": "Gaussian splatting can effectively render shiny, reflective objects as well as long and thin details. Splatting also excels at capturing large, expansive spaces without sacrificing smaller details."}
+{"idx": 3, "title": "SynGauss: Real-Time 3 D Gaussian Splatting for... | IEEE DataPort", "date": "", "ddg_snippet": "Datasets . Standard Dataset . SynGauss: Real-Time 3 D Gaussian Splatting for Audio-Driven Talking Head Synthesis.", "subpage_snippet": "", "source": "ieee-dataport.org", "link": "https://ieee-dataport.org/documents/syngauss-real-time-3d-gaussian-splatting-audio-driven-talking-head-synthesis", "content": "Datasets . Standard Dataset . SynGauss: Real-Time 3 D Gaussian Splatting for Audio-Driven Talking Head Synthesis."}
+{"idx": 4, "title": "DiGS- 3 D : Diffusion Transformer as unstructured 3 D Gaussian ...", "date": "", "ddg_snippet": "We introduce DiGS- 3 D , an efficient method for generation of 3 D Gaussian Splatting representations generation using a diffusion transformer.", "subpage_snippet": "", "source": "sigport.org", "link": "https://sigport.org/documents/digs-3d-diffusion-transformer-unstructured-3d-gaussian-splatting-generator-supplementary", "content": "We introduce DiGS- 3 D , an efficient method for generation of 3 D Gaussian Splatting representations generation using a diffusion transformer."}
+{"idx": 5, "title": "PlayCanvas Open Sources SOG: The WebP of Gaussian Splatting", "date": "", "ddg_snippet": "SuperSplat, the #1 platform publishing 3 D Gaussian Splats, has been updated to compress your scans with SOG. Since it provides around 2-3x the compression of Compressed PLY, your creations will load much faster and will load on more memory constrained devices.", "subpage_snippet": "", "source": "blog.playcanvas.com", "link": "https://blog.playcanvas.com/playcanvas-open-sources-sog-format-for-gaussian-splatting/", "content": "SuperSplat, the #1 platform publishing 3 D Gaussian Splats, has been updated to compress your scans with SOG. Since it provides around 2-3x the compression of Compressed PLY, your creations will load much faster and will load on more memory constrained devices."}
+{"idx": 6, "title": "lilujunai/awesome- 3 D - gaussian - splatting - Giters", "date": "", "ddg_snippet": "2 papers added: Gaussian Splatting SLAM and Denoising Scores for 3 D Generation. ScaffoldGS code released.In this paper, we present Gaussian Splatting based text-to- 3 D generation (GSGEN), a novel approach for generating high-quality 3 D objects .", "subpage_snippet": "", "source": "giters.com", "link": "https://giters.com/lilujunai/awesome-3D-gaussian-splatting", "content": "2 papers added: Gaussian Splatting SLAM and Denoising Scores for 3 D Generation. ScaffoldGS code released.In this paper, we present Gaussian Splatting based text-to- 3 D generation (GSGEN), a novel approach for generating high-quality 3 D objects ."}
+{"idx": 7, "title": "UVGS Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=1uMuXsJScvY", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 8, "title": "GitHub - sailing-innocent/DailyArxiv: from https...", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping . 3 D Gaussian Splatting (3DGS) is a process that enables the direct creation of 3 D objects from 2D images. This representation offers numerous advantages, including rapid training and rendering.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sailing-innocent/DailyArxiv", "content": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping . 3 D Gaussian Splatting (3DGS) is a process that enables the direct creation of 3 D objects from 2D images. This representation offers numerous advantages, including rapid training and rendering."}
+{"idx": 9, "title": "Monocular Online Reconstruction with Enhanced Detail...", "date": "", "ddg_snippet": "[3] UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping . 3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. AAashish RaiDDilin Wang.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/monocular-online-reconstruction-with-enhanced-detail-preservation/1129360498676990011-108597", "content": "[3] UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping . 3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. AAashish RaiDDilin Wang."}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_branched_mapping_layers.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_branched_mapping_layers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1320027497a96acb98cf035e9d1b9ef8f47ec8ad
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_branched_mapping_layers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Abstract: 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33266", "content": "Abstract: 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation ..."}
+{"idx": 1, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... Images UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... (PDF) UVGS: Reimagining Unstructured 3D Gaussian Splatting ... GitHub - aashishrai3799/uvgs [PDF] UVGS: Reimagining Unstructured 3D Gaussian Splatting ... UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… (PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using U… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Feb 3, 2025 · 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... View all Abstract: 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation ... Feb 3, 2025 · UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping February 2025 DOI: 10.48550/arXiv.2502.01846 License CC BY 4.0 UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Arthur Chen, Srinath Sridhar, Aayush Prakash Feb 3, 2025 · This work utilizes spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS , and discovers that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging ... 3D Gaussian Splatting (3DGS) has demonstrated supe- rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... What is 3D Gaussian splatting? 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. Can 3D Gaussian splatting be integrated with existing image foundational models? We introduced a novel method to solve the underlying issues with 3D Gaussian Splatting (3DGS) that prevent the direct integration of them with the large number of existing image foundational models. We proposed UVGS - a structured representation for 3DGS obtained by spherical mapping of 3DGS primitives to UV maps. How is a Super uvgs mapped back to a 3DGS object? The Super UVGS is mapped back to UVGS through branched inverse mapping, which in turn can be reconstructed back to the 3DGS object through inverse spherical mapping . 3D Generative/Reconstruction Models for Objects: Generation or reconstruction of 3D assets has been a long standing task . Are uvgs maps effective in capturing a complex real-world scene? To show the effectiv eness of proposed UVGS maps in capturing the intricacies of a complex real-world scene, we used a 12 layer UV map to reconstruct the 3D scenes. PDF | 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. What is a structured uvgs map? The structured UVGS maps provides local and global features that can be learned by a CNN . Forward Mapping The first layer is a set of three mapping branches for position, transform, and appearance (ϕ P f, ϕ T f, ϕ A f) respectively. We refer to them as position, transformation, and appearance branch. Can a single uvgs map match a 3DGS reconstruction quality? From the table, it can be seen that by using four layers of UVGS maps (K=4), we can almost match the reconstruction quality of fitted 3DGS results, while we realized that simply with a single layer UVGS , we are able to maintain the overall geometry and appearance of the object for our dataset with a PSNR of more than 30. To address these shortcomings, we introduce UV Gaussian Splatting ( UVGS ), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [43] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "Feb 3, 2025 · 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... View all Abstract: 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation ... Feb 3, 2025 · UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping February 2025 DOI: 10.48550/arXiv.2502.01846 License CC BY 4.0 UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Arthur Chen, Srinath Sridhar, Aayush Prakash Feb 3, 2025 · This work utilizes spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS , and discovers that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging ... 3D Gaussian Splatting (3DGS) has demonstrated supe- rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... What is 3D Gaussian splatting? 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. Can 3D Gaussian splatting be integrated with existing image foundational models? We introduced a novel method to solve the underlying issues with 3D Gaussian Splatting (3DGS) that prevent the direct integration of them with the large number of existing image foundational models. We proposed UVGS - a structured representation for 3DGS obtained by spherical mapping of 3DGS primitives to UV maps. How is a Super uvgs mapped back to a 3DGS object? The Super UVGS is mapped back to UVGS through branched inverse mapping, which in turn can be reconstructed back to the 3DGS object through inverse spherical mapping . 3D Generative/Reconstruction Models for Objects: Generation or reconstruction of 3D assets has been a long standing task . Are uvgs maps effective in capturing a complex real-world scene? To show the effectiv eness of proposed UVGS maps in capturing the intricacies of a complex real-world scene, we used a 12 layer UV map to reconstruct the 3D scenes. PDF | 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. What is a structured uvgs map? The structured UVGS maps provides local and global features that can be learned by a CNN . Forward Mapping The first layer is a set of three mapping branches for position, transform, and appearance (ϕ P f, ϕ T f, ϕ A f) respectively. We refer to them as position, transformation, and appearance branch. Can a single uvgs map match a 3DGS reconstruction quality? From the table, it can be seen that by using four layers of UVGS maps (K=4), we can almost match the reconstruction quality of fitted 3DGS results, while we realized that simply with a single layer UVGS , we are able to maintain the overall geometry and appearance of the object for our dataset with a PSNR of more than 30. To address these shortcomings, we introduce UV Gaussian Splatting ( UVGS ), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [43] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ..."}
+{"idx": 2, "title": "(PDF) UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "Feb 3, 2025 · UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping February 2025 DOI: 10.48550/arXiv.2502.01846 License CC BY 4.0", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "Feb 3, 2025 · UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping February 2025 DOI: 10.48550/arXiv.2502.01846 License CC BY 4.0"}
+{"idx": 3, "title": "GitHub - aashishrai3799/uvgs", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Arthur Chen, Srinath Sridhar, Aayush Prakash", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aashishrai3799/uvgs", "content": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Arthur Chen, Srinath Sridhar, Aayush Prakash"}
+{"idx": 4, "title": "[PDF] UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "Feb 3, 2025 · This work utilizes spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS , and discovers that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/UVGS:-Reimagining-Unstructured-3D-Gaussian-using-UV-Rai-Wang/10f0d88160981f85fd6c270e65496ec5948a98d2", "content": "Feb 3, 2025 · This work utilizes spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS , and discovers that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging ..."}
+{"idx": 5, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated supe- rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/assets/UVGS.pdf", "content": "3D Gaussian Splatting (3DGS) has demonstrated supe- rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 6, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "To address these shortcomings, we introduce UV Gaussian Splatting ( UVGS ), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [43] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v3", "content": "To address these shortcomings, we introduce UV Gaussian Splatting ( UVGS ), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [43] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ..."}
+{"idx": 7, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "by A Rai · 2025 · Cited by 2 — We present quantitative ablation study for number of. UVGS layers (K), UVGS map resolution, and the effect of branch - ing in mapping network on the Objaverse ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "by A Rai · 2025 · Cited by 2 — We present quantitative ablation study for number of. UVGS layers (K), UVGS map resolution, and the effect of branch - ing in mapping network on the Objaverse ... 11 pages"}
+{"idx": 8, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "11 Feb 2025 — Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v2", "content": "11 Feb 2025 — Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the ..."}
+{"idx": 9, "title": "[2502.01846] UVGS: Reimagining Unstructured 3D Gaussian ...", "date": "", "ddg_snippet": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images , with feature ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.01846", "content": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images , with feature ..."}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_spherical_mapping_formula.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_spherical_mapping_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fde97dd1ec6b99b09c6a498d8bf27a6074d48247
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_spherical_mapping_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "The paper presents UV Gaussian Splatting ( UVGS ), a method that transforms unstructured 3D Gaussian Splatting into a structured 2D representation, enhancing the ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/105135", "content": "The paper presents UV Gaussian Splatting ( UVGS ), a method that transforms unstructured 3D Gaussian Splatting into a structured 2D representation, enhancing the ..."}
+{"idx": 1, "title": "Graphics Programming Weekly - Database", "date": "", "ddg_snippet": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping . The paper introduces turning 3D Gaussian Splatting from unstructured into structured ...", "subpage_snippet": "", "source": "www.jendrikillner.com", "link": "https://www.jendrikillner.com/article_database/", "content": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping . The paper introduces turning 3D Gaussian Splatting from unstructured into structured ..."}
+{"idx": 2, "title": "DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation", "date": "", "ddg_snippet": "DyTact employs markerless contact capture (left) and uses a dynamic articulated representation based on 2D Gaussians to accurately model hand-object ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03103v1", "content": "DyTact employs markerless contact capture (left) and uses a dynamic articulated representation based on 2D Gaussians to accurately model hand-object ..."}
+{"idx": 3, "title": "Daily Papers", "date": "", "ddg_snippet": "5 days ago — Instead of splatting 3D Gaussians directly onto the spherical surface, 360-GS projects them onto the tangent plane of the unit sphere and then ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=2D+Gaussian+splat+coefficients", "content": "5 days ago — Instead of splatting 3D Gaussians directly onto the spherical surface, 360-GS projects them onto the tangent plane of the unit sphere and then ..."}
+{"idx": 4, "title": "arXiv:2503.01448v1 [cs.CV] 3 Mar 2025", "date": "", "ddg_snippet": "by X Tang · 2025 — Reimagining unstructured 3d gaussian splatting using uv mapping ... Lifting stylegan-human into a 3d generative model using pixel ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01448", "content": "by X Tang · 2025 — Reimagining unstructured 3d gaussian splatting using uv mapping ... Lifting stylegan-human into a 3d generative model using pixel ..."}
+{"idx": 5, "title": "Track: Poster Session 2 - CVPR", "date": "", "ddg_snippet": "13 Jun 2025 — To pave the way for efficient 3D reconstruction, we present Spherical Gaussians, a simple and effective representation for 3D geometric ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/session/35266", "content": "13 Jun 2025 — To pave the way for efficient 3D reconstruction, we present Spherical Gaussians, a simple and effective representation for 3D geometric ..."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "1 day ago — Instead of splatting 3D Gaussians directly onto the spherical surface, 360-GS projects them onto the tangent plane of the unit sphere and then ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=spherical+mirroring", "content": "1 day ago — Instead of splatting 3D Gaussians directly onto the spherical surface, 360-GS projects them onto the tangent plane of the unit sphere and then ..."}
+{"idx": 7, "title": "52CV/CVPR-2025-Papers", "date": "", "ddg_snippet": "S plat : Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration · UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/52CV/CVPR-2025-Papers", "content": "S plat : Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration · UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ..."}
+{"idx": 8, "title": "2025 IEEE/CVF Conference on Computer Vision and ...", "date": "", "ddg_snippet": "10 Jun 2025 — UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping pp. 5927-5937. PUP 3D-GS: Principled Uncertainty Pruning for 3D ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings/cvpr/2025/2998kzfEzK0", "content": "10 Jun 2025 — UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping pp. 5927-5937. PUP 3D-GS: Principled Uncertainty Pruning for 3D ..."}
+{"idx": 9, "title": "Computer Vision and Pattern Recognition Feb 2025", "date": "", "ddg_snippet": "Title: UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping . Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Kefan Chen, Srinath ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CV/2025-02?skip=50&show=1000", "content": "Title: UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping . Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Kefan Chen, Srinath ..."}
diff --git a/data/sampled_jsons/UVGS_custom_3DGS_dataset_Objaverse_objects_count_1000.jsonl b/data/sampled_jsons/UVGS_custom_3DGS_dataset_Objaverse_objects_count_1000.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e8a2d3f2ed923404a2f23125f151e1d82f87cc26
--- /dev/null
+++ b/data/sampled_jsons/UVGS_custom_3DGS_dataset_Objaverse_objects_count_1000.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "This way, we create a high-quality and large-scale 3DGS dataset of ∼400K objects and scenes from Objaverse . We only use static scenes or objects from Objaverse .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "This way, we create a high-quality and large-scale 3DGS dataset of ∼400K objects and scenes from Objaverse . We only use static scenes or objects from Objaverse ."}
+{"idx": 1, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting", "date": "", "ddg_snippet": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/", "content": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 2, "title": "uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025", "date": "", "ddg_snippet": "VGS maps, and the resolution of UVGS maps. We performed our experiments on our custom Objaverse [10] 3DGS dataset and evaluate the performance of o r model in terms of PSNR, SSIM, and LP", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846v1", "content": "VGS maps, and the resolution of UVGS maps. We performed our experiments on our custom Objaverse [10] 3DGS dataset and evaluate the performance of o r model in terms of PSNR, SSIM, and LP"}
+{"idx": 3, "title": "Objaverse", "date": "", "ddg_snippet": "Objaverse 1.0 is a Massive Dataset of 800K+ Annotated 3D Objects", "subpage_snippet": "", "source": "objaverse.allenai.org", "link": "https://objaverse.allenai.org/", "content": "Objaverse 1.0 is a Massive Dataset of 800K+ Annotated 3D Objects"}
+{"idx": 4, "title": "(PDF) UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "Feb 3, 2025 · The top three rows show the unconditional generation results of our method using ShapeNet dataset , while the bottom 3 show from Objaverse dataset .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "Feb 3, 2025 · The top three rows show the unconditional generation results of our method using ShapeNet dataset , while the bottom 3 show from Objaverse dataset ."}
+{"idx": 5, "title": "GitHub - Li-Hao-yuan/UniGS: Paper: UniGS: Unified Language ...", "date": "", "ddg_snippet": "For example, MVImgNet is saved in COLMAP format, thus can skip the process of converting data format. Meanwhile, the optimization commands for different data sets are different in \"sample_.py\". We thanks Zero123 for [the rendered images of objaverse , you can download it from the Dataset ( Objaverse Renderings) part of Zero123.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Li-Hao-yuan/UniGS", "content": "For example, MVImgNet is saved in COLMAP format, thus can skip the process of converting data format. Meanwhile, the optimization commands for different data sets are different in \"sample_.py\". We thanks Zero123 for [the rendered images of objaverse , you can download it from the Dataset ( Objaverse Renderings) part of Zero123."}
+{"idx": 6, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.html", "content": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "4 days ago — 3D Gaussian Splatting ( 3DGS ) has shown its impressive power in novel view synthesis. However, creating relightable 3D assets, especially for ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=compositional+3DGS+refinement+module", "content": "4 days ago — 3D Gaussian Splatting ( 3DGS ) has shown its impressive power in novel view synthesis. However, creating relightable 3D assets, especially for ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "3D Gaussian Splatting ( 3DGS ) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Gaussian2Scene", "content": "3D Gaussian Splatting ( 3DGS ) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed ..."}
+{"idx": 9, "title": "CVPR 2025 Friday 06/13", "date": "", "ddg_snippet": "... 3D object digital twin dataset . A digital twin of a 3D object is a highly detailed, virtually indistinguishable representation of a physical object ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/day/6/13", "content": "... 3D object digital twin dataset . A digital twin of a 3D object is a highly detailed, virtually indistinguishable representation of a physical object ..."}
diff --git a/data/sampled_jsons/UVGS_paper_limitations_of_NeRF_real-time_rendering.jsonl b/data/sampled_jsons/UVGS_paper_limitations_of_NeRF_real-time_rendering.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9dc861949d6eb13faf328e96411d943c87e17f8c
--- /dev/null
+++ b/data/sampled_jsons/UVGS_paper_limitations_of_NeRF_real-time_rendering.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR 2025 Papers", "date": "", "ddg_snippet": "... in the Detail: Towards Injecting Fine Details of ... SplatAD: Real - Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/papers.html", "content": "... in the Detail: Towards Injecting Fine Details of ... SplatAD: Real - Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving"}
+{"idx": 1, "title": "How to Design and Train Your Implicit Neural Representation for", "date": "", "ddg_snippet": "However, with equal training time , such by allowing all methods the amount of time it takes NeRV to run 300 epochs as measured on a single NVIDIA ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.24127v1", "content": "However, with equal training time , such by allowing all methods the amount of time it takes NeRV to run 300 epochs as measured on a single NVIDIA ..."}
+{"idx": 2, "title": "DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation", "date": "", "ddg_snippet": "... realistic manipulation in AI character animation, XR, and robotics, yet it remains challenging due to heavy occlusions, complex surface details, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03103v1", "content": "... realistic manipulation in AI character animation, XR, and robotics, yet it remains challenging due to heavy occlusions, complex surface details, and ..."}
+{"idx": 3, "title": "Inpainting", "date": "", "ddg_snippet": "When performing 3D inpainting using novel-view rendering methods like Neural Radiance Field ( NeRF ) or 3D Gaussian Splatting (3DGS), how to achieve ...", "subpage_snippet": "", "source": "paperreading.club", "link": "http://paperreading.club/category?cate=Inpainting", "content": "When performing 3D inpainting using novel-view rendering methods like Neural Radiance Field ( NeRF ) or 3D Gaussian Splatting (3DGS), how to achieve ..."}
+{"idx": 4, "title": "Inpainting", "date": "", "ddg_snippet": "In this paper , we address these concerns with GHOST 2.0, which consists of two problem-specific modules. ... of -the-art methods, our proposed ...", "subpage_snippet": "", "source": "paperreading.club", "link": "http://paperreading.club/category?cate=Inpainting&page=0", "content": "In this paper , we address these concerns with GHOST 2.0, which consists of two problem-specific modules. ... of -the-art methods, our proposed ..."}
+{"idx": 5, "title": "CVPR 2025 Schedule", "date": "", "ddg_snippet": "Emergent Visual Abilities and Limits of Foundation ... The 5th Workshop of Adversarial Machine Learning on Computer Vision: Foundation Models + X", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/calendar", "content": "Emergent Visual Abilities and Limits of Foundation ... The 5th Workshop of Adversarial Machine Learning on Computer Vision: Foundation Models + X"}
+{"idx": 6, "title": "Shop Online, kettlebells, courses, certifications | StrongFirst", "date": "", "ddg_snippet": "Shop online in the official StrongFirst online shop, get your kettlebells, books, course or certification here.", "subpage_snippet": "", "source": "www.strongfirst.com", "link": "https://www.strongfirst.com/shop/", "content": "Shop online in the official StrongFirst online shop, get your kettlebells, books, course or certification here."}
+{"idx": 7, "title": "Shop Bodyweight Instructor SFB, Chicago, IL—April 25-26, 2026 |...", "date": "", "ddg_snippet": "StrongFirst Bodyweight Instructor SFB, Chicago, IL—April 25-26, 2026 | Save $400 when you register now through December 4, 2025—pay only $895 with the Total Commitment Price Early Price—Save $200 when you register from December 5, 2025 through February 24, 2026—pay only $1095 Standard Price of $1295 available now and ends April 11, 2026 Late Price of $1395 begins April 12, 2026", "subpage_snippet": "", "source": "www.strongfirst.com", "link": "https://www.strongfirst.com/shop/certifications/bodyweight-instructor-sfb/chicago-il-april-25-26-2026/", "content": "StrongFirst Bodyweight Instructor SFB, Chicago, IL—April 25-26, 2026 | Save $400 when you register now through December 4, 2025—pay only $895 with the Total Commitment Price Early Price—Save $200 when you register from December 5, 2025 through February 24, 2026—pay only $1095 Standard Price of $1295 available now and ends April 11, 2026 Late Price of $1395 begins April 12, 2026"}
+{"idx": 8, "title": "Shop Professional Seminars, StrongFirst RESILIENT—Rijeka,...", "date": "", "ddg_snippet": "StrongFirst Professional Seminars, StrongFirst RESILIENT—Rijeka, Croatia—May 30-31, 2026 | Save €175 when you register now through December 31, 2025—pay only €515 with the Total Commitment Price Standard Price of €690 available now and ends May 16, 2026 Late Price of €770 begins May 17, 2026", "subpage_snippet": "", "source": "www.strongfirst.com", "link": "https://www.strongfirst.com/shop/special-events/strongfirst-resilient-rijeka-croatia-may-30-31-2026/", "content": "StrongFirst Professional Seminars, StrongFirst RESILIENT—Rijeka, Croatia—May 30-31, 2026 | Save €175 when you register now through December 31, 2025—pay only €515 with the Total Commitment Price Standard Price of €690 available now and ends May 16, 2026 Late Price of €770 begins May 17, 2026"}
+{"idx": 9, "title": "Shop Barbell Instructor SFL, Denver, CO—May 15-17, 2026 |...", "date": "", "ddg_snippet": "May 15, 2017 · StrongFirst Barbell Instructor SFL, Denver, CO—May 15-17, 2026 | Save $400 when you register now through December 16, 2025—pay only $1195 with the Total Commitment Price Early Price—Save $200 when you register from December 17, 2025 through March 16, 2026—pay only $1395 Standard Price of $1595 available now and ends May 1, 2026 Late Price of $1695 begins May 2, 2026", "subpage_snippet": "", "source": "www.strongfirst.com", "link": "https://www.strongfirst.com/shop/certifications/barbell-instructor-sfl/denver-co-may-15-17-2026/", "content": "May 15, 2017 · StrongFirst Barbell Instructor SFL, Denver, CO—May 15-17, 2026 | Save $400 when you register now through December 16, 2025—pay only $1195 with the Total Commitment Price Early Price—Save $200 when you register from December 17, 2025 through March 16, 2026—pay only $1395 Standard Price of $1595 available now and ends May 1, 2026 Late Price of $1695 begins May 2, 2026"}
diff --git a/data/sampled_jsons/UVGS_spherical_mapping_to_UV_map_formula_equation.jsonl b/data/sampled_jsons/UVGS_spherical_mapping_to_UV_map_formula_equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..04b9b210ce9e83ff01be874f5baa483e31d48bd4
--- /dev/null
+++ b/data/sampled_jsons/UVGS_spherical_mapping_to_UV_map_formula_equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Track: Poster Session 2 - CVPR", "date": "", "ddg_snippet": "13 Jun 2025 — We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/session/35266", "content": "13 Jun 2025 — We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images ..."}
+{"idx": 1, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "by SM Ahmed — ... UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping. Aashish Rai,. Dilin Wang,. Mihir Jain,. Nikolaos Sarafianos,. Kefan Chen,. Srinath ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/CVPR2025?day=2025-06-13", "content": "by SM Ahmed — ... UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping. Aashish Rai,. Dilin Wang,. Mihir Jain,. Nikolaos Sarafianos,. Kefan Chen,. Srinath ..."}
+{"idx": 2, "title": "Quantum State Engineering and Information Processing ...", "date": "", "ddg_snippet": "by BE King · 1999 · Cited by 39 — Schrödinger equation or the Bloch equations : such solutions are well known. However, here I will introduce another point of view which ...", "subpage_snippet": "", "source": "www.nist.gov", "link": "https://www.nist.gov/system/files/documents/2017/05/09/bkthesis.pdf", "content": "by BE King · 1999 · Cited by 39 — Schrödinger equation or the Bloch equations : such solutions are well known. However, here I will introduce another point of view which ..."}
+{"idx": 3, "title": "Computer Vision and Pattern Recognition Feb 2025", "date": "", "ddg_snippet": "Title: UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping ... Title: Improved Partial Differential Equation and Fast Approximation ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CV/2025-02?skip=100&show=2000", "content": "Title: UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping ... Title: Improved Partial Differential Equation and Fast Approximation ..."}
+{"idx": 4, "title": "CIRES FY 2005 Annual Report", "date": "", "ddg_snippet": "29 Sept 2005 — grid and map . The map -making tool provides the user with a set of calculated default values or allows the user to enter their own custom ...", "subpage_snippet": "", "source": "cires.colorado.edu", "link": "https://cires.colorado.edu/api/files/sites/default/files/2022-10/CIRES_Annual_Report_FY05-2.pdf", "content": "29 Sept 2005 — grid and map . The map -making tool provides the user with a set of calculated default values or allows the user to enter their own custom ..."}
+{"idx": 5, "title": "Graphics Programming Weekly - Database", "date": "", "ddg_snippet": "The talk introduces a novel sphere tracing algorithm tailored for harmonic functions. The method allows for larger step sizes than traditional ray marching.", "subpage_snippet": "", "source": "www.jendrikillner.com", "link": "https://www.jendrikillner.com/article_database/", "content": "The talk introduces a novel sphere tracing algorithm tailored for harmonic functions. The method allows for larger step sizes than traditional ray marching."}
+{"idx": 6, "title": "Power Quality in Microgrids: Issues, Challenges and ...", "date": "", "ddg_snippet": "This book provides a brief insight of various challenges and its mitigation techniques in microgrid due to power quality", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/power-quality-in-microgrids-issues-challenges-and-mitigation-techniques-9819920655-9789819920655.html", "content": "This book provides a brief insight of various challenges and its mitigation techniques in microgrid due to power quality"}
+{"idx": 7, "title": "Bicyclist & Pedestrian Safety", "date": "", "ddg_snippet": "Providing information about local laws and helpful safety tips to bicyclists, pedestriansand drivers will help to make roads and streets safe for everyone.", "subpage_snippet": "", "source": "www.peoplepoweredmovement.org", "link": "https://www.peoplepoweredmovement.org/bicyclist-pedestrian-safety/", "content": "Providing information about local laws and helpful safety tips to bicyclists, pedestriansand drivers will help to make roads and streets safe for everyone."}
+{"idx": 8, "title": "Categories For The Working Mathematician | PDF", "date": "", "ddg_snippet": "Categories for the Working Mathematician - Free download as PDF File (.pdf) or view presentation slides online. Teoría de Categorías para científicos en ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/doc/290433151/Categories-for-the-Working-Mathematician", "content": "Categories for the Working Mathematician - Free download as PDF File (.pdf) or view presentation slides online. Teoría de Categorías para científicos en ..."}
+{"idx": 9, "title": "University of Colorado at Boulder", "date": "", "ddg_snippet": "Edward Dorn passed away in mid-December 1999. He was a professor emeritus of the Department of English. Marilyn Krysl, a professor in the Department of ...", "subpage_snippet": "", "source": "spl.cde.state.co.us", "link": "https://spl.cde.state.co.us/artemis/ucbserials/ucb112internet/ucb112200001internet.pdf", "content": "Edward Dorn passed away in mid-December 1999. He was a professor emeritus of the Department of English. Marilyn Krysl, a professor in the Department of ..."}
diff --git a/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020_abstract_year_2020.jsonl b/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7343c36168eb9bfca5167b5943b399b709a4689b
--- /dev/null
+++ b/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "2017. Unbiased learning -to-rank with biased feedback .Yuta Saito , Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020 . Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3409256.3409812", "content": "2017. Unbiased learning -to-rank with biased feedback .Yuta Saito , Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020 . Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback ."}
+{"idx": 1, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "Yuta Saito . September 2020 .A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner.", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/2020/ictir2020/", "content": "Yuta Saito . September 2020 .A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner."}
+{"idx": 2, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control. Unbiased Learning to Rank with Biased Continuous Feedback .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Unbiased-Pairwise-Learning-from-Biased-Implicit-Saito/2aff8673be4db3e7c9c81e7fe37c7dcd08b52405", "content": "Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control. Unbiased Learning to Rank with Biased Continuous Feedback ."}
+{"idx": 3, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "View. Show abstract . ... Existing studies have developed unbiased recommender learning methods [33,38,39,63] to estimate true user preferences from implicit feedback under the missing-not-at-random (MNAR) assumption [29,43,62]. They formulated a new loss function to eliminate...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/347579058_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback", "content": "View. Show abstract . ... Existing studies have developed unbiased recommender learning methods [33,38,39,63] to estimate true user preferences from implicit feedback under the missing-not-at-random (MNAR) assumption [29,43,62]. They formulated a new loss function to eliminate..."}
+{"idx": 4, "title": "GitHub - usaito/ unbiased - pairwise -rec: (ICTIR 2020 ) \" Unbiased ...\"", "date": "", "ddg_snippet": "(ICTIR 2020 ) \" Unbiased Pairwise Learning from Biased Implicit Feedback \".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/usaito/unbiased-pairwise-rec", "content": "(ICTIR 2020 ) \" Unbiased Pairwise Learning from Biased Implicit Feedback \"."}
+{"idx": 5, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback .", "date": "", "ddg_snippet": "↓ скачать. Saito , Y. ( 2020 ). Unbiased Pairwise Learning from Biased Implicit Feedback . Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. doi:10.1145/3409256.3409812.", "subpage_snippet": "", "source": "sci-hub.se", "link": "https://sci-hub.se/10.1145/3409256.3409812", "content": "↓ скачать. Saito , Y. ( 2020 ). Unbiased Pairwise Learning from Biased Implicit Feedback . Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. doi:10.1145/3409256.3409812."}
+{"idx": 6, "title": "Yuta Saito - Google Scholar", "date": "", "ddg_snippet": "Unbiased pairwise learning from biased implicit feedback .Y Saito , S Yasui. International Conference on Machine Learning , 8398-8407, 2020 .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=pw4hwS8AAAAJ&hl=en", "content": "Unbiased pairwise learning from biased implicit feedback .Y Saito , S Yasui. International Conference on Machine Learning , 8398-8407, 2020 ."}
+{"idx": 7, "title": "Unbiased Pairwise Learning from Implicit Feedback for... | DeepAI", "date": "", "ddg_snippet": "Nonetheless, the existing unbiased pairwise learning method suffers from high variance. To get satisfactory performance, non-negative estimator is utilized for practical variance control but introduces additional bias .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/unbiased-pairwise-learning-from-implicit-feedback-for-recommender-systems-without-biased-variance-control", "content": "Nonetheless, the existing unbiased pairwise learning method suffers from high variance. To get satisfactory performance, non-negative estimator is utilized for practical variance control but introduces additional bias ."}
+{"idx": 8, "title": "Unbiased Pairwise Learning from Implicit Feedback", "date": "", "ddg_snippet": "Unbiased learning -to-rank with biased feedback . In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pages 781–789.", "subpage_snippet": "", "source": "bpb-us-w2.wpmucdn.com", "link": "https://bpb-us-w2.wpmucdn.com/sites.coecis.cornell.edu/dist/a/238/files/2019/12/Id_20_final.pdf", "content": "Unbiased learning -to-rank with biased feedback . In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pages 781–789."}
+{"idx": 9, "title": "Publications - Yuta Saito", "date": "", "ddg_snippet": "Yuta Saito . Unbiased Pairwise Learning from Biased Implicit Feedback International Conference on the Theory of Information Retrieval (ICTIR 2020 ).", "subpage_snippet": "", "source": "usaito.github.io", "link": "https://usaito.github.io/publications/", "content": "Yuta Saito . Unbiased Pairwise Learning from Biased Implicit Feedback International Conference on the Theory of Information Retrieval (ICTIR 2020 )."}
diff --git a/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_Equation_6_R_weak(g).jsonl b/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_Equation_6_R_weak(g).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9318a3d7e376ccdab50dba5ab90ab7e65c0d8950
--- /dev/null
+++ b/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_Equation_6_R_weak(g).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - usaito/unbiased-implicit-rec-real: (WSDM2020 ...", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback About This repository accompanies the real-world experiment conducted in the paper \"Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback \" by Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata, which has been accepted to ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/usaito/unbiased-implicit-rec-real", "content": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback About This repository accompanies the real-world experiment conducted in the paper \"Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback \" by Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata, which has been accepted to ..."}
+{"idx": 1, "title": "ICML Poster Unbiased Recommender Learning from Implicit ...", "date": "", "ddg_snippet": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples."}
+{"idx": 2, "title": "Unbiased Recommender Learning from Implicit Feedback via ...", "date": "", "ddg_snippet": "Specifically, we first reformulate the ideal risk and construct an empirical estimator that obviates the need for negative feedback . We show that this estimator is unbiased, consistent with the ideal risk, and amenable to optimization via stochastic gradient descent, with unique at-tainable optimum for representative recommender families.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0E5rZOGA13", "content": "Specifically, we first reformulate the ideal risk and construct an empirical estimator that obviates the need for negative feedback . We show that this estimator is unbiased, consistent with the ideal risk, and amenable to optimization via stochastic gradient descent, with unique at-tainable optimum for representative recommender families."}
+{"idx": 3, "title": "Unbiased Recommender Learning from Biased Graded Implicit ...", "date": "", "ddg_snippet": "In response, we formulate a recommendation with graded implicit feedback as a statistical estimation problem and define an ideal loss function of interest, which should ideally be optimized to maximize the user experience. Subsequently, we propose an unbiased estimator for the inverse propensity score ideal loss, building on the estimator.", "subpage_snippet": "", "source": "decisionmaking4ir.github.io", "link": "https://decisionmaking4ir.github.io/WSDM-2022/papers/Suguru.pdf", "content": "In response, we formulate a recommendation with graded implicit feedback as a statistical estimation problem and define an ideal loss function of interest, which should ideally be optimized to maximize the user experience. Subsequently, we propose an unbiased estimator for the inverse propensity score ideal loss, building on the estimator."}
+{"idx": 4, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "Sep 14, 2020 · Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems. However, the application of implicit feedback data is much more complicated than its explicit counterpart because it provides only positive feedback , and we cannot know whether the non-interacted feedback is positive or negative.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3409256.3409812", "content": "Sep 14, 2020 · Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems. However, the application of implicit feedback data is much more complicated than its explicit counterpart because it provides only positive feedback , and we cannot know whether the non-interacted feedback is positive or negative."}
+{"idx": 5, "title": "Unbiased Recommender Learning from Biased Graded Implicit ...", "date": "", "ddg_snippet": "Binary user-behavior logs such as clicks or views, called implicit feedback , are often used to build recommender systems because of its general availability in real practice. Most existing studies formulate implicit feedback as binary relevance feedback . However, in numerous applications, implicit feedback is observed not only as a binary indicator but also in a graded form, such as the number ...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/workshops/wsdm2022/", "content": "Binary user-behavior logs such as clicks or views, called implicit feedback , are often used to build recommender systems because of its general availability in real practice. Most existing studies formulate implicit feedback as binary relevance feedback . However, in numerous applications, implicit feedback is observed not only as a binary indicator but also in a graded form, such as the number ..."}
+{"idx": 6, "title": "Unbiased Recommender Learning from Implicit Feedback via...", "date": "", "ddg_snippet": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13&referrer=[the+profile+of+Licheng+Pan](/profile?id=~Licheng_Pan1)", "content": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback ."}
+{"idx": 7, "title": "Unbiased Recommender Learning from Missing-Not-At-Random...", "date": "", "ddg_snippet": "7 Conclusion. References. Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . arXiv:1909.03601v3 [stat.ML] 9 Feb 2020. Yuta Saito.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.03601", "content": "7 Conclusion. References. Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . arXiv:1909.03601v3 [stat.ML] 9 Feb 2020. Yuta Saito."}
+{"idx": 8, "title": "(PDF) Unbiased Pairwise Learning from Implicit Feedback for...", "date": "", "ddg_snippet": "Recommender Systems; Unbiased Learning ; Implicit Feedback ; Missing-. Not-At-Random; Inverse Propensity Score. ACM Reference Format: Yi Ren, Hongyan Tang, Jiangpeng Rong, and Siwen Zhu. 2023. Unbiased .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/369946401_Unbiased_Pairwise_Learning_from_Implicit_Feedback_for_Recommender_Systems_without_Biased_Variance_Control", "content": "Recommender Systems; Unbiased Learning ; Implicit Feedback ; Missing-. Not-At-Random; Inverse Propensity Score. ACM Reference Format: Yi Ren, Hongyan Tang, Jiangpeng Rong, and Siwen Zhu. 2023. Unbiased ."}
+{"idx": 9, "title": "VAE-IPS: A Deep Generative Recommendation Method for Unbiased ...", "date": "", "ddg_snippet": "2021. Dual Unbiased Recommender Learning for Implicit Feedback .2020. Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on.", "subpage_snippet": "", "source": "staff.fnwi.uva.nl", "link": "https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/gupta-2022-vae-ips.pdf", "content": "2021. Dual Unbiased Recommender Learning for Implicit Feedback .2020. Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on."}
diff --git a/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning.jsonl b/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8b1535e6de17ac289701f7ae847dfc3f399691b5
--- /dev/null
+++ b/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — This paper formulates implicit feedback recommendation as a weakly supervised learning problem , obtaining an unbiased positive-negative ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — This paper formulates implicit feedback recommendation as a weakly supervised learning problem , obtaining an unbiased positive-negative ..."}
+{"idx": 1, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — To address this issue, we introduce WeaklyRec , a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — To address this issue, we introduce WeaklyRec , a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, ..."}
+{"idx": 2, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "Task Targeted. The paper focuses on enhancing implicit feedback recommendation systems , specifically through a model-agnostic framework called WeaklyRec.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165506", "content": "Task Targeted. The paper focuses on enhancing implicit feedback recommendation systems , specifically through a model-agnostic framework called WeaklyRec."}
+{"idx": 3, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "This research introduces WeaklyRec , a new method for recommendation systems that relies on weakly supervised learning to solve a common problem: not having ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46694/authors?_c=eyJ2IjoxLCJyZWxhdGVkIjpbImNvZGUiLCJyZWZlcmVuY2VzIiwiY29uZmVyZW5jZSJdfQ==", "content": "This research introduces WeaklyRec , a new method for recommendation systems that relies on weakly supervised learning to solve a common problem: not having ..."}
+{"idx": 4, "title": "A Survey of Real-World Recommender Systems", "date": "", "ddg_snippet": "7 Sept 2025 — Learning and optimization of implicit negative feedback for industrial short-video recommender system. In Proceedings of the 32nd ACM ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06002v1", "content": "7 Sept 2025 — Learning and optimization of implicit negative feedback for industrial short-video recommender system. In Proceedings of the 32nd ACM ..."}
+{"idx": 5, "title": "Practically Unbiased Pairwise Loss for Recommendation ...", "date": "", "ddg_snippet": "by T Cao · 2025 · Cited by 2 — In this paper, we focus on unbiased ranking loss weighted by inversed propensity scores (IPS), which are widely used in recommendations with implicit feedback ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/04/10810273/22O7O9fFhOo", "content": "by T Cao · 2025 · Cited by 2 — In this paper, we focus on unbiased ranking loss weighted by inversed propensity scores (IPS), which are widely used in recommendations with implicit feedback ..."}
+{"idx": 6, "title": "Tianqiao Liu", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning . H Wang, Z Chen, H Wang, Y Tan, L Pan, T Liu, X Chen, H Li, Z Lin. Forty ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=HIuCuFkAAAAJ&hl=en", "content": "Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning . H Wang, Z Chen, H Wang, Y Tan, L Pan, T Liu, X Chen, H Li, Z Lin. Forty ..."}
+{"idx": 7, "title": "Downloads 2025", "date": "", "ddg_snippet": "Unbiased Evaluation of Large Language Models from a Causal Perspective · Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "Unbiased Evaluation of Large Language Models from a Causal Perspective · Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning ..."}
+{"idx": 8, "title": "Publication", "date": "", "ddg_snippet": "Dual Unbiased Recommender Learning for Implicit Feedback [paper] [video] · MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical ...", "subpage_snippet": "", "source": "dial.skku.edu", "link": "https://dial.skku.edu/publication", "content": "Dual Unbiased Recommender Learning for Implicit Feedback [paper] [video] · MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback · Recommender systems widely use implicit feedback such as click data because of ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=unbiased+representation", "content": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback · Recommender systems widely use implicit feedback such as click data because of ..."}
diff --git a/data/sampled_jsons/Undifferentiated_Attention_vs_Scaling_Contribution_mechanistic_difference_ASR_increase_safety_heads.jsonl b/data/sampled_jsons/Undifferentiated_Attention_vs_Scaling_Contribution_mechanistic_difference_ASR_increase_safety_heads.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4075acd28398c14e2deddb5705390753dff37a33
--- /dev/null
+++ b/data/sampled_jsons/Undifferentiated_Attention_vs_Scaling_Contribution_mechanistic_difference_ASR_increase_safety_heads.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention Is All You Need - Wikipedia", "date": "", "ddg_snippet": "Scaled dot-product attention & self- attention .For their 100M-parameter Transformer model, the authors increased the learning rate linearly for the first 4000 (warmup) steps and decreased it proportionally to inverse square root of the current step number.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Attention_Is_All_You_Need", "content": "Scaled dot-product attention & self- attention .For their 100M-parameter Transformer model, the authors increased the learning rate linearly for the first 4000 (warmup) steps and decreased it proportionally to inverse square root of the current step number."}
+{"idx": 1, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "However, existing research tends to overlook the safety impact of multi-head attention mechanisms, despite their crucial role in various model functionalities. Hence, in this paper, we aim to explore the connection between standard attention mechanisms and safety capability to fill this gap in the safety -related mechanistic interpretability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v1", "content": "However, existing research tends to overlook the safety impact of multi-head attention mechanisms, despite their crucial role in various model functionalities. Hence, in this paper, we aim to explore the connection between standard attention mechanisms and safety capability to fill this gap in the safety -related mechanistic interpretability."}
+{"idx": 2, "title": "Attention heads of large language models - ScienceDirect", "date": "", "ddg_snippet": "Feb 14, 2025 · Attention heads play a pivotal role in reasoning and are thought to share similarities with human brain functions. In this review, we explore the roles and mechanisms of attention heads to help demystify the internal reasoning processes of LLMs. We first introduce a four-stage framework inspired by the human thought process.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666389925000248", "content": "Feb 14, 2025 · Attention heads play a pivotal role in reasoning and are thought to share similarities with human brain functions. In this review, we explore the roles and mechanisms of attention heads to help demystify the internal reasoning processes of LLMs. We first introduce a four-stage framework inspired by the human thought process."}
+{"idx": 3, "title": "GitHub - IAAR-Shanghai/Awesome-Attention-Heads: An awesome ...", "date": "", "ddg_snippet": "Attention Heads of Large Language Models: A Survey (Awesome Attention Heads ) Important About this repo. This is a platform to get the latest research on different kinds of LLM's Attention Heads . Also, we released a survey based on these fantastic works. If you want to cite our work, here is our bibtex entry: CITATION.bib.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IAAR-Shanghai/Awesome-Attention-Heads", "content": "Attention Heads of Large Language Models: A Survey (Awesome Attention Heads ) Important About this repo. This is a platform to get the latest research on different kinds of LLM's Attention Heads . Also, we released a survey based on these fantastic works. If you want to cite our work, here is our bibtex entry: CITATION.bib."}
+{"idx": 4, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Two methods, Undifferentiated Attention and Scaling Contribution , are used to ablate the attention head. Safety Head ImPortant Score (Ships) is defined to evaluate the importance of attention heads based on the change in the probability of rejection responses to harmful queries.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper/arxiv/2410.13708", "content": "Two methods, Undifferentiated Attention and Scaling Contribution , are used to ablate the attention head. Safety Head ImPortant Score (Ships) is defined to evaluate the importance of attention heads based on the change in the probability of rejection responses to harmful queries."}
+{"idx": 5, "title": "What does increasing number of heads do in the Multi-head ...", "date": "", "ddg_snippet": "Nov 2, 2020 · The next bit is more of an opinion. When you have several heads per layer the heads are independent of each other. This means that the model can learn different patterns with each head. For example, one head might pay most attention to the next word in each sentence, and another head might pay attention to how nouns and adjectives combine.", "subpage_snippet": "", "source": "discuss.huggingface.co", "link": "https://discuss.huggingface.co/t/what-does-increasing-number-of-heads-do-in-the-multi-head-attention/1847", "content": "Nov 2, 2020 · The next bit is more of an opinion. When you have several heads per layer the heads are independent of each other. This means that the model can learn different patterns with each head. For example, one head might pay most attention to the next word in each sentence, and another head might pay attention to how nouns and adjectives combine."}
+{"idx": 6, "title": "(PDF) On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Undifferentiated Attention and Scaling Contribution , regarding their impact on the safety capability.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety", "content": "Undifferentiated Attention and Scaling Contribution , regarding their impact on the safety capability."}
+{"idx": 7, "title": "【论文阅读】 ON THE ROLE OF ATTENTION HEADS IN LARGE...", "date": "", "ddg_snippet": "Safety Attention Head AttRibution Algorithm (Sahara).", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/xianshuiyihui/article/details/149698061", "content": "Safety Attention Head AttRibution Algorithm (Sahara)."}
+{"idx": 8, "title": "【DL輪読会】 On The Role of Attention Heads in Large Language...", "date": "", "ddg_snippet": "は ASR （attack success rate） – プロンプト • direct • template 11.– Undifferentiated Attention > Scaling Contribution 12.", "subpage_snippet": "", "source": "www.docswell.com", "link": "https://www.docswell.com/s/DeepLearning2023/Z3GEV8-2024-11-21-153626", "content": "は ASR （attack success rate） – プロンプト • direct • template 11.– Undifferentiated Attention > Scaling Contribution 12."}
+{"idx": 9, "title": "30 Questions - Attention mechanism applications Quiz - Deep Learning", "date": "", "ddg_snippet": "Multi- Head Attention and Interpretability. Advanced users of attention mechanisms harness multi- head attention to capture different aspects of the input data simultaneously.", "subpage_snippet": "", "source": "talktome.ai", "link": "https://talktome.ai/cat12-attention-mechanism-applications-quiz/", "content": "Multi- Head Attention and Interpretability. Advanced users of attention mechanisms harness multi- head attention to capture different aspects of the input data simultaneously."}
diff --git a/data/sampled_jsons/Universal_Transformers_Dehghani_2019_parallel-in-time_self-attentive_recurrent_year_2019.jsonl b/data/sampled_jsons/Universal_Transformers_Dehghani_2019_parallel-in-time_self-attentive_recurrent_year_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9430617819133c594cc9bb973cd2d7a60c9f7cda
--- /dev/null
+++ b/data/sampled_jsons/Universal_Transformers_Dehghani_2019_parallel-in-time_self-attentive_recurrent_year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1807.03819] Universal Transformers", "date": "", "ddg_snippet": "by M Dehghani · 2018 · Cited by 1160 — We propose the Universal Transformer (UT), a parallel -in- time self - attentive recurrent sequence model which can be cast as a generalization of the Transformer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1807.03819", "content": "by M Dehghani · 2018 · Cited by 1160 — We propose the Universal Transformer (UT), a parallel -in- time self - attentive recurrent sequence model which can be cast as a generalization of the Transformer ..."}
+{"idx": 1, "title": "UNIVERSAL TRANSFORMERS", "date": "", "ddg_snippet": "In this paper, we introduce the Universal Transformer (UT), a parallel -in- time recurrent self - attentive sequence model which can be cast as a generalization of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/6ee41939003eaa38439a2607d081864b4ba5fea4.pdf", "content": "In this paper, we introduce the Universal Transformer (UT), a parallel -in- time recurrent self - attentive sequence model which can be cast as a generalization of ..."}
+{"idx": 2, "title": "[PDF] Universal Transformers", "date": "", "ddg_snippet": "The Universal Transformer (UT), a parallel -in- time self - attentive recurrent sequence model which can be cast as a generalization of the Transformer model ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Universal-Transformers-Dehghani-Gouws/ac4dafdef1d2b685b7f28a11837414573d39ff4e", "content": "The Universal Transformer (UT), a parallel -in- time self - attentive recurrent sequence model which can be cast as a generalization of the Transformer model ..."}
+{"idx": 3, "title": "(PDF) Universal Transformers", "date": "", "ddg_snippet": "We propose the Universal Transformer (UT), a parallel -in- time self - attentive recurrent sequence model which can be cast as a generalization of... ... Universal ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/74420506/Universal_Transformers", "content": "We propose the Universal Transformer (UT), a parallel -in- time self - attentive recurrent sequence model which can be cast as a generalization of... ... Universal ..."}
+{"idx": 4, "title": "Universal Transformers", "date": "", "ddg_snippet": "7 Sept 2019 — The Universal Transformer is a “ parallel -in- time self - attentive recurrent sequence model” which is parallelizable over the input sequence.", "subpage_snippet": "", "source": "glassboxmedicine.com", "link": "https://glassboxmedicine.com/2019/09/07/universal-transformers/", "content": "7 Sept 2019 — The Universal Transformer is a “ parallel -in- time self - attentive recurrent sequence model” which is parallelizable over the input sequence."}
+{"idx": 5, "title": "End-to-end Algorithm Synthesis with Recurrent Networks", "date": "", "ddg_snippet": "2019 . TLDR. The Universal Transformer (UT), a parallel -in- time self - attentive recurrent sequence model which can be cast as a generalization of the Transformer ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/c9143b978f91ee35429f1644a2266e5b036dad3a", "content": "2019 . TLDR. The Universal Transformer (UT), a parallel -in- time self - attentive recurrent sequence model which can be cast as a generalization of the Transformer ..."}
+{"idx": 6, "title": "A survey of transformers", "date": "", "ddg_snippet": "by T Lin · 2022 · Cited by 1884 — Furthermore, the self -attention modules in the decoder are adapted to prevent each position from attending to subsequent positions. The overall architecture of ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666651022000146", "content": "by T Lin · 2022 · Cited by 1884 — Furthermore, the self -attention modules in the decoder are adapted to prevent each position from attending to subsequent positions. The overall architecture of ..."}
+{"idx": 7, "title": "Theoretical Limitations of Self-Attention in Neural ...", "date": "", "ddg_snippet": "by M Hahn · 2020 · Cited by 385 — Eschewing recurrent computations, transformers are entirely based on self -attention, performing their computations largely in parallel . This enables them to ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2020.tacl-1.11.pdf", "content": "by M Hahn · 2020 · Cited by 385 — Eschewing recurrent computations, transformers are entirely based on self -attention, performing their computations largely in parallel . This enables them to ..."}
+{"idx": 8, "title": "A Transformer network with effective methods for learning ...", "date": "", "ddg_snippet": "by Y Shi · 2021 · Cited by 1 — Universal Transformer (UT) is a parallel - in- time self - attentive recurrent sequence model that can be cast as a generalization of the Transformer model and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2107.00653", "content": "by Y Shi · 2021 · Cited by 1 — Universal Transformer (UT) is a parallel - in- time self - attentive recurrent sequence model that can be cast as a generalization of the Transformer model and ..."}
+{"idx": 9, "title": "The Transformer Family Version 2.0 - Lil'Log", "date": "", "ddg_snippet": "27 Jan 2023 — Universal Transformer ( Dehghani , et al. 2019 ) combines self -attention in Transformer with the recurrent mechanism in RNN, aiming to benefit from ...", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2023-01-27-the-transformer-family-v2/", "content": "27 Jan 2023 — Universal Transformer ( Dehghani , et al. 2019 ) combines self -attention in Transformer with the recurrent mechanism in RNN, aiming to benefit from ..."}
diff --git a/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_arXiv.jsonl b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e4e34a3c07ce037f9c07f3c47c753aa4875452f
--- /dev/null
+++ b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fine - tuning (deep learning) - Wikipedia", "date": "", "ddg_snippet": "In deep learning, fine - tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. Fine - tuning can be done on the entire neural network, or on only a subset of its layers...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)", "content": "In deep learning, fine - tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. Fine - tuning can be done on the entire neural network, or on only a subset of its layers..."}
+{"idx": 1, "title": "Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model.View a PDF of the paper titled Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting , by Sunny Sanyal and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02797", "content": "Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model.View a PDF of the paper titled Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting , by Sunny Sanyal and 4 other authors."}
+{"idx": 2, "title": "Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "The sample -wise weights are computed once. 3. Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting . and used throughout the entire fine - tuning process.ICLR, 2021. 10. Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797", "content": "The sample -wise weights are computed once. 3. Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting . and used throughout the entire fine - tuning process.ICLR, 2021. 10. Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting ."}
+{"idx": 3, "title": "Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "To address this challenge, we propose a sample weighting scheme for the fine - tuning data solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2502.02797", "content": "To address this challenge, we propose a sample weighting scheme for the fine - tuning data solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model."}
+{"idx": 4, "title": "sanyalsunny111/FLOW_ finetuning : Upweighting Easy Samples in ...", "date": "", "ddg_snippet": "Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting .FLOW fine - tuning for vision models are performed as follows. Download a pre-trained model to be fine - tuned . Perform linear probing on model from step 1, using a target dataset to develop a linear probe (lp) model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sanyalsunny111/FLOW_finetuning", "content": "Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting .FLOW fine - tuning for vision models are performed as follows. Download a pre-trained model to be fine - tuned . Perform linear probing on model from step 1, using a target dataset to develop a linear probe (lp) model."}
+{"idx": 5, "title": "ICML Poster Upweighting Easy Samples in Fine - Tuning Mitigates ...", "date": "", "ddg_snippet": "To address this challenge, we propose a * sample weighting scheme for the fine - tuning data* solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46655", "content": "To address this challenge, we propose a * sample weighting scheme for the fine - tuning data* solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model."}
+{"idx": 6, "title": "[Literature Review] Upweighting Easy Samples in Fine - Tuning ...", "date": "", "ddg_snippet": "Upweighting . Fine - Tuning . Catastrophic Forgetting . Sample Weighting .In the paper \" Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting ,\" the authors address the challenge of catastrophic forgetting during the fine - tuning of pre-trained models.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/upweighting-easy-samples-in-fine-tuning-mitigates-forgetting", "content": "Upweighting . Fine - Tuning . Catastrophic Forgetting . Sample Weighting .In the paper \" Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting ,\" the authors address the challenge of catastrophic forgetting during the fine - tuning of pre-trained models."}
+{"idx": 7, "title": "Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "The paper \" Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting \" presents several innovative ideas and methods aimed at addressing the challenge of catastrophic forgetting in machine learning models, particularly in the context of continual learning.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-upweighting-easy-samples-in-fine-tuning-mitigates-cm6ttqb31frmi07s30f6nsl30", "content": "The paper \" Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting \" presents several innovative ideas and methods aimed at addressing the challenge of catastrophic forgetting in machine learning models, particularly in the context of continual learning."}
+{"idx": 8, "title": "Fine - tuning RL Models is Secretly a Forgetting Mitigation Problem...", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=seB93xaHhiM", "content": ""}
+{"idx": 9, "title": "Computer Science", "date": "", "ddg_snippet": "... from the Simons Foundation, member institutions , and all contributors. ... Title: Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs/pastweek?skip=740&show=25", "content": "... from the Simons Foundation, member institutions , and all contributors. ... Title: Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting"}
diff --git a/data/sampled_jsons/VBench_A_Comprehensive_Benchmark_for_Video_Generation.jsonl b/data/sampled_jsons/VBench_A_Comprehensive_Benchmark_for_Video_Generation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0b5d48512d95b7796c60386580b427bee0e0ced3
--- /dev/null
+++ b/data/sampled_jsons/VBench_A_Comprehensive_Benchmark_for_Video_Generation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VBench: Comprehensive Benchmark Suite for Video Generative", "date": "", "ddg_snippet": "We propose VBench , a comprehensive benchmark suite for video generative models. ... VBench }: Comprehensive Benchmark Suite for Video Generative ...", "subpage_snippet": "", "source": "vchitect.github.io", "link": "https://vchitect.github.io/VBench-project/", "content": "We propose VBench , a comprehensive benchmark suite for video generative models. ... VBench }: Comprehensive Benchmark Suite for Video Generative ..."}
+{"idx": 1, "title": "VBench-2.0: Advancing Video Generation Benchmark Suite for\\\\", "date": "", "ddg_snippet": "VBench -2.0 : Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness ... these video generative models, benchmarks such as VBench have ...", "subpage_snippet": "", "source": "vchitect.github.io", "link": "https://vchitect.github.io/VBench-2.0-project/", "content": "VBench -2.0 : Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness ... these video generative models, benchmarks such as VBench have ..."}
+{"idx": 2, "title": "[2311.17982] VBench: Comprehensive Benchmark Suite for Video", "date": "", "ddg_snippet": "A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.17982", "content": "A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human ..."}
+{"idx": 3, "title": "Less is Enough: Training-Free Video Diffusion Acceleration via", "date": "", "ddg_snippet": "Within this landscape, video generation has garnered significant attention due to the growing demand for dynamic content creation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02860v1", "content": "Within this landscape, video generation has garnered significant attention due to the growing demand for dynamic content creation."}
+{"idx": 4, "title": "Hunyuan: Transform Text into Stunning Videos - novita.ai", "date": "", "ddg_snippet": "VBench is a robust and comprehensive benchmark suite designed to evaluate video generative models. ... Video stands at the forefront of AI-powered ...", "subpage_snippet": "", "source": "blogs.novita.ai", "link": "https://blogs.novita.ai/hunyuanvideo/", "content": "VBench is a robust and comprehensive benchmark suite designed to evaluate video generative models. ... Video stands at the forefront of AI-powered ..."}
+{"idx": 5, "title": "Wan2.1: An Open-Source AI Model for Video Generation - novita.ai", "date": "", "ddg_snippet": "VBench is a robust and comprehensive benchmark suite designed to evaluate video generative models. ... automated generation of high-quality videos for ...", "subpage_snippet": "", "source": "blogs.novita.ai", "link": "https://blogs.novita.ai/wan2-1/", "content": "VBench is a robust and comprehensive benchmark suite designed to evaluate video generative models. ... automated generation of high-quality videos for ..."}
+{"idx": 6, "title": "Introduction to Wan 2.1 and How to Use WanVideo to Create Magic", "date": "", "ddg_snippet": "Wan 2.1 has topped the VBench leaderboard, a comprehensive benchmark for video generation models, scoring particularly well in areas like movement ...", "subpage_snippet": "", "source": "wanvideo.io", "link": "https://wanvideo.io/hub/introduction-to-wan-21-and-use-wanvideo-create-magic-video", "content": "Wan 2.1 has topped the VBench leaderboard, a comprehensive benchmark for video generation models, scoring particularly well in areas like movement ..."}
+{"idx": 7, "title": "CVPR 2024 Datasets and Benchmarks - Part 2: Benchmarks - Voxel51", "date": "", "ddg_snippet": "VBench : Comprehensive Benchmark Suite for Video Generative Models ... Each of these benchmarks presents unique challenges and opportunities for ...", "subpage_snippet": "", "source": "voxel51.com", "link": "https://voxel51.com/blog/cvpr-2024-datasets-and-benchmarks-part-2-benchmarks/", "content": "VBench : Comprehensive Benchmark Suite for Video Generative Models ... Each of these benchmarks presents unique challenges and opportunities for ..."}
+{"idx": 8, "title": "Alibaba Cloud Open Sources its AI Models for Video Generation -", "date": "", "ddg_snippet": "... in following instructions has propelled Wan2.1 to the top of the VBench leaderboard, a comprehensive benchmark suite for video generative models.", "subpage_snippet": "", "source": "mid-east.info", "link": "https://mid-east.info/alibaba-cloud-open-sources-its-ai-models-for-video-generation/", "content": "... in following instructions has propelled Wan2.1 to the top of the VBench leaderboard, a comprehensive benchmark suite for video generative models."}
+{"idx": 9, "title": "Yuanhan Zhang", "date": "", "ddg_snippet": "Our experiments demonstrate that LLaVA- Video achieves strong performance across various video benchmarks , highlighting the effectiveness of our ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Yuanhan+Zhang", "content": "Our experiments demonstrate that LLaVA- Video achieves strong performance across various video benchmarks , highlighting the effectiveness of our ..."}
diff --git a/data/sampled_jsons/VRSBench_Li_et_al._2024_filetypehtml.jsonl b/data/sampled_jsons/VRSBench_Li_et_al._2024_filetypehtml.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c019747bc75e78735ee4a30fa75bd5ebb839a0c4
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_Li_et_al._2024_filetypehtml.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote ...", "date": "", "ddg_snippet": "VRSBench comprises 29,614 images, each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets. This dataset facilitates the training and evaluation of vision-language models across a spectrum of remote sensing image understanding tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v2", "content": "VRSBench comprises 29,614 images, each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets. This dataset facilitates the training and evaluation of vision-language models across a spectrum of remote sensing image understanding tasks."}
+{"idx": 1, "title": "GitHub - lx709/VRSBench", "date": "", "ddg_snippet": "[ 2024 .06.19] We release the VRSBench , A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. VRSBench contains 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. check VRSBench Project Page.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "[ 2024 .06.19] We release the VRSBench , A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. VRSBench contains 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. check VRSBench Project Page."}
+{"idx": 2, "title": "VRSBench:", "date": "", "ddg_snippet": "VRSBench provides large-scale human-verified annotations that feature several advantages: VRSBench provides a large-scale collection of human-verified, high-quality captions rich in object details. VRSBench offers more realistic object refers in which each referring sentence unambiguously identifies an object among multiple similar ones within the same category. VRSBench features a diverse ...", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "VRSBench provides large-scale human-verified annotations that feature several advantages: VRSBench provides a large-scale collection of human-verified, high-quality captions rich in object details. VRSBench offers more realistic object refers in which each referring sentence unambiguously identifies an object among multiple similar ones within the same category. VRSBench features a diverse ..."}
+{"idx": 3, "title": "ZFIN Publication: Li et al., 2024", "date": "", "ddg_snippet": "Li , Y., Yu, S., Jia, X., Qiu, X., He, J. ID ZDB-PUB-240217-1 Date 2024 Source PLoS Biology 22: e3002506e3002506 (Journal) Generate reference Registered Authors He, Jie, Jia, Xinling, Li , Yan, Yu, Shuguang Keywords none MeSH Terms Mammals Transcription Factors/metabolism Zebrafish* Cholinergic Agents/metabolism Animals Retina/metabolism Amacrine ...", "subpage_snippet": "", "source": "zfin.org", "link": "https://zfin.org/ZDB-PUB-240217-1", "content": "Li , Y., Yu, S., Jia, X., Qiu, X., He, J. ID ZDB-PUB-240217-1 Date 2024 Source PLoS Biology 22: e3002506e3002506 (Journal) Generate reference Registered Authors He, Jie, Jia, Xinling, Li , Yan, Yu, Shuguang Keywords none MeSH Terms Mammals Transcription Factors/metabolism Zebrafish* Cholinergic Agents/metabolism Animals Retina/metabolism Amacrine ..."}
+{"idx": 4, "title": "Neurlps2024论文解析|VRSBench A Versatile Vision ... - CSDN博客", "date": "", "ddg_snippet": "论文标题 VRSBench : A Versatile Vision -Language Benchmark Dataset for Remote Sensing Image Understanding VRSBench : 用于遥感图像理解的多功能视觉-语言基准数据集 论文链接 VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding论文下载 论文作者 Xiang Li , Jian Ding, Mohamed Elhoseiny 内容简介 本文介绍了一种新的 ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/SJ_HP/article/details/145682696", "content": "论文标题 VRSBench : A Versatile Vision -Language Benchmark Dataset for Remote Sensing Image Understanding VRSBench : 用于遥感图像理解的多功能视觉-语言基准数据集 论文链接 VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding论文下载 论文作者 Xiang Li , Jian Ding, Mohamed Elhoseiny 内容简介 本文介绍了一种新的 ..."}
+{"idx": 5, "title": "VRSBench/README.md at main · lx709/VRSBench · GitHub", "date": "", "ddg_snippet": "[ 2024 .06.19] We release the VRSBench , A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. VRSBench contains 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. check VRSBench Project Page.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench/blob/main/README.md", "content": "[ 2024 .06.19] We release the VRSBench , A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. VRSBench contains 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. check VRSBench Project Page."}
+{"idx": 6, "title": "VRSBench | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li , Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2021.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738022", "content": "Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li , Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2021."}
+{"idx": 7, "title": "(PDF) VRSBench: A Versatile Vision-Language Benchmark Dataset for ...", "date": "", "ddg_snippet": "We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381518535_VRSBench_A_Versatile_Vision-Language_Benchmark_Dataset_for_Remote_Sensing_Image_Understanding", "content": "We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in ..."}
+{"idx": 8, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote ...", "date": "", "ddg_snippet": "We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ..."}
+{"idx": 9, "title": "GitHub - Lavender105/RSGPT", "date": "", "ddg_snippet": "[ 2024 .06.19] We release the VRSBench , A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. VRSBench contains 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. check VRSBench Project Page.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Lavender105/RSGPT", "content": "[ 2024 .06.19] We release the VRSBench , A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. VRSBench contains 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. check VRSBench Project Page."}
diff --git a/data/sampled_jsons/VRSBench_Li_et_al._2024_remote_sensing_image_size_year_2024.jsonl b/data/sampled_jsons/VRSBench_Li_et_al._2024_remote_sensing_image_size_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5e805e2e695f58462ae82279ad202c2cd16cfc8a
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_Li_et_al._2024_remote_sensing_image_size_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2406.12384] VRSBench : A Versatile Vision-Language Benchmark...", "date": "", "ddg_snippet": "View a PDF of the paper titled VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding, by Xiang Li and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "View a PDF of the paper titled VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding, by Xiang Li and 2 other authors."}
+{"idx": 1, "title": "GitHub - lx709/ VRSBench", "date": "", "ddg_snippet": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding.Vision-language models in remote sensing : Current progress and future trends.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding.Vision-language models in remote sensing : Current progress and future trends."}
+{"idx": 2, "title": "VRSBench : A Versatile Vision-Language Benchmark", "date": "", "ddg_snippet": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding."}
+{"idx": 3, "title": "VRSBench : A Versatile Vision-Language Benchmark... | OpenReview", "date": "", "ddg_snippet": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kwDOxOmGE0&noteId=CgRIAIjiiX", "content": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding."}
+{"idx": 4, "title": "ESSD - ChatEarthNet: a global-scale image –text dataset empowering...", "date": "", "ddg_snippet": "The recent work VRSBench ( Li et al ., 2024 a) offers a versatile benchmark featuring human-verified captions with detailed object information for remote sensing images .", "subpage_snippet": "", "source": "essd.copernicus.org", "link": "https://essd.copernicus.org/articles/17/1245/2025/", "content": "The recent work VRSBench ( Li et al ., 2024 a) offers a versatile benchmark featuring human-verified captions with detailed object information for remote sensing images ."}
+{"idx": 5, "title": "xiang709/ VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "The remote sensing image sourced from GoogleEarth displays a landscape with agricultural fields, a settlement of houses, and a small dam positioned to the right side of the image . VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "The remote sensing image sourced from GoogleEarth displays a landscape with agricultural fields, a settlement of houses, and a small dam positioned to the right side of the image . VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding."}
+{"idx": 6, "title": "Comparison between existing remote sensing vision-language...", "date": "", "ddg_snippet": "VRSBench comprises 29,614 images , each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Comparison-between-existing-remote-sensing-vision-language-datasets-and-our-VRSBench_tbl1_381518535", "content": "VRSBench comprises 29,614 images , each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets."}
+{"idx": 7, "title": "Introducing VRSBench : Advancing Remote Sensing Image Analysis", "date": "", "ddg_snippet": "Remote sensing uses different types of images taken from satellites or aircraft to gather information about the Earth's surface.Title: VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-27-introducing-vrsbench-advancing-remote-sensing-image-analysis--ak6l6or", "content": "Remote sensing uses different types of images taken from satellites or aircraft to gather information about the Earth's surface.Title: VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding."}
+{"idx": 8, "title": "AI and remote sensing data sets advance sustainable mining and...", "date": "", "ddg_snippet": "Among the new possibilities for using remote sensing images in mining is the increasing application of Artificial Intelligence (AI).Weikang Yu et al , MineNetCD: A Benchmark for Global Mining Change Detection on Remote Sensing Imagery , arXiv ( 2024 ).", "subpage_snippet": "", "source": "phys.org", "link": "https://phys.org/news/2024-08-ai-remote-advance-sustainable-earth.html?&target=_blank&trk=article-ssr-frontend-pulse_little-text-block", "content": "Among the new possibilities for using remote sensing images in mining is the increasing application of Artificial Intelligence (AI).Weikang Yu et al , MineNetCD: A Benchmark for Global Mining Change Detection on Remote Sensing Imagery , arXiv ( 2024 )."}
+{"idx": 9, "title": "Advancements in Vision–Language Models for Remote Sensing ...", "date": "", "ddg_snippet": "* Remote Sensing Image Change Captioning (RSICC): A new task aiming to generate human-like language descriptions for the land-cover changes in multitemporal RS images .", "subpage_snippet": "", "source": "www.thefreelibrary.com", "link": "https://www.thefreelibrary.com/Advancements+in+Vision–Language+Models+for+Remote+Sensing:+Datasets,...-a0823355239", "content": "* Remote Sensing Image Change Captioning (RSICC): A new task aiming to generate human-like language descriptions for the land-cover changes in multitemporal RS images ."}
diff --git a/data/sampled_jsons/VRSBench_arxiv_abstract_image_size_year_2024.jsonl b/data/sampled_jsons/VRSBench_arxiv_abstract_image_size_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1f3df3faeefcbda21be9c5938d9edca3c7412f23
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_arxiv_abstract_image_size_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "by X Li · 2024 · Cited by 50 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "by X Li · 2024 · Cited by 50 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ..."}
+{"idx": 1, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "18 Jun 2024 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v1", "content": "18 Jun 2024 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ..."}
+{"idx": 2, "title": "VRSBench", "date": "", "ddg_snippet": "This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 3, "title": "Enhancing Ultra High Resolution Remote Sensing Imagery ...", "date": "", "ddg_snippet": "12 Mar 2025 — In this paper, we introduce ImageRAG for RS, a framework to address the complexities of analyzing UHR remote sensing imagery with a little training requirement.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07688v2", "content": "12 Mar 2025 — In this paper, we introduce ImageRAG for RS, a framework to address the complexities of analyzing UHR remote sensing imagery with a little training requirement."}
+{"idx": 4, "title": "Enhancing Ultrahigh Resolution Remote Sensing Imagery ...", "date": "", "ddg_snippet": "Ultrahigh resolution (UHR) remote sensing imagery. (RSI) (e.g. 10,000 # 10,000 pixels) poses a significant challenge for current RS vision-language models ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6245518/8976286/11039502.pdf", "content": "Ultrahigh resolution (UHR) remote sensing imagery. (RSI) (e.g. 10,000 # 10,000 pixels) poses a significant challenge for current RS vision-language models ..."}
+{"idx": 5, "title": "AeroLite: Tag-Guided Lightweight Generation of Aerial Image", "date": "", "ddg_snippet": "Remote sensing imagery is indispensable in areas such as environmental monitoring, urban planning, agricultural management, and disaster response ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09528v1", "content": "Remote sensing imagery is indispensable in areas such as environmental monitoring, urban planning, agricultural management, and disaster response ..."}
+{"idx": 6, "title": "CVPR Poster XLRS-Bench: Could Your Multimodal LLMs ...", "date": "", "ddg_snippet": "14 Jun 2025 — XLRS-Bench boasts the largest average image size (8500 × 8500) observed thus far, with all evaluation samples meticulously annotated manually, ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/35068", "content": "14 Jun 2025 — XLRS-Bench boasts the largest average image size (8500 × 8500) observed thus far, with all evaluation samples meticulously annotated manually, ..."}
+{"idx": 7, "title": "GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large ...", "date": "", "ddg_snippet": "27 May 2025 — LRS-VQA lrsvqa constructs a large remote sensing VQA benchmark with 7,333 QA pairs across 8 categories, with image length up to 27,328 pixels.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.21375v1", "content": "27 May 2025 — LRS-VQA lrsvqa constructs a large remote sensing VQA benchmark with 7,333 QA pairs across 8 categories, with image length up to 27,328 pixels."}
+{"idx": 8, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "31 Mar 2025 — XLRS-Bench boasts the largest average image size (8500 × \\times × 8500) observed thus far, with all evaluation samples meticulously annotated ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.23771v1", "content": "31 Mar 2025 — XLRS-Bench boasts the largest average image size (8500 × \\times × 8500) observed thus far, with all evaluation samples meticulously annotated ..."}
+{"idx": 9, "title": "UrBench: A Comprehensive Benchmark for Evaluating Large", "date": "", "ddg_snippet": "For instance, geo-localization tasks require satellite-view imagery for spatial orientation and street-view imagery for detailed contexts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17267v3", "content": "For instance, geo-localization tasks require satellite-view imagery for spatial orientation and street-view imagery for detailed contexts."}
diff --git a/data/sampled_jsons/VRSBench_dataset_image_dimensions_pixels_1024_2048_512_size_year_2024.jsonl b/data/sampled_jsons/VRSBench_dataset_image_dimensions_pixels_1024_2048_512_size_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7d18b27a5ad7633737d4499a9e1d114a07716258
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_dataset_image_dimensions_pixels_1024_2048_512_size_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "List of common display resolutions - Wikipedia", "date": "", "ddg_snippet": "The extra pixels are used to form the increased area to the sides of the D1 image . The pixel density of 960H is identical to standard D1 resolution so it does not give any improvement in image quality, merely a wider aspect ratio.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/List_of_common_display_resolutions", "content": "The extra pixels are used to form the increased area to the sides of the D1 image . The pixel density of 960H is identical to standard D1 resolution so it does not give any improvement in image quality, merely a wider aspect ratio."}
+{"idx": 1, "title": "Effect of Matrix Size on the Image Quality of Ultra-high-resolution CT ...", "date": "", "ddg_snippet": "Slit phantoms and 11 cadaveric lungs were scanned on U-HRCT. Slit phantom scans were reconstructed using a 20-mm field of view (FOV) with 1024 matrix size and a 320-mm FOV with 512 , 1024 , and 2048 matrix sizes . Cadaveric lung scans were reconstructed using 512 , 1024 , and 2048 matrix sizes . Three observers subjectively scored the images on a three-point scale (1 = worst, 3 = best), in terms of ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1076633217305044", "content": "Slit phantoms and 11 cadaveric lungs were scanned on U-HRCT. Slit phantom scans were reconstructed using a 20-mm field of view (FOV) with 1024 matrix size and a 320-mm FOV with 512 , 1024 , and 2048 matrix sizes . Cadaveric lung scans were reconstructed using 512 , 1024 , and 2048 matrix sizes . Three observers subjectively scored the images on a three-point scale (1 = worst, 3 = best), in terms of ..."}
+{"idx": 2, "title": "Do 2048,1024,512 etc are a must texture size? - Rendering - Epic ...", "date": "", "ddg_snippet": "It's a thing about how graphics cards work. For 3D rendering, specifically, they are optimized to draw faster pixel buffers that are sized \"power of 2\" bits both width and height: 0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512 , 1024 , 2048 , 4096, etc… So you can either have a texture square 2048 x 2048 or 2048 x 512 , or 256 x 4096 and etc.", "subpage_snippet": "", "source": "forums.unrealengine.com", "link": "https://forums.unrealengine.com/t/do-2048-1024-512-etc-are-a-must-texture-size/107574", "content": "It's a thing about how graphics cards work. For 3D rendering, specifically, they are optimized to draw faster pixel buffers that are sized \"power of 2\" bits both width and height: 0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512 , 1024 , 2048 , 4096, etc… So you can either have a texture square 2048 x 2048 or 2048 x 512 , or 256 x 4096 and etc."}
+{"idx": 3, "title": "A novel strategy to develop deep learning for image super-resolution ...", "date": "", "ddg_snippet": "In the SHR mode, 1792 detector channels with 0.25 × 0.25 mm size are used, and 512 × 512 , 1024 × 1024 , and 2048 × 2048 matrix sizes with a 0.25 mm slice thickness are available, thereby providing high-resolution images in both the in-plane and body-axis directions.", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8315896/", "content": "In the SHR mode, 1792 detector channels with 0.25 × 0.25 mm size are used, and 512 × 512 , 1024 × 1024 , and 2048 × 2048 matrix sizes with a 0.25 mm slice thickness are available, thereby providing high-resolution images in both the in-plane and body-axis directions."}
+{"idx": 4, "title": "Rendering 512, 1024, 2048, 4096, 8192, - planetside.co.uk", "date": "", "ddg_snippet": "Rendering 512 , 1024 , 2048 , 4096, 8192,Can you show some examples? What would be ideal is 1: a downsampled version of the full image for one of the problem renders 2: a crop at full resolution of a problem area on one of the renders. Some more details about your scene would also help. Are you rendering straight down and/or orthographically? How big is your terrain? You're not possibly seeing ...", "subpage_snippet": "", "source": "planetside.co.uk", "link": "https://planetside.co.uk/forums/index.php?topic=11384.0", "content": "Rendering 512 , 1024 , 2048 , 4096, 8192,Can you show some examples? What would be ideal is 1: a downsampled version of the full image for one of the problem renders 2: a crop at full resolution of a problem area on one of the renders. Some more details about your scene would also help. Are you rendering straight down and/or orthographically? How big is your terrain? You're not possibly seeing ..."}
+{"idx": 5, "title": "Visual difference between 2048 x 1024 texture & 1024 x 512 texture on ...", "date": "", "ddg_snippet": "Good morning, I wanted to get some thoughts on this before testing myself. Is there much difference on the simulator (p3D) when applying a 2048x1024 texture and a 1024x512 texture. I obviuosly know that there should be, but my question is centered around whether the difference is noticed when...", "subpage_snippet": "", "source": "www.fsdeveloper.com", "link": "https://www.fsdeveloper.com/forum/threads/visual-difference-between-2048-x-1024-texture-1024-x-512-texture-on-building.448182/", "content": "Good morning, I wanted to get some thoughts on this before testing myself. Is there much difference on the simulator (p3D) when applying a 2048x1024 texture and a 1024x512 texture. I obviuosly know that there should be, but my question is centered around whether the difference is noticed when..."}
+{"idx": 6, "title": "1. Переведите: 16 бит = ... байт 2,5 байта = ... бит 2048 байт", "date": "", "ddg_snippet": "1 КБ = 8192 бит (так как 1 Кб = 1024 байта и 1 байт = 8 бит, то 1024 × 8 = 8192).", "subpage_snippet": "", "source": "www.euroki.org", "link": "https://www.euroki.org/koza/perevedite-bit---bayt-bayta---bit-bayt---kb-bayt---mb-bayta---bit-kb---bayt-kb---bit-kb---mb", "content": "1 КБ = 8192 бит (так как 1 Кб = 1024 байта и 1 байт = 8 бит, то 1024 × 8 = 8192)."}
+{"idx": 7, "title": "dalle-mini/open- images · Datasets at Hugging Face", "date": "", "ddg_snippet": "Dataset card. Data Studio.1,620. Use this dataset . Size of downloaded dataset files: 3.12 GB.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/dalle-mini/open-images", "content": "Dataset card. Data Studio.1,620. Use this dataset . Size of downloaded dataset files: 3.12 GB."}
+{"idx": 8, "title": "(PDF) WxC- Bench : A Novel Dataset for Weather and Climate...", "date": "", "ddg_snippet": "We provide a comprehensive description of the dataset and also present a technical validation for baseline analysis.search results for individual parameters. The validation is done by training the encoder-decoder model for 100 epochs on input images of size 32x25.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386455093_WxC-Bench_A_Novel_Dataset_for_Weather_and_Climate_Downstream_Tasks", "content": "We provide a comprehensive description of the dataset and also present a technical validation for baseline analysis.search results for individual parameters. The validation is done by training the encoder-decoder model for 100 epochs on input images of size 32x25."}
+{"idx": 9, "title": "Les dimensions des images sur les Réseaux Sociaux en 2023", "date": "", "ddg_snippet": "Les dimensions d’une image partagée sur Twitter : 1024 x 512 pixels minimum – 1600 x 900 recommandées.", "subpage_snippet": "", "source": "www.ludosln.net", "link": "https://www.ludosln.net/dimensions-images-reseaux-sociaux/", "content": "Les dimensions d’une image partagée sur Twitter : 1024 x 512 pixels minimum – 1600 x 900 recommandées."}
diff --git a/data/sampled_jsons/Vanilla_Hybrid_Training_VHT_underperforms_blendfake_data_BF-only_deepfake_detector.jsonl b/data/sampled_jsons/Vanilla_Hybrid_Training_VHT_underperforms_blendfake_data_BF-only_deepfake_detector.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a6481c1592ce921b4b29f07eca3b1df566085e2
--- /dev/null
+++ b/data/sampled_jsons/Vanilla_Hybrid_Training_VHT_underperforms_blendfake_data_BF-only_deepfake_detector.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "This is likely because previous empirical observations suggest that vanilla hybrid training ( VHT ), which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called \"1+1<2\").", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17052", "content": "This is likely because previous empirical observations suggest that vanilla hybrid training ( VHT ), which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called \"1+1<2\")."}
+{"idx": 1, "title": "Blendfake Data: A New Way to Detect Deepfakes - Simple Science", "date": "", "ddg_snippet": "This is likely because previous empirical observations suggest that vanilla hybrid training ( VHT ), which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called \"1+1 Exploring blendfake data's effectiveness in deepfake detection methods.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-19-blendfake-data-a-new-way-to-detect-deepfakes--akxyq01", "content": "This is likely because previous empirical observations suggest that vanilla hybrid training ( VHT ), which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called \"1+1 Exploring blendfake data's effectiveness in deepfake detection methods."}
+{"idx": 2, "title": "prithivMLmods/Deep-Fake-Detector-v2-Model · Hugging Face", "date": "", "ddg_snippet": "The Deep-Fake- Detector -v2-Model is a state-of-the-art deep learning model designed to detect deepfake images. It leverages the Vision Transformer (ViT) architecture, specifically the google/vit-base-patch16-224-in21k model, fine-tuned on a dataset of real and deepfake images.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/prithivMLmods/Deep-Fake-Detector-v2-Model", "content": "The Deep-Fake- Detector -v2-Model is a state-of-the-art deep learning model designed to detect deepfake images. It leverages the Vision Transformer (ViT) architecture, specifically the google/vit-base-patch16-224-in21k model, fine-tuned on a dataset of real and deepfake images."}
+{"idx": 3, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Interestingly, current SoTA methods utilize blendfake without incorporating any deepfake data in their training process. This is likely because previous empirical observations suggest that vanilla hybrid training ( VHT ), which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called 1 ...", "subpage_snippet": "", "source": "deepfake-total.com", "link": "https://deepfake-total.com/related_work/2408.17052", "content": "Interestingly, current SoTA methods utilize blendfake without incorporating any deepfake data in their training process. This is likely because previous empirical observations suggest that vanilla hybrid training ( VHT ), which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called 1 ..."}
+{"idx": 4, "title": "Compression-Aware Hybrid Framework for Deep Fake Detection in Low ...", "date": "", "ddg_snippet": "Compression-Aware Hybrid Framework for Deep Fake Detection in Low-Quality Video Deep fakes pose a growing threat to digital media integrity by generating highly realistic fake videos that are difficult to detect, especially under the high compression levels commonly used on social media platforms.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11095666", "content": "Compression-Aware Hybrid Framework for Deep Fake Detection in Low-Quality Video Deep fakes pose a growing threat to digital media integrity by generating highly realistic fake videos that are difficult to detect, especially under the high compression levels commonly used on social media platforms."}
+{"idx": 5, "title": "Can We Leave Deepfake Data Behind in Training Deep", "date": "", "ddg_snippet": "represents using only blendfake data (i.e., CBI [27] and SBI [40]) to train the deepfake detector . Vanilla Hybrid Training ( VHT ) represents simply incorporates all three data (i.e., Deepfake , CBI [27],", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/790279987/Can-We-Leave-Deepfake-Data-Behind-in-Training-Deep", "content": "represents using only blendfake data (i.e., CBI [27] and SBI [40]) to train the deepfake detector . Vanilla Hybrid Training ( VHT ) represents simply incorporates all three data (i.e., Deepfake , CBI [27],"}
+{"idx": 6, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "The table compares the baseline of using only blendfake data ( BF-only ) and vanilla hybrid training ( VHT ) with different combinations of the proposed components: Oriented Progressive Regularizer (OPR) with various classification strategies (multi-class, multi-label, triplet binary), feature bridging, and transition loss.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/vh9yepleyd/", "content": "The table compares the baseline of using only blendfake data ( BF-only ) and vanilla hybrid training ( VHT ) with different combinations of the proposed components: Oriented Progressive Regularizer (OPR) with various classification strategies (multi-class, multi-label, triplet binary), feature bridging, and transition loss."}
+{"idx": 7, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "This is likely because previous empirical observations suggest that vanilla hybrid training ( VHT ), which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called \"1+1<2\").", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "This is likely because previous empirical observations suggest that vanilla hybrid training ( VHT ), which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called \"1+1<2\")."}
+{"idx": 8, "title": "A Hybrid Approach for Deep Fake Detection Using Deep ... - Springer", "date": "", "ddg_snippet": "Abstract Deep Fake technology has emerged as a potent tool for generating highly realistic synthetic media, raising concerns about its potential misuse and impact on society. This paper presents an innovative approach to address the growing threat of Deep Fake proliferation through a robust detection system.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-97-6714-4_11", "content": "Abstract Deep Fake technology has emerged as a potent tool for generating highly realistic synthetic media, raising concerns about its potential misuse and impact on society. This paper presents an innovative approach to address the growing threat of Deep Fake proliferation through a robust detection system."}
+{"idx": 9, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "process. This is likely because previous empirical observations suggest that vanilla hybrid training ( VHT ), which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called \"1", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.17052", "content": "process. This is likely because previous empirical observations suggest that vanilla hybrid training ( VHT ), which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called \"1"}
diff --git a/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_Equation_(5).jsonl b/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_Equation_(5).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b98da86885b334be78808365f8f1df1bc91feabe
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_Equation_(5).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.19009] Video-ColBERT: Contextualized Late Interaction ... Video-ColBERT: Contextualized Late Interaction for Text-to ... GitHub - yogesh-iitj/Video-ColBERT Alexander Martin CVPR 2025 Open Access Repository Video-ColBERT: Contextualized Late Interaction for Text-to ... Video -ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval Video - ColBERT : Contextualized Late Interaction for Text-to- Video Retrie… Video - ColBERT : Contextualized Late Interaction for Text-to- Video Retrie… Video - ColBERT : Contextualized Late Interaction for Text-to- Video Retrie… Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "Mar 24, 2025 · In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ... Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expan-sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong indi-vidual, yet compatible, representations for encoding video content. This repository implements Video-ColBERT , a contextualized late interaction model for text-to- video retrieval. Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ... Aug 20, 2025 · Inspired by the success of late interaction techniques in text-document, text-image, and text- video re- trieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assess- ment between queries and videos . In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. How does video-Colbert work? Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. Is video-Colbert a good method for text-to-video retrieval? This finding, again, depends heavily on the types of videos being retrieved. In this work, we introduced Video - ColBERT , a novel approach for text-to- video retrieval that uses efficient fine-grained interactions with both spatial and spatio-temporal visual features. Why do we use contextualized video representations in video-Colbert? Unlike previous works that only use features after temporal modeling, the contextualized video representations in Video - ColBERT can encode more temporal information because the temporal layers have less need to preserve purely spatial concepts and can instead focus on capturing higher-level cross-frame and global interactions. How does Colbert improve text document retrieval? In the domain of text document retrieval, ColBERT showed the promise of contextualized late interaction techniques , which enable fine-grained interaction between queries and documents while maintaining efficiency at retrieval time. Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible representations for encoding video content.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "Mar 24, 2025 · In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ... Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expan-sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong indi-vidual, yet compatible, representations for encoding video content. This repository implements Video-ColBERT , a contextualized late interaction model for text-to- video retrieval. Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ... Aug 20, 2025 · Inspired by the success of late interaction techniques in text-document, text-image, and text- video re- trieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assess- ment between queries and videos . In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. How does video-Colbert work? Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. Is video-Colbert a good method for text-to-video retrieval? This finding, again, depends heavily on the types of videos being retrieved. In this work, we introduced Video - ColBERT , a novel approach for text-to- video retrieval that uses efficient fine-grained interactions with both spatial and spatio-temporal visual features. Why do we use contextualized video representations in video-Colbert? Unlike previous works that only use features after temporal modeling, the contextualized video representations in Video - ColBERT can encode more temporal information because the temporal layers have less need to preserve purely spatial concepts and can instead focus on capturing higher-level cross-frame and global interactions. How does Colbert improve text document retrieval? In the domain of text document retrieval, ColBERT showed the promise of contextualized late interaction techniques , which enable fine-grained interaction between queries and documents while maintaining efficiency at retrieval time. Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible representations for encoding video content."}
+{"idx": 1, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expan-sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong indi-vidual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expan-sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong indi-vidual, yet compatible, representations for encoding video content."}
+{"idx": 2, "title": "GitHub - yogesh-iitj/Video-ColBERT", "date": "", "ddg_snippet": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to- video retrieval. Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yogesh-iitj/Video-ColBERT", "content": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to- video retrieval. Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ..."}
+{"idx": 3, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009", "content": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content."}
+{"idx": 4, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible representations for encoding video content.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33381", "content": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible representations for encoding video content."}
+{"idx": 5, "title": "Video - ColBERT : Contextualized Late Interaction for Text-to-Video...", "date": "", "ddg_snippet": "* Equal contribution. Video Similarity. COLBERT , a multi-level late - interaction retrieval model that provides better retrieval effectiveness with comparable model size. 2. We introduce a richer form of tokenwise interaction by performing MMS at both the spatial and spatio-temporal levels.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.19009", "content": "* Equal contribution. Video Similarity. COLBERT , a multi-level late - interaction retrieval model that provides better retrieval effectiveness with comparable model size. 2. We introduce a richer form of tokenwise interaction by performing MMS at both the spatial and spatio-temporal levels."}
+{"idx": 6, "title": "Video - ColBERT : Contextualized Late Interaction for... | alphaXiv", "date": "", "ddg_snippet": "We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. These representations lead to increases in performance on common text-to- video retrieval benchmarks compared to other bi-encoder methods.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2503.19009", "content": "We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. These representations lead to increases in performance on common text-to- video retrieval benchmarks compared to other bi-encoder methods."}
+{"idx": 7, "title": "ColBERT : Contextualized Late Interaction BERT explained... | Medium", "date": "", "ddg_snippet": "The Colbert architecture is a neural IR technique which does late -stage interaction between each query token and each token in a passage in the dataset.Taken from this YouTube video by Chris Potts. The authors remove the punctuation before this late interaction .", "subpage_snippet": "", "source": "athekunal.medium.com", "link": "https://athekunal.medium.com/colbert-contextualized-late-interaction-bert-explained-with-a-tutorial-2ae72a7aa055", "content": "The Colbert architecture is a neural IR technique which does late -stage interaction between each query token and each token in a passage in the dataset.Taken from this YouTube video by Chris Potts. The authors remove the punctuation before this late interaction ."}
+{"idx": 8, "title": "Video - ColBERT : Contextualized Late Interaction for Text-to-Video...", "date": "", "ddg_snippet": "Inspired by the success of late interaction techniques in text-document, text-image, and text-video retrieval, our approach, Video - ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval@CVPR2025@CVF", "content": "Inspired by the success of late interaction techniques in text-document, text-image, and text-video retrieval, our approach, Video - ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos."}
+{"idx": 9, "title": "Video - ColBERT : Contextualized Late Interaction for Text-to-Video...", "date": "", "ddg_snippet": "Inspired by the success of late interaction techniques in text-document, text-image, and text-video retrieval, our approach, Video - ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/bff2f91c763830a2d14dbbbeca150e92ede02323/Video+ColBERT:-Contextualized-Late-Interaction-for-Text+to+Video-Retrieval/graph", "content": "Inspired by the success of late interaction techniques in text-document, text-image, and text-video retrieval, our approach, Video - ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos."}
diff --git a/data/sampled_jsons/Video-ColBERT_MSVD_8_frames_OR_16_frames_OR_32_frames_OR_64_frames.jsonl b/data/sampled_jsons/Video-ColBERT_MSVD_8_frames_OR_16_frames_OR_32_frames_OR_64_frames.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..938eb661de676982acb0c32507787561c4af9266
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_MSVD_8_frames_OR_16_frames_OR_32_frames_OR_64_frames.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "24 Mar 2025 — In line with previous work, we sample 12 frames for MSR-VTT, MSVD and VATEX, while using 64 frames for DiDeMo and ActivityNet. Report issue ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "24 Mar 2025 — In line with previous work, we sample 12 frames for MSR-VTT, MSVD and VATEX, while using 64 frames for DiDeMo and ActivityNet. Report issue ..."}
+{"idx": 1, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "by A Reddy · 2025 · Cited by 5 — ing VIDEO-COLBERT . ... In line with previous work, we sample 12 frames for. MSR-VTT, MSVD and VATEX, while using 64 frames for. DiDeMo and ActivityNet. 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "by A Reddy · 2025 · Cited by 5 — ing VIDEO-COLBERT . ... In line with previous work, we sample 12 frames for. MSR-VTT, MSVD and VATEX, while using 64 frames for. DiDeMo and ActivityNet. 11 pages"}
+{"idx": 2, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In line with previous work, we sample 12 frames for MSR-VTT, MSVD and VATEX, while using 64 frames for DiDeMo and ActivityNet. 5.3. Baselines & Evaluation ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33381", "content": "In line with previous work, we sample 12 frames for MSR-VTT, MSVD and VATEX, while using 64 frames for DiDeMo and ActivityNet. 5.3. Baselines & Evaluation ..."}
+{"idx": 3, "title": "Find Glasses Eye Frame - Get Glasses Eye Frame On eBay Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Try the eBay way-getting what you want doesn't have to be a splurge. Browse Glasses eye frame! Find the deal you deserve on eBay. Discover discounts from sellers across the globe.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=ebay.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=sgW1qw0Xa68kxIukvEFgXOx-AhP3--AqXbRr8tyxAqJdIaOX5I4prpPJVCQcq1FizdV_68TGNx_b_LnmdgBhc8MUxdFUyVFAve3F1tJXjpSUeaG8_hy7fwpEGya1VErJ.SdUVOrbYkKeXieh8VfCnAg&rut=8d0c5257c1b3de5c7a8cfdc50233cded80be164aa93e5235147c98cf8d85a1d2&u3=https://www.bing.com/aclick?ld=e8ENcjvwBSB8QkvXgb9Djk-DVUCUwTipLurch7M7zVsuWGFwwqu_X3OE5TQuZmYNgJBhTSiLk0vJdkpKZmjpxcMm2iqi9favfhx9z4h7Mae8Yq9hYVvEQeEgjVKpwzLVAEAxHkzbMJELmv2nedDAwok3PFD5rGego5owI---2AwgLXw89fMhN0uVwWb8q6JQp9JiNlIw&u=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&rlid=38e4c5d4956a1e069700ad05c52590d7&vqd=4-32551228070215093812872885978787130430&iurl={1}IG=6869E9B49B8F4CAABFF77E7A17719AEF&CID=1710BAF382AE6BC11A28AC9C839B6AF0&ID=DevEx,5046.1", "content": "Try the eBay way-getting what you want doesn't have to be a splurge. Browse Glasses eye frame! Find the deal you deserve on eBay. Discover discounts from sellers across the globe."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 5, "title": "Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual", "date": "", "ddg_snippet": "... or subsequent human-creator curation (such as titles, or search-optimized descriptions), that go beyond the information conveyed directly in that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10202v1", "content": "... or subsequent human-creator curation (such as titles, or search-optimized descriptions), that go beyond the information conveyed directly in that ..."}
diff --git a/data/sampled_jsons/Video-ColBERT_MSVD_how_many_frames_per_video.jsonl b/data/sampled_jsons/Video-ColBERT_MSVD_how_many_frames_per_video.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3323d5e44b6852188bee4c430ee10c2bf7b4e582
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_MSVD_how_many_frames_per_video.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How to Check Any Video Resolution & Frame Rate in... - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=2i50LVZVE0w", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features."}
+{"idx": 1, "title": "Video Frame Rate Converter - Adjust Video Frame Rate - VEED.IO", "date": "", "ddg_snippet": "Video frame rate, measured in frames per second (FPS), is how many frames or still images a video shows per second.", "subpage_snippet": "", "source": "www.veed.io", "link": "https://www.veed.io/tools/video-frame-rate-converter", "content": "Video frame rate, measured in frames per second (FPS), is how many frames or still images a video shows per second."}
+{"idx": 2, "title": "Frames To Seconds Calculator - Savvy Calculator", "date": "", "ddg_snippet": "Easily convert frames to seconds with our intuitive Frames to Seconds Calculator. Streamline video editing and animation tasks effortlessly.", "subpage_snippet": "", "source": "savvycalculator.com", "link": "https://savvycalculator.com/frames-to-seconds-calculator", "content": "Easily convert frames to seconds with our intuitive Frames to Seconds Calculator. Streamline video editing and animation tasks effortlessly."}
+{"idx": 3, "title": "Video File Size Calculator - SNXP Studio", "date": "", "ddg_snippet": "Frame Rate: How it Affects File Size. Frame rate, measured in frames per second (fps), determines how many frames are shown per second of video .", "subpage_snippet": "", "source": "snxpstudio.co", "link": "https://snxpstudio.co/resources/video-file-size-calculator/", "content": "Frame Rate: How it Affects File Size. Frame rate, measured in frames per second (fps), determines how many frames are shown per second of video ."}
+{"idx": 4, "title": "Frames To Ms Calculator - Calculator Academy", "date": "", "ddg_snippet": "Therefore, converting frames to milliseconds can help synchronize video with audio or other time-based elements. How to Calculate Frames To Ms? The following steps outline how to calculate the Frames To Ms.", "subpage_snippet": "", "source": "calculator.academy", "link": "https://calculator.academy/frames-to-ms-calculator/", "content": "Therefore, converting frames to milliseconds can help synchronize video with audio or other time-based elements. How to Calculate Frames To Ms? The following steps outline how to calculate the Frames To Ms."}
+{"idx": 5, "title": "Increase Video Frame Rate by 2x or 4x up to 240FPS with Media.io AI...", "date": "", "ddg_snippet": "Online Video Frame InterpolationAI-Powered. Enhance video with Media.io AI Frame Interpolation on your browser to unlock smoother video playback. Say goodbye to stuttering and jittering in your video . Upload Files Now Smooth Video Online.", "subpage_snippet": "", "source": "www.media.io", "link": "https://www.media.io/ai-video-frame-interpolation.html", "content": "Online Video Frame InterpolationAI-Powered. Enhance video with Media.io AI Frame Interpolation on your browser to unlock smoother video playback. Say goodbye to stuttering and jittering in your video . Upload Files Now Smooth Video Online."}
+{"idx": 6, "title": "FramePack - Practical Video Diffusion on Consumer GPUs", "date": "", "ddg_snippet": "Create smooth, professional-quality videos at 30 frames per second, up to 60 seconds long. Open Source & Extensible. Built for researchers and developers with a modular Python codebase that's easy to extend.", "subpage_snippet": "", "source": "framepack-ai.com", "link": "https://framepack-ai.com/", "content": "Create smooth, professional-quality videos at 30 frames per second, up to 60 seconds long. Open Source & Extensible. Built for researchers and developers with a modular Python codebase that's easy to extend."}
+{"idx": 7, "title": "Fig. 7: Example video frames (3 non-consecutive frames per clip) and...", "date": "", "ddg_snippet": "For each video in the TACoS corpus, three levels of descriptions were collected that include: (1) detailed description of video with no more than 15 sentences per video ; (2) a short description that comprises 3-5 sentences per video ; and finally (3)...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Example-video-frames-3-non-consecutive-frames-per-clip-and-captions-from-the-various_fig6_325543786", "content": "For each video in the TACoS corpus, three levels of descriptions were collected that include: (1) detailed description of video with no more than 15 sentences per video ; (2) a short description that comprises 3-5 sentences per video ; and finally (3)..."}
+{"idx": 8, "title": "How many frames per second can the human eye really... | PC Gamer", "date": "", "ddg_snippet": "Here’s how it works. I spend far too many of my first tender minutes in a new game with a framerate counter running in the corner of my screen. I play, hyper-sensitive to the smallest hitches, dipping in and out of the graphics settings to optimise, and worry, and optimise and worry again.", "subpage_snippet": "", "source": "www.pcgamer.com", "link": "https://www.pcgamer.com/how-many-frames-per-second-can-the-human-eye-really-see/", "content": "Here’s how it works. I spend far too many of my first tender minutes in a new game with a framerate counter running in the corner of my screen. I play, hyper-sensitive to the smallest hitches, dipping in and out of the graphics settings to optimise, and worry, and optimise and worry again."}
+{"idx": 9, "title": "MSVD Dataset Corpus", "date": "", "ddg_snippet": "The Microsoft Research Video Description Corpus ( MSVD ) dataset consists of about 120K sentences collected during the summer of 2010. Workers on Mechanical Turk were paid to watch a short video snippet and then summarize the action in a single sentence.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/vtrnanh/msvd-dataset-corpus", "content": "The Microsoft Research Video Description Corpus ( MSVD ) dataset consists of about 120K sentences collected during the summer of 2010. Workers on Mechanical Turk were paid to watch a short video snippet and then summarize the action in a single sentence."}
diff --git a/data/sampled_jsons/Video-ColBERT_Table_3_MMSF_MMSV_R@1_scores_year_2023.jsonl b/data/sampled_jsons/Video-ColBERT_Table_3_MMSF_MMSV_R@1_scores_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5eba247fd0858877aeefc7e9665f406c8e0bf57a
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_Table_3_MMSF_MMSV_R@1_scores_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "by A Reddy · 2025 · Cited by 5 — The final query-video similar- ity score in VIDEO - COLBERT is a sum of the frame-level and video-level MMS scores : MMSFV (Q, V ) = MMSF (Q, V ) + MMSV (Q, V ). ( ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "by A Reddy · 2025 · Cited by 5 — The final query-video similar- ity score in VIDEO - COLBERT is a sum of the frame-level and video-level MMS scores : MMSFV (Q, V ) = MMSF (Q, V ) + MMSV (Q, V ). ( ... 11 pages"}
+{"idx": 1, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33381", "content": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss ..."}
+{"idx": 2, "title": "Football Live Scores , Latest Football Results | Flashscore.com.ng", "date": "", "ddg_snippet": "Football live scores page on Flashscore.com.ng offers all the latest football results from FIFA World Cup U20 2025 and more than 1000+ football leagues all around the world including EPL, LaLiga, Serie A, Bundesliga, UEFA Champions League and more.", "subpage_snippet": "", "source": "www.flashscore.com.ng", "link": "https://www.flashscore.com.ng/", "content": "Football live scores page on Flashscore.com.ng offers all the latest football results from FIFA World Cup U20 2025 and more than 1000+ football leagues all around the world including EPL, LaLiga, Serie A, Bundesliga, UEFA Champions League and more."}
+{"idx": 3, "title": "cgdirector.com/cinebench- r 23- scores -updated-results", "date": "", "ddg_snippet": "Cinebench R 23 single and multi scores .", "subpage_snippet": "", "source": "www.cgdirector.com", "link": "https://www.cgdirector.com/cinebench-r23-scores-updated-results/", "content": "Cinebench R 23 single and multi scores ."}
+{"idx": 4, "title": "MLB Scores : Scoreboard, Results and Highlights | MLB.com", "date": "", "ddg_snippet": "Get up-to-date MLB scores from today’s games, as well as scores from all the 2023 MLB season games.", "subpage_snippet": "", "source": "www.mlb.com", "link": "https://www.mlb.com/scores", "content": "Get up-to-date MLB scores from today’s games, as well as scores from all the 2023 MLB season games."}
+{"idx": 5, "title": "https://huggingface.co/datasets/open-source-metric...", "date": "", "ddg_snippet": "... 1 ,327587 @@ +{ + \"name\": [ + \"apapiu/transformer_latent_diffusion\", + ... video .js\", + \"birehan/Automated-Storyboard-Synthesis-for-Digital-Advertising ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/open-source-metrics/transformers-dependents/commit/2cb60a3d2f4cd754ecf19c097f04eeb2ad19d04e.diff?file=data/2024/02/18.json", "content": "... 1 ,327587 @@ +{ + \"name\": [ + \"apapiu/transformer_latent_diffusion\", + ... video .js\", + \"birehan/Automated-Storyboard-Synthesis-for-Digital-Advertising ..."}
+{"idx": 6, "title": "Сводки ополчения Новороссии Z.O.V. (ДНР, ЛНР, Украина, Война)...", "date": "", "ddg_snippet": "175K videos . 94 files. 51.2K links. Реквизиты для помощи: НА Сберкарту: 4276 1609 2548 3621 Бот обратной связи: @swodka_bot с администрацией напрямую. Здесь можно оставить информацию, которая поможет нашей Победе!", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/swodki", "content": "175K videos . 94 files. 51.2K links. Реквизиты для помощи: НА Сберкарту: 4276 1609 2548 3621 Бот обратной связи: @swodka_bot с администрацией напрямую. Здесь можно оставить информацию, которая поможет нашей Победе!"}
+{"idx": 7, "title": "Play Rock Paper Scissors but it... keeps going?", "date": "", "ddg_snippet": "Scores . Trophies. Scores and trophies are going away soon. Screenshot or copy and paste to save them.", "subpage_snippet": "", "source": "www.whatbeatsrock.com", "link": "https://www.whatbeatsrock.com/user/m1nc3r4f573r", "content": "Scores . Trophies. Scores and trophies are going away soon. Screenshot or copy and paste to save them."}
+{"idx": 8, "title": "r 6.tracker.network/ r 6siege/app", "date": "", "ddg_snippet": "View rank, operator, map, weapon, rival and ally stats, and smart video ...", "subpage_snippet": "", "source": "r6.tracker.network", "link": "https://r6.tracker.network/r6siege/app", "content": "View rank, operator, map, weapon, rival and ally stats, and smart video ..."}
+{"idx": 9, "title": "PDF to Word Converter | Convert PDF to Word Online for Free", "date": "", "ddg_snippet": "Convert PDFs to fully editable Word documents online for free. Keep fonts, formatting, and layouts intact. No sign-ups, no downloads, no watermarks.", "subpage_snippet": "", "source": "smallpdf.com", "link": "https://smallpdf.com/pdf-to-word", "content": "Convert PDFs to fully editable Word documents online for free. Keep fonts, formatting, and layouts intact. No sign-ups, no downloads, no watermarks."}
diff --git a/data/sampled_jsons/Video-ColBERT_equation_5.jsonl b/data/sampled_jsons/Video-ColBERT_equation_5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e006ca823d6a9a09b0e9de1c0d6b1109af0540ab
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_equation_5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stephen Colbert (character) - Wikipedia", "date": "", "ddg_snippet": "Colbert filmed a satirical music video poking fun at Rain's popular single \"How to Avoid the Sun\" and referencing several stereotypical South Korean ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stephen_Colbert_(character)", "content": "Colbert filmed a satirical music video poking fun at Rain's popular single \"How to Avoid the Sun\" and referencing several stereotypical South Korean ..."}
+{"idx": 1, "title": "A Colbert Report: By the Numbers", "date": "", "ddg_snippet": "But Kimmel has a heavier commercial ad load (500 more) compared to Colbert from an airings perspective, while Colbert has over 400 more network promo ...", "subpage_snippet": "", "source": "www.themeasure.net", "link": "https://www.themeasure.net/a-colbert-report-by-the-numbers/", "content": "But Kimmel has a heavier commercial ad load (500 more) compared to Colbert from an airings perspective, while Colbert has over 400 more network promo ..."}
+{"idx": 2, "title": "KAKU ON COLBERT: THE GOD EQUATION : Official Website of Dr.", "date": "", "ddg_snippet": "... at a young age, Michio Kaku has spent most of his life seeking out a universal “ Theory of Everything ” or so-called “ God Equation ...", "subpage_snippet": "", "source": "mkaku.org", "link": "https://mkaku.org/home/kaku-on-colbert-the-god-equation/", "content": "... at a young age, Michio Kaku has spent most of his life seeking out a universal “ Theory of Everything ” or so-called “ God Equation ..."}
+{"idx": 3, "title": "Stephen Colbert Delivers Epic 5-Minute Rant Against Donald", "date": "", "ddg_snippet": "... Colbert rationalizes, has been able to continue his shameless musings thanks to the support of Rand \" Man Who Asked His Barber To Make Him Look ...", "subpage_snippet": "", "source": "www.gq.com", "link": "https://www.gq.com/story/stephen-colbert-rant-trump", "content": "... Colbert rationalizes, has been able to continue his shameless musings thanks to the support of Rand \" Man Who Asked His Barber To Make Him Look ..."}
+{"idx": 4, "title": "WATCH: Colbert Trots Out Kinzinger, Who Insanely Equates", "date": "", "ddg_snippet": "WATCH: Colbert Trots Out Kinzinger, Who Insanely Equates Speaker Johnson With the Taliban ... trotted out on the set of CBS' \" The Stephen Colbert ...", "subpage_snippet": "", "source": "redstate.com", "link": "https://redstate.com/mike_miller/2023/11/02/watch-colbert-trots-out-kinzinger-to-equate-house-speaker-johnson-with-the-taliban-n2165830", "content": "WATCH: Colbert Trots Out Kinzinger, Who Insanely Equates Speaker Johnson With the Taliban ... trotted out on the set of CBS' \" The Stephen Colbert ..."}
+{"idx": 5, "title": "Stars Over Washington: Colbert Report on being \"Poor\" in", "date": "", "ddg_snippet": "... are no longer Live--SO'W will enter its 18th year on October 16, 2022 with 5 ,000+ entries! Also, some content providers may have removed their videos ...", "subpage_snippet": "", "source": "www.starsoverwashington.com", "link": "https://www.starsoverwashington.com/2011/08/colbert-report-on-being-poor-in-america.html", "content": "... are no longer Live--SO'W will enter its 18th year on October 16, 2022 with 5 ,000+ entries! Also, some content providers may have removed their videos ..."}
+{"idx": 6, "title": "Colbert: Biden a ‘Great President’ Without a Brain", "date": "", "ddg_snippet": "On and on it went, as if Colbert hadn ’ t pretended all was well in Biden land up until five minutes ago. ... Colbert mentioned that he met ...", "subpage_snippet": "", "source": "www.hollywoodintoto.com", "link": "https://www.hollywoodintoto.com/colbert-biden-great-president-brain/", "content": "On and on it went, as if Colbert hadn ’ t pretended all was well in Biden land up until five minutes ago. ... Colbert mentioned that he met ..."}
+{"idx": 7, "title": "Crossing the Equator, Going Apple Picking, Deciphering Bathroom", "date": "", "ddg_snippet": "Nick: You must, right? And what's interesting, like the Royal Caribbean video I mentioned, they do a lot of these same things.", "subpage_snippet": "", "source": "www.wereyouraisedbywolves.com", "link": "https://www.wereyouraisedbywolves.com/crossing-the-equator-going-apple-picking-deciphering-bathroom-signs-and-more/", "content": "Nick: You must, right? And what's interesting, like the Royal Caribbean video I mentioned, they do a lot of these same things."}
+{"idx": 8, "title": "EtHD - TPB", "date": "", "ddg_snippet": "Stephen. Colbert .2020.06.22.James.Corden.720p.WEB.h264-TRUMP[e... ... Yukon.Men.S05E04.Gut.Check.CONVERT.720p.WEB.H264- EQUATION [ett...", "subpage_snippet": "", "source": "thepiratebay0.org", "link": "https://thepiratebay0.org/user/EtHD/1/13/0", "content": "Stephen. Colbert .2020.06.22.James.Corden.720p.WEB.h264-TRUMP[e... ... Yukon.Men.S05E04.Gut.Check.CONVERT.720p.WEB.H264- EQUATION [ett..."}
+{"idx": 9, "title": "Zach Braff, Donald Faison and more return for Scrubs reboot", "date": "", "ddg_snippet": "On a map, the lines that run parallel to the equator are called lines of latitude , also known as parallels. These lines circle the Earth from ...", "subpage_snippet": "", "source": "gorgenewscenter.com", "link": "https://gorgenewscenter.com/2025/07/11/zach-braff-donald-faison-and-more-return-for-scrubs-reboot/", "content": "On a map, the lines that run parallel to the equator are called lines of latitude , also known as parallels. These lines circle the Earth from ..."}
diff --git a/data/sampled_jsons/WWW_2024_The_Web_Conference_accepted_papers_top_tier_year_2024.jsonl b/data/sampled_jsons/WWW_2024_The_Web_Conference_accepted_papers_top_tier_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..073b0af595fe72e04c4a2d293cc9851b5a3a6460
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_The_Web_Conference_accepted_papers_top_tier_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Most Influential WWW Papers (2024-05 Version)", "date": "", "ddg_snippet": "10 May 2024 — Paper Digest Team analyzes all papers published on WWW in the past years, and presents the 15 most influential papers for each year.", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2024/05/most-influential-www-papers-2024-05/", "content": "10 May 2024 — Paper Digest Team analyzes all papers published on WWW in the past years, and presents the 15 most influential papers for each year."}
+{"idx": 1, "title": "The Web Conference - Accepted Papers, Deadline, Impact ...", "date": "", "ddg_snippet": "5 May 2023 — Top affiliations were grouped by their rank into the following tiers : top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a ...", "subpage_snippet": "", "source": "research.com", "link": "https://research.com/conference/the-web-conference", "content": "5 May 2023 — Top affiliations were grouped by their rank into the following tiers : top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a ..."}
+{"idx": 2, "title": "Accepted Main Conference Papers", "date": "", "ddg_snippet": "Accepted Main Conference Papers . On this page. Long Papers; Short Papers. Long Papers. Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized ...", "subpage_snippet": "", "source": "2024.aclweb.org", "link": "https://2024.aclweb.org/program/main_conference_papers/", "content": "Accepted Main Conference Papers . On this page. Long Papers; Short Papers. Long Papers. Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized ..."}
+{"idx": 3, "title": "Proceedings of the ACM Web Conference 2024", "date": "", "ddg_snippet": "13 May 2024 — This conference has been the premier venue to present and discuss progress in research, development, standards and applications of topics related to the Web.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3589334?tocHeading=heading9", "content": "13 May 2024 — This conference has been the premier venue to present and discuss progress in research, development, standards and applications of topics related to the Web."}
+{"idx": 4, "title": "Proceedings of the ACM Web Conference 2024", "date": "", "ddg_snippet": "13 May 2024 — This conference has been the premier venue to present and discuss progress in research, development, standards and applications of topics related to the Web.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3589334?tocHeading=heading3", "content": "13 May 2024 — This conference has been the premier venue to present and discuss progress in research, development, standards and applications of topics related to the Web."}
+{"idx": 5, "title": "[D] WWW 2025 Reviews (TheWebConference)", "date": "", "ddg_snippet": "Anyone know any historical scores of papers that get in?", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/1h56hno/d_www_2025_reviews_thewebconference/", "content": "Anyone know any historical scores of papers that get in?"}
+{"idx": 6, "title": "Accepted Papers", "date": "", "ddg_snippet": "Accepted Papers ; fp0422, Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effects, Dong Yao,Caizhi Tang,Qing Cui,Longfei Li.", "subpage_snippet": "", "source": "cikm2024.org", "link": "https://cikm2024.org/accepted-papers/", "content": "Accepted Papers ; fp0422, Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effects, Dong Yao,Caizhi Tang,Qing Cui,Longfei Li."}
+{"idx": 7, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Start here, schedule, tutorials, main conference , invited talks, orals, spotlights, papers , paper visualization, competitions, datasets & benchmarks.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "Start here, schedule, tutorials, main conference , invited talks, orals, spotlights, papers , paper visualization, competitions, datasets & benchmarks."}
+{"idx": 8, "title": "CVPR 2024 Accepted Papers", "date": "", "ddg_snippet": "CVPR 2024 Accepted Papers . Papers are assigned to poster sessions such that topics are maximally spread over sessions (attendees will find interesting papers ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2024/AcceptedPapers", "content": "CVPR 2024 Accepted Papers . Papers are assigned to poster sessions such that topics are maximally spread over sessions (attendees will find interesting papers ..."}
+{"idx": 9, "title": "ICML 2024 Papers", "date": "", "ddg_snippet": "IMLS Archives, Getting Started, Schedule, Tutorials, Main Conference , Invited Talks, Orals, Spotlight, Posters, Awards, Test of Time Award Papers , Workshops", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/papers.html", "content": "IMLS Archives, Getting Started, Schedule, Tutorials, Main Conference , Invited Talks, Orals, Spotlight, Posters, Awards, Test of Time Award Papers , Workshops"}
diff --git a/data/sampled_jsons/WWW_2024_Top-Tier_Information_Retrieval_papers_year_2024.jsonl b/data/sampled_jsons/WWW_2024_Top-Tier_Information_Retrieval_papers_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd21fa207cce33e9988882691347b892a427daa4
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_Top-Tier_Information_Retrieval_papers_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Most Influential ArXiv ( Information Retrieval ) Papers ( 2024 -10)", "date": "", "ddg_snippet": "October 1, 2024 November 2, 2024 admin. The field of Information Retrieval in arXiv covers indexing, dictionaries, retrieval , content and analysis.To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW .", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2024/10/most-influential-arxiv-information-retrieval-papers-2024-10/", "content": "October 1, 2024 November 2, 2024 admin. The field of Information Retrieval in arXiv covers indexing, dictionaries, retrieval , content and analysis.To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW ."}
+{"idx": 1, "title": "Information Retrieval Papers · GitHub", "date": "", "ddg_snippet": "Information Retrieval Papers . GitHub Gist: instantly share code, notes, and snippets.- InPars: Data Augmentation for Information Retrieval using Large Language Models (Bonifacio et al., 2022).", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/DominguesM/8552c396e10c83cb2b5b6bb19a9888ac", "content": "Information Retrieval Papers . GitHub Gist: instantly share code, notes, and snippets.- InPars: Data Augmentation for Information Retrieval using Large Language Models (Bonifacio et al., 2022)."}
+{"idx": 2, "title": "Important Information Retrieval Papers | Restackio", "date": "", "ddg_snippet": "Important Information Retrieval Papers . Last updated on 05/29/25. Explore key papers in information retrieval that shape the field and enhance understanding of core concepts and methodologies. On this page.", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/information-retrieval-answer-important-papers-cat-ai", "content": "Important Information Retrieval Papers . Last updated on 05/29/25. Explore key papers in information retrieval that shape the field and enhance understanding of core concepts and methodologies. On this page."}
+{"idx": 3, "title": "Substack newsletter: Top Information Retrieval Papers | LinkedIn", "date": "", "ddg_snippet": "My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper /codebase.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/reachsumit_informationretrieval-researchpapers-curatedcontent-activity-7210682000020582402-L3vB", "content": "My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper /codebase."}
+{"idx": 4, "title": "Top Information Retrieval Papers of the Week | Sumit | Substack", "date": "", "ddg_snippet": "A weekly curated newsletter about the latest research papers in the Information Retrieval domain, including Recommender Systems, Search, Retrieval , and Ranking.", "subpage_snippet": "", "source": "recsys.substack.com", "link": "https://recsys.substack.com/", "content": "A weekly curated newsletter about the latest research papers in the Information Retrieval domain, including Recommender Systems, Search, Retrieval , and Ranking."}
+{"idx": 5, "title": "InteGround: On the Evaluation of Verification and Retrieval Planning in...", "date": "", "ddg_snippet": "is obtained through the top -n retrieval results.(2023) , especially when it contains conflicting information Jiayang et al. ( 2024 ) . While related to our verification component, our work focuses on a comprehensive evaluation of integrative retrieval and planning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16534v1", "content": "is obtained through the top -n retrieval results.(2023) , especially when it contains conflicting information Jiayang et al. ( 2024 ) . While related to our verification component, our work focuses on a comprehensive evaluation of integrative retrieval and planning."}
+{"idx": 6, "title": "SciHub 2024 Official Websites - All Sci Hub Proxy Links", "date": "", "ddg_snippet": "Updated for 2024 . Search for papers by DOI. www .sci-hub.se – Working July 2024 with VPN set to United States.", "subpage_snippet": "", "source": "scihub.help", "link": "https://scihub.help/", "content": "Updated for 2024 . Search for papers by DOI. www .sci-hub.se – Working July 2024 with VPN set to United States."}
+{"idx": 7, "title": "Several More Good Information Retrieval Papers Published... | Medium", "date": "", "ddg_snippet": "Papers are drawn from those in my citation database, started when I began graduate school. I’m no doubt missing references in areas I have yet to publish extensively in (e.g. efficiency, user studies). I’m focusing on information retrieval papers , as opposed to those from other communities.", "subpage_snippet": "", "source": "diazf.medium.com", "link": "https://diazf.medium.com/several-more-good-information-retrieval-papers-published-before-2002-201203f324f8", "content": "Papers are drawn from those in my citation database, started when I began graduate school. I’m no doubt missing references in areas I have yet to publish extensively in (e.g. efficiency, user studies). I’m focusing on information retrieval papers , as opposed to those from other communities."}
+{"idx": 8, "title": "What We Learned at SIGIR 2025", "date": "", "ddg_snippet": "SIGIR (Special Interest Group on Information Retrieval ) is a top - tier information retrieval conference bringing together researchers, developers, industry experts, and educators from across the globe to share the latest ground-breaking research.", "subpage_snippet": "", "source": "jina.ai", "link": "https://jina.ai/news/what-we-learned-at-sigir-2025/", "content": "SIGIR (Special Interest Group on Information Retrieval ) is a top - tier information retrieval conference bringing together researchers, developers, industry experts, and educators from across the globe to share the latest ground-breaking research."}
+{"idx": 9, "title": "Small but Mighty: Using Answer.ai's ColBERT embedding... | Vespa Blog", "date": "", "ddg_snippet": "14 Aug 2024 .It is one of the most cited recent information retrieval papers , with over 800 citations. Later improvements (distillation, compression) were incorporated in ColBERT v2 . ColBERT learns token-level vectors usable for retrieval and ranking.", "subpage_snippet": "", "source": "blog.vespa.ai", "link": "https://blog.vespa.ai/introducing-answerai-colbert-small/", "content": "14 Aug 2024 .It is one of the most cited recent information retrieval papers , with over 800 citations. Later improvements (distillation, compression) were incorporated in ColBERT v2 . ColBERT learns token-level vectors usable for retrieval and ranking."}
diff --git a/data/sampled_jsons/WWW_2024_conference_papers_blockchain_reinforcement_learning.jsonl b/data/sampled_jsons/WWW_2024_conference_papers_blockchain_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..690896e20c01927fb79d6115d4508bcd5f6a31df
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_conference_papers_blockchain_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - jianyu-niu/ blockchain _ conference _ paper : The existing...", "date": "", "ddg_snippet": "The existing blockchain -related academic papers . All papers are sorted according to the conference and published year. Welcome developers or researchers to add more published papers to this list.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jianyu-niu/blockchain_conference_paper", "content": "The existing blockchain -related academic papers . All papers are sorted according to the conference and published year. Welcome developers or researchers to add more published papers to this list."}
+{"idx": 1, "title": "Recent Reinforcement Learning and Blockchain Based Security...", "date": "", "ddg_snippet": "Conference Paper . Jun 2024 . Blockchain and deep reinforcement learning (DRL) are two separate transaction systems committed to the credibility and usefulness of system functionality.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372858036_Recent_Reinforcement_Learning_and_Blockchain_Based_Security_Solutions_for_Internet_of_Things_Survey", "content": "Conference Paper . Jun 2024 . Blockchain and deep reinforcement learning (DRL) are two separate transaction systems committed to the credibility and usefulness of system functionality."}
+{"idx": 2, "title": "Optimizing the Valid Transaction Using Reinforcement ... | SpringerLink", "date": "", "ddg_snippet": "Conference paper . First Online: 20 April 2021. Blockchain . Reinforcement learning . Wireless sensor network.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-33-6081-5_48", "content": "Conference paper . First Online: 20 April 2021. Blockchain . Reinforcement learning . Wireless sensor network."}
+{"idx": 3, "title": "AI and Crypto: How Machine Learning is Enhancing Blockchain ...", "date": "", "ddg_snippet": "Discover how machine learning helps to maintain the immutable nature of blockchain technology. Blockchain technology is what every cryptocurrency or crypto-related item is grounded on.", "subpage_snippet": "", "source": "techstartups.com", "link": "https://techstartups.com/2024/10/17/ai-and-crypto-how-machine-learning-is-enhancing-blockchain-security/", "content": "Discover how machine learning helps to maintain the immutable nature of blockchain technology. Blockchain technology is what every cryptocurrency or crypto-related item is grounded on."}
+{"idx": 4, "title": "Blockchain Life 2025 | October 28-29, Dubai, UAE | Top...", "date": "", "ddg_snippet": "The 15th International Forum/ Conference on Web3, Cryptocurrencies and Mining in Dubai, UAE. Join 15,000 attendees from 130 countries. The world's largest crypto event, featuring an exhibition, networking and startup competitions.", "subpage_snippet": "", "source": "blockchain-life.com", "link": "https://blockchain-life.com/", "content": "The 15th International Forum/ Conference on Web3, Cryptocurrencies and Mining in Dubai, UAE. Join 15,000 attendees from 130 countries. The world's largest crypto event, featuring an exhibition, networking and startup competitions."}
+{"idx": 5, "title": "(PDF) Blockchain -based AI Methods for Managing Industrial IoT...", "date": "", "ddg_snippet": "Blockchain -based Deep Learning to Process IoT Data Acquisition in Cognitive Data. Blockchain for IoT Applications: taxonomy, platforms, recent advances, challenges and future research directions.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/130017235/Blockchain_based_AI_Methods_for_Managing_Industrial_IoT_Recent_Developments_Integration_Challenges_and_Opportunities", "content": "Blockchain -based Deep Learning to Process IoT Data Acquisition in Cognitive Data. Blockchain for IoT Applications: taxonomy, platforms, recent advances, challenges and future research directions."}
+{"idx": 6, "title": "Conference in Europe 2025 – Upcoming International Conferences", "date": "", "ddg_snippet": "Find the list of upcoming international conferences in Europe 2025, tailored to advance knowledge exchange and innovation across a multitude of disciplines.", "subpage_snippet": "", "source": "www.conferenceineurope.org", "link": "https://www.conferenceineurope.org/", "content": "Find the list of upcoming international conferences in Europe 2025, tailored to advance knowledge exchange and innovation across a multitude of disciplines."}
+{"idx": 7, "title": "Blockchain -based AI Methods for Managing Industrial IoT: Recent...", "date": "", "ddg_snippet": "Blockchain -based Deep Learning to Process IoT Data Acquisition in Cognitive Data. Reinforcement learning , IIoT. The simulation based result does not include real world sce-nario. The paper focuses on the small and medium enterprises ex-cluding large sectors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.12550", "content": "Blockchain -based Deep Learning to Process IoT Data Acquisition in Cognitive Data. Reinforcement learning , IIoT. The simulation based result does not include real world sce-nario. The paper focuses on the small and medium enterprises ex-cluding large sectors."}
+{"idx": 8, "title": "Yann LeCun: Reinforcement Learning Requires An Enormous Amount", "date": "", "ddg_snippet": "Blockchain . Cryptocurrency.“To learn the game Starcraft, Reinforcement Learning required 200 years to train… A lot of the situations where the objective is very clear and you don’t need to… We can use supervised learning .” Professor Yann LeCun.", "subpage_snippet": "", "source": "www.rebellionresearch.com", "link": "https://www.rebellionresearch.com/yann-lecun-reinforcement-learning-requires-an-enormous-amount-of-data-to-perform-well", "content": "Blockchain . Cryptocurrency.“To learn the game Starcraft, Reinforcement Learning required 200 years to train… A lot of the situations where the objective is very clear and you don’t need to… We can use supervised learning .” Professor Yann LeCun."}
+{"idx": 9, "title": "Approximate Multiagent Reinforcement Learning for On-Demand...", "date": "", "ddg_snippet": "Garces, D, Bhattacharya, S, Bertsekas, D & Gil, S 2024 , Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map. in 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 .", "subpage_snippet": "", "source": "asu.elsevierpure.com", "link": "https://asu.elsevierpure.com/en/publications/approximate-multiagent-reinforcement-learning-for-on-demand-urban", "content": "Garces, D, Bhattacharya, S, Bertsekas, D & Gil, S 2024 , Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map. in 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 ."}
diff --git a/data/sampled_jsons/WWW_2024_official_conference_statistics_year_2024.jsonl b/data/sampled_jsons/WWW_2024_official_conference_statistics_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..875a502fbaed9c5b91066638f788b16110c91623
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_official_conference_statistics_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "COMPSTAT 2024", "date": "", "ddg_snippet": "The 26th International Conference on Computational Statistics will take place at the University of Giessen, 27-30 August 2024 .", "subpage_snippet": "", "source": "www.compstat2024.org", "link": "http://www.compstat2024.org/", "content": "The 26th International Conference on Computational Statistics will take place at the University of Giessen, 27-30 August 2024 ."}
+{"idx": 1, "title": "15th International Conference on Ordered Statistical Data ...", "date": "", "ddg_snippet": "15th International Conference on Ordered Statistical Data ( OSD2024 ). June 11, 2024 - June 14, 2024 . Coimbra, Portugal. Meeting Website. Not Sponsored. This ...", "subpage_snippet": "", "source": "imstat.org", "link": "https://imstat.org/meetings-calendar/15th-international-conference-on-ordered-statistical-data-osd2024/", "content": "15th International Conference on Ordered Statistical Data ( OSD2024 ). June 11, 2024 - June 14, 2024 . Coimbra, Portugal. Meeting Website. Not Sponsored. This ..."}
+{"idx": 2, "title": "Home| Artificial Intelligence and Statistics Conference", "date": "", "ddg_snippet": "The 27th International Conference on Artificial Intelligence and Statistics (AISTATS) will be held on May 2nd - May 4th, 2024 .", "subpage_snippet": "", "source": "aistats.org", "link": "http://aistats.org/aistats2024/", "content": "The 27th International Conference on Artificial Intelligence and Statistics (AISTATS) will be held on May 2nd - May 4th, 2024 ."}
+{"idx": 3, "title": "FAO at the 8th International Conference on Big Data ...", "date": "", "ddg_snippet": "FAO at the 8th International Conference on Big Data and Data Science for Official Statistics . Hybrid Event, 10/06/2024 - 14/06/2024. FAO is joining the 8th ...", "subpage_snippet": "", "source": "www.fao.org", "link": "https://www.fao.org/statistics/events/events-detail/fao-at-the-8th-international-conference-on-big-data-and-data-science-for-official-statistics/en", "content": "FAO at the 8th International Conference on Big Data and Data Science for Official Statistics . Hybrid Event, 10/06/2024 - 14/06/2024. FAO is joining the 8th ..."}
+{"idx": 4, "title": "2024 INFORMS Conference on Quality, Statistics and ...", "date": "", "ddg_snippet": "Dates: Tuesday, July 2, 2024 - Thursday, July 4, 2024 . Location: Villa Parravicini Revel. Country: Italy (ITA). Sponsor: INFORMS Quality, Statistics, and ...", "subpage_snippet": "", "source": "www.informs.org", "link": "https://www.informs.org/Meetings-Conferences/INFORMS-Conference-Calendar/2024-INFORMS-Conference-on-Quality-Statistics-and-Reliability-ICQSR", "content": "Dates: Tuesday, July 2, 2024 - Thursday, July 4, 2024 . Location: Villa Parravicini Revel. Country: Italy (ITA). Sponsor: INFORMS Quality, Statistics, and ..."}
+{"idx": 5, "title": "72nd plenary session of the Conference of European ...", "date": "", "ddg_snippet": "The 72nd plenary session of the Conference of European Statisticians (CES) will take place on 20-21 June 2024 in Palais des Nations, Geneva.", "subpage_snippet": "", "source": "unece.org", "link": "https://unece.org/statistics/events/CES2024", "content": "The 72nd plenary session of the Conference of European Statisticians (CES) will take place on 20-21 June 2024 in Palais des Nations, Geneva."}
+{"idx": 6, "title": "11th European Conference on Quality in Official Statistics ...", "date": "", "ddg_snippet": "4 Jun 2024 — This conference will gather statisticians, academics, and external stakeholders to foster the dissemination of knowledge and research on emerging issues ...", "subpage_snippet": "", "source": "www.fao.org", "link": "https://www.fao.org/statistics/events/events-detail/11th-european-conference-on-quality-in-official-statistics-(q2024)/en", "content": "4 Jun 2024 — This conference will gather statisticians, academics, and external stakeholders to foster the dissemination of knowledge and research on emerging issues ..."}
+{"idx": 7, "title": "IMS International Conference on Statistics and Data ...", "date": "", "ddg_snippet": "IMS International Conference on Statistics and Data Science (ICSDS). December 16, 2024 - December 19, 2024 . Nice, France. Meeting Website. Sponsored. The ...", "subpage_snippet": "", "source": "imstat.org", "link": "https://imstat.org/meetings-calendar/ims-international-conference-on-statistics-and-data-science-icsds-3/", "content": "IMS International Conference on Statistics and Data Science (ICSDS). December 16, 2024 - December 19, 2024 . Nice, France. Meeting Website. Sponsored. The ..."}
+{"idx": 8, "title": "UNESCO Conference of Education Data and Statistics First ...", "date": "", "ddg_snippet": "The first session of the UNESCO Conference of Education Data and Statistics will take place from 7 to 9 February 2024, comprising three full days of activities. 6 pages", "subpage_snippet": "", "source": "tcg.uis.unesco.org", "link": "https://tcg.uis.unesco.org/wp-content/uploads/sites/4/2023/10/ConferenceNote_EN-2023.09.29.pdf", "content": "The first session of the UNESCO Conference of Education Data and Statistics will take place from 7 to 9 February 2024, comprising three full days of activities. 6 pages"}
+{"idx": 9, "title": "2024 Joint Statistical Meetings - Statistics & Data Science", "date": "", "ddg_snippet": "JSM offers a unique opportunity for statisticians in academia, industry, and government to exchange ideas and explore opportunities for collaboration.", "subpage_snippet": "", "source": "ww2.amstat.org", "link": "https://ww2.amstat.org/meetings/jsm/2024/", "content": "JSM offers a unique opportunity for statisticians in academia, industry, and government to exchange ideas and explore opportunities for collaboration."}
diff --git a/data/sampled_jsons/WWW_2024_top_tier_papers_information_retrieval_year_2024.jsonl b/data/sampled_jsons/WWW_2024_top_tier_papers_information_retrieval_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d9a6b8ccb74d78af1b28e2f997a101c750e3db1c
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_top_tier_papers_information_retrieval_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International English Language Testing System - Wikipedia", "date": "", "ddg_snippet": "Listening: 40 minutes (including 10-minute transfer time in paper -based test), Reading: 60 minutes, Writing: 60 minutes, Speaking: 10-15 minutes.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/International_English_Language_Testing_System", "content": "Listening: 40 minutes (including 10-minute transfer time in paper -based test), Reading: 60 minutes, Writing: 60 minutes, Speaking: 10-15 minutes."}
+{"idx": 1, "title": "Punched card - Wikipedia", "date": "", "ddg_snippet": "A punched card (also punch card 1 or punched-card 2 ) is a stiff paper -based medium used to store digital information via the presence or absence of ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Punched_card", "content": "A punched card (also punch card 1 or punched-card 2 ) is a stiff paper -based medium used to store digital information via the presence or absence of ..."}
+{"idx": 2, "title": "Most Influential ArXiv (Information Retrieval) Papers (2024-10)", "date": "", "ddg_snippet": "Oct 1, 2024 · The field of Information Retrieval in arXiv covers indexing, dictionaries, retrieval , content and analysis. Roughly it includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4. Paper Digest Team analyzes all papers published in this field in the past years, and presents up to 30 most influential papers for each year.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2024/10/most-influential-arxiv-information-retrieval-papers-2024-10/", "content": "Oct 1, 2024 · The field of Information Retrieval in arXiv covers indexing, dictionaries, retrieval , content and analysis. Roughly it includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4. Paper Digest Team analyzes all papers published in this field in the past years, and presents up to 30 most influential papers for each year."}
+{"idx": 3, "title": "Information Retrieval Sep 2024 - arXiv.org", "date": "", "ddg_snippet": "Information Retrieval Authors and titles for September 2024 Total of 23 entries Showing up to 25 entries per page: fewer | more | all [1] arXiv:2409.00009 [pdf, html, other]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.IR/2024-09", "content": "Information Retrieval Authors and titles for September 2024 Total of 23 entries Showing up to 25 entries per page: fewer | more | all [1] arXiv:2409.00009 [pdf, html, other]"}
+{"idx": 4, "title": "SIGIR-AP 2024", "date": "", "ddg_snippet": "Best Papers Best paper honorable mention ★ LLMs can be Fooled into Labelling a Document as Relevant Marwah Alaofi, Paul Thomas, Mark Sanderson and Falk Scholer Best paper award ★ Generative Retrieval with Semantic Tree-Structured Identifiers and Contrastive Learning Zihua Si, Zhongxiang Sun, Jiale Chen, Guozhang Chen, Xiaoxue Zang, Kai Zheng, Yang Song, Xiao Zhang, Jun Xu and Kun Gai Best ...", "subpage_snippet": "", "source": "www.sigir-ap.org", "link": "https://www.sigir-ap.org/sigir-ap-2024/bestpapers/", "content": "Best Papers Best paper honorable mention ★ LLMs can be Fooled into Labelling a Document as Relevant Marwah Alaofi, Paul Thomas, Mark Sanderson and Falk Scholer Best paper award ★ Generative Retrieval with Semantic Tree-Structured Identifiers and Contrastive Learning Zihua Si, Zhongxiang Sun, Jiale Chen, Guozhang Chen, Xiaoxue Zang, Kai Zheng, Yang Song, Xiao Zhang, Jun Xu and Kun Gai Best ..."}
+{"idx": 5, "title": "Journal Top 100 of 2024 - Nature", "date": "", "ddg_snippet": "Nov 14, 2024 · Collection 05 March 2025 Journal Top 100 of 2024 This collection highlights the most downloaded* Scientific Reports research papers published in 2024 .", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/collections/afjdaicdhg", "content": "Nov 14, 2024 · Collection 05 March 2025 Journal Top 100 of 2024 This collection highlights the most downloaded* Scientific Reports research papers published in 2024 ."}
+{"idx": 6, "title": "Information Retrieval – Paper Digest", "date": "", "ddg_snippet": "Jul 13, 2025 · SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ) is one of the top information retrieval conferences in the world. Paper Digest Team analyzes all papers published on SIGIR in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/category/information-retrieval/", "content": "Jul 13, 2025 · SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ) is one of the top information retrieval conferences in the world. Paper Digest Team analyzes all papers published on SIGIR in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research ..."}
+{"idx": 7, "title": "Proceedings of the 2024 ACM SIGIR International Conference on ...", "date": "", "ddg_snippet": "Aug 5, 2024 · Welcome to ACM ICTIR 2024 , the 10th conference with that name to be fully sponsored by the ACM Special Interest Group on Information Retrieval (SIGIR), and the 14th International Conference on the Theory of Information Retrieval . ICTIR is the premier forum for presenting and discussing research on theoretical and foundational aspects of Information Retrieval .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3664190", "content": "Aug 5, 2024 · Welcome to ACM ICTIR 2024 , the 10th conference with that name to be fully sponsored by the ACM Special Interest Group on Information Retrieval (SIGIR), and the 14th International Conference on the Theory of Information Retrieval . ICTIR is the premier forum for presenting and discussing research on theoretical and foundational aspects of Information Retrieval ."}
+{"idx": 8, "title": "[2403.00784] Utilizing BERT for Information Retrieval: Survey ...", "date": "", "ddg_snippet": "Feb 18, 2024 · Abstract page for arXiv paper 2403.00784: Utilizing BERT for Information Retrieval : Survey, Applications, Resources, and Challenges", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.00784", "content": "Feb 18, 2024 · Abstract page for arXiv paper 2403.00784: Utilizing BERT for Information Retrieval : Survey, Applications, Resources, and Challenges"}
+{"idx": 9, "title": "HKU IDS Scholar Prof Chao Huang’s research team have papers", "date": "", "ddg_snippet": "... Papers in Version 2024 -05 in WWW (The Web Conference) & SIGIR (Annual International ACM SIGIR Conference on Research and Development in ...", "subpage_snippet": "", "source": "datascience.hku.hk", "link": "https://datascience.hku.hk/2024/06/hku-ids-scholar-prof-chao-huangs-research-team-have-papers-listed-as-the-most-influential-sigir-2024-05-papers/", "content": "... Papers in Version 2024 -05 in WWW (The Web Conference) & SIGIR (Annual International ACM SIGIR Conference on Research and Development in ..."}
diff --git a/data/sampled_jsons/WWW_2025_blockchain_sharding_reinforcement_learning_AERO.jsonl b/data/sampled_jsons/WWW_2025_blockchain_sharding_reinforcement_learning_AERO.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9938272075a34212ddf6779fe8a0c71208db12a0
--- /dev/null
+++ b/data/sampled_jsons/WWW_2025_blockchain_sharding_reinforcement_learning_AERO.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain Sharding", "date": "", "ddg_snippet": "To design an experimental tool that supports sharding mechanisms, it is necessary to determine the layered architecture of the sharded blockchain and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.03612v5", "content": "To design an experimental tool that supports sharding mechanisms, it is necessary to determine the layered architecture of the sharded blockchain and ..."}
+{"idx": 1, "title": "Paper Digest: WWW 2025 Papers & Highlights –", "date": "", "ddg_snippet": "To summarize the latest research published at WWW - 2025 on a specific topic, you can utilize the review by venue ( WWW - 2025 ) service.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/04/www-2025-papers-highlights/", "content": "To summarize the latest research published at WWW - 2025 on a specific topic, you can utilize the review by venue ( WWW - 2025 ) service."}
+{"idx": 2, "title": "The Future of Jobs Report 2025 | World Economic Forum", "date": "", "ddg_snippet": "Jan 7, 2025 · Technological change, geoeconomic fragmentation, economic uncertainty, demographic shifts and the green transition – individually and in combination are among the major drivers expected to shape and transform the global labour market by 2030. The Future of Jobs Report 2025 brings together the perspective of over 1,000 leading global employers—collectively representing more than 14 million ...", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/publications/the-future-of-jobs-report-2025/digest/", "content": "Jan 7, 2025 · Technological change, geoeconomic fragmentation, economic uncertainty, demographic shifts and the green transition – individually and in combination are among the major drivers expected to shape and transform the global labour market by 2030. The Future of Jobs Report 2025 brings together the perspective of over 1,000 leading global employers—collectively representing more than 14 million ..."}
+{"idx": 3, "title": "The Future of Jobs Report 2025 - The World Economic Forum", "date": "", "ddg_snippet": "Jan 7, 2025 · Learn how global trends like tech innovation and green transition will transform jobs, skills, and workforce strategies in The Future of Jobs Report 2025", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/publications/the-future-of-jobs-report-2025/in-full/3-skills-outlook/", "content": "Jan 7, 2025 · Learn how global trends like tech innovation and green transition will transform jobs, skills, and workforce strategies in The Future of Jobs Report 2025"}
+{"idx": 4, "title": "In charts: 7 global shifts defining 2025 so far | World Economic...", "date": "", "ddg_snippet": "Aug 5, 2025 · 2025 has been marked by significant global shifts, including increased geopolitical instability, the accelerating impact of AI and a changing labour market.", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/stories/2025/08/inflection-points-7-global-shifts-defining-2025-so-far-in-charts/", "content": "Aug 5, 2025 · 2025 has been marked by significant global shifts, including increased geopolitical instability, the accelerating impact of AI and a changing labour market."}
+{"idx": 5, "title": "These are the Top 10 Emerging Technologies of 2025", "date": "", "ddg_snippet": "Jun 24, 2025 · The World Economic Forum’s latest Top 10 Emerging Technologies report explores the tech on the cusp of making a massive impact on our lives.", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/stories/2025/06/top-10-emerging-technologies-of-2025/", "content": "Jun 24, 2025 · The World Economic Forum’s latest Top 10 Emerging Technologies report explores the tech on the cusp of making a massive impact on our lives."}
+{"idx": 6, "title": "Annual Report 2024-2025 - World Economic Forum", "date": "", "ddg_snippet": "Sep 5, 2025 · The World Economic Forum’s Annual Report outlines the key developments in 2024- 2025 . It provides an overview of the Forum’s centres and initiatives, progress from coalitions and key reports, and takeaways from select meetings, as well as the latest progress from the Forum’s core functions and operations.", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/publications/annual-report-2024-2025/", "content": "Sep 5, 2025 · The World Economic Forum’s Annual Report outlines the key developments in 2024- 2025 . It provides an overview of the Forum’s centres and initiatives, progress from coalitions and key reports, and takeaways from select meetings, as well as the latest progress from the Forum’s core functions and operations."}
+{"idx": 7, "title": "World Economic Forum Annual Meeting 2025", "date": "", "ddg_snippet": "Jan 24, 2025 · The Annual Meeting 2025 of The World Economic Forum will take place at Davos-Klosters from 20th to 24th January 2025 .", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/meetings/world-economic-forum-annual-meeting-2025/", "content": "Jan 24, 2025 · The Annual Meeting 2025 of The World Economic Forum will take place at Davos-Klosters from 20th to 24th January 2025 ."}
+{"idx": 8, "title": "Top 10 Emerging Technologies of 2025 | World Economic Forum", "date": "", "ddg_snippet": "Jun 24, 2025 · The Top 10 Emerging Technologies of 2025 report highlights 10 innovations with the potential to reshape industries and societies.", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/publications/top-10-emerging-technologies-of-2025/", "content": "Jun 24, 2025 · The Top 10 Emerging Technologies of 2025 report highlights 10 innovations with the potential to reshape industries and societies."}
+{"idx": 9, "title": "2025: the year companies prepare to disrupt how work gets done", "date": "", "ddg_snippet": "Jan 20, 2025 · 80% of C-suite executives believing AI will kickstart a culture shift towards greater innovation; this year will see businesses use it to functionally disrupt how work gets done", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/stories/2025/01/ai-2025-workplace/", "content": "Jan 20, 2025 · 80% of C-suite executives believing AI will kickstart a culture shift towards greater innovation; this year will see businesses use it to functionally disrupt how work gets done"}
diff --git a/data/sampled_jsons/Wampler_2010.jsonl b/data/sampled_jsons/Wampler_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..abf58c63ac9e75e80d5517936dce2aa40aaa8dc0
--- /dev/null
+++ b/data/sampled_jsons/Wampler_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "United States v. Wampler, 4th Cir. (2010) | PDF - Scribd", "date": "", "ddg_snippet": "United States v. Wampler , 4th Cir. ( 2010 ) - Free download as PDF File (.pdf) or read online for free. This document summarizes a court case in which Cecil Edward Wampler , Jr. sought to appeal the district court's denial of his 28 U.S.C.A. § 2255 motion. The circuit court judges independently reviewed the record and determined that Wampler did not make the requisite showing to receive a ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/321556471/United-States-v-Wampler-4th-Cir-2010", "content": "United States v. Wampler , 4th Cir. ( 2010 ) - Free download as PDF File (.pdf) or read online for free. This document summarizes a court case in which Cecil Edward Wampler , Jr. sought to appeal the district court's denial of his 28 U.S.C.A. § 2255 motion. The circuit court judges independently reviewed the record and determined that Wampler did not make the requisite showing to receive a ..."}
+{"idx": 1, "title": "US v. Wampler (2010) | FindLaw", "date": "", "ddg_snippet": "US v. Wampler ( 2010 ) United States Tenth Circuit US v. Wampler 09-6229 Decided: November 16, 2010 Before: Kimberly Gorsuch; John W. Coyle, III for defendant; Scott E. Williams for plaintiff; FindLaw is currently processing this opinion. In the meantime, you can access a copy of the opinion here. Was this helpful?", "subpage_snippet": "", "source": "caselaw.findlaw.com", "link": "https://caselaw.findlaw.com/court/us-10th-circuit/1545012.html", "content": "US v. Wampler ( 2010 ) United States Tenth Circuit US v. Wampler 09-6229 Decided: November 16, 2010 Before: Kimberly Gorsuch; John W. Coyle, III for defendant; Scott E. Williams for plaintiff; FindLaw is currently processing this opinion. In the meantime, you can access a copy of the opinion here. Was this helpful?"}
+{"idx": 2, "title": "United States v. Wampler, No. 09-6229 (10th Cir. 2010) :: Justia", "date": "", "ddg_snippet": "United States v. Wampler , No. 09-6229 (10th Cir. 2010 ) case opinion from the US Court of Appeals for the Tenth Circuit", "subpage_snippet": "", "source": "law.justia.com", "link": "https://law.justia.com/cases/federal/appellate-courts/ca10/09-6229/09-6229-2011-05-05.html", "content": "United States v. Wampler , No. 09-6229 (10th Cir. 2010 ) case opinion from the US Court of Appeals for the Tenth Circuit"}
+{"idx": 3, "title": "Wampler, B. (2010). Participatory Budgeting in Brazil Contestation ...", "date": "", "ddg_snippet": "Article citations More>> Wampler , B. ( 2010 ). Participatory Budgeting in Brazil: Contestation, Cooperation, and Accountability. Penn State Press. has been cited by the following article: TITLE: The Dynamics of Public Policy Effectiveness in Africa: Between Intentions and Realities AUTHORS: Elie Nguekam Wambe KEYWORDS: Public Policies, Sub-Saharan Africa, Sustainable Governance, Citizen ...", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=3954681", "content": "Article citations More>> Wampler , B. ( 2010 ). Participatory Budgeting in Brazil: Contestation, Cooperation, and Accountability. Penn State Press. has been cited by the following article: TITLE: The Dynamics of Public Policy Effectiveness in Africa: Between Intentions and Realities AUTHORS: Elie Nguekam Wambe KEYWORDS: Public Policies, Sub-Saharan Africa, Sustainable Governance, Citizen ..."}
+{"idx": 4, "title": "Participatory Budgeting in Brazil - Google Books", "date": "", "ddg_snippet": "About the author ( 2010 ) Brian Wampler is Associate Professor of Political Science at Boise State University.", "subpage_snippet": "", "source": "books.google.com", "link": "https://books.google.com/books/about/Participatory_Budgeting_in_Brazil.html?id=Y6-14Zpr0lkC", "content": "About the author ( 2010 ) Brian Wampler is Associate Professor of Political Science at Boise State University."}
+{"idx": 5, "title": "Participatory budgeting in Brazil : contestation, cooperation, and ...", "date": "", "ddg_snippet": "Most of the scholarly literature has focused on the successful case of Porto Alegre and has neglected to analyze how it fared elsewhere.\" \"In this first rigorous comparative study of the phenomenon, Brian Wampler draws evidence from eight municipalities in Brazil to show the varying degrees of success and failure PB has experienced.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/participatorybud0000wamp", "content": "Most of the scholarly literature has focused on the successful case of Porto Alegre and has neglected to analyze how it fared elsewhere.\" \"In this first rigorous comparative study of the phenomenon, Brian Wampler draws evidence from eight municipalities in Brazil to show the varying degrees of success and failure PB has experienced."}
+{"idx": 6, "title": "Participatory Budgeting in Brazil: Contestation, Cooperation, and ...", "date": "", "ddg_snippet": "Participatory Budgeting in Brazil Contestation, Cooperation, and Accountability Brian Wampler \"With its comparative analysis of eight cases of participatory budgeting (PB) in Brazil, varying from success to failure, Wampler's book is a significant contribution to a literature heretofore dominated by single-case or two-case analyses (usually of only successful cases). His argument that ...", "subpage_snippet": "", "source": "www.psupress.org", "link": "https://www.psupress.org/books/titles/978-0-271-03252-8.html", "content": "Participatory Budgeting in Brazil Contestation, Cooperation, and Accountability Brian Wampler \"With its comparative analysis of eight cases of participatory budgeting (PB) in Brazil, varying from success to failure, Wampler's book is a significant contribution to a literature heretofore dominated by single-case or two-case analyses (usually of only successful cases). His argument that ..."}
+{"idx": 7, "title": "Participatory Budgeting in Brazil: Contestation, Cooperation, and ...", "date": "", "ddg_snippet": "Participatory budgeting is the most well-known and widespread example of this ( Wampler 2010 ) and Extinction Rebellion's call for Citizens' Assem- blies to fight climate change is one of the most ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/43981343_Participatory_Budgeting_in_Brazil_Contestation_Cooperation_and_Accountability", "content": "Participatory budgeting is the most well-known and widespread example of this ( Wampler 2010 ) and Extinction Rebellion's call for Citizens' Assem- blies to fight climate change is one of the most ..."}
+{"idx": 8, "title": "Contestation in Participatory Budgeting: Spaces, Boundaries, and Agency", "date": "", "ddg_snippet": "Wampler ( 2010 ) shows, moreover, that mayors' varying political commitments and orientations affect whether participants feel entitled to question or object to how projects of participation are framed.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/full/10.1177/0002764220941226", "content": "Wampler ( 2010 ) shows, moreover, that mayors' varying political commitments and orientations affect whether participants feel entitled to question or object to how projects of participation are framed."}
+{"idx": 9, "title": "Participatory Budgeting in Brazil : Contestation, Cooperation, and ...", "date": "", "ddg_snippet": "Find many great new & used options and get the best deals for Participatory Budgeting in Brazil : Contestation, Cooperation, and Accountability by Brian Wampler ( 2010 , Trade Paperback) at the best online prices at eBay! Free shipping for many products!", "subpage_snippet": "", "source": "www.ebay.com", "link": "https://www.ebay.com/p/80468824", "content": "Find many great new & used options and get the best deals for Participatory Budgeting in Brazil : Contestation, Cooperation, and Accountability by Brian Wampler ( 2010 , Trade Paperback) at the best online prices at eBay! Free shipping for many products!"}
diff --git a/data/sampled_jsons/Wampler_2010_game_behaviors_strategies.jsonl b/data/sampled_jsons/Wampler_2010_game_behaviors_strategies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bbb491ecf425ea5d85b4e8bc20c4633e9c9ea0e1
--- /dev/null
+++ b/data/sampled_jsons/Wampler_2010_game_behaviors_strategies.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "20the players' behaviors into action spaces at action-level and explore strategies based on them Wampler 21et al. [ 2010 ], Won et al. [2021a]. Among commonly seen beahaviors in real-world games , Feint 22behaviors is a class of tactic behaviors which are used to mislead opponents to gain future strategic 23advantages.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "20the players' behaviors into action spaces at action-level and explore strategies based on them Wampler 21et al. [ 2010 ], Won et al. [2021a]. Among commonly seen beahaviors in real-world games , Feint 22behaviors is a class of tactic behaviors which are used to mislead opponents to gain future strategic 23advantages."}
+{"idx": 1, "title": "Feint behaviors and strategies: - ACM Digital Library", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games . Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738032", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games . Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ..."}
+{"idx": 2, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games ."}
+{"idx": 3, "title": "PDF Character Animation in Two-Player Adversarial Games", "date": "", "ddg_snippet": "1. INTRODUCTION Some of the most complicated and intricate human behaviors arise out of interactions with other people in competitive games . In many competitive sports, players compete for certain goals while simulta-neously preventing the opponents from achieving their goals. These scenarios create very dynamic and unpredictable situations: the players need to make decisions considering both ...", "subpage_snippet": "", "source": "grail.cs.washington.edu", "link": "https://grail.cs.washington.edu/wp-content/uploads/2015/08/wampler2010cai.pdf", "content": "1. INTRODUCTION Some of the most complicated and intricate human behaviors arise out of interactions with other people in competitive games . In many competitive sports, players compete for certain goals while simulta-neously preventing the opponents from achieving their goals. These scenarios create very dynamic and unpredictable situations: the players need to make decisions considering both ..."}
+{"idx": 4, "title": "In-Game Behavior - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "In- game behaviors refer to the actions and conduct of students during the Good Behavior Game , where specific undesired behaviors , such as disruptive or aggressive actions, are tracked and influence team rewards and consequences. These behaviors are managed collectively by team members in a competitive format aimed at encouraging positive peer support and self-regulation. AI generated ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/psychology/in-game-behavior", "content": "In- game behaviors refer to the actions and conduct of students during the Good Behavior Game , where specific undesired behaviors , such as disruptive or aggressive actions, are tracked and influence team rewards and consequences. These behaviors are managed collectively by team members in a competitive format aimed at encouraging positive peer support and self-regulation. AI generated ..."}
+{"idx": 5, "title": "PDF Ec2010a: Game Theory Section Notes - Scholars at Harvard", "date": "", "ddg_snippet": "In a finite extensive game with perfect recall, (i) every mixed strategy has an equivalent behavioral strategy, and (ii) every behavioral strategy has an equivalent mixed strategy.", "subpage_snippet": "", "source": "scholar.harvard.edu", "link": "https://scholar.harvard.edu/files/changliu/files/ec2010a_gt_sectionnotes_all_2021.pdf", "content": "In a finite extensive game with perfect recall, (i) every mixed strategy has an equivalent behavioral strategy, and (ii) every behavioral strategy has an equivalent mixed strategy."}
+{"idx": 6, "title": "Game Theory and Behavior", "date": "", "ddg_snippet": "Mixed-Strategy Nash Equilibrium 92 Solving for Mixed-Strategy Nash Equilibria 93 The Princess Bride Game 94 Stag Hunt: A Game with Pure- and Mixed-Strategy Nash Equilibria 94 Mixing over Multiple Pure Strategies 96 Best Responding, Dominated Strategies , and Rationalizability Behavior in Games 100 97 Playing Mixed-up Strategies 100 Evidence-Based Adaptations of the Standard Model 103 Exercises 104", "subpage_snippet": "", "source": "urn.ub.unibe.ch", "link": "https://urn.ub.unibe.ch/urn:ch:slsp:0262047292:ihv:pdf", "content": "Mixed-Strategy Nash Equilibrium 92 Solving for Mixed-Strategy Nash Equilibria 93 The Princess Bride Game 94 Stag Hunt: A Game with Pure- and Mixed-Strategy Nash Equilibria 94 Mixing over Multiple Pure Strategies 96 Best Responding, Dominated Strategies , and Rationalizability Behavior in Games 100 97 Playing Mixed-up Strategies 100 Evidence-Based Adaptations of the Standard Model 103 Exercises 104"}
+{"idx": 7, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "In most real-world Multi-Player Games (e.g., boxing, basketball, motor racing, and etc.), players exhibit complex behaviors , which imply hard-to-be-quantified interactions. Simulating these games requires to model the players' behaviors into action spaces as the elements (i.e., which we denote such a decision as \"action-level\"), and explore strategies based on these elements [34, 35]. As ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2024/file/064ae24cdbb3eaacc801ee7f4fe0e4f2-Paper-Conference.pdf", "content": "In most real-world Multi-Player Games (e.g., boxing, basketball, motor racing, and etc.), players exhibit complex behaviors , which imply hard-to-be-quantified interactions. Simulating these games requires to model the players' behaviors into action spaces as the elements (i.e., which we denote such a decision as \"action-level\"), and explore strategies based on these elements [34, 35]. As ..."}
+{"idx": 8, "title": "Game Theory in Psychology: Examples and Strategies", "date": "", "ddg_snippet": "Game theory is a theoretical framework that is used for the optimal decision-making of players in a strategic setting. A key characteristic of game theory is that a player's payoff is dependent on the strategy of other players.", "subpage_snippet": "", "source": "www.simplypsychology.org", "link": "https://www.simplypsychology.org/game-theory.html", "content": "Game theory is a theoretical framework that is used for the optimal decision-making of players in a strategic setting. A key characteristic of game theory is that a player's payoff is dependent on the strategy of other players."}
+{"idx": 9, "title": "PDF feint_video_slides - neurips.cc", "date": "", "ddg_snippet": "Background and Motivation Action-Level Formalization Existing works: Animate of Feint behaviors as proof-of-concept of character animation techniques [1]. Learn control strategies from motion clips which incorporate nuanced behaviors like Feint behaviors [2]. Uniqueness of Feint behaviors : (Semi-)palindrome Incorporation with other behaviors", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96274.pdf", "content": "Background and Motivation Action-Level Formalization Existing works: Animate of Feint behaviors as proof-of-concept of character animation techniques [1]. Learn control strategies from motion clips which incorporate nuanced behaviors like Feint behaviors [2]. Uniqueness of Feint behaviors : (Semi-)palindrome Incorporation with other behaviors"}
diff --git a/data/sampled_jsons/Wampler_et_al._2010_feint_behaviors_abstract_year_2010.jsonl b/data/sampled_jsons/Wampler_et_al._2010_feint_behaviors_abstract_year_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c4d09227dcaf673809a93417917a82f0a1f8563
--- /dev/null
+++ b/data/sampled_jsons/Wampler_et_al._2010_feint_behaviors_abstract_year_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reply to Wampler et al .: Deforestation and biodiversity loss should not...", "date": "", "ddg_snippet": "Wampler et al . (3) refer to our exclusion of other forest types, but these types were not relevant because the bulk of biodiversity is in the primary forest.Those extinct species will not reappear if denuded mountaintops are reforested. Although not mentioned by Wampler et al .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6431194/", "content": "Wampler et al . (3) refer to our exclusion of other forest types, but these types were not relevant because the bulk of biodiversity is in the primary forest.Those extinct species will not reappear if denuded mountaintops are reforested. Although not mentioned by Wampler et al ."}
+{"idx": 1, "title": "Modeling wildfire effects on streamflow in the Cascade Mountains...", "date": "", "ddg_snippet": "K.A. Wampler et al . here, calibrating for both streamflow and sediment increases confidence that the underlying physical processes are being correctly represented in the model.", "subpage_snippet": "", "source": "fews.forestry.oregonstate.edu", "link": "https://fews.forestry.oregonstate.edu/publications/Wampler_JoH_2023.pdf", "content": "K.A. Wampler et al . here, calibrating for both streamflow and sediment increases confidence that the underlying physical processes are being correctly represented in the model."}
+{"idx": 2, "title": "Оцени свою фигуру онлайн - профессиональный анализ... | GymBot", "date": "", "ddg_snippet": "WHO Technical Report on Waist Circumference and Waist-Hip Ratio (2008)Dixson B.J. et al . ( 2010 ). Male preferences for female waist-to-hip ratio", "subpage_snippet": "", "source": "gymbot.ru", "link": "https://gymbot.ru/figure_assessment_calculator", "content": "WHO Technical Report on Waist Circumference and Waist-Hip Ratio (2008)Dixson B.J. et al . ( 2010 ). Male preferences for female waist-to-hip ratio"}
+{"idx": 3, "title": "AMP: Adversarial Motion Priors for Stylized Physics-Based Character...", "date": "", "ddg_snippet": "2002, 2010 b]. Given a motion dataset, controllers can be constructed to select an appropriate motion clip to play back for a particular scenario [Agrawal and van de Panne 2016; Safonova and Hodgins 2007; Treuille et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2104.02180", "content": "2002, 2010 b]. Given a motion dataset, controllers can be constructed to select an appropriate motion clip to play back for a particular scenario [Agrawal and van de Panne 2016; Safonova and Hodgins 2007; Treuille et al ."}
+{"idx": 4, "title": "US7753645.pdf", "date": "", "ddg_snippet": "(12) United States Patent. Wampler et al .13, 2010 . (54) rotary blood pump with opposing spindle magnets and contoured housing. (75) Inventors: Richard K. Wampler , Loomis, CA (US); David M. Lancisi, Folsom, CA.", "subpage_snippet": "", "source": "patentimages.storage.googleapis.com", "link": "https://patentimages.storage.googleapis.com/pdfs/US7753645.pdf", "content": "(12) United States Patent. Wampler et al .13, 2010 . (54) rotary blood pump with opposing spindle magnets and contoured housing. (75) Inventors: Richard K. Wampler , Loomis, CA (US); David M. Lancisi, Folsom, CA."}
+{"idx": 5, "title": "Recommendations for Collective Training for the Battle Management...", "date": "", "ddg_snippet": "Wampler et al . (2006) highlight that the trainer should be able to observe the trainee during important tasks, and that this capability usually needs to be incorporated into the system design, as it is often too expensive to modify the system after purchase.", "subpage_snippet": "", "source": "www.dst.defence.gov.au", "link": "https://www.dst.defence.gov.au/sites/default/files/publications/documents/DSTO-TR-2685+PR.pdf", "content": "Wampler et al . (2006) highlight that the trainer should be able to observe the trainee during important tasks, and that this capability usually needs to be incorporated into the system design, as it is often too expensive to modify the system after purchase."}
+{"idx": 6, "title": "BG - The influence of burn severity on dissolved organic carbon...", "date": "", "ddg_snippet": "Katie A. Wampler et al . Abstract . Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds.", "subpage_snippet": "", "source": "bg.copernicus.org", "link": "https://bg.copernicus.org/articles/21/3093/2024/", "content": "Katie A. Wampler et al . Abstract . Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds."}
+{"idx": 7, "title": "Information Leakage in Encrypted IP Video Traffic | Request PDF", "date": "", "ddg_snippet": "C. Wampler et al . [14] analyzed information leaking in video over IP traffic, including for encrypted payloads. The authors verified leakage by analyzing the metadata of network traffic including video bandwidth, packet size, and arrival time between packets. ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/304416216_Information_Leakage_in_Encrypted_IP_Video_Traffic", "content": "C. Wampler et al . [14] analyzed information leaking in video over IP traffic, including for encrypted payloads. The authors verified leakage by analyzing the metadata of network traffic including video bandwidth, packet size, and arrival time between packets. ..."}
+{"idx": 8, "title": "Scala autoboxing and Java Map - Stack Overflow", "date": "", "ddg_snippet": "edited Dec 16, 2010 at 16:48. Vasil Remeniuk's user avatar.Commented Dec 16, 2010 at 18:06. I cannot find anything on it in any of my Scala books (Odersky et al , Wampler et al , or Loverdos et al ).", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/4462762/scala-autoboxing-and-java-map", "content": "edited Dec 16, 2010 at 16:48. Vasil Remeniuk's user avatar.Commented Dec 16, 2010 at 18:06. I cannot find anything on it in any of my Scala books (Odersky et al , Wampler et al , or Loverdos et al )."}
+{"idx": 9, "title": "Сверхпроводники обнаружили сразу в двух метеоритах", "date": "", "ddg_snippet": "Их критическая температура — около 5 кельвин. График MFMMS для метеорита Мандрабилла. James Wampler et al ./ PNAS, 2020. Метеориты Мандрабилла и GRA 95205 совсем не похожи друг на друга.", "subpage_snippet": "", "source": "nplus1.ru", "link": "https://nplus1.ru/news/2020/03/26/extraterrestrial-superconductivity", "content": "Их критическая температура — около 5 кельвин. График MFMMS для метеорита Мандрабилла. James Wampler et al ./ PNAS, 2020. Метеориты Мандрабилла и GRA 95205 совсем не похожи друг на друга."}
diff --git a/data/sampled_jsons/Waymo_Open_Dataset_1000_segments_200k_frames_statistics_original_paper_year_2020.jsonl b/data/sampled_jsons/Waymo_Open_Dataset_1000_segments_200k_frames_statistics_original_paper_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4255161f1f3047c7be8f1c64296c9c68bb940c2f
--- /dev/null
+++ b/data/sampled_jsons/Waymo_Open_Dataset_1000_segments_200k_frames_statistics_original_paper_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "About – Waymo Open Dataset", "date": "", "ddg_snippet": "The Waymo Open Dataset provided here includes an unlabeled mixture of data from both manual and autonomous driving operations. The Dataset should only be used ...", "subpage_snippet": "", "source": "waymo.com", "link": "https://waymo.com/open/about/", "content": "The Waymo Open Dataset provided here includes an unlabeled mixture of data from both manual and autonomous driving operations. The Dataset should only be used ..."}
+{"idx": 1, "title": "Waymo Open Dataset: Sharing our self-driving data for ...", "date": "", "ddg_snippet": "Each segment captures 20 seconds of continuous driving, corresponding to 200,000 frames at 10 Hz per sensor. Such continuous footage gives ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/waymo/waymo-open-dataset-6c6ac227ab1a", "content": "Each segment captures 20 seconds of continuous driving, corresponding to 200,000 frames at 10 Hz per sensor. Such continuous footage gives ..."}
+{"idx": 2, "title": "Waymo Driverless Car Data Analysis and Driving Modeling ...", "date": "", "ddg_snippet": "29 Apr 2025 — Here each segment is of 20 seconds of driving data collected by AV. Each segment is comprised of 200,000 frames captures at 10 Hz per sensor.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.01446v1", "content": "29 Apr 2025 — Here each segment is of 20 seconds of driving data collected by AV. Each segment is comprised of 200,000 frames captures at 10 Hz per sensor."}
+{"idx": 3, "title": "Waymo is making some of its self-driving car data available ...", "date": "", "ddg_snippet": "21 Aug 2019 — ... 1,000 driving segments , with each segment capturing 20 seconds of continuous driving. Those 20-second clips correspond to 200,000 frames at ...", "subpage_snippet": "", "source": "www.theverge.com", "link": "https://www.theverge.com/2019/8/21/20822755/waymo-self-driving-car-data-set-free-research", "content": "21 Aug 2019 — ... 1,000 driving segments , with each segment capturing 20 seconds of continuous driving. Those 20-second clips correspond to 200,000 frames at ..."}
+{"idx": 4, "title": "Waymo opens-up data treasure trove for autonomous ...", "date": "", "ddg_snippet": "This release contains data from 1,000 driving segments , each capturing 20 seconds of continuous driving, corresponding to 200,000 frames at 10 Hz per sensor.", "subpage_snippet": "", "source": "www.telecoms.com", "link": "https://www.telecoms.com/enterprise-telecoms/waymo-opens-up-data-treasure-trove-for-autonomous-vehicles", "content": "This release contains data from 1,000 driving segments , each capturing 20 seconds of continuous driving, corresponding to 200,000 frames at 10 Hz per sensor."}
+{"idx": 5, "title": "Waymo Driverless Car Data Analysis and Driving Modeling ...", "date": "", "ddg_snippet": "by AK Misraa · 2025 · Cited by 1 — In this project we have presented multiple ways to predict acceleration of the autonomous vehicle using Waymo's open dataset . Our main approach ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.01446", "content": "by AK Misraa · 2025 · Cited by 1 — In this project we have presented multiple ways to predict acceleration of the autonomous vehicle using Waymo's open dataset . Our main approach ..."}
+{"idx": 6, "title": "Waymo shares some of its self-driving car data to help ...", "date": "", "ddg_snippet": "21 Aug 2019 — It only covers 1,000 driving segments of 20 seconds each, but that's 200,000 frames per sensor, 12 million 3D object labels and 1.2 million ...", "subpage_snippet": "", "source": "tech.yahoo.com", "link": "https://tech.yahoo.com/ai/article/2019-08-21-waymo-shares-self-driving-car-data.html", "content": "21 Aug 2019 — It only covers 1,000 driving segments of 20 seconds each, but that's 200,000 frames per sensor, 12 million 3D object labels and 1.2 million ..."}
+{"idx": 7, "title": "15 Best Open-Source Autonomous Driving Datasets", "date": "", "ddg_snippet": "The Waymo Open dataset is an open-source multimodal sensor dataset for autonomous driving. Extracted from Waymo self-driving vehicles, the data ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/analytics-vidhya/15-best-open-source-autonomous-driving-datasets-34324676c8d7", "content": "The Waymo Open dataset is an open-source multimodal sensor dataset for autonomous driving. Extracted from Waymo self-driving vehicles, the data ..."}
+{"idx": 8, "title": "Machine Learning Datasets", "date": "", "ddg_snippet": "The Waymo Open Dataset is comprised of high resolution sensor data collected by autonomous vehicles operated by the Waymo Driver in a wide variety of ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/datasets?q=+OpenSubtitles", "content": "The Waymo Open Dataset is comprised of high resolution sensor data collected by autonomous vehicles operated by the Waymo Driver in a wide variety of ..."}
+{"idx": 9, "title": "Machine Learning Datasets - Lane Detection", "date": "", "ddg_snippet": "The dataset collects valuable contents from public perception dataset Waymo Open Dataset and provides lane&closest-in-path object(CIPO) annotation for 1000 ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/datasets?task=lane-detection&page=1", "content": "The dataset collects valuable contents from public perception dataset Waymo Open Dataset and provides lane&closest-in-path object(CIPO) annotation for 1000 ..."}
diff --git a/data/sampled_jsons/Waymo_Open_Dataset_Sun_et_al._2020_number_of_frames_images_sequences_breakdown_year_2020.jsonl b/data/sampled_jsons/Waymo_Open_Dataset_Sun_et_al._2020_number_of_frames_images_sequences_breakdown_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ea0e29f69df6d601a6252ad2fa52c0fbe85fc65e
--- /dev/null
+++ b/data/sampled_jsons/Waymo_Open_Dataset_Sun_et_al._2020_number_of_frames_images_sequences_breakdown_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scalability in Perception for Autonomous Driving: Waymo Open ...", "date": "", "ddg_snippet": "Geiger et al . introduced the multi-sensor KITTI Dataset [9, 8] in 2012, which provides synchronized stereo camera as well as LiDAR sensor data for 22 sequences , enabling tasks such as 3D object detection and tracking, visual odometry, and scene flow estimation.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Scalability_in_Perception_for_Autonomous_Driving_Waymo_Open_Dataset_CVPR_2020_paper.pdf", "content": "Geiger et al . introduced the multi-sensor KITTI Dataset [9, 8] in 2012, which provides synchronized stereo camera as well as LiDAR sensor data for 22 sequences , enabling tasks such as 3D object detection and tracking, visual odometry, and scene flow estimation."}
+{"idx": 1, "title": "Scalability in Perception for Autonomous Driving: Waymo Open ...", "date": "", "ddg_snippet": "The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the over-all viability of the technology. In an effort to ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9156973", "content": "The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the over-all viability of the technology. In an effort to ..."}
+{"idx": 2, "title": "Processing, assessing, and enhancing the Waymo autonomous ...", "date": "", "ddg_snippet": "Jan 1, 2022 · Sharma et al . (2018) proposed a pattern recognition algorithm for assessing trajectory completeness, and the same algorithm is applied for the Waymo Open Dataset.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0968090X21004769", "content": "Jan 1, 2022 · Sharma et al . (2018) proposed a pattern recognition algorithm for assessing trajectory completeness, and the same algorithm is applied for the Waymo Open Dataset."}
+{"idx": 3, "title": "waymo-research/waymo-open-dataset - GitHub", "date": "", "ddg_snippet": "The Waymo Open Dataset is a collection of datasets and evaluation code that we have released publicly to aid the research community in making advancements in machine perception and autonomous driving technology. The Waymo Open Dataset includes three datasets: The Perception dataset, with high resolution sensor data and labels for various tasks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/waymo-research/waymo-open-dataset", "content": "The Waymo Open Dataset is a collection of datasets and evaluation code that we have released publicly to aid the research community in making advancements in machine perception and autonomous driving technology. The Waymo Open Dataset includes three datasets: The Perception dataset, with high resolution sensor data and labels for various tasks."}
+{"idx": 4, "title": "About – Waymo Open Dataset", "date": "", "ddg_snippet": "The Waymo Open Dataset is composed of three datasets - the Perception Dataset with high resolution sensor data and labels for 2,030 segments, the Motion Dataset with object trajectories and corresponding 3D maps for 103,354 segments, and the End-to-End Driving Dataset with camera images providing 360-degree coverage and routing instructions for ...", "subpage_snippet": "", "source": "waymo.com", "link": "https://waymo.com/open/about/", "content": "The Waymo Open Dataset is composed of three datasets - the Perception Dataset with high resolution sensor data and labels for 2,030 segments, the Motion Dataset with object trajectories and corresponding 3D maps for 103,354 segments, and the End-to-End Driving Dataset with camera images providing 360-degree coverage and routing instructions for ..."}
+{"idx": 5, "title": "Waymo Open dataset - Dataset - LDM - service.tib.eu", "date": "", "ddg_snippet": "The Waymo Open dataset provides 798 training sequences and 202 validation sequences . Each sequence contains 200 frames within 20 seconds.", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/waymo-open-dataset", "content": "The Waymo Open dataset provides 798 training sequences and 202 validation sequences . Each sequence contains 200 frames within 20 seconds."}
+{"idx": 6, "title": "MIT Open Access Articles Generalized self-cueing real- ...", "date": "", "ddg_snippet": "by S Liu · 2023 · Cited by 3 — Results obtained on Waymo Open dataset ( Sun et al , 2020 ). 7 Empirical Optimization. In this section, we list some practical considerations and empirical ...", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/150910/11241_2023_9396_ReferencePDF.pdf?sequence=1&isAllowed=y", "content": "by S Liu · 2023 · Cited by 3 — Results obtained on Waymo Open dataset ( Sun et al , 2020 ). 7 Empirical Optimization. In this section, we list some practical considerations and empirical ..."}
+{"idx": 7, "title": "Emma Paper | PDF | Waymo | Lidar", "date": "", "ddg_snippet": "29 Oct 2024 — 1 3D Object Detection. We validate our 3D object detection performance on the 3D camera-primary detection benchmark from the Waymo Open Dataset ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/786090543/Emma-Paper", "content": "29 Oct 2024 — 1 3D Object Detection. We validate our 3D object detection performance on the 3D camera-primary detection benchmark from the Waymo Open Dataset ..."}
+{"idx": 8, "title": "how Waymo's Open Dataset Challenges shape AI ...", "date": "", "ddg_snippet": "by S Hind · 2025 · Cited by 9 — This article analyses these challenges, situating them within the context of the 'Grand Challenges' of artificial intelligence (AI).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00146-024-01927-x", "content": "by S Hind · 2025 · Cited by 9 — This article analyses these challenges, situating them within the context of the 'Grand Challenges' of artificial intelligence (AI)."}
+{"idx": 9, "title": "The Waymo Open Sim Agents Challenge", "date": "", "ddg_snippet": "by N Montali · Cited by 79 — The goal of the challenge is to stimulate the design of realistic simulators that can be used to evaluate and train a behavior model for autonomous driving. We ...", "subpage_snippet": "", "source": "ml4ad.github.io", "link": "https://ml4ad.github.io/files/papers2023/The+Waymo+Open+Sim+Agents+Challenge.pdf", "content": "by N Montali · Cited by 79 — The goal of the challenge is to stimulate the design of realistic simulators that can be used to evaluate and train a behavior model for autonomous driving. We ..."}
diff --git a/data/sampled_jsons/Waymo_Open_Dataset_Sun_et_al._2020_paper.jsonl b/data/sampled_jsons/Waymo_Open_Dataset_Sun_et_al._2020_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ebcbf803d330a3f27bbca67a94e2cac94897e24
--- /dev/null
+++ b/data/sampled_jsons/Waymo_Open_Dataset_Sun_et_al._2020_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scalability in Perception for Autonomous Driving: Waymo Open Dataset", "date": "", "ddg_snippet": "View a PDF of the paper titled Scalability in Perception for Autonomous Driving: Waymo Open Dataset , by Pei Sun and 24 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1912.04838", "content": "View a PDF of the paper titled Scalability in Perception for Autonomous Driving: Waymo Open Dataset , by Pei Sun and 24 other authors"}
+{"idx": 1, "title": "PDF Scalability in Perception for Autonomous Driving: Waymo Open Dataset", "date": "", "ddg_snippet": "Geiger et al. introduced the multi-sensor KITTI Dataset [9, 8] in 2012, which provides synchronized stereo camera as well as LiDAR sensor data for 22 sequences, enabling tasks such as 3D object detection and tracking, visual odometry, and scene flow estimation.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Scalability_in_Perception_for_Autonomous_Driving_Waymo_Open_Dataset_CVPR_2020_paper.pdf", "content": "Geiger et al. introduced the multi-sensor KITTI Dataset [9, 8] in 2012, which provides synchronized stereo camera as well as LiDAR sensor data for 22 sequences, enabling tasks such as 3D object detection and tracking, visual odometry, and scene flow estimation."}
+{"idx": 2, "title": "Scalability in Perception for Autonomous Driving: Waymo Open Dataset ...", "date": "", "ddg_snippet": "The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the over-all viability of the technology. In an effort to ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9156973", "content": "The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the over-all viability of the technology. In an effort to ..."}
+{"idx": 3, "title": "Processing, assessing, and enhancing the Waymo autonomous vehicle open ...", "date": "", "ddg_snippet": "Abstract Recently released Autonomous Vehicle (AV) trajectory datasets can potentially catalyze research progress on AV-oriented traffic flow analysis. This paper aims to comprehensively and systematically process and assess one of the AV-oriented open datasets , i.e., Waymo Open Dataset , with a focus on car following paired trajectories.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0968090X21004769", "content": "Abstract Recently released Autonomous Vehicle (AV) trajectory datasets can potentially catalyze research progress on AV-oriented traffic flow analysis. This paper aims to comprehensively and systematically process and assess one of the AV-oriented open datasets , i.e., Waymo Open Dataset , with a focus on car following paired trajectories."}
+{"idx": 4, "title": "Scalability in Perception for Autonomous Driving: Waymo Open Dataset", "date": "", "ddg_snippet": "Request PDF | On Jun 1, 2020 , Pei Sun and others published Scalability in Perception for Autonomous Driving: Waymo Open Dataset | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/343460935_Scalability_in_Perception_for_Autonomous_Driving_Waymo_Open_Dataset", "content": "Request PDF | On Jun 1, 2020 , Pei Sun and others published Scalability in Perception for Autonomous Driving: Waymo Open Dataset | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 5, "title": "waymo-research/waymo-open-dataset - GitHub", "date": "", "ddg_snippet": "The Waymo Open Dataset is a collection of datasets and evaluation code that we have released publicly to aid the research community in making advancements in machine perception and autonomous driving technology. The Waymo Open Dataset includes three datasets : The Perception dataset , with high resolution sensor data and labels for various tasks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/waymo-research/waymo-open-dataset", "content": "The Waymo Open Dataset is a collection of datasets and evaluation code that we have released publicly to aid the research community in making advancements in machine perception and autonomous driving technology. The Waymo Open Dataset includes three datasets : The Perception dataset , with high resolution sensor data and labels for various tasks."}
+{"idx": 6, "title": "CVPR 2020 Open Access Repository", "date": "", "ddg_snippet": "Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Scalability_in_Perception_for_Autonomous_Driving_Waymo_Open_Dataset_CVPR_2020_paper.html", "content": "Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies."}
+{"idx": 7, "title": "Scalability in Perception for Autonomous Driving: Waymo Open Dataset", "date": "", "ddg_snippet": "Geiger et al. introduced the multi-sensor KITTI Dataset [9, 8] in 2012, which provides synchronized stereo camera as well as LiDAR sensor data for 22 sequences, enabling tasks such as 3D object detection and tracking, visual odometry, and scene flow estimation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1912.04838", "content": "Geiger et al. introduced the multi-sensor KITTI Dataset [9, 8] in 2012, which provides synchronized stereo camera as well as LiDAR sensor data for 22 sequences, enabling tasks such as 3D object detection and tracking, visual odometry, and scene flow estimation."}
+{"idx": 8, "title": "Processing, assessing, and enhancing the Waymo autonomous vehicle open ...", "date": "", "ddg_snippet": "The Waymo Open Dataset ( Sun et al ., 2020b) enables such detailed assessment as it includes fineresolution motion information of AVs in mixed autonomy traffic from 1,000 segments (2019 release) at ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/356748756_Processing_assessing_and_enhancing_the_Waymo_autonomous_vehicle_open_dataset_for_driving_behavior_research", "content": "The Waymo Open Dataset ( Sun et al ., 2020b) enables such detailed assessment as it includes fineresolution motion information of AVs in mixed autonomy traffic from 1,000 segments (2019 release) at ..."}
+{"idx": 9, "title": "Processing, assessing, and enhancing the Waymo autonomous vehicle open ...", "date": "", "ddg_snippet": "Recently released Autonomous Vehicle (AV) trajectory datasets can potentially catalyze research progress on AV-oriented traffic flow analysis. This paper aims to comprehensively and systematically process and assess one of the AV-oriented open datasets , i.e., Waymo Open Dataset , with a focus on car following paired trajectories.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0968090X21004769", "content": "Recently released Autonomous Vehicle (AV) trajectory datasets can potentially catalyze research progress on AV-oriented traffic flow analysis. This paper aims to comprehensively and systematically process and assess one of the AV-oriented open datasets , i.e., Waymo Open Dataset , with a focus on car following paired trajectories."}
diff --git a/data/sampled_jsons/WcuXvn3HVk_AERO-_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration.jsonl b/data/sampled_jsons/WcuXvn3HVk_AERO-_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fa0bc7d717857125effb3213ee0c2459324859fe
--- /dev/null
+++ b/data/sampled_jsons/WcuXvn3HVk_AERO-_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "To address these scalability issues, account migration ofers a promising solution. However, existing migration solutions struggle with the high computational overhead and insuficient capture of complex transaction patterns. We propose AERO, a deep reinforcement learning framework to facilitate eficient account migration in sharding blockchains .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "To address these scalability issues, account migration ofers a promising solution. However, existing migration solutions struggle with the high computational overhead and insuficient capture of complex transaction patterns. We propose AERO, a deep reinforcement learning framework to facilitate eficient account migration in sharding blockchains ."}
+{"idx": 1, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "We propose AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains . AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "We propose AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains . AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts ."}
+{"idx": 2, "title": "Deep Learning Approaches for Blockchain Scalability Through Sharding ...", "date": "", "ddg_snippet": "Sharding technology creates new difficulties even as it helps traditional blockchain networks overcome performance issues. The distribution of malicious nodes may be unequal because of random node allocation, resulting in performance variations and security threats. Current reputation-based sharding techniques frequently ignore node performance features and don't take care of leader election ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10830433", "content": "Sharding technology creates new difficulties even as it helps traditional blockchain networks overcome performance issues. The distribution of malicious nodes may be unequal because of random node allocation, resulting in performance variations and security threats. Current reputation-based sharding techniques frequently ignore node performance features and don't take care of leader election ..."}
+{"idx": 3, "title": "A Review of Dynamic Sharding in Blockchain with Deep Reinforcement ...", "date": "", "ddg_snippet": "Blockchain networks face significant scalability and efficiency challenges as transaction volumes increase. Sharding , a partitioning technique that divides the blockchain into smaller, parallel-processing units, offers a promising solution. However, traditional static sharding lacks adaptability, leading to inefficiencies in resource utilization and security vulnerabilities. This paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11069521", "content": "Blockchain networks face significant scalability and efficiency challenges as transaction volumes increase. Sharding , a partitioning technique that divides the blockchain into smaller, parallel-processing units, offers a promising solution. However, traditional static sharding lacks adaptability, leading to inefficiencies in resource utilization and security vulnerabilities. This paper ..."}
+{"idx": 4, "title": "PDF AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "Motivated by the need for an eficient and decentralized account migration mechanism, we explore applying deep reinforcement learning (DRL) [27] to this problem. DRL is highly efective in handling sequential decision-making tasks and has demonstrated significant potential in optimizing complex systems with expansive state and action spaces [24]. In the context of account migration , the account ...", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "Motivated by the need for an eficient and decentralized account migration mechanism, we explore applying deep reinforcement learning (DRL) [27] to this problem. DRL is highly efective in handling sequential decision-making tasks and has demonstrated significant potential in optimizing complex systems with expansive state and action spaces [24]. In the context of account migration , the account ..."}
+{"idx": 5, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "This work proposes AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains , which employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts . Sharding blockchain networks face significant scalability challenges due to high frequencies of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/AERO:-Enhancing-Sharding-Blockchain-via-Deep-for-Song-Li/a4ab4e5da2ec84ea76d9176e2d31eed7990bcdd3", "content": "This work proposes AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains , which employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts . Sharding blockchain networks face significant scalability challenges due to high frequencies of ..."}
+{"idx": 6, "title": "Papers - Zhen Xiao", "date": "", "ddg_snippet": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Proc. of the Web Conference 2025 (WWW 2025), May 2025. Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan, Rongkai Zhang, Pengze Li, and Zhen Xiao.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/", "content": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Proc. of the Web Conference 2025 (WWW 2025), May 2025. Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan, Rongkai Zhang, Pengze Li, and Zhen Xiao."}
+{"idx": 7, "title": "Mingxuan Song", "date": "", "ddg_snippet": "\"AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .\" Proceedings of the Web Conference, May 2025. [PDF] CVPR 2024 CCF-A Shenglin Yin, Zhen Xiao*, Mingxuan Song, and Jieyi Long. \"Adversarial Distillation Based on Slack Matching and Attribution Region Alignment.\"", "subpage_snippet": "", "source": "www.songmingxuan.com", "link": "https://www.songmingxuan.com/", "content": "\"AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .\" Proceedings of the Web Conference, May 2025. [PDF] CVPR 2024 CCF-A Shenglin Yin, Zhen Xiao*, Mingxuan Song, and Jieyi Long. \"Adversarial Distillation Based on Slack Matching and Attribution Region Alignment.\""}
+{"idx": 8, "title": "Zhen Xiao | Semantic Scholar", "date": "", "ddg_snippet": "This work proposes AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains , which employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/author/Zhen-Xiao/2257215916", "content": "This work proposes AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains , which employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts ."}
+{"idx": 9, "title": "Mingxuan Song - OpenReview", "date": "", "ddg_snippet": "Publications AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Mingxuan_Song1", "content": "Publications AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement"}
diff --git a/data/sampled_jsons/We_introduce_physics_informed_neural_networks_loss_function_partial_differential_equations_Raissi_20.jsonl b/data/sampled_jsons/We_introduce_physics_informed_neural_networks_loss_function_partial_differential_equations_Raissi_20.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5e6dc8f77ce69f20cabc992e3eced710d5b7fc1
--- /dev/null
+++ b/data/sampled_jsons/We_introduce_physics_informed_neural_networks_loss_function_partial_differential_equations_Raissi_20.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Authors | Physics Informed Deep Learning", "date": "", "ddg_snippet": "Abstract We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations .", "subpage_snippet": "", "source": "maziarraissi.github.io", "link": "https://maziarraissi.github.io/PINNs/", "content": "Abstract We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations ."}
+{"idx": 1, "title": "Physics-informed neural networks: A deep learning framework ...", "date": "", "ddg_snippet": "Feb 1, 2019 · We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999118307125", "content": "Feb 1, 2019 · We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations ."}
+{"idx": 2, "title": "Physics-Informed Neural Networks and Extensions - arXiv.org", "date": "", "ddg_snippet": "Aug 29, 2024 · M. Raissi , P. Perdikaris, G. E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , Journal of Computational Physics 378, ( 2019 ), 686–707.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.16806v1", "content": "Aug 29, 2024 · M. Raissi , P. Perdikaris, G. E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , Journal of Computational Physics 378, ( 2019 ), 686–707."}
+{"idx": 3, "title": "Physics Informed Neural Networks(PINNs) for Burgers' equation", "date": "", "ddg_snippet": "PINNs are neural networks that undergo training to execute supervised learning tasks while adhering to certain laws of physics, which are typically defined by basic nonlinear partial differential equations . This paper concentrates on the advancements in addressing burger equation problems using data-driven approaches for partial differential equations . Here the algorithms are separated into ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10870712", "content": "PINNs are neural networks that undergo training to execute supervised learning tasks while adhering to certain laws of physics, which are typically defined by basic nonlinear partial differential equations . This paper concentrates on the advancements in addressing burger equation problems using data-driven approaches for partial differential equations . Here the algorithms are separated into ..."}
+{"idx": 4, "title": "Physics-informed neural networks | Proceedings of the Thirty ...", "date": "", "ddg_snippet": "The residual loss in Physics - Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, resulting in a loss landscape that is inherently different from those of common supervised problems. Therefore, relying on the existing theory leads to unjustified design choices and suboptimal performance. In this ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.24963/ijcai.2024/647", "content": "The residual loss in Physics - Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, resulting in a loss landscape that is inherently different from those of common supervised problems. Therefore, relying on the existing theory leads to unjustified design choices and suboptimal performance. In this ..."}
+{"idx": 5, "title": "Physics-informed neural networks: A deep learning framework ...", "date": "", "ddg_snippet": "Jun 13, 2018 · Physics Informed Deep Learning Data-driven solutions and discovery of Nonlinear Partial Differential Equations View on GitHub Authors Maziar Raissi , Paris Perdikaris, and George Em Karniadakis Abstract We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear ...", "subpage_snippet": "", "source": "neuralfields.cs.brown.edu", "link": "https://neuralfields.cs.brown.edu/paper_4.html", "content": "Jun 13, 2018 · Physics Informed Deep Learning Data-driven solutions and discovery of Nonlinear Partial Differential Equations View on GitHub Authors Maziar Raissi , Paris Perdikaris, and George Em Karniadakis Abstract We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear ..."}
+{"idx": 6, "title": "Physics-Informed Neural Networks: Minimizing Residual Loss ...", "date": "", "ddg_snippet": "Abstract The residual loss in Physics - Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, resulting in a loss landscape that is inherently different from those of common supervised problems. Therefore, relying on the existing theory leads to unjustified design choices and suboptimal performance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.01680", "content": "Abstract The residual loss in Physics - Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, resulting in a loss landscape that is inherently different from those of common supervised problems. Therefore, relying on the existing theory leads to unjustified design choices and suboptimal performance ..."}
+{"idx": 7, "title": "(PDF) Physics Informed Deep Learning (Part II): Data-driven...", "date": "", "ddg_snippet": "We introduce physics informed neural networks – neural networks that are. trained to solve supervised learning tasks while respecting any given law of. physics described by general nonlinear partial differential equations .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/321375771_Physics_Informed_Deep_Learning_Part_II_Data-driven_Discovery_of_Nonlinear_Partial_Differential_Equations", "content": "We introduce physics informed neural networks – neural networks that are. trained to solve supervised learning tasks while respecting any given law of. physics described by general nonlinear partial differential equations ."}
+{"idx": 8, "title": "[PDF] Physics Informed Deep Learning (Part II)... | Semantic Scholar", "date": "", "ddg_snippet": "We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Physics-Informed-Deep-Learning-(Part-II):-Discovery-Raissi-Perdikaris/25903eabbb1830aefa82048212e643eec660de0b", "content": "We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations ."}
+{"idx": 9, "title": "fPINNs: Fractional Physics - Informed Neural Networks -Bohrium", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs), introduced in [M. Raissi , P. Perdikaris, and G. Karniadakis, J. Comput. Phys., 378 ( 2019 ), pp. 686--707], are effective in solving integer-order partial differential equations (PDEs) based on scattered and noisy data.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/fpinns-fractional-physics-informed-neural-networks/812741931774771201-883", "content": "Physics - informed neural networks (PINNs), introduced in [M. Raissi , P. Perdikaris, and G. Karniadakis, J. Comput. Phys., 378 ( 2019 ), pp. 686--707], are effective in solving integer-order partial differential equations (PDEs) based on scattered and noisy data."}
diff --git a/data/sampled_jsons/WeaklyRec_class_prior_estimation_Progressive_Proximal_Transport_implicit_feedback_year_2024.jsonl b/data/sampled_jsons/WeaklyRec_class_prior_estimation_Progressive_Proximal_Transport_implicit_feedback_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..005173f567f2f098150eb2c5e030d30a2a58898b
--- /dev/null
+++ b/data/sampled_jsons/WeaklyRec_class_prior_estimation_Progressive_Proximal_Transport_implicit_feedback_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning Eric Wang · Zhichao Chen · Haotian Wang · Yanchao Tan · Licheng Pan · Tianqiao Liu · Xu Chen · Haoxuan Li · Zhouchen Lin", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning Eric Wang · Zhichao Chen · Haotian Wang · Yanchao Tan · Licheng Pan · Tianqiao Liu · Xu Chen · Haoxuan Li · Zhouchen Lin"}
+{"idx": 1, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — To address this, we develop a Pro- gressive Proximal Transport (PPT) method which achieves automatic class prior estimation by minimizing the proximal .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — To address this, we develop a Pro- gressive Proximal Transport (PPT) method which achieves automatic class prior estimation by minimizing the proximal ."}
+{"idx": 2, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "Progressive Proximal Transport (PPT): A method designed to achieve automatic class prior estimation by minimizing the transport cost between positive and ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/165506", "content": "Progressive Proximal Transport (PPT): A method designed to achieve automatic class prior estimation by minimizing the transport cost between positive and ..."}
+{"idx": 3, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "However, its unbiasedness hinges on the accurate estimation of the class prior . To address this challenge, we propose Progressive Proximal Transport (PPT), which estimates the class prior by minimizing the proximal transport cost between positive and unlabeled samples.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13", "content": "However, its unbiasedness hinges on the accurate estimation of the class prior . To address this challenge, we propose Progressive Proximal Transport (PPT), which estimates the class prior by minimizing the proximal transport cost between positive and unlabeled samples."}
+{"idx": 4, "title": "GitHub - Lorenzo-Perini/Active_PU_Learning: Class Prior Estimation in ...", "date": "", "ddg_snippet": "Estimating the proportion of positive examples (i.e., the class prior ) from positive and unlabeled (PU) data is an important task that facilitates learning a classifier from such data. We explore how to tackle this problem when the observed labels were acquired via active learning. This introduces ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Lorenzo-Perini/Active_PU_Learning", "content": "Estimating the proportion of positive examples (i.e., the class prior ) from positive and unlabeled (PU) data is an important task that facilitates learning a classifier from such data. We explore how to tackle this problem when the observed labels were acquired via active learning. This introduces ..."}
+{"idx": 5, "title": "Class Prior Estimation with Biased Positives and Unlabeled Examples", "date": "", "ddg_snippet": "Although this introduces problems in all aspects of positive-unlabeled learning, we begin to address this challenge by focusing on the estimation of class priors , quantities central to the estimation of posterior probabilities and the recovery of true classification performance. We start by making a set of assumptions to model the sampling bias.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/5848", "content": "Although this introduces problems in all aspects of positive-unlabeled learning, we begin to address this challenge by focusing on the estimation of class priors , quantities central to the estimation of posterior probabilities and the recovery of true classification performance. We start by making a set of assumptions to model the sampling bias."}
+{"idx": 6, "title": "Practically Unbiased Pairwise Loss for Recommendation With Implicit ...", "date": "", "ddg_snippet": "Practically Unbiased Pairwise Loss for Recommendation With Implicit Feedback Recommender systems have been widely employed on various online platforms to improve user experience. In these systems, recommendation models are often learned from the users' historical behaviors that are automatically collected.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10810273", "content": "Practically Unbiased Pairwise Loss for Recommendation With Implicit Feedback Recommender systems have been widely employed on various online platforms to improve user experience. In these systems, recommendation models are often learned from the users' historical behaviors that are automatically collected."}
+{"idx": 7, "title": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback", "date": "", "ddg_snippet": "This leads to a difficulty in predicting the users' preferences from implicit feedback . Previous studies addressed the positive-unlabeled problem by uniformly upweighting the loss for the positive feedback data or estimating the confidence of each data having relevance information via the EM-algorithm.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.03601", "content": "This leads to a difficulty in predicting the users' preferences from implicit feedback . Previous studies addressed the positive-unlabeled problem by uniformly upweighting the loss for the positive feedback data or estimating the confidence of each data having relevance information via the EM-algorithm."}
+{"idx": 8, "title": "Rethinking Class-Prior Estimation for Positive-Unlabeled Learning", "date": "", "ddg_snippet": "Contribute to a5507203/Rethinking- Class - Prior - Estimation -for-Positive-Unlabeled-Learning development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/a5507203/Rethinking-Class-Prior-Estimation-for-Positive-Unlabeled-Learning", "content": "Contribute to a5507203/Rethinking- Class - Prior - Estimation -for-Positive-Unlabeled-Learning development by creating an account on GitHub."}
+{"idx": 9, "title": "Modeling Dynamic Missingness of Implicit Feedback for Recommendation", "date": "", "ddg_snippet": "Abstract Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of values that are missing not at random (MNAR); and the missing data is a mixture of negative and unknown feedback , making it difficult to learn users' negative preferences. Recent studies modeled exposure, a latent missingness ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6453574/", "content": "Abstract Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of values that are missing not at random (MNAR); and the missing data is a mixture of negative and unknown feedback , making it difficult to learn users' negative preferences. Recent studies modeled exposure, a latent missingness ..."}
diff --git a/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_A_Mechanistic_Study_Samyak_Jain.jsonl b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_A_Mechanistic_Study_Samyak_Jain.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f13cd2af2cf4b81cbd21ebf6aec6df2aa979202
--- /dev/null
+++ b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_A_Mechanistic_Study_Samyak_Jain.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "View a PDF of the paper titled What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study , by Samyak Jain and 6 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.10264", "content": "View a PDF of the paper titled What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study , by Samyak Jain and 6 other authors"}
+{"idx": 1, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Authors Samyak Jain , Ekdeep Singh Lubana, Kemal Oksuz, Tom Joy, Philip H.S. Torr, Amartya Sanyal, Puneet K. Dokania Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a9bef53eb7b0e5950d4f2d9c74a16006-Abstract-Conference.html", "content": "Authors Samyak Jain , Ekdeep Singh Lubana, Kemal Oksuz, Tom Joy, Philip H.S. Torr, Amartya Sanyal, Puneet K. Dokania Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient ..."}
+{"idx": 2, "title": "PDF What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks, and adversarial attacks. We make careful design choices to adhere to the properties of natural language instructions and the jailbreaks taxonomy of Wei et al. (2023), thus facilitating a thorough ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/a9bef53eb7b0e5950d4f2d9c74a16006-Paper-Conference.pdf", "content": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks, and adversarial attacks. We make careful design choices to adhere to the properties of natural language instructions and the jailbreaks taxonomy of Wei et al. (2023), thus facilitating a thorough ..."}
+{"idx": 3, "title": "What Makes and Breaks Safety Fine-tuning? Mechanistic Study", "date": "", "ddg_snippet": "Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the un-derlying factors that make models safe via safety fine-tuning , we design a synthetic data genera-tion framework that captures salient aspects of an unsafe input by modeling the interaction be-tween the task the model is asked to perform (e.g ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BS2CbUkJpy", "content": "Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the un-derlying factors that make models safe via safety fine-tuning , we design a synthetic data genera-tion framework that captures salient aspects of an unsafe input by modeling the interaction be-tween the task the model is asked to perform (e.g ..."}
+{"idx": 4, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "The official implementation of \" What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study \". This work is accepted to NeurIPS 2024. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "The official implementation of \" What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study \". This work is accepted to NeurIPS 2024. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient ..."}
+{"idx": 5, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JEflV4nRlH", "content": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design ..."}
+{"idx": 6, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study | Empirical Inference - Max Planck Institute for Intelligent Systems Back Empirical InferenceConference Paper2024", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/ei/publications/jainetal24-721b15d0-65bd-4334-a544-01904e7d1787", "content": "What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study | Empirical Inference - Max Planck Institute for Intelligent Systems Back Empirical InferenceConference Paper2024"}
+{"idx": 7, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study | Max Planck Institute for Intelligent Systems Back Empirical InferenceConference Paper2024", "subpage_snippet": "", "source": "mms.is.mpg.de", "link": "https://mms.is.mpg.de/publications/jainetal24-721b15d0-65bd-4334-a544-01904e7d1787", "content": "What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study | Max Planck Institute for Intelligent Systems Back Empirical InferenceConference Paper2024"}
+{"idx": 8, "title": "What Makes and Breaks Safety Fine-tuning? Mechanistic Study", "date": "", "ddg_snippet": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., ``design ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240710264J/abstract", "content": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., ``design ..."}
+{"idx": 9, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Poster What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study Samyak Jain · Ekdeep S Lubana · Kemal Oksuz · Tom Joy · Philip Torr · Amartya Sanyal · Puneet Dokania East Exhibit Hall A-C #3306", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95726", "content": "Poster What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study Samyak Jain · Ekdeep S Lubana · Kemal Oksuz · Tom Joy · Philip Torr · Amartya Sanyal · Puneet Dokania East Exhibit Hall A-C #3306"}
diff --git a/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_minGPT_n_layer_transformer_blocks_configuration_synthetic_year_2024.jsonl b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_minGPT_n_layer_transformer_blocks_configuration_synthetic_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3f5790a433e6a6c8a4caf62c9a2036cbc2d73839
--- /dev/null
+++ b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_minGPT_n_layer_transformer_blocks_configuration_synthetic_year_2024.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "A Spin Glass Characterization of Neural Networks", "date": "", "ddg_snippet": "... a tool inspired by the replica method and replica symmetry breaking (RSB) for characterizing the thermodynamic signatures of neural networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.07397v1", "content": "... a tool inspired by the replica method and replica symmetry breaking (RSB) for characterizing the thermodynamic signatures of neural networks."}
+{"idx": 1, "title": "The Principles of Deep Learning Theory | Hacker News", "date": "", "ddg_snippet": "I highly recommended reading the papers “How to Train a 10,000 Layer Neural Network” [1], and “ReZero is All You Need: Fast Convergence at ...", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=31051540", "content": "I highly recommended reading the papers “How to Train a 10,000 Layer Neural Network” [1], and “ReZero is All You Need: Fast Convergence at ..."}
+{"idx": 2, "title": "Actually, Othello-GPT Has A Linear Emergent World", "date": "", "ddg_snippet": "The headline result is that Othello-GPT learns an emergent world representation - despite never being explicitly given the state of the board, and ...", "subpage_snippet": "", "source": "www.neelnanda.io", "link": "https://www.neelnanda.io/mechanistic-interpretability/othello", "content": "The headline result is that Othello-GPT learns an emergent world representation - despite never being explicitly given the state of the board, and ..."}
diff --git a/data/sampled_jsons/When_comparing_human_and_machine_performance_Geirhos_error_consistency_abstract.jsonl b/data/sampled_jsons/When_comparing_human_and_machine_performance_Geirhos_error_consistency_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..322bbf90b7b9b73757894bfe19196a14ac0fcc0f
--- /dev/null
+++ b/data/sampled_jsons/When_comparing_human_and_machine_performance_Geirhos_error_consistency_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Check For AI & Humanize Text - Human and machine Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Boost clarity and tone in your AI-powered business content. Make AI-generated content sound more natural and brand-consistent for your team", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=sidekicker.ai&ad_provider=bingv7aa&ad_type=txad&click_metadata=CXJHy4wL6I2d7tw7mSXjNRBxV3fZnaunTDyDKs_jZG29C0BuAj4RCnXg1pxhTmac-vZyF3kxsZ_qgFRGLQa8hF-2VenPk1mNsCZuvdLd4BIceK4y8WxyHJwqc0YElQSj.sEJhXxivkowb-T4Y1qvw1w&rut=615a3229d1d78a625bf6f7ea527688ad59d7c1f5a182723dfb513ab8e11c7f0c&u3=https://www.bing.com/aclick?ld=e8ZMO4Ru6G-cMCnWx8xiw9SDVUCUyBYi-D2ioBTx_ctOzKn0vsWeFugLZjdkaGPoKrNLytjYXw7qJEX96EJoj0YZ1goouH6dacrkaIaPG0C0CtDsKnL4lyK0blxjgpOjQ2TtG8E0IaBHy9IvQeT01UlKWFdL6am3G6_C9flQwN3Z3-4PSNXzTEVsmRmrS36-oWPlUacrha-8d5uRVWChi-Y3Bld4w&u=aHR0cHMlM2ElMmYlMmZzaWRla2lja2VyLmFpJTJmJTNmdXRtX3NvdXJjZSUzZGJpbmclMjZ1dG1fbWVkaXVtJTNkY3BjJTI2dXRtX2NhbXBhaWduJTNkNDg2NzQ1MjYyJTI2dXRtX2NvbnRlbnQlM2QxMjM2OTUxNzM2ODQ1NjExJTI2dXRtX2Fkc2V0X2lkJTNkMTIzNjk1MTczNjg0NTYxMSUyNnV0bV90ZXJtJTNkaHVtYW4lMjUyMGFuZCUyNTIwbWFjaGluZSUyNnV0bV9uZXR3b3JrJTNkcyUyNnV0bV9tYXRjaHR5cGUlM2RwJTI2bXNjbGtpZCUzZDM2NWFlYWE5ZDBkZDE3ZTI4MGYxODI3OTA4NjdhNTQ0&rlid=365aeaa9d0dd17e280f182790867a544&vqd=4-122334033087150767566441481660378427964&iurl={1}IG=708076E3CD764E9AAC7F11BC5C037C8C&CID=0FE7475DD0A76B1F18A45132D1926AAE&ID=DevEx,5028.1", "content": "Boost clarity and tone in your AI-powered business content. Make AI-generated content sound more natural and brand-consistent for your team"}
+{"idx": 1, "title": "quantifying trial-by-trial behaviour of CNNs and humans ...", "date": "", "ddg_snippet": "by R Geirhos · 2020 · Cited by 160 — Here we introduce trial-by-trial error consistency , a quantitative analysis for measuring whether two decision making systems systematically make errors on the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.16736", "content": "by R Geirhos · 2020 · Cited by 160 — Here we introduce trial-by-trial error consistency , a quantitative analysis for measuring whether two decision making systems systematically make errors on the ..."}
+{"idx": 2, "title": "quantifying trial-by-trial behaviour of CNNs and humans by ...", "date": "", "ddg_snippet": "by R Geirhos · 2020 · Cited by 160 — Taken together, error consistency analysis suggests that the strategies used by human and machine vision are still very different—but we envision that error ... 13 pages", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2020/file/9f6992966d4c363ea0162a056cb45fe5-Paper.pdf", "content": "by R Geirhos · 2020 · Cited by 160 — Taken together, error consistency analysis suggests that the strategies used by human and machine vision are still very different—but we envision that error ... 13 pages"}
+{"idx": 3, "title": "Partial success in closing the gap between human and ...", "date": "", "ddg_snippet": "by R Geirhos · 2021 · Cited by 320 — The longstanding distortion robustness gap between humans and CNNs is closing, with the best models now exceeding human feedforward performance .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2106.07411", "content": "by R Geirhos · 2021 · Cited by 320 — The longstanding distortion robustness gap between humans and CNNs is closing, with the best models now exceeding human feedforward performance ."}
+{"idx": 4, "title": "quantifying trial-by-trial behaviour of CNNs and humans ...", "date": "", "ddg_snippet": "6 Dec 2020 — Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency . AUTHORs: Robert Geirhos .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3495724.3496889", "content": "6 Dec 2020 — Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency . AUTHORs: Robert Geirhos ."}
+{"idx": 5, "title": "Five points to check when comparing visual perception in ...", "date": "", "ddg_snippet": "by CM Funke · 2021 · Cited by 99 — In order to compare human and machine performance at learning abstract relationships between shapes, Fleuret et al. ... humans by measuring error consistency .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7980041/", "content": "by CM Funke · 2021 · Cited by 99 — In order to compare human and machine performance at learning abstract relationships between shapes, Fleuret et al. ... humans by measuring error consistency ."}
+{"idx": 6, "title": "Partial success in closing the gap between human and ...", "date": "", "ddg_snippet": "by R Geirhos · Cited by 320 — This paper follows a trend of studies comparing human behavioral performance on an object recognition task with machine vision models. Studies ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QkljT4mrfs", "content": "by R Geirhos · Cited by 320 — This paper follows a trend of studies comparing human behavioral performance on an object recognition task with machine vision models. Studies ..."}
+{"idx": 7, "title": "The Notorious Difficulty of Comparing Human and Machine ...", "date": "", "ddg_snippet": "It is shown that, despite their ability to solve closed-contour tasks, the authors' neural networks use different decision-making strategies than humans , ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Notorious-Difficulty-of-Comparing-Human-and-Funke-Borowski/e1e2ebf3c931d21cba6b43bfd24103e4ca991bcc", "content": "It is shown that, despite their ability to solve closed-contour tasks, the authors' neural networks use different decision-making strategies than humans , ..."}
+{"idx": 8, "title": "Decision-margin consistency: a principled metric for human ...", "date": "", "ddg_snippet": "by GA Alvarez · Cited by 1 — We compared error consistency and decision margin consistency between models and humans , computed over three experiments conducted by Geirhos et al., 2020 [2].", "subpage_snippet": "", "source": "konklab.fas.harvard.edu", "link": "https://konklab.fas.harvard.edu/Papers/Alvarez_2024_UniReps.pdf", "content": "by GA Alvarez · Cited by 1 — We compared error consistency and decision margin consistency between models and humans , computed over three experiments conducted by Geirhos et al., 2020 [2]."}
+{"idx": 9, "title": "A comparison between humans and AI at recognizing ...", "date": "", "ddg_snippet": "Error consistency compares observed consistency to expected consistency , and allows us to quantify whether the observed consistency is larger than would have ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/ff3970fa5adb91f089f85b2edb97353c0083c420.pdf", "content": "Error consistency compares observed consistency to expected consistency , and allows us to quantify whether the observed consistency is larger than would have ..."}
diff --git a/data/sampled_jsons/WiSE-FT_Wortsman_et_al.,_2021_arXiv.jsonl b/data/sampled_jsons/WiSE-FT_Wortsman_et_al.,_2021_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..026ea0b2bc14c6592ecb96c22edfbee57efc77e8
--- /dev/null
+++ b/data/sampled_jsons/WiSE-FT_Wortsman_et_al.,_2021_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2109.01903] Robust fine-tuning of zero-shot models - arXiv.org", "date": "", "ddg_snippet": "Sep 4, 2021 · We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.01903", "content": "Sep 4, 2021 · We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution."}
+{"idx": 1, "title": "GitHub - mlfoundations/wise-ft: Robust fine-tuning of zero ... CVPR 2022 Open Access Repository Robust fine-tuning of zero-shot models - NASA/ADS Robust fine-tuning of zero-shot models - OpenReview Robust ne-tuning of zero-shot models - arXiv.org Robust fine-tuning of zero-shot models - OpenReview Robust ne-tuning of zero-shot models - arXiv .org Robust fine-tuning of zero-shot models - OpenReview Robust ne-tuning of zero-shot models - arXiv .org Robust fine-tuning of zero-shot models - OpenReview [2109.01903] Robust fine-tuning of zero-shot models - arXiv .org Paper page - Robust fine-tuning of zero-shot models", "date": "", "ddg_snippet": "This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman *, Gabriel Ilharco*, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt. TLDR: We fine-tune zero-shot models while preserving or improving OOD accuracy at no extra computational cost during fine-tuning or inference. Abstract Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning approaches substantially improve accuracy in-distribution, they often reduce out-of-distribution robustness. We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE - FT ). Compared to standard fine-tuning, WiSE - FT provides large accuracy improvements out-of-distribution, while preserving high in-distribution accuracy. On ImageNet (in-distribution) and five derived distribution shifts, WiSE - FT improves out-of-distribution accuracy by 4 to 6 percentage points (pp) over prior work while increasing in-distribution accuracy by 1.6 pp. WiSE - FT achieves similarly large robustness improvements (2 to 23 pp) on a diverse set of six further distribution shifts, and in-distribution accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on seven commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference. See full list on github.com Overview WiSE - FT can be implemented in a few lines of code in addition to standard fine-tuning, as shown below. See src/wise_ft.py for more details. Download data When necessary, please refer to datasets.md for instructions on how to download datasets. Run WiSE - FT Sample command when zeroshot and fine-tuned models are available:Sample command for running WiSE - FT from scratch using ViT-B/32:Note: the flag --freeze-encoder controls whether only a linear classifier is fine-tuned, or if all weights are fine-tuned (end-to-end). See full list on github.com If you found this repository useful, please consider citing: See full list on github.com We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. Sep 28, 2021 · We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy. Weight-space ensembles for ne-tuning ( WiSE-FT ) substantially improve accuracy under distribution shift compared to prior work while maintaining high performance on the target distribution. Concretely, on ImageNet [17] and ve of the natural distribution shifts studied by Radford et al . [82], WiSE-FT applied to standard end-to-end ne-tuning improves accuracy under distribution shift by 4 to 6 ... What is wise-ft? One-sentence Summary: We propose WiSE-FT, a method for fine-tuning zero-shot models that improves out-of-distribution accuracy while maintaining or improving in-distribution accuracy compared to standard fine-tuning. Supplementary Material: zip Community Implementations: [! Does Wise-ft improve NE-tuning accuracy under distribution shift? formance on the target distribution. Concretely, on ImageNet and ve of the natural distribution shifts studied by Radford et al. , WiSE-FT applied to standard end-to-end ne-tuning improves accuracy under distribution shift by 4 to 6 percentage points (pp) over prior work while maintaining or improving the ImageNet How does Wise-ft improve ImageNet distribution accuracy? On ImageNet (in-distribution) and five derived distribution shifts, WiSE - FT improves out-of-distribution accuracy by 2 to 10 percentage points (pp) while increasing in-distribution accuracy by nearly 1 pp relative to standard fine-tuning. Does Wise-ft improve performance under distribution shift? s uses such as surveillance systems. WiSE-FT is a ne-tuning method that builds on such models, and thus ay perpetua e their negative impact.Conclusion. WiSE-FT can substantially improve performance under distribution shift with minimal or no loss in accuracy on the target distrib How robust is wise-ft compared to standard fine-tuning? WiSE-FT achieves similarly large robustness improvements (2 to 15 pp) on a diverse set of six further distribution shifts, and in-distribution accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on seven commonly used transfer learning datasets. How accurate are pre-trained models for Zero-shot inference? Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. Sep 4, 2021 · Robust fine-tuning of zero-shot models ... Mitchell Wortsman , Gabriel Ilharco , Jong Wook Kim , Mike Li ,", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mlfoundations/wise-ft", "content": "This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman *, Gabriel Ilharco*, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt. TLDR: We fine-tune zero-shot models while preserving or improving OOD accuracy at no extra computational cost during fine-tuning or inference. Abstract Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning approaches substantially improve accuracy in-distribution, they often reduce out-of-distribution robustness. We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE - FT ). Compared to standard fine-tuning, WiSE - FT provides large accuracy improvements out-of-distribution, while preserving high in-distribution accuracy. On ImageNet (in-distribution) and five derived distribution shifts, WiSE - FT improves out-of-distribution accuracy by 4 to 6 percentage points (pp) over prior work while increasing in-distribution accuracy by 1.6 pp. WiSE - FT achieves similarly large robustness improvements (2 to 23 pp) on a diverse set of six further distribution shifts, and in-distribution accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on seven commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference. See full list on github.com Overview WiSE - FT can be implemented in a few lines of code in addition to standard fine-tuning, as shown below. See src/wise_ft.py for more details. Download data When necessary, please refer to datasets.md for instructions on how to download datasets. Run WiSE - FT Sample command when zeroshot and fine-tuned models are available:Sample command for running WiSE - FT from scratch using ViT-B/32:Note: the flag --freeze-encoder controls whether only a linear classifier is fine-tuned, or if all weights are fine-tuned (end-to-end). See full list on github.com If you found this repository useful, please consider citing: See full list on github.com We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. Sep 28, 2021 · We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy. Weight-space ensembles for ne-tuning ( WiSE-FT ) substantially improve accuracy under distribution shift compared to prior work while maintaining high performance on the target distribution. Concretely, on ImageNet [17] and ve of the natural distribution shifts studied by Radford et al . [82], WiSE-FT applied to standard end-to-end ne-tuning improves accuracy under distribution shift by 4 to 6 ... What is wise-ft? One-sentence Summary: We propose WiSE-FT, a method for fine-tuning zero-shot models that improves out-of-distribution accuracy while maintaining or improving in-distribution accuracy compared to standard fine-tuning. Supplementary Material: zip Community Implementations: [! Does Wise-ft improve NE-tuning accuracy under distribution shift? formance on the target distribution. Concretely, on ImageNet and ve of the natural distribution shifts studied by Radford et al. , WiSE-FT applied to standard end-to-end ne-tuning improves accuracy under distribution shift by 4 to 6 percentage points (pp) over prior work while maintaining or improving the ImageNet How does Wise-ft improve ImageNet distribution accuracy? On ImageNet (in-distribution) and five derived distribution shifts, WiSE - FT improves out-of-distribution accuracy by 2 to 10 percentage points (pp) while increasing in-distribution accuracy by nearly 1 pp relative to standard fine-tuning. Does Wise-ft improve performance under distribution shift? s uses such as surveillance systems. WiSE-FT is a ne-tuning method that builds on such models, and thus ay perpetua e their negative impact.Conclusion. WiSE-FT can substantially improve performance under distribution shift with minimal or no loss in accuracy on the target distrib How robust is wise-ft compared to standard fine-tuning? WiSE-FT achieves similarly large robustness improvements (2 to 15 pp) on a diverse set of six further distribution shifts, and in-distribution accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on seven commonly used transfer learning datasets. How accurate are pre-trained models for Zero-shot inference? Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. Sep 4, 2021 · Robust fine-tuning of zero-shot models ... Mitchell Wortsman , Gabriel Ilharco , Jong Wook Kim , Mike Li ,"}
+{"idx": 2, "title": "Robust ne-tuning of zero-shot models - arXiv.org", "date": "", "ddg_snippet": "Weight-space ensembles for ne-tuning ( WiSE-FT ) substantially improve accuracy under distribution shift compared to prior work while maintaining high performance on the target distribution. Concretely, on ImageNet [17] and ve of the natural distribution shifts studied by Radford et al . [82], WiSE-FT applied to standard end-to-end ne-tuning improves accuracy under distribution shift by 4 to 6 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.01903", "content": "Weight-space ensembles for ne-tuning ( WiSE-FT ) substantially improve accuracy under distribution shift compared to prior work while maintaining high performance on the target distribution. Concretely, on ImageNet [17] and ve of the natural distribution shifts studied by Radford et al . [82], WiSE-FT applied to standard end-to-end ne-tuning improves accuracy under distribution shift by 4 to 6 ..."}
+{"idx": 3, "title": "Figure 4: FLOW is complementary with model averaging ( WiSE - FT ) in...", "date": "", "ddg_snippet": "We compare WiSE - FT [ Wortsman et al ., 2021 ] with a standard model fine-tuning and with FLOW after fine-tuning Gemma 2 2B on MetaMathQA.The results indicate that combining Wise - FT with FLOW outperforms vanilla WiSE - FT with standard fine-tuning.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/FLOW-is-complementary-with-model-averaging-WiSE-FT-in-language-modeling-We-compare_fig2_388754394", "content": "We compare WiSE - FT [ Wortsman et al ., 2021 ] with a standard model fine-tuning and with FLOW after fine-tuning Gemma 2 2B on MetaMathQA.The results indicate that combining Wise - FT with FLOW outperforms vanilla WiSE - FT with standard fine-tuning."}
+{"idx": 4, "title": "CVPR 2022 Open Access Repository", "date": "", "ddg_snippet": "We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/html/Wortsman_Robust_Fine-Tuning_of_Zero-Shot_Models_CVPR_2022_paper.html", "content": "We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution."}
+{"idx": 5, "title": "Robust fine-tuning of zero-shot models - NASA/ADS", "date": "", "ddg_snippet": "We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2021arXiv210901903W/abstract", "content": "We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution."}
+{"idx": 6, "title": "Robust fine-tuning of zero-shot models - OpenReview", "date": "", "ddg_snippet": "Sep 28, 2021 · We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=yrbF6ekqQ9w", "content": "Sep 28, 2021 · We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy."}
+{"idx": 7, "title": "Paper page - Robust fine-tuning of zero-shot models", "date": "", "ddg_snippet": "Sep 4, 2021 · Robust fine-tuning of zero-shot models ... Mitchell Wortsman , Gabriel Ilharco , Jong Wook Kim , Mike Li ,", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2109.01903", "content": "Sep 4, 2021 · Robust fine-tuning of zero-shot models ... Mitchell Wortsman , Gabriel Ilharco , Jong Wook Kim , Mike Li ,"}
+{"idx": 8, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "WiSE - FT ( Wortsman et al ., 2021 ) : Learning without Forgetting (Li & Hoiem, 2016) : G.2 Language Model Hyper-Parameters.with WiSE - FT ( Wortsman et al ., 2021 ) to mitigate the effects of catastrophic forgetting when fine-tuning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02797v2", "content": "WiSE - FT ( Wortsman et al ., 2021 ) : Learning without Forgetting (Li & Hoiem, 2016) : G.2 Language Model Hyper-Parameters.with WiSE - FT ( Wortsman et al ., 2021 ) to mitigate the effects of catastrophic forgetting when fine-tuning."}
+{"idx": 9, "title": "Proceedings of the International Conference on Machine Learning 2022", "date": "", "ddg_snippet": "Wortsman et al . ( 2021 ) introduce WiSE - FT , a method for improving the robustness of a model θ1 which is fine-tuned from initialization θ0 by linearly interpolating θ1 and θ0.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/wortsman22a/wortsman22a.pdf", "content": "Wortsman et al . ( 2021 ) introduce WiSE - FT , a method for improving the robustness of a model θ1 which is fine-tuned from initialization θ0 by linearly interpolating θ1 and θ0."}
diff --git a/data/sampled_jsons/Witness_complex_vs_Vietoris-Rips_computational_complexity_landmarks.jsonl b/data/sampled_jsons/Witness_complex_vs_Vietoris-Rips_computational_complexity_landmarks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7a1c81f141c01413393c015b45502904a2a8f5e3
--- /dev/null
+++ b/data/sampled_jsons/Witness_complex_vs_Vietoris-Rips_computational_complexity_landmarks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Vietoris-Rips complex - Wikipedia", "date": "", "ddg_snippet": "A Vietoris - Rips complex of a set of 23 points in the Euclidean plane. This complex has sets of up to four points: the points themselves (shown as red circles), pairs of points (black edges), triples of points (pale blue triangles), and quadruples of points (dark blue tetrahedrons). In topology, the Vietoris - Rips complex , also called the Vietoris complex or Rips complex , is a way of forming ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Vietoris–Rips_complex", "content": "A Vietoris - Rips complex of a set of 23 points in the Euclidean plane. This complex has sets of up to four points: the points themselves (shown as red circles), pairs of points (black edges), triples of points (pale blue triangles), and quadruples of points (dark blue tetrahedrons). In topology, the Vietoris - Rips complex , also called the Vietoris complex or Rips complex , is a way of forming ..."}
+{"idx": 1, "title": "Fast construction of the Vietoris-Rips complex - ScienceDirect", "date": "", "ddg_snippet": "In this paper, we present fast algorithms for constructing the filtered Vietoris - Rips complex of a point set. Our software can compute arbitrary dimensional complexes for point sets in arbitrary dimensions, and may be applied toward constructing other clique complexes, such as the weak witness complex . Fig. 1 shows the performance of our fastest algorithm to inspire interest. On this point set ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0097849310000464", "content": "In this paper, we present fast algorithms for constructing the filtered Vietoris - Rips complex of a point set. Our software can compute arbitrary dimensional complexes for point sets in arbitrary dimensions, and may be applied toward constructing other clique complexes, such as the weak witness complex . Fig. 1 shows the performance of our fastest algorithm to inspire interest. On this point set ..."}
+{"idx": 2, "title": "Topological Data Analysis with -net Induced Lazy Witness Complex", "date": "", "ddg_snippet": "We adopt the notion of -cover to de ne -net. We prove that -net, as a choice of landmarks , is an -approximate representation of the point cloud and the induced lazy witness complex is a 3-approximation of the induced Vietoris - Rips complex . Furthermore, we propose three al-gorithms to construct -net landmarks .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1906.06122", "content": "We adopt the notion of -cover to de ne -net. We prove that -net, as a choice of landmarks , is an -approximate representation of the point cloud and the induced lazy witness complex is a 3-approximation of the induced Vietoris - Rips complex . Furthermore, we propose three al-gorithms to construct -net landmarks ."}
+{"idx": 3, "title": "Mastering Vietoris-Rips Complexes for Topological Insights", "date": "", "ddg_snippet": "Explore the theoretical foundations and practical applications of Vietoris - Rips complexes in Computational Topology, and discover how to extract meaningful topological insights from complex datasets.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/mastering-vietoris-rips-complexes-topological-insights", "content": "Explore the theoretical foundations and practical applications of Vietoris - Rips complexes in Computational Topology, and discover how to extract meaningful topological insights from complex datasets."}
+{"idx": 4, "title": "Comparison of Rips and witness complex with the same landmarks and ...", "date": "", "ddg_snippet": "Comparison of Rips and witness complex with the same landmarks and. Blue dots are synthetic point cloud and red circles are landmarks . (a) constructs the complex only using landmarks regardless of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Comparison-of-Rips-and-witness-complex-with-the-same-landmarks-and-Blue-dots-are_fig2_311361367", "content": "Comparison of Rips and witness complex with the same landmarks and. Blue dots are synthetic point cloud and red circles are landmarks . (a) constructs the complex only using landmarks regardless of ..."}
+{"idx": 5, "title": "The Čech Complex and the Vietoris-Rips Complex - Math ∩ Programming", "date": "", "ddg_snippet": "So in this example the Vietoris - Rips complex is a graph with a 2-simplex, while the Čech complex is just a graph. One obvious question is: do we still get the benefits of the nerve theorem with Vietoris - Rips complexes?", "subpage_snippet": "", "source": "www.jeremykun.com", "link": "https://www.jeremykun.com/2015/08/06/cech-vietoris-rips-complex/", "content": "So in this example the Vietoris - Rips complex is a graph with a 2-simplex, while the Čech complex is just a graph. One obvious question is: do we still get the benefits of the nerve theorem with Vietoris - Rips complexes?"}
+{"idx": 6, "title": "PDF Topological Data Analysis with -net Induced Lazy Witness Complex - Springer", "date": "", "ddg_snippet": "We adopt the notion of -cover to define -net. We prove that -net, as a choice of landmarks , is an -approximate representation of the point cloud and the induced lazy witness complex is a 3-approximation of the induced Vietoris - Rips complex . Furthermore, we propose three algo-rithms to construct -net landmarks . We establish the relationship of these algorithms with the existing landmark ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-27618-8_28.pdf", "content": "We adopt the notion of -cover to define -net. We prove that -net, as a choice of landmarks , is an -approximate representation of the point cloud and the induced lazy witness complex is a 3-approximation of the induced Vietoris - Rips complex . Furthermore, we propose three algo-rithms to construct -net landmarks . We establish the relationship of these algorithms with the existing landmark ..."}
+{"idx": 7, "title": "A New Construction of the Vietoris-Rips Complex - arXiv.org", "date": "", "ddg_snippet": "In doing so, we achieve a significant reduction in the number of comparisons required to construct the Vietoris - Rips compared to state-of-the-art algorithms, which is seen here by examining the computational complexity of the critical step in the algorithms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2301.07191", "content": "In doing so, we achieve a significant reduction in the number of comparisons required to construct the Vietoris - Rips compared to state-of-the-art algorithms, which is seen here by examining the computational complexity of the critical step in the algorithms."}
+{"idx": 8, "title": "Vietoris-Rips Complexes: The Backbone of Persistent Homology", "date": "", "ddg_snippet": "Mathematical Background of Vietoris - Rips Complexes Vietoris - Rips complexes are a fundamental concept in topological data analysis (TDA), particularly in the context of Persistent Homology. To understand Vietoris - Rips complexes, we first need to grasp some basic notions from topology and simplicial complexes.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/vietoris-rips-complexes-persistent-homology-deep-dive", "content": "Mathematical Background of Vietoris - Rips Complexes Vietoris - Rips complexes are a fundamental concept in topological data analysis (TDA), particularly in the context of Persistent Homology. To understand Vietoris - Rips complexes, we first need to grasp some basic notions from topology and simplicial complexes."}
+{"idx": 9, "title": "How to compare different type of simplicial complex?", "date": "", "ddg_snippet": "The Vietoris - Rips complex computations are much easier because the ambient dimension does not hinder computations, as the Vietoris - Rips complex depends only on pairwise distances.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/331502/how-to-compare-different-type-of-simplicial-complex", "content": "The Vietoris - Rips complex computations are much easier because the ambient dimension does not hinder computations, as the Vietoris - Rips complex depends only on pairwise distances."}
diff --git a/data/sampled_jsons/Wolpert_No_Free_Lunch_theorems_abstract.jsonl b/data/sampled_jsons/Wolpert_No_Free_Lunch_theorems_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..56f459d627ade6bae60fe6be8313999f44258f33
--- /dev/null
+++ b/data/sampled_jsons/Wolpert_No_Free_Lunch_theorems_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No free lunch theorem - Wikipedia", "date": "", "ddg_snippet": "No free lunch theorem In mathematical folklore, the \" no free lunch \" (NFL) theorem (sometimes pluralized) of David Wolpert and William Macready, alludes to the saying \" no such thing as a free lunch \", that is, there are no easy shortcuts to success. It appeared in the 1997 \" No Free Lunch Theorems for Optimization\". [1]", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/No_free_lunch_theorem", "content": "No free lunch theorem In mathematical folklore, the \" no free lunch \" (NFL) theorem (sometimes pluralized) of David Wolpert and William Macready, alludes to the saying \" no such thing as a free lunch \", that is, there are no easy shortcuts to success. It appeared in the 1997 \" No Free Lunch Theorems for Optimization\". [1]"}
+{"idx": 1, "title": "PDF No Free Lunch Theorems For Optimization - Evolutionary Computation ...", "date": "", "ddg_snippet": "Abstract — A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by perfor- mance over another class. These theorems result in a geometric interpretation of ...", "subpage_snippet": "", "source": "www.cs.ubc.ca", "link": "https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf", "content": "Abstract — A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by perfor- mance over another class. These theorems result in a geometric interpretation of ..."}
+{"idx": 2, "title": "No Free Lunch Theorems for Search - jmvidal.cse.sc.edu", "date": "", "ddg_snippet": "Cited by 353 - Google Scholar @TechReport{wolpert95a, author = {David H. Wolpert and William G. Macready}, title = { No Free Lunch Theorems for Search}, institution = {Santa Fe Institute}, year = 1995, abstract = {We show that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions In particular, if algorithm A ...", "subpage_snippet": "", "source": "jmvidal.cse.sc.edu", "link": "https://jmvidal.cse.sc.edu/lib/wolpert95a.html", "content": "Cited by 353 - Google Scholar @TechReport{wolpert95a, author = {David H. Wolpert and William G. Macready}, title = { No Free Lunch Theorems for Search}, institution = {Santa Fe Institute}, year = 1995, abstract = {We show that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions In particular, if algorithm A ..."}
+{"idx": 3, "title": "No free lunch theorems for optimization - IEEE Xplore", "date": "", "ddg_snippet": "A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/585893", "content": "A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem."}
+{"idx": 4, "title": "(PDF) No Free Lunch Theorems for Optimization - ResearchGate", "date": "", "ddg_snippet": "PDF | On Jan 1, 1997, D. H. Wolpert and others published No Free Lunch Theorems for Optimization | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/281266055_No_Free_Lunch_Theorems_for_Optimization", "content": "PDF | On Jan 1, 1997, D. H. Wolpert and others published No Free Lunch Theorems for Optimization | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 5, "title": "No Free Lunch Theorems", "date": "", "ddg_snippet": "No Free Lunch Theorems Broadly speaking, there are two no free lunch theorems . One for supervised machine learning ( Wolpert 1996) and one for search/optimization ( Wolpert and Macready 1997). For an overview of the ( no ) free lunch and associated theorems , see David Wolpert's What does dinner cost?", "subpage_snippet": "", "source": "no-free-lunch.org", "link": "http://no-free-lunch.org/", "content": "No Free Lunch Theorems Broadly speaking, there are two no free lunch theorems . One for supervised machine learning ( Wolpert 1996) and one for search/optimization ( Wolpert and Macready 1997). For an overview of the ( no ) free lunch and associated theorems , see David Wolpert's What does dinner cost?"}
+{"idx": 6, "title": "No Free Lunch Theorems for Search | Santa Fe Institute", "date": "", "ddg_snippet": "No Free Lunch Theorems for Search William Macready, David Wolpert Paper #: 95-02-010", "subpage_snippet": "", "source": "www.santafe.edu", "link": "https://www.santafe.edu/research/results/working-papers/no-free-lunch-theorems-for-search", "content": "No Free Lunch Theorems for Search William Macready, David Wolpert Paper #: 95-02-010"}
+{"idx": 7, "title": "No Free Lunch Theorems for Optimization - PhilPapers", "date": "", "ddg_snippet": "Abstract A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/rec/WOLNFL", "content": "Abstract A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it ..."}
+{"idx": 8, "title": "No free lunch theorems for optimization | IEEE Transactions on ...", "date": "", "ddg_snippet": "A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/4235.585893", "content": "A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem."}
+{"idx": 9, "title": "arXiv:2007.10928v1 [cs.LG] 21 Jul 2020", "date": "", "ddg_snippet": "David H. Wolpert Abstract The No Free Lunch theorems prove that under a uniform distribution over induction problems (search problems or learning problems), all induction algorithms performequally. As I discuss in this chapter, the importance of the theorems arises by using them to analyze scenarios involvingnon-uniform distributions, and to compare different algorithms, without any assumption ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2007.10928", "content": "David H. Wolpert Abstract The No Free Lunch theorems prove that under a uniform distribution over induction problems (search problems or learning problems), all induction algorithms performequally. As I discuss in this chapter, the importance of the theorems arises by using them to analyze scenarios involvingnon-uniform distributions, and to compare different algorithms, without any assumption ..."}
diff --git a/data/sampled_jsons/Won_et_al._2021_deceptive_strategies_multi-agent_reinforcement_learning.jsonl b/data/sampled_jsons/Won_et_al._2021_deceptive_strategies_multi-agent_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f953a1ceb5b5bcb326b574fe6297745b35961e0
--- /dev/null
+++ b/data/sampled_jsons/Won_et_al._2021_deceptive_strategies_multi-agent_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Deceptive Strategies in Adversarial Settings: A Two-Player ...", "date": "", "ddg_snippet": "This study explores strategic deception and counter-deception in multi-agent reinforcement learning environments for a police officer-robber game. The research is motivated by real-world scenarios where agents must operate with partial observability and adversarial intent. We develop a suite of progressively complex grid-based environments featuring dynamic goals, fake targets, and ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/14/7805", "content": "This study explores strategic deception and counter-deception in multi-agent reinforcement learning environments for a police officer-robber game. The research is motivated by real-world scenarios where agents must operate with partial observability and adversarial intent. We develop a suite of progressively complex grid-based environments featuring dynamic goals, fake targets, and ..."}
+{"idx": 1, "title": "Deception in Social Learning: A Multi-Agent Reinforcement Learning ...", "date": "", "ddg_snippet": "1 Introduction Recent successes in Artificial Intelligence have brought Reinforcement Learning (RL) to the fore-front attention of the research community, through examples such as learning how to play Go, Chess and Atari Games with the same algorithm [Schrittwieser et al ., 2019], solving a physical Rubik's cube [OpenAI et al ., 2019], controlling power grids [Glavic et al ., 2017], routing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2106.05402", "content": "1 Introduction Recent successes in Artificial Intelligence have brought Reinforcement Learning (RL) to the fore-front attention of the research community, through examples such as learning how to play Go, Chess and Atari Games with the same algorithm [Schrittwieser et al ., 2019], solving a physical Rubik's cube [OpenAI et al ., 2019], controlling power grids [Glavic et al ., 2017], routing ..."}
+{"idx": 2, "title": "Multi-agent deep reinforcement learning: a survey - Springer", "date": "", "ddg_snippet": "The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single- agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address problems with real-world complexity. This article provides an overview of the current developments in ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-021-09996-w", "content": "The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single- agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address problems with real-world complexity. This article provides an overview of the current developments in ..."}
+{"idx": 3, "title": "Learning to Deceive in Multi-agent Hidden Role Games", "date": "", "ddg_snippet": "Deception is prevalent in human social settings. However, studies into the effect of deception on reinforcement learning algorithms have been limited to simplistic settings, restricting their applicability to complex real-world problems. This paper addresses this by introducing a new mixed competitive-cooperative multi-agent reinforcement learning (MARL) environment, inspired by popular role ...", "subpage_snippet": "", "source": "openresearch-repository.anu.edu.au", "link": "https://openresearch-repository.anu.edu.au/items/6bc574cc-e780-4a88-845a-9108a4116cb4", "content": "Deception is prevalent in human social settings. However, studies into the effect of deception on reinforcement learning algorithms have been limited to simplistic settings, restricting their applicability to complex real-world problems. This paper addresses this by introducing a new mixed competitive-cooperative multi-agent reinforcement learning (MARL) environment, inspired by popular role ..."}
+{"idx": 4, "title": "Decentralized Multi-Agent Pursuit Using Deep Reinforcement Learning", "date": "", "ddg_snippet": "Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We use deep reinforcement learning for pursuing an omnidirectional target with multiple, homogeneous agents that are subject to unicycle kinematic constraints. We use shared experience to train a policy for a given number of pursuers, executed independently by each agent at run-time. The training uses ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9387125", "content": "Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We use deep reinforcement learning for pursuing an omnidirectional target with multiple, homogeneous agents that are subject to unicycle kinematic constraints. We use shared experience to train a policy for a given number of pursuers, executed independently by each agent at run-time. The training uses ..."}
+{"idx": 5, "title": "Deception in Social Learning: A Multi-Agent Reinforcement Learning ...", "date": "", "ddg_snippet": "Within the framework of Multi-Agent Reinforcement Learning , Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/352308945_Deception_in_Social_Learning_A_Multi-Agent_Reinforcement_Learning_Perspective", "content": "Within the framework of Multi-Agent Reinforcement Learning , Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of ..."}
+{"idx": 6, "title": "A survey on multi-agent reinforcement learning and its application", "date": "", "ddg_snippet": "Multi-agent reinforcement learning (MARL) is an important subfield in the community of machine learning . The emergence of MARL marks a significant advancement in artificial intelligence, particularly in handling complex and dynamic environments with multiple interacting agents . Unlike traditional reinforcement learning (RL) that is applicable to single- agent systems, MARL concentrates on the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2949855424000042", "content": "Multi-agent reinforcement learning (MARL) is an important subfield in the community of machine learning . The emergence of MARL marks a significant advancement in artificial intelligence, particularly in handling complex and dynamic environments with multiple interacting agents . Unlike traditional reinforcement learning (RL) that is applicable to single- agent systems, MARL concentrates on the ..."}
+{"idx": 7, "title": "Learning to Deceive in Multi-agent Hidden Role Games - Springer", "date": "", "ddg_snippet": "Abstract. Deception is prevalent in human social settings. However, studies into the effect of deception on reinforcement learning algorithms have been limited to simplistic settings, restricting their applicability to complex real-world problems. This paper addresses this by introducing a new mixed competitive-cooperative multi-agent reinforcement learning (MARL) environment, inspired by ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-91779-1_5", "content": "Abstract. Deception is prevalent in human social settings. However, studies into the effect of deception on reinforcement learning algorithms have been limited to simplistic settings, restricting their applicability to complex real-world problems. This paper addresses this by introducing a new mixed competitive-cooperative multi-agent reinforcement learning (MARL) environment, inspired by ..."}
+{"idx": 8, "title": "Deep Multiagent Reinforcement Learning: Challenges and Directions", "date": "", "ddg_snippet": "This paper surveys the field of deep multiagent reinforcement learning . The combination of deep neural networks with reinforcement learning has gained increased traction in recent years and is slowly shifting the focus from single- agent to multiagent environments. Dealing with multiple agents is inherently more complex as (a) the future rewards depend on multiple players' joint actions and (b ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2106.15691", "content": "This paper surveys the field of deep multiagent reinforcement learning . The combination of deep neural networks with reinforcement learning has gained increased traction in recent years and is slowly shifting the focus from single- agent to multiagent environments. Dealing with multiple agents is inherently more complex as (a) the future rewards depend on multiple players' joint actions and (b ..."}
+{"idx": 9, "title": "Deceptive Reinforcement Learning for Privacy-Preserving Planning", "date": "", "ddg_snippet": "Some work in deceptive reinforcement learning investigates techniques to counter the deceptive strategies of other agents , such as the agent being fed incorrect reward signals [12], deception in games and multi-agent systems [4, 17, 28].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2102.03022", "content": "Some work in deceptive reinforcement learning investigates techniques to counter the deceptive strategies of other agents , such as the agent being fed incorrect reward signals [12], deception in games and multi-agent systems [4, 17, 28]."}
diff --git a/data/sampled_jsons/Wonjun_Lee_ELITE_Table_3_E-ASR_results_presentation_slides.jsonl b/data/sampled_jsons/Wonjun_Lee_ELITE_Table_3_E-ASR_results_presentation_slides.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3265161ab7a80a96c5b5c2a8b54ff246c8fdd71d
--- /dev/null
+++ b/data/sampled_jsons/Wonjun_Lee_ELITE_Table_3_E-ASR_results_presentation_slides.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Computation and Language May 2025", "date": "", "ddg_snippet": "Title: Can Large Language Models be Effective Online Opinion Miners? Ryang Heo, Yongsik Seo, Junseong Lee , Dongha Lee . Comments: Accepted to EMNLP 2025 Main.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CL/2025-05?skip=550&show=2000", "content": "Title: Can Large Language Models be Effective Online Opinion Miners? Ryang Heo, Yongsik Seo, Junseong Lee , Dongha Lee . Comments: Accepted to EMNLP 2025 Main."}
+{"idx": 1, "title": "Computer Science Feb 2025", "date": "", "ddg_snippet": "Title: ELITE : Enhanced Language-Image Toxicity Evaluation for Safety. Wonjun Lee , Doehyeon Lee, Eugene Choi, Sangyoon Yu, Ashkan Yousefpour, Haon Park ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-02?skip=1225&show=2000", "content": "Title: ELITE : Enhanced Language-Image Toxicity Evaluation for Safety. Wonjun Lee , Doehyeon Lee, Eugene Choi, Sangyoon Yu, Ashkan Yousefpour, Haon Park ..."}
+{"idx": 2, "title": "TECHNICAL PROGRAM EXHIBITION GUIDE", "date": "", "ddg_snippet": "26 Feb 2025 — Come hear presentations from leading speakers across the globe. Plenary sessions include world-class speakers, and the poster session brings ... 101 pages", "subpage_snippet": "", "source": "spie.org", "link": "https://spie.org/documents/ConferencesExhibitions/Programs/2025/AL25-TechnicalProgram-ExGuide.pdf", "content": "26 Feb 2025 — Come hear presentations from leading speakers across the globe. Plenary sessions include world-class speakers, and the poster session brings ... 101 pages"}
+{"idx": 3, "title": "Accepted Main Conference Papers", "date": "", "ddg_snippet": "Accepted Main Conference Papers. EcomScriptBench: A Multi-task Benchmark for E -commerce Script Planning via Step-wise Intention-Driven Product Association", "subpage_snippet": "", "source": "2025.aclweb.org", "link": "https://2025.aclweb.org/program/main_papers/", "content": "Accepted Main Conference Papers. EcomScriptBench: A Multi-task Benchmark for E -commerce Script Planning via Step-wise Intention-Driven Product Association"}
+{"idx": 4, "title": "Annual Meeting of the Association for Computational ...", "date": "", "ddg_snippet": "Annual Meeting of the Association for Computational Linguistics (2025). Volumes. Proceedings of the 63rd Annual Meeting of the Association for Computational ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/events/acl-2025/", "content": "Annual Meeting of the Association for Computational Linguistics (2025). Volumes. Proceedings of the 63rd Annual Meeting of the Association for Computational ..."}
+{"idx": 5, "title": "ICML 2025 Thursday 07/17", "date": "", "ddg_snippet": "This hands-on session will give you practical tips and exercises to craft a short, effective and accessible overview of your work for a wide range of audiences ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/17", "content": "This hands-on session will give you practical tips and exercises to craft a short, effective and accessible overview of your work for a wide range of audiences ..."}
+{"idx": 6, "title": "ACL 2025 The 63rd Annual Meeting of the Association for ...", "date": "", "ddg_snippet": "by W Che · 2025 — It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for. Computational Linguistics (ACL 2025), ... 153 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.0.pdf", "content": "by W Che · 2025 — It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for. Computational Linguistics (ACL 2025), ... 153 pages"}
+{"idx": 7, "title": "Acceptance of Internet-based learning medium: the role ...", "date": "", "ddg_snippet": "This study is one of the few attempts to investigate students' acceptance of an Internet-based learning medium (ILM). By integrating a motivational ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/3166789/Acceptance_of_Internet_based_learning_medium_the_role_of_extrinsic_and_intrinsic_motivation", "content": "This study is one of the few attempts to investigate students' acceptance of an Internet-based learning medium (ILM). By integrating a motivational ..."}
+{"idx": 8, "title": "absorptiometry dexa scan: Topics by ...", "date": "", "ddg_snippet": "Body fat distribution was assessed using anthropometric measurements and DEXA scans at baseline and then at 3 , 6, 12, 18, and 24 months after commencing ART.", "subpage_snippet": "", "source": "www.science.gov", "link": "https://www.science.gov/topicpages/a/absorptiometry+dexa+scan", "content": "Body fat distribution was assessed using anthropometric measurements and DEXA scans at baseline and then at 3 , 6, 12, 18, and 24 months after commencing ART."}
+{"idx": 9, "title": "Tools to conquer the adversary - The Daily Universe - BYU", "date": "", "ddg_snippet": "President Boyd K. Packer of the Quorum of the Twelve taught Education Week participants the importance of arming themselves with gospel doctrines to ...", "subpage_snippet": "", "source": "newsnet.byu.edu", "link": "https://newsnet.byu.edu/pdf/du20090819.pdf", "content": "President Boyd K. Packer of the Quorum of the Twelve taught Education Week participants the importance of arming themselves with gospel doctrines to ..."}
diff --git a/data/sampled_jsons/XLRS-Bench_45942_annotations_dataset_scale_year_2024.jsonl b/data/sampled_jsons/XLRS-Bench_45942_annotations_dataset_scale_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..984dfa93cb1098e2d1ac9109689c1cc309605bf8
--- /dev/null
+++ b/data/sampled_jsons/XLRS-Bench_45942_annotations_dataset_scale_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand Extremely ...", "date": "", "ddg_snippet": "Finally, XLRS-Bench incorpo-rates 45,942 annotations covering 10 ability indicators for perception and 6 for reasoning, comprising 32,389 VQA pairs, 12,619 visual grounding instances, and 934 detailed captions, as shown in Fig. 1.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "Finally, XLRS-Bench incorpo-rates 45,942 annotations covering 10 ability indicators for perception and 6 for reasoning, comprising 32,389 VQA pairs, 12,619 visual grounding instances, and 934 detailed captions, as shown in Fig. 1."}
+{"idx": 1, "title": "GitHub - AI9Stars/XLRS-Bench: [CVPR 2025 HIghlight] XLRS ...", "date": "", "ddg_snippet": "Apr 1, 2025 · We present XLRS-Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars/XLRS-Bench", "content": "Apr 1, 2025 · We present XLRS-Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts."}
+{"idx": 2, "title": "XLRS-Bench", "date": "", "ddg_snippet": "We present XLRS-Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts.", "subpage_snippet": "", "source": "xlrs-bench.github.io", "link": "https://xlrs-bench.github.io/home_page.html", "content": "We present XLRS-Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts."}
+{"idx": 3, "title": "[2503.23771] XLRS-Bench: Could Your Multimodal LLMs ... README.md · initiacms/XLRS-Bench-lite at main - Hugging Face XLRS-Bench/README.md at main · AI9Stars/XLRS-Bench · GitHub XLRS-Bench: Welcome", "date": "", "ddg_snippet": "Mar 31, 2025 · Abstract page for arXiv paper 2503.23771: XLRS-Bench : Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery? The data users should include the following acknowledgement in any publication resulting from the datasets : “*The authors would like to acknowledge the provision of the Downtown Toronto data set by Optech Inc., First Base Solutions Inc., GeoICT Lab at York University, and ISPRS WG III/4.*” Apr 1, 2025 · The main advantages of XLRS-Bench compared to existing MLLM benchmarks as follows: Ultra-high Resolution. XLRS-Bench features the largest image sizes available, 10∼20× than that of existing datasets , with 840 images out of all images at a resolution of 10,000×10,000 pixels High-quality Annotation . Benchmarking Multimodal LLMs in Real-World Ultra-High-Resolution Remote Sensing with Human-Verified Annotations", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.23771", "content": "Mar 31, 2025 · Abstract page for arXiv paper 2503.23771: XLRS-Bench : Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery? The data users should include the following acknowledgement in any publication resulting from the datasets : “*The authors would like to acknowledge the provision of the Downtown Toronto data set by Optech Inc., First Base Solutions Inc., GeoICT Lab at York University, and ISPRS WG III/4.*” Apr 1, 2025 · The main advantages of XLRS-Bench compared to existing MLLM benchmarks as follows: Ultra-high Resolution. XLRS-Bench features the largest image sizes available, 10∼20× than that of existing datasets , with 840 images out of all images at a resolution of 10,000×10,000 pixels High-quality Annotation . Benchmarking Multimodal LLMs in Real-World Ultra-High-Resolution Remote Sensing with Human-Verified Annotations"}
+{"idx": 4, "title": "README.md · initiacms/XLRS-Bench-lite at main - Hugging Face", "date": "", "ddg_snippet": "The data users should include the following acknowledgement in any publication resulting from the datasets : “*The authors would like to acknowledge the provision of the Downtown Toronto data set by Optech Inc., First Base Solutions Inc., GeoICT Lab at York University, and ISPRS WG III/4.*”", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/initiacms/XLRS-Bench-lite/blob/main/README.md", "content": "The data users should include the following acknowledgement in any publication resulting from the datasets : “*The authors would like to acknowledge the provision of the Downtown Toronto data set by Optech Inc., First Base Solutions Inc., GeoICT Lab at York University, and ISPRS WG III/4.*”"}
+{"idx": 5, "title": "XLRS-Bench/README.md at main · AI9Stars/XLRS-Bench · GitHub", "date": "", "ddg_snippet": "Apr 1, 2025 · The main advantages of XLRS-Bench compared to existing MLLM benchmarks as follows: Ultra-high Resolution. XLRS-Bench features the largest image sizes available, 10∼20× than that of existing datasets , with 840 images out of all images at a resolution of 10,000×10,000 pixels High-quality Annotation .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars/XLRS-Bench/blob/main/README.md", "content": "Apr 1, 2025 · The main advantages of XLRS-Bench compared to existing MLLM benchmarks as follows: Ultra-high Resolution. XLRS-Bench features the largest image sizes available, 10∼20× than that of existing datasets , with 840 images out of all images at a resolution of 10,000×10,000 pixels High-quality Annotation ."}
+{"idx": 6, "title": "XLRS-Bench: Welcome", "date": "", "ddg_snippet": "Benchmarking Multimodal LLMs in Real-World Ultra-High-Resolution Remote Sensing with Human-Verified Annotations", "subpage_snippet": "", "source": "xlrs-bench.github.io", "link": "https://xlrs-bench.github.io/", "content": "Benchmarking Multimodal LLMs in Real-World Ultra-High-Resolution Remote Sensing with Human-Verified Annotations"}
+{"idx": 7, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "31 Mar 2025 — Finally, XLRS - Bench incorporates 45,942 annotations covering 10 ability indicators for perception and 6 for reasoning, comprising 32,389 VQA ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.23771v1", "content": "31 Mar 2025 — Finally, XLRS - Bench incorporates 45,942 annotations covering 10 ability indicators for perception and 6 for reasoning, comprising 32,389 VQA ..."}
+{"idx": 8, "title": "CVPR Poster XLRS-Bench: Could Your Multimodal LLMs ...", "date": "", "ddg_snippet": "14 Jun 2025 — Finally, XLRS - Bench incorporates 45,942 annotations covering 10 ability indicators for perception and 6 for reasoning, comprising 32,389 VQA ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/35068", "content": "14 Jun 2025 — Finally, XLRS - Bench incorporates 45,942 annotations covering 10 ability indicators for perception and 6 for reasoning, comprising 32,389 VQA ..."}
+{"idx": 9, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "Researchers from multiple Chinese institutions introduce XLRS - Bench , a benchmark dataset of 1,400 ultra-high-resolution (8,500x8,500 pixel) remote sensing ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.23771v1", "content": "Researchers from multiple Chinese institutions introduce XLRS - Bench , a benchmark dataset of 1,400 ultra-high-resolution (8,500x8,500 pixel) remote sensing ..."}
diff --git a/data/sampled_jsons/YFCC100M_dataset_YFCC_stands_for.jsonl b/data/sampled_jsons/YFCC100M_dataset_YFCC_stands_for.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b6009346a8ac57580b5956e6a28f0b1cd5a5a856
--- /dev/null
+++ b/data/sampled_jsons/YFCC100M_dataset_YFCC_stands_for.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Building Better Datasets: Seven Recommendations for Responsible", "date": "", "ddg_snippet": "Despite their institutional differences, all participants expressed encountering common challenges of dataset creation such as striving for data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.00252v1", "content": "Despite their institutional differences, all participants expressed encountering common challenges of dataset creation such as striving for data ..."}
+{"idx": 1, "title": "DenseFusion-1M: Merging Vision Experts for Comprehensive", "date": "", "ddg_snippet": "... 66 ] .To fully harness the capabilities of MLLMs in comprehensive visual perception, there is a critical demand for high-quality image-text datasets ...", "subpage_snippet": "", "source": "kingsleylo.com", "link": "https://kingsleylo.com/article/densefusion-1m-merging-vision-experts-for-comprehensive-multimodal-perception", "content": "... 66 ] .To fully harness the capabilities of MLLMs in comprehensive visual perception, there is a critical demand for high-quality image-text datasets ..."}
+{"idx": 2, "title": "yahoo – Interdependent Thoughts", "date": "", "ddg_snippet": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... YFCC -100M covers the years 2004-2014 and I ...", "subpage_snippet": "", "source": "www.zylstra.org", "link": "https://www.zylstra.org/blog/tag/yahoo/", "content": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... YFCC -100M covers the years 2004-2014 and I ..."}
+{"idx": 3, "title": "creative commons – Interdependent Thoughts", "date": "", "ddg_snippet": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... YFCC -100M covers the years 2004-2014 and I ...", "subpage_snippet": "", "source": "www.zylstra.org", "link": "https://www.zylstra.org/blog/tag/creative-commons/", "content": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... YFCC -100M covers the years 2004-2014 and I ..."}
+{"idx": 4, "title": "Innovation – Page 3 – Interdependent Thoughts", "date": "", "ddg_snippet": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... YFCC -100M covers the years 2004-2014 and I ...", "subpage_snippet": "", "source": "www.zylstra.org", "link": "https://www.zylstra.org/blog/category/innovation-2/page/3/", "content": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... YFCC -100M covers the years 2004-2014 and I ..."}
+{"idx": 5, "title": "machinelearning – Interdependent Thoughts", "date": "", "ddg_snippet": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... YFCC -100M covers the years 2004-2014 and I ...", "subpage_snippet": "", "source": "www.zylstra.org", "link": "https://www.zylstra.org/blog/tag/machinelearning/", "content": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... YFCC -100M covers the years 2004-2014 and I ..."}
+{"idx": 6, "title": "creativecommons – Page 2 – Interdependent Thoughts", "date": "", "ddg_snippet": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... YFCC -100M covers the years 2004-2014 and I ...", "subpage_snippet": "", "source": "www.zylstra.org", "link": "https://www.zylstra.org/blog/tag/creativecommons/page/2/", "content": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... YFCC -100M covers the years 2004-2014 and I ..."}
+{"idx": 7, "title": "Basic visual analytics - LBSN Structure", "date": "", "ddg_snippet": "... as those found in the YFCC100M dataset ... For example, to select the 100 most frequented locations for the topic \"nature\" and the YFCC100M dataset :", "subpage_snippet": "", "source": "lbsn.vgiscience.org", "link": "https://lbsn.vgiscience.org/tutorial/yfcc-geohash/", "content": "... as those found in the YFCC100M dataset ... For example, to select the 100 most frequented locations for the topic \"nature\" and the YFCC100M dataset :"}
+{"idx": 8, "title": "TiC-CLIP: Continual Training of CLIP Models", "date": "", "ddg_snippet": "TiC -DataComp We collect timestamps for the CommonPool dataset introduced in DataComp which contains 12.7B image-text pairs (not including 0.1B ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.16226v3", "content": "TiC -DataComp We collect timestamps for the CommonPool dataset introduced in DataComp which contains 12.7B image-text pairs (not including 0.1B ..."}
+{"idx": 9, "title": "Visual Genome: Connecting Language and Vision Using", "date": "", "ddg_snippet": "Footnote 2 Instead, for each image in the Visual Genome dataset , we collect more than \\(50\\) descriptions for different regions in the image ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-016-0981-7", "content": "Footnote 2 Instead, for each image in the Visual Genome dataset , we collect more than \\(50\\) descriptions for different regions in the image ..."}
diff --git a/data/sampled_jsons/YOpa6dTrpt_'Section_4.3'_'LiDAR_improves'_'depth_information'_'3D_reconstruction'_year_2025.jsonl b/data/sampled_jsons/YOpa6dTrpt_'Section_4.3'_'LiDAR_improves'_'depth_information'_'3D_reconstruction'_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4a4ee0e63c857a6042009787c852792a367ca5bb
--- /dev/null
+++ b/data/sampled_jsons/YOpa6dTrpt_'Section_4.3'_'LiDAR_improves'_'depth_information'_'3D_reconstruction'_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Lidar - Wikipedia", "date": "", "ddg_snippet": "Lidar is a method for determining ranges by targeting an object or a surface with a laser and measuring the time for the reflected light to return to the receiver. Lidar may operate in a fixed direction or it may scan multiple directions, in a specia...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Lidar", "content": "Lidar is a method for determining ranges by targeting an object or a surface with a laser and measuring the time for the reflected light to return to the receiver. Lidar may operate in a fixed direction or it may scan multiple directions, in a specia..."}
+{"idx": 1, "title": "Для чего нужен сканер LIDAR (Лидар) в Айфоне и что это такое?", "date": "", "ddg_snippet": "Что такое 3 D сканер LIDAR в про-версиях iPhone и на iPad Pro и для чего он нужен?", "subpage_snippet": "", "source": "yablyk.com", "link": "https://yablyk.com/940169-what-is-the-lidar-scanner-for/", "content": "Что такое 3 D сканер LIDAR в про-версиях iPhone и на iPad Pro и для чего он нужен?"}
+{"idx": 2, "title": "Best Android Phones With LiDAR : Android's Finest - Aosmark", "date": "", "ddg_snippet": "Better Depth Perception: LiDAR sensors provide precise depth information , allowing for sharper focus and improved subject separation, especially in portrait mode and low- light photography.", "subpage_snippet": "", "source": "aosmark.com", "link": "https://aosmark.com/best-android-phones-with-lidar/", "content": "Better Depth Perception: LiDAR sensors provide precise depth information , allowing for sharper focus and improved subject separation, especially in portrait mode and low- light photography."}
+{"idx": 3, "title": "Level 6 ( LiDAR ) Correct Passage | Backrooms: Escape... - YouTube", "date": "", "ddg_snippet": "I was trying my best to explain how to pick the right door to my buddy and where to go from there. Thought a shortened version would help some people.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=ET4lTfCxg3E", "content": "I was trying my best to explain how to pick the right door to my buddy and where to go from there. Thought a shortened version would help some people."}
+{"idx": 4, "title": "LuNaDrone", "date": "", "ddg_snippet": "3. Depth information is estimated for every pixels by using multi-baseline stereo, and then, it is. optimized by considering space continuity. Basically, these methods [51, 52, 53] are designed for fast and online 3 D modeling.Chapter 3. 3.4.5 RGB-D vSLAM.", "subpage_snippet": "", "source": "webthesis.biblio.polito.it", "link": "https://webthesis.biblio.polito.it/17032/1/tesi.pdf", "content": "3. Depth information is estimated for every pixels by using multi-baseline stereo, and then, it is. optimized by considering space continuity. Basically, these methods [51, 52, 53] are designed for fast and online 3 D modeling.Chapter 3. 3.4.5 RGB-D vSLAM."}
+{"idx": 5, "title": "Схема предохранителей — Hyundai Elantra... | DRIVE2", "date": "", "ddg_snippet": "Блок реле и предохранителей Блок реле и предохранителей под капотом: № Номинал, А Назначение предохранителя 1 120 BATT. Главный предохранитель 2 50 BATT. Стеклоподъемники, задние фонари, предохрани…", "subpage_snippet": "", "source": "www.drive2.ru", "link": "https://www.drive2.ru/l/702811406960970179/", "content": "Блок реле и предохранителей Блок реле и предохранителей под капотом: № Номинал, А Назначение предохранителя 1 120 BATT. Главный предохранитель 2 50 BATT. Стеклоподъемники, задние фонари, предохрани…"}
+{"idx": 6, "title": "Повёрнутые на войне 🇷🇺 – Telegram", "date": "", "ddg_snippet": "Предложка и вопросы: @voenacher_zov_bot По вопросам рекламы: @VoenacherAdvert Сборка дронов: @Sborka_upyr_bot Передай информацию об обнаружении скопления инопланетян: @pnv_dozor_bot РКН: https://clck.ru/3G7dKG.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/voenacher", "content": "Предложка и вопросы: @voenacher_zov_bot По вопросам рекламы: @VoenacherAdvert Сборка дронов: @Sborka_upyr_bot Передай информацию об обнаружении скопления инопланетян: @pnv_dozor_bot РКН: https://clck.ru/3G7dKG."}
+{"idx": 7, "title": "Номер 2, страница 6 - гдз по английскому языку 6 класс (spotlight)...", "date": "", "ddg_snippet": "Английский язык (english), 6 класс Рабочая тетрадь (workbook), авторы: Ваулина Юлия Евгеньевна (Vaulina Julia), Дули Дженни (Dooley Jenny), Подоляко Ольга Евгеньевна (Podolyako Olga), Эванс Вирджиния (Evans Virginia), издательство Просвещение, Москва...", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/6-klass/english/vaulina-spotlight-rabochaja-tetrad/01-2-2", "content": "Английский язык (english), 6 класс Рабочая тетрадь (workbook), авторы: Ваулина Юлия Евгеньевна (Vaulina Julia), Дули Дженни (Dooley Jenny), Подоляко Ольга Евгеньевна (Podolyako Olga), Эванс Вирджиния (Evans Virginia), издательство Просвещение, Москва..."}
+{"idx": 8, "title": "Бесплатная нейросеть онлайн | Чат GPT на русском для... — ruGPT", "date": "", "ddg_snippet": "Нейросеть и искусственный интеллект онлайн: чат бот GPT на русском, генерация текста и помощь в задачах — бесплатно, без регистрации и VPN...", "subpage_snippet": "", "source": "rugpt.io", "link": "https://rugpt.io/", "content": "Нейросеть и искусственный интеллект онлайн: чат бот GPT на русском, генерация текста и помощь в задачах — бесплатно, без регистрации и VPN..."}
+{"idx": 9, "title": "Constructions - 3 D | 3 D Concrete Printers", "date": "", "ddg_snippet": "Constructions - 3 D is the leading manufacturer of mobile 3 D concrete printers. We provide advanced housing construction technologies through 3 D printing machines, available worldwide.", "subpage_snippet": "", "source": "www.constructions-3d.com", "link": "https://www.constructions-3d.com/", "content": "Constructions - 3 D is the leading manufacturer of mobile 3 D concrete printers. We provide advanced housing construction technologies through 3 D printing machines, available worldwide."}
diff --git a/data/sampled_jsons/Yao_et_al.,_2023_Tree_of_Thoughts_abstract_year_2023.jsonl b/data/sampled_jsons/Yao_et_al.,_2023_Tree_of_Thoughts_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ac2157220ccd3adcf11e1f3e8924f9fa08c792f
--- /dev/null
+++ b/data/sampled_jsons/Yao_et_al.,_2023_Tree_of_Thoughts_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.10601] Tree of Thoughts: Deliberate Problem Solving ...", "date": "", "ddg_snippet": "May 17, 2023 · To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text ( thoughts ) that serve as intermediate steps toward problem solving.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.10601", "content": "May 17, 2023 · To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text ( thoughts ) that serve as intermediate steps toward problem solving."}
+{"idx": 1, "title": "Tree of thoughts | Proceedings of the 37th International ...", "date": "", "ddg_snippet": "Dec 10, 2023 · To surmount these challenges, we introduce a new framework for language model inference, \" Tree of Thoughts \" (ToT), which generalizes over the popular \"Chain of Thought \" approach to prompting language models, and enables exploration over coherent units of text (\" thoughts \") that serve as intermediate steps toward problem solving.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3666639", "content": "Dec 10, 2023 · To surmount these challenges, we introduce a new framework for language model inference, \" Tree of Thoughts \" (ToT), which generalizes over the popular \"Chain of Thought \" approach to prompting language models, and enables exploration over coherent units of text (\" thoughts \") that serve as intermediate steps toward problem solving."}
+{"idx": 2, "title": "Tree of Thoughts: Deliberate Problem Solving with Large ...", "date": "", "ddg_snippet": "To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text ( thoughts ) that serve as intermediate steps toward problem solving.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/271db9922b8d1f4dd7aaef84ed5ac703-Abstract-Conference.html", "content": "To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text ( thoughts ) that serve as intermediate steps toward problem solving."}
+{"idx": 3, "title": "yao_tree_2023 | TransferLab — appliedAI Institute", "date": "", "ddg_snippet": "Jul 3, 2023 · Reference abstract : Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference.", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/refs/yao_tree_2023/", "content": "Jul 3, 2023 · Reference abstract : Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference."}
+{"idx": 4, "title": "Shunyu_yao Tree of Thoughts 2023 | Sukai Huang", "date": "", "ddg_snippet": "May 24, 2023 · We thus propose the Tree of Thoughts (ToT) framework for general problem solving with language models.", "subpage_snippet": "", "source": "sino-huang.github.io", "link": "https://sino-huang.github.io/posts/shunyu_yao-tree-of-thoughts-2023/", "content": "May 24, 2023 · We thus propose the Tree of Thoughts (ToT) framework for general problem solving with language models."}
+{"idx": 5, "title": "Tree of Thoughts (ToT) | Prompt Engineering Guide", "date": "", "ddg_snippet": "Yao et el . ( 2023 ) and Long ( 2023 ) recently proposed Tree of Thoughts (ToT), a framework that generalizes over chain-of-thought prompting and encourages exploration over thoughts that serve as intermediate steps for general problem solving with language models.", "subpage_snippet": "", "source": "www.promptingguide.ai", "link": "https://www.promptingguide.ai/techniques/tot", "content": "Yao et el . ( 2023 ) and Long ( 2023 ) recently proposed Tree of Thoughts (ToT), a framework that generalizes over chain-of-thought prompting and encourages exploration over thoughts that serve as intermediate steps for general problem solving with language models."}
+{"idx": 6, "title": "Tree of Thoughts: Deliberate Problem Solving with Large ...", "date": "", "ddg_snippet": "May 17, 2023 · To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370869723_Tree_of_Thoughts_Deliberate_Problem_Solving_with_Large_Language_Models", "content": "May 17, 2023 · To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting..."}
+{"idx": 7, "title": "Tree of Thoughts : Deliberate Problem Solving with... - AI Empower", "date": "", "ddg_snippet": "Tree of Thoughts (ToT) Framework: Employs a tree structure to maintain multiple reasoning paths, allowing for the exploration of diverse alternatives and the evaluation of intermediate thoughts towards solving a task.", "subpage_snippet": "", "source": "aiempower.org", "link": "https://aiempower.org/kb/tree-of-thoughts-deliberate-problem-solving-with-large-language-models/", "content": "Tree of Thoughts (ToT) Framework: Employs a tree structure to maintain multiple reasoning paths, allowing for the exploration of diverse alternatives and the evaluation of intermediate thoughts towards solving a task."}
+{"idx": 8, "title": "Unlocking LLMs’ Potential with Tree - of - Thought Prompting | Medium", "date": "", "ddg_snippet": "Schematically, the Tree - of - Thought approach was presented in comparison with other prompting methods in a study by Yao et al . ( 2023 ), as shown below", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@albert_88839/unlocking-llms-potential-with-tree-of-thought-prompting-31e9a34f4830?responsesOpen=true&sortBy=REVERSE_CHRON", "content": "Schematically, the Tree - of - Thought approach was presented in comparison with other prompting methods in a study by Yao et al . ( 2023 ), as shown below"}
+{"idx": 9, "title": "Prompt Engineering 101 — Exploring Tree - of - Thoughts ...", "date": "", "ddg_snippet": "Among these advancements stands Tree - of - Thoughts (ToT) Prompting, a groundbreaking framework introduced by Yao et al . ( 2023 ) and Long ( 2023 ), aimed at facilitating complex problem-solving through structured exploration and strategic lookahead.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/prompt-engineering-101-exploring-tree-of-thoughts-tot-prompting-a-new-frontier-in-ai-reasoning-aff1208cdb5b", "content": "Among these advancements stands Tree - of - Thoughts (ToT) Prompting, a groundbreaking framework introduced by Yao et al . ( 2023 ) and Long ( 2023 ), aimed at facilitating complex problem-solving through structured exploration and strategic lookahead."}
diff --git a/data/sampled_jsons/Yicheng_Pan_Renjie_Chen_Hierarchical_Overlapping_Clustering_on_Graphs_cost_function_overlaps.jsonl b/data/sampled_jsons/Yicheng_Pan_Renjie_Chen_Hierarchical_Overlapping_Clustering_on_Graphs_cost_function_overlaps.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8a62bfbf99578a09941904a85ad046f181756a9
--- /dev/null
+++ b/data/sampled_jsons/Yicheng_Pan_Renjie_Chen_Hierarchical_Overlapping_Clustering_on_Graphs_cost_function_overlaps.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm ...", "date": "", "ddg_snippet": "Abstract Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have ex-amined these two structures separately, character-izing and evaluating their hybrid forms remains an open challenge. To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "Abstract Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have ex-amined these two structures separately, character-izing and evaluating their hybrid forms remains an open challenge. To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and ..."}
+{"idx": 1, "title": "Overlapping Hierarchical Clustering (OHC) | SpringerLink", "date": "", "ddg_snippet": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-44584-3_21", "content": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering ."}
+{"idx": 2, "title": "PDF Overlapping Hierarchical Clustering (OHC) - inria.hal.science", "date": "", "ddg_snippet": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and com-bines the advantages of hierarchies with the precision of a less arbitrary clustering .", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and com-bines the advantages of hierarchies with the precision of a less arbitrary clustering ."}
+{"idx": 3, "title": "(PDF) Overlapping Hierarchical Clustering (OHC) - ResearchGate", "date": "", "ddg_snippet": "PDF | Agglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/340823458_Overlapping_Hierarchical_Clustering_OHC", "content": "PDF | Agglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures."}
+{"idx": 4, "title": "Hierarchical overlapping clustering: cost function, algorithm and ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 5, "title": "Yicheng Pan's research works | Beihang University (BUAA), Beijing (BUAA ...", "date": "", "ddg_snippet": "A combinatorial cost function for hierarchical clustering was introduced by Dasgupta \\cite {dasgupta2016cost}. It has been generalized by Cohen-Addad et al. \\cite {cohen2019hierarchical} to a ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Yicheng-Pan-2199691002", "content": "A combinatorial cost function for hierarchical clustering was introduced by Dasgupta \\cite {dasgupta2016cost}. It has been generalized by Cohen-Addad et al. \\cite {cohen2019hierarchical} to a ..."}
+{"idx": 6, "title": "Yicheng Pan - OpenReview", "date": "", "ddg_snippet": "Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan , Renjie Chen , Pengyu Long, Bingchen Fan Published: 01 May 2025, Last Modified: 13 Aug 2025 ICML 2025 poster", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Yicheng_Pan1", "content": "Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan , Renjie Chen , Pengyu Long, Bingchen Fan Published: 01 May 2025, Last Modified: 13 Aug 2025 ICML 2025 poster"}
+{"idx": 7, "title": "Bingchen Fan - OpenReview", "date": "", "ddg_snippet": "Hierarchical overlapping clustering : cost function , algorithm and scalability Yicheng Pan , Renjie Chen , Pengyu Long, Bingchen Fan 26 Sept 2024 (modified: 04 Feb 2025) Submitted to ICLR 2025", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Bingchen_Fan1", "content": "Hierarchical overlapping clustering : cost function , algorithm and scalability Yicheng Pan , Renjie Chen , Pengyu Long, Bingchen Fan 26 Sept 2024 (modified: 04 Feb 2025) Submitted to ICLR 2025"}
+{"idx": 8, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function...", "date": "", "ddg_snippet": "Yicheng Pan , Renjie Chen , Pengyu Long, Bingchen Fan Published: 01 May 2025, Last Modified: 13 Aug 2025 ICML 2025 poster Everyone Revisions BibTeX CC BY 4.0 TL;DR: We have proposed a cost function for hierarchical overlapping clustering on graphs , and developed an approximation algorithm for it.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "Yicheng Pan , Renjie Chen , Pengyu Long, Bingchen Fan Published: 01 May 2025, Last Modified: 13 Aug 2025 ICML 2025 poster Everyone Revisions BibTeX CC BY 4.0 TL;DR: We have proposed a cost function for hierarchical overlapping clustering on graphs , and developed an approximation algorithm for it."}
+{"idx": 9, "title": "Renjie Chen - OpenReview", "date": "", "ddg_snippet": "Publications Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan , Renjie Chen , Pengyu Long, Bingchen Fan Published: 01 May 2025, Last Modified: 18 Jun 2025 ICML 2025 poster", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Renjie_Chen1", "content": "Publications Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan , Renjie Chen , Pengyu Long, Bingchen Fan Published: 01 May 2025, Last Modified: 18 Jun 2025 ICML 2025 poster"}
diff --git a/data/sampled_jsons/Yicheng_Pan_Renjie_Chen_Pengyu_Long_Bingchen_Fan_Hierarchical_Overlapping_Clustering_Algorithm_2_tim.jsonl b/data/sampled_jsons/Yicheng_Pan_Renjie_Chen_Pengyu_Long_Bingchen_Fan_Hierarchical_Overlapping_Clustering_Algorithm_2_tim.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eff746e60c4e4c243d78c4dc21e890b2e0d5837e
--- /dev/null
+++ b/data/sampled_jsons/Yicheng_Pan_Renjie_Chen_Pengyu_Long_Bingchen_Fan_Hierarchical_Overlapping_Clustering_Algorithm_2_tim.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "by Y Pan — We initiate the theoretical study of hierarchical overlapping clustering from the perspectives of cost function, algorithm and experiments.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "by Y Pan — We initiate the theoretical study of hierarchical overlapping clustering from the perspectives of cost function, algorithm and experiments."}
+{"idx": 1, "title": "ICML Poster Hierarchical Overlapping Clustering on Graphs ...", "date": "", "ddg_snippet": "Poster Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46447", "content": "Poster Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009"}
+{"idx": 2, "title": "An Information-theoretic Perspective of Hierarchical Clustering", "date": "", "ddg_snippet": "In this paper, we investigate the hierarchical clustering from the perspective of information theory. Our study is based on Li and Pan ’s structural information theory [11] whose core concept named structural entropy measures the complexity of hierarchical networks.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2108.06036", "content": "In this paper, we investigate the hierarchical clustering from the perspective of information theory. Our study is based on Li and Pan ’s structural information theory [11] whose core concept named structural entropy measures the complexity of hierarchical networks."}
+{"idx": 3, "title": "dblp: Yicheng Pan 0001", "date": "", "ddg_snippet": "Yicheng Pan , Wei Luo, Feng Zheng, Shaojiang Wang, Yuan Yao, Hongan Wang, Waqas Nazeer: Corrections to \"Flow-Based Clustering on Directed Graphs: A Structural Entropy Minimization Approach\".", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/14/721-1", "content": "Yicheng Pan , Wei Luo, Feng Zheng, Shaojiang Wang, Yuan Yao, Hongan Wang, Waqas Nazeer: Corrections to \"Flow-Based Clustering on Directed Graphs: A Structural Entropy Minimization Approach\"."}
+{"idx": 4, "title": "Yicheng Pan's research works | Beihang University (BUAA ...", "date": "", "ddg_snippet": "An Information-theoretic Perspective of Hierarchical Clustering Preprint Full-text available Aug 2021 Yicheng Pan Feng Zheng Bingchen Fan", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Yicheng-Pan-2199691002", "content": "An Information-theoretic Perspective of Hierarchical Clustering Preprint Full-text available Aug 2021 Yicheng Pan Feng Zheng Bingchen Fan"}
+{"idx": 5, "title": "Yicheng Pan | IEEE Xplore Author Details", "date": "", "ddg_snippet": "Yicheng Pan was born in 1983. He received the Ph.D. degree from the Institute of Software, Chinese Academy of Science, Beijing, China, in 2012. In 2015, he completed his Postdoctoral study at the Institute of Information Engineering, Chinese Academy of Sciences. He is currently with the State Key ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/author/37085821768", "content": "Yicheng Pan was born in 1983. He received the Ph.D. degree from the Institute of Software, Chinese Academy of Science, Beijing, China, in 2012. In 2015, he completed his Postdoctoral study at the Institute of Information Engineering, Chinese Academy of Sciences. He is currently with the State Key ..."}
+{"idx": 6, "title": "Bingchen Fan - OpenReview", "date": "", "ddg_snippet": "Hierarchical overlapping clustering : cost function, algorithm and scalability Yicheng Pan , Renjie Chen , Pengyu Long , Bingchen Fan 26 Sept 2024 (modified: 04 Feb 2025) Submitted to ICLR 2025", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Bingchen_Fan1", "content": "Hierarchical overlapping clustering : cost function, algorithm and scalability Yicheng Pan , Renjie Chen , Pengyu Long , Bingchen Fan 26 Sept 2024 (modified: 04 Feb 2025) Submitted to ICLR 2025"}
+{"idx": 7, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — TL;DR: We have proposed a cost function for hierarchical overlapping clustering on graphs, and developed an approximation algorithm for it.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "by Y Pan — TL;DR: We have proposed a cost function for hierarchical overlapping clustering on graphs, and developed an approximation algorithm for it."}
+{"idx": 8, "title": "A Comprehensive Survey on Long Context Language ...", "date": "", "ddg_snippet": "25 Mar 2025 — This repository provides a collection of papers and resources focused on Long Context Language Modeling.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LCLM-Horizon/A-Comprehensive-Survey-For-Long-Context-Language-Modeling", "content": "25 Mar 2025 — This repository provides a collection of papers and resources focused on Long Context Language Modeling."}
+{"idx": 9, "title": "Date/Time", "date": "", "ddg_snippet": "Tuning-free Long Video Diffusion. Jingyuan Chen ,Fuchen Long ,Jie An,Zhaofan Qiu,Ting. Yao,Jiebo Luo,Tao Mei. Friday February 28,. 12:30pm- 2 :30pm. 13097. DynASyn ...", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/wp-content/uploads/2025/02/AAAI-25-Main-Track-Poster-Schedule-2.21.pdf", "content": "Tuning-free Long Video Diffusion. Jingyuan Chen ,Fuchen Long ,Jie An,Zhaofan Qiu,Ting. Yao,Jiebo Luo,Tao Mei. Friday February 28,. 12:30pm- 2 :30pm. 13097. DynASyn ..."}
diff --git a/data/sampled_jsons/You_Only_Look_at_Screens_Multimodal_Chain-of-Action_Agents_technique_summary_year_2023.jsonl b/data/sampled_jsons/You_Only_Look_at_Screens_Multimodal_Chain-of-Action_Agents_technique_summary_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b7cfb84b5e0c63fd814d933d4bcd597274a96f43
--- /dev/null
+++ b/data/sampled_jsons/You_Only_Look_at_Screens_Multimodal_Chain-of-Action_Agents_technique_summary_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "You Only Look at Screens: Multimodal Chain-of-Action Agents", "date": "", "ddg_snippet": "To further strengthen the agent ’s action prediction capability, we propose a novel chain-of-action technique , where a chain of action is a series of intermediate previous action histories and future action plans.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2309.11436", "content": "To further strengthen the agent ’s action prediction capability, we propose a novel chain-of-action technique , where a chain of action is a series of intermediate previous action histories and future action plans."}
+{"idx": 1, "title": "You Only Look at Screens: Multimodal Chain-of-Action Agents", "date": "", "ddg_snippet": "To improve the agent's action prediction capability, we propose a novel chain-of-action technique , where a chain of action is a series of intermediate previous action histories and future action plans that lead to action prediction.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cooelf/Auto-GUI/blob/main/README.md", "content": "To improve the agent's action prediction capability, we propose a novel chain-of-action technique , where a chain of action is a series of intermediate previous action histories and future action plans that lead to action prediction."}
+{"idx": 2, "title": "You Only Look at Screens: Multimodal Chain-of-Action Agents", "date": "", "ddg_snippet": "Moreover, we propose a chain-of-action technique —leveraging a series of intermedi-ate previous action histories and future action plans—to help the agent decide what action to execute.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=f7r8pMQ9Up", "content": "Moreover, we propose a chain-of-action technique —leveraging a series of intermedi-ate previous action histories and future action plans—to help the agent decide what action to execute."}
+{"idx": 3, "title": "You Only Look at Screens: Multimodal Chain of Action Agents", "date": "", "ddg_snippet": "You Only Look at Screens : Multimodal Chain of Action Agents Autonomous user interface (UI) agents aim to facilitate task automation by interacting with the user interface without manual intervention. Recent studies have investigated eliciting the capabilities of large language models (LLMs) for effective engagement in diverse environments.", "subpage_snippet": "", "source": "superagi.com", "link": "https://superagi.com/research_papers/multimodal-chain-of-action-agents/", "content": "You Only Look at Screens : Multimodal Chain of Action Agents Autonomous user interface (UI) agents aim to facilitate task automation by interacting with the user interface without manual intervention. Recent studies have investigated eliciting the capabilities of large language models (LLMs) for effective engagement in diverse environments."}
+{"idx": 4, "title": "You Only Look at Screens: Multimodal Chain-of-Action Agents", "date": "", "ddg_snippet": "This work introduces Auto-GUI, a multimodal solution that directly interacts with the interface, bypassing the need for environment parsing or reliance on application-dependent APIs, and proposes a chain-of-action technique to help the agent decide what action to execute.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/You-Only-Look-at-Screens:-Multimodal-Agents-Zhang-Zhang/6ab33b17cd45e7cbd2cb9b0c5a2d56e5eac1c814/figure/15", "content": "This work introduces Auto-GUI, a multimodal solution that directly interacts with the interface, bypassing the need for environment parsing or reliance on application-dependent APIs, and proposes a chain-of-action technique to help the agent decide what action to execute."}
+{"idx": 5, "title": "You Only Look at Screens: Multimodal Chain-of-Action Agents", "date": "", "ddg_snippet": "Sep 20, 2023 · Moreover, we propose a chain-of-action technique -- leveraging a series of intermediate previous action histories and future action plans -- to help the agent decide what action to execute.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2309.11436", "content": "Sep 20, 2023 · Moreover, we propose a chain-of-action technique -- leveraging a series of intermediate previous action histories and future action plans -- to help the agent decide what action to execute."}
+{"idx": 6, "title": "dblp: You Only Look at Screens: Multimodal Chain-of-Action ...", "date": "", "ddg_snippet": "Sep 25, 2023 · Bibliographic details on You Only Look at Screens : Multimodal Chain-of-Action Agents .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2309-11436", "content": "Sep 25, 2023 · Bibliographic details on You Only Look at Screens : Multimodal Chain-of-Action Agents ."}
+{"idx": 7, "title": "You Only Look at Screens: Multimodal Chain-of-Action Agents", "date": "", "ddg_snippet": "You Only Look at Screens : Multimodal Chain - of - Action Agents ... agent ’s action prediction capability, we propose a novel chain - of - action technique ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2309.11436v4", "content": "You Only Look at Screens : Multimodal Chain - of - Action Agents ... agent ’s action prediction capability, we propose a novel chain - of - action technique ..."}
+{"idx": 8, "title": "Coding Agents with Multimodal Browsing are Generalist Problem", "date": "", "ddg_snippet": "Figure 1: Comparison of \\AgentName with previously published SOTA agents and OpenHands across GAIA, SWE-Bench Multimodal (SWE-Bench M) and The Agent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03011v1", "content": "Figure 1: Comparison of \\AgentName with previously published SOTA agents and OpenHands across GAIA, SWE-Bench Multimodal (SWE-Bench M) and The Agent ..."}
+{"idx": 9, "title": "Smoothing Grounding and Reasoning for MLLM-Powered GUI Agents", "date": "", "ddg_snippet": "... pre-training, particularly through grounding techniques , is widely adopted to enhance the performance of graphical user interface (GUI) agents ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00401v2", "content": "... pre-training, particularly through grounding techniques , is widely adopted to enhance the performance of graphical user interface (GUI) agents ..."}
diff --git a/data/sampled_jsons/YuMEUNNpeb_Position_Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity.jsonl b/data/sampled_jsons/YuMEUNNpeb_Position_Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0c7161c0232b47bb2f62997b147308db9f243052
--- /dev/null
+++ b/data/sampled_jsons/YuMEUNNpeb_Position_Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Medical Large Language Model Benchmarks Should Prioritize ...", "date": "", "ddg_snippet": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks —a tradition inherited from mainstream machine learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10694v1", "content": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks —a tradition inherited from mainstream machine learning."}
+{"idx": 1, "title": "Latest AI Research LLM Agents, Medical LLMs, And More", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity argues that medical LLM benchmarks should prioritize construct validity . This means that the benchmarks should accurately measure the underlying abilities they are intended to assess.", "subpage_snippet": "", "source": "codemeld.org", "link": "https://codemeld.org/blog/latest-ai-research-llm-agents", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity argues that medical LLM benchmarks should prioritize construct validity . This means that the benchmarks should accurately measure the underlying abilities they are intended to assess."}
+{"idx": 2, "title": "ICML 2025 Accepted Paper List: Position Track - Paper Copilot", "date": "", "ddg_snippet": "Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/icml-paper-list/icml-2025-paper-list-position-track/", "content": "Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance."}
+{"idx": 3, "title": "Why Language Models Hallucinate", "date": "", "ddg_snippet": "Many language - model benchmarks mirror standardized human exams, using binary metrics such as accuracy or pass-rate.2025. Medical large language models are vulnerable to data-poisoning attacks.", "subpage_snippet": "", "source": "readwise-assets.s3.amazonaws.com", "link": "https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/why-language-models-hallucinat/2509.04664v1.pdf", "content": "Many language - model benchmarks mirror standardized human exams, using binary metrics such as accuracy or pass-rate.2025. Medical large language models are vulnerable to data-poisoning attacks."}
+{"idx": 4, "title": "ICML 2025 Sneak Peek: The New Laws of AI | Medium", "date": "", "ddg_snippet": "Another paper, “ Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity ,” argues that current benchmarks for high-stakes domains like medicine fail to meet the rigorous standards of scientific validity required for trustworthy evaluation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/foundation-models-deep-dive/icml-2025-sneak-peek-the-new-laws-of-ai-1210789c66a2", "content": "Another paper, “ Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity ,” argues that current benchmarks for high-stakes domains like medicine fail to meet the rigorous standards of scientific validity required for trustworthy evaluation."}
+{"idx": 5, "title": "Inioluwa Deborah Raji's research works | University of California...", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Preprint.In this position paper, we argue that medical LLM benchmarks should (and indeed can) be empirically evaluated for their construct validity .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Inioluwa-Deborah-Raji-2148191730", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Preprint.In this position paper, we argue that medical LLM benchmarks should (and indeed can) be empirically evaluated for their construct validity ."}
+{"idx": 6, "title": "Articles by Frances Dean | Synthical", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 12 March 2025 by Ahmed Alaa and others. Computation and Language .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/d8b88aa7-cbcf-4d81-a315-a1ff4be5f516/articles", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 12 March 2025 by Ahmed Alaa and others. Computation and Language ."}
+{"idx": 7, "title": "Franny Dean - Google Scholar", "date": "", "ddg_snippet": "2021. Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Y04UhM4AAAAJ&hl=en", "content": "2021. Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity ."}
+{"idx": 8, "title": "Latest 15 Papers - June 08, 2025 - Githubissues", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity .A Survey on Medical Large Language Models : Technology, Application, Trustworthiness, and Future Directions. 2024-12-09.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/somewordstoolate/DailyArXiv/17", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity .A Survey on Medical Large Language Models : Technology, Application, Trustworthiness, and Future Directions. 2024-12-09."}
+{"idx": 9, "title": "GitHub - Xuchen-Li/llm-arxiv-daily: Automatically update arXiv papers...", "date": "", "ddg_snippet": "PRS-Med: Position Reasoning Segmentation with Vision- Language Model in Medical Imaging. Quoc-Huy Trinh et.al. Medical Large Language Model Benchmarks Should Prioritize Construct Validity .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xuchen-Li/llm-arxiv-daily", "content": "PRS-Med: Position Reasoning Segmentation with Vision- Language Model in Medical Imaging. Quoc-Huy Trinh et.al. Medical Large Language Model Benchmarks Should Prioritize Construct Validity ."}
diff --git a/data/sampled_jsons/Zhang_2024_initialization_neural_networks_transformers.jsonl b/data/sampled_jsons/Zhang_2024_initialization_neural_networks_transformers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..078984bfe720b85ba6712bf744aad82db282bcd9
--- /dev/null
+++ b/data/sampled_jsons/Zhang_2024_initialization_neural_networks_transformers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Initialization is Critical to Whether Transformers Fit Composite ...", "date": "", "ddg_snippet": "In this work, we use anchor functions ( Zhang et al., 2024 ), a framework for creating controlled synthetic data, to investigate how transformers generalize to unseen compositional tasks. In our setup, each sequence contains an anchor pair, a key item, and noise items unrelated to the output (right side of Fig. 1(a)).", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/19c145aaad40927c51f4d10eaa339c20-Paper-Conference.pdf", "content": "In this work, we use anchor functions ( Zhang et al., 2024 ), a framework for creating controlled synthetic data, to investigate how transformers generalize to unseen compositional tasks. In our setup, each sequence contains an anchor pair, a key item, and noise items unrelated to the output (right side of Fig. 1(a))."}
+{"idx": 1, "title": "Initialization Is Critical to Whether Transformer fits Composite ...", "date": "", "ddg_snippet": "In this work, we use anchor functions ( Zhang et al., 2024 ) to investigate how transformers generalize to unseen compositional tasks. In our setup, each sequence contains a composite anchor pair, a key item, and noise items unrelated to the output (right side of Fig. 1 (a)).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.05409v1", "content": "In this work, we use anchor functions ( Zhang et al., 2024 ) to investigate how transformers generalize to unseen compositional tasks. In our setup, each sequence contains a composite anchor pair, a key item, and noise items unrelated to the output (right side of Fig. 1 (a))."}
+{"idx": 2, "title": "Zhongwang Zhang (张众望) - Homepage", "date": "", "ddg_snippet": "An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang †, Zhi-Qin John Xu† This paper reveals how initialization scales shape transformer -based models' task preferences: smaller scales induce reasoning bias through structured embeddings, while larger scales promote memorization. We attribute this to differential label-driven embedding ...", "subpage_snippet": "", "source": "sjtuzzw.github.io", "link": "https://sjtuzzw.github.io/", "content": "An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang †, Zhi-Qin John Xu† This paper reveals how initialization scales shape transformer -based models' task preferences: smaller scales induce reasoning bias through structured embeddings, while larger scales promote memorization. We attribute this to differential label-driven embedding ..."}
+{"idx": 3, "title": "Trained Transformers Learn Linear Models In-Context", "date": "", "ddg_snippet": "Trained Transformers Learn Linear Models In-Context Ruiqi Zhang , Spencer Frei, Peter L. Bartlett; 25 (49):1−55, 2024 . Abstract Attention-based neural networks such as transformers have demonstrated a remarkable ability to exhibit in-context learning (ICL): Given a short prompt sequence of tokens from an unseen task, they can formulate relevant per-token and next-token predictions without any ...", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/v25/23-1042.html", "content": "Trained Transformers Learn Linear Models In-Context Ruiqi Zhang , Spencer Frei, Peter L. Bartlett; 25 (49):1−55, 2024 . Abstract Attention-based neural networks such as transformers have demonstrated a remarkable ability to exhibit in-context learning (ICL): Given a short prompt sequence of tokens from an unseen task, they can formulate relevant per-token and next-token predictions without any ..."}
+{"idx": 4, "title": "PDF Local to Global: Learning Dynamics and Effect of Initialization for ...", "date": "", "ddg_snippet": "To address this gap, in this paper, we focus on the canonical setting of first-order Markov chains and single-layer transformers and analyze the learning dynamics in this context. Specifically, we prove (Thms. 2, 3, and 8) that the input data properties and the parameter initialization play a significant role in the convergence of the transformer parameters to either local or global minima on ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/9cdb4f8c4dfa13284d2d5a6e7853e5a2-Paper-Conference.pdf", "content": "To address this gap, in this paper, we focus on the canonical setting of first-order Markov chains and single-layer transformers and analyze the learning dynamics in this context. Specifically, we prove (Thms. 2, 3, and 8) that the input data properties and the parameter initialization play a significant role in the convergence of the transformer parameters to either local or global minima on ..."}
+{"idx": 5, "title": "PDF Initialization Is Critical to Reasoning Ability of Transformer", "date": "", "ddg_snippet": "Zhongwang Zhang , Pengxiao Lin, Zhiwei Wang, Yaoyu Zhang , Zhi-Qin John Xu*, Initialization is Critical to Whether Transformers Fit Composite Functions by Inference or Memorizing, NeurIPS 2024", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/94702.pdf", "content": "Zhongwang Zhang , Pengxiao Lin, Zhiwei Wang, Yaoyu Zhang , Zhi-Qin John Xu*, Initialization is Critical to Whether Transformers Fit Composite Functions by Inference or Memorizing, NeurIPS 2024"}
+{"idx": 6, "title": "Initialization is Critical to Whether Transformers Fit Composite...", "date": "", "ddg_snippet": "Transformers have shown impressive capabilities across various tasks, but their performance on compositional problems remains a topic of debate. In this work, we investigate the mechanisms of how transformers behave on unseen compositional tasks. We discover that the parameter initialization scale plays a critical role in determining whether the model learns inferential (reasoning-based ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YOBGdVaYTS", "content": "Transformers have shown impressive capabilities across various tasks, but their performance on compositional problems remains a topic of debate. In this work, we investigate the mechanisms of how transformers behave on unseen compositional tasks. We discover that the parameter initialization scale plays a critical role in determining whether the model learns inferential (reasoning-based ..."}
+{"idx": 7, "title": "Initialization is Critical to Whether Transformers Fit Composite ...", "date": "", "ddg_snippet": "In order to clearly study the behaviors and internal mechanisms of language models, Zhang et al. introduced anchor functions as benchmark functions for investigating transformer behavior. Our work builds on the anchor function setting to explore how different initialization scales affect model solutions and mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.05409v3", "content": "In order to clearly study the behaviors and internal mechanisms of language models, Zhang et al. introduced anchor functions as benchmark functions for investigating transformer behavior. Our work builds on the anchor function setting to explore how different initialization scales affect model solutions and mechanisms."}
+{"idx": 8, "title": "Initialization is Critical to Whether Transformers Fit Composite ...", "date": "", "ddg_snippet": "The core of this research lies in understanding how transformers handle compositional tasks, specifically examining the dichotomy between reasoning and memorization. The authors posit that parameter initialization plays a crucial role in determining whether a transformer solves a problem through genuine reasoning or rote memorization.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/yobgdvayts/", "content": "The core of this research lies in understanding how transformers handle compositional tasks, specifically examining the dichotomy between reasoning and memorization. The authors posit that parameter initialization plays a crucial role in determining whether a transformer solves a problem through genuine reasoning or rote memorization."}
+{"idx": 9, "title": "Initialization is Critical to Whether Transformers Fit Composite ...", "date": "", "ddg_snippet": "Authors Zhongwang Zhang , Pengxiao Lin, Zhiwei Wang, Yaoyu Zhang , Zhi-Qin John Xu Abstract Transformers have shown impressive capabilities across various tasks, but their performance on compositional problems remains a topic of debate. In this work, we investigate the mechanisms of how transformers behave on unseen compositional tasks. We discover that the parameter initialization scale plays a ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/19c145aaad40927c51f4d10eaa339c20-Abstract-Conference.html", "content": "Authors Zhongwang Zhang , Pengxiao Lin, Zhiwei Wang, Yaoyu Zhang , Zhi-Qin John Xu Abstract Transformers have shown impressive capabilities across various tasks, but their performance on compositional problems remains a topic of debate. In this work, we investigate the mechanisms of how transformers behave on unseen compositional tasks. We discover that the parameter initialization scale plays a ..."}
diff --git a/data/sampled_jsons/Zhang_et_al._2024a_initialization_scale_language_models.jsonl b/data/sampled_jsons/Zhang_et_al._2024a_initialization_scale_language_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..433ce1f33d6e55c2d9251fc4b1aec8a5b9327eb4
--- /dev/null
+++ b/data/sampled_jsons/Zhang_et_al._2024a_initialization_scale_language_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "An Analysis for Reasoning Bias of Language Models with Small ...", "date": "", "ddg_snippet": "An alternative approach to enhancing the reasoning abil-ity of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was ob-served that the scale of model parameter initialization sig-nificantly impacts the model ’s reasoning behavior ( Zhang et al ., 2024a ; 2025). Specifically, smaller ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375", "content": "An alternative approach to enhancing the reasoning abil-ity of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was ob-served that the scale of model parameter initialization sig-nificantly impacts the model ’s reasoning behavior ( Zhang et al ., 2024a ; 2025). Specifically, smaller ..."}
+{"idx": 2, "title": "\"TinyLlama: An Open-Source Small Language Model\", Zhang et al ...", "date": "", "ddg_snippet": "7K subscribers in the mlscaling community. ML/AI/DL research on approaches using large models , datasets, and compute: \"more is different\"", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/mlscaling/comments/18z9gy8/tinyllama_an_opensource_small_language_model/", "content": "7K subscribers in the mlscaling community. ML/AI/DL research on approaches using large models , datasets, and compute: \"more is different\""}
+{"idx": 3, "title": "MM-LLMs: Recent Advances in MultiModal Large Language Models", "date": "", "ddg_snippet": "How- ever, as the scale of models and datasets continues to expand, traditional MM models incur substan- tial computational costs, particularly when trained *Equal contributions. Corresponding authors. This work was done when Duzhen Zhang was interning at Tencent, AI Lab, Beijing, China.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.738.pdf", "content": "How- ever, as the scale of models and datasets continues to expand, traditional MM models incur substan- tial computational costs, particularly when trained *Equal contributions. Corresponding authors. This work was done when Duzhen Zhang was interning at Tencent, AI Lab, Beijing, China."}
+{"idx": 4, "title": "LLaMA-Adapter: Efficient Fine-tuning of Language Models with ...", "date": "", "ddg_snippet": "LLaMA-Adapter: Eficient Fine-tuning of Language Models with Zero-init Attention Renrui Zhang ∗1,2, Jiaming Han∗1, Aojun Zhou2, Xiangfei Hu1, Shilin Yan1 Pan Lu3, Hongsheng Li2, Peng Gao1, Yu Qiao1", "subpage_snippet": "", "source": "lupantech.github.io", "link": "https://lupantech.github.io/papers/iclr24_llama.pdf", "content": "LLaMA-Adapter: Eficient Fine-tuning of Language Models with Zero-init Attention Renrui Zhang ∗1,2, Jiaming Han∗1, Aojun Zhou2, Xiangfei Hu1, Shilin Yan1 Pan Lu3, Hongsheng Li2, Peng Gao1, Yu Qiao1"}
+{"idx": 5, "title": "INITIALIZATION USING UPDATE APPROXIMATION IS A S B EXTREMELY ...", "date": "", "ddg_snippet": "Parameter-eficient fine-tuning (PEFT) has become vital for adapting large language models (LLMs) under computational constraints. Low-rank methods like LoRA (Hu et al ., 2021) address this by reducing learnable parameters via low-rank updates, sparking advancements in optimization, initialization , structured matrices, and adaptive rank selection ( Zhang et al ., 2023; Wang et al ., 2024b;a). Low ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WQZFXVpQow", "content": "Parameter-eficient fine-tuning (PEFT) has become vital for adapting large language models (LLMs) under computational constraints. Low-rank methods like LoRA (Hu et al ., 2021) address this by reducing learnable parameters via low-rank updates, sparking advancements in optimization, initialization , structured matrices, and adaptive rank selection ( Zhang et al ., 2023; Wang et al ., 2024b;a). Low ..."}
+{"idx": 6, "title": "TrainingofScaffoldedLanguageModels withLanguageSupervision ...", "date": "", "ddg_snippet": "Recent works (Yüksekgönül et al ., 2024; Cheng et al ., 2024) propose to optimize the parameters of the system in an end-to-end manner using an LLM. In particular, using an LLM as an optimizer of-fers an eficient alternative to gradient-based (Chen et al ., 2024b) and RL-based ( Zhang et al ., 2022) techniques. An LLM can optimize complex het-erogeneous parameters such as instructions and tool ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.16392", "content": "Recent works (Yüksekgönül et al ., 2024; Cheng et al ., 2024) propose to optimize the parameters of the system in an end-to-end manner using an LLM. In particular, using an LLM as an optimizer of-fers an eficient alternative to gradient-based (Chen et al ., 2024b) and RL-based ( Zhang et al ., 2022) techniques. An LLM can optimize complex het-erogeneous parameters such as instructions and tool ..."}
+{"idx": 7, "title": "Model Composition for Multimodal Large Language Models", "date": "", "ddg_snippet": "Recent advancements in Multimodal Large Language Models (MLLMs) have established them as the forefront of multimodal learning paradigms (Liu et al .,2023b,a). The prevalent ap- proach involves aligning modality encoders with large language models (LLMs) through extensive modality-text paired data and then ne-tuning with modality-specic instruction data. This paradigm", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.606.pdf", "content": "Recent advancements in Multimodal Large Language Models (MLLMs) have established them as the forefront of multimodal learning paradigms (Liu et al .,2023b,a). The prevalent ap- proach involves aligning modality encoders with large language models (LLMs) through extensive modality-text paired data and then ne-tuning with modality-specic instruction data. This paradigm"}
+{"idx": 8, "title": "(PDF) An Analysis for Reasoning Bias of Language Models with Small...", "date": "", "ddg_snippet": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848015_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization", "content": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task..."}
+{"idx": 9, "title": "Working Memory Identifies Reasoning Limits in Language Models", "date": "", "ddg_snippet": "zhang - etal - 2024 -working %X This study explores the inherent limitations of large language models (LLMs) from a scaling perspective, focusing on the upper bounds of their cognitive capabilities.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.938/", "content": "zhang - etal - 2024 -working %X This study explores the inherent limitations of large language models (LLMs) from a scaling perspective, focusing on the upper bounds of their cognitive capabilities."}
diff --git a/data/sampled_jsons/Zhao_et_al._2023b_peeling_bandit_abstract_year_2023.jsonl b/data/sampled_jsons/Zhao_et_al._2023b_peeling_bandit_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9e38bef2afe967e88ffb3039564ba88512036c7a
--- /dev/null
+++ b/data/sampled_jsons/Zhao_et_al._2023b_peeling_bandit_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — Zhao et al . ( 2023b ) develop a peeling approach for the unknown variance case without variance es- timation in linear settings, and Pacchiano ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — Zhao et al . ( 2023b ) develop a peeling approach for the unknown variance case without variance es- timation in linear settings, and Pacchiano ( ..."}
+{"idx": 1, "title": "Noise-Adaptive Confidence Sets for Linear Bandits and ...", "date": "", "ddg_snippet": "by KS Jun · 2024 · Cited by 4 — This proof is inspired by Zhao et al . ( 2023b ), but details differ since we leverage the regret equality (Lemma C.7), which helps shorten the proof and ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10548723", "content": "by KS Jun · 2024 · Cited by 4 — This proof is inspired by Zhao et al . ( 2023b ), but details differ since we leverage the regret equality (Lemma C.7), which helps shorten the proof and ..."}
+{"idx": 2, "title": "Unifying (Federated) (Private) High-Dimensional Bandits ...", "date": "", "ddg_snippet": "∥v( ¯D) − v( ¯D′)∥ ≤ D, then Algorithm 5 is (ϵ, δ)-DP. 815. We further have the following bounds on that establish that peeling is indeed a contraction Cai et ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=icM2HffLwD&name=pdf", "content": "∥v( ¯D) − v( ¯D′)∥ ≤ D, then Algorithm 5 is (ϵ, δ)-DP. 815. We further have the following bounds on that establish that peeling is indeed a contraction Cai et ..."}
+{"idx": 3, "title": "Anytime-valid off-policy Inference for Contextual Bandits", "date": "", "ddg_snippet": "by I Waudby-Smith · 2024 · Cited by 51 — Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in healthcare and the tech industry. They involve online learning ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3643693", "content": "by I Waudby-Smith · 2024 · Cited by 51 — Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in healthcare and the tech industry. They involve online learning ..."}
+{"idx": 4, "title": "Variance-Dependent Regret Lower Bounds for Contextual ...", "date": "", "ddg_snippet": "by J He · 2025 · Cited by 1 — In this paper, we study variance-dependent lower bounds for linear contextual bandits in different settings. For both prefixed and adaptive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.12020", "content": "by J He · 2025 · Cited by 1 — In this paper, we study variance-dependent lower bounds for linear contextual bandits in different settings. For both prefixed and adaptive ..."}
+{"idx": 5, "title": "Revisiting Simple Regret: Fast Rates for Returning a Good Arm", "date": "", "ddg_snippet": "by Y Zhao · 2023 · Cited by 23 — Abstract. Simple regret is a natural and parameter-free per- formance criterion for pure exploration in multi- armed bandits. However, possibly due to the ... 49 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/zhao23g/zhao23g.pdf", "content": "by Y Zhao · 2023 · Cited by 23 — Abstract. Simple regret is a natural and parameter-free per- formance criterion for pure exploration in multi- armed bandits. However, possibly due to the ... 49 pages"}
+{"idx": 6, "title": "Noise-Adaptive Thompson Sampling for Linear Contextual ...", "date": "", "ddg_snippet": "by R Xu · 2023 · Cited by 11 — (2022b); Zhao et al . ( 2023 ) explored the unknown variance case. Nevertheless, there is a scarcity of results concerning TS algorithms in this context. 28 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/4a6824f8f137e78f18e73d9cfc1d22ed-Paper-Conference.pdf", "content": "by R Xu · 2023 · Cited by 11 — (2022b); Zhao et al . ( 2023 ) explored the unknown variance case. Nevertheless, there is a scarcity of results concerning TS algorithms in this context. 28 pages"}
+{"idx": 7, "title": "Second-Order Bounds for Reinforcement Learning", "date": "", "ddg_snippet": "by K Wang · Cited by 22 — Abstract . In this paper, we prove that Distributional Re- inforcement Learning (DistRL), which learns the return distribution, can obtain second-order.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kZBCFQe1Ej", "content": "by K Wang · Cited by 22 — Abstract . In this paper, we prove that Distributional Re- inforcement Learning (DistRL), which learns the return distribution, can obtain second-order."}
+{"idx": 8, "title": "Experiments on individual strategy updating in iterated ...", "date": "", "ddg_snippet": "by H Qi · 2015 · Cited by 20 — Through reinforcement learning , Zhao et al . ( Zhao & Jia, 2015) investigated the impact of an uncontrolled intersection on traffic flow. Show abstract .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S002251931400705X", "content": "by H Qi · 2015 · Cited by 20 — Through reinforcement learning , Zhao et al . ( Zhao & Jia, 2015) investigated the impact of an uncontrolled intersection on traffic flow. Show abstract ."}
+{"idx": 9, "title": "Constant or Logarithmic Regret in Asynchronous Multiplayer ...", "date": "", "ddg_snippet": "by H Richard · 2024 · Cited by 3 — Multiplayer bandits have recently garnered significant attention due to their relevance in cognitive radio networks. While the ex-.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/richard24a/richard24a.pdf", "content": "by H Richard · 2024 · Cited by 3 — Multiplayer bandits have recently garnered significant attention due to their relevance in cognitive radio networks. While the ex-."}
diff --git a/data/sampled_jsons/Zhao_et_al._peeling_approach_bandits_unknown_variance_abstract_year_2023.jsonl b/data/sampled_jsons/Zhao_et_al._peeling_approach_bandits_unknown_variance_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..74e11c844ecb7647db1d6846f781a70ce0ba1dd4
--- /dev/null
+++ b/data/sampled_jsons/Zhao_et_al._peeling_approach_bandits_unknown_variance_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025", "date": "", "ddg_snippet": "tting are Zhao et al . (2023b); Pacchiano (2024). Zhao et al . (2023b) develop a peeling approach for the unknown variance case without variance es-timation in linear settings, and Pacchiano (2024) extend", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "tting are Zhao et al . (2023b); Pacchiano (2024). Zhao et al . (2023b) develop a peeling approach for the unknown variance case without variance es-timation in linear settings, and Pacchiano (2024) extend"}
+{"idx": 1, "title": "Variance-Dependent Regret Bounds for Linear Bandits and ...", "date": "", "ddg_snippet": "Abstract Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance -dependent regret bounds for linear contextual bandits , which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v195/zhao23a/zhao23a.pdf", "content": "Abstract Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance -dependent regret bounds for linear contextual bandits , which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the ..."}
+{"idx": 2, "title": "Variance-Dependent Regret Bounds for Non-stationary Linear ...", "date": "", "ddg_snippet": "These algorithms ad-dress cases where the variance information of the rewards is known and unknown , respec-VK tively. Notably, when the total variance K is much smaller than , our algorithms out-perform previous state-of-the-art results on non-stationary stochastic linear bandits under diferent settings.", "subpage_snippet": "", "source": "www.cse.cuhk.edu.hk", "link": "https://www.cse.cuhk.edu.hk/~cslui/PUBLICATION/2025_AISTATS_Variance_Dependent_Regret_Bounds_for_Nonstationary_Linear_Bandits.pdf", "content": "These algorithms ad-dress cases where the variance information of the rewards is known and unknown , respec-VK tively. Notably, when the total variance K is much smaller than , our algorithms out-perform previous state-of-the-art results on non-stationary stochastic linear bandits under diferent settings."}
+{"idx": 3, "title": "How Does Variance Shape the Regret in Contextual Bandits?", "date": "", "ddg_snippet": "Abstract We consider realizable contextual bandits with general function approximation, investigating how small reward variance can lead to better-than-minimax regret bounds. Unlike in minimax regret bounds, we show that the eluder dimension delu—a measure of the complexity of the function class—plays a crucial role in variance -dependent ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/9861a7c3972ed5d36dda3826d44bb246-Paper-Conference.pdf", "content": "Abstract We consider realizable contextual bandits with general function approximation, investigating how small reward variance can lead to better-than-minimax regret bounds. Unlike in minimax regret bounds, we show that the eluder dimension delu—a measure of the complexity of the function class—plays a crucial role in variance -dependent ..."}
+{"idx": 4, "title": "Variance-Dependent Regret Bounds for Linear Bandits and ... Variance-DependentRegretBoundsforLinearBandits ... Noise-Adaptive Thompson Sampling for Linear Contextual Bandits", "date": "", "ddg_snippet": "Feb 21, 2023 · Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance -dependent regret bounds for linear contextual bandits , which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the noise. In ... Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance -dependent regret bounds for linear contextual bandits , which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the noise. In ... Abstract Linear contextual bandits represent a fundamental class of models with numer-ous real-world applications, and it is critical to developing algorithms that can effectively manage noise with unknown variance , ensuring provable guarantees for both worst-case constant- variance noise and deterministic reward scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2302.10371", "content": "Feb 21, 2023 · Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance -dependent regret bounds for linear contextual bandits , which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the noise. In ... Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance -dependent regret bounds for linear contextual bandits , which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the noise. In ... Abstract Linear contextual bandits represent a fundamental class of models with numer-ous real-world applications, and it is critical to developing algorithms that can effectively manage noise with unknown variance , ensuring provable guarantees for both worst-case constant- variance noise and deterministic reward scenarios."}
+{"idx": 5, "title": "Variance-DependentRegretBoundsforLinearBandits ...", "date": "", "ddg_snippet": "Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance -dependent regret bounds for linear contextual bandits , which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the noise. In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2302.10371", "content": "Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance -dependent regret bounds for linear contextual bandits , which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the noise. In ..."}
+{"idx": 6, "title": "Noise-Adaptive Thompson Sampling for Linear Contextual Bandits", "date": "", "ddg_snippet": "Abstract Linear contextual bandits represent a fundamental class of models with numer-ous real-world applications, and it is critical to developing algorithms that can effectively manage noise with unknown variance , ensuring provable guarantees for both worst-case constant- variance noise and deterministic reward scenarios.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/4a6824f8f137e78f18e73d9cfc1d22ed-Paper-Conference.pdf", "content": "Abstract Linear contextual bandits represent a fundamental class of models with numer-ous real-world applications, and it is critical to developing algorithms that can effectively manage noise with unknown variance , ensuring provable guarantees for both worst-case constant- variance noise and deterministic reward scenarios."}
+{"idx": 7, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Zhao et al . (2023b) develop a peeling approach for the unknown variance case without variance estimation in linear settings, and Pacchiano (2024) extend this technique to general function approxima-tion.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "Zhao et al . (2023b) develop a peeling approach for the unknown variance case without variance estimation in linear settings, and Pacchiano (2024) extend this technique to general function approxima-tion."}
+{"idx": 8, "title": "Variance -Dependent Regret Bounds for Linear Bandits", "date": "", "ddg_snippet": "The second approach (Zhang et al ., 2021b; Kim et al ., 2021) follows the test-based framework.eroscedastic bandit setting (Kirschner and Krause, 2018; Zhou et al ., 2021a; Zhou and Gu, 2022). for the known variance case.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/variance-dependent-regret-bounds-for-linear-bandits-and-28zvdvdp.pdf", "content": "The second approach (Zhang et al ., 2021b; Kim et al ., 2021) follows the test-based framework.eroscedastic bandit setting (Kirschner and Krause, 2018; Zhou et al ., 2021a; Zhou and Gu, 2022). for the known variance case."}
+{"idx": 9, "title": "(PDF) Selective Reviews of Bandit Problems in AI via a Statistical View", "date": "", "ddg_snippet": "explore applied bandit problems and bandits with unknown variance proxies, emphasizing.the subsample means. Cai et al . [106. ] extended the use of covariates in MAB problems by addressing transfer.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389111146_Selective_Reviews_of_Bandit_Problems_in_AI_via_a_Statistical_View", "content": "explore applied bandit problems and bandits with unknown variance proxies, emphasizing.the subsample means. Cai et al . [106. ] extended the use of covariates in MAB problems by addressing transfer."}
diff --git a/data/sampled_jsons/Zhao_et_al._variance-dependent_regret_peeling_bandit_year_2023.jsonl b/data/sampled_jsons/Zhao_et_al._variance-dependent_regret_peeling_bandit_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9592b83f3fd4da1cf78ad4532da602459f97b902
--- /dev/null
+++ b/data/sampled_jsons/Zhao_et_al._variance-dependent_regret_peeling_bandit_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Variance-Dependent Regret Bounds for Linear Bandits and ...", "date": "", "ddg_snippet": "Abstract Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance-dependent regret bounds for linear contextual bandits, which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v195/zhao23a/zhao23a.pdf", "content": "Abstract Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance-dependent regret bounds for linear contextual bandits, which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the ..."}
+{"idx": 1, "title": "How Does Variance Shape the Regret in Contextual Bandits?", "date": "", "ddg_snippet": "Such variance-dependent regret bounds that replace the dependence on the number of rounds T by the total variance Λ have been shown in multi-armed bandits (Audibert et al ., 2009), linear bandits (Ito and Takemura, 2023), and linear contextual bandits ( Zhao et al ., 2023).", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/9861a7c3972ed5d36dda3826d44bb246-Paper-Conference.pdf", "content": "Such variance-dependent regret bounds that replace the dependence on the number of rounds T by the total variance Λ have been shown in multi-armed bandits (Audibert et al ., 2009), linear bandits (Ito and Takemura, 2023), and linear contextual bandits ( Zhao et al ., 2023)."}
+{"idx": 2, "title": "Variance-Dependent Regret Bounds for Linear Bandits and ...", "date": "", "ddg_snippet": "Feb 21, 2023 · Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance-dependent regret bounds for linear contextual bandits, which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the noise. In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2302.10371", "content": "Feb 21, 2023 · Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance-dependent regret bounds for linear contextual bandits, which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the noise. In ..."}
+{"idx": 3, "title": "[2503.12020] Variance-Dependent Regret Lower Bounds for ... Variance-Dependent Regret Lower Bounds for Contextual Bandits How Does Variance Shape the Regret in Contextual Bandits? Variance-Dependent Regret Bounds for Non-stationary Linear ... Variance-Dependent Regret Bounds for Linear Bandits and ... NeurIPS Poster How Does Variance Shape the Regret in ...", "date": "", "ddg_snippet": "Mar 15, 2025 · However, most existing works focus on the regret upper bounds instead of lower bounds. To our knowledge, the only lower bound is from Jia et al . (2024), which proved that for any eluder dimension delu and total variance budget Λ, there exists an instance with ∑K k=1σ2 k ≤ Λ for which any algorithm incurs a variance-dependent lower bound ... Dec 31, 2024 · In this paper, to overcome the limitations of Jia et al . (2024), we consider the general variance sequence under two settings. For a prefixed sequence, where the entire variance sequence is revealed to the learner at the beginning of the learning process, we establish a variance-dependent lower bound of $\\Omega (d \\sqrt {\\sum_ {k=1}^K\\sigma_k^2 ... Such variance-dependent regret bounds that replace the dependence on the number of rounds T by the total variance Λ have been shown in multi-armed bandits (Audibert et al ., 2009), linear bandits (Ito and Takemura, 2023), and linear contextual bandits ( Zhao et al ., 2023). The only exception of utilizing the variance information in the non-stationary ban-dit setting is Wei et al . (2016), which proposed the Rerun-UCB-V algorithm for the non-stationary MAB setting with a regret dependent on the action set size |A| . Feb 21, 2023 · Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance-dependent regret bounds for linear contextual bandits, which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the noise. In ... Unlike in minimax regret bounds, we show that the eluder dimension d elu − a measure of the complexity of the function class − plays a crucial role in variance-dependent bounds. We consider two types of adversary: (1) Weak adversary: The adversary sets the reward variance before observing the learner's action.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.12020", "content": "Mar 15, 2025 · However, most existing works focus on the regret upper bounds instead of lower bounds. To our knowledge, the only lower bound is from Jia et al . (2024), which proved that for any eluder dimension delu and total variance budget Λ, there exists an instance with ∑K k=1σ2 k ≤ Λ for which any algorithm incurs a variance-dependent lower bound ... Dec 31, 2024 · In this paper, to overcome the limitations of Jia et al . (2024), we consider the general variance sequence under two settings. For a prefixed sequence, where the entire variance sequence is revealed to the learner at the beginning of the learning process, we establish a variance-dependent lower bound of $\\Omega (d \\sqrt {\\sum_ {k=1}^K\\sigma_k^2 ... Such variance-dependent regret bounds that replace the dependence on the number of rounds T by the total variance Λ have been shown in multi-armed bandits (Audibert et al ., 2009), linear bandits (Ito and Takemura, 2023), and linear contextual bandits ( Zhao et al ., 2023). The only exception of utilizing the variance information in the non-stationary ban-dit setting is Wei et al . (2016), which proposed the Rerun-UCB-V algorithm for the non-stationary MAB setting with a regret dependent on the action set size |A| . Feb 21, 2023 · Recently, several studies (Zhou et al ., 2021a; Zhang et al ., 2021b; Kim et al ., 2021; Zhou and Gu, 2022) have provided variance-dependent regret bounds for linear contextual bandits, which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computationally intractable or unable to handle unknown variance of the noise. In ... Unlike in minimax regret bounds, we show that the eluder dimension d elu − a measure of the complexity of the function class − plays a crucial role in variance-dependent bounds. We consider two types of adversary: (1) Weak adversary: The adversary sets the reward variance before observing the learner's action."}
+{"idx": 4, "title": "Variance-Dependent Regret Lower Bounds for Contextual Bandits", "date": "", "ddg_snippet": "Dec 31, 2024 · In this paper, to overcome the limitations of Jia et al . (2024), we consider the general variance sequence under two settings. For a prefixed sequence, where the entire variance sequence is revealed to the learner at the beginning of the learning process, we establish a variance-dependent lower bound of $\\Omega (d \\sqrt {\\sum_ {k=1}^K\\sigma_k^2 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qIiBqYxQJ7", "content": "Dec 31, 2024 · In this paper, to overcome the limitations of Jia et al . (2024), we consider the general variance sequence under two settings. For a prefixed sequence, where the entire variance sequence is revealed to the learner at the beginning of the learning process, we establish a variance-dependent lower bound of $\\Omega (d \\sqrt {\\sum_ {k=1}^K\\sigma_k^2 ..."}
+{"idx": 5, "title": "Variance-Dependent Regret Bounds for Non-stationary Linear ...", "date": "", "ddg_snippet": "The only exception of utilizing the variance information in the non-stationary ban-dit setting is Wei et al . (2016), which proposed the Rerun-UCB-V algorithm for the non-stationary MAB setting with a regret dependent on the action set size |A| .", "subpage_snippet": "", "source": "www.cse.cuhk.edu.hk", "link": "https://www.cse.cuhk.edu.hk/~cslui/PUBLICATION/2025_AISTATS_Variance_Dependent_Regret_Bounds_for_Nonstationary_Linear_Bandits.pdf", "content": "The only exception of utilizing the variance information in the non-stationary ban-dit setting is Wei et al . (2016), which proposed the Rerun-UCB-V algorithm for the non-stationary MAB setting with a regret dependent on the action set size |A| ."}
+{"idx": 6, "title": "NeurIPS Poster How Does Variance Shape the Regret in ...", "date": "", "ddg_snippet": "Unlike in minimax regret bounds, we show that the eluder dimension d elu − a measure of the complexity of the function class − plays a crucial role in variance-dependent bounds. We consider two types of adversary: (1) Weak adversary: The adversary sets the reward variance before observing the learner's action.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96778", "content": "Unlike in minimax regret bounds, we show that the eluder dimension d elu − a measure of the complexity of the function class − plays a crucial role in variance-dependent bounds. We consider two types of adversary: (1) Weak adversary: The adversary sets the reward variance before observing the learner's action."}
+{"idx": 7, "title": "Bandit Learning with General Function Classes... | DeepAI", "date": "", "ddg_snippet": "by Heyang Zhao , et al .For generalized linear bandits , we further propose an algorithm based on follow-the-regularized-leader (FTRL) subroutine and online-to-confidence-set conversion, which can achieve a tighter variance - dependent regret under certain conditions.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/bandit-learning-with-general-function-classes-heteroscedastic-noise-and-variance-dependent-regret-bounds", "content": "by Heyang Zhao , et al .For generalized linear bandits , we further propose an algorithm based on follow-the-regularized-leader (FTRL) subroutine and online-to-confidence-set conversion, which can achieve a tighter variance - dependent regret under certain conditions."}
+{"idx": 8, "title": "ICML Poster Improved Regret Analysis in Gaussian Process Bandits ...", "date": "", "ddg_snippet": "We study the Gaussian process (GP) bandit problem, whose goal is to minimize regret under an unknown reward function lying in some reproducing kernel Hilbert space (RKHS).", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43519", "content": "We study the Gaussian process (GP) bandit problem, whose goal is to minimize regret under an unknown reward function lying in some reproducing kernel Hilbert space (RKHS)."}
+{"idx": 9, "title": "Variance - Dependent Regret Bounds for Linear Bandits ... | alphaXiv", "date": "", "ddg_snippet": "We propose a variance -adaptive algorithm for linear mixture MDPs, which achieves a problem- dependent horizon-free regret bound that can gracefully reduce to a nearly constant regret for deterministic MDPs.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2302.10371", "content": "We propose a variance -adaptive algorithm for linear mixture MDPs, which achieves a problem- dependent horizon-free regret bound that can gracefully reduce to a nearly constant regret for deterministic MDPs."}
diff --git a/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_abstract_Olah_2020_year_2020.jsonl b/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_abstract_Olah_2020_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ccd61cca8867ce33f269c5ba4cd94fa9e8f4466
--- /dev/null
+++ b/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_abstract_Olah_2020_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Explainable artificial intelligence - Wikipedia", "date": "", "ddg_snippet": "Human users of such a system can understand the AI's cognition (both in real-time and after the fact) and can determine whether to trust the AI.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Explainable_artificial_intelligence", "content": "Human users of such a system can understand the AI's cognition (both in real-time and after the fact) and can determine whether to trust the AI."}
+{"idx": 1, "title": "Zoom In: An Introduction to Circuits — LessWrong", "date": "", "ddg_snippet": "Chris Olah and the rest of the rest of the OpenAI Clarity team just published “ Zoom In : An Introduction to Circuits ,” a Distill article about ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/MG4ZjWQDrdpgeu8wG/zoom-in-an-introduction-to-circuits", "content": "Chris Olah and the rest of the rest of the OpenAI Clarity team just published “ Zoom In : An Introduction to Circuits ,” a Distill article about ..."}
+{"idx": 2, "title": "Zoom In: An Introduction to Circuits — AI Alignment Forum", "date": "", "ddg_snippet": "Chris Olah and the rest of the rest of the OpenAI Clarity team just published “ Zoom In : An Introduction to Circuits ,” a Distill article about ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/MG4ZjWQDrdpgeu8wG/zoom-in-an-introduction-to-circuits", "content": "Chris Olah and the rest of the rest of the OpenAI Clarity team just published “ Zoom In : An Introduction to Circuits ,” a Distill article about ..."}
+{"idx": 3, "title": "Thread: Circuits", "date": "", "ddg_snippet": "The Circuits thread is open to articles exploring individual features, circuits , and their organization within neural networks.", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/", "content": "The Circuits thread is open to articles exploring individual features, circuits , and their organization within neural networks."}
+{"idx": 4, "title": "Rational Animations' intro to mechanistic interpretability", "date": "", "ddg_snippet": "Each layer usually detects more and more abstract properties of the input image until we get to the last part of the network, which is structured ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/tSNygWGHdpiBvzp4D/rational-animations-intro-to-mechanistic-interpretability", "content": "Each layer usually detects more and more abstract properties of the input image until we get to the last part of the network, which is structured ..."}
+{"idx": 5, "title": "EIS VI: Critiques of Mechanistic Interpretability Work in AI", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits ( Olah et al., 2020 ) ... The feature visualization ( Olah et al., 2017) and zoom in ( Olah et al., 2020 ) papers ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/wt7HXaCWzuKQipqz3/eis-vi-critiques-of-mechanistic-interpretability-work-in-ai", "content": "Zoom In : An Introduction to Circuits ( Olah et al., 2020 ) ... The feature visualization ( Olah et al., 2017) and zoom in ( Olah et al., 2020 ) papers ..."}
+{"idx": 6, "title": "Matryoshka Sparse Autoencoders - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "And if the circuit depends on both an abstract and fine-grained feature in one location, no single vanilla SAE size is sufficient and it is unclear ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/zbebxYCqsryPALh8C/matryoshka-sparse-autoencoders", "content": "And if the circuit depends on both an abstract and fine-grained feature in one location, no single vanilla SAE size is sufficient and it is unclear ..."}
+{"idx": 7, "title": "EIS VI: Critiques of Mechanistic Interpretability Work in AI", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits ( Olah et al., 2020 ) ... The feature visualization ( Olah et al., 2017) and zoom in ( Olah et al., 2020 ) papers ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/wt7HXaCWzuKQipqz3/eis-vi-critiques-of-mechanistic-interpretability-work-in-ai", "content": "Zoom In : An Introduction to Circuits ( Olah et al., 2020 ) ... The feature visualization ( Olah et al., 2017) and zoom in ( Olah et al., 2020 ) papers ..."}
+{"idx": 8, "title": "From Flat to Hierarchical : Extracting Sparse Representations", "date": "", "ddg_snippet": "A) Linearly accessible concepts : abstract directions that are approximately orthogonal and independently interpretable, as assumed by the Linear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03093v1", "content": "A) Linearly accessible concepts : abstract directions that are approximately orthogonal and independently interpretable, as assumed by the Linear ..."}
+{"idx": 9, "title": "In Dialogue with Intelligence: Rethinking Large Language Models", "date": "", "ddg_snippet": "It seeks to infer structure from behaviour and activity, typically without access to complete architectural specifications, training histories, or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22767v1", "content": "It seeks to infer structure from behaviour and activity, typically without access to complete architectural specifications, training histories, or ..."}
diff --git a/data/sampled_jsons/ablation_study_user_engagement_subreddit_affluence_Causal_Modeling_of_Climate_Activism_on_Reddit.jsonl b/data/sampled_jsons/ablation_study_user_engagement_subreddit_affluence_Causal_Modeling_of_Climate_Activism_on_Reddit.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cbda5b62bfe2c27ee9357154434605e848381bf8
--- /dev/null
+++ b/data/sampled_jsons/ablation_study_user_engagement_subreddit_affluence_Causal_Modeling_of_Climate_Activism_on_Reddit.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — In this work, we develop a comprehensive causal model of how and why. Reddit users engage with activist communities driving mass climate protests (mainly the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — In this work, we develop a comprehensive causal model of how and why. Reddit users engage with activist communities driving mass climate protests (mainly the ..."}
+{"idx": 2, "title": "USENIX Security '25 Technical Sessions", "date": "", "ddg_snippet": "... study using our user - study framework and survey. Our findings suggest that ... Our ablation study further offers insights into how various interaction ...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity25/technical-sessions", "content": "... study using our user - study framework and survey. Our findings suggest that ... Our ablation study further offers insights into how various interaction ..."}
+{"idx": 3, "title": "Findings of the Association for Computational Linguistics", "date": "", "ddg_snippet": "by H Bouamor · 2023 · Cited by 1 — Our work opens up the research avenues to bring user opinions as an important ingredient in aligning language models . pdf bib abs. CCSRD: Content-Centric ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2023.findings-emnlp/", "content": "by H Bouamor · 2023 · Cited by 1 — Our work opens up the research avenues to bring user opinions as an important ingredient in aligning language models . pdf bib abs. CCSRD: Content-Centric ..."}
+{"idx": 4, "title": "36th Annual - Undergraduate Research Center - UC Davis", "date": "", "ddg_snippet": "25 Apr 2025 — Our scholarship and research address some of society's most critical challenges, including climate change , feeding the world and sustaining ... 314 pages", "subpage_snippet": "", "source": "urc.ucdavis.edu", "link": "https://urc.ucdavis.edu/sites/g/files/dgvnsk3561/files/media/documents/25_URC_Abstract_Book_V2.pdf", "content": "25 Apr 2025 — Our scholarship and research address some of society's most critical challenges, including climate change , feeding the world and sustaining ... 314 pages"}
+{"idx": 5, "title": "NLPCSS 2022 The Fifth Workshop on Natural Language ...", "date": "", "ddg_snippet": "7 Nov 2022 — Ablation Study . Next, we analyze the components in our classification models through an ablation study for the model using all the features.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.nlpcss-1.pdf", "content": "7 Nov 2022 — Ablation Study . Next, we analyze the components in our classification models through an ablation study for the model using all the features."}
+{"idx": 6, "title": "Findings of the Association for Computational Linguistics", "date": "", "ddg_snippet": "by Y Al-Onaizan · 2024 · Cited by 22 — Exploring Multilingual Concepts of Human Values in Large Language Models : Is Value Alignment Consistent, Transferable and Controllable across Languages?", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2024.findings-emnlp/", "content": "by Y Al-Onaizan · 2024 · Cited by 22 — Exploring Multilingual Concepts of Human Values in Large Language Models : Is Value Alignment Consistent, Transferable and Controllable across Languages?"}
+{"idx": 7, "title": "CODI 2023 4th Workshop on Computational Approaches to ...", "date": "", "ddg_snippet": "13 Jul 2023 — The workshop consists of invited talks, contributed papers, extended abstracts, and ACL Findings pre- sentations.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.codi-1.pdf", "content": "13 Jul 2023 — The workshop consists of invited talks, contributed papers, extended abstracts, and ACL Findings pre- sentations."}
+{"idx": 8, "title": "Advanced Data Mining and Applications", "date": "", "ddg_snippet": "The 18th International Conference on Advanced Data Mining and Applications (ADMA. 2022) was held in Brisbane, Australia, during November 28–30, 2022.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-22137-8.pdf", "content": "The 18th International Conference on Advanced Data Mining and Applications (ADMA. 2022) was held in Brisbane, Australia, during November 28–30, 2022."}
+{"idx": 9, "title": "From Keyboard to Clinic Proceedings of the Fifth Workshop", "date": "", "ddg_snippet": "12 Dec 2005 — Language technology can support mental health clinicians, service organizations, and individuals with lived experience in many ways.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/W18-06.pdf", "content": "12 Dec 2005 — Language technology can support mental health clinicians, service organizations, and individuals with lived experience in many ways."}
diff --git a/data/sampled_jsons/abstract_Diversified_In-domain_Synthesis_with_Efficient_Fine-tuning_for_few-shot_classification.jsonl b/data/sampled_jsons/abstract_Diversified_In-domain_Synthesis_with_Efficient_Fine-tuning_for_few-shot_classification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cbb9110afcc037f145e014878d1eae32e59554f1
--- /dev/null
+++ b/data/sampled_jsons/abstract_Diversified_In-domain_Synthesis_with_Efficient_Fine-tuning_for_few-shot_classification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fine - tuning (deep learning) - Wikipedia", "date": "", "ddg_snippet": "Fine - tuning is common in natural language processing (NLP), especially in the domain of language modeling. Few - Shot Parameter- Efficient Fine - Tuning is Better and Cheaper than In-Context Learning (PDF). Advances in Neural Information Processing Systems.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)", "content": "Fine - tuning is common in natural language processing (NLP), especially in the domain of language modeling. Few - Shot Parameter- Efficient Fine - Tuning is Better and Cheaper than In-Context Learning (PDF). Advances in Neural Information Processing Systems."}
+{"idx": 1, "title": "Diversified in - domain synthesis with efficient fine - tuning for ...", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning (DISEF), a novel approach which addresses the generalization challenge in few - shot learning using synthetic data. DISEF consists of two main components.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning (DISEF), a novel approach which addresses the generalization challenge in few - shot learning using synthetic data. DISEF consists of two main components."}
+{"idx": 2, "title": "vturrisi/disef: Pytorch implementation of \" Diversified in - domain ...\"", "date": "", "ddg_snippet": "Diversified in - domain synthesis with efficient fine - tuning for few - shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vturrisi/disef", "content": "Diversified in - domain synthesis with efficient fine - tuning for few - shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci."}
+{"idx": 3, "title": "Zero-Shot vs One-Shot vs Few - Shot Learning - GeeksforGeeks", "date": "", "ddg_snippet": "In artificial intelligence (AI), zero-shot and few - shot learning are groundbreaking concepts that have significantly advanced the capabilities of machine learning models. These techniques enable models to recognize and classify new data with little or no training examples...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/zero-shot-vs-one-shot-vs-few-shot-learning/", "content": "In artificial intelligence (AI), zero-shot and few - shot learning are groundbreaking concepts that have significantly advanced the capabilities of machine learning models. These techniques enable models to recognize and classify new data with little or no training examples..."}
+{"idx": 4, "title": "Few Shot Domain Adaptation Using Transformer and GNN-Based...", "date": "", "ddg_snippet": "The vanilla Few - shot Learning (FSL) learns to build a classifier for a new concept from one or very few target examples, with the general assumption that source and target classes are sampled from the same domain .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380830222_Few_Shot_Domain_Adaptation_Using_Transformer_and_GNN-Based_Fine_Tuning", "content": "The vanilla Few - shot Learning (FSL) learns to build a classifier for a new concept from one or very few target examples, with the general assumption that source and target classes are sampled from the same domain ."}
+{"idx": 5, "title": "Unleashing the Power of Meta- tuning for Few - shot Generalization...", "date": "", "ddg_snippet": "Abstract . Recent successes suggest that parameter- efficient fine - tuning of foundation models is becoming the state-of-the-art method for transfer learning in vision, gradually replacing the rich literature of alternatives such as meta-learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QhHMx51ir6", "content": "Abstract . Recent successes suggest that parameter- efficient fine - tuning of foundation models is becoming the state-of-the-art method for transfer learning in vision, gradually replacing the rich literature of alternatives such as meta-learning."}
+{"idx": 6, "title": "DataDream: Few - Shot Guided Dataset Generation | SpringerLink", "date": "", "ddg_snippet": "We propose DataDream, a framework for synthesizing classification datasets that more faithfully represents the real data distribution when guided by few - shot examples of the target classes. DataDream fine - tunes LoRA weights for the image generation model on the few real...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-73209-6_15", "content": "We propose DataDream, a framework for synthesizing classification datasets that more faithfully represents the real data distribution when guided by few - shot examples of the target classes. DataDream fine - tunes LoRA weights for the image generation model on the few real..."}
+{"idx": 7, "title": "Articles by Victor G. Turrisi Da Costa | Synthical", "date": "", "ddg_snippet": "Diversified in - domain synthesis with efficient fine - tuning for few - shot classification .Dual-Head Contrastive Domain Adaptation for Video Action Recognition.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/b10b5cef-74be-4f7d-a4b2-2bf482ff6b41/articles", "content": "Diversified in - domain synthesis with efficient fine - tuning for few - shot classification .Dual-Head Contrastive Domain Adaptation for Video Action Recognition."}
+{"idx": 8, "title": "Nicola Dall'Asen - Google Akademik", "date": "", "ddg_snippet": "Diversified in - domain synthesis with efficient fine - tuning for few - shot classification .", "subpage_snippet": "", "source": "scholar.google.com.tr", "link": "https://scholar.google.com.tr/citations?user=e7lgiYYAAAAJ&hl=tr", "content": "Diversified in - domain synthesis with efficient fine - tuning for few - shot classification ."}
+{"idx": 9, "title": "publications by categories in reversed chronological order.", "date": "", "ddg_snippet": "Diversified in - domain synthesis with efficient fine - tuning for few - shot classification .We validate our method in ten different benchmarks, consistently outperforming baselines and establishing a new state-of-the-art for few - shot classification .", "subpage_snippet": "", "source": "fodark.xyz", "link": "https://fodark.xyz/publications/", "content": "Diversified in - domain synthesis with efficient fine - tuning for few - shot classification .We validate our method in ten different benchmarks, consistently outperforming baselines and establishing a new state-of-the-art for few - shot classification ."}
diff --git a/data/sampled_jsons/abstract_of_IPO_Your_Language_Model_is_Secretly_a_Preference_Classifier.jsonl b/data/sampled_jsons/abstract_of_IPO_Your_Language_Model_is_Secretly_a_Preference_Classifier.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..df97aadd99fd4f1dbc3524d14bbb34c4ee9cf485
--- /dev/null
+++ b/data/sampled_jsons/abstract_of_IPO_Your_Language_Model_is_Secretly_a_Preference_Classifier.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "IPO : Your Language Model is Secretly a Preference Classifier", "date": "", "ddg_snippet": "Abstract :Reinforcement learning from human feedback (RLHF) has emerged as the primary method for aligning large language models (LLMs) with human preferences .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.16182", "content": "Abstract :Reinforcement learning from human feedback (RLHF) has emerged as the primary method for aligning large language models (LLMs) with human preferences ."}
+{"idx": 1, "title": "(PDF) IPO : Your Language Model is Secretly a Preference Classifier", "date": "", "ddg_snippet": "Aligning Large Language Models (LLMs) with diverse human preferences is a pivotal technique for controlling model behaviors and enhancing generation quality. Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO)...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389315556_IPO_Your_Language_Model_is_Secretly_a_Preference_Classifier", "content": "Aligning Large Language Models (LLMs) with diverse human preferences is a pivotal technique for controlling model behaviors and enhancing generation quality. Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO)..."}
+{"idx": 2, "title": "IPO : Your Language Model is Secretly a Preference ...", "date": "", "ddg_snippet": "[1] Direct Preference Optimization: Your Language Model is Secretly a Reward Model .This paper examines how language characteristics affect initial public offerings ( IPO ) underpricing.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/ipo-your-language-model-is-secretly-a-preference-classifier/1111303342392344576-2000000", "content": "[1] Direct Preference Optimization: Your Language Model is Secretly a Reward Model .This paper examines how language characteristics affect initial public offerings ( IPO ) underpricing."}
+{"idx": 3, "title": "GitHub - shivank21/Implicit_ Preference _Optimization: https...", "date": "", "ddg_snippet": "IPO : Your Language Model is Secretly a Preference Classifier .Reinforcement learning from human feedback (RLHF) has emerged as the primary method for aligning large language models (LLMs) with human preferences .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shivank21/Implicit_Preference_Optimization", "content": "IPO : Your Language Model is Secretly a Preference Classifier .Reinforcement learning from human feedback (RLHF) has emerged as the primary method for aligning large language models (LLMs) with human preferences ."}
+{"idx": 4, "title": "Bengaluru-based AI Lab LossFunk Introduces IPO , a Novel Approach...", "date": "", "ddg_snippet": "...‘ IPO : Your Language Model is Secretly a Preference Classifier ,’ the researchers said that their new technique offers a more efficient and scalable method for aligning LLMs with human preferences by reducing dependence on human-labelled data and external reward models .", "subpage_snippet": "", "source": "analyticsindiamag.com", "link": "https://analyticsindiamag.com/ai-news-updates/bengaluru-based-ai-lab-lossfunk-introduces-ipo-a-novel-approach-to-aligning-llms-without-external-feedback/", "content": "...‘ IPO : Your Language Model is Secretly a Preference Classifier ,’ the researchers said that their new technique offers a more efficient and scalable method for aligning LLMs with human preferences by reducing dependence on human-labelled data and external reward models ."}
+{"idx": 5, "title": "Direct Preference Optimization: Your Language Model is Secretly ...", "date": "", "ddg_snippet": "Direct Preference Optimization: Your Language Model is Secretly a Reward Model .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2305.18290", "content": "Direct Preference Optimization: Your Language Model is Secretly a Reward Model ."}
+{"idx": 6, "title": "Direct Preference Optimization: Your language model is secretly ...", "date": "", "ddg_snippet": "Direct Preference Optimization: Your Language Model is Secretly a Reward Model (View Highlight.", "subpage_snippet": "", "source": "www.pelayoarbues.com", "link": "https://www.pelayoarbues.com/literature-notes/Articles/Direct-Preference-Optimization-Your-language-model-is-secretly-a-reward-model", "content": "Direct Preference Optimization: Your Language Model is Secretly a Reward Model (View Highlight."}
+{"idx": 7, "title": "AI-Powered Paper Summarization about the arXiv paper 2502.16182v1", "date": "", "ddg_snippet": "IPO : Your Language Model is Secretly a Preference Classifier . IPO aims to reduce reliance on external feedback or reward models by using generative LLMs as preference classifiers .", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/2502.16182v1/", "content": "IPO : Your Language Model is Secretly a Preference Classifier . IPO aims to reduce reliance on external feedback or reward models by using generative LLMs as preference classifiers ."}
+{"idx": 8, "title": "Ayush Singh - ACL Anthology", "date": "", "ddg_snippet": "Large language models (LLMs) on the other hand, have performed impressive feats in natural language processing (NLP), that too in a zero-shot manner, i.e. without any labeled data. To that end, we propose using LLMs to identify relevant section headers.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/ayush-singh/", "content": "Large language models (LLMs) on the other hand, have performed impressive feats in natural language processing (NLP), that too in a zero-shot manner, i.e. without any labeled data. To that end, we propose using LLMs to identify relevant section headers."}
+{"idx": 9, "title": "Articles by Ayush Singh | Synthical", "date": "", "ddg_snippet": "IPO : Your Language Model is Secretly a Preference Classifier .Agent-based Model for Microbial Populations Exposed to Radiation (AMMPER) simulates yeast growth for deep-space experiments.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/search/by_author/Ayush+Singh", "content": "IPO : Your Language Model is Secretly a Preference Classifier .Agent-based Model for Microbial Populations Exposed to Radiation (AMMPER) simulates yeast growth for deep-space experiments."}
diff --git a/data/sampled_jsons/adaptive_class_distribution_estimation_open-world_learning_unknown_classes.jsonl b/data/sampled_jsons/adaptive_class_distribution_estimation_open-world_learning_unknown_classes.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..68b44dcde849bc2a1a46967814ce71379e1ecc28
--- /dev/null
+++ b/data/sampled_jsons/adaptive_class_distribution_estimation_open-world_learning_unknown_classes.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Redefining Object Detection for Open - World Settings...", "date": "", "ddg_snippet": "and unknown classes , we propose the Open World Novelty. Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386206593_Redefining_Object_Detection_for_Open-World_Settings_A_Framework_for_Simultaneous_Identification_of_Known_and_Unknown_Classes", "content": "and unknown classes , we propose the Open World Novelty. Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones."}
+{"idx": 1, "title": "AGO: Adaptive Grounding for Open World 3D Occupancy... | alphaXiv", "date": "", "ddg_snippet": "Abstract: Open - world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2504.10117v1", "content": "Abstract: Open - world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects."}
+{"idx": 2, "title": "Open - world Machine Learning : A Review and New", "date": "", "ddg_snippet": "5 Class -incremental Learning . After detecting unknown samples via OOD detection or open -set recognition techniques, those samples should be labeled by humans or novel class discovery strategies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.01759", "content": "5 Class -incremental Learning . After detecting unknown samples via OOD detection or open -set recognition techniques, those samples should be labeled by humans or novel class discovery strategies."}
+{"idx": 3, "title": "PROB: Probabilistic Objectness for Open World Object Detection", "date": "", "ddg_snippet": "DETR for Open World Learning . DETR-type mod-els [1, 35] have transformed the object detection eld due to their simplied design, which has less inductive bias. Open - world object detection via discriminative class prototype learning .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Zohar_PROB_Probabilistic_Objectness_for_Open_World_Object_Detection_CVPR_2023_paper.pdf", "content": "DETR for Open World Learning . DETR-type mod-els [1, 35] have transformed the object detection eld due to their simplied design, which has less inductive bias. Open - world object detection via discriminative class prototype learning ."}
+{"idx": 4, "title": "Open - World Machine Learning or Set Recognition or Classification", "date": "", "ddg_snippet": "Classic machine learning makes the closed world assumption, i.e., the classes that the agent sees in training are what it will see in testing (no new objects or classes can appear in testing) (Fei and Liu 2016). A more realistic scenario is to expect unseen classes during testing ( open world ).", "subpage_snippet": "", "source": "www.cs.uic.edu", "link": "https://www.cs.uic.edu/~liub/open-classification.html", "content": "Classic machine learning makes the closed world assumption, i.e., the classes that the agent sees in training are what it will see in testing (no new objects or classes can appear in testing) (Fei and Liu 2016). A more realistic scenario is to expect unseen classes during testing ( open world )."}
+{"idx": 5, "title": "(PDF) Model Evaluation for Domain Identification of Unknown Classes ...", "date": "", "ddg_snippet": "Open - world learning is a growing area that has an objective to elevate the model to have the ability to reject unknown classes and to learn continually from the rejected samples.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/128876385/Model_Evaluation_for_Domain_Identification_of_Unknown_Classes_in_Open_World_Recognition_A_Proposal", "content": "Open - world learning is a growing area that has an objective to elevate the model to have the ability to reject unknown classes and to learn continually from the rejected samples."}
+{"idx": 6, "title": "Towards Distribution -Agnostic Generalized Category", "date": "", "ddg_snippet": "Open - World Learning tackling the scenario where some unknown (open-set) visual concepts or categories are involved during training or real-world applications. Class -imbalanced semi-supervised learning with adaptive thresholding.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=cczH4Xl7Zo", "content": "Open - World Learning tackling the scenario where some unknown (open-set) visual concepts or categories are involved during training or real-world applications. Class -imbalanced semi-supervised learning with adaptive thresholding."}
+{"idx": 7, "title": "Prompt-Driven Feature Diffusion for Open - World Semi-Supervised...", "date": "", "ddg_snippet": "This paper proposes a novel approach called \"Prompt-Driven Feature Diffusion\" for open - world semi-supervised learning . The method leverages language prompts to guide the feature learning process, enabling the model to adapt to new classes without requiring any additional training.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/prompt-driven-feature-diffusion-open-world-semi", "content": "This paper proposes a novel approach called \"Prompt-Driven Feature Diffusion\" for open - world semi-supervised learning . The method leverages language prompts to guide the feature learning process, enabling the model to adapt to new classes without requiring any additional training."}
+{"idx": 8, "title": "Bayesian Embeddings for Few-Shot Open World Recognition", "date": "", "ddg_snippet": "Ntest contains both unknown -known and unknown - unknown class data. This setting can also be interpreted as an extension of the the current few-shot paradigm of k-shot n-way few-shot learning to the open - world recognition setting.", "subpage_snippet": "", "source": "www.arxiv-vanity.com", "link": "https://www.arxiv-vanity.com/papers/2107.13682/", "content": "Ntest contains both unknown -known and unknown - unknown class data. This setting can also be interpreted as an extension of the the current few-shot paradigm of k-shot n-way few-shot learning to the open - world recognition setting."}
+{"idx": 9, "title": "Fully- and Partially- Distributed Adaptive Consensus of... - SCIEPublish", "date": "", "ddg_snippet": "Based on the techniques of classical adaptive control ([15,16]), the unknown acceleration of the leaderϕ0T (t)θ^0i compensate the unknown dynamics of each agent and leader acceleration, respectively. Before giving the adaptive laws for estimated parameter arrays.", "subpage_snippet": "", "source": "www.sciepublish.com", "link": "https://www.sciepublish.com/article/pii/330", "content": "Based on the techniques of classical adaptive control ([15,16]), the unknown acceleration of the leaderϕ0T (t)θ^0i compensate the unknown dynamics of each agent and leader acceleration, respectively. Before giving the adaptive laws for estimated parameter arrays."}
diff --git a/data/sampled_jsons/adaptive_class_distribution_estimation_techniques_semi-supervised_learning.jsonl b/data/sampled_jsons/adaptive_class_distribution_estimation_techniques_semi-supervised_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c720a7fd504fe9808f2a052d46295dfe11c7ad54
--- /dev/null
+++ b/data/sampled_jsons/adaptive_class_distribution_estimation_techniques_semi-supervised_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Semiparametric semi-supervised learning for general targets", "date": "", "ddg_snippet": "In this paper, we propose a general semiparametric framework for estimation and inference in semi - supervised settings under distribution shift and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06452v1", "content": "In this paper, we propose a general semiparametric framework for estimation and inference in semi - supervised settings under distribution shift and ..."}
+{"idx": 1, "title": "CaliMatch: Adaptive Calibration for Improving Safe", "date": "", "ddg_snippet": "To address this issue, several deep learning -based SSL methods, known as safe semi - supervised learning , have been proposed [ 1 , 4 , 9 , 14 , 24 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.00922v1", "content": "To address this issue, several deep learning -based SSL methods, known as safe semi - supervised learning , have been proposed [ 1 , 4 , 9 , 14 , 24 ..."}
+{"idx": 2, "title": "Density Ratio Estimation-based Bayesian Optimization with", "date": "", "ddg_snippet": "... to unlabeled points, e.g., predefined fixed-size pools, we propose density ratio estimation -based Bayesian optimization with semi - supervised learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.15612v4", "content": "... to unlabeled points, e.g., predefined fixed-size pools, we propose density ratio estimation -based Bayesian optimization with semi - supervised learning ..."}
+{"idx": 3, "title": "SST: Self-training with Self-adaptive Thresholding for", "date": "", "ddg_snippet": "Semi - supervised learning \\sep Self-training \\sep Pseudo-labeling \\sep Self- adaptive thresholding ... Adaptive Thresholding (SAT) mechanism, which ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00467v1", "content": "Semi - supervised learning \\sep Self-training \\sep Pseudo-labeling \\sep Self- adaptive thresholding ... Adaptive Thresholding (SAT) mechanism, which ..."}
+{"idx": 4, "title": "Semi-Supervised Federated Learning via Dual Contrastive", "date": "", "ddg_snippet": "In [ 26 ] , Monte Carlo-based adaptive threshold selection was utilized to enable semi - supervised IFD with limited labels, while a contrastive image ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14181v1", "content": "In [ 26 ] , Monte Carlo-based adaptive threshold selection was utilized to enable semi - supervised IFD with limited labels, while a contrastive image ..."}
+{"idx": 5, "title": "Learning under distribution shift — The Dan MacKinlay stable", "date": "", "ddg_snippet": "... learning and domain adaptation and such, these are all concepts that arise in the NN framing, and sometimes the methods overlap with statistical ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/domain_adaptation.html", "content": "... learning and domain adaptation and such, these are all concepts that arise in the NN framing, and sometimes the methods overlap with statistical ..."}
+{"idx": 6, "title": "Semi-Supervised Learning: Techniques & Examples [2024]", "date": "", "ddg_snippet": "Semi - supervised learning is a broad category of machine learning techniques that utilizes both labeled and unlabeled data; in this way, as the name ...", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/semi-supervised-learning-guide", "content": "Semi - supervised learning is a broad category of machine learning techniques that utilizes both labeled and unlabeled data; in this way, as the name ..."}
+{"idx": 7, "title": "Semi Supervised Learning: 2025 Best Practices for Model", "date": "", "ddg_snippet": "Good semi - supervised machine learning techniques try to learn this manifold and separate classes along regions of low data density.", "subpage_snippet": "", "source": "labelyourdata.com", "link": "https://labelyourdata.com/articles/semi-supervised-learning", "content": "Good semi - supervised machine learning techniques try to learn this manifold and separate classes along regions of low data density."}
+{"idx": 8, "title": "Learning with not Enough Data Part 1: Semi-Supervised Learning", "date": "", "ddg_snippet": "Several hypotheses have been discussed in literature to support certain design decisions in semi - supervised learning methods.", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2021-12-05-semi-supervised/", "content": "Several hypotheses have been discussed in literature to support certain design decisions in semi - supervised learning methods."}
+{"idx": 9, "title": "US Patent Application for Semi-Supervised Learning for Training", "date": "", "ddg_snippet": "US Nonprovisional patent application titled “ SEMI - SUPERVISED LEARNING FOR TRAINING AN ENSEMBLE OF DEEP CONVOLUTIONAL NEURAL NETWORKS,” by ...", "subpage_snippet": "", "source": "patents.justia.com", "link": "https://patents.justia.com/patent/20190114544", "content": "US Nonprovisional patent application titled “ SEMI - SUPERVISED LEARNING FOR TRAINING AN ENSEMBLE OF DEEP CONVOLUTIONAL NEURAL NETWORKS,” by ..."}
diff --git a/data/sampled_jsons/adaptive_replanning_strategy_reinforcement_learning_robotics_ICRA_2024.jsonl b/data/sampled_jsons/adaptive_replanning_strategy_reinforcement_learning_robotics_ICRA_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ec9c0d14248f5daa89ea72b190120e79b3e05d5
--- /dev/null
+++ b/data/sampled_jsons/adaptive_replanning_strategy_reinforcement_learning_robotics_ICRA_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Context-Aware Replanning with Pre-explored Semantic Map for", "date": "", "ddg_snippet": "CLIP [ 8 ] achieves this alignment through contrastive learning , forming the foundation for various training-free robotic applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.04837v2", "content": "CLIP [ 8 ] achieves this alignment through contrastive learning , forming the foundation for various training-free robotic applications."}
+{"idx": 1, "title": "Robotics and Perception Group", "date": "", "ddg_snippet": "Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making ...", "subpage_snippet": "", "source": "rpg.ifi.uzh.ch", "link": "https://rpg.ifi.uzh.ch/", "content": "Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making ..."}
+{"idx": 2, "title": "Publications · Robot Learning Lab", "date": "", "ddg_snippet": "... Visual Self-Supervised Traversability Learning for Off-road Navigation IEEE International Conference on Robotics and Automation ( ICRA ), 2024 S.", "subpage_snippet": "", "source": "robotlearning.cs.washington.edu", "link": "https://robotlearning.cs.washington.edu/publications/", "content": "... Visual Self-Supervised Traversability Learning for Off-road Navigation IEEE International Conference on Robotics and Automation ( ICRA ), 2024 S."}
+{"idx": 3, "title": "(PDF) A System for Learning Continuous Human-Robot Interactions", "date": "", "ddg_snippet": "We present a data-driven imitation learning system for learning human-robot interactions from human-human demonstrations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/317389424_A_System_for_Learning_Continuous_Human-Robot_Interactions_from_Human-Human_Demonstrations", "content": "We present a data-driven imitation learning system for learning human-robot interactions from human-human demonstrations."}
+{"idx": 4, "title": "Publications · Personal Robotics Lab", "date": "", "ddg_snippet": "... Learning : An online framework for ... Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding .", "subpage_snippet": "", "source": "personalrobotics.cs.washington.edu", "link": "https://personalrobotics.cs.washington.edu/publications/", "content": "... Learning : An online framework for ... Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding ."}
+{"idx": 5, "title": "News – HKUST Aerial Robotics Group", "date": "", "ddg_snippet": "... Yin's work on \"Modeling Point Uncertainty in Radar SLAM\" has been chosen for the 2024 IEEE International Conference on Robotics and Automation ( ICRA ...", "subpage_snippet": "", "source": "uav.hkust.edu.hk", "link": "https://uav.hkust.edu.hk/news/", "content": "... Yin's work on \"Modeling Point Uncertainty in Radar SLAM\" has been chosen for the 2024 IEEE International Conference on Robotics and Automation ( ICRA ..."}
+{"idx": 6, "title": "From Seeing to Experiencing: Scaling Navigation Foundation", "date": "", "ddg_snippet": "We analyze the effectiveness of Reinforcement Learning (RL) versus Supervised Fine-Tuning (SFT) in post-training for robot learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22028v1", "content": "We analyze the effectiveness of Reinforcement Learning (RL) versus Supervised Fine-Tuning (SFT) in post-training for robot learning ."}
+{"idx": 7, "title": "Publications | Dynamic Robot Systems Group", "date": "", "ddg_snippet": "Real-Time Trajectory Adaptation for Quadrupedal Locomotion using Deep Reinforcement Learning ” , Siddhant Gangapurwala, Mathieu Geisert, Romeo ...", "subpage_snippet": "", "source": "dynamic.robots.ox.ac.uk", "link": "https://dynamic.robots.ox.ac.uk/publications/", "content": "Real-Time Trajectory Adaptation for Quadrupedal Locomotion using Deep Reinforcement Learning ” , Siddhant Gangapurwala, Mathieu Geisert, Romeo ..."}
+{"idx": 8, "title": "reading_group [LL4MA lab]", "date": "", "ddg_snippet": "We would like to pick our papers from recent robotics and machine learning conferences such as RSS, ICRA , IROS, ICML, NeurIPS, and Humanoids.", "subpage_snippet": "", "source": "robot-learning.cs.utah.edu", "link": "https://robot-learning.cs.utah.edu/reading_group", "content": "We would like to pick our papers from recent robotics and machine learning conferences such as RSS, ICRA , IROS, ICML, NeurIPS, and Humanoids."}
+{"idx": 9, "title": "ThinkAct: Vision-Language-Action Reasoning via Reinforced", "date": "", "ddg_snippet": "We demonstrate that our learned reasoning VLA enables capabilities of few-shot adaptation, long-horizon planning, and self-correction across diverse ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16815v1", "content": "We demonstrate that our learned reasoning VLA enables capabilities of few-shot adaptation, long-horizon planning, and self-correction across diverse ..."}
diff --git a/data/sampled_jsons/advanced_techniques_for_accelerating_inference_in_autoregressive_transformer_models_beyond_KV_cachin_year_2023.jsonl b/data/sampled_jsons/advanced_techniques_for_accelerating_inference_in_autoregressive_transformer_models_beyond_KV_cachin_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..73e86942a9cdec30456c4e652fd754f0246c022c
--- /dev/null
+++ b/data/sampled_jsons/advanced_techniques_for_accelerating_inference_in_autoregressive_transformer_models_beyond_KV_cachin_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer Inference: Techniques for Faster AI Models", "date": "", "ddg_snippet": "Sep 19, 2024 · Transformer inference powers tasks in NLP and vision, but is computationally intense, requiring optimizations. Large models like GPT-3 need extensive memory and FLOPs, with techniques like KV caching , quantization, and parallelism reducing costs.", "subpage_snippet": "", "source": "blog.premai.io", "link": "https://blog.premai.io/transformer-inference-techniques-for-faster-ai-models/", "content": "Sep 19, 2024 · Transformer inference powers tasks in NLP and vision, but is computationally intense, requiring optimizations. Large models like GPT-3 need extensive memory and FLOPs, with techniques like KV caching , quantization, and parallelism reducing costs."}
+{"idx": 1, "title": "Beyond KV caching: Shared attention for efficient LLMs", "date": "", "ddg_snippet": "The self-attention mechanism in transformer models , softmax (Q K T d) V, requires computing attention weights at each layer. While the KV -cache mitigates recomputing Keys (K) and Values (V) for past tokens during inference , the calculation of the attention scores remains a layer-wise computational step.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231225012597", "content": "The self-attention mechanism in transformer models , softmax (Q K T d) V, requires computing attention weights at each layer. While the KV -cache mitigates recomputing Keys (K) and Values (V) for past tokens during inference , the calculation of the attention scores remains a layer-wise computational step."}
+{"idx": 2, "title": "Reducing Transformer Key-Value Cache Size with Cross-Layer ...", "date": "", "ddg_snippet": "Abstract Key-value ( KV ) caching plays an essential role in accelerating decoding for transformer -based autoregressive large language models (LLMs). However, the amount of memory required to store the KV cache can become prohibitive at long sequence lengths and large batch sizes. Since the invention of the transformer , two of the most effective interventions discovered for reducing the size of ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/9e23d020c18e4c40d81c6a0fc7a46f68-Paper-Conference.pdf", "content": "Abstract Key-value ( KV ) caching plays an essential role in accelerating decoding for transformer -based autoregressive large language models (LLMs). However, the amount of memory required to store the KV cache can become prohibitive at long sequence lengths and large batch sizes. Since the invention of the transformer , two of the most effective interventions discovered for reducing the size of ..."}
+{"idx": 3, "title": "Caching and Reuse Optimizations - Aussie AI", "date": "", "ddg_snippet": "Aug 29, 2025 · Transformer Caching Optimizations Transformer caching optimizations are the use of datastores of cached information for LLM inference optimization. There are several ways that Transformer architectures can use caching to speed up LLM inference . There are two main ways: Outside the Transformer — Inference cache, Semantic cache, RAG caching , etc. Inside the Transformer — KV caching (basic ...", "subpage_snippet": "", "source": "www.aussieai.com", "link": "https://www.aussieai.com/research/caching", "content": "Aug 29, 2025 · Transformer Caching Optimizations Transformer caching optimizations are the use of datastores of cached information for LLM inference optimization. There are several ways that Transformer architectures can use caching to speed up LLM inference . There are two main ways: Outside the Transformer — Inference cache, Semantic cache, RAG caching , etc. Inside the Transformer — KV caching (basic ..."}
+{"idx": 4, "title": "Advanced Inference Optimization: KV-Caching, Speculative ...", "date": "", "ddg_snippet": "May 31, 2025 · The autoregressive nature of transformer -based language models introduces unique computational patterns that traditional optimization techniques struggle to address effectively.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@rizqimulkisrc/advanced-inference-optimization-kv-caching-speculative-decoding-and-parallelism-c16d1163d3a1", "content": "May 31, 2025 · The autoregressive nature of transformer -based language models introduces unique computational patterns that traditional optimization techniques struggle to address effectively."}
+{"idx": 5, "title": "ALISA: Accelerating Large Language Model Inference via ...", "date": "", "ddg_snippet": "Jun 29, 2024 · The Transformer architecture has significantly advanced natural language processing (NLP) and has been foundational in developing large language models (LLMs) such as LLaMA and OPT, which have come to dominate a broad range of NLP tasks. Despite their superior accuracy, LLMs present unique challenges in practical inference , concerning the compute and memory-intensive nature. Thanks to the ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10609626", "content": "Jun 29, 2024 · The Transformer architecture has significantly advanced natural language processing (NLP) and has been foundational in developing large language models (LLMs) such as LLaMA and OPT, which have come to dominate a broad range of NLP tasks. Despite their superior accuracy, LLMs present unique challenges in practical inference , concerning the compute and memory-intensive nature. Thanks to the ..."}
+{"idx": 6, "title": "Accelerating Inference in Large Language Models with a", "date": "", "ddg_snippet": "Apr 10, 2024 · Similarly, for the KV caching technique that often used in autoregressive prediction models , if the depth of computation for a subsequent token exceeds that of a cached token, the current cache contents need to be recalculated, leading to computational inefficiency and waste.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.06954v1", "content": "Apr 10, 2024 · Similarly, for the KV caching technique that often used in autoregressive prediction models , if the depth of computation for a subsequent token exceeds that of a cached token, the current cache contents need to be recalculated, leading to computational inefficiency and waste."}
+{"idx": 7, "title": "KV Cache: Optimizing Transformer Inference", "date": "", "ddg_snippet": "As models grow larger, advanced KV Cache techniques , such as FlashAttention and paged caching, will play a pivotal role in balancing efficiency ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@aisagescribe/kv-cache-optimizing-transformer-inference-b49f5ea4f824", "content": "As models grow larger, advanced KV Cache techniques , such as FlashAttention and paged caching, will play a pivotal role in balancing efficiency ..."}
+{"idx": 8, "title": "KV Caching in LLM Inference A Comprehensive Review", "date": "", "ddg_snippet": "5 May 2025 — Other Techniques: There are other inference optimizations like quantization (reducing precision to speed up math and memory), pruning or ...", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/kv-caching-in-llm-inference-a-comprehensive", "content": "5 May 2025 — Other Techniques: There are other inference optimizations like quantization (reducing precision to speed up math and memory), pruning or ..."}
+{"idx": 9, "title": "Beyond KV Caching: Shared Attention for Efficient LLMs", "date": "", "ddg_snippet": "13 Jul 2024 — In this paper, we introduce a novel method termed Shared Attention (SA), which significantly reduces the KV cache requirements and computational ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.12866v1", "content": "13 Jul 2024 — In this paper, we introduce a novel method termed Shared Attention (SA), which significantly reduces the KV cache requirements and computational ..."}
diff --git a/data/sampled_jsons/advanced_techniques_to_accelerate_inference_in_autoregressive_Transformer_models_year_2023.jsonl b/data/sampled_jsons/advanced_techniques_to_accelerate_inference_in_autoregressive_Transformer_models_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3eec5cbd67e3ceb31b1640a971b099da91f42a1
--- /dev/null
+++ b/data/sampled_jsons/advanced_techniques_to_accelerate_inference_in_autoregressive_Transformer_models_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Survey of Techniques for Optimizing Transformer Inference", "date": "", "ddg_snippet": "This paper presents a comprehensive survey of techniques for optimizing the inference phase of transformer networks. We survey techniques such as knowledge distillation, pruning, quantization, neural architecture search and lightweight network design at the algorithmic level.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.07982", "content": "This paper presents a comprehensive survey of techniques for optimizing the inference phase of transformer networks. We survey techniques such as knowledge distillation, pruning, quantization, neural architecture search and lightweight network design at the algorithmic level."}
+{"idx": 1, "title": "Transformer Autoregressive Inference Optimization", "date": "", "ddg_snippet": "In the transformer decoder autoregressive inference , using the \"vanilla\" recipe, at the time step n, the transformer decoder would have to consume n tokens and the inference for this time step is an O (n 2) operation due to the self-attention mechanism.", "subpage_snippet": "", "source": "leimao.github.io", "link": "https://leimao.github.io/article/Transformer-Autoregressive-Inference-Optimization/", "content": "In the transformer decoder autoregressive inference , using the \"vanilla\" recipe, at the time step n, the transformer decoder would have to consume n tokens and the inference for this time step is an O (n 2) operation due to the self-attention mechanism."}
+{"idx": 2, "title": "PIM GPT a hybrid process in memory accelerator for autoregressive ...", "date": "", "ddg_snippet": "PIM-GPT is a memory-centric acceleration system aimed to support Transformer -based autoregressive token generation models including GPT. The PIM-GPT system is shown in Fig. 3, which composes of ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s44335-024-00004-2", "content": "PIM-GPT is a memory-centric acceleration system aimed to support Transformer -based autoregressive token generation models including GPT. The PIM-GPT system is shown in Fig. 3, which composes of ..."}
+{"idx": 3, "title": "Accelerated Inference for Large Transformer Models Using ... - NVIDIA", "date": "", "ddg_snippet": "Optimizations in FasterTransformer FT enables you to get a faster inference pipeline, with lower latency and higher throughput for the transformer -based NNs in comparison to the common frameworks for deep learning training. Some of the optimization techniques that allow FT to have the fastest inference for the GPT-3 and other large transformer models include: Layer fusion - The set of ...", "subpage_snippet": "", "source": "resources.nvidia.com", "link": "https://resources.nvidia.com/en-us-ai-inference-large-language-models/accelerated-inferenc", "content": "Optimizations in FasterTransformer FT enables you to get a faster inference pipeline, with lower latency and higher throughput for the transformer -based NNs in comparison to the common frameworks for deep learning training. Some of the optimization techniques that allow FT to have the fastest inference for the GPT-3 and other large transformer models include: Layer fusion - The set of ..."}
+{"idx": 4, "title": "Transformer Inference: Techniques for Faster AI Models", "date": "", "ddg_snippet": "Transformer inference powers tasks in NLP and vision, but is computationally intense, requiring optimizations. Large models like GPT-3 need extensive memory and FLOPs, with techniques like KV caching, quantization, and parallelism reducing costs.", "subpage_snippet": "", "source": "blog.premai.io", "link": "https://blog.premai.io/transformer-inference-techniques-for-faster-ai-models/", "content": "Transformer inference powers tasks in NLP and vision, but is computationally intense, requiring optimizations. Large models like GPT-3 need extensive memory and FLOPs, with techniques like KV caching, quantization, and parallelism reducing costs."}
+{"idx": 5, "title": "A survey of FPGA and ASIC designs for transformer inference ...", "date": "", "ddg_snippet": "This study investigates and analyzes the model compression methods, various optimization techniques , and architectures of accelerators related to FPGA- and ASIC-based transformer designs. We expect this study to serve as a valuable guide for hardware research in the transformer field.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S138376212400184X", "content": "This study investigates and analyzes the model compression methods, various optimization techniques , and architectures of accelerators related to FPGA- and ASIC-based transformer designs. We expect this study to serve as a valuable guide for hardware research in the transformer field."}
+{"idx": 6, "title": "\"Towards faster inference of transformers: Strategies for accelerating ...", "date": "", "ddg_snippet": "This thesis delves into the acceleration and optimization of Transformer inference , a subject of increasing importance with the emergence of Large Language Models (LLMs). The study primarily addresses the challenges posed by two inherent properties of Transformers during inference : the quadratic complexity of the attention mechanism and the sequential nature of autoregressive inference . The ...", "subpage_snippet": "", "source": "ink.library.smu.edu.sg", "link": "https://ink.library.smu.edu.sg/etd_coll/613/", "content": "This thesis delves into the acceleration and optimization of Transformer inference , a subject of increasing importance with the emergence of Large Language Models (LLMs). The study primarily addresses the challenges posed by two inherent properties of Transformers during inference : the quadratic complexity of the attention mechanism and the sequential nature of autoregressive inference . The ..."}
+{"idx": 7, "title": "A Guide to Optimizing Transformer-based Models for Faster Inference", "date": "", "ddg_snippet": "Throughout this blog post, we saw various techniques for reducing the inference time and size of the model without losing much accuracy. We managed to decrease the model size by more than half its size and accelerate inference time by x7.73!", "subpage_snippet": "", "source": "www.edge-ai-vision.com", "link": "https://www.edge-ai-vision.com/2023/10/a-guide-to-optimizing-transformer-based-models-for-faster-inference/", "content": "Throughout this blog post, we saw various techniques for reducing the inference time and size of the model without losing much accuracy. We managed to decrease the model size by more than half its size and accelerate inference time by x7.73!"}
+{"idx": 8, "title": "Fast Inference from Transformers via Speculative Decoding", "date": "", "ddg_snippet": "Inference from large autoregressive models like Transformers is slow - decoding K tokens takes K serial runs of the model . In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by computing several tokens in parallel. At the heart of our approach lie the observations that (1) hard language-modeling tasks ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/leviathan23a.html", "content": "Inference from large autoregressive models like Transformers is slow - decoding K tokens takes K serial runs of the model . In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by computing several tokens in parallel. At the heart of our approach lie the observations that (1) hard language-modeling tasks ..."}
+{"idx": 9, "title": "Accelerating Transformer Inference for Translation via Parallel Decoding", "date": "", "ddg_snippet": "Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network architectures and learning-based methods to solve this issue, which are expensive and require changes to the MT model , trading inference speed at the cost of the translation quality. In this paper, we propose to address the problem from the point of view of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.10427", "content": "Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network architectures and learning-based methods to solve this issue, which are expensive and require changes to the MT model , trading inference speed at the cost of the translation quality. In this paper, we propose to address the problem from the point of view of ..."}
diff --git a/data/sampled_jsons/advantage_of_Witness_complex_over_Vietoris-Rips_complex.jsonl b/data/sampled_jsons/advantage_of_Witness_complex_over_Vietoris-Rips_complex.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c4ce191059667a19183cd75090f406c11cb004d
--- /dev/null
+++ b/data/sampled_jsons/advantage_of_Witness_complex_over_Vietoris-Rips_complex.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Vietoris – Rips complex - Wikipedia", "date": "", "ddg_snippet": "A Vietoris – Rips complex of a set of 23 points in the Euclidean plane. This complex has sets of up to four points: the points themselves, pairs of points, triples of points, and quadruples of points. In topology, the Vietoris – Rips complex , also called...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Vietoris–Rips_complex", "content": "A Vietoris – Rips complex of a set of 23 points in the Euclidean plane. This complex has sets of up to four points: the points themselves, pairs of points, triples of points, and quadruples of points. In topology, the Vietoris – Rips complex , also called..."}
+{"idx": 1, "title": "at.algebraic topology - How to compare different type of... - MathOverflow", "date": "", "ddg_snippet": "Witness complexes are an approximation to Cech, Alpha, or Vietoris - Rips complexes which only use a subset of the data points as vertices --- the remaining data points are used to help decide when higher-dimensional simplices are added.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/331502/how-to-compare-different-type-of-simplicial-complex", "content": "Witness complexes are an approximation to Cech, Alpha, or Vietoris - Rips complexes which only use a subset of the data points as vertices --- the remaining data points are used to help decide when higher-dimensional simplices are added."}
+{"idx": 2, "title": "Graph Induced Complex on Point Data", "date": "", "ddg_snippet": "Witness complex Rips complex Graph induced complex .It refers to the singular homology when the argument is a manifold or a compact set, and to the simplicial homology when it is a simplicial complex . Also, all homology groups are assumed to be dened over the nite eld Z2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1304.0662", "content": "Witness complex Rips complex Graph induced complex .It refers to the singular homology when the argument is a manifold or a compact set, and to the simplicial homology when it is a simplicial complex . Also, all homology groups are assumed to be dened over the nite eld Z2."}
+{"idx": 3, "title": "(PDF) Topological estimation using witness complexes", "date": "", "ddg_snippet": "Witness complexes have several advantages .The Witness Complex was chosen for its efficiency and scalability, as it constructs a simplicial complex using landmark points, reducing computational load compared to methods like the Vietoris - Rips and Čech complexes .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/248117036_Topological_estimation_using_witness_complexes", "content": "Witness complexes have several advantages .The Witness Complex was chosen for its efficiency and scalability, as it constructs a simplicial complex using landmark points, reducing computational load compared to methods like the Vietoris - Rips and Čech complexes ."}
+{"idx": 4, "title": "Persistence stability for geometric complexes | Geometriae Dedicata", "date": "", "ddg_snippet": "Attali, D., Lieutier, A., Salinas, D.: Vietoris – Rips complexes also provide topologically correct reconstructions of sampled shapes. In: Proceedings of the 27th Annual ACM Symposium on Computational geometry, SoCG ’11, pp. 491–500.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10711-013-9937-z", "content": "Attali, D., Lieutier, A., Salinas, D.: Vietoris – Rips complexes also provide topologically correct reconstructions of sampled shapes. In: Proceedings of the 27th Annual ACM Symposium on Computational geometry, SoCG ’11, pp. 491–500."}
+{"idx": 5, "title": "Witness Autoencoder", "date": "", "ddg_snippet": "Vietoris - Rips complexes construct a simplicial complex from the entire dataset. For large datasets this gets computationally infeasible, yet the topology of a space can normally be captured by a smaller subset already. Witness complexes that we use in our work, build around that idea.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1gQfXt_U5a-", "content": "Vietoris - Rips complexes construct a simplicial complex from the entire dataset. For large datasets this gets computationally infeasible, yet the topology of a space can normally be captured by a smaller subset already. Witness complexes that we use in our work, build around that idea."}
+{"idx": 6, "title": "Linear-size approximations to the vietoris - rips filtration", "date": "", "ddg_snippet": "The sparse Vietoris – Rips complexes can be arranged into a zigzag ltra-tion Q as follows.The advantage of the non-zigzagging ltration is that it allows one to use the standard persistence algorithm, but it has larger size in the intermediate complexes .", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-01111878/document", "content": "The sparse Vietoris – Rips complexes can be arranged into a zigzag ltra-tion Q as follows.The advantage of the non-zigzagging ltration is that it allows one to use the standard persistence algorithm, but it has larger size in the intermediate complexes ."}
+{"idx": 7, "title": "The Čech Complex and the Vietoris - Rips Complex", "date": "", "ddg_snippet": "The Vietoris - Rips complex is essentially the same as the Čech complex , except instead of adding a $ d$-simplex when there is a common point of intersection of all the $ (\\varepsilon/2)$-balls, we just do so when all the balls have pairwise intersections.", "subpage_snippet": "", "source": "www.jeremykun.com", "link": "https://www.jeremykun.com/2015/08/06/cech-vietoris-rips-complex/", "content": "The Vietoris - Rips complex is essentially the same as the Čech complex , except instead of adding a $ d$-simplex when there is a common point of intersection of all the $ (\\varepsilon/2)$-balls, we just do so when all the balls have pairwise intersections."}
+{"idx": 8, "title": "Chapter 5", "date": "", "ddg_snippet": "the Vietoris - Rips complex .This complex again has theoretical guarantees of approximating the Vietoris - Rips complex on P ∪ Q. Exercise 5.19. Let P, Q be point sets and G(P) a graph with P as its vertex set.", "subpage_snippet": "", "source": "ti.inf.ethz.ch", "link": "https://ti.inf.ethz.ch/ew/courses/TDA24/Chapter5.pdf", "content": "the Vietoris - Rips complex .This complex again has theoretical guarantees of approximating the Vietoris - Rips complex on P ∪ Q. Exercise 5.19. Let P, Q be point sets and G(P) a graph with P as its vertex set."}
+{"idx": 9, "title": "Persistent and Tangential Homology for", "date": "", "ddg_snippet": "An introduction to topological data analysisSimplices and simplicial complexes Filtrations. The Vietoris - Rips complex", "subpage_snippet": "", "source": "ediss.sub.uni-hamburg.de", "link": "https://ediss.sub.uni-hamburg.de/bitstream/ediss/7104/1/Dissertation.pdf", "content": "An introduction to topological data analysisSimplices and simplicial complexes Filtrations. The Vietoris - Rips complex"}
diff --git a/data/sampled_jsons/adversarial_target_behavior_multi-target_tracking_agent_nearby.jsonl b/data/sampled_jsons/adversarial_target_behavior_multi-target_tracking_agent_nearby.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..55b0df5e3c95d2c9a30b260afe1143b94300d86f
--- /dev/null
+++ b/data/sampled_jsons/adversarial_target_behavior_multi-target_tracking_agent_nearby.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diffusion Models for Multi-target Adversarial Tracking", "date": "", "ddg_snippet": "Jan 12, 2024 · As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents C onstrained A gent -based D iffusion for EN han CE d Multi - Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.06244v2", "content": "Jan 12, 2024 · As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents C onstrained A gent -based D iffusion for EN han CE d Multi - Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information."}
+{"idx": 1, "title": "Diffusion Models for Multi-target Adversarial Tracking | IEEE ...", "date": "", "ddg_snippet": "Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target ’s location is often limited. Improving autonomous tracking systems will enable unmanned aerial, surface, and underwater vehicles to better assist in interdicting smugglers that use manned surface, semisubmersible, and aerial vessels. As ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10416775", "content": "Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target ’s location is often limited. Improving autonomous tracking systems will enable unmanned aerial, surface, and underwater vehicles to better assist in interdicting smugglers that use manned surface, semisubmersible, and aerial vessels. As ..."}
+{"idx": 2, "title": "Collaborative Target Tracking Algorithm for Multi-Agent Based ...", "date": "", "ddg_snippet": "Jul 24, 2025 · Target tracking is a representative task in multi - agent reinforcement learning (MARL), where agents must collaborate effectively in environments with dense obstacles, evasive targets, and high-dimensional observations—conditions that often lead to local optima and training inefficiencies. To address these challenges, this paper proposes a collaborative tracking algorithm for UAVs that ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2504-446X/9/8/521", "content": "Jul 24, 2025 · Target tracking is a representative task in multi - agent reinforcement learning (MARL), where agents must collaborate effectively in environments with dense obstacles, evasive targets, and high-dimensional observations—conditions that often lead to local optima and training inefficiencies. To address these challenges, this paper proposes a collaborative tracking algorithm for UAVs that ..."}
+{"idx": 3, "title": "Diffusion Based Multi-Agent Adversarial Tracking - DeepAI", "date": "", "ddg_snippet": "Jul 12, 2023 · As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents Constrained Agent -based Diffusion for Enhanced Multi - Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/diffusion-based-multi-agent-adversarial-tracking", "content": "Jul 12, 2023 · As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents Constrained Agent -based Diffusion for Enhanced Multi - Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information."}
+{"idx": 4, "title": "Diffusion Models for Multi-target Adversarial Tracking", "date": "", "ddg_snippet": "As unmanned drones prolif-erate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents Constrained Agent -based Diffusion for ENhanCEd Multi - Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information.", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10595409", "content": "As unmanned drones prolif-erate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents Constrained Agent -based Diffusion for ENhanCEd Multi - Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information."}
+{"idx": 5, "title": "Multi-Agent Systems: The Next Frontier in AI-Driven Cyber ...", "date": "", "ddg_snippet": "Sep 9, 2025 · Research exploring ideas such as Multi - Agent Reinforcement Learning (MARL) is looking at how agents may be able to learn and adapt to changing dynamics in a cyber environment, in addition to adversarial robustness to elevate agent robustness against advanced attacks that target the AI.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/multi-agent-systems-the-next-frontier-in-ai-driven-cyber-defense/", "content": "Sep 9, 2025 · Research exploring ideas such as Multi - Agent Reinforcement Learning (MARL) is looking at how agents may be able to learn and adapt to changing dynamics in a cyber environment, in addition to adversarial robustness to elevate agent robustness against advanced attacks that target the AI."}
+{"idx": 6, "title": "Resilient Multi-Robot Target Tracking with Sensing and ...", "date": "", "ddg_snippet": "Sep 17, 2024 · Multi -robot collaboration for target tracking in adversarial environments poses significant challenges, including system failures, dynamic priority shifts, and other unpredictable factors. These challenges become even more pronounced when the environment is unknown. In this paper, we propose a resilient coordination framework for multi -robot, multi-target tracking in environments with unknown ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.11230", "content": "Sep 17, 2024 · Multi -robot collaboration for target tracking in adversarial environments poses significant challenges, including system failures, dynamic priority shifts, and other unpredictable factors. These challenges become even more pronounced when the environment is unknown. In this paper, we propose a resilient coordination framework for multi -robot, multi-target tracking in environments with unknown ..."}
+{"idx": 7, "title": "Resilient and Adaptive Replanning for Multi -Robot Target Tracking", "date": "", "ddg_snippet": "• We formulate multi -robot target tracking with initially unknown danger zones as a partially centralized non-linear optimization problem. The attack and recovery mechanisms are modeled for both sensing and commu-nication danger zones.danger zones, and adversarial targets .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.11230", "content": "• We formulate multi -robot target tracking with initially unknown danger zones as a partially centralized non-linear optimization problem. The attack and recovery mechanisms are modeled for both sensing and commu-nication danger zones.danger zones, and adversarial targets ."}
+{"idx": 8, "title": "Adversarial Search and Tracking with Multiagent Reinforcement", "date": "", "ddg_snippet": "Object Search and Track . Opponent Modeling. Multi - Agent Reinforcement Learning (MARL).Opponent modeling usually uses neural networks for target or opponent prediction. For instance, DRON is proposed to model the behavior of opponents from opponent observations [25].", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/adversarial-search-and-track-with-multiagent-reinforcement-3hlxsm0e.pdf", "content": "Object Search and Track . Opponent Modeling. Multi - Agent Reinforcement Learning (MARL).Opponent modeling usually uses neural networks for target or opponent prediction. For instance, DRON is proposed to model the behavior of opponents from opponent observations [25]."}
+{"idx": 9, "title": "Tree Search Techniques for Adversarial Target Tracking With...", "date": "", "ddg_snippet": "Multi - target visual tracking with aerial robots.This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/351720334_Tree_Search_Techniques_for_Adversarial_Target_Tracking_With_Distance-Dependent_Measurement_Noise", "content": "Multi - target visual tracking with aerial robots.This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position."}
diff --git a/data/sampled_jsons/ah2ac2_GitHub_README_dataset_games_number_abstract.jsonl b/data/sampled_jsons/ah2ac2_GitHub_README_dataset_games_number_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07897f9024b1c1855d972c72d71a3d2050b6e1d5
--- /dev/null
+++ b/data/sampled_jsons/ah2ac2_GitHub_README_dataset_games_number_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Introduction - Ad-Hoc Human-AI Coordination Challenge (AH2AC2 ...", "date": "", "ddg_snippet": "Using the Dataset : We provide two utilities (HanabiLiveGamesDataset and HanabiLiveGamesDataloader) for easier data handling, batching, shuffling, and integration with JaxMARL. Tutorial: Unrolling Games : Find out how to use our datasets and dataloaders to unroll game trajectories within the JaxMARL framework.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/datasets/", "content": "Using the Dataset : We provide two utilities (HanabiLiveGamesDataset and HanabiLiveGamesDataloader) for easier data handling, batching, shuffling, and integration with JaxMARL. Tutorial: Unrolling Games : Find out how to use our datasets and dataloaders to unroll game trajectories within the JaxMARL framework."}
+{"idx": 1, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) - GitHub", "date": "", "ddg_snippet": "Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ). Contribute to FLAIROx/ ah2ac2 development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2", "content": "Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ). Contribute to FLAIROx/ ah2ac2 development by creating an account on GitHub ."}
+{"idx": 2, "title": "ah2ac2/README.md at production · FLAIROx/ah2ac2 · GitHub", "date": "", "ddg_snippet": "Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ). Contribute to FLAIROx/ ah2ac2 development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2/blob/production/README.md", "content": "Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ). Contribute to FLAIROx/ ah2ac2 development by creating an account on GitHub ."}
+{"idx": 3, "title": "AH2AC2", "date": "", "ddg_snippet": "🎉 Official website for the AH2AC2 research paper accepted to ICML 2025 for a spotlight poster presentation!", "subpage_snippet": "", "source": "ah2ac2.com", "link": "https://ah2ac2.com/", "content": "🎉 Official website for the AH2AC2 research paper accepted to ICML 2025 for a spotlight poster presentation!"}
+{"idx": 4, "title": "Enhance Your GitHub README with Badges: A Shields.io... - YouTube", "date": "", "ddg_snippet": "In this video, we'll show you how to level up your GitHub README files with eye-catching badges using Shields.io.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=4cgpu9L2AE8", "content": "In this video, we'll show you how to level up your GitHub README files with eye-catching badges using Shields.io."}
+{"idx": 5, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 6, "title": "Releases · Flowseal/zapret-discord-youtube · GitHub", "date": "", "ddg_snippet": "GitHub Sponsors. Fund open source developers. The ReadME Project.IPset и Game Filter теперь по стандарту выключены. Game Filter без включённого ipset работать не будет. Включить обратно можно в service.bat. Обновлена стратегия general (FAKE TLS AUTO ALT).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Flowseal/zapret-discord-youtube/releases", "content": "GitHub Sponsors. Fund open source developers. The ReadME Project.IPset и Game Filter теперь по стандарту выключены. Game Filter без включённого ipset работать не будет. Включить обратно можно в service.bat. Обновлена стратегия general (FAKE TLS AUTO ALT)."}
+{"idx": 7, "title": "Оформление README файла репозитория на github ... | Пикабу", "date": "", "ddg_snippet": "Приветствую, это полный гайд по оформлению REAMDE файла репозитория на github . Начнём с самого простого, и закончим понастоящему класными решениями.", "subpage_snippet": "", "source": "pikabu.ru", "link": "https://pikabu.ru/story/oformlenie_readme_fayla_repozitoriya_na_github_12685418", "content": "Приветствую, это полный гайд по оформлению REAMDE файла репозитория на github . Начнём с самого простого, и закончим понастоящему класными решениями."}
+{"idx": 8, "title": "GitHub README File Syntax and Templates | Tle Apps", "date": "", "ddg_snippet": "GitHub README File Syntax. In this post, we’ll cover everything from the syntax for writing in Markdown, to adding images and links.", "subpage_snippet": "", "source": "tleapps.com", "link": "https://tleapps.com/github-readme-file-syntax/", "content": "GitHub README File Syntax. In this post, we’ll cover everything from the syntax for writing in Markdown, to adding images and links."}
+{"idx": 9, "title": "SQL for Marketing: Querying a Marketing Campaign dataset for...", "date": "", "ddg_snippet": "Abstract .Our dataset consists of 2,240 records and 30 fields. Now to find out the dates of enrolment of the first customer and the last customer.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/sql-for-marketing-querying-a-marketing-campaign-dataset-for-customer-segmentation-03333118983f", "content": "Abstract .Our dataset consists of 2,240 records and 30 fields. Now to find out the dates of enrolment of the first customer and the last customer."}
diff --git a/data/sampled_jsons/algebraic_combinatorics_dataset_repository_S18_characters_100000_training_examples.jsonl b/data/sampled_jsons/algebraic_combinatorics_dataset_repository_S18_characters_100000_training_examples.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e20034c7b2e14dbc8f1dd9e25fa0ece91d0dff06
--- /dev/null
+++ b/data/sampled_jsons/algebraic_combinatorics_dataset_repository_S18_characters_100000_training_examples.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algebraic combinatorial geometry: the polynomial method in...", "date": "", "ddg_snippet": "...a technique in combinatorics and number theory for controlling a relevant set of points by comparing it with the zero set of a suitably chosen polynomial, and then using tools from algebraic geometry (e.g. Bezout’s theorem) on that zero set .", "subpage_snippet": "", "source": "terrytao.wordpress.com", "link": "https://terrytao.wordpress.com/2013/10/25/algebraic-combinatorial-geometry-the-polynomial-method-in-arithmetic-combinatorics-incidence-combinatorics-and-number-theory/", "content": "...a technique in combinatorics and number theory for controlling a relevant set of points by comparing it with the zero set of a suitably chosen polynomial, and then using tools from algebraic geometry (e.g. Bezout’s theorem) on that zero set ."}
+{"idx": 1, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 2, "title": "UCI Machine Learning Repository | Discover datasets around the world!", "date": "", "ddg_snippet": "Dataset Characteristics . Tabular.Convergence and Margin of Adversarial Training on Separable Data . By Zachary Charles, Shashank Rajput, Stephen Wright, Dimitris Papailiopoulos. 2019.", "subpage_snippet": "", "source": "archive.ics.uci.edu", "link": "https://archive.ics.uci.edu/dataset/53/iris", "content": "Dataset Characteristics . Tabular.Convergence and Margin of Adversarial Training on Separable Data . By Zachary Charles, Shashank Rajput, Stephen Wright, Dimitris Papailiopoulos. 2019."}
+{"idx": 3, "title": "Combinatorial Algebraic Geometry: Selected Papers... | SpringerLink", "date": "", "ddg_snippet": "This book covers a range of topics in combinatorial algebraic geometry including curves, surfaces, Grassmannians, and convexity.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/book/10.1007/978-1-4939-7486-3", "content": "This book covers a range of topics in combinatorial algebraic geometry including curves, surfaces, Grassmannians, and convexity."}
+{"idx": 4, "title": "Combinatorial Algebraic Topology (Algorithms and Computation in...)", "date": "", "ddg_snippet": "Purchase options and add-ons. Combinatorial algebraic topology is a fascinating and dynamic field at the crossroads of algebraic topology and discrete mathematics. This volume is the first comprehensive treatment of the subject in book form.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/Combinatorial-Algebraic-Algorithms-Computation-Mathematics/dp/3540730516", "content": "Purchase options and add-ons. Combinatorial algebraic topology is a fascinating and dynamic field at the crossroads of algebraic topology and discrete mathematics. This volume is the first comprehensive treatment of the subject in book form."}
+{"idx": 5, "title": "combinatorics - Grothendieck polynomials and divided difference...", "date": "", "ddg_snippet": "All the examples I have suggest that this might be true, but I haven't been able to prove it yet. Do you have any counterexamples? algebraic - combinatorics . schubert-calculus.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/5097377/grothendieck-polynomials-and-divided-difference-operators", "content": "All the examples I have suggest that this might be true, but I haven't been able to prove it yet. Do you have any counterexamples? algebraic - combinatorics . schubert-calculus."}
+{"idx": 6, "title": "Datasets", "date": "", "ddg_snippet": "Dataset repository .Collaborate on models, datasets and Spaces. Faster examples with accelerated inference. Switch between documentation themes.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/datasets/index", "content": "Dataset repository .Collaborate on models, datasets and Spaces. Faster examples with accelerated inference. Switch between documentation themes."}
+{"idx": 7, "title": "Github Repository for Top LLM Datasets | Analytics Vidhya", "date": "", "ddg_snippet": "Explore the Github Repository for LLM Datasets and transform your AI projects with quality data for model training .", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/09/github-repository-for-top-llm-datasets/", "content": "Explore the Github Repository for LLM Datasets and transform your AI projects with quality data for model training ."}
+{"idx": 8, "title": "Sparse Combinatorial Dataset | IEEE DataPort", "date": "", "ddg_snippet": "This paper discusses a novel adoption of a combinatorial interaction strategy to generate a sparse combinatorial data table (SCDT) for machine learning.", "subpage_snippet": "", "source": "ieee-dataport.org", "link": "https://ieee-dataport.org/documents/sparse-combinatorial-dataset", "content": "This paper discusses a novel adoption of a combinatorial interaction strategy to generate a sparse combinatorial data table (SCDT) for machine learning."}
+{"idx": 9, "title": "Начинаем работу с PyTorch 2.0 и Hugging Face Transformers / Хабр", "date": "", "ddg_snippet": "18K.random_id = randrange(len(raw_ dataset [' train '])) raw_ dataset [' train '][random_id] # {'text': \"I can't get google pay to work right.\", 'label': 2}. Для обучения модели нужно предварительно преобразовать текст в токены. Это делается токенизатором.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/726468/", "content": "18K.random_id = randrange(len(raw_ dataset [' train '])) raw_ dataset [' train '][random_id] # {'text': \"I can't get google pay to work right.\", 'label': 2}. Для обучения модели нужно предварительно преобразовать текст в токены. Это делается токенизатором."}
diff --git a/data/sampled_jsons/allintitle_Semi-Bandit_Learning_for_Monotone_Stochastic_Optimization_filetypepdf.jsonl b/data/sampled_jsons/allintitle_Semi-Bandit_Learning_for_Monotone_Stochastic_Optimization_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b2002721411eeb2cb6071deacbc738e499ebd947
--- /dev/null
+++ b/data/sampled_jsons/allintitle_Semi-Bandit_Learning_for_Monotone_Stochastic_Optimization_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Semi-Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "Aug 14, 2025 · We demonstrate the generality of our framework by obtaining polynomial time online learning algorithms for a number of stochastic optimization problems arising in domains of optimal stopping, revenue management, submodular optimization , knapsack and matching.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.15427v2", "content": "Aug 14, 2025 · We demonstrate the generality of our framework by obtaining polynomial time online learning algorithms for a number of stochastic optimization problems arising in domains of optimal stopping, revenue management, submodular optimization , knapsack and matching."}
+{"idx": 1, "title": "Beating Stochastic and Adversarial Semi-bandits Optimally and ...", "date": "", "ddg_snippet": "We propose a simple and general semi - bandit algo-rithm based on the Follow-the-Regularized-Leader (FTRL) framework with a novel regularizer (Sec-tion 2.1).", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v97/zimmert19a/zimmert19a.pdf", "content": "We propose a simple and general semi - bandit algo-rithm based on the Follow-the-Regularized-Leader (FTRL) framework with a novel regularizer (Sec-tion 2.1)."}
+{"idx": 2, "title": "Semi-Bandit feedback: A survey of results - UMD", "date": "", "ddg_snippet": "In this report, we survey the recent results for the Semi - Bandit problems in the stochastic setting. For a motivation of the semi - bandit setting, we describe the first paper which looks at it in the adversarial setting.", "subpage_snippet": "", "source": "www.cs.umd.edu", "link": "https://www.cs.umd.edu/sites/default/files/scholarly_papers/Sankararaman.pdf", "content": "In this report, we survey the recent results for the Semi - Bandit problems in the stochastic setting. For a motivation of the semi - bandit setting, we describe the first paper which looks at it in the adversarial setting."}
+{"idx": 3, "title": "Stochastic Online Greedy Learning with Semi-bandit Feedbacks", "date": "", "ddg_snippet": "In this paper, we address the online learning problem when the input to the greedy algorithm is stochastic with unknown parameters that have to be learned over time.", "subpage_snippet": "", "source": "people.iiis.tsinghua.edu.cn", "link": "http://people.iiis.tsinghua.edu.cn/~jianli/paper/nips2015_full_og.pdf", "content": "In this paper, we address the online learning problem when the input to the greedy algorithm is stochastic with unknown parameters that have to be learned over time."}
+{"idx": 4, "title": "Semi-Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "Our frame-work applies to several fundamental problems in stochastic optimization such as prophet inequality, Pandora’s box, stochastic knapsack, stochastic matchings and stochastic submodular optimization .", "subpage_snippet": "", "source": "ieee-focs.org", "link": "https://ieee-focs.org/FOCS-2024-Papers/pdfs/FOCS2024-1oojWxXs5YAKfs3z3lBRMF/167400b260/167400b260.pdf", "content": "Our frame-work applies to several fundamental problems in stochastic optimization such as prophet inequality, Pandora’s box, stochastic knapsack, stochastic matchings and stochastic submodular optimization ."}
+{"idx": 5, "title": "Bandit Optimization: Theory and Applications - SIGMETRICS", "date": "", "ddg_snippet": "Gai, Krishnamachari, and Jain, Combinatorial network optimization with unknown variables: Multi-armed bandits with linear rewards and individual observations, 2012", "subpage_snippet": "", "source": "www.sigmetrics.org", "link": "https://www.sigmetrics.org/sigmetrics2015/tutorial_sigmetrics.pdf", "content": "Gai, Krishnamachari, and Jain, Combinatorial network optimization with unknown variables: Multi-armed bandits with linear rewards and individual observations, 2012"}
+{"idx": 6, "title": "Semi - Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algo-rithms have been obtained for several fundamental problems in this area.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.15427", "content": "Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algo-rithms have been obtained for several fundamental problems in this area."}
+{"idx": 7, "title": "Semi - Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "Abstract: Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.15427", "content": "Abstract: Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area."}
+{"idx": 8, "title": "Semi - Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10756016", "content": "Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area."}
+{"idx": 9, "title": "Semi - Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/semi-bandit-learning-for-monotone-stochastic", "content": "Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area."}
diff --git a/data/sampled_jsons/alpha_parameter_noise_variance_estimation_challenging_distribution_regression_manifold.jsonl b/data/sampled_jsons/alpha_parameter_noise_variance_estimation_challenging_distribution_regression_manifold.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0186deb0022808cd1457e7a409b897501ef9a4e8
--- /dev/null
+++ b/data/sampled_jsons/alpha_parameter_noise_variance_estimation_challenging_distribution_regression_manifold.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generative Learning for Slow Manifolds and Bifurcation Diagrams", "date": "", "ddg_snippet": "Provided a label that parametrizes the manifold , a trained generative model can quickly produce a distribution that is consistent with the prescribed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.20375v1", "content": "Provided a label that parametrizes the manifold , a trained generative model can quickly produce a distribution that is consistent with the prescribed ..."}
+{"idx": 1, "title": "A Survey of Dimension Estimation Methods", "date": "", "ddg_snippet": "We study how estimators respond to practical challenges such as choosing appropriate hyperparameters, and the presence of noise and curvature in data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.13887v1", "content": "We study how estimators respond to practical challenges such as choosing appropriate hyperparameters, and the presence of noise and curvature in data."}
+{"idx": 2, "title": "Reducing Variance in Meta-Learning via Laplace Approximation", "date": "", "ddg_snippet": "... the variance that each data point carries over the parameters and optimally aggregate these posterior distributions into the task-adapted parameters .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.01476v2", "content": "... the variance that each data point carries over the parameters and optimally aggregate these posterior distributions into the task-adapted parameters ."}
+{"idx": 3, "title": "Adversarial Manifold Estimation | Request PDF", "date": "", "ddg_snippet": "For instance, in manifold learning [MM10, FMN16, FIK + 18, AK24, KWW25], trajectory planning [BH75,Zha02], nonlinear regression , and geometric data ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364381816_Adversarial_Manifold_Estimation", "content": "For instance, in manifold learning [MM10, FMN16, FIK + 18, AK24, KWW25], trajectory planning [BH75,Zha02], nonlinear regression , and geometric data ..."}
+{"idx": 4, "title": "A Statistical Physics of Language Model Reasoning", "date": "", "ddg_snippet": "... D > 2048 𝐷 2048 D > 2048 italic_D > 2048 for most LMs) is impractical, we use a lower-dimensional manifold capturing significant variance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.04374v1", "content": "... D > 2048 𝐷 2048 D > 2048 italic_D > 2048 for most LMs) is impractical, we use a lower-dimensional manifold capturing significant variance ..."}
+{"idx": 5, "title": "Combine predictors using stacking — scikit-learn 1.6.1", "date": "", "ddg_snippet": "... of Gaussian process regression (GPR) to estimate data noise ... Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html", "content": "... of Gaussian process regression (GPR) to estimate data noise ... Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)"}
+{"idx": 6, "title": "ICML 2021 Papers", "date": "", "ddg_snippet": "... Learning? Application to Human Age Estimation ... Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/papers.html", "content": "... Learning? Application to Human Age Estimation ... Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold"}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "alpha $-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression ... with an Application to Function-On-Scalar Regression", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "alpha $-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression ... with an Application to Function-On-Scalar Regression"}
+{"idx": 8, "title": "Score-Guided Diffusion for 3D Human Recovery", "date": "", "ddg_snippet": "... the case for the human category, since parametric models [ 42 , 64 ] of the human body exist, and most approaches in this paradigm learn to regress ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09623v1", "content": "... the case for the human category, since parametric models [ 42 , 64 ] of the human body exist, and most approaches in this paradigm learn to regress ..."}
+{"idx": 9, "title": "Sensitivity analysis and inverse uncertainty quantification for", "date": "", "ddg_snippet": "... challenges of establishing structural identifiability for complex nonlinear systems, global sensitivity analysis approaches the question slightly ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10237-022-01571-8", "content": "... challenges of establishing structural identifiability for complex nonlinear systems, global sensitivity analysis approaches the question slightly ..."}
diff --git a/data/sampled_jsons/alternatives_to_neuroevolution_for_efficient_neural_network_training.jsonl b/data/sampled_jsons/alternatives_to_neuroevolution_for_efficient_neural_network_training.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..315fc2c3b2573eff0d1ba2f5e536e6a8d594d2a0
--- /dev/null
+++ b/data/sampled_jsons/alternatives_to_neuroevolution_for_efficient_neural_network_training.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neuroevolution of augmenting topologies - Wikipedia", "date": "", "ddg_snippet": "NeuroEvolution of Augmenting Topologies ( NEAT ) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies", "content": "NeuroEvolution of Augmenting Topologies ( NEAT ) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a ..."}
+{"idx": 1, "title": "Deep Neuroevolution: Genetic Algorithms are a ...", "date": "", "ddg_snippet": "by FP Such · 2017 · Cited by 1030 — Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning . Figure 1. Image Hard Maze Domain and Results ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1712.06567", "content": "by FP Such · 2017 · Cited by 1030 — Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning . Figure 1. Image Hard Maze Domain and Results ..."}
+{"idx": 2, "title": "Neuroevolution with box mutation: An adaptive and ...", "date": "", "ddg_snippet": "by FJJB Santos · 2023 · Cited by 4 — This work presents a novel method for evolving deep neural networks by adapting the principles of Geometric Semantic Genetic Programming.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1568494623007858", "content": "by FJJB Santos · 2023 · Cited by 4 — This work presents a novel method for evolving deep neural networks by adapting the principles of Geometric Semantic Genetic Programming."}
+{"idx": 3, "title": "Neuroevolution — The current state | by Stanko Jankovic", "date": "", "ddg_snippet": "An alternative would be to use neuroevolution, but that approach apparently does not scale very well.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@stankoja/neuroevolution-the-current-state-58be2778c8d4", "content": "An alternative would be to use neuroevolution, but that approach apparently does not scale very well."}
+{"idx": 4, "title": "Efficient training and design of photonic neural network ...", "date": "", "ddg_snippet": "by T Zhang · 2019 · Cited by 92 — In this article, we propose a novel learning strategy based on neuroevolution to design and train ONNs. Two typical neuroevolution algorithms are used to ...", "subpage_snippet": "", "source": "opg.optica.org", "link": "https://opg.optica.org/abstract.cfm?uri=oe-27-26-37150", "content": "by T Zhang · 2019 · Cited by 92 — In this article, we propose a novel learning strategy based on neuroevolution to design and train ONNs. Two typical neuroevolution algorithms are used to ..."}
+{"idx": 5, "title": "Scalable training of artificial neural networks with adaptive ...", "date": "", "ddg_snippet": "by DC Mocanu · 2018 · Cited by 855 — Deep neuroevolution : genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning . Preprint ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-018-04316-3", "content": "by DC Mocanu · 2018 · Cited by 855 — Deep neuroevolution : genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning . Preprint ..."}
+{"idx": 6, "title": "A GPU-accelerated Library for NeuroEvolution of ...", "date": "", "ddg_snippet": "11 Apr 2025 — Deep neuroevolution : Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning . arXiv ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.08339v1", "content": "11 Apr 2025 — Deep neuroevolution : Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning . arXiv ..."}
+{"idx": 7, "title": "A Decentralized Approach to Federated NeuroEvolution of ...", "date": "", "ddg_snippet": "14 Jul 2024 — In the past few years, Federated Learning (FL) has emerged as an effective approach for training neural networks (NNs) over a computing network ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3638529.3654029", "content": "14 Jul 2024 — In the past few years, Federated Learning (FL) has emerged as an effective approach for training neural networks (NNs) over a computing network ..."}
+{"idx": 8, "title": "Neuroevolving Electronic Dynamical Networks", "date": "", "ddg_snippet": "In recent years, FPGAs have gained popularity for implementing myriad types of artificial neural networks (ANNs) due to their high performance and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.04587v2", "content": "In recent years, FPGAs have gained popularity for implementing myriad types of artificial neural networks (ANNs) due to their high performance and ..."}
+{"idx": 9, "title": "Neuroevolution of Physics-Informed Neural Nets: Benchmark", "date": "", "ddg_snippet": "In this section, we first present the problem setup for PINNs, contrasting it to standard deep neural networks (DNNs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.07624v3", "content": "In this section, we first present the problem setup for PINNs, contrasting it to standard deep neural networks (DNNs)."}
diff --git a/data/sampled_jsons/another_model_using_sigmoid_loss_for_contrastive_learning.jsonl b/data/sampled_jsons/another_model_using_sigmoid_loss_for_contrastive_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..70aa723b2c683bde4c6e62876508f031de042c17
--- /dev/null
+++ b/data/sampled_jsons/another_model_using_sigmoid_loss_for_contrastive_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning Analysis of Using Sigmoid Loss for Contrastive Learning GitHub - filipbasara0/sigmoid-contrastive-loss ... Analysis of Using Sigmoid Loss for Contrastive Learning sigmoid-contrastive-learning · PyPI Analysis of Using Sigmoid Loss for Contrastive Learning [2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learnin… [2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learnin… Implementation of modulated sigmoid pairwise contrastive loss for self-supervised learning on images - GitHub [2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learnin… [2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learnin… SigLIP Paper - hola sigmoid!", "date": "", "ddg_snippet": "Feb 20, 2024 · In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings. This paper investigated the embedding structure learned by contrastive learning with the sigmoid loss , motivated by the recent success of SigLIP, a variant of CLIP that uses sigmoid loss instead of InfoNCE loss. A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract. This paper proposes a unified framework for understanding contrastive learning through the lens of cosine similarity, and presents two key theoretical insights derived from this framework, including an auxiliary loss that reduces the variance of negative-pair similarities in mini-batch settings. Apr 7, 2024 · A PyTorch implementation of the sigmoid pairwise loss for contrastive self-supervised learning on images. The training architecture consists of an online and a target encoder (EMA) with a simple critic MLP projector and is based on Representation Learning via Invariant Causal Mechanisms (ReLIC). Analysis of Using Sigmoid Loss for Contrastive Learning . In Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li, editors, International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. Can sigmoid loss be used in contrastive learning? However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning, in the perspective of the geometric structure of learned embeddings. Can siglip be used in contrastive learning? Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss. SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . Does PyTorch implement modulated sigmoid pairwise loss for contrastive self-supervised learning? A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract. Models are pretrained on training subsets - for CIFAR10 50,000 and for STL10 100,000 images. Does CCEM contain the optimal embedding minimizing sigmoid loss for contrastive learning? Interestingly, the proposed CCEM is proven to contain the optimal embedding with respect to the sigmoid loss. Second, we mathematically analyze the optimal embedding minimizing the sigmoid loss for contrastive learning. What is the optimal embedding structure for sigmoid loss? The optimal embedding ranges from simplex equiangular-tight-frame to antipodal structure , depending on the temperature parameter used in the sigmoid loss. Third, our experimental results on synthetic datasets coincide with the theoretical results on the optimal embedding structures. Sigmoid loss usage: The study explored two language-image pre-training models , SigLiT and SigLIP, both using sigmoid loss . Performance comparison: Sigmoid loss outperformed the softmax baseline, particularly for smaller batch sizes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.12613", "content": "Feb 20, 2024 · In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings. This paper investigated the embedding structure learned by contrastive learning with the sigmoid loss , motivated by the recent success of SigLIP, a variant of CLIP that uses sigmoid loss instead of InfoNCE loss. A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract. This paper proposes a unified framework for understanding contrastive learning through the lens of cosine similarity, and presents two key theoretical insights derived from this framework, including an auxiliary loss that reduces the variance of negative-pair similarities in mini-batch settings. Apr 7, 2024 · A PyTorch implementation of the sigmoid pairwise loss for contrastive self-supervised learning on images. The training architecture consists of an online and a target encoder (EMA) with a simple critic MLP projector and is based on Representation Learning via Invariant Causal Mechanisms (ReLIC). Analysis of Using Sigmoid Loss for Contrastive Learning . In Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li, editors, International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. Can sigmoid loss be used in contrastive learning? However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning, in the perspective of the geometric structure of learned embeddings. Can siglip be used in contrastive learning? Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss. SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . Does PyTorch implement modulated sigmoid pairwise loss for contrastive self-supervised learning? A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract. Models are pretrained on training subsets - for CIFAR10 50,000 and for STL10 100,000 images. Does CCEM contain the optimal embedding minimizing sigmoid loss for contrastive learning? Interestingly, the proposed CCEM is proven to contain the optimal embedding with respect to the sigmoid loss. Second, we mathematically analyze the optimal embedding minimizing the sigmoid loss for contrastive learning. What is the optimal embedding structure for sigmoid loss? The optimal embedding ranges from simplex equiangular-tight-frame to antipodal structure , depending on the temperature parameter used in the sigmoid loss. Third, our experimental results on synthetic datasets coincide with the theoretical results on the optimal embedding structures. Sigmoid loss usage: The study explored two language-image pre-training models , SigLiT and SigLIP, both using sigmoid loss . Performance comparison: Sigmoid loss outperformed the softmax baseline, particularly for smaller batch sizes."}
+{"idx": 1, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "This paper investigated the embedding structure learned by contrastive learning with the sigmoid loss , motivated by the recent success of SigLIP, a variant of CLIP that uses sigmoid loss instead of InfoNCE loss.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/lee24a/lee24a.pdf", "content": "This paper investigated the embedding structure learned by contrastive learning with the sigmoid loss , motivated by the recent success of SigLIP, a variant of CLIP that uses sigmoid loss instead of InfoNCE loss."}
+{"idx": 2, "title": "GitHub - filipbasara0/sigmoid-contrastive-loss ...", "date": "", "ddg_snippet": "A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/filipbasara0/sigmoid-contrastive-loss", "content": "A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract."}
+{"idx": 3, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "This paper proposes a unified framework for understanding contrastive learning through the lens of cosine similarity, and presents two key theoretical insights derived from this framework, including an auxiliary loss that reduces the variance of negative-pair similarities in mini-batch settings.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Analysis-of-Using-Sigmoid-Loss-for-Contrastive-Lee-Chang/8948d8d2ac358587f1a09264239137791c3ccc22/figure/2", "content": "This paper proposes a unified framework for understanding contrastive learning through the lens of cosine similarity, and presents two key theoretical insights derived from this framework, including an auxiliary loss that reduces the variance of negative-pair similarities in mini-batch settings."}
+{"idx": 4, "title": "sigmoid-contrastive-learning · PyPI", "date": "", "ddg_snippet": "Apr 7, 2024 · A PyTorch implementation of the sigmoid pairwise loss for contrastive self-supervised learning on images. The training architecture consists of an online and a target encoder (EMA) with a simple critic MLP projector and is based on Representation Learning via Invariant Causal Mechanisms (ReLIC).", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/sigmoid-contrastive-learning/", "content": "Apr 7, 2024 · A PyTorch implementation of the sigmoid pairwise loss for contrastive self-supervised learning on images. The training architecture consists of an online and a target encoder (EMA) with a simple critic MLP projector and is based on Representation Learning via Invariant Causal Mechanisms (ReLIC)."}
+{"idx": 5, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "Analysis of Using Sigmoid Loss for Contrastive Learning . In Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li, editors, International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain.", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/LeeCS24-0", "content": "Analysis of Using Sigmoid Loss for Contrastive Learning . In Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li, editors, International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain."}
+{"idx": 6, "title": "SigLIP Paper - hola sigmoid!", "date": "", "ddg_snippet": "Sigmoid loss usage: The study explored two language-image pre-training models , SigLiT and SigLIP, both using sigmoid loss . Performance comparison: Sigmoid loss outperformed the softmax baseline, particularly for smaller batch sizes.", "subpage_snippet": "", "source": "www.himanshustwts.com", "link": "https://www.himanshustwts.com/posts/siglip", "content": "Sigmoid loss usage: The study explored two language-image pre-training models , SigLiT and SigLIP, both using sigmoid loss . Performance comparison: Sigmoid loss outperformed the softmax baseline, particularly for smaller batch sizes."}
+{"idx": 7, "title": "Simplifying Language-Image Pre-Training with Sigmoid Loss", "date": "", "ddg_snippet": "Together with the memory efficient advantage for the sigmoid loss , it allows one to train the best language-image model with much fewer amount of ...", "subpage_snippet": "", "source": "joanfihu.com", "link": "https://joanfihu.com/2024/07/17/simplifying-language-image-pre-training-with-sigmoid-loss/", "content": "Together with the memory efficient advantage for the sigmoid loss , it allows one to train the best language-image model with much fewer amount of ..."}
+{"idx": 8, "title": "machine learning - In Gaussian Process binary classification,", "date": "", "ddg_snippet": "My main question is: for binary classification with $f$ modelled as a Gaussian Process, why use sigmoid functions at all (in either equation) instead ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/281498/in-gaussian-process-binary-classification-why-are-sigmoid-functions-preferred-o", "content": "My main question is: for binary classification with $f$ modelled as a Gaussian Process, why use sigmoid functions at all (in either equation) instead ..."}
+{"idx": 9, "title": "ML Interview Q Series: NLL loss is used with softmax for", "date": "", "ddg_snippet": "ML Interview Q Series: NLL loss is used with softmax for probabilistic outputs; alternatives depend on specific modeling needs.", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/ml-interview-q-series-nll-loss-is", "content": "ML Interview Q Series: NLL loss is used with softmax for probabilistic outputs; alternatives depend on specific modeling needs."}
diff --git a/data/sampled_jsons/ar5iv_2405.17618_Section_5.4_analysis_PPO_instability.jsonl b/data/sampled_jsons/ar5iv_2405.17618_Section_5.4_analysis_PPO_instability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a7f236d7d131bfe6b3f17ac2388bfe4f456b9b9c
--- /dev/null
+++ b/data/sampled_jsons/ar5iv_2405.17618_Section_5.4_analysis_PPO_instability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Secrets of RLHF in Large Language Models Part I: PPO - ar5iv", "date": "", "ddg_snippet": "Therefore, we explore the PPO -max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model. Based on our main results, we perform a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2307.04964", "content": "Therefore, we explore the PPO -max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model. Based on our main results, we perform a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT."}
+{"idx": 1, "title": "[2502.16944] Lean and Mean: Decoupled Value Policy ... Improving the Language Understanding Capabilities of Large ... [2103.01955] The Surprising Effectiveness of PPO in ... - ar5iv [1707.06347] Proximal Policy Optimization Algorithms - arXiv.org Secrets of RLHF in Large Language Models Part I: PPO Secrets of RLHF in Large Language Models Part I: PPO Secrets of RLHF in Large Language Models Part I: PPO PPO-BR: Dual-Signal Entropy-Reward Adaptation for Trust ...", "date": "", "ddg_snippet": "Feb 24, 2025 · Proximal Policy Optimization ( PPO )-based Reinforcement Learning from Human Feedback (RLHF) is essential for aligning large language models (LLMs) with human preferences. It requires joint training of an actor and critic with a pretrained, fixed reward model for guidance. This approach increases computational complexity and instability due to actor-critic interdependence. Additionally, PPO ... In this paper, we explore two approaches—supervised fine-tuning and proximal policy optimization ( PPO )—to enhance the NLU capabilities of LLMs. To reduce the computational cost of full-model fine-tuning, we integrate low-rank adaptation (LoRA) layers, restricting updates to these layers during both supervised fine-tuning and PPO stages. Our empirical analysis of PPO ’s implementation and hyperparameter factors in multi-agent settings is similar to the studies of policy-gradient methods in single-agent RL [34, 17, 9, 1]. Jul 20, 2017 · We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a \"surrogate\" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of ... Does PPO-Max improve the training stability of the policy model? Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model . Based on our main results, we perform a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT. How does PPO normalize the advantage value based on Gae? After calculating the advantage based on GAE, PPO normalizes the advantage value by subtracting its mean and dividing it by its standard deviation . Andrychowicz first attempt to apply Advantages Normalization in gaming domain and reported that this trick didn’t exhibit significant improvements. Why is PPO sensitive to hyperparameters? In the new language environment, PPO suffers from sparse reward and inefficient exploration in word space , making it sensitive to hyperparameters. Models trained solely through repeated experiments, failed runs, and hyperparameter sweeps achieve far inferior results. PPO -BR’s simplicity, stability , and minimal overhead make it especially suited for real-world deployment. In safety-critical domains such as autonomous drones, swarm robotics, and surgical assistance systems, adaptive trust region control offers a promising path toward safer, more reliable policy learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.16944", "content": "Feb 24, 2025 · Proximal Policy Optimization ( PPO )-based Reinforcement Learning from Human Feedback (RLHF) is essential for aligning large language models (LLMs) with human preferences. It requires joint training of an actor and critic with a pretrained, fixed reward model for guidance. This approach increases computational complexity and instability due to actor-critic interdependence. Additionally, PPO ... In this paper, we explore two approaches—supervised fine-tuning and proximal policy optimization ( PPO )—to enhance the NLU capabilities of LLMs. To reduce the computational cost of full-model fine-tuning, we integrate low-rank adaptation (LoRA) layers, restricting updates to these layers during both supervised fine-tuning and PPO stages. Our empirical analysis of PPO ’s implementation and hyperparameter factors in multi-agent settings is similar to the studies of policy-gradient methods in single-agent RL [34, 17, 9, 1]. Jul 20, 2017 · We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a \"surrogate\" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of ... Does PPO-Max improve the training stability of the policy model? Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model . Based on our main results, we perform a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT. How does PPO normalize the advantage value based on Gae? After calculating the advantage based on GAE, PPO normalizes the advantage value by subtracting its mean and dividing it by its standard deviation . Andrychowicz first attempt to apply Advantages Normalization in gaming domain and reported that this trick didn’t exhibit significant improvements. Why is PPO sensitive to hyperparameters? In the new language environment, PPO suffers from sparse reward and inefficient exploration in word space , making it sensitive to hyperparameters. Models trained solely through repeated experiments, failed runs, and hyperparameter sweeps achieve far inferior results. PPO -BR’s simplicity, stability , and minimal overhead make it especially suited for real-world deployment. In safety-critical domains such as autonomous drones, swarm robotics, and surgical assistance systems, adaptive trust region control offers a promising path toward safer, more reliable policy learning."}
+{"idx": 2, "title": "[2103.01955] The Surprising Effectiveness of PPO in ... - ar5iv", "date": "", "ddg_snippet": "Our empirical analysis of PPO ’s implementation and hyperparameter factors in multi-agent settings is similar to the studies of policy-gradient methods in single-agent RL [34, 17, 9, 1].", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2103.01955", "content": "Our empirical analysis of PPO ’s implementation and hyperparameter factors in multi-agent settings is similar to the studies of policy-gradient methods in single-agent RL [34, 17, 9, 1]."}
+{"idx": 3, "title": "PPO-BR: Dual-Signal Entropy-Reward Adaptation for Trust ...", "date": "", "ddg_snippet": "PPO -BR’s simplicity, stability , and minimal overhead make it especially suited for real-world deployment. In safety-critical domains such as autonomous drones, swarm robotics, and surgical assistance systems, adaptive trust region control offers a promising path toward safer, more reliable policy learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.17714", "content": "PPO -BR’s simplicity, stability , and minimal overhead make it especially suited for real-world deployment. In safety-critical domains such as autonomous drones, swarm robotics, and surgical assistance systems, adaptive trust region control offers a promising path toward safer, more reliable policy learning."}
+{"idx": 4, "title": "[ 2405 . 17618 ] Symmetric Reinforcement Learning Loss for Robust...", "date": "", "ddg_snippet": "arXiv: 2405 . 17618 (cs). [Submitted on 27 May 2024 ( v 1), last revised 23 Jun 2025 (this version, v 3)]. Title:Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "arXiv: 2405 . 17618 (cs). [Submitted on 27 May 2024 ( v 1), last revised 23 Jun 2025 (this version, v 3)]. Title:Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales."}
+{"idx": 5, "title": "TGame | Daily Lives Of My Countryside character section ... - YouTube", "date": "", "ddg_snippet": "Daily Lives Of My Countryside experience part Mae- The main thing is to have fun.- The video I made is a guide to the events in the game, I didn't make the u...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=BirIRgPd6QM", "content": "Daily Lives Of My Countryside experience part Mae- The main thing is to have fun.- The video I made is a guide to the events in the game, I didn't make the u..."}
+{"idx": 6, "title": "Improving the Language Understanding Capabilities of Large ...", "date": "", "ddg_snippet": "In this paper, we explore two approaches—supervised fine-tuning and proximal policy optimization ( PPO )—to enhance the NLU capabilities of LLMs. To reduce the computational cost of full-model fine-tuning, we integrate low-rank adaptation (LoRA) layers, restricting updates to these layers during both supervised fine-tuning and PPO stages.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.11020v2", "content": "In this paper, we explore two approaches—supervised fine-tuning and proximal policy optimization ( PPO )—to enhance the NLU capabilities of LLMs. To reduce the computational cost of full-model fine-tuning, we integrate low-rank adaptation (LoRA) layers, restricting updates to these layers during both supervised fine-tuning and PPO stages."}
+{"idx": 7, "title": "Instagram Profile Viewer Private", "date": "", "ddg_snippet": "Profile Analyzer .Read our blog to learn more about some new techniques for Instagram promotion. If you have any questions about our service, visit our FAQ section .", "subpage_snippet": "", "source": "inflact.com", "link": "https://inflact.com/instagram-viewer/profile/", "content": "Profile Analyzer .Read our blog to learn more about some new techniques for Instagram promotion. If you have any questions about our service, visit our FAQ section ."}
+{"idx": 8, "title": "ГДЗ по английскому языку Enjoy English Биболетова 4 класс...", "date": "", "ddg_snippet": "Section 1. What is your favourite season? - Раздел 1. Какое твоё любимое время года? Exercise 1.", "subpage_snippet": "", "source": "english-gdz.ru", "link": "https://english-gdz.ru/04/EnjoyEnglish/005.html", "content": "Section 1. What is your favourite season? - Раздел 1. Какое твоё любимое время года? Exercise 1."}
+{"idx": 9, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss. We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2405.17618", "content": "In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss. We demonstrate performance improvements across various tasks and scales."}
diff --git a/data/sampled_jsons/ar5iv_RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_3.1.jsonl b/data/sampled_jsons/ar5iv_RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_3.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..046e5af1886ab7f97733f5df7da0bdbee7eb97ef
--- /dev/null
+++ b/data/sampled_jsons/ar5iv_RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_3.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Retrieval-augmented generation - Wikipedia", "date": "", "ddg_snippet": "Retrieval-augmented generation is a technique that enables large language models to retrieve and incorporate new information . With RAG , LLMs do not respond to user queries until they refer to a specified set of documents.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Retrieval-augmented_generation", "content": "Retrieval-augmented generation is a technique that enables large language models to retrieve and incorporate new information . With RAG , LLMs do not respond to user queries until they refer to a specified set of documents."}
+{"idx": 1, "title": "RAGGED : Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "A well- designed RAG system should be scalable , meaning it should benefit from increasing.By introducing a systematic framework for assessing RAG stability and scalability , our study provides actionable insights for building more reliable and robust AI systems .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v3", "content": "A well- designed RAG system should be scalable , meaning it should benefit from increasing.By introducing a systematic framework for assessing RAG stability and scalability , our study provides actionable insights for building more reliable and robust AI systems ."}
+{"idx": 2, "title": "RAGGED : Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4ufjBV6S4I", "content": "Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation."}
+{"idx": 3, "title": "Top Research Papers on RAG", "date": "", "ddg_snippet": "RAGGED : Towards Informed Design of Scalable and Stable RAG Systems .This study emphasizes that the RAG architecture’s ability to retrieve information by dynamically using the learnings obtained from large datasets of LLMs strengthens applications in the field of NLP.", "subpage_snippet": "", "source": "paperguide.ai", "link": "https://paperguide.ai/papers/top/research-papers-rag/", "content": "RAGGED : Towards Informed Design of Scalable and Stable RAG Systems .This study emphasizes that the RAG architecture’s ability to retrieve information by dynamically using the learnings obtained from large datasets of LLMs strengthens applications in the field of NLP."}
+{"idx": 4, "title": "AI Insights from ICML 2025 Part 1: Context engineering and multimodal...", "date": "", "ddg_snippet": "Meanwhile, RAGGED : Towards Informed Design of Scalable and Stable RAG Systems introduced robustness metrics that revealed more retrieval isn’t always better — especially in multi-hop settings.", "subpage_snippet": "", "source": "instabase.com", "link": "https://instabase.com/blog/ai-insights-from-icml-2025-part-1-context-engineering-and-multimodal-reasoning/", "content": "Meanwhile, RAGGED : Towards Informed Design of Scalable and Stable RAG Systems introduced robustness metrics that revealed more retrieval isn’t always better — especially in multi-hop settings."}
+{"idx": 5, "title": "RAG (Retrieval Augmented Generation) — простое и понятное... / Хабр", "date": "", "ddg_snippet": "RAG (Retrieval Augmented Generation) — это метод работы с большими языковыми моделями, когда пользователь пишет свой вопросы, а вы программно к этому вопросу «подмешиваете» дополнительную информацию из...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/779526/", "content": "RAG (Retrieval Augmented Generation) — это метод работы с большими языковыми моделями, когда пользователь пишет свой вопросы, а вы программно к этому вопросу «подмешиваете» дополнительную информацию из..."}
+{"idx": 6, "title": "RAG — от первой версии к рабочему решению - DeepSchool", "date": "", "ddg_snippet": "Разбираем каждый компонент RAG -системы, объясняем типичные ошибки и даём план действий по улучшению ванильной версии.", "subpage_snippet": "", "source": "blog.deepschool.ru", "link": "https://blog.deepschool.ru/llm/rag-ot-pervoj-versii-k-rabochemu-resheniyu/", "content": "Разбираем каждый компонент RAG -системы, объясняем типичные ошибки и даём план действий по улучшению ванильной версии."}
+{"idx": 7, "title": "Длинный контекст LLM в RAG -системах и не только... — AI на vc.ru", "date": "", "ddg_snippet": "Длинный контекст LLM, RAG -системы, генерация ответов, эффективность, архитектуры, проблемы и преимущества использования, сравнение методов.", "subpage_snippet": "", "source": "vc.ru", "link": "https://vc.ru/ai/1926251-dlinnyy-kontekst-llm-i-rag-sistemy", "content": "Длинный контекст LLM, RAG -системы, генерация ответов, эффективность, архитектуры, проблемы и преимущества использования, сравнение методов."}
+{"idx": 8, "title": "Paper page - RAGGED : Towards Informed Design of Retrieval...", "date": "", "ddg_snippet": "The RAGGED framework optimizes Retrieval-augmented Generation systems by analyzing different retrievers and language model architectures, revealing insights into their optimal configurations and context usage. AI-generated summary.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2403.09040", "content": "The RAGGED framework optimizes Retrieval-augmented Generation systems by analyzing different retrievers and language model architectures, revealing insights into their optimal configurations and context usage. AI-generated summary."}
+{"idx": 9, "title": "What is Retrieval-Augmented Generation ( RAG )? - YouTube", "date": "", "ddg_snippet": "Ready to become a certified GenAI engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdGhCFLearn about the technology...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=T-D1OfcDW1M", "content": "Ready to become a certified GenAI engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdGhCFLearn about the technology..."}
diff --git a/data/sampled_jsons/arXiv2502.00136_Adversarial_Review_algorithm_Table_1.jsonl b/data/sampled_jsons/arXiv2502.00136_Adversarial_Review_algorithm_Table_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c991268c9e75631a193443e555142263a9a95774
--- /dev/null
+++ b/data/sampled_jsons/arXiv2502.00136_Adversarial_Review_algorithm_Table_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "[2508.10104] DINOv3 - arXiv.org", "date": "", "ddg_snippet": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.10104", "content": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ..."}
+{"idx": 2, "title": "Log in to arXiv | arXiv e-print repository", "date": "", "ddg_snippet": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/login", "content": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy."}
+{"idx": 3, "title": "YOLOv12: Attention-Centric Real-Time Object Detectors - arXiv.org", "date": "", "ddg_snippet": "Feb 18, 2025 · Abstract page for arXiv paper 2502 .12524: YOLOv12: Attention-Centric Real-Time Object Detectors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12524", "content": "Feb 18, 2025 · Abstract page for arXiv paper 2502 .12524: YOLOv12: Attention-Centric Real-Time Object Detectors"}
+{"idx": 4, "title": "Computer Science - arXiv.org", "date": "", "ddg_snippet": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/archive/cs", "content": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:"}
+{"idx": 5, "title": "[2212.10156] Planning-oriented Autonomous Driving - arXiv.org", "date": "", "ddg_snippet": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.10156", "content": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ..."}
+{"idx": 6, "title": "[1706.03762] Attention Is All You Need - arXiv.org", "date": "", "ddg_snippet": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ..."}
+{"idx": 7, "title": "[2506.01844] SmolVLA: A Vision-Language-Action Model for ... -...", "date": "", "ddg_snippet": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.01844", "content": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics"}
+{"idx": 8, "title": "[2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in...", "date": "", "ddg_snippet": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.12948", "content": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning"}
+{"idx": 9, "title": "[2410.10762] AFlow: Automating Agentic Workflow Generation -...", "date": "", "ddg_snippet": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10762", "content": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation"}
diff --git a/data/sampled_jsons/arXiv2503.06366_Machine_Learning_meets_Algebraic_Combinatorics_Table_1.jsonl b/data/sampled_jsons/arXiv2503.06366_Machine_Learning_meets_Algebraic_Combinatorics_Table_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..767e77f93c3e7cafb9fd10ff3ddc7415724ccd12
--- /dev/null
+++ b/data/sampled_jsons/arXiv2503.06366_Machine_Learning_meets_Algebraic_Combinatorics_Table_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "什么是arXiv？ - 知乎", "date": "", "ddg_snippet": "论文讲究时效性，你想了一个 idea， 然后做了仿真，写了论文。但是考虑到投稿问题，有些会议或者期刊 “call for paper ” 是有时间限制的，比如可能多几个月才是论文的收稿期。一方面为了证明自己论文的原创性，将论文放到 arXiv 上挂起来；另一方面，也是为了竞争，谁的论文在 arXiv 挂的早，谁 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/31864895?sort=created", "content": "论文讲究时效性，你想了一个 idea， 然后做了仿真，写了论文。但是考虑到投稿问题，有些会议或者期刊 “call for paper ” 是有时间限制的，比如可能多几个月才是论文的收稿期。一方面为了证明自己论文的原创性，将论文放到 arXiv 上挂起来；另一方面，也是为了竞争，谁的论文在 arXiv 挂的早，谁 ..."}
+{"idx": 1, "title": "arxiv国内有镜像网站吗？ - 知乎", "date": "", "ddg_snippet": "国内确实有arxiv的镜像网站，旨在提升访问速度和下载体验。有两个推荐的镜像站点： CN.ARXIV.ORG：这是官方提供的中国镜像，访问速度快，适合国内用户下载arxiv上的PDF文件。您可以直接访问这个域名获取资料。 中科院镜像 (XXX.ITP.AC.CN)：另一个高效的选择，通过将arxiv.org的链接中的域名替换为 http ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/1911390291929306874", "content": "国内确实有arxiv的镜像网站，旨在提升访问速度和下载体验。有两个推荐的镜像站点： CN.ARXIV.ORG：这是官方提供的中国镜像，访问速度快，适合国内用户下载arxiv上的PDF文件。您可以直接访问这个域名获取资料。 中科院镜像 (XXX.ITP.AC.CN)：另一个高效的选择，通过将arxiv.org的链接中的域名替换为 http ..."}
+{"idx": 2, "title": "论文挂在arxiv上会影响之后的投稿吗，查重或者算一稿多投啥的? - 知乎", "date": "", "ddg_snippet": "2. 预留充足的投稿时间：考虑到 arXiv 预印本可能带来的影响，在投稿时要预留比平时更充足的时间 。 如果论文在 arXiv 上发布后，需要进行大量修改才能满足投稿要求，要确保有足够的时间进行修改和完善，避免因时间紧迫而影响论文质量。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/509528796", "content": "2. 预留充足的投稿时间：考虑到 arXiv 预印本可能带来的影响，在投稿时要预留比平时更充足的时间 。 如果论文在 arXiv 上发布后，需要进行大量修改才能满足投稿要求，要确保有足够的时间进行修改和完善，避免因时间紧迫而影响论文质量。"}
+{"idx": 3, "title": "如果一个论文只在arXiv上挂着，没有在什么会和期刊发表过，却有几万的...", "date": "", "ddg_snippet": "如果一个论文只在arXiv上挂着，没有在什么会和期刊发表过，却有几万的引用量，如何评价这类论文？ 关注者 755 被浏览", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/458164481", "content": "如果一个论文只在arXiv上挂着，没有在什么会和期刊发表过，却有几万的引用量，如何评价这类论文？ 关注者 755 被浏览"}
+{"idx": 4, "title": "如何看待将投往双盲会议的论文提前公布到arXiv的行为? - 知乎", "date": "", "ddg_snippet": "我就算有10张嘴，怎么能骂得过黑子10000张嘴？ 所以，对自己的paper没有相当程度的自信，是不敢传arxiv的。 而往往大佬组的paper质量高的可能性大，也更自信，所以你观测到的大佬传arxiv的数量就更多，背后的原因其实不是大佬想给reviewer施加压力。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/427222067", "content": "我就算有10张嘴，怎么能骂得过黑子10000张嘴？ 所以，对自己的paper没有相当程度的自信，是不敢传arxiv的。 而往往大佬组的paper质量高的可能性大，也更自信，所以你观测到的大佬传arxiv的数量就更多，背后的原因其实不是大佬想给reviewer施加压力。"}
+{"idx": 5, "title": "在arxiv发表论文的意义体现在哪里？ - 知乎", "date": "", "ddg_snippet": "arXiv 拥有庞大的学术用户群，论文一旦上传，就可以被全球的研究者搜索、阅读和引用。 许多研究人员习惯直接从 arXiv 获取最新的研究动态，而不必等正式出版。 高质量的预印本往往能带来更多关注、下载和引用，甚至有可能在正式发表前就产生影响。 4.", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/8772574507", "content": "arXiv 拥有庞大的学术用户群，论文一旦上传，就可以被全球的研究者搜索、阅读和引用。 许多研究人员习惯直接从 arXiv 获取最新的研究动态，而不必等正式出版。 高质量的预印本往往能带来更多关注、下载和引用，甚至有可能在正式发表前就产生影响。 4."}
+{"idx": 6, "title": "把文章发到arXiv上别人认可嘛？ - 知乎", "date": "", "ddg_snippet": "发表到arXiv上，确切来讲不能叫发表，因为arXiv只是一个共享平台，并不能说明文章水平，会议和期刊就不一样了，要经过好多专家评审，而且不同会议和期刊也代表不同水平。 个人认为发到 arXiv 上的有以下情况： 第一种，就是为了 占坑，因为无论会议和期刊从投出到最终可以检索，都需要一半年 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/30131676", "content": "发表到arXiv上，确切来讲不能叫发表，因为arXiv只是一个共享平台，并不能说明文章水平，会议和期刊就不一样了，要经过好多专家评审，而且不同会议和期刊也代表不同水平。 个人认为发到 arXiv 上的有以下情况： 第一种，就是为了 占坑，因为无论会议和期刊从投出到最终可以检索，都需要一半年 ..."}
+{"idx": 7, "title": "如何知道arXiv上的论文投到哪里了？ - 知乎", "date": "", "ddg_snippet": "Nov 23, 2020 · 如何知道arXiv上的论文投到哪里了？ 比如我在arXiv上看到一篇不错的论文，想引用它，我想直接按录取它的期刊或者会议的格式引用，怎么做？", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/431622372", "content": "Nov 23, 2020 · 如何知道arXiv上的论文投到哪里了？ 比如我在arXiv上看到一篇不错的论文，想引用它，我想直接按录取它的期刊或者会议的格式引用，怎么做？"}
+{"idx": 8, "title": "arXiv怎么发音？ - 知乎", "date": "", "ddg_snippet": "Via arXiv : The arXiv (pronounced \" archive \", as if the \"X\" were the Greek letter Chi, χ) is a repository of electronic preprints , known as e-prints , of scientific papers in the fields of mathematics , physics , astronomy , computer science , quantitative biology , statistics , and quantitative finance, which can be accessed online 展开 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/28517169", "content": "Via arXiv : The arXiv (pronounced \" archive \", as if the \"X\" were the Greek letter Chi, χ) is a repository of electronic preprints , known as e-prints , of scientific papers in the fields of mathematics , physics , astronomy , computer science , quantitative biology , statistics , and quantitative finance, which can be accessed online 展开 ..."}
+{"idx": 9, "title": "如何看待朝鲜国家科学院数学研究所发布 arXiv 论文称千禧年难题纳维-...", "date": "", "ddg_snippet": "Aug 28, 2025 · 如何看待朝鲜国家科学院数学研究所发布 arXiv 论文称千禧年难题纳维-斯托克斯方程已被攻克？ 2025/8/28，arXiv 上挂出了一篇题为 《Global regularity of Leray-Hopf weak solutions to…", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/1944357449965494499", "content": "Aug 28, 2025 · 如何看待朝鲜国家科学院数学研究所发布 arXiv 论文称千禧年难题纳维-斯托克斯方程已被攻克？ 2025/8/28，arXiv 上挂出了一篇题为 《Global regularity of Leray-Hopf weak solutions to…"}
diff --git a/data/sampled_jsons/arXiv2503.19009_Video-ColBERT_references_[63].jsonl b/data/sampled_jsons/arXiv2503.19009_Video-ColBERT_references_[63].jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e8ed376f019e29ba6648be22955695066ae04fdb
--- /dev/null
+++ b/data/sampled_jsons/arXiv2503.19009_Video-ColBERT_references_[63].jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2503 . 19009 ] Video - ColBERT : Contextualized Late Interaction for...", "date": "", "ddg_snippet": "arXiv : 2503 . 19009 (cs). [Submitted on 24 Mar 2025]. Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "arXiv : 2503 . 19009 (cs). [Submitted on 24 Mar 2025]. Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training."}
+{"idx": 1, "title": "Video - ColBERT : Contextualized Late Interaction for... | alphaXiv", "date": "", "ddg_snippet": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2503.19009", "content": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training."}
+{"idx": 2, "title": "Обзоры препринтов научных статей «astro-ph/ arxiv .org» за... / Хабр", "date": "", "ddg_snippet": "Ежемесячный обзор научных статей в области астрофизики от профессора МГУ Сергея Попова. Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv .org.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948996/", "content": "Ежемесячный обзор научных статей в области астрофизики от профессора МГУ Сергея Попова. Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv .org."}
+{"idx": 3, "title": "Wan 2.2 14b | Image to Video", "date": "", "ddg_snippet": "Animate images in <30s with Wan 2.2 i2v 14b. Fast video inference, fast AI image to video model.For a complete list of restrictions and details regarding your rights, please refer to the full text of the license.", "subpage_snippet": "", "source": "replicate.com", "link": "https://replicate.com/wan-video/wan-2.2-i2v-a14b", "content": "Animate images in <30s with Wan 2.2 i2v 14b. Fast video inference, fast AI image to video model.For a complete list of restrictions and details regarding your rights, please refer to the full text of the license."}
+{"idx": 4, "title": "Alexander Martin (@alexdmartin314) | Aguea", "date": "", "ddg_snippet": "I'll be presenting two papers on video retrieval: Video - ColBERT (late interaction for video retrieval) and MultiVENT 2.0 (a challenging, multimodal event-centric IR benchmark!)", "subpage_snippet": "", "source": "aguea.net", "link": "https://aguea.net/alexdmartin314", "content": "I'll be presenting two papers on video retrieval: Video - ColBERT (late interaction for video retrieval) and MultiVENT 2.0 (a challenging, multimodal event-centric IR benchmark!)"}
+{"idx": 5, "title": "George Russell/Max Verstappen - Works | Archive of Our Own", "date": "", "ddg_snippet": "2. 6312、3312，包含 63 /33/12三人行情节！Enemies to Friends to Lovers. Slow Romance. Mythology References .", "subpage_snippet": "", "source": "archiveofourown.org", "link": "https://archiveofourown.org/tags/George+Russell*s*Max+Verstappen/works", "content": "2. 6312、3312，包含 63 /33/12三人行情节！Enemies to Friends to Lovers. Slow Romance. Mythology References ."}
+{"idx": 6, "title": "Машина Едет По Трассе Над Ней Ангел | TikTok", "date": "", "ddg_snippet": "TikTok video from anatol4ik (@anatol4ik): “Відповідь користувачу @T_R_D ЯК ЗРОБИТИ ФОТО АНГЕЛ НАД ФУРОЮ! #далекобійник #ангелнадфурою#фотозангелом#дальнобой”.На рахате #миягиэндшпиль #ночь #природа #дача #мясо #mercedes #eclass #amg #amg 63 .", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/машина-едет-по-трассе-над-ней-ангел", "content": "TikTok video from anatol4ik (@anatol4ik): “Відповідь користувачу @T_R_D ЯК ЗРОБИТИ ФОТО АНГЕЛ НАД ФУРОЮ! #далекобійник #ангелнадфурою#фотозангелом#дальнобой”.На рахате #миягиэндшпиль #ночь #природа #дача #мясо #mercedes #eclass #amg #amg 63 ."}
+{"idx": 7, "title": "Конвертировать HEIC в JPG онлайн — Convertio", "date": "", "ddg_snippet": "GIF Editor. Video Translator.", "subpage_snippet": "", "source": "convertio.co", "link": "https://convertio.co/ru/heic-jpg/", "content": "GIF Editor. Video Translator."}
+{"idx": 8, "title": "Attention Is All You Need - YouTube", "date": "", "ddg_snippet": "https:// arxiv .org/abs/1706.03762Abstract:The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an enco...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=iDulhoQ2pro", "content": "https:// arxiv .org/abs/1706.03762Abstract:The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an enco..."}
+{"idx": 9, "title": "All SHiFT Codes and How to Redeem... | Borderlands 4｜Game8", "date": "", "ddg_snippet": "The copyrights of videos of games used in our content and other intellectual property rights belong to the provider of the game. The contents we provide on this site were created personally by members of the Game8 editorial department.", "subpage_snippet": "", "source": "game8.co", "link": "https://game8.co/games/Borderlands-4/archives/548406", "content": "The copyrights of videos of games used in our content and other intellectual property rights belong to the provider of the game. The contents we provide on this site were created personally by members of the Game8 editorial department."}
diff --git a/data/sampled_jsons/arXiv_2110.10277_abstract_year_2022.jsonl b/data/sampled_jsons/arXiv_2110.10277_abstract_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a726660b6f8d6f65920b3bef083b1fc44a72fa47
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2110.10277_abstract_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "D20211030 arxiv 2110 14054 microboone ratio observed candidate...", "date": "", "ddg_snippet": "D20211030 arxiv 2110 14054 microboone ratio observed candidate electron low energy excess. Por Francisco R. Villatoro, el 31 octubre, 2021. Categoría(s):.", "subpage_snippet": "", "source": "francis.naukas.com", "link": "https://francis.naukas.com/2021/10/31/microoboone-descarta-un-neutrino-esteril-como-causa-de-la-anomalia-de-miniboone/d20211030-arxiv-2110-14054-microboone-ratio-observed-candidate-electron-low-energy-excess/", "content": "D20211030 arxiv 2110 14054 microboone ratio observed candidate electron low energy excess. Por Francisco R. Villatoro, el 31 octubre, 2021. Categoría(s):."}
+{"idx": 1, "title": "arXiv : 2110 .13848 Abstract | arXiv Analytics", "date": "", "ddg_snippet": "arXiv : 2110 .13848 [astro-ph.HE] Abstract References Reviews Resources. A Simple Model of Radiation from a Magnetized Neutron Star: Accreted Matter and Polar Hotspots. D. G. Yakovlev.QR code for arXiv : 2110 .13848 [astro-ph.HE].", "subpage_snippet": "", "source": "arxitics.com", "link": "http://arxitics.com/articles/2110.13848", "content": "arXiv : 2110 .13848 [astro-ph.HE] Abstract References Reviews Resources. A Simple Model of Radiation from a Magnetized Neutron Star: Accreted Matter and Polar Hotspots. D. G. Yakovlev.QR code for arXiv : 2110 .13848 [astro-ph.HE]."}
+{"idx": 2, "title": "ahajournals.org/doi/10.1161/circ.132.suppl_3.10277", "date": "", "ddg_snippet": "The site owner hides the web page description.", "subpage_snippet": "", "source": "www.ahajournals.org", "link": "https://www.ahajournals.org/doi/10.1161/circ.132.suppl_3.10277", "content": "The site owner hides the web page description."}
+{"idx": 3, "title": "A Deep Generative Approach to Conditional Sampling A Deep Generative Approach to Conditional Sampling A Deep Generative Approach to Conditional Sampling [Paper Note] Evolving Language Models without Labels ... - GitHub Proof-Theoretic Semantics - Stanford Encyclopedia of Philosophy Graph-Based Approximate Nearest Neighbor Search Revisited ... LLM-I: LLMs are Naturally Interleaved Multimodal Creators", "date": "", "ddg_snippet": "Oct 19, 2021 · Abstract page for arXiv paper 2110.10277 : A Deep Generative Approach to Conditional Sampling Abstract :Conditional distribution is a fundamental quantity for describing the relationship between a response and a predictor. We propose a Wasserstein generative approach to learning a conditional distribution. The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution. The conditional generator is estimated by matching a joint ... Abstract We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. 3 days ago · Abstract Large language models (LLMs) are increasingly trained with reinforcement learning from verifiable rewards (RLVR), yet real-world deployment demands models that can self-improve without labels or external judges. However, though this is prima facie obvious and proof-theoretic semanticists would normally agree with this abstract claim, the practice in proof-theoretic semantics is often different, and the topic of the identity of proofs is much neglected. It very frequently happens that rules which are equally powerful are identified. Abstract Graph-based approaches to approximate nearest neighbor search (ANNS) have achieved remarkable success in enabling fast, high-recall retrieval on billion-scale vector datasets. Among them, the Sparse Neighborhood Graph (SNG) has emerged as a widely adopted graph structure due to its superior search performance. 5 days ago · We propose LLM-Interleaved (LLM-I), a flexible and dynamic framework that reframes interleaved image-text generation as a tool-use problem. LLM-I is designed to overcome the \"one-tool\" bottleneck of current unified models, which are limited to synthetic imagery and struggle with tasks requiring factual grounding or programmatic precision. Our framework empowers a central LLM or MLLM agent to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2110.10277", "content": "Oct 19, 2021 · Abstract page for arXiv paper 2110.10277 : A Deep Generative Approach to Conditional Sampling Abstract :Conditional distribution is a fundamental quantity for describing the relationship between a response and a predictor. We propose a Wasserstein generative approach to learning a conditional distribution. The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution. The conditional generator is estimated by matching a joint ... Abstract We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. 3 days ago · Abstract Large language models (LLMs) are increasingly trained with reinforcement learning from verifiable rewards (RLVR), yet real-world deployment demands models that can self-improve without labels or external judges. However, though this is prima facie obvious and proof-theoretic semanticists would normally agree with this abstract claim, the practice in proof-theoretic semantics is often different, and the topic of the identity of proofs is much neglected. It very frequently happens that rules which are equally powerful are identified. Abstract Graph-based approaches to approximate nearest neighbor search (ANNS) have achieved remarkable success in enabling fast, high-recall retrieval on billion-scale vector datasets. Among them, the Sparse Neighborhood Graph (SNG) has emerged as a widely adopted graph structure due to its superior search performance. 5 days ago · We propose LLM-Interleaved (LLM-I), a flexible and dynamic framework that reframes interleaved image-text generation as a tool-use problem. LLM-I is designed to overcome the \"one-tool\" bottleneck of current unified models, which are limited to synthetic imagery and struggle with tasks requiring factual grounding or programmatic precision. Our framework empowers a central LLM or MLLM agent to ..."}
+{"idx": 4, "title": "A Deep Generative Approach to Conditional Sampling", "date": "", "ddg_snippet": "Abstract :Conditional distribution is a fundamental quantity for describing the relationship between a response and a predictor. We propose a Wasserstein generative approach to learning a conditional distribution. The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution. The conditional generator is estimated by matching a joint ...", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2110.10277", "content": "Abstract :Conditional distribution is a fundamental quantity for describing the relationship between a response and a predictor. We propose a Wasserstein generative approach to learning a conditional distribution. The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution. The conditional generator is estimated by matching a joint ..."}
+{"idx": 5, "title": "A Deep Generative Approach to Conditional Sampling", "date": "", "ddg_snippet": "Abstract We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2110.10277", "content": "Abstract We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma."}
+{"idx": 6, "title": "[Paper Note] Evolving Language Models without Labels ... - GitHub", "date": "", "ddg_snippet": "3 days ago · Abstract Large language models (LLMs) are increasingly trained with reinforcement learning from verifiable rewards (RLVR), yet real-world deployment demands models that can self-improve without labels or external judges.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AkihikoWatanabe/paper_notes/issues/2886", "content": "3 days ago · Abstract Large language models (LLMs) are increasingly trained with reinforcement learning from verifiable rewards (RLVR), yet real-world deployment demands models that can self-improve without labels or external judges."}
+{"idx": 7, "title": "Proof-Theoretic Semantics - Stanford Encyclopedia of Philosophy", "date": "", "ddg_snippet": "However, though this is prima facie obvious and proof-theoretic semanticists would normally agree with this abstract claim, the practice in proof-theoretic semantics is often different, and the topic of the identity of proofs is much neglected. It very frequently happens that rules which are equally powerful are identified.", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/archives/fall2025/entries/proof-theoretic-semantics/", "content": "However, though this is prima facie obvious and proof-theoretic semanticists would normally agree with this abstract claim, the practice in proof-theoretic semantics is often different, and the topic of the identity of proofs is much neglected. It very frequently happens that rules which are equally powerful are identified."}
+{"idx": 8, "title": "Graph-Based Approximate Nearest Neighbor Search Revisited ...", "date": "", "ddg_snippet": "Abstract Graph-based approaches to approximate nearest neighbor search (ANNS) have achieved remarkable success in enabling fast, high-recall retrieval on billion-scale vector datasets. Among them, the Sparse Neighborhood Graph (SNG) has emerged as a widely adopted graph structure due to its superior search performance.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2025arXiv250915531M/abstract", "content": "Abstract Graph-based approaches to approximate nearest neighbor search (ANNS) have achieved remarkable success in enabling fast, high-recall retrieval on billion-scale vector datasets. Among them, the Sparse Neighborhood Graph (SNG) has emerged as a widely adopted graph structure due to its superior search performance."}
+{"idx": 9, "title": "A Deep Generative Approach to Conditional Sampling", "date": "", "ddg_snippet": "B Proofs and additional technical details. arXiv : 2110 . 10277 v1 [math.ST] 19 Oct 2021. A Deep Generative Approach to Conditional Sampling.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2110.10277", "content": "B Proofs and additional technical details. arXiv : 2110 . 10277 v1 [math.ST] 19 Oct 2021. A Deep Generative Approach to Conditional Sampling."}
diff --git a/data/sampled_jsons/arXiv_2210.12468_Discrepancy_Minimization_Input-Sparsity_Time_technical_overview.jsonl b/data/sampled_jsons/arXiv_2210.12468_Discrepancy_Minimization_Input-Sparsity_Time_technical_overview.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f5c6d9bdf6b14295ee91643a3d7dd81c3228199
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2210.12468_Discrepancy_Minimization_Input-Sparsity_Time_technical_overview.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2210.12468] Discrepancy Minimization in Input-Sparsity Time Discrepancy Minimization in Input-Sparsity Time - NASA/ADS Yichuan Deng - Google Scholar \"Discrepancy Minimization in Input-Sparsity Time.\" - dblp Table 1 from Discrepancy Minimization in Input-Sparsity Time ... Discrepancy Minimization in Input-Sparsity Time,arXiv - CS ... 2210.12468 - Discrepancy Minimization in Input-Sparsity Time", "date": "", "ddg_snippet": "Oct 22, 2022 · View a PDF of the paper titled Discrepancy Minimization in Input-Sparsity Time , by Yichuan Deng and 3 other authors Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n^ {2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach. Ph.D. Student, University of Washington - Cited by 364 - Language Models - 3D Computer Vision - Biomedical AI Bibliographic details on Discrepancy Minimization in Input-Sparsity Time . Table 1: Progress on approximate discrepancy - minimization algorithms of real-valued m × n matrices (m ≥ n). For simplicity, we ignore no(1) and poly(log n) factors in the table. “[ES14]*” refers to a (black-box) combination of our Theorem 1.3 with [ES14] and using state-of-art LP solvers for square and tall matrices [LS14, BLSS20, JSWZ21]. - \" Discrepancy Minimization in Input-Sparsity Time \" Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n^ {2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach. Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n{2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2210.12468", "content": "Oct 22, 2022 · View a PDF of the paper titled Discrepancy Minimization in Input-Sparsity Time , by Yichuan Deng and 3 other authors Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n^ {2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach. Ph.D. Student, University of Washington - Cited by 364 - Language Models - 3D Computer Vision - Biomedical AI Bibliographic details on Discrepancy Minimization in Input-Sparsity Time . Table 1: Progress on approximate discrepancy - minimization algorithms of real-valued m × n matrices (m ≥ n). For simplicity, we ignore no(1) and poly(log n) factors in the table. “[ES14]*” refers to a (black-box) combination of our Theorem 1.3 with [ES14] and using state-of-art LP solvers for square and tall matrices [LS14, BLSS20, JSWZ21]. - \" Discrepancy Minimization in Input-Sparsity Time \" Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n^ {2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach. Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n{2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach."}
+{"idx": 1, "title": "Discrepancy Minimization in Input-Sparsity Time - NASA/ADS", "date": "", "ddg_snippet": "Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n^ {2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2022arXiv221012468D/abstract", "content": "Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n^ {2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach."}
+{"idx": 2, "title": "\"Discrepancy Minimization in Input-Sparsity Time.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Discrepancy Minimization in Input-Sparsity Time .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2210-12468.html", "content": "Bibliographic details on Discrepancy Minimization in Input-Sparsity Time ."}
+{"idx": 3, "title": "Table 1 from Discrepancy Minimization in Input-Sparsity Time ...", "date": "", "ddg_snippet": "Table 1: Progress on approximate discrepancy - minimization algorithms of real-valued m × n matrices (m ≥ n). For simplicity, we ignore no(1) and poly(log n) factors in the table. “[ES14]*” refers to a (black-box) combination of our Theorem 1.3 with [ES14] and using state-of-art LP solvers for square and tall matrices [LS14, BLSS20, JSWZ21]. - \" Discrepancy Minimization in Input-Sparsity Time \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Discrepancy-Minimization-in-Input-Sparsity-Time-Deng-Song/5b704a532353ac574fd8fb1aa61e63870a54080b/figure/0", "content": "Table 1: Progress on approximate discrepancy - minimization algorithms of real-valued m × n matrices (m ≥ n). For simplicity, we ignore no(1) and poly(log n) factors in the table. “[ES14]*” refers to a (black-box) combination of our Theorem 1.3 with [ES14] and using state-of-art LP solvers for square and tall matrices [LS14, BLSS20, JSWZ21]. - \" Discrepancy Minimization in Input-Sparsity Time \""}
+{"idx": 4, "title": "Discrepancy Minimization in Input-Sparsity Time,arXiv - CS ...", "date": "", "ddg_snippet": "Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n^ {2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach.", "subpage_snippet": "", "source": "www.x-mol.com", "link": "https://www.x-mol.com/paper/1584961519860846592", "content": "Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n^ {2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach."}
+{"idx": 5, "title": "2210.12468 - Discrepancy Minimization in Input-Sparsity Time", "date": "", "ddg_snippet": "Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n{2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2210.12468", "content": "Using a more intricate analysis and fast matrix-multiplication, we achieve $\\widetilde {O} (\\mathrm {nnz} (A) + n{2.53})$ time , which breaks cubic runtime for square matrices, and bypasses the barrier of linear-programming approaches [ES14] for which input-sparsity time is currently out of reach."}
+{"idx": 6, "title": "(PDF) Discrepancy Minimization in Input - Sparsity Time", "date": "", "ddg_snippet": "which input - sparsity time coloring was very recently obtained [JSS23].‡omri@cs.columbia.edu. Hebrew University and Columbia University. arXiv : 2210 . 12468 v1 [cs.DS] 22 Oct 2022.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364690363_Discrepancy_Minimization_in_Input-Sparsity_Time", "content": "which input - sparsity time coloring was very recently obtained [JSS23].‡omri@cs.columbia.edu. Hebrew University and Columbia University. arXiv : 2210 . 12468 v1 [cs.DS] 22 Oct 2022."}
+{"idx": 7, "title": "ICML Poster Discrepancy Minimization in Input - Sparsity Time", "date": "", "ddg_snippet": "updates with low-rank corrections. Our results nearly close the computational gap between real-valued and binary matrices, for which input - sparsity time coloring was recently obtained by [Jain, Sah and Sawhney, SODA 2023].", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45157", "content": "updates with low-rank corrections. Our results nearly close the computational gap between real-valued and binary matrices, for which input - sparsity time coloring was recently obtained by [Jain, Sah and Sawhney, SODA 2023]."}
+{"idx": 8, "title": "Discrepancy Minimization in Input - Sparsity Time | DeepAI", "date": "", "ddg_snippet": "[ES14] for which input - sparsity time is currently out of reach.Our result nearly closes the computational gap between real-valued and binary matrices (set-systems), for which input - sparsity time coloring was very recently obtained [JSS23].", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/discrepancy-minimization-in-input-sparsity-time", "content": "[ES14] for which input - sparsity time is currently out of reach.Our result nearly closes the computational gap between real-valued and binary matrices (set-systems), for which input - sparsity time coloring was very recently obtained [JSS23]."}
+{"idx": 9, "title": "Spencer's theorem in nearly input - sparsity time (2022) | Vishesh Jain", "date": "", "ddg_snippet": "TL;DR: In this paper , a width reduction technique for linear programs, not of covering/packing type, was proposed in near input - sparsity time using the multiplicative weights update method.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/spencer-s-theorem-in-nearly-input-sparsity-time-6dc6tahz", "content": "TL;DR: In this paper , a width reduction technique for linear programs, not of covering/packing type, was proposed in near input - sparsity time using the multiplicative weights update method."}
diff --git a/data/sampled_jsons/arXiv_2502.00136_Section_3.2_four-step_self-supervised_learning_behaviors_emotions_mapping.jsonl b/data/sampled_jsons/arXiv_2502.00136_Section_3.2_four-step_self-supervised_learning_behaviors_emotions_mapping.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b31551138bfafc3937e5842c3215ad538b3ad8be
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2502.00136_Section_3.2_four-step_self-supervised_learning_behaviors_emotions_mapping.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.In the learning process emotions are backpropagated by a mechanism of secondary reinforcement.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.In the learning process emotions are backpropagated by a mechanism of secondary reinforcement."}
+{"idx": 1, "title": "[2301.05712] A Survey on Self-supervised Learning: Algorithms ... [2408.17059] A Survey of the Self Supervised Learning ... A Cookbook of Self-Supervised Learning - arXiv.org A Comprehensive Survey on Self-Supervised Learning for ... LLM-Based Agents for Tool Learning: A Survey - Springer (PDF) ALERT: Adapt Language Models to Reasoning Tasks", "date": "", "ddg_snippet": "Jan 13, 2023 · Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning , aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has ... Aug 30, 2024 · Abstract page for arXiv paper 2408.17059: A Survey of the Self Supervised Learning Mechanisms for Vision Transformers Contents 1 What is Self-Supervised Learning and Why Bother? 1.1 Why a Cookbook for Self-Supervised Learning ? In Section 3, we present our proposed taxonomy for understanding self-supervised learning within the context of recommendation systems. The main content of the survey is covered in Section 4 , where we conduct separate reviews of the three self-supervised learning paradigms across various recommendation scenarios. Jun 26, 2025 · In this section , we provide a detailed introduction to constructing a tool- learning environment based on user queries. After receiving feedback signals from the environment, the structured framework filters input data, employs reflection to address hallucination, and ultimately plans and utilizes tools. Recent advancements in large language models have enabled them to perform well on complex tasks that require step -by- step reasoning with few-shot learning . However, it is unclear whether these models are applying reasoning skills they have learned", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.05712", "content": "Jan 13, 2023 · Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning , aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has ... Aug 30, 2024 · Abstract page for arXiv paper 2408.17059: A Survey of the Self Supervised Learning Mechanisms for Vision Transformers Contents 1 What is Self-Supervised Learning and Why Bother? 1.1 Why a Cookbook for Self-Supervised Learning ? In Section 3, we present our proposed taxonomy for understanding self-supervised learning within the context of recommendation systems. The main content of the survey is covered in Section 4 , where we conduct separate reviews of the three self-supervised learning paradigms across various recommendation scenarios. Jun 26, 2025 · In this section , we provide a detailed introduction to constructing a tool- learning environment based on user queries. After receiving feedback signals from the environment, the structured framework filters input data, employs reflection to address hallucination, and ultimately plans and utilizes tools. Recent advancements in large language models have enabled them to perform well on complex tasks that require step -by- step reasoning with few-shot learning . However, it is unclear whether these models are applying reasoning skills they have learned"}
+{"idx": 2, "title": "[2408.17059] A Survey of the Self Supervised Learning ...", "date": "", "ddg_snippet": "Aug 30, 2024 · Abstract page for arXiv paper 2408.17059: A Survey of the Self Supervised Learning Mechanisms for Vision Transformers", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17059", "content": "Aug 30, 2024 · Abstract page for arXiv paper 2408.17059: A Survey of the Self Supervised Learning Mechanisms for Vision Transformers"}
+{"idx": 3, "title": "A Cookbook of Self-Supervised Learning - arXiv.org", "date": "", "ddg_snippet": "Contents 1 What is Self-Supervised Learning and Why Bother? 1.1 Why a Cookbook for Self-Supervised Learning ?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2304.12210", "content": "Contents 1 What is Self-Supervised Learning and Why Bother? 1.1 Why a Cookbook for Self-Supervised Learning ?"}
+{"idx": 4, "title": "A Comprehensive Survey on Self-Supervised Learning for ...", "date": "", "ddg_snippet": "In Section 3, we present our proposed taxonomy for understanding self-supervised learning within the context of recommendation systems. The main content of the survey is covered in Section 4 , where we conduct separate reviews of the three self-supervised learning paradigms across various recommendation scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.03354", "content": "In Section 3, we present our proposed taxonomy for understanding self-supervised learning within the context of recommendation systems. The main content of the survey is covered in Section 4 , where we conduct separate reviews of the three self-supervised learning paradigms across various recommendation scenarios."}
+{"idx": 5, "title": "LLM-Based Agents for Tool Learning: A Survey - Springer", "date": "", "ddg_snippet": "Jun 26, 2025 · In this section , we provide a detailed introduction to constructing a tool- learning environment based on user queries. After receiving feedback signals from the environment, the structured framework filters input data, employs reflection to address hallucination, and ultimately plans and utilizes tools.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s41019-025-00296-9", "content": "Jun 26, 2025 · In this section , we provide a detailed introduction to constructing a tool- learning environment based on user queries. After receiving feedback signals from the environment, the structured framework filters input data, employs reflection to address hallucination, and ultimately plans and utilizes tools."}
+{"idx": 6, "title": "[ 2502 . 00136 ] A Checks-and-Balances Framework for Context-Aware...", "date": "", "ddg_snippet": "arXiv : 2502 . 00136 (cs). [Submitted on 31 Jan 2025 (v1), last revised 26 May 2025 (this version, v2)]. Title:A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment.Beyond structural separation, we address a fundamental challenge: regulating emotion to shape behaviors .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "arXiv : 2502 . 00136 (cs). [Submitted on 31 Jan 2025 (v1), last revised 26 May 2025 (this version, v2)]. Title:A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment.Beyond structural separation, we address a fundamental challenge: regulating emotion to shape behaviors ."}
+{"idx": 7, "title": "Machine Learning Tutorial - GeeksforGeeks", "date": "", "ddg_snippet": "Machine Learning is mainly divided into three core types: Supervised, Unsupervised and Reinforcement Learning along with two additional types, Semi-Supervised and Self - Supervised Learning .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/machine-learning/", "content": "Machine Learning is mainly divided into three core types: Supervised, Unsupervised and Reinforcement Learning along with two additional types, Semi-Supervised and Self - Supervised Learning ."}
+{"idx": 8, "title": "Motivation and Learning Strategies for College Success...", "date": "", "ddg_snippet": "What Learning Strategies Promote Learning and Retention?Exercise 5.2: Identifying Irrational Thinking Patterns 119 How Can the Rational Emotive Approach Be Used to Change Emotions ? How Does Self -Talk Influence My Emotions and Behavior ?", "subpage_snippet": "", "source": "samarnhpang.wordpress.com", "link": "https://samarnhpang.wordpress.com/wp-content/uploads/2011/06/theories-in-learning.pdf", "content": "What Learning Strategies Promote Learning and Retention?Exercise 5.2: Identifying Irrational Thinking Patterns 119 How Can the Rational Emotive Approach Be Used to Change Emotions ? How Does Self -Talk Influence My Emotions and Behavior ?"}
+{"idx": 9, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "Drawing from psychological theories where managing emotional responses prevents harmful behaviors , we develop a self-supervised learning pipeline that maps emo-tions to linguistic behaviors , enabling precise be-havioral modulation through emotional condition-ing.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "Drawing from psychological theories where managing emotional responses prevents harmful behaviors , we develop a self-supervised learning pipeline that maps emo-tions to linguistic behaviors , enabling precise be-havioral modulation through emotional condition-ing."}
diff --git a/data/sampled_jsons/arXiv_2502.12892_equation_2_stability_calculation.jsonl b/data/sampled_jsons/arXiv_2502.12892_equation_2_stability_calculation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..39d638d6532c085a102d95cf6116359acd427629
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2502.12892_equation_2_stability_calculation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "We measure stability based on Equation 2 : the optimal average cosine similarity between the dictionaries across 4 runs after finding the best alignment via the Hungarian algorithm. Archetypal-SAEs improve stability without compromising reconstruction fidelity, performing better on the stability -reconstruction tradeoff than existing methods.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.12892", "content": "We measure stability based on Equation 2 : the optimal average cosine similarity between the dictionaries across 4 runs after finding the best alignment via the Hungarian algorithm. Archetypal-SAEs improve stability without compromising reconstruction fidelity, performing better on the stability -reconstruction tradeoff than existing methods."}
+{"idx": 1, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2502.12892 : Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "Abstract page for arXiv paper 2502.12892 : Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models"}
+{"idx": 2, "title": "arXiv:2509.16583v1 [cond-mat.soft] 20 Sep 2025", "date": "", "ddg_snippet": "We herein summarise here the derivation of the model and outline the stability calculations performed for dif- ferent jet geometries, the analysis of which is presented in detail in the companion article [18].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.16583", "content": "We herein summarise here the derivation of the model and outline the stability calculations performed for dif- ferent jet geometries, the analysis of which is presented in detail in the companion article [18]."}
+{"idx": 3, "title": "Essential renormalization group equation for gravity coupled to a ...", "date": "", "ddg_snippet": "We study the essential renormalization group equation , in which inessential couplings are removed via field redefinitions, for Einstein gravity coupled to a massive scalar field in the presence of a cosmological constant. Our results indicate that perturbatively nonrenormalizable terms can be eliminated due to the cosmological term, in contrast to the case of perturbation around flat spacetime ...", "subpage_snippet": "", "source": "journals.aps.org", "link": "https://journals.aps.org/prd/abstract/10.1103/16c5-73q2", "content": "We study the essential renormalization group equation , in which inessential couplings are removed via field redefinitions, for Einstein gravity coupled to a massive scalar field in the presence of a cosmological constant. Our results indicate that perturbatively nonrenormalizable terms can be eliminated due to the cosmological term, in contrast to the case of perturbation around flat spacetime ..."}
+{"idx": 4, "title": "Theory of active self-organization of dense nematic structures in the ...", "date": "", "ddg_snippet": "Reviewer # 2 (Public Review): Summary: The article by Waleed et al discusses the self organization of actin cytoskeleton using the theory of active nematics. Linear stability analysis of the governing equations and computer simulations show that the system is unstable to density fluctuations and self organized structures can emerge.", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/93097v2/reviews", "content": "Reviewer # 2 (Public Review): Summary: The article by Waleed et al discusses the self organization of actin cytoskeleton using the theory of active nematics. Linear stability analysis of the governing equations and computer simulations show that the system is unstable to density fluctuations and self organized structures can emerge."}
+{"idx": 5, "title": "Dimensionality effect on exceptional fermionic superfluidity with spin ...", "date": "", "ddg_snippet": "Finally, we solve the NH gap equation , evaluate the condensation energy, and obtain the phase diagrams for t⊥ = 0, 0.3, 0.5, and 1, as shown in Fig. 5. It is known that, in the two-dimensional limit with t⊥ = 1, the superfluid state appears for arbi-trary U, and the introduction of asymmetric hopping γ induce the exceptional SF [26].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.17414", "content": "Finally, we solve the NH gap equation , evaluate the condensation energy, and obtain the phase diagrams for t⊥ = 0, 0.3, 0.5, and 1, as shown in Fig. 5. It is known that, in the two-dimensional limit with t⊥ = 1, the superfluid state appears for arbi-trary U, and the introduction of asymmetric hopping γ induce the exceptional SF [26]."}
+{"idx": 6, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "We measure stability based on Equation 2 : the optimal average cosine similarity between the dictionaries across 4 runs after finding the best alignment via the Hungarian algorithm. Archetypal-SAEs improve stability without compromising reconstruction fidelity, performing better on the stability -reconstruction tradeoff than existing methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v1", "content": "We measure stability based on Equation 2 : the optimal average cosine similarity between the dictionaries across 4 runs after finding the best alignment via the Hungarian algorithm. Archetypal-SAEs improve stability without compromising reconstruction fidelity, performing better on the stability -reconstruction tradeoff than existing methods."}
+{"idx": 7, "title": "PDF Quasinormal modes and dynamical evolution of scalar fields in the ...", "date": "", "ddg_snippet": "Section 2 of this manuscript provides a brief overview of the Einstein-bumblebee gravity model and its underlying geometrical background, and derives equations of motion for the scalar field. Section 3 describes the WKB method [67], employs numerical techniques to find the scalar field QNMs, and presents the associated numerical results.", "subpage_snippet": "", "source": "comptes-rendus.academie-sciences.fr", "link": "https://comptes-rendus.academie-sciences.fr/physique/item/10.5802/crphys.260.pdf", "content": "Section 2 of this manuscript provides a brief overview of the Einstein-bumblebee gravity model and its underlying geometrical background, and derives equations of motion for the scalar field. Section 3 describes the WKB method [67], employs numerical techniques to find the scalar field QNMs, and presents the associated numerical results."}
+{"idx": 8, "title": "Improvement associated with mesh geometry for the decomposition of ...", "date": "", "ddg_snippet": "2.2.1. Geometry calculation The spatial resolution of the Sentinel-1 products is approximately 20 m × 5 m (azimuth and range) (Copernicus Land Monitoring Service 2024b). Given that the exact position of a scatterer within a pixel is unknown, we accounted for the most restrictive scenario - placing the scatterer in a corner of the pixel (", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/10095020.2025.2547948", "content": "2.2.1. Geometry calculation The spatial resolution of the Sentinel-1 products is approximately 20 m × 5 m (azimuth and range) (Copernicus Land Monitoring Service 2024b). Given that the exact position of a scatterer within a pixel is unknown, we accounted for the most restrictive scenario - placing the scatterer in a corner of the pixel ("}
+{"idx": 9, "title": "Adaptive kernel predictors from feature-learning infinite limits of ...", "date": "", "ddg_snippet": "This work studies the large-width limit of feature-learning Bayesian networks, and studies gradient flow training of randomly initialized networks trained with weight decay in the infinite-width limit using dynamical mean field theory (DMFT). Previous influential work showed that infinite width limits of neural networks in the lazy training regime are described by kernel machines. Here, we ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Adaptive-kernel-predictors-from-feature-learning-of-Lauditi-Bordelon/6b4fbfb839daf68c842c5548b6aaed6eebb08276", "content": "This work studies the large-width limit of feature-learning Bayesian networks, and studies gradient flow training of randomly initialized networks trained with weight decay in the infinite-width limit using dynamical mean field theory (DMFT). Previous influential work showed that infinite width limits of neural networks in the lazy training regime are described by kernel machines. Here, we ..."}
diff --git a/data/sampled_jsons/arXiv_2503.01485_FlowDec_Table_2_NDAC-75_NDAC-25_codebooks_embedding_dimension.jsonl b/data/sampled_jsons/arXiv_2503.01485_FlowDec_Table_2_NDAC-75_NDAC-25_codebooks_embedding_dimension.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3bebc7062f07a114d963eecce2dda606c8077104
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2503.01485_FlowDec_Table_2_NDAC-75_NDAC-25_codebooks_embedding_dimension.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compar…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compar…"}
+{"idx": 2, "title": "Table 6 from FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "Table 6: Mean ± 95% confidence interval of objective metrics for DAC- 75 , compared to a variant (\"+Cw\") trained with the original adversarial and non-adversarial losses as well as our proposed CQT and waveform losses used for DAC- 75 (Section 3.4). Bitrates are in kbit/s, and FAD is multiplied by 100 for readability. - \" FlowDec : A flow-based full-band general audio codec with high ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/FlowDec:-A-flow-based-full-band-general-audio-codec-Welker-Le/ec40a4902b277f0f9e3704c1b23634cad4fe0dcf/figure/7", "content": "Table 6: Mean ± 95% confidence interval of objective metrics for DAC- 75 , compared to a variant (\"+Cw\") trained with the original adversarial and non-adversarial losses as well as our proposed CQT and waveform losses used for DAC- 75 (Section 3.4). Bitrates are in kbit/s, and FAD is multiplied by 100 for readability. - \" FlowDec : A flow-based full-band general audio codec with high ..."}
+{"idx": 3, "title": "Topic-VQ-VAE: Leveraging Latent Codebooks for - arXiv.org", "date": "", "ddg_snippet": "Abstract This paper introduces a novel approach for topic modeling utilizing latent codebooks from Vector-Quantized Variational Auto-Encoder (VQ-VAE), discretely encapsulating the rich information of the pre-trained embeddings such as the pre-trained language model. From the novel interpretation of the latent codebooks and embeddings as conceptual bag-of-words, we propose a new generative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.11532v2", "content": "Abstract This paper introduces a novel approach for topic modeling utilizing latent codebooks from Vector-Quantized Variational Auto-Encoder (VQ-VAE), discretely encapsulating the rich information of the pre-trained embeddings such as the pre-trained language model. From the novel interpretation of the latent codebooks and embeddings as conceptual bag-of-words, we propose a new generative ..."}
+{"idx": 4, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389580542_FlowDec_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow ..."}
+{"idx": 5, "title": "PDF arXiv:1205.2949v1 [hep-ph] 14 May 2012", "date": "", "ddg_snippet": "Naive dimensional analysis (NDA) is a widely used ansatz to estimate coupling con-stants among composite states emerging from dynamics of a strongly coupled gauge theory. However, the validity of NDA is still unclear because of the difficulty in calcu-lating these quantities in strongly coupled theories. We examine the NDA ansatz using gauge/string duality, by estimating glueball coupling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1205.2949v1.pdf", "content": "Naive dimensional analysis (NDA) is a widely used ansatz to estimate coupling con-stants among composite states emerging from dynamics of a strongly coupled gauge theory. However, the validity of NDA is still unclear because of the difficulty in calcu-lating these quantities in strongly coupled theories. We examine the NDA ansatz using gauge/string duality, by estimating glueball coupling ..."}
+{"idx": 6, "title": "Abstract 1. Introduction arXiv:2505.22865v1 [cs.SD] 28 May 2025", "date": "", "ddg_snippet": "This paper discusses a novel framework for accelerating reasoning in large language models while maintaining high-quality results.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.22865", "content": "This paper discusses a novel framework for accelerating reasoning in large language models while maintaining high-quality results."}
+{"idx": 7, "title": "SwitchCodec: A High-Fidelity Nerual Audio Codec With Sparse Quantization", "date": "", "ddg_snippet": "The impact on bandwidth depends on the audio length. For example, a 2-second audio clip in-curs an extra Nr/2 bps cost. Additionally, Table 4 demonstrates the performance improvement of our algorithm, showing that it maintains competitiveness with state-of-the-art codecs even in low-latency scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.24437v2", "content": "The impact on bandwidth depends on the audio length. For example, a 2-second audio clip in-curs an extra Nr/2 bps cost. Additionally, Table 4 demonstrates the performance improvement of our algorithm, showing that it maintains competitiveness with state-of-the-art codecs even in low-latency scenarios."}
+{"idx": 8, "title": "NDA/BLA Multi-Disciplinary Review and Evaluation (Dupixent)", "date": "", "ddg_snippet": "The immunogenicity incidences in phase 3 trial R668-AD-1526 are summarized in Table 2 . The incidences for treatment emergent ADA were 16% (13/81) and 20.7% (17/82) for the 200 mg/300 mg Q2W and ...", "subpage_snippet": "", "source": "www.fda.gov", "link": "https://www.fda.gov/media/175317/download", "content": "The immunogenicity incidences in phase 3 trial R668-AD-1526 are summarized in Table 2 . The incidences for treatment emergent ADA were 16% (13/81) and 20.7% (17/82) for the 200 mg/300 mg Q2W and ..."}
+{"idx": 9, "title": "SwitchCodec: A High-Fidelity Nerual Audio Codec With Sparse Quantization", "date": "", "ddg_snippet": "The channel dimension progressively doubles from 64 to 1024, while the temporal dimension is compressed with ratios progressing from 1× to 512×. A final 3×1 convolution outputs a 1024-dimensional feature vector. The decoder employs a symmetric architecture to the en-coder, leveraging transposed convolutions for signal recon-struction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.24437", "content": "The channel dimension progressively doubles from 64 to 1024, while the temporal dimension is compressed with ratios progressing from 1× to 512×. A final 3×1 convolution outputs a 1024-dimensional feature vector. The decoder employs a symmetric architecture to the en-coder, leveraging transposed convolutions for signal recon-struction."}
diff --git a/data/sampled_jsons/arXiv_2503.01485_table_2_specifications_NDAC-75_NDAC-25_parameters.jsonl b/data/sampled_jsons/arXiv_2503.01485_table_2_specifications_NDAC-75_NDAC-25_parameters.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3cf47f4322e39f6978c898df1b50d3311972b24b
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2503.01485_table_2_specifications_NDAC-75_NDAC-25_parameters.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.01485] FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "NDAC-75 is targeted at 48 kHz audio with a whole-number feature rate ( 75 Hz) and whole-number bitrates. NDAC-25 is a variant tailored for downstream generative audio tasks, with a lower feature rate ( 25 Hz) and feature dimension which are advantageous for audio generation due to more efficient memory usage and decreased modeling difficulties.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2503.01485", "content": "NDAC-75 is targeted at 48 kHz audio with a whole-number feature rate ( 75 Hz) and whole-number bitrates. NDAC-25 is a variant tailored for downstream generative audio tasks, with a lower feature rate ( 25 Hz) and feature dimension which are advantageous for audio generation due to more efficient memory usage and decreased modeling difficulties."}
+{"idx": 1, "title": "arXiv.org", "date": "", "ddg_snippet": "arXiv .org", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485", "content": "arXiv .org"}
+{"idx": 2, "title": "Car Specifications and Features, Equipment and... | VinDecoderz.com", "date": "", "ddg_snippet": "Get detailed car specifications , performance, standard equipment and comprehensive auto data.", "subpage_snippet": "", "source": "www.vindecoderz.com", "link": "https://www.vindecoderz.com/EN/car-specifications-features-performance-equipment", "content": "Get detailed car specifications , performance, standard equipment and comprehensive auto data."}
+{"idx": 3, "title": "Обзоры препринтов научных статей «astro-ph/ arxiv .org» за... / Хабр", "date": "", "ddg_snippet": "обзор arxiv :2506.02907 Высококонтрастная коронография (High-Contrast Coronagraphy)Authors: Matthew A. Kenworthy, Sebastiaan Y. HaffertComments: Invited review article. 41 pages, 12 figures, 1 table .", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948996/", "content": "обзор arxiv :2506.02907 Высококонтрастная коронография (High-Contrast Coronagraphy)Authors: Matthew A. Kenworthy, Sebastiaan Y. HaffertComments: Invited review article. 41 pages, 12 figures, 1 table ."}
+{"idx": 4, "title": "Mazda MX-5 2019 JM1 NDAC 75 K0306493 from Copart - Autoplatforma", "date": "", "ddg_snippet": "Купите Mazda MX-5 2019 JM1 NDAC 75 K0306493 по лучшей цене с авто аукциона США copart с быстрой и дешевой доставкой в вашу страну.Подробная информация JM1 NDAC 75 K0306493: Номер лота", "subpage_snippet": "", "source": "autoplatforma.com", "link": "https://autoplatforma.com/ru/auction/lot/mazda-mx-5-2019-white-jm1ndac75k0306493-copart-84008724", "content": "Купите Mazda MX-5 2019 JM1 NDAC 75 K0306493 по лучшей цене с авто аукциона США copart с быстрой и дешевой доставкой в вашу страну.Подробная информация JM1 NDAC 75 K0306493: Номер лота"}
+{"idx": 5, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "NDAC-75 is targeted at 48 kHz audio with a whole-number feature rate ( 75 Hz) and whole-number bitrates. NDAC-25 is a variant tailored for downstream generative audio tasks, with a lower feature rate ( 25 Hz) and feature dimension which are advantageous for audio generation due to more efficient memory usage and decreased modeling difficulties.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "NDAC-75 is targeted at 48 kHz audio with a whole-number feature rate ( 75 Hz) and whole-number bitrates. NDAC-25 is a variant tailored for downstream generative audio tasks, with a lower feature rate ( 25 Hz) and feature dimension which are advantageous for audio generation due to more efficient memory usage and decreased modeling difficulties."}
+{"idx": 6, "title": "NADAC Equivalency Metrics - Medicaid.gov", "date": "", "ddg_snippet": "Dec 30, 2024 · NADAC Equivalency Metrics Table 1: Illustrates the aggregate mean and median discounts for single source legend drugs and innovator multiple source legend drugs (both commonly known as brand legend drugs), as well as non‐innovator multiple source legend drugs (commonly known as generic legend drugs) comparing the published National Average Drug Acquisition Cost (NADAC) values to that of the ...", "subpage_snippet": "", "source": "www.medicaid.gov", "link": "https://www.medicaid.gov/medicaid/prescription-drugs/downloads/retail-price-survey/nadac-equiv-metrics.pdf", "content": "Dec 30, 2024 · NADAC Equivalency Metrics Table 1: Illustrates the aggregate mean and median discounts for single source legend drugs and innovator multiple source legend drugs (both commonly known as brand legend drugs), as well as non‐innovator multiple source legend drugs (commonly known as generic legend drugs) comparing the published National Average Drug Acquisition Cost (NADAC) values to that of the ..."}
+{"idx": 7, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio codec...", "date": "", "ddg_snippet": "You can for instance use our pre-trained NDAC variants - see the \"Inference\" section for how to run them. The expected input format for FlowDec datasets is a file containing a comma-separated list of paths, e.g.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "You can for instance use our pre-trained NDAC variants - see the \"Inference\" section for how to run them. The expected input format for FlowDec datasets is a file containing a comma-separated list of paths, e.g."}
+{"idx": 8, "title": "UPDATE SQL — 95 универсальных примеров для всех реализаций...", "date": "", "ddg_snippet": "25 . Массовое изменение номера версии: UPDATE demo_ table _ 25 SET value = 'Updated 25 ' WHERE id = 25", "subpage_snippet": "", "source": "sqller.com", "link": "https://sqller.com/sql/update-sql", "content": "25 . Массовое изменение номера версии: UPDATE demo_ table _ 25 SET value = 'Updated 25 ' WHERE id = 25"}
+{"idx": 9, "title": "2016 mazda CX-3 CX-3 touri gas - JM1DKBC76G0115924", "date": "", "ddg_snippet": "Mazda MX-5 miata club. JM1 NDAC 75 G0104843. 2 км.", "subpage_snippet": "", "source": "autoconsultant.com.ua", "link": "https://autoconsultant.com.ua/automobile/mazda/cx_3/bec2a2dbc368995-2016-mazda-cx3-jm1dkbc76g0115924/", "content": "Mazda MX-5 miata club. JM1 NDAC 75 G0104843. 2 км."}
diff --git a/data/sampled_jsons/arXiv_2503.16979_Equation_(6)_motion_interpolation.jsonl b/data/sampled_jsons/arXiv_2503.16979_Equation_(6)_motion_interpolation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cdd05f89421e2d81b6c1a4b18c4a2e8d037e2f8c
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2503.16979_Equation_(6)_motion_interpolation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Solve the equation : 6 x - x = 8.4 - 0.9", "date": "", "ddg_snippet": "Банк заданий | Подробное решение | Смотреть ответ Solve the equation : 6 x - x = 8.4 - 0.9.", "subpage_snippet": "", "source": "www.euroki.org", "link": "https://www.euroki.org/koza/solve-the-equation-x--x-5391978", "content": "Банк заданий | Подробное решение | Смотреть ответ Solve the equation : 6 x - x = 8.4 - 0.9."}
+{"idx": 1, "title": "Find an equation of the tangent line to the... | Wyzant Ask An Expert", "date": "", "ddg_snippet": "Hi Celine, To find an equation of the tangent line I always start in point - slope formfind the limit as it approaches -3 in the equation ( 6 x+9)/x^4+6x^3+9x^2.", "subpage_snippet": "", "source": "www.wyzant.com", "link": "https://www.wyzant.com/resources/answers/747606/find-an-equation-of-the-tangent-line-to-the-graph-of-the-function-f-x-x-2-3", "content": "Hi Celine, To find an equation of the tangent line I always start in point - slope formfind the limit as it approaches -3 in the equation ( 6 x+9)/x^4+6x^3+9x^2."}
+{"idx": 2, "title": "Solved 1 ) Method 1 Uses equation [ 6 ], conservation of | Chegg.com", "date": "", "ddg_snippet": "Using equation [ 6 ], solve for v. 2) Equation [3]. conservation of momentum. is used to find the initial velocity of the ball, vb.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/1-method-1-uses-equation-6-conservation-energy-determine-initial-velocity-pendulum-ball-v--q7763607", "content": "Using equation [ 6 ], solve for v. 2) Equation [3]. conservation of momentum. is used to find the initial velocity of the ball, vb."}
+{"idx": 3, "title": "Inhomogeneous Electromagnetic Wave Equation • Physics Forums", "date": "", "ddg_snippet": "Inhomogeneous Electromagnetic Wave Equation. Thread starter Warr. Start date Jan 25, 2007.but I couldn't get that to work out either, using the earlier vector calc identity ( equation 6 ). So basically I am either approaching this wrong, or have made a bad assumption.", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/inhomogeneous-electromagnetic-wave-equation.153084/", "content": "Inhomogeneous Electromagnetic Wave Equation. Thread starter Warr. Start date Jan 25, 2007.but I couldn't get that to work out either, using the earlier vector calc identity ( equation 6 ). So basically I am either approaching this wrong, or have made a bad assumption."}
+{"idx": 4, "title": "[FREE] The solution to the equation $ 6 \\left(2^{x+4}\\right)... - brainly.com", "date": "", "ddg_snippet": "Problem Setup We are given the equation 6 (2x+4)=36 and asked to find the value of x that satisfies it. We will solve this equation step-by-step. Isolating the Exponential Term First, divide both sides of the equation by 6 to isolate the exponential term:2x+4=636 =6.", "subpage_snippet": "", "source": "brainly.com", "link": "https://brainly.com/question/62589672", "content": "Problem Setup We are given the equation 6 (2x+4)=36 and asked to find the value of x that satisfies it. We will solve this equation step-by-step. Isolating the Exponential Term First, divide both sides of the equation by 6 to isolate the exponential term:2x+4=636 =6."}
+{"idx": 5, "title": "To model the motion of a swimmer using the parametric... | Nail IB", "date": "", "ddg_snippet": "From Newton’s 2nd Law of Motion , the force applied on anybody is equal to the product of mass and acceleration of the body. Using the principle, equation ( 6 ) could be reframed asFrom the equation (2), the displacement time equation for the horizontal motion would be", "subpage_snippet": "", "source": "nailib.com", "link": "https://nailib.com/ia-sample/ib-math-ai-hl/64bf4a2140d5d5163181df29", "content": "From Newton’s 2nd Law of Motion , the force applied on anybody is equal to the product of mass and acceleration of the body. Using the principle, equation ( 6 ) could be reframed asFrom the equation (2), the displacement time equation for the horizontal motion would be"}
+{"idx": 6, "title": "Move One Matchstick To Fix The Equation 6 +4=4 | Matchstick Puzzle...", "date": "", "ddg_snippet": "Today, we're looking at the puzzling equation : 6 +4=4. But don't worry, with just one clever matchstick move, We'll turn it into a proper math problem.Let's uncover the solution to this matchstick equation! To correct \"6+4=4\" with just one move, follow these simple steps", "subpage_snippet": "", "source": "www.mindyourlogic.com", "link": "https://www.mindyourlogic.com/daily-matchstick-puzzles/move-one-matchstick-to-fix-the-equation-6+4=4-matchstick-puzzle", "content": "Today, we're looking at the puzzling equation : 6 +4=4. But don't worry, with just one clever matchstick move, We'll turn it into a proper math problem.Let's uncover the solution to this matchstick equation! To correct \"6+4=4\" with just one move, follow these simple steps"}
+{"idx": 7, "title": "Solve Quadratic equations 36x2-12x+1=0 Tiger Algebra Solver", "date": "", "ddg_snippet": "Solving a Single Variable Equation Since all these terms are equal to each other, it actually means : 6x-1 = 0. Add 1 to both sides of the equation : 6 x = 1 Divide both sides of the equation by 6: x = 1/6 = 0.167.", "subpage_snippet": "", "source": "www.tiger-algebra.com", "link": "https://www.tiger-algebra.com/drill/36x2-12x_1=0/", "content": "Solving a Single Variable Equation Since all these terms are equal to each other, it actually means : 6x-1 = 0. Add 1 to both sides of the equation : 6 x = 1 Divide both sides of the equation by 6: x = 1/6 = 0.167."}
+{"idx": 8, "title": "quantum mechanics - Why did Dirac choose a linear equation in...", "date": "", "ddg_snippet": "The Dirac equation arose out of the need for a quantum mechanical wave equation that treats position and time on an equal basis, just as relativity expects them to be. Now the Schrodinger equation is.", "subpage_snippet": "", "source": "physics.stackexchange.com", "link": "https://physics.stackexchange.com/questions/546824/why-did-dirac-choose-a-linear-equation-in-momentum-for-formulating-a-relativisti", "content": "The Dirac equation arose out of the need for a quantum mechanical wave equation that treats position and time on an equal basis, just as relativity expects them to be. Now the Schrodinger equation is."}
+{"idx": 9, "title": "If one root of the equation 2x^2- 6 x+c=0 is (3+5i)/2 , then the real v", "date": "", "ddg_snippet": "If both roots of the equation ax2+bx+c=0 lies in(3,8) then possible integral value of one of the root of the equation a(xx−4)2+b(xx−4)+c=0 is.", "subpage_snippet": "", "source": "www.doubtnut.com", "link": "https://www.doubtnut.com/qna/56421", "content": "If both roots of the equation ax2+bx+c=0 lies in(3,8) then possible integral value of one of the root of the equation a(xx−4)2+b(xx−4)+c=0 is."}
diff --git a/data/sampled_jsons/arxiv2111.02801_gPINN_diffusion_equation_experiment_setup_Jeremy_Yu.jsonl b/data/sampled_jsons/arxiv2111.02801_gPINN_diffusion_equation_experiment_setup_Jeremy_Yu.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..afac06bae4f1e613a3f760a394b8bebc0166ca6d
--- /dev/null
+++ b/data/sampled_jsons/arxiv2111.02801_gPINN_diffusion_equation_experiment_setup_Jeremy_Yu.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2111.02801] Gradient-enhanced physics-informed neural networks for ...", "date": "", "ddg_snippet": "Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss function of the neural network, and have been successfully employed to solve diverse forward and inverse PDE problems. However, one disadvantage of the first generation of PINNs is that they usually ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.02801", "content": "Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss function of the neural network, and have been successfully employed to solve diverse forward and inverse PDE problems. However, one disadvantage of the first generation of PINNs is that they usually ..."}
+{"idx": 1, "title": "[2111.02801] Gradient-enhanced physics-informed neural networks for ...", "date": "", "ddg_snippet": "In the Poisson equation , the performance of gPINN depends on the value of the weight, but in this diffusion -reaction system, gPINN is not sensitive to the value of w. gPINN with the values of w = 0.01, 0.1, and 1 all outperform PINN by up to two orders of magnitude for the L 2 relative errors of u, d u d x and d u d t, and the mean absolute ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2111.02801", "content": "In the Poisson equation , the performance of gPINN depends on the value of the weight, but in this diffusion -reaction system, gPINN is not sensitive to the value of w. gPINN with the values of w = 0.01, 0.1, and 1 all outperform PINN by up to two orders of magnitude for the L 2 relative errors of u, d u d x and d u d t, and the mean absolute ..."}
+{"idx": 2, "title": "gpinn/README.md at main · lu-group/gpinn · GitHub", "date": "", "ddg_snippet": "gPINN : Gradient-enhanced physics-informed neural networks The data and code for the paper J. Yu , L. Lu, X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering, 393, 114823, 2022.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lu-group/gpinn/blob/main/README.md", "content": "gPINN : Gradient-enhanced physics-informed neural networks The data and code for the paper J. Yu , L. Lu, X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering, 393, 114823, 2022."}
+{"idx": 3, "title": "A gradient-enhanced physics-informed neural network (gPINN) scheme for ...", "date": "", "ddg_snippet": "In their cutting-edge research, Yu et al. (2022) developed the gradient-enhanced physics-informed neural network ( gPINN ) to enhance accuracy. In their investigation into considering the gradients of PDE residual with respect to the input variables, Yu et al. (2022) suggest that it can ameliorate the total performance of the neural network.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197623010928", "content": "In their cutting-edge research, Yu et al. (2022) developed the gradient-enhanced physics-informed neural network ( gPINN ) to enhance accuracy. In their investigation into considering the gradients of PDE residual with respect to the input variables, Yu et al. (2022) suggest that it can ameliorate the total performance of the neural network."}
+{"idx": 4, "title": "[2111.02559] Schwarz Waveform Relaxation Physics-Informed Neural ...", "date": "", "ddg_snippet": "This paper develops a physics-informed neural network (PINN) based on the Schwarz waveform relaxation (SWR) method for solving local and nonlocal advection- diffusion -reaction equations . Specifically, we derive the formulation by constructing subdomain-dependent local solutions by minimizing local loss functions, allowing the decomposition of the training process in different domains in an ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.02559", "content": "This paper develops a physics-informed neural network (PINN) based on the Schwarz waveform relaxation (SWR) method for solving local and nonlocal advection- diffusion -reaction equations . Specifically, we derive the formulation by constructing subdomain-dependent local solutions by minimizing local loss functions, allowing the decomposition of the training process in different domains in an ..."}
+{"idx": 5, "title": "gPINN: Gradient-enhanced physics-informed neural networks", "date": "", "ddg_snippet": "The data and code for the paper J. Yu , L. Lu, X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. arXiv preprint arXiv:2111.02801 , 2021.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dalerxli/gpinn-1", "content": "The data and code for the paper J. Yu , L. Lu, X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. arXiv preprint arXiv:2111.02801 , 2021."}
+{"idx": 6, "title": "Solving Multi-Group Neutron Diffusion Eigenvalue Problem with ...", "date": "", "ddg_snippet": "In the midst of the neural network's success in solving partial differential equations , tackling eigenvalue problems using neural networks remains a challenging task. However, the Physics Constrained-General Inverse Power Method Neural Network (PC-GIPMNN) approach was proposed and successfully applied to solve the single-group critical problems in reactor physics. This paper aims to solve ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.15693", "content": "In the midst of the neural network's success in solving partial differential equations , tackling eigenvalue problems using neural networks remains a challenging task. However, the Physics Constrained-General Inverse Power Method Neural Network (PC-GIPMNN) approach was proposed and successfully applied to solve the single-group critical problems in reactor physics. This paper aims to solve ..."}
+{"idx": 7, "title": "gPINN: Gradient-enhanced physics-informed neural networks", "date": "", "ddg_snippet": "The data and code for the paper J. Yu , L. Lu, X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering, 393, 114823, 2022 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lu-group/gPINN", "content": "The data and code for the paper J. Yu , L. Lu, X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering, 393, 114823, 2022 ..."}
+{"idx": 8, "title": "Gradient-enhanced physics-informed neural networks for forward and ...", "date": "", "ddg_snippet": "In the Poisson equation , the performance of gPINN depends on the value of the weight, but in this diffusion -reaction system, gPINN is not sensitive to the value of w. gPINN with the values of w = 0 01, 0.1, and 1 all outperform PINN by up to two orders of magnitude for the L 2 relative errors of u, d u d x and d u d t, and the mean absolute ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782522001438", "content": "In the Poisson equation , the performance of gPINN depends on the value of the weight, but in this diffusion -reaction system, gPINN is not sensitive to the value of w. gPINN with the values of w = 0 01, 0.1, and 1 all outperform PINN by up to two orders of magnitude for the L 2 relative errors of u, d u d x and d u d t, and the mean absolute ..."}
+{"idx": 9, "title": "Gradient-enhanced physics-informed neural networks for forward and ...", "date": "", "ddg_snippet": "We demonstrated the effectiveness of gPINN in both forward and inverse PDE problems, including Poisson equation , diffusion -reaction equation , Brinkman-Forchheimer model, Burgers' equation , and Allen-Cahn equation .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0045782522001438", "content": "We demonstrated the effectiveness of gPINN in both forward and inverse PDE problems, including Poisson equation , diffusion -reaction equation , Brinkman-Forchheimer model, Burgers' equation , and Allen-Cahn equation ."}
diff --git a/data/sampled_jsons/arxiv_2406.14532_Section_5_spurious_correlations_year_2024.jsonl b/data/sampled_jsons/arxiv_2406.14532_Section_5_spurious_correlations_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd34ab460316c56e6054903e8b69331e2a4ea458
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2406.14532_Section_5_spurious_correlations_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Correlation - Wikipedia", "date": "", "ddg_snippet": "1.1 Correlation and independence. 1.2 Sample correlation coefficient. 1.3 Example. 2 Rank correlation coefficients. 3 Other measures of dependence among random variables. 4 Sensitivity to the data distribution.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Correlation", "content": "1.1 Correlation and independence. 1.2 Sample correlation coefficient. 1.3 Example. 2 Rank correlation coefficients. 3 Other measures of dependence among random variables. 4 Sensitivity to the data distribution."}
+{"idx": 1, "title": "[ 2406 . 14532 ] RL on Incorrect Synthetic Data Scales the Efficiency of...", "date": "", "ddg_snippet": "At the same time, training on model-generated positives can amplify various spurious correlations , resulting in flat or even inverse scaling trends as the amount of data increases.Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL). Cite as: arXiv : 2406 . 14532 [cs.LG].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "At the same time, training on model-generated positives can amplify various spurious correlations , resulting in flat or even inverse scaling trends as the amount of data increases.Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL). Cite as: arXiv : 2406 . 14532 [cs.LG]."}
+{"idx": 2, "title": "The 10 Most Bizarre Correlations", "date": "", "ddg_snippet": "One of the first things you learn in any statistics class is that correlation doesn't imply causation. Nonetheless, it's fun to consider the causal relationships one could infer from these correlations .", "subpage_snippet": "", "source": "www.buzzfeednews.com", "link": "https://www.buzzfeednews.com/article/kjh2110/the-10-most-bizarre-correlations", "content": "One of the first things you learn in any statistics class is that correlation doesn't imply causation. Nonetheless, it's fun to consider the causal relationships one could infer from these correlations ."}
+{"idx": 3, "title": "Обзоры препринтов научных статей «astro-ph/ arxiv .org» за... / Хабр", "date": "", "ddg_snippet": "Ежемесячный обзор научных статей в области астрофизики от профессора МГУ Сергея Попова. Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv .org.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948996/", "content": "Ежемесячный обзор научных статей в области астрофизики от профессора МГУ Сергея Попова. Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv .org."}
+{"idx": 4, "title": "Current Home Page Section Hero | Hero Section Design Design", "date": "", "ddg_snippet": "Learn to design a compelling Hero Section Design to boost user engagement and interface.", "subpage_snippet": "", "source": "www.unsection.com", "link": "https://www.unsection.com/section/current-home-page-section-hero", "content": "Learn to design a compelling Hero Section Design to boost user engagement and interface."}
+{"idx": 5, "title": "Spurious Correlations - Why we need Regression Models ? - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=XChnhgSFVg0", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "Implementation Details. arXiv : 2406 . 14532 v 1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "Implementation Details. arXiv : 2406 . 14532 v 1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 7, "title": "Adaptive Model Selection for Expanded Post Hoc Debiasing and...", "date": "", "ddg_snippet": "EvA: erasing spurious correlations with activations. Q He. K Xu. A Yao. On feature learning in the presence of spurious correlations .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395577997_Adaptive_Model_Selection_for_Expanded_Post_Hoc_Debiasing_and_Mitigating_Varying_Degrees_of_Spurious_Correlations", "content": "EvA: erasing spurious correlations with activations. Q He. K Xu. A Yao. On feature learning in the presence of spurious correlations ."}
+{"idx": 8, "title": "Popularity of the 'all your base' meme correlates with Google searches...", "date": "", "ddg_snippet": "Show prompt for explanation Model: gpt-3. 5 -turbo-1106 (fine-tuned) System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations . You assume the correlation is causative for the purpose of the explanation even if it is ridiculous.", "subpage_snippet": "", "source": "tylervigen.com", "link": "https://tylervigen.com/spurious/correlation/4843_popularity-of-the-all-your-base-meme_correlates-with_google-searches-for-black-holes", "content": "Show prompt for explanation Model: gpt-3. 5 -turbo-1106 (fine-tuned) System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations . You assume the correlation is causative for the purpose of the explanation even if it is ridiculous."}
+{"idx": 9, "title": "比率の疑似相関 — HiSoUR История культуры", "date": "", "ddg_snippet": "Spurious _ correlation _of_ratios.PMID 25775355。 image Spurious _ correlation _of_ratios&oldid=1010842460″.", "subpage_snippet": "", "source": "www.hisour.com", "link": "https://www.hisour.com/ru/data/spurious_correlation_of_ratios/", "content": "Spurious _ correlation _of_ratios.PMID 25775355。 image Spurious _ correlation _of_ratios&oldid=1010842460″."}
diff --git a/data/sampled_jsons/arxiv_2410.09536.jsonl b/data/sampled_jsons/arxiv_2410.09536.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..efb5115dcd7f32bdf7555ec58fd8c6abd01135ab
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2410.09536.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.09536] TOP-ERL: Transformer-based Off-Policy Episodic ...", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09536", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ..."}
+{"idx": 1, "title": "[2410.09536] Untitled Document [ar5iv.labs.arxiv.org]", "date": "", "ddg_snippet": "Conversion to HTML had a Fatal error and exited abruptly. This document may be truncated or damaged. Feeling", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.09536", "content": "Conversion to HTML had a Fatal error and exited abruptly. This document may be truncated or damaged. Feeling"}
+{"idx": 2, "title": "\"TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement ... - dblp", "date": "", "ddg_snippet": "Details and statistics show external API response JSON @ openalex.org see also: API doc @ openalex.org DOI: 10.48550/ ARXIV .2410.09536 access: open type: Informal or Other Publication metadata version: 2024-11-22 view electronic edition via DOI (open access) authority control:", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2410-09536", "content": "Details and statistics show external API response JSON @ openalex.org see also: API doc @ openalex.org DOI: 10.48550/ ARXIV .2410.09536 access: open type: Informal or Other Publication metadata version: 2024-11-22 view electronic edition via DOI (open access) authority control:"}
+{"idx": 3, "title": "Machine Learning Oct 2024", "date": "", "ddg_snippet": "[1101] arXiv:2410.09536 [pdf, other]. Title: TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning. Ge Li, Dong Tian, Hongyi Zhou, Xinkai ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.LG/2024-10?skip=1050&show=500", "content": "[1101] arXiv:2410.09536 [pdf, other]. Title: TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning. Ge Li, Dong Tian, Hongyi Zhou, Xinkai ..."}
+{"idx": 4, "title": "Machine Learning Oct 2024", "date": "", "ddg_snippet": "21 Oct 2024 — [1101] arXiv:2410.09536 [pdf, other]. Title: TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning. Ge Li, Dong Tian, Hongyi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.LG/2024-10?skip=325&show=2000", "content": "21 Oct 2024 — [1101] arXiv:2410.09536 [pdf, other]. Title: TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning. Ge Li, Dong Tian, Hongyi ..."}
+{"idx": 5, "title": "A Transformer-based Soft Actor-Critic with N-Step Returns", "date": "", "ddg_snippet": "Top-erl: Transformer-based off-policy episodic reinforcement learning. arXiv preprint arXiv:2410.09536 , 2024a. Ge Li, Hongyi Zhou, Dominik Roth, Serge ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2503.03660/paper", "content": "Top-erl: Transformer-based off-policy episodic reinforcement learning. arXiv preprint arXiv:2410.09536 , 2024a. Ge Li, Hongyi Zhou, Dominik Roth, Serge ..."}
+{"idx": 6, "title": "transformer-based off-policy episodic reinforcement learning", "date": "", "ddg_snippet": "by G Li · 2024 · Cited by 7 — This work introduces Transformer-based Off-Policy Episodic Reinforcement. Learning (TOP-ERL), a novel algorithm that enables off-policy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "by G Li · 2024 · Cited by 7 — This work introduces Transformer-based Off-Policy Episodic Reinforcement. Learning (TOP-ERL), a novel algorithm that enables off-policy ..."}
+{"idx": 7, "title": "Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "This page provides the most accurate and concise summary worldwide for the paper titled TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/top-erl-transformer-based-off-policy-episodic-reinforcement-learning", "content": "This page provides the most accurate and concise summary worldwide for the paper titled TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning ."}
+{"idx": 8, "title": "Reinforcement Learning with Action Chunking", "date": "", "ddg_snippet": "We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/fe95604beeb8813dfe73e2988998dacf80355101.pdf", "content": "We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks."}
+{"idx": 9, "title": "Rethinking Transformers in Solving POMDPs", "date": "", "ddg_snippet": "Key takeaway: 'Transformers are insufficient for partially observable Markov decision processes, and the Deep Linear Recurrent Unit (LRU) is a better ...", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/rethinking-transformers-in-solving-pomdps-xu-du/924be86c972f53329257e414b8818bf8/", "content": "Key takeaway: 'Transformers are insufficient for partially observable Markov decision processes, and the Deep Linear Recurrent Unit (LRU) is a better ..."}
diff --git a/data/sampled_jsons/arxiv_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_Few-shot_Lea.jsonl b/data/sampled_jsons/arxiv_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_Few-shot_Lea.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..33825f4a600bb08e4c8911f47087a3a494957c27
--- /dev/null
+++ b/data/sampled_jsons/arxiv_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_Few-shot_Lea.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - HaoyuanYang-2023/ImagineFSL: Official implementation of ...", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "This repository contains the official code for \" ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 1, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set", "date": "", "ddg_snippet": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/ImagineFSL/index.html", "content": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning"}
+{"idx": 2, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "Poster ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Haoyuan Yang · Xiaoou Li · Jiaming Lv · Xianjun Cheng · Qilong Wang · Peihua Li ExHall D Poster #370", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32717", "content": "Poster ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Haoyuan Yang · Xiaoou Li · Jiaming Lv · Xianjun Cheng · Qilong Wang · Peihua Li ExHall D Poster #370"}
+{"idx": 3, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023/ImagineFSL · GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "This repository contains the official code for \" ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 4, "title": "PDF Supplementary Material for \"ImagineFSL: Self-Supervised Pretraining ...", "date": "", "ddg_snippet": "Supplementary Material for \" ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning \" Haoyuan Yang 1,∗", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "Supplementary Material for \" ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning \" Haoyuan Yang 1,∗"}
+{"idx": 5, "title": "PDF ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Haoyuan Yang 1,*, Xiaoou Li 2, *, Jiaming Lv 1, Xianjun Cheng 1, Qilong Wang 3, Peihua Li †", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/media/cvpr-2025/Slides/32717_jI4FPCp.pdf", "content": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Haoyuan Yang 1,*, Xiaoou Li 2, *, Jiaming Lv 1, Xianjun Cheng 1, Qilong Wang 3, Peihua Li †"}
+{"idx": 6, "title": "few - shot - learning · GitHub Topics · GitHub", "date": "", "ddg_snippet": "deep-learning loss-functions meta-learning few - shot - learning .Official implementation of \" ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning \" [CVPR 2025 Highlight].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/few-shot-learning?o=desc&s=updated", "content": "deep-learning loss-functions meta-learning few - shot - learning .Official implementation of \" ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning \" [CVPR 2025 Highlight]."}
+{"idx": 7, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.html", "content": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning"}
+{"idx": 8, "title": "CVPR Daily - Sunday", "date": "", "ddg_snippet": "...Mitigating Hallucinations in MLLMs with Attention Causal Decoding [5-168] Align3R: Aligned Monocular Depth Estimation for Dynamic Videos [Highlight] [6-370] ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning [6-250].", "subpage_snippet": "", "source": "www.rsipvision.com", "link": "https://www.rsipvision.com/CVPR2025-Sunday/", "content": "...Mitigating Hallucinations in MLLMs with Attention Causal Decoding [5-168] Align3R: Aligned Monocular Depth Estimation for Dynamic Videos [Highlight] [6-370] ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning [6-250]."}
+{"idx": 9, "title": "Homepage of Qilong Wang", "date": "", "ddg_snippet": "ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning .", "subpage_snippet": "", "source": "csqlwang.github.io", "link": "https://csqlwang.github.io/homepage/", "content": "ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning ."}
diff --git a/data/sampled_jsons/arxiv_html_2502.20099_Figure_3_MCC.jsonl b/data/sampled_jsons/arxiv_html_2502.20099_Figure_3_MCC.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d5628828be1bd7514a204a1347444d1fa9f06bc8
--- /dev/null
+++ b/data/sampled_jsons/arxiv_html_2502.20099_Figure_3_MCC.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "什么是arXiv？ - 知乎", "date": "", "ddg_snippet": "arXiv网站简介 arXiv是一个收集物理学、数学、计算机科学、生物学与数理经济学的论文预印本的网站。其中arXiv发音同“archive”，因为“X”代表希腊字母 χ，国际音标为 [kai]。它于1991年8月14日成立，现由美国康奈尔大学维护。 ——维基百科 对于理论物理方向的科研工作者来说，arXiv是一个了解相关 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/31864895?sort=created", "content": "arXiv网站简介 arXiv是一个收集物理学、数学、计算机科学、生物学与数理经济学的论文预印本的网站。其中arXiv发音同“archive”，因为“X”代表希腊字母 χ，国际音标为 [kai]。它于1991年8月14日成立，现由美国康奈尔大学维护。 ——维基百科 对于理论物理方向的科研工作者来说，arXiv是一个了解相关 ..."}
+{"idx": 1, "title": "什么是arXiv？ - 知乎", "date": "", "ddg_snippet": "arXivは物理学、数学、コンピュータ科学、生物学、数理経済学などの論文を収集するウェブサイトで、論文のオリジナリティを証明し研究成果を共有できます。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/31864895/answers/updated", "content": "arXivは物理学、数学、コンピュータ科学、生物学、数理経済学などの論文を収集するウェブサイトで、論文のオリジナリティを証明し研究成果を共有できます。"}
+{"idx": 2, "title": "arxiv国内有镜像网站吗？ - 知乎", "date": "", "ddg_snippet": "国内确实有arxiv的镜像网站，旨在提升访问速度和下载体验。有两个推荐的镜像站点： CN.ARXIV.ORG：这是官方提供的中国镜像，访问速度快，适合国内用户下载arxiv上的PDF文件。您可以直接访问这个域名获取资料。 中科院镜像 (XXX.ITP.AC.CN)：另一个高效的选择，通过将arxiv.org的链接中的域名替换为 http ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/1911390291929306874", "content": "国内确实有arxiv的镜像网站，旨在提升访问速度和下载体验。有两个推荐的镜像站点： CN.ARXIV.ORG：这是官方提供的中国镜像，访问速度快，适合国内用户下载arxiv上的PDF文件。您可以直接访问这个域名获取资料。 中科院镜像 (XXX.ITP.AC.CN)：另一个高效的选择，通过将arxiv.org的链接中的域名替换为 http ..."}
+{"idx": 3, "title": "把文章发到arXiv上别人认可嘛？ - 知乎", "date": "", "ddg_snippet": "发表到arXiv上，确切来讲不能叫发表，因为arXiv只是一个共享平台，并不能说明文章水平，会议和期刊就不一样了，要经过好多专家评审，而且不同会议和期刊也代表不同水平。 个人认为发到 arXiv 上的有以下情况： 第一种，就是为了 占坑，因为无论会议和期刊从投出到最终可以检索，都需要一半年 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/30131676", "content": "发表到arXiv上，确切来讲不能叫发表，因为arXiv只是一个共享平台，并不能说明文章水平，会议和期刊就不一样了，要经过好多专家评审，而且不同会议和期刊也代表不同水平。 个人认为发到 arXiv 上的有以下情况： 第一种，就是为了 占坑，因为无论会议和期刊从投出到最终可以检索，都需要一半年 ..."}
+{"idx": 4, "title": "什么是arXiv？ - 知乎", "date": "", "ddg_snippet": "论文讲究时效性，你想了一个 idea， 然后做了仿真，写了论文。但是考虑到投稿问题，有些会议或者期刊 “call for paper ” 是有时间限制的，比如可能多几个月才是论文的收稿期。一方面为了证明自己论文的原创性，将论文放到 arXiv 上挂起来；另一方面，也是为了竞争，谁的论文在 arXiv 挂的早，谁 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/31864895", "content": "论文讲究时效性，你想了一个 idea， 然后做了仿真，写了论文。但是考虑到投稿问题，有些会议或者期刊 “call for paper ” 是有时间限制的，比如可能多几个月才是论文的收稿期。一方面为了证明自己论文的原创性，将论文放到 arXiv 上挂起来；另一方面，也是为了竞争，谁的论文在 arXiv 挂的早，谁 ..."}
+{"idx": 5, "title": "在arxiv发表论文的意义体现在哪里？ - 知乎", "date": "", "ddg_snippet": "arXiv 拥有庞大的学术用户群，论文一旦上传，就可以被全球的研究者搜索、阅读和引用。 许多研究人员习惯直接从 arXiv 获取最新的研究动态，而不必等正式出版。 高质量的预印本往往能带来更多关注、下载和引用，甚至有可能在正式发表前就产生影响。 4.", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/8772574507", "content": "arXiv 拥有庞大的学术用户群，论文一旦上传，就可以被全球的研究者搜索、阅读和引用。 许多研究人员习惯直接从 arXiv 获取最新的研究动态，而不必等正式出版。 高质量的预印本往往能带来更多关注、下载和引用，甚至有可能在正式发表前就产生影响。 4."}
+{"idx": 6, "title": "请问挂arxiv上的文章投稿期刊时可以更改作者顺序和新增作者吗? - 知乎", "date": "", "ddg_snippet": "Jul 29, 2022 · 请问挂arxiv上的文章投稿期刊时可以更改作者顺序和新增作者吗? arxiv上本身的作者可以改变顺序，增加新作者吗？ 显示全部 关注者 7", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/546044299?write", "content": "Jul 29, 2022 · 请问挂arxiv上的文章投稿期刊时可以更改作者顺序和新增作者吗? arxiv上本身的作者可以改变顺序，增加新作者吗？ 显示全部 关注者 7"}
+{"idx": 7, "title": "投稿计算机顶会像CVPR，ICCV，AAAI等之后可以把文章发布在arxiv上吗？...", "date": "", "ddg_snippet": "关于arXiv：可以发。 CVPR/ ICCV / ECCV；NeurIPS/ ICML /ICLR等几乎全部计算机视觉和模式识别顶会明确地允许作者在投稿之后上传arXiv。 关于模板：大部分计算机视觉和模式识别顶会都没有直接提供Word模板。 可能像 MM 、 BMVC 这样的会议会提供Word模板，但是不太建议用。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/617798431", "content": "关于arXiv：可以发。 CVPR/ ICCV / ECCV；NeurIPS/ ICML /ICLR等几乎全部计算机视觉和模式识别顶会明确地允许作者在投稿之后上传arXiv。 关于模板：大部分计算机视觉和模式识别顶会都没有直接提供Word模板。 可能像 MM 、 BMVC 这样的会议会提供Word模板，但是不太建议用。"}
+{"idx": 8, "title": "论文挂在arxiv上会影响之后的投稿吗，查重或者算一稿多投啥的? - 知乎", "date": "", "ddg_snippet": "2. 预留充足的投稿时间：考虑到 arXiv 预印本可能带来的影响，在投稿时要预留比平时更充足的时间 。 如果论文在 arXiv 上发布后，需要进行大量修改才能满足投稿要求，要确保有足够的时间进行修改和完善，避免因时间紧迫而影响论文质量。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/509528796", "content": "2. 预留充足的投稿时间：考虑到 arXiv 预印本可能带来的影响，在投稿时要预留比平时更充足的时间 。 如果论文在 arXiv 上发布后，需要进行大量修改才能满足投稿要求，要确保有足够的时间进行修改和完善，避免因时间紧迫而影响论文质量。"}
+{"idx": 9, "title": "哪里能够查到 arXiv 上的论文被引用的情况？ - 知乎", "date": "", "ddg_snippet": "Apr 21, 2024 · 知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容，聚集了中文互联网科技、商业、影视 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/653649543?write", "content": "Apr 21, 2024 · 知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容，聚集了中文互联网科技、商业、影视 ..."}
diff --git a/data/sampled_jsons/attention_head_caching_optimization_language_models_inference_efficiency_year_2024.jsonl b/data/sampled_jsons/attention_head_caching_optimization_language_models_inference_efficiency_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1390817bac9bf50d2ae651ca940bbf207257b3f8
--- /dev/null
+++ b/data/sampled_jsons/attention_head_caching_optimization_language_models_inference_efficiency_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "KV Cache Compression for Inference Efficiency in LLMs: A Review", "date": "", "ddg_snippet": "Therefore, optimizing the KV cache during inference is crucial for enhancing performance and efficiency . This review systematically examines current KV cache optimization techniques, including compression strategies such as selective token strategies, quantization, and attention compression.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06297v1", "content": "Therefore, optimizing the KV cache during inference is crucial for enhancing performance and efficiency . This review systematically examines current KV cache optimization techniques, including compression strategies such as selective token strategies, quantization, and attention compression."}
+{"idx": 1, "title": "Understanding KV Cache and Paged Attention in LLMs: A Deep ... - Medium", "date": "", "ddg_snippet": "As Large Language Models (LLMs) continue to grow in size and complexity, efficient inference becomes increasingly crucial. Two key techniques that have emerged to optimize LLM inference are KV ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/my-musings-with-llms/understanding-kv-cache-and-paged-attention-in-llms-a-deep-dive-into-efficient-inference-62fa372432ce", "content": "As Large Language Models (LLMs) continue to grow in size and complexity, efficient inference becomes increasingly crucial. Two key techniques that have emerged to optimize LLM inference are KV ..."}
+{"idx": 2, "title": "Beyond KV caching: Shared attention for efficient LLMs", "date": "", "ddg_snippet": "The rapid scaling of Large Language Models (LLMs) necessitates advancements in computational and memory efficiency during inference . While methods like Multi-Query Attention (MQA), Grouped-Query Attention (GQA), and Cross-Layer Attention (CLA) reduce Key-Value (KV) cache size by sharing K/V pairs, strategies to further reduce computational load include reusing computed attention weights ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231225012597", "content": "The rapid scaling of Large Language Models (LLMs) necessitates advancements in computational and memory efficiency during inference . While methods like Multi-Query Attention (MQA), Grouped-Query Attention (GQA), and Cross-Layer Attention (CLA) reduce Key-Value (KV) cache size by sharing K/V pairs, strategies to further reduce computational load include reusing computed attention weights ..."}
+{"idx": 3, "title": "PDF Optimizing Attention for Efficient LLM Inference: A Review", "date": "", "ddg_snippet": "Keywords—Large Language Models , Attention Mechanism Optimization , Inference Efficiency , Long-Sequence Modeling I. INTRODUCTION With the rapid development of deep learning, large lan-guage models (LLMs) have achieved groundbreaking pro-gress in the field of natural language processing (NLP).", "subpage_snippet": "", "source": "oulurepo.oulu.fi", "link": "https://oulurepo.oulu.fi/bitstream/handle/10024/57525/nbnfioulu-202507015041.pdf?sequence=1", "content": "Keywords—Large Language Models , Attention Mechanism Optimization , Inference Efficiency , Long-Sequence Modeling I. INTRODUCTION With the rapid development of deep learning, large lan-guage models (LLMs) have achieved groundbreaking pro-gress in the field of natural language processing (NLP)."}
+{"idx": 4, "title": "Techniques for KV Cache Optimization in Large Language Models", "date": "", "ddg_snippet": "This post explores techniques for optimizing the Key-Value (KV) cache in large language models , from Grouped-query attention to PagedAttention and distributed cache management.", "subpage_snippet": "", "source": "www.omrimallis.com", "link": "https://www.omrimallis.com/posts/techniques-for-kv-cache-optimization/", "content": "This post explores techniques for optimizing the Key-Value (KV) cache in large language models , from Grouped-query attention to PagedAttention and distributed cache management."}
+{"idx": 5, "title": "GitHub - manncodes/inference-opt: Implementation of inference ...", "date": "", "ddg_snippet": "Implementation of advanced inference optimization techniques for modern language models : Radix Attention : Efficient KV cache reuse during runtime Multi- head Latent Attention (MLA): DeepSeek's KV cache compression technique Speculative Decoding: Accelerated inference through parallel token prediction", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/manncodes/inference-opt", "content": "Implementation of advanced inference optimization techniques for modern language models : Radix Attention : Efficient KV cache reuse during runtime Multi- head Latent Attention (MLA): DeepSeek's KV cache compression technique Speculative Decoding: Accelerated inference through parallel token prediction"}
+{"idx": 6, "title": "The Power of Focus: Understanding Attention Mechanisms in LLM", "date": "", "ddg_snippet": "The Power of Focus: Understanding Attention Mechanisms in LLM [TOC] Background Nowadays, there are several fronts on improving Large Language Models at both inference and training. Techniques such as quantization, distillation and optimizations at the attention mechanism are continuously been improved to either reduce model size or memory ...", "subpage_snippet": "", "source": "marcelcastrobr.github.io", "link": "https://marcelcastrobr.github.io/posts/2025-01-03-OptimizingLLMAttention.html", "content": "The Power of Focus: Understanding Attention Mechanisms in LLM [TOC] Background Nowadays, there are several fronts on improving Large Language Models at both inference and training. Techniques such as quantization, distillation and optimizations at the attention mechanism are continuously been improved to either reduce model size or memory ..."}
+{"idx": 7, "title": "CHAI: Clustered Head Attention for Efficient LLM Inference", "date": "", "ddg_snippet": "Abstract Large Language Models (LLMs) with hundreds of billions of parameters have transformed the field of machine learning. However, serving these models at inference time is both compute and memory intensive, where a single request can require multiple GPUs and tens of Gigabytes of memory. Multi- Head Attention is one of the key components of LLMs, which can account for over 50% of LLMs ...", "subpage_snippet": "", "source": "ai.meta.com", "link": "https://ai.meta.com/research/publications/chai-clustered-head-attention-for-efficient-llm-inference/", "content": "Abstract Large Language Models (LLMs) with hundreds of billions of parameters have transformed the field of machine learning. However, serving these models at inference time is both compute and memory intensive, where a single request can require multiple GPUs and tens of Gigabytes of memory. Multi- Head Attention is one of the key components of LLMs, which can account for over 50% of LLMs ..."}
+{"idx": 8, "title": "KV Cache in Large Language Models: Design, Optimization, and Inference ...", "date": "", "ddg_snippet": "Transformers are the backbone of modern large language models (LLMs), but inference over long contexts becomes computationally expensive. The KV Cache —short for Key/Value Cache—is one of the most important optimizations that enable scalable and low-latency LLM deployment, particularly for autoregressive decoding.", "subpage_snippet": "", "source": "markbyun.blogspot.com", "link": "https://markbyun.blogspot.com/2025/05/kv-cache-in-large-language-models.html", "content": "Transformers are the backbone of modern large language models (LLMs), but inference over long contexts becomes computationally expensive. The KV Cache —short for Key/Value Cache—is one of the most important optimizations that enable scalable and low-latency LLM deployment, particularly for autoregressive decoding."}
+{"idx": 9, "title": "What is KV Cache in LLMs and How Does It Help?", "date": "", "ddg_snippet": "TL;DR: KV cache is a memory optimization central to efficient LLM inference . It enables faster, longer, and more cost-effective generation by caching previously computed attention keys and values—unlocking the practical deployment of models like GPT-4o, Llama 3, etc. 1. Introduction Generative AI, powered largely today by Large language models (LLMs) such as GPT-4o, Llama 3, etc., is ...", "subpage_snippet": "", "source": "blog.desigeek.com", "link": "https://blog.desigeek.com/post/2025/06/what-is-kv-cache-in-llms/", "content": "TL;DR: KV cache is a memory optimization central to efficient LLM inference . It enables faster, longer, and more cost-effective generation by caching previously computed attention keys and values—unlocking the practical deployment of models like GPT-4o, Llama 3, etc. 1. Introduction Generative AI, powered largely today by Large language models (LLMs) such as GPT-4o, Llama 3, etc., is ..."}
diff --git a/data/sampled_jsons/automatic_concept_discovery_for_concept_bottleneck_models_data-efficient_CBMs_alternatives_after_202_year_2024.jsonl b/data/sampled_jsons/automatic_concept_discovery_for_concept_bottleneck_models_data-efficient_CBMs_alternatives_after_202_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c3800c015ff3f757939bc6c49527d9962ad1669
--- /dev/null
+++ b/data/sampled_jsons/automatic_concept_discovery_for_concept_bottleneck_models_data-efficient_CBMs_alternatives_after_202_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "by K Prasse — DCBM is more data-efficient than conventional CBMs and automatically generates concepts using segmentation and detection models. However, it ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BdO4R6XxUH", "content": "by K Prasse — DCBM is more data-efficient than conventional CBMs and automatically generates concepts using segmentation and detection models. However, it ..."}
+{"idx": 1, "title": "Causally Reliable Concept Bottleneck Models", "date": "", "ddg_snippet": "6 Mar 2025 — A class of concept -based architectures that enforce reasoning through a bottleneck of concepts structured according to a model of the real-world causal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.04363v1", "content": "6 Mar 2025 — A class of concept -based architectures that enforce reasoning through a bottleneck of concepts structured according to a model of the real-world causal ..."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "2 Jul 2025 — We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "2 Jul 2025 — We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 3, "title": "Beyond Concept Bottleneck Models: How to Make Black ...", "date": "", "ddg_snippet": "by S Laguna · 2024 · Cited by 27 — Additionally, these works explore automated concept discovery using VLMs . ... For the concept prediction, CBM outperforms black-box models, even after fine-tuning. 39 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/9a439efaa34fe37177eba00737624824-Paper-Conference.pdf", "content": "by S Laguna · 2024 · Cited by 27 — Additionally, these works explore automated concept discovery using VLMs . ... For the concept prediction, CBM outperforms black-box models, even after fine-tuning. 39 pages"}
+{"idx": 4, "title": "CVPR Poster Hybrid Concept Bottleneck Models", "date": "", "ddg_snippet": "In this work, we propose Hybrid Concept Bottleneck Model (HybridCBM), a novel CBM framework to address the challenge of incomplete predefined concepts .", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33050", "content": "In this work, we propose Hybrid Concept Bottleneck Model (HybridCBM), a novel CBM framework to address the challenge of incomplete predefined concepts ."}
+{"idx": 5, "title": "Automated Essential Concept Discovery for Few-Shot Out ...", "date": "", "ddg_snippet": "Our proposed method, AECD, when integrated with LoCoOp, consistently outperforms existing approaches on both individual OOD datasets and the overall average.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvprw/2025/999400d964/2a1UYNFijQY", "content": "Our proposed method, AECD, when integrated with LoCoOp, consistently outperforms existing approaches on both individual OOD datasets and the overall average."}
+{"idx": 6, "title": "Stochastic Concept Bottleneck Models", "date": "", "ddg_snippet": "by M Vandenhirtz · 2024 · Cited by 20 — We propose Stochastic Concept Bottleneck Models 1 (SCBM), a novel concept -based method that relaxes the implicit CBM assumption of independent concepts . SCBM ... 24 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/5c7894ac8788555f1cecf536f1e0fd35-Paper-Conference.pdf", "content": "by M Vandenhirtz · 2024 · Cited by 20 — We propose Stochastic Concept Bottleneck Models 1 (SCBM), a novel concept -based method that relaxes the implicit CBM assumption of independent concepts . SCBM ... 24 pages"}
+{"idx": 7, "title": "Enhancing Bottleneck Concept Learning in Image ...", "date": "", "ddg_snippet": "by X Cheng · 2025 — This paper introduces the Enhanced Bottleneck Concept Learner (E-BotCL), a self-supervised framework that autonomously discovers task-relevant, interpretable ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12031560/", "content": "by X Cheng · 2025 — This paper introduces the Enhanced Bottleneck Concept Learner (E-BotCL), a self-supervised framework that autonomously discovers task-relevant, interpretable ..."}
+{"idx": 8, "title": "Cross-Modal Conceptualization in Bottleneck Models", "date": "", "ddg_snippet": "by D Alukaev · Cited by 7 — We consider LCBM as an alternative to XCB in our experiments. Yuksekgonul et al. ( 2023 ) introduced a method that posthoc transforms any neural ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.emnlp-main.318.pdf?utm_source=consensus", "content": "by D Alukaev · Cited by 7 — We consider LCBM as an alternative to XCB in our experiments. Yuksekgonul et al. ( 2023 ) introduced a method that posthoc transforms any neural ..."}
+{"idx": 9, "title": "Discover-then-Name: Task-Agnostic Concept Bottlenecks ...", "date": "", "ddg_snippet": "by S Rao · 2024 · Cited by 29 — In this work, we leverage recent advances in mechanistic interpretability and propose a novel CBM approach—called Discover-then-Name- CBM (DN- CBM )—that inverts ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/978-3-031-72980-5_26", "content": "by S Rao · 2024 · Cited by 29 — In this work, we leverage recent advances in mechanistic interpretability and propose a novel CBM approach—called Discover-then-Name- CBM (DN- CBM )—that inverts ..."}
diff --git a/data/sampled_jsons/beautyremainProDet_loss_hyperparameters_beta_gamma.jsonl b/data/sampled_jsons/beautyremainProDet_loss_hyperparameters_beta_gamma.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f3e0c45fb1601313f4c85c8241257dae9bbadc24
--- /dev/null
+++ b/data/sampled_jsons/beautyremainProDet_loss_hyperparameters_beta_gamma.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - beautyremain/ProDet: The official code for paper \"Can We Leave ...", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well. You may find the overall-best checkpoint of our method from Google Drive, which is recommended for ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well. You may find the overall-best checkpoint of our method from Google Drive, which is recommended for ..."}
+{"idx": 1, "title": "Tune XGBoost \"gamma\" Parameter | XGBoosting", "date": "", "ddg_snippet": "The gamma parameter in XGBoost controls the minimum loss reduction required for a split to occur in a leaf node. An alias for the gamma parameter is min_split_loss. It acts as a regularization term that helps control the model's complexity. Higher values of gamma make the algorithm more conservative, requiring a larger reduction in the loss function to create a new split. This can help ...", "subpage_snippet": "", "source": "xgboosting.com", "link": "https://xgboosting.com/tune-xgboost-gamma-parameter/", "content": "The gamma parameter in XGBoost controls the minimum loss reduction required for a split to occur in a leaf node. An alias for the gamma parameter is min_split_loss. It acts as a regularization term that helps control the model's complexity. Higher values of gamma make the algorithm more conservative, requiring a larger reduction in the loss function to create a new split. This can help ..."}
+{"idx": 2, "title": "How to Tune XGBoost Hyperparameters - ML Journey", "date": "", "ddg_snippet": "Learning how to tune XGBoost hyperparameters effectively is a skill that can dramatically improve your model performance. By following a systematic approach, understanding the impact of key parameters, and avoiding common pitfalls, you can unlock the full potential of XGBoost for your machine learning projects.", "subpage_snippet": "", "source": "mljourney.com", "link": "https://mljourney.com/how-to-tune-xgboost-hyperparameters/", "content": "Learning how to tune XGBoost hyperparameters effectively is a skill that can dramatically improve your model performance. By following a systematic approach, understanding the impact of key parameters, and avoiding common pitfalls, you can unlock the full potential of XGBoost for your machine learning projects."}
+{"idx": 3, "title": "Fine-Tuning Your XGBoost Model with Key Hyperparameters", "date": "", "ddg_snippet": "A larger value for gamma means that the model will require a larger improvement in the loss to make a split, which can help to prevent overfitting.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@srishtisawla/fine-tuning-your-xgboost-model-with-key-hyperparameters-afe2f1df5cd4", "content": "A larger value for gamma means that the model will require a larger improvement in the loss to make a split, which can help to prevent overfitting."}
+{"idx": 4, "title": "The AUC of the CDF has been 0.77 · Issue #7 · beautyremain/ProDet", "date": "", "ddg_snippet": "My dataset and code were reproduced based on DeepfakeBench and your implementation, with no modifications made to the hyperparameters . The model achieved its optimal performance of 0.77 AUC in the first few epochs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/issues/7", "content": "My dataset and code were reproduced based on DeepfakeBench and your implementation, with no modifications made to the hyperparameters . The model achieved its optimal performance of 0.77 AUC in the first few epochs."}
+{"idx": 5, "title": "Most Important XGBoost Hyperparameters to Tune | XGBoosting", "date": "", "ddg_snippet": "Additional Hyperparameters to Consider Tuning While max_depth, min_child_weight, subsample, colsample_bytree, and learning_rate are considered the most important hyperparameters to tune, there are a few others that can also impact model performance: gamma : This parameter defines the minimum loss reduction required to make a split.", "subpage_snippet": "", "source": "xgboosting.com", "link": "https://xgboosting.com/most-important-xgboost-hyperparameters-to-tune/", "content": "Additional Hyperparameters to Consider Tuning While max_depth, min_child_weight, subsample, colsample_bytree, and learning_rate are considered the most important hyperparameters to tune, there are a few others that can also impact model performance: gamma : This parameter defines the minimum loss reduction required to make a split."}
+{"idx": 6, "title": "ProDet/README.md at main · beautyremain/ProDet · GitHub", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet/README.md at main · beautyremain/ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/blob/main/README.md", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet/README.md at main · beautyremain/ProDet"}
+{"idx": 7, "title": "The Ultimate Guide to XGBoost Parameter Tuning", "date": "", "ddg_snippet": "XGBoost Parameters Gradient boosting algorithms like XGBoost have two main types of hyperparameters : tree parameters which control the decision tree trained at each boosting round and boosting parameters which control the boosting procedure itself. Below I'll highlight my favorite parameters, but you can see the full list in the documentation. Tree Parameters In theory you can use any kind ...", "subpage_snippet": "", "source": "randomrealizations.com", "link": "https://randomrealizations.com/posts/xgboost-parameter-tuning-with-optuna/", "content": "XGBoost Parameters Gradient boosting algorithms like XGBoost have two main types of hyperparameters : tree parameters which control the decision tree trained at each boosting round and boosting parameters which control the boosting procedure itself. Below I'll highlight my favorite parameters, but you can see the full list in the documentation. Tree Parameters In theory you can use any kind ..."}
+{"idx": 8, "title": "Parameters | XGBoosting", "date": "", "ddg_snippet": "XGBoost ParametersThey are parameters in the programming sense (e.g. arguments to functions), but hyperparameters in the model sense (e.g. influence model behavior). Technically, model parameters are the trees and weights found by the learning algorithm.", "subpage_snippet": "", "source": "xgboosting.com", "link": "https://xgboosting.com/parameters/", "content": "XGBoost ParametersThey are parameters in the programming sense (e.g. arguments to functions), but hyperparameters in the model sense (e.g. influence model behavior). Technically, model parameters are the trees and weights found by the learning algorithm."}
+{"idx": 9, "title": "XGBoost Hyperparameter Optimization | XGBoosting", "date": "", "ddg_snippet": "Key hyperparameters to tune include learning rate, max depth, subsample, colsample_bytree, and n_estimators. Techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization.", "subpage_snippet": "", "source": "xgboosting.com", "link": "https://xgboosting.com/xgboost-hyperparameter-optimization/", "content": "Key hyperparameters to tune include learning rate, max depth, subsample, colsample_bytree, and n_estimators. Techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization."}
diff --git a/data/sampled_jsons/behavior_emotion_mapping_self-supervised_learning_four_steps_AI_alignment.jsonl b/data/sampled_jsons/behavior_emotion_mapping_self-supervised_learning_four_steps_AI_alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cc2979df49c9f9c90619e16607718199f4ae0fc0
--- /dev/null
+++ b/data/sampled_jsons/behavior_emotion_mapping_self-supervised_learning_four_steps_AI_alignment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Behavior - Wikipedia", "date": "", "ddg_snippet": "While some behavior is produced in response to an organism's environment (extrinsic motivation), behavior can also be the product of intrinsic motivation, also referred to as \"agency\" or \"free will\".", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Behavior", "content": "While some behavior is produced in response to an organism's environment (extrinsic motivation), behavior can also be the product of intrinsic motivation, also referred to as \"agency\" or \"free will\"."}
+{"idx": 1, "title": "Machine learning - Wikipedia", "date": "", "ddg_snippet": "A special type of unsupervised learning called, self - supervised learning involves training a model by generating the supervisory signal from the data itself.[50][51].", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Machine_learning", "content": "A special type of unsupervised learning called, self - supervised learning involves training a model by generating the supervisory signal from the data itself.[50][51]."}
+{"idx": 2, "title": "BEHAVIOR Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of BEHAVIOR is the way in which someone conducts oneself or behaves; also : an instance of such behavior . How to use behavior in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/behavior", "content": "The meaning of BEHAVIOR is the way in which someone conducts oneself or behaves; also : an instance of such behavior . How to use behavior in a sentence."}
+{"idx": 3, "title": "BEHAVIOR | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "BEHAVIOR definition: 1. the way that someone behaves: 2. the way that a person, an animal, a substance, etc. behaves in…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/behavior", "content": "BEHAVIOR definition: 1. the way that someone behaves: 2. the way that a person, an animal, a substance, etc. behaves in…. Learn more."}
+{"idx": 4, "title": "BEHAVIOR Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Behavior , conduct, deportment, comportment refer to one's actions before or toward others, especially on a particular occasion. Behavior refers to actions usually measured by commonly accepted standards: His behavior at the party was childish.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/behavior", "content": "Behavior , conduct, deportment, comportment refer to one's actions before or toward others, especially on a particular occasion. Behavior refers to actions usually measured by commonly accepted standards: His behavior at the party was childish."}
+{"idx": 5, "title": "Behavior - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "Behavior refers to how you conduct yourself. Generally, it’s wise to engage in good behavior , even if you're really bored. The noun behavior is a spin-off of the verb behave. Get rid of the be in behave and you're left with have, which makes sense: you could say that to behave is to \"have\" or \"own\" yourself — to control yourself.", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/behavior", "content": "Behavior refers to how you conduct yourself. Generally, it’s wise to engage in good behavior , even if you're really bored. The noun behavior is a spin-off of the verb behave. Get rid of the be in behave and you're left with have, which makes sense: you could say that to behave is to \"have\" or \"own\" yourself — to control yourself."}
+{"idx": 6, "title": "BEHAVIOR definition in American English | Collins English ...", "date": "", "ddg_snippet": "an instance of behavior ; specif., one of a recurring or characteristic pattern of observable actions or responses", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/us/dictionary/english/behavior", "content": "an instance of behavior ; specif., one of a recurring or characteristic pattern of observable actions or responses"}
+{"idx": 7, "title": "Behavior Analysis In Psychology", "date": "", "ddg_snippet": "Feb 2, 2024 · Behavior analysis posits that people's and organisms’ environments can be arranged so that desirable behaviors become more probable and undesirable behaviors become less probable.", "subpage_snippet": "", "source": "www.simplypsychology.org", "link": "https://www.simplypsychology.org/what-is-behavior-analysis.html", "content": "Feb 2, 2024 · Behavior analysis posits that people's and organisms’ environments can be arranged so that desirable behaviors become more probable and undesirable behaviors become less probable."}
+{"idx": 8, "title": "The Great AI Alignment Debate: Why Your ChatGPT... | Medium", "date": "", "ddg_snippet": "Step -by- step diagram showing Constitutional AI . The results are pretty impressive.What’s Next: The Future of AI Alignment . Advanced AI alignment techniques such as dynamic principle weighting and cross-model auditing. The field isn’t standing still.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@beyondbytes2/the-great-ai-alignment-debate-why-your-chatgpt-conversations-matter-more-than-you-think-e4a6026ce4d8", "content": "Step -by- step diagram showing Constitutional AI . The results are pretty impressive.What’s Next: The Future of AI Alignment . Advanced AI alignment techniques such as dynamic principle weighting and cross-model auditing. The field isn’t standing still."}
+{"idx": 9, "title": "What Is Supervised Learning ? | IBM", "date": "", "ddg_snippet": "Self - supervised learning (SSL) mimics supervised learning with unlabeled data. Rather than use the manually created labels of supervised learning datasets, SSL tasks are configured so that the model can generate implicit labels from unstructured data.", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/topics/supervised-learning", "content": "Self - supervised learning (SSL) mimics supervised learning with unlabeled data. Rather than use the manually created labels of supervised learning datasets, SSL tasks are configured so that the model can generate implicit labels from unstructured data."}
diff --git a/data/sampled_jsons/beta_sigma_similarity_comparison_squared_distance_spatial_encoding_objective_neurips_2024.jsonl b/data/sampled_jsons/beta_sigma_similarity_comparison_squared_distance_spatial_encoding_objective_neurips_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b6023a9a43222001404b661e338a6b9656d83dd3
--- /dev/null
+++ b/data/sampled_jsons/beta_sigma_similarity_comparison_squared_distance_spatial_encoding_objective_neurips_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "6.2 Similarity measures | Multivariate Statistics - GitHub Pages", "date": "", "ddg_snippet": "Here, we use the \\ (L_1\\) distance , which is also known as the Manhattan distance . We have to subtract this from the largest possible distance , which is 6 in this case, to get the similarity , and then divide by 6 to get the SMC.", "subpage_snippet": "", "source": "rich-d-wilkinson.github.io", "link": "https://rich-d-wilkinson.github.io/MATH3030/6.2-similarity.html", "content": "Here, we use the \\ (L_1\\) distance , which is also known as the Manhattan distance . We have to subtract this from the largest possible distance , which is 6 in this case, to get the similarity , and then divide by 6 to get the SMC."}
+{"idx": 1, "title": "More sophisticated is not always better: comparison of ...", "date": "", "ddg_snippet": "For set B, the best overall clustering was obtained using Euclidean distance with n t and smoothing (σ = 5 𝜎 5 \\ sigma =5 italic_σ = 5), followed by Wasserstein distance (also with n t) and Procrustes similarity .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01725v1", "content": "For set B, the best overall clustering was obtained using Euclidean distance with n t and smoothing (σ = 5 𝜎 5 \\ sigma =5 italic_σ = 5), followed by Wasserstein distance (also with n t) and Procrustes similarity ."}
+{"idx": 2, "title": "A comparative analysis of trajectory similarity measures", "date": "", "ddg_snippet": "A set of four experiments: a. creates a measurement baseline by comparing similarity measures to a single trajectory subjected to various transformations; b. explores the behavior of similarity measures on network-constrained bus trajectories, grouped based on spatial and on temporal similarity ; c. assesses similarity with respect to known ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/pdf/10.1080/15481603.2021.1908927", "content": "A set of four experiments: a. creates a measurement baseline by comparing similarity measures to a single trajectory subjected to various transformations; b. explores the behavior of similarity measures on network-constrained bus trajectories, grouped based on spatial and on temporal similarity ; c. assesses similarity with respect to known ..."}
+{"idx": 3, "title": "A similarity measure based on subspace distance for spectral ...", "date": "", "ddg_snippet": "Mar 1, 2025 · A similarity matrix based on subspace distance has been introduced by SMDS that effectively captures the similarity between data points. This section explores how the proposed matrix can be leveraged for SC.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224019581", "content": "Mar 1, 2025 · A similarity matrix based on subspace distance has been introduced by SMDS that effectively captures the similarity between data points. This section explores how the proposed matrix can be leveraged for SC."}
+{"idx": 4, "title": "Encoding Spatial Distribution of Convolutional ... - NeurIPS", "date": "", "ddg_snippet": "The proposed FE in this work distinguishes itself from the aforementioned methods by encoding the regularity on the spatial distribution of convolutional features, providing additional discrimination.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf", "content": "The proposed FE in this work distinguishes itself from the aforementioned methods by encoding the regularity on the spatial distribution of convolutional features, providing additional discrimination."}
+{"idx": 5, "title": "Spatial similarity metrics | ESPADON", "date": "", "ddg_snippet": "The espadon package calculates several spatial similarity metrics, such as : the volumetric DICE similarity defined by DSC = 2 (VA ∩ VB) / (VA+VB) the Dice-jaccard coefficient defined by DJC = (VA ∩ VB) / (VA ∪ VB) the mean distance to conformity MDC, over-contouring mean distance over.MDC and under-contouring mean distance under.MDC, defined by Jena et al [1] the maximum , mean and ...", "subpage_snippet": "", "source": "espadon.cnrs.fr", "link": "https://espadon.cnrs.fr/get-started/sp-similarity-metrics/", "content": "The espadon package calculates several spatial similarity metrics, such as : the volumetric DICE similarity defined by DSC = 2 (VA ∩ VB) / (VA+VB) the Dice-jaccard coefficient defined by DJC = (VA ∩ VB) / (VA ∪ VB) the mean distance to conformity MDC, over-contouring mean distance over.MDC and under-contouring mean distance under.MDC, defined by Jena et al [1] the maximum , mean and ..."}
+{"idx": 6, "title": "A Survey on Measurement Metrics for Shape Matching Based on ...", "date": "", "ddg_snippet": "Jan 29, 2020 · In the present study, principal metrics for comparing geometric data are studied. In each metric, one, two or three features out of similarity, scaling, and spatial distance is considered. Evaluation for metrics based on three perspectives is discussed and results are provided in a detailed table.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-37309-2_2", "content": "Jan 29, 2020 · In the present study, principal metrics for comparing geometric data are studied. In each metric, one, two or three features out of similarity, scaling, and spatial distance is considered. Evaluation for metrics based on three perspectives is discussed and results are provided in a detailed table."}
+{"idx": 7, "title": "BWCache: Accelerating Video Diffusion Transformers through", "date": "", "ddg_snippet": "... existing baselines in terms of inference speed and video generation quality, justified through both ablation studies and qualitative comparisons .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13789v2", "content": "... existing baselines in terms of inference speed and video generation quality, justified through both ablation studies and qualitative comparisons ."}
+{"idx": 8, "title": "ArtifactGen: Benchmarking WGAN-GP vs Diffusion for Label-Aware", "date": "", "ddg_snippet": "Power spectral density (PSD) via Welch’s method provides band-power comparisons in canonical δ \\delta / θ \\theta / α \\alpha / β \\ beta bands ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08188v1", "content": "Power spectral density (PSD) via Welch’s method provides band-power comparisons in canonical δ \\delta / θ \\theta / α \\alpha / β \\ beta bands ..."}
+{"idx": 9, "title": "DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete", "date": "", "ddg_snippet": "Thereby, the discrete latents directly learn to encode information that is beneficial for reducing the DM’s score matching objective and making the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.03300v1", "content": "Thereby, the discrete latents directly learn to encode information that is beneficial for reducing the DM’s score matching objective and making the ..."}
diff --git a/data/sampled_jsons/causal_disentanglement_noise_robust_method_2024.jsonl b/data/sampled_jsons/causal_disentanglement_noise_robust_method_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0eb2265d5723de185ec68a44fe6c812edc9535b
--- /dev/null
+++ b/data/sampled_jsons/causal_disentanglement_noise_robust_method_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causality - Wikipedia", "date": "", "ddg_snippet": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Causality", "content": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future."}
+{"idx": 1, "title": "CAUSAL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/causal", "content": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence."}
+{"idx": 2, "title": "CAUSAL | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/causal", "content": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more."}
+{"idx": 3, "title": "CAUSAL Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/causal", "content": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence."}
+{"idx": 4, "title": "CAUSAL definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "If there is a causal relationship between two things, one thing is responsible for causing the other thing.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/causal", "content": "If there is a causal relationship between two things, one thing is responsible for causing the other thing."}
+{"idx": 5, "title": "Causal - definition of causal by The Free Dictionary", "date": "", "ddg_snippet": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/causal", "content": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause."}
+{"idx": 6, "title": "causal adjective - Definition, pictures, pronunciation and usage...", "date": "", "ddg_snippet": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/causal", "content": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 7, "title": "causal , adj. & n. meanings, etymology and more | Oxford English...", "date": "", "ddg_snippet": "causal , adj. & n. meanings, etymology, pronunciation and more in the Oxford English Dictionary", "subpage_snippet": "", "source": "www.oed.com", "link": "https://www.oed.com/dictionary/causal_adj", "content": "causal , adj. & n. meanings, etymology, pronunciation and more in the Oxford English Dictionary"}
+{"idx": 8, "title": "causal - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark.", "subpage_snippet": "", "source": "en.m.wiktionary.org", "link": "https://en.m.wiktionary.org/wiki/causal", "content": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark."}
+{"idx": 9, "title": "Causal - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "Causal is a variation of the word cause , which should be a clue to its meaning. A cause is what makes something happen: the notebook flew across the room because you threw it, so your throwing it was causal. If a bolt of lightning set a statue on fire, the lightning was causal for the fire.", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/causal", "content": "Causal is a variation of the word cause , which should be a clue to its meaning. A cause is what makes something happen: the notebook flew across the room because you threw it, so your throwing it was causal. If a bolt of lightning set a statue on fire, the lightning was causal for the fire."}
diff --git a/data/sampled_jsons/causal_representation_learning_noise_robust_2024_arxiv.jsonl b/data/sampled_jsons/causal_representation_learning_noise_robust_2024_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f835539db02f478994ace02210d41483f0d4701
--- /dev/null
+++ b/data/sampled_jsons/causal_representation_learning_noise_robust_2024_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Robust Intervention Representations with Delta", "date": "", "ddg_snippet": "In this paper, we introduce Causal Delta Embedding (CDE), a novel framework for learning robust representations of interventions from image pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.04492v1", "content": "In this paper, we introduce Causal Delta Embedding (CDE), a novel framework for learning robust representations of interventions from image pairs."}
+{"idx": 1, "title": "Efficiently Disentangle Causal Representations", "date": "", "ddg_snippet": "This paper proposes an efficient approach to learning disentangled representations with causal mechanisms based on the difference of conditional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2201.01942v2", "content": "This paper proposes an efficient approach to learning disentangled representations with causal mechanisms based on the difference of conditional ..."}
+{"idx": 2, "title": "Identifying Causal Direction via Dense Functional Classes", "date": "", "ddg_snippet": "Bivariate causality, dense functional classes, low noise , cubic spline regression, minimum description length. ... causal relations between X X and Y ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.00538v1", "content": "Bivariate causality, dense functional classes, low noise , cubic spline regression, minimum description length. ... causal relations between X X and Y ..."}
+{"idx": 3, "title": "Position: Causal Machine Learning Requires Rigorous Synthetic", "date": "", "ddg_snippet": "In this context, Loftus ( 2024 ) argued that wide adoption of causal inference requires a mindset shift towards human-centered scientific pragmatism ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08883v1", "content": "In this context, Loftus ( 2024 ) argued that wide adoption of causal inference requires a mindset shift towards human-centered scientific pragmatism ..."}
+{"idx": 4, "title": "Most Influential ArXiv (Machine Learning) Papers (2024-10)", "date": "", "ddg_snippet": "... Learning in arXiv covers papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning , bandit problems, and so ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2024/10/most-influential-arxiv-machine-learning-papers-2024-10/", "content": "... Learning in arXiv covers papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning , bandit problems, and so ..."}
+{"idx": 5, "title": "Causal3D: A Comprehensive Benchmark for Causal Learning from", "date": "", "ddg_snippet": "We assess multiple state-of-the-art methods, including classical causal discovery, causal representation learning , and large/vision-language models ...", "subpage_snippet": "", "source": "burfon.com", "link": "https://burfon.com/article/causal3d-a-comprehensive-benchmark-for-causal-learning-from-visual-data", "content": "We assess multiple state-of-the-art methods, including classical causal discovery, causal representation learning , and large/vision-language models ..."}
+{"idx": 6, "title": "Mark Crowley | Causality", "date": "", "ddg_snippet": "We propose Generative Causal Representation Learning (GCRL) which leverages causality to facilitate knowledge transfer under distribution shifts.", "subpage_snippet": "", "source": "markcrowley.ca", "link": "https://markcrowley.ca/causality/", "content": "We propose Generative Causal Representation Learning (GCRL) which leverages causality to facilitate knowledge transfer under distribution shifts."}
+{"idx": 7, "title": "Quantifying First-Order Markov Violations in Noisy", "date": "", "ddg_snippet": "This framework offers a principled mechanism for developing robust policies, informing representation learning , and addressing partial observability ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00206v1", "content": "This framework offers a principled mechanism for developing robust policies, informing representation learning , and addressing partial observability ..."}
+{"idx": 8, "title": "Welcome to Jikai Jin's website", "date": "", "ddg_snippet": "2024 Our paper Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity is accepted by NeurIPS 2024 as ...", "subpage_snippet": "", "source": "jkjin.com", "link": "https://jkjin.com/", "content": "2024 Our paper Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity is accepted by NeurIPS 2024 as ..."}
+{"idx": 9, "title": "Hongjun Wang - Google Scholar", "date": "", "ddg_snippet": "Causal -Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=vRMZqW4AAAAJ&hl=de", "content": "Causal -Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention"}
diff --git a/data/sampled_jsons/clarity_precision_validity_systematization_operationalization_measurement_framework_GenAI.jsonl b/data/sampled_jsons/clarity_precision_validity_systematization_operationalization_measurement_framework_GenAI.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6105fa2716830f9278127405bf18342fb046eb5b
--- /dev/null
+++ b/data/sampled_jsons/clarity_precision_validity_systematization_operationalization_measurement_framework_GenAI.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Frameworks, Modeling and Simulations of Misinformation and", "date": "", "ddg_snippet": "... in the literature, this paper surveys the existing frameworks , models and simulations of mis/disinformation dynamics by performing a systematic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.09343v1", "content": "... in the literature, this paper surveys the existing frameworks , models and simulations of mis/disinformation dynamics by performing a systematic ..."}
+{"idx": 1, "title": "Clarity Connections - Rally Integration - Webinars Seguridad", "date": "", "ddg_snippet": "With this out-of-the-box connection, creating and syncing Clarity investments & Rally initiatives has never been easier.", "subpage_snippet": "", "source": "www.seguridadmania.com", "link": "https://www.seguridadmania.com/webinar/53879/clarity-connections-rally-integration/valueops-from-broadcom", "content": "With this out-of-the-box connection, creating and syncing Clarity investments & Rally initiatives has never been easier."}
+{"idx": 2, "title": "Toward understanding the role of generative AI in ...", "date": "", "ddg_snippet": "These AI-powered approaches improve the flexibility and precision of EE while strengthening students' decision-making abilities in complex market environments. However, existing research primarily examines AI's short-term impact on learning performance, with limited systematic analysis of its long-term effects on entrepreneurial success.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666920X25001109", "content": "These AI-powered approaches improve the flexibility and precision of EE while strengthening students' decision-making abilities in complex market environments. However, existing research primarily examines AI's short-term impact on learning performance, with limited systematic analysis of its long-term effects on entrepreneurial success."}
+{"idx": 3, "title": "Methodological Foundations for AI-Driven Survey Question", "date": "", "ddg_snippet": "As the use of GenAI in research grows, so too does the need for frameworks that support transparency, validation, and trustworthiness.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.01150v1", "content": "As the use of GenAI in research grows, so too does the need for frameworks that support transparency, validation, and trustworthiness."}
+{"idx": 4, "title": "Opticon 2025: Forward Deployed Engineers Step Into the Spotlight", "date": "", "ddg_snippet": "Validation: Testing solutions against real-world constraints, building evaluation frameworks and scaling validation processes.", "subpage_snippet": "", "source": "www.cmswire.com", "link": "https://www.cmswire.com/digital-experience/big-reveal-at-opticon-2025-the-rise-of-forward-deployed-software-engineers/", "content": "Validation: Testing solutions against real-world constraints, building evaluation frameworks and scaling validation processes."}
+{"idx": 5, "title": "Course Catalog – TEI | The Evaluators' Institute", "date": "", "ddg_snippet": "Measurement , whether it results from self-report survey, interview/focus groups, observation, document review, or administrative data must be ...", "subpage_snippet": "", "source": "tei.cgu.edu", "link": "https://tei.cgu.edu/program-at-a-glance/tei-curriculum/course-catalog/", "content": "Measurement , whether it results from self-report survey, interview/focus groups, observation, document review, or administrative data must be ..."}
+{"idx": 6, "title": "Publications – sonsoleslp", "date": "", "ddg_snippet": "This systematic literature review maps the empirical research of 41 papers utilizing GenAI and LA and interprets the results through the lens of the ...", "subpage_snippet": "", "source": "sonsoles.me", "link": "https://sonsoles.me/publications.html", "content": "This systematic literature review maps the empirical research of 41 papers utilizing GenAI and LA and interprets the results through the lens of the ..."}
+{"idx": 7, "title": "Resources", "date": "", "ddg_snippet": "WHAT: A CRMP establishes a structured, risk-driven framework that systematically identifies, assesses, mitigates, and monitors cybersecurity risks ...", "subpage_snippet": "", "source": "www.fairinstitute.org", "link": "https://www.fairinstitute.org/resources", "content": "WHAT: A CRMP establishes a structured, risk-driven framework that systematically identifies, assesses, mitigates, and monitors cybersecurity risks ..."}
+{"idx": 8, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "... systematized concept reflect what we want it to reflect?—from operational debates—i.e., did we operationalize the systematized concept via ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "... systematized concept reflect what we want it to reflect?—from operational debates—i.e., did we operationalize the systematized concept via ..."}
+{"idx": 9, "title": "How Leaders in Regulated Industries Are Scaling Enterprise AI -", "date": "", "ddg_snippet": "... operational processes: Prioritizing process-first design ensures AI deployments address real business needs, making it easier to integrate systems ...", "subpage_snippet": "", "source": "emerj.com", "link": "https://emerj.com/how-leaders-in-regulated-industries-are-scaling-enterprise-ai-with-leaders-from-searce-blue-cross-blue-shield-us-bank-and-citi/", "content": "... operational processes: Prioritizing process-first design ensures AI deployments address real business needs, making it easier to integrate systems ..."}
diff --git a/data/sampled_jsons/class_prior_kp_weakly_supervised_learning_recommendation_unbiased.jsonl b/data/sampled_jsons/class_prior_kp_weakly_supervised_learning_recommendation_unbiased.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..30b892a26c1d45f20e3678eab85e3892fca164de
--- /dev/null
+++ b/data/sampled_jsons/class_prior_kp_weakly_supervised_learning_recommendation_unbiased.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What does .class mean in Java? - Stack Overflow", "date": "", "ddg_snippet": "Feb 26, 2013 · What does . class mean in Java? For example, if I created a class called Print. What does Print. class return?", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/15078935/what-does-class-mean-in-java", "content": "Feb 26, 2013 · What does . class mean in Java? For example, if I created a class called Print. What does Print. class return?"}
+{"idx": 1, "title": "How do I sign in to Classroom? - Computer - Classroom Help", "date": "", "ddg_snippet": "Depending on your learning setting, you can sign in to Classroom with one of the following accounts: School account An accredited educational institution creates this account, typically referred t", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/edu/classroom/answer/6072460?hl=en&co=GENIE.Platform=Desktop", "content": "Depending on your learning setting, you can sign in to Classroom with one of the following accounts: School account An accredited educational institution creates this account, typically referred t"}
+{"idx": 2, "title": "class - Python decorators in classes - Stack Overflow", "date": "", "ddg_snippet": "Putting the decorator outside the class doesn't answer the question, which was how to put a decorator inside a class . One example of where your approach wouldn't work is where the decorator depends on a class attribute", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/1263451/python-decorators-in-classes", "content": "Putting the decorator outside the class doesn't answer the question, which was how to put a decorator inside a class . One example of where your approach wouldn't work is where the decorator depends on a class attribute"}
+{"idx": 3, "title": "Classroom Help", "date": "", "ddg_snippet": "Official Google Classroom Help Center where you can find tips and tutorials on using Google Classroom and other answers to frequently asked questions.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/edu/classroom/?hl=en", "content": "Official Google Classroom Help Center where you can find tips and tutorials on using Google Classroom and other answers to frequently asked questions."}
+{"idx": 4, "title": "What is ::class in PHP? - Stack Overflow", "date": "", "ddg_snippet": "Jun 11, 2015 · The special :: class constant is available as of PHP 5.5.0, and allows for fully qualified class name resolution at compile time, this is useful for namespaced classes:", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/30770148/what-is-class-in-php", "content": "Jun 11, 2015 · The special :: class constant is available as of PHP 5.5.0, and allows for fully qualified class name resolution at compile time, this is useful for namespaced classes:"}
+{"idx": 5, "title": "How can I solve \"java.lang.NoClassDefFoundError\"?", "date": "", "ddg_snippet": "I've tried both the examples in Oracle's Java Tutorials. They both compile fine, but at run time, both come up with this error: Exception in thread \"main\" java.lang.NoClassDefFoundError:", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/17973970/how-can-i-solve-java-lang-noclassdeffounderror", "content": "I've tried both the examples in Oracle's Java Tutorials. They both compile fine, but at run time, both come up with this error: Exception in thread \"main\" java.lang.NoClassDefFoundError:"}
+{"idx": 6, "title": "What does \"Could not find or load main class\" mean?", "date": "", "ddg_snippet": "Aug 7, 2013 · The Main class could not be found when there is a typo or wrong syntax in the fully qualified class name or it does not exist in the provided classpath. The Main class could not be loaded when the class cannot be initiated.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/18093928/what-does-could-not-find-or-load-main-class-mean", "content": "Aug 7, 2013 · The Main class could not be found when there is a typo or wrong syntax in the fully qualified class name or it does not exist in the provided classpath. The Main class could not be loaded when the class cannot be initiated."}
+{"idx": 7, "title": "How do I resolve ClassNotFoundException? - Stack Overflow", "date": "", "ddg_snippet": "Sep 9, 2016 · I am trying to run a Java application, but getting this error: java.lang.ClassNotFoundException: After the colon comes the location of the class that is missing. However, I know that that locat...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/17408769/how-do-i-resolve-classnotfoundexception", "content": "Sep 9, 2016 · I am trying to run a Java application, but getting this error: java.lang.ClassNotFoundException: After the colon comes the location of the class that is missing. However, I know that that locat..."}
+{"idx": 8, "title": "Join a class with a class code in Google Classroom", "date": "", "ddg_snippet": "To join a class , you just need to enter the class code once. After you join, you don’t need to enter the code again. If you forget, lose, or delete the code before you join the class , ask your teacher to share it with you.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/edu/classroom/answer/15605102?hl=en-EN&co=GENIE.Platform=Desktop", "content": "To join a class , you just need to enter the class code once. After you join, you don’t need to enter the code again. If you forget, lose, or delete the code before you join the class , ask your teacher to share it with you."}
+{"idx": 9, "title": "In c# what does 'where T : class' mean? - Stack Overflow", "date": "", "ddg_snippet": "Dec 7, 2012 · 0 public class MyGenericClass where T : SomeType { // Class implementation } Type Constraints: You can apply various constraints to type parameters, such as: Class constraint (where T : class ): This ensures that the type argument must be a reference type (a class or interface).", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/3786774/in-c-sharp-what-does-where-t-class-mean", "content": "Dec 7, 2012 · 0 public class MyGenericClass where T : SomeType { // Class implementation } Type Constraints: You can apply various constraints to type parameters, such as: Class constraint (where T : class ): This ensures that the type argument must be a reference type (a class or interface)."}
diff --git a/data/sampled_jsons/climate_change_social_media_studies_events_natural_disasters_policy_changes_protests.jsonl b/data/sampled_jsons/climate_change_social_media_studies_events_natural_disasters_policy_changes_protests.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3faa09ceafb4f7f69d7944f31225272f3be488df
--- /dev/null
+++ b/data/sampled_jsons/climate_change_social_media_studies_events_natural_disasters_policy_changes_protests.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "From Disruptive Protests to Disrupted Networks? Analyzing ...", "date": "", "ddg_snippet": "Examining how different forms of climate protest affect social media debates is critical to understanding their role within societal climate policy discourse.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/20563051251337400?int.sj-full-text.similar-articles.2=", "content": "Examining how different forms of climate protest affect social media debates is critical to understanding their role within societal climate policy discourse."}
+{"idx": 1, "title": "Unraveling anthropogenic climate change narratives on ...", "date": "", "ddg_snippet": "by O Elroy · 2024 · Cited by 7 — Social media platforms have a key role in spreading narratives about climate change , and therefore it is crucial to understand the discussion about climate ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666049024000161", "content": "by O Elroy · 2024 · Cited by 7 — Social media platforms have a key role in spreading narratives about climate change , and therefore it is crucial to understand the discussion about climate ..."}
+{"idx": 2, "title": "Climate summits and protests have a strong impact on ...", "date": "", "ddg_snippet": "by JH Lochner · 2024 · Cited by 9 — Here we assess how different sociopolitical and extreme weather events affect climate change media coverage , both immediately and in the weeks following the ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s43247-024-01434-3", "content": "by JH Lochner · 2024 · Cited by 9 — Here we assess how different sociopolitical and extreme weather events affect climate change media coverage , both immediately and in the weeks following the ..."}
+{"idx": 3, "title": "Raising awareness of climate change: Nature, activists, ...", "date": "", "ddg_snippet": "by D Grechyna · 2025 · Cited by 7 — This paper evaluates the relative importance of natural and human factors in shaping public awareness of climate change .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0921800924002714", "content": "by D Grechyna · 2025 · Cited by 7 — This paper evaluates the relative importance of natural and human factors in shaping public awareness of climate change ."}
+{"idx": 4, "title": "Relative engagement with sources of climate ...", "date": "", "ddg_snippet": "by S Storani · 2025 · Cited by 1 — We explore the discourse on climate change across multiple social media platforms, examining the evolution of user engagement with climate-related content.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-03082-9", "content": "by S Storani · 2025 · Cited by 1 — We explore the discourse on climate change across multiple social media platforms, examining the evolution of user engagement with climate-related content."}
+{"idx": 5, "title": "Climate Denial Fuels Climate Change Discussions More ...", "date": "", "ddg_snippet": "by M Shah · 2021 · Cited by 11 — The findings suggest that collective discussions on climate change are driven more by political leaders' controversial positions than life-threatening local ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8426507/", "content": "by M Shah · 2021 · Cited by 11 — The findings suggest that collective discussions on climate change are driven more by political leaders' controversial positions than life-threatening local ..."}
+{"idx": 6, "title": "Gen Z, Millennials Stand Out for Climate Change Activism ...", "date": "", "ddg_snippet": "26 May 2021 — Majorities of Americans support an array of measures to address climate change but stop short of a full break with fossil fuels.", "subpage_snippet": "", "source": "www.pewresearch.org", "link": "https://www.pewresearch.org/science/2021/05/26/gen-z-millennials-stand-out-for-climate-change-activism-social-media-engagement-with-issue/", "content": "26 May 2021 — Majorities of Americans support an array of measures to address climate change but stop short of a full break with fossil fuels."}
+{"idx": 7, "title": "Climate Change Communication on Facebook, Twitter, Sina ...", "date": "", "ddg_snippet": "First, social media platforms have been used by scientists, activists, journalists, and ordinary people to share and receive reports about climate change .", "subpage_snippet": "", "source": "oxfordre.com", "link": "https://oxfordre.com/climatescience/oso/viewentry/10.1093$002facrefore$002f9780190228620.001.0001$002facrefore-9780190228620-e-361?p=emailAUFisPiMAxigE&d=/10.1093/acrefore/9780190228620.001.0001/acrefore-9780190228620-e-361", "content": "First, social media platforms have been used by scientists, activists, journalists, and ordinary people to share and receive reports about climate change ."}
+{"idx": 8, "title": "Social media usage patterns during natural hazards | PLOS One", "date": "", "ddg_snippet": "by MT Niles · 2019 · Cited by 145 — Natural hazards are becoming increasingly expensive as climate change and development are exposing communities to greater risks.", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210484", "content": "by MT Niles · 2019 · Cited by 145 — Natural hazards are becoming increasingly expensive as climate change and development are exposing communities to greater risks."}
+{"idx": 9, "title": "4 How Climate Events Can Lead to Social and Political ...", "date": "", "ddg_snippet": "Climate change can result in events that create shocks to globalized systems that support human life and well-being and that can therefore affect populations ...", "subpage_snippet": "", "source": "nap.nationalacademies.org", "link": "https://nap.nationalacademies.org/read/14682/chapter/6", "content": "Climate change can result in events that create shocks to globalized systems that support human life and well-being and that can therefore affect populations ..."}
diff --git a/data/sampled_jsons/computer_science_standards_development_history_measurement_rigor_effort_justified.jsonl b/data/sampled_jsons/computer_science_standards_development_history_measurement_rigor_effort_justified.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a0b5578db25af754ae050d3678071ea714f0c02
--- /dev/null
+++ b/data/sampled_jsons/computer_science_standards_development_history_measurement_rigor_effort_justified.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Computer science - Wikipedia", "date": "", "ddg_snippet": "Main article: History of computer science .Gottfried Wilhelm Leibniz (1646–1716) developed logic in a binary number system and has been called the \"founder of computer science \".[13].", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Computer_science", "content": "Main article: History of computer science .Gottfried Wilhelm Leibniz (1646–1716) developed logic in a binary number system and has been called the \"founder of computer science \".[13]."}
+{"idx": 1, "title": "Computer science | Definition, Types, & Facts | Britannica", "date": "", "ddg_snippet": "Computer science is the study of computers and computing , including their theoretical and algorithmic foundations, hardware and software, and their uses for processing information.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/science/computer-science", "content": "Computer science is the study of computers and computing , including their theoretical and algorithmic foundations, hardware and software, and their uses for processing information."}
+{"idx": 2, "title": "Международный научный журнал « Endless light in science »", "date": "", "ddg_snippet": "ОФ “Международный научно-исследовательский центр “Endless Light in Science ”. Impact Factor: SJIF 2023 - 5.95 2024 - 5.99. Исторические науки historical sciences .", "subpage_snippet": "", "source": "irc-els.com", "link": "https://irc-els.com/docs/СБОРНИК+МНЖ+31+АВГУСТА+2025+ІІ+ЧАСТЬ.pdf", "content": "ОФ “Международный научно-исследовательский центр “Endless Light in Science ”. Impact Factor: SJIF 2023 - 5.95 2024 - 5.99. Исторические науки historical sciences ."}
+{"idx": 3, "title": "Ada Lovelace: The First Computer Programmer - Data Science", "date": "", "ddg_snippet": "Ada Lovelace is celebrated as the first computer programmer for her visionary work on Charles Babbage’s Analytical Engine. Discover her pioneering insights into computational theory, which laid the foundation for modern computing .", "subpage_snippet": "", "source": "diogoribeiro7.github.io", "link": "https://diogoribeiro7.github.io/biographies/ada_lovelace_the_first_computer_programmer/", "content": "Ada Lovelace is celebrated as the first computer programmer for her visionary work on Charles Babbage’s Analytical Engine. Discover her pioneering insights into computational theory, which laid the foundation for modern computing ."}
+{"idx": 4, "title": "Effort Justification - Aseem Gupta", "date": "", "ddg_snippet": "Effort justification profoundly affects our perceptions, attachments, and decision-making processes. The stories of John and Mark illuminate how our cognitive biases can lead us to overvalue the outcomes of tasks that demand significant effort .", "subpage_snippet": "", "source": "aseemgupta.com", "link": "https://aseemgupta.com/effort-justification/", "content": "Effort justification profoundly affects our perceptions, attachments, and decision-making processes. The stories of John and Mark illuminate how our cognitive biases can lead us to overvalue the outcomes of tasks that demand significant effort ."}
+{"idx": 5, "title": "(PDF) Robert Gilpin. The Realist Quest for Dynamics of Power.", "date": "", "ddg_snippet": "The historical development of IPE in the last few centuries also informs Gilpin’s.PD avoidance and promotion are part. of liberal capitalism’s self- justificatory efforts , but they also transcend the. politics of the contemporary era. One should really study the interaction of.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/5063991_Robert_Gilpin_The_Realist_Quest_for_Dynamics_of_Power", "content": "The historical development of IPE in the last few centuries also informs Gilpin’s.PD avoidance and promotion are part. of liberal capitalism’s self- justificatory efforts , but they also transcend the. politics of the contemporary era. One should really study the interaction of."}
+{"idx": 6, "title": "How Intel's Nvidia deal could help Intel's next generation of... | Reu...", "date": "", "ddg_snippet": "The primary loser is AMD, which designs different types of chips that compete with Nvidia and Intel in their respective markets, according to industry analyst Gold. \"Having two major competitors combining their efforts is not exactly a positive outcome for AMD,\" Gold wrote.", "subpage_snippet": "", "source": "www.reuters.com", "link": "https://www.reuters.com/business/how-intels-nvidia-deal-could-help-intels-next-generation-chip-manufacturing-2025-09-18/", "content": "The primary loser is AMD, which designs different types of chips that compete with Nvidia and Intel in their respective markets, according to industry analyst Gold. \"Having two major competitors combining their efforts is not exactly a positive outcome for AMD,\" Gold wrote."}
+{"idx": 7, "title": "Basics Of Communication Research [g0r91k11ge0k]", "date": "", "ddg_snippet": "THE HISTORY OF SAMPLING Sampling in social scientific research has developed hand in hand with political polling. This is the case, no doubt, because political polling is one of the few opportunities that social scientists have to discover the accuracy of their estimates.", "subpage_snippet": "", "source": "doku.pub", "link": "https://doku.pub/documents/basics-of-communication-research-g0r91k11ge0k", "content": "THE HISTORY OF SAMPLING Sampling in social scientific research has developed hand in hand with political polling. This is the case, no doubt, because political polling is one of the few opportunities that social scientists have to discover the accuracy of their estimates."}
+{"idx": 8, "title": "How Tyranny Becomes Entrenched: 9/11 and the Police...", "date": "", "ddg_snippet": "This is the danger of double standards in politics: every tyranny is rationalized in the moment by its chorus of defenders. But history teaches that what goes around comes around. If you justify it now, you’ll have no defense when the tables turn.", "subpage_snippet": "", "source": "eraoflight.com", "link": "https://eraoflight.com/2025/09/21/how-tyranny-becomes-entrenched-9-11-and-the-police-states-endless-power-grabs/", "content": "This is the danger of double standards in politics: every tyranny is rationalized in the moment by its chorus of defenders. But history teaches that what goes around comes around. If you justify it now, you’ll have no defense when the tables turn."}
+{"idx": 9, "title": "The Future of International Relation: Masters in the Making?", "date": "", "ddg_snippet": "10 Alexander Wendt: a social scientist struggling with history .Such is the usual story told that justifies and, indeed, perpetuates the separation of the two enterprises.", "subpage_snippet": "", "source": "hostnezt.com", "link": "https://hostnezt.com/cssfiles/internationalrelations/Future+of+international+relations.pdf", "content": "10 Alexander Wendt: a social scientist struggling with history .Such is the usual story told that justifies and, indeed, perpetuates the separation of the two enterprises."}
diff --git a/data/sampled_jsons/concept_bottleneck_models_alternatives_foundation_models_2024_year_2024.jsonl b/data/sampled_jsons/concept_bottleneck_models_alternatives_foundation_models_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dec0acb1ce8eb8d5fd724187964295a5a97243ef
--- /dev/null
+++ b/data/sampled_jsons/concept_bottleneck_models_alternatives_foundation_models_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "If Concept Bottlenecks are the Question, are Foundation ...", "date": "", "ddg_snippet": "29 Apr 2025 — Concept Bottleneck Models (CBMs ) are neural networks designed to conjoin high performance with ante-hoc interpretability. CBMs work by first ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.19774v2", "content": "29 Apr 2025 — Concept Bottleneck Models (CBMs ) are neural networks designed to conjoin high performance with ante-hoc interpretability. CBMs work by first ..."}
+{"idx": 1, "title": "NeurIPS Poster Coarse-to-Fine Concept Bottleneck Models", "date": "", "ddg_snippet": "This work targets ante hoc interpretability, and specifically Concept Bottleneck Models (CBMs). Our goal is to design a framework that admits a highly ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95178", "content": "This work targets ante hoc interpretability, and specifically Concept Bottleneck Models (CBMs). Our goal is to design a framework that admits a highly ..."}
+{"idx": 2, "title": "Concept Bottleneck Generative Models", "date": "", "ddg_snippet": "by AA Ismail · Cited by 30 — The authors propose to introduce a concept - bottleneck layer into deep generative models , including GANs, VAEs, and diffusion models . The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=L9U5MJJleF", "content": "by AA Ismail · Cited by 30 — The authors propose to introduce a concept - bottleneck layer into deep generative models , including GANs, VAEs, and diffusion models . The ..."}
+{"idx": 3, "title": "An Analysis of Concept Bottleneck Models: Measuring ...", "date": "", "ddg_snippet": "22 May 2025 — Concept bottleneck models (CBMs ) ensure interpretability by decomposing predictions into human interpretable concepts. Yet the annotations used ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16705v1", "content": "22 May 2025 — Concept bottleneck models (CBMs ) ensure interpretability by decomposing predictions into human interpretable concepts. Yet the annotations used ..."}
+{"idx": 4, "title": "improving the predictability of concept bottleneck models", "date": "", "ddg_snippet": "We validate our approach with experiments on CIFAR-10 and MetaShift, showing that concept-bottleneck models outperform the foundational embedding counterpart , ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93623", "content": "We validate our approach with experiments on CIFAR-10 and MetaShift, showing that concept-bottleneck models outperform the foundational embedding counterpart , ..."}
+{"idx": 5, "title": "Hybrid Concept Bottleneck Models - CVF Open Access", "date": "", "ddg_snippet": "by Y Liu · 2025 — In this work, we propose Hybrid Concept . Bottleneck Model (HybridCBM), a novel CBM framework to address the challenge of incomplete predefined concepts . Our ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Hybrid_Concept_Bottleneck_Models_CVPR_2025_paper.pdf", "content": "by Y Liu · 2025 — In this work, we propose Hybrid Concept . Bottleneck Model (HybridCBM), a novel CBM framework to address the challenge of incomplete predefined concepts . Our ... 11 pages"}
+{"idx": 6, "title": "Interpretable prognostics with concept bottleneck models", "date": "", "ddg_snippet": "by F Forest · 2025 · Cited by 3 — We propose concept bottleneck models for more interpretable prognostics, where degradation modes of an asset are used as intermediate concepts.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253525005007", "content": "by F Forest · 2025 · Cited by 3 — We propose concept bottleneck models for more interpretable prognostics, where degradation modes of an asset are used as intermediate concepts."}
+{"idx": 7, "title": "ICML 2024 Workshop on Foundation Models in the Wild", "date": "", "ddg_snippet": "Link. Ayush Kaushal · Tejas Vaidhya · Tejas Pandey · Aaryan Bhagat · Irina Rish. -. Adaptive Concept Bottleneck for Foundation Models ( Poster ) > link · Link.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/workshop/29954", "content": "Link. Ayush Kaushal · Tejas Vaidhya · Tejas Pandey · Aaryan Bhagat · Irina Rish. -. Adaptive Concept Bottleneck for Foundation Models ( Poster ) > link · Link."}
+{"idx": 8, "title": "Interpretable Generative Models through Post-hoc Concept ...", "date": "", "ddg_snippet": "by A Kulkarni · 2025 · Cited by 1 — Concept bottleneck models (CBM ) aim to produce inherently interpretable models that rely on human-understandable con- cepts for their predictions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kulkarni_Interpretable_Generative_Models_through_Post-hoc_Concept_Bottlenecks_CVPR_2025_paper.pdf", "content": "by A Kulkarni · 2025 · Cited by 1 — Concept bottleneck models (CBM ) aim to produce inherently interpretable models that rely on human-understandable con- cepts for their predictions."}
+{"idx": 9, "title": "Coarse-to-fine concept bottleneck models - ACM Digital Library", "date": "", "ddg_snippet": "5 Jun 2025 — Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models . Computer Vision – ECCV 2024 .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3741256", "content": "5 Jun 2025 — Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models . Computer Vision – ECCV 2024 ."}
diff --git a/data/sampled_jsons/concept_bottleneck_models_without_foundation_models_alternatives_2024_year_2024.jsonl b/data/sampled_jsons/concept_bottleneck_models_without_foundation_models_alternatives_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1036524f3773ea2f25a8c769d246859e0438cfc3
--- /dev/null
+++ b/data/sampled_jsons/concept_bottleneck_models_without_foundation_models_alternatives_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Concept Bottleneck Models Without Predefined Concepts", "date": "", "ddg_snippet": "Unsupervised Concept Bottleneck Models . Learning the concept space.Example Input to the Large Vision-Language Model . arXiv:2407.03921v1 [cs.LG] 4 Jul 2024 . Concept bottleneck models without predefined concepts .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.03921", "content": "Unsupervised Concept Bottleneck Models . Learning the concept space.Example Input to the Large Vision-Language Model . arXiv:2407.03921v1 [cs.LG] 4 Jul 2024 . Concept bottleneck models without predefined concepts ."}
+{"idx": 1, "title": "GitHub - riverback/Awesome- Concept - Bottleneck - Models : A list of...", "date": "", "ddg_snippet": "The original Concept Bottleneck Model maps each concept to a single (probabilistic) value to construct the concept bottleneck layer, followed by a linear layer that predicts image-level class labels based on these concept values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/riverback/Awesome-Concept-Bottleneck-Models", "content": "The original Concept Bottleneck Model maps each concept to a single (probabilistic) value to construct the concept bottleneck layer, followed by a linear layer that predicts image-level class labels based on these concept values."}
+{"idx": 2, "title": "Locality-aware Concept Bottleneck Model | OpenReview", "date": "", "ddg_snippet": "Concept bottleneck models (CBMs) are inherently interpretable models that make predictions based on human-understandable visual cues, referred to as concepts .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9LrbWulBfa&noteId=9LrbWulBfa", "content": "Concept bottleneck models (CBMs) are inherently interpretable models that make predictions based on human-understandable visual cues, referred to as concepts ."}
+{"idx": 3, "title": "Explain via Any Concept : Concept Bottleneck", "date": "", "ddg_snippet": "The concept bottleneck model (CBM) is an interpretable-by-design framework that makes decisions by first predicting a set of interpretable concepts , and then predicting the class label based on the given concepts .", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/11588.pdf", "content": "The concept bottleneck model (CBM) is an interpretable-by-design framework that makes decisions by first predicting a set of interpretable concepts , and then predicting the class label based on the given concepts ."}
+{"idx": 4, "title": "ICML Poster DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "2024 . Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46104", "content": "2024 . Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting..."}
+{"idx": 5, "title": "Are They the Same Picture? Adapting Concept Bottleneck Models for...", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) are another recent and promising method to facilitate collaboration [Koh et al., 2020]. They allow humans to interact with AI models by viewing and ma-nipulating intermediate, high-level concepts (e.g., whether a bird has a blue wing), which...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0866.pdf", "content": "Concept Bottleneck Models (CBMs) are another recent and promising method to facilitate collaboration [Koh et al., 2020]. They allow humans to interact with AI models by viewing and ma-nipulating intermediate, high-level concepts (e.g., whether a bird has a blue wing), which..."}
+{"idx": 6, "title": "Concept Bottleneck Models Without Predefined Concepts", "date": "", "ddg_snippet": "Traditional concept bottleneck models rely on predefined concepts , which can limit their flexibility and interpretability. This new approach allows the model to automatically learn meaningful concepts from the data, rather than using predefined ones.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/concept-bottleneck-models-without-predefined-concepts", "content": "Traditional concept bottleneck models rely on predefined concepts , which can limit their flexibility and interpretability. This new approach allows the model to automatically learn meaningful concepts from the data, rather than using predefined ones."}
+{"idx": 7, "title": "Relational Concept Bottleneck Models", "date": "", "ddg_snippet": "Relational Concept Bottleneck Models . Part of Advances in Neural Information Processing Systems 37 (NeurIPS 2024 ) Main Conference Track.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/8deff86036ee6a73cda883dc7d2d6b07-Abstract-Conference.html", "content": "Relational Concept Bottleneck Models . Part of Advances in Neural Information Processing Systems 37 (NeurIPS 2024 ) Main Conference Track."}
+{"idx": 8, "title": "(PDF) Causally Reliable Concept Bottleneck Models", "date": "", "ddg_snippet": "a Concept Bottleneck Model (CBM) (Koh et al.,2020) with.between models ’ baseline performance ( without interventions) and their performance when all variables are intervened.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389648547_Causally_Reliable_Concept_Bottleneck_Models", "content": "a Concept Bottleneck Model (CBM) (Koh et al.,2020) with.between models ’ baseline performance ( without interventions) and their performance when all variables are intervened."}
+{"idx": 9, "title": "Integrating Clinical Knowledge into Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept bottleneck models (CBMs), which predict human-interpretable concepts (e.g., nucleus shapes in cell images) before predicting the final output (e.g., cell type), provide insights into the decision-making processes of the model .", "subpage_snippet": "", "source": "papers.miccai.org", "link": "https://papers.miccai.org/miccai-2024/415-Paper1786.html", "content": "Abstract Concept bottleneck models (CBMs), which predict human-interpretable concepts (e.g., nucleus shapes in cell images) before predicting the final output (e.g., cell type), provide insights into the decision-making processes of the model ."}
diff --git a/data/sampled_jsons/conceptual_debates_operational_debates_systematized_concept_what_we_want_it_to_reflect_GenAI_evaluat.jsonl b/data/sampled_jsons/conceptual_debates_operational_debates_systematized_concept_what_we_want_it_to_reflect_GenAI_evaluat.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f4dc01493145b9295e24f47725359777f18600c0
--- /dev/null
+++ b/data/sampled_jsons/conceptual_debates_operational_debates_systematized_concept_what_we_want_it_to_reflect_GenAI_evaluat.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Political Concepts - Political Science - iResearchNet", "date": "", "ddg_snippet": "... conceptual analysis as well as the ... Since its origins in ancient Greece, Western philosophy has been debating the nature and meaning of concepts .", "subpage_snippet": "", "source": "political-science.iresearchnet.com", "link": "https://political-science.iresearchnet.com/political-concepts/", "content": "... conceptual analysis as well as the ... Since its origins in ancient Greece, Western philosophy has been debating the nature and meaning of concepts ."}
+{"idx": 1, "title": "All Azimuth | Conceptual Cultivation and Homegrown Theorizing:", "date": "", "ddg_snippet": "Second, a novel definition of the concept of influence is propounded since a rigorous and systematic conceptual cultivation of influence is still ...", "subpage_snippet": "", "source": "www.allazimuth.com", "link": "https://www.allazimuth.com/2017/09/20/conceptual-cultivation-and-homegrown-theorizing-the-case-offor-the-concept-of-influence/", "content": "Second, a novel definition of the concept of influence is propounded since a rigorous and systematic conceptual cultivation of influence is still ..."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems is a Social Science...", "date": "", "ddg_snippet": "As we explain below, this separation has two important implications for designing and evaluating evaluations of GenAI systems: First, it can broaden the expertise involved in such evaluations by enabling stakeholders with different perspectives to participate in conceptual debates .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "As we explain below, this separation has two important implications for designing and evaluating evaluations of GenAI systems: First, it can broaden the expertise involved in such evaluations by enabling stakeholders with different perspectives to participate in conceptual debates ."}
+{"idx": 3, "title": "(PDF) Position: Evaluating Generative AI Systems is a Social Science...", "date": "", "ddg_snippet": "we want it to reflect ?—from operational debates —i.e., did we operationalize the systematized concept via. GenAI systems: First, it can broaden the expertise involved. in such evaluations by enabling stakeholders with different. perspectives to participate in conceptual debates .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388657599_Position_Evaluating_Generative_AI_Systems_is_a_Social_Science_Measurement_Challenge", "content": "we want it to reflect ?—from operational debates —i.e., did we operationalize the systematized concept via. GenAI systems: First, it can broaden the expertise involved. in such evaluations by enabling stakeholders with different. perspectives to participate in conceptual debates ."}
+{"idx": 4, "title": "ISSA Proceedings 2014 – Consideration On The Notion Of", "date": "", "ddg_snippet": "... what he called the theory of ... For instance, we can of course complete an abstract operation of mathematical proof by systematic calculation.", "subpage_snippet": "", "source": "rozenbergquarterly.com", "link": "https://rozenbergquarterly.com/issa-proceedings-2014-consideration-on-the-notion-of-reasoning/", "content": "... what he called the theory of ... For instance, we can of course complete an abstract operation of mathematical proof by systematic calculation."}
+{"idx": 5, "title": "ISSA : Rozenberg Quarterly", "date": "", "ddg_snippet": "The reader wants to know not what has happened, but also, more importantly, what is going to happen. ... we argue that the distinction between ...", "subpage_snippet": "", "source": "rozenbergquarterly.com", "link": "https://rozenbergquarterly.com/category/language/issa/page/54/", "content": "The reader wants to know not what has happened, but also, more importantly, what is going to happen. ... we argue that the distinction between ..."}
+{"idx": 6, "title": "The Politics of Conflicting Concerns | The Ethics of", "date": "", "ddg_snippet": "The Ethics of Conceptualization: Tailoring Thought and Language to Need ... Non-Western Philosophy ... Operating Department Practice", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/book/59533/chapter/502350751", "content": "The Ethics of Conceptualization: Tailoring Thought and Language to Need ... Non-Western Philosophy ... Operating Department Practice"}
+{"idx": 7, "title": "Nosology - Bibliography - PhilPapers", "date": "", "ddg_snippet": "... debate about cancer classification and about obesity as a disease I also argue that the proper target of conceptual engineering in medical research ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/nosology", "content": "... debate about cancer classification and about obesity as a disease I also argue that the proper target of conceptual engineering in medical research ..."}
+{"idx": 8, "title": "The Representation-Enabling Approach to Campaign Finance", "date": "", "ddg_snippet": "... democracy— what I will call a representation-enabling account—can systematically bring into view the full range of relevant concerns (Section VII).", "subpage_snippet": "", "source": "freeandequaljournal.org", "link": "https://freeandequaljournal.org/article/id/18650/", "content": "... democracy— what I will call a representation-enabling account—can systematically bring into view the full range of relevant concerns (Section VII)."}
+{"idx": 9, "title": "Experimental Philosophy of Science - Bibliography - PhilPapers", "date": "", "ddg_snippet": "We found that researchers’ general disposition to endorse or reject realism was better predicted by their views regarding scientific progress than ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/experimental-philosophy-of-science", "content": "We found that researchers’ general disposition to endorse or reject realism was better predicted by their views regarding scientific progress than ..."}
diff --git a/data/sampled_jsons/conditional_variance_squared_random_variable_kurtosis_heavy_tails_robust_statistics.jsonl b/data/sampled_jsons/conditional_variance_squared_random_variable_kurtosis_heavy_tails_robust_statistics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cc2fb3b0fa896b07d0cc3ee565f5c25f48249d17
--- /dev/null
+++ b/data/sampled_jsons/conditional_variance_squared_random_variable_kurtosis_heavy_tails_robust_statistics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "List of statistics articles - Wikipedia", "date": "", "ddg_snippet": "... two-stage least squares) – redirects to instrumental variable ... Asymptotic relative efficiency – redirects to Efficiency ( statistics )", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/List_of_statistics_articles", "content": "... two-stage least squares) – redirects to instrumental variable ... Asymptotic relative efficiency – redirects to Efficiency ( statistics )"}
+{"idx": 1, "title": "Kurtosis - Wikipedia", "date": "", "ddg_snippet": "... statistics , kurtosis (from Greek : κυρτός , kyrtos or kurtos , meaning \"curved, arching\") is a measure of the \"tailedness\" of the probability ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Kurtosis", "content": "... statistics , kurtosis (from Greek : κυρτός , kyrtos or kurtos , meaning \"curved, arching\") is a measure of the \"tailedness\" of the probability ..."}
+{"idx": 2, "title": "Kurtosis: The Tails of Kurtosis: Skewness and Risk in", "date": "", "ddg_snippet": "Kurtosis is a statistical measure that describes the shape of a distribution's tails in relation to its overall shape.", "subpage_snippet": "", "source": "fastercapital.com", "link": "https://fastercapital.com/content/Kurtosis--The-Tails-of-Kurtosis--Skewness-and-Risk-in-Investment-Portfolios.html", "content": "Kurtosis is a statistical measure that describes the shape of a distribution's tails in relation to its overall shape."}
+{"idx": 3, "title": "Skewness and Kurtosis: How to Measure the Asymmetry and Tail", "date": "", "ddg_snippet": "Kurtosis can be calculated as the fourth standardized moment of a distribution, or as the ratio of the fourth moment to the square of the second ...", "subpage_snippet": "", "source": "fastercapital.com", "link": "https://fastercapital.com/content/Skewness-and-Kurtosis--How-to-Measure-the-Asymmetry-and-Tail-Risk-of-Your-Investment-Returns.html", "content": "Kurtosis can be calculated as the fourth standardized moment of a distribution, or as the ratio of the fourth moment to the square of the second ..."}
+{"idx": 4, "title": "Normal Distribution: Beyond the Bell Curve: Kurtosis in Normal", "date": "", "ddg_snippet": "Kurtosis is a statistical measure that describes the shape of a distribution's tails in relation to its overall profile.", "subpage_snippet": "", "source": "fastercapital.com", "link": "https://fastercapital.com/content/Normal-Distribution--Beyond-the-Bell-Curve--Kurtosis-in-Normal-Distribution.html", "content": "Kurtosis is a statistical measure that describes the shape of a distribution's tails in relation to its overall profile."}
+{"idx": 5, "title": "Newest 'heavy-tailed' Questions - Cross Validated", "date": "", "ddg_snippet": "... p(x)$, which has finite moments $\\left $ defined upto $k=N$, is it possible to say something about the heavyness of the tails by ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/tagged/heavy-tailed", "content": "... p(x)$, which has finite moments $\\left $ defined upto $k=N$, is it possible to say something about the heavyness of the tails by ..."}
+{"idx": 6, "title": "mathematical statistics - Intuition for moments about the mean", "date": "", "ddg_snippet": "... will get a measure that gives non-negative values, with higher values occurring if the tails of the distribution of the standardised random variable ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/123251/intuition-for-moments-about-the-mean-of-a-distribution", "content": "... will get a measure that gives non-negative values, with higher values occurring if the tails of the distribution of the standardised random variable ..."}
+{"idx": 7, "title": "Comparing the variances of two dependent variables | Journal of", "date": "", "ddg_snippet": "Let \\({\\sigma ^{2}_{1}}\\) and \\({\\sigma ^{2}_{2}}\\) be the population variances associated with the random variables X and Y , respectively, where X ...", "subpage_snippet": "", "source": "jsdajournal.springeropen.com", "link": "https://jsdajournal.springeropen.com/articles/10.1186/s40488-015-0030-z", "content": "Let \\({\\sigma ^{2}_{1}}\\) and \\({\\sigma ^{2}_{2}}\\) be the population variances associated with the random variables X and Y , respectively, where X ..."}
+{"idx": 8, "title": "Slash Distribution - Statistics How To", "date": "", "ddg_snippet": "... statistical procedures such as investigating how variables perform under extreme conditions [3], including robustness studies (e.g., [4, 5]) and ...", "subpage_snippet": "", "source": "www.statisticshowto.com", "link": "https://www.statisticshowto.com/slash-distribution/", "content": "... statistical procedures such as investigating how variables perform under extreme conditions [3], including robustness studies (e.g., [4, 5]) and ..."}
+{"idx": 9, "title": "Kurtosis - Infogalactic: the planetary knowledge core", "date": "", "ddg_snippet": "... statistics , kurtosis (from Greek : κυρτός , kyrtos or kurtos , meaning \"curved, arching\") is a measure of the \"tailedness\" of the probability ...", "subpage_snippet": "", "source": "infogalactic.com", "link": "https://infogalactic.com/info/Kurtosis", "content": "... statistics , kurtosis (from Greek : κυρτός , kyrtos or kurtos , meaning \"curved, arching\") is a measure of the \"tailedness\" of the probability ..."}
diff --git a/data/sampled_jsons/conf(i,_k)_equation_7_ATA_Adaptive_Task_Allocation_distributed_machine_learning.jsonl b/data/sampled_jsons/conf(i,_k)_equation_7_ATA_Adaptive_Task_Allocation_distributed_machine_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4178a941a58b204319a70221aac549729270a7e9
--- /dev/null
+++ b/data/sampled_jsons/conf(i,_k)_equation_7_ATA_Adaptive_Task_Allocation_distributed_machine_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "Asynchronous methods are fundamental for parallelizing computations in distributed machine learning .In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "Asynchronous methods are fundamental for parallelizing computations in distributed machine learning .In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 1, "title": "Postdoc, KAUST - Cited by 12 - Online learning - Bandits theory", "date": "", "ddg_snippet": "Ata : Adaptive task allocation for efficient resource management in distributed machine learning .2025. Covariance- adaptive best arm identification. EM Saad, G Blanchard, N Verzelen.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Jih_bwsAAAAJ&hl=en", "content": "Ata : Adaptive task allocation for efficient resource management in distributed machine learning .2025. Covariance- adaptive best arm identification. EM Saad, G Blanchard, N Verzelen."}
+{"idx": 2, "title": "Joint Task and Resource Allocation for Mobile Edge Learning (2020)", "date": "", "ddg_snippet": "(DOI: 10.1109/GLOBECOM42002.2020.9322399) The exploding increase in the number of connected devices and growing sizes of their generated data gave more opportunities for distributed learning to dominate fast data analytics in mobile edge environments.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/joint-task-and-resource-allocation-for-mobile-edge-learning-10hpi1t00k", "content": "(DOI: 10.1109/GLOBECOM42002.2020.9322399) The exploding increase in the number of connected devices and growing sizes of their generated data gave more opportunities for distributed learning to dominate fast data analytics in mobile edge environments."}
+{"idx": 3, "title": "(PDF) Task Allocation for Asynchronous Mobile Edge Learning with...", "date": "", "ddg_snippet": "Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/346555823_Task_Allocation_for_Asynchronous_Mobile_Edge_Learning_with_Delay_and_Energy_Constraints", "content": "Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning ."}
+{"idx": 4, "title": "ATA : Adaptive Task Allocation", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning .", "subpage_snippet": "", "source": "artomaranjyan.github.io", "link": "https://artomaranjyan.github.io/assets/pdf/posters/ATA_SNSL.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ."}
+{"idx": 5, "title": "Heterogeneity Aware Distributed Machine Learning at... | SpringerLink", "date": "", "ddg_snippet": "Distributed Machine Learning and Computing. Umair Mohammad3 &.Mohammad, U., Saeed, F. (2024). Heterogeneity Aware Distributed Machine Learning at the Wireless Edge for Health IoT Applications: An EEG Data Case Study.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-57567-9_3", "content": "Distributed Machine Learning and Computing. Umair Mohammad3 &.Mohammad, U., Saeed, F. (2024). Heterogeneity Aware Distributed Machine Learning at the Wireless Edge for Health IoT Applications: An EEG Data Case Study."}
+{"idx": 6, "title": "Dynamic Task Allocation for Mobile Edge Learning", "date": "", "ddg_snippet": "Environment, Heterogeneous Nodes, Distributed Machine Learning , Machine Learning Models, Number Of Updates, Local Updates, Global Cycle, MNIST Dataset, Global Update, Mobile Edge Computing, Edge Server, Computational Capabilities, Model Size, Interior Point Method.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tm/2023/12/09658132/1zw1hXJhfJS", "content": "Environment, Heterogeneous Nodes, Distributed Machine Learning , Machine Learning Models, Number Of Updates, Local Updates, Global Cycle, MNIST Dataset, Global Update, Mobile Edge Computing, Edge Server, Computational Capabilities, Model Size, Interior Point Method."}
+{"idx": 7, "title": "TAN: a Distributed Algorithm for Dynamic Task", "date": "", "ddg_snippet": "Therefore, in this paper, we propose an adaptive and decentralised Task Allocation Negotiation algorithm (TAN) based on non-cooperative game theory, where neighbouring nodes engage in negotiations to maximize their own utility functions.", "subpage_snippet": "", "source": "iris.unica.it", "link": "https://iris.unica.it/bitstream/11584/105474/5/IEEE+checked+-+Task+Allocation+Negotiation+a+Distributed+Algorithm+for+the+Dynamic+Deployment+of+Applications+in+WSNs.pdf", "content": "Therefore, in this paper, we propose an adaptive and decentralised Task Allocation Negotiation algorithm (TAN) based on non-cooperative game theory, where neighbouring nodes engage in negotiations to maximize their own utility functions."}
+{"idx": 8, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 9, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "May 1, 2025 · The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "May 1, 2025 · The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods."}
diff --git a/data/sampled_jsons/consequential_validity_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge.jsonl b/data/sampled_jsons/consequential_validity_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8c3cca6d48442fc766d44af220292ed890c20cd1
--- /dev/null
+++ b/data/sampled_jsons/consequential_validity_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Evaluating Generative AI Systems is a Social ...", "date": "", "ddg_snippet": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge Hanna Wallach Meera Desai A. Feder Cooper Angelina Wang Chad Atalla Solon Barocas Su Lin Blodgett Alexandra Chouldechova Emily Corvi P. Alex Dow Jean Garcia-Gathright Alexandra Olteanu Nicholas Pangakis Stefanie Reed Emily Sheng Dan Vann Jennifer Wortman Vaughan Matthew Vogel Hannah Washington Abigail Z. Jacobs", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge Hanna Wallach Meera Desai A. Feder Cooper Angelina Wang Chad Atalla Solon Barocas Su Lin Blodgett Alexandra Chouldechova Emily Corvi P. Alex Dow Jean Garcia-Gathright Alexandra Olteanu Nicholas Pangakis Stefanie Reed Emily Sheng Dan Vann Jennifer Wortman Vaughan Matthew Vogel Hannah Washington Abigail Z. Jacobs"}
+{"idx": 1, "title": "(PDF) Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "validity,hypothesis validity, and consequential validity [e.g., 13. ]. Each lens constitutes a different. source of evidence about validity. For example, content validity focuses on whether a measurement . instrument captures all relevant aspects of a systematized concept, while convergent...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385920305_Evaluating_Generative_AI_Systems_is_a_Social_Science_Measurement_Challenge", "content": "validity,hypothesis validity, and consequential validity [e.g., 13. ]. Each lens constitutes a different. source of evidence about validity. For example, content validity focuses on whether a measurement . instrument captures all relevant aspects of a systematized concept, while convergent..."}
+{"idx": 2, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Nov 17, 2024 · Abstract Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.10939v1", "content": "Nov 17, 2024 · Abstract Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the ..."}
+{"idx": 3, "title": "AI Red Teaming Through the Lens of Measurement Theory", "date": "", "ddg_snippet": "Red teaming—i.e., simulating attacks on computer systems to identify vulnerabilities and improve system defenses [e.g., 11]—has emerged as a widely used approach for surfacing both qualitative and quantitative information about system behavior in evaluations of generative AI (GenAI) systems . The expanding variety of both red-teaming practices and the information they produce makes the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=KEggQCeDUA&name=pdf", "content": "Red teaming—i.e., simulating attacks on computer systems to identify vulnerabilities and improve system defenses [e.g., 11]—has emerged as a widely used approach for surfacing both qualitative and quantitative information about system behavior in evaluations of generative AI (GenAI) systems . The expanding variety of both red-teaming practices and the information they produce makes the ..."}
+{"idx": 4, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge Hanna Wallach 1 2 Meera Desai A. Feder Cooper 3 1 1 Angelina Wang Chad Atalla Solon Barocas", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561", "content": "Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge Hanna Wallach 1 2 Meera Desai A. Feder Cooper 3 1 1 Angelina Wang Chad Atalla Solon Barocas"}
+{"idx": 5, "title": "Position - Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "\" consequential validity , which is concerned with the consequences of measurement, including the consequences of the systematization process and the systematized concept.\" (Page 6)", "subpage_snippet": "", "source": "notebook.ddeepak95.com", "link": "https://notebook.ddeepak95.com/reference/position-evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/", "content": "\" consequential validity , which is concerned with the consequences of measurement, including the consequences of the systematization process and the systematized concept.\" (Page 6)"}
+{"idx": 6, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "Jun 6, 2025 · Abstract The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as “ a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "Jun 6, 2025 · Abstract The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as “ a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using ..."}
+{"idx": 7, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "1 Measurement and its Role in Evaluating GenAI Systems .Robert Adcock and David Collier. Measurement validity : A shared standard for qualitative and quantitative research. American political science review, 95(3):529–546, 2001.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.10939v1/", "content": "1 Measurement and its Role in Evaluating GenAI Systems .Robert Adcock and David Collier. Measurement validity : A shared standard for qualitative and quantitative research. American political science review, 95(3):529–546, 2001."}
+{"idx": 8, "title": "Measurement as Bricolage: Examining How Data Scientists", "date": "", "ddg_snippet": "However, the traditional top-down conceptualization of measurement is in tension with our understanding of data science as a bottom-up process ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02819v1", "content": "However, the traditional top-down conceptualization of measurement is in tension with our understanding of data science as a bottom-up process ..."}
+{"idx": 9, "title": "AI Red Teaming Through the Lens of", "date": "", "ddg_snippet": "consequential validity .Validity issues arise as systematic gaps across the four levels, such as using a US-centric systematization of hate speech when evaluating a system in the UK context.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KEggQCeDUA", "content": "consequential validity .Validity issues arise as systematic gaps across the four levels, such as using a US-centric systematization of hate speech when evaluating a system in the UK context."}
diff --git a/data/sampled_jsons/constant-Q_transform_CQT_advantages_over_STFT_music_signal_analysis.jsonl b/data/sampled_jsons/constant-Q_transform_CQT_advantages_over_STFT_music_signal_analysis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..71347055944f00243f116908f2ed1326295a1ee4
--- /dev/null
+++ b/data/sampled_jsons/constant-Q_transform_CQT_advantages_over_STFT_music_signal_analysis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constant-Q transform - Wikipedia", "date": "", "ddg_snippet": "In mathematics and signal processing , the constant - Q transform and variable- Q transform , simply known as CQT and VQT , transforms a data series to ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Constant-Q_transform", "content": "In mathematics and signal processing , the constant - Q transform and variable- Q transform , simply known as CQT and VQT , transforms a data series to ..."}
+{"idx": 1, "title": "Spectral Representations: Mel Spectrograms, CQT, and HCQT", "date": "", "ddg_snippet": "... explore three advanced spectral representations that address the limitations of STFT -based spectrograms: Mel Spectrograms, Constant - Q Transform ( CQT ...", "subpage_snippet": "", "source": "www.jhonatanlopez.com", "link": "https://www.jhonatanlopez.com/advanced-spectral-representations-audio-analysis/", "content": "... explore three advanced spectral representations that address the limitations of STFT -based spectrograms: Mel Spectrograms, Constant - Q Transform ( CQT ..."}
+{"idx": 2, "title": "Constant-Q transform - HandWiki", "date": "", "ddg_snippet": "In mathematics and signal processing , the constant - Q transform and variable- Q transform , simply known as CQT and VQT , transforms a data series to ...", "subpage_snippet": "", "source": "handwiki.org", "link": "https://handwiki.org/wiki/Constant-Q_transform", "content": "In mathematics and signal processing , the constant - Q transform and variable- Q transform , simply known as CQT and VQT , transforms a data series to ..."}
+{"idx": 3, "title": "(PDF) A Real-Time Variable-Q Non-Stationary Gabor Transform for", "date": "", "ddg_snippet": "... to retain natural sounding pitch shifted speech, a hybrid of smoothly varying Q scheme is used to retain the formant structure of the original signal ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/292361602_A_Real-Time_Variable-Q_Non-Stationary_Gabor_Transform_for_Pitch_Shifting", "content": "... to retain natural sounding pitch shifted speech, a hybrid of smoothly varying Q scheme is used to retain the formant structure of the original signal ..."}
+{"idx": 4, "title": "Advanced Signal Analysis in Detecting Replay Attacks for", "date": "", "ddg_snippet": "... constant Q transform ( CQT ) method) and efficacy, respectively, in analyzing speech signals , making them promising to utilize in music and speech ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01130v3", "content": "... constant Q transform ( CQT ) method) and efficacy, respectively, in analyzing speech signals , making them promising to utilize in music and speech ..."}
+{"idx": 5, "title": "An Intro to Wavelets for Computer Musicians | Nathan Ho", "date": "", "ddg_snippet": "I should first address a question some of my readers have: what are wavelet transforms ? The best answer I can give is that a wavelet transform is any ...", "subpage_snippet": "", "source": "nathan.ho.name", "link": "https://nathan.ho.name/posts/wavelets/", "content": "I should first address a question some of my readers have: what are wavelet transforms ? The best answer I can give is that a wavelet transform is any ..."}
+{"idx": 6, "title": "(PDF) \"An efficient algorithm for the calculation of a", "date": "", "ddg_snippet": "An efficient method of transforming a discrete Fourier transform (DFT) into a constant Q transform , where Q is the ratio of center frequency to ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/230554907_An_efficient_algorithm_for_the_calculation_of_a_constant_Q_transform", "content": "An efficient method of transforming a discrete Fourier transform (DFT) into a constant Q transform , where Q is the ratio of center frequency to ..."}
+{"idx": 7, "title": "Diffusion Models for Audio Restoration Invited paper for the", "date": "", "ddg_snippet": "Figure 1 : A continuous-time diffusion model [ 8 ] transforms (left) a Gaussian distribution to (right) an intractable data distribution through a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.09821v3", "content": "Figure 1 : A continuous-time diffusion model [ 8 ] transforms (left) a Gaussian distribution to (right) an intractable data distribution through a ..."}
+{"idx": 8, "title": "fft - Most efficient way to extract frequency components of", "date": "", "ddg_snippet": "At this point, in order to answer directly your concern about reflecting human perception, some people derived the [\" constant - Q transform \" ( CQT ...", "subpage_snippet": "", "source": "dsp.stackexchange.com", "link": "https://dsp.stackexchange.com/questions/3580/most-efficient-way-to-extract-frequency-components-of-audio-signal-with-near-hum", "content": "At this point, in order to answer directly your concern about reflecting human perception, some people derived the [\" constant - Q transform \" ( CQT ..."}
+{"idx": 9, "title": "Literature | SigSep", "date": "", "ddg_snippet": "... transform ( STFT ) [ 16 ], which has complex entries: the angle accounts for the phase, i.e., the actual shift of the corresponding sinusoid at that ...", "subpage_snippet": "", "source": "sigsep.github.io", "link": "https://sigsep.github.io/literature/", "content": "... transform ( STFT ) [ 16 ], which has complex entries: the angle accounts for the phase, i.e., the actual shift of the corresponding sinusoid at that ..."}
diff --git a/data/sampled_jsons/construct_validity_medical_LLM_task_distribution_psychiatry_cardiology_findings.jsonl b/data/sampled_jsons/construct_validity_medical_LLM_task_distribution_psychiatry_cardiology_findings.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f879d1dd80bb47f96f74ac96f0b9be5ad58430f
--- /dev/null
+++ b/data/sampled_jsons/construct_validity_medical_LLM_task_distribution_psychiatry_cardiology_findings.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Artificial intelligence: revolutionizing cardiology with large ...", "date": "", "ddg_snippet": "by MJ Boonstra · 2024 · Cited by 76 — Large language models are very valuable assets in the field of cardiology as LLMs are able to perform numerous NLP tasks such as speech-to-text tools to ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10834163/", "content": "by MJ Boonstra · 2024 · Cited by 76 — Large language models are very valuable assets in the field of cardiology as LLMs are able to perform numerous NLP tasks such as speech-to-text tools to ..."}
+{"idx": 1, "title": "Large Language Models in Medical Diagnostics: Scoping ...", "date": "", "ddg_snippet": "by H Su · 2025 · Cited by 3 — The integration of large language models (LLMs) into medical diagnostics has garnered substantial attention due to their potential to enhance diagnostic ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12186007/", "content": "by H Su · 2025 · Cited by 3 — The integration of large language models (LLMs) into medical diagnostics has garnered substantial attention due to their potential to enhance diagnostic ..."}
+{"idx": 2, "title": "A systematic review of large language model (LLM ...", "date": "", "ddg_snippet": "by S Shool · 2025 · Cited by 63 — This systematic review examines the evaluation parameters and methodologies applied to LLMs in clinical medicine , highlighting their capabilities, limitations, ...", "subpage_snippet": "", "source": "bmcmedinformdecismak.biomedcentral.com", "link": "https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-025-02954-4", "content": "by S Shool · 2025 · Cited by 63 — This systematic review examines the evaluation parameters and methodologies applied to LLMs in clinical medicine , highlighting their capabilities, limitations, ..."}
+{"idx": 3, "title": "Zodiac: A Cardiologist-Level LLM Framework for Multi- ...", "date": "", "ddg_snippet": "2 Oct 2024 — In this work , we introduce Zodiac, an LLM -powered framework with cardiologist -level professionalism designed to engage LLMs in cardiological diagnostics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02026v1", "content": "2 Oct 2024 — In this work , we introduce Zodiac, an LLM -powered framework with cardiologist -level professionalism designed to engage LLMs in cardiological diagnostics."}
+{"idx": 4, "title": "Cardiology Knowledge Assessment of Retrieval ...", "date": "", "ddg_snippet": "12 Sept 2025 — Discussion Large language models (LLMs) are increasingly integrated into clinical workflows, yet their performance in cardiovascular medicine ...", "subpage_snippet": "", "source": "www.medrxiv.org", "link": "https://www.medrxiv.org/content/10.1101/2025.09.11.25335607v1.full", "content": "12 Sept 2025 — Discussion Large language models (LLMs) are increasingly integrated into clinical workflows, yet their performance in cardiovascular medicine ..."}
+{"idx": 5, "title": "A Novel Evaluation Benchmark for Medical LLMs", "date": "", "ddg_snippet": "13 Aug 2025 — Domain-specific medical LLMs showed consistent performance advantages over general-purpose models, with relatively higher top scores in safety ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23486v3", "content": "13 Aug 2025 — Domain-specific medical LLMs showed consistent performance advantages over general-purpose models, with relatively higher top scores in safety ( ..."}
+{"idx": 6, "title": "Large Language Models in Medical Diagnostics: Scoping ...", "date": "", "ddg_snippet": "9 Jun 2025 — This scoping review aimed to provide an overview of the current state of research regarding the use of LLMs in medical diagnostics.", "subpage_snippet": "", "source": "www.jmir.org", "link": "https://www.jmir.org/2025/1/e72062", "content": "9 Jun 2025 — This scoping review aimed to provide an overview of the current state of research regarding the use of LLMs in medical diagnostics."}
+{"idx": 7, "title": "Evaluation of a large language model to simplify discharge ...", "date": "", "ddg_snippet": "by P Rust · 2025 · Cited by 1 — This study explored using a large language model ( LLM ) to enhance discharge summary readability and augment it with lifestyle recommendations.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s43856-025-00927-2", "content": "by P Rust · 2025 · Cited by 1 — This study explored using a large language model ( LLM ) to enhance discharge summary readability and augment it with lifestyle recommendations."}
+{"idx": 8, "title": "Applications of large language models in cardiovascular ...", "date": "", "ddg_snippet": "by J Ferreira Santos · 2025 · Cited by 1 — In medicine , LLMs hold promise by enabling the analysis of vast volumes of medical literature, patient records, and clinical research, ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/ehjdh/article/6/4/540/8102875", "content": "by J Ferreira Santos · 2025 · Cited by 1 — In medicine , LLMs hold promise by enabling the analysis of vast volumes of medical literature, patient records, and clinical research, ..."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/contrastive_learning_frameworks_using_sigmoid_loss_apart_from_SigLIP.jsonl b/data/sampled_jsons/contrastive_learning_frameworks_using_sigmoid_loss_apart_from_SigLIP.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e9783618770cfe8203e7d9139d2393f9903e1810
--- /dev/null
+++ b/data/sampled_jsons/contrastive_learning_frameworks_using_sigmoid_loss_apart_from_SigLIP.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "Contrastive learning has emerged as a prominent branch of self-supervised learning for several years. Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss . SigLIP achieves the performance comparable to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.12613", "content": "Contrastive learning has emerged as a prominent branch of self-supervised learning for several years. Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss . SigLIP achieves the performance comparable to ..."}
+{"idx": 1, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning - PMLR", "date": "", "ddg_snippet": "Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss . SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/lee24a.html", "content": "Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss . SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored."}
+{"idx": 2, "title": "SigLIP vs. CLIP: The Sigmoid Advantage | by heping_LU | Medium", "date": "", "ddg_snippet": "Contrastive Learning & Sigmoid Loss SigLIP outperforms CLIP at small batch sizes (e.g., 4-8k), but both reach saturation at 32k batch size despite claims that larger batches improve performance.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jiangmen28/siglip-vs-clip-the-sigmoid-advantage-457f1cb872ab", "content": "Contrastive Learning & Sigmoid Loss SigLIP outperforms CLIP at small batch sizes (e.g., 4-8k), but both reach saturation at 32k batch size despite claims that larger batches improve performance."}
+{"idx": 3, "title": "GitHub - filipbasara0/sigmoid-contrastive-loss: Implementation of ...", "date": "", "ddg_snippet": "The loss function is a sigmoid constrastive loss adapted from SigLIP , with an addition of a confidence penalty gamma that balances the ratio of positive and negative samples per batch, amplifying learning from harder examples and improving training stability.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/filipbasara0/sigmoid-contrastive-loss", "content": "The loss function is a sigmoid constrastive loss adapted from SigLIP , with an addition of a confidence penalty gamma that balances the ratio of positive and negative samples per batch, amplifying learning from harder examples and improving training stability."}
+{"idx": 4, "title": "SigLIP: Sigmoid Loss in Language Image Pretraining", "date": "", "ddg_snippet": "And, yes that's all. Instead of performing the pre-training using cross-entropy loss , it uses a sigmoid loss function. This new approach helps the AI get even better at matching images and text, making it smarter and more accurate. but why sigmoid loss is a game-changer? Here's where SigLIP really shines because of the sigmoid loss :", "subpage_snippet": "", "source": "sushant-kumar.com", "link": "https://sushant-kumar.com/blog/siglip", "content": "And, yes that's all. Instead of performing the pre-training using cross-entropy loss , it uses a sigmoid loss function. This new approach helps the AI get even better at matching images and text, making it smarter and more accurate. but why sigmoid loss is a game-changer? Here's where SigLIP really shines because of the sigmoid loss :"}
+{"idx": 5, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss . SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LqGa3nqvJQ", "content": "Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss . SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored."}
+{"idx": 6, "title": "[2303.15343] Sigmoid Loss for Language Image Pre-Training", "date": "", "ddg_snippet": "We propose a simple pairwise Sigmoid loss for Language-Image Pre-training ( SigLIP ). Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. The sigmoid loss simultaneously allows further scaling up the batch size, while also performing better at ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.15343", "content": "We propose a simple pairwise Sigmoid loss for Language-Image Pre-training ( SigLIP ). Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. The sigmoid loss simultaneously allows further scaling up the batch size, while also performing better at ..."}
+{"idx": 7, "title": "sigmoid-contrastive-learning · PyPI", "date": "", "ddg_snippet": "The loss function is a sigmoid constrastive loss adapted from SigLIP , with an addition of a confidence penalty gamma that balances the ratio of positive and negative samples per batch, amplifying learning from harder examples and improving training stability.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/sigmoid-contrastive-learning/", "content": "The loss function is a sigmoid constrastive loss adapted from SigLIP , with an addition of a confidence penalty gamma that balances the ratio of positive and negative samples per batch, amplifying learning from harder examples and improving training stability."}
+{"idx": 8, "title": "PDF Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss . SigLIP achieves the performance comparable to CLIP in a more eficient manner by eliminating the need for a global view. However, theoreti-cal understanding of using the sigmoid loss in contrastive learning is underexplored.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/lee24a/lee24a.pdf", "content": "Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss . SigLIP achieves the performance comparable to CLIP in a more eficient manner by eliminating the need for a global view. However, theoreti-cal understanding of using the sigmoid loss in contrastive learning is underexplored."}
+{"idx": 9, "title": "Sigmoid Loss for Language Image Pre-Training", "date": "", "ddg_snippet": "SigLIP's key idea is to use a sigmoid operation instead of a softmax operation. CLIP uses a softmax function and, accordingly, the loss for a given positive pair (image-text) is dependent on every negative pair within a mini-batch as shown in Eq.", "subpage_snippet": "", "source": "ahmdtaha.medium.com", "link": "https://ahmdtaha.medium.com/sigmoid-loss-for-language-image-pre-training-2dd5e7d1af84", "content": "SigLIP's key idea is to use a sigmoid operation instead of a softmax operation. CLIP uses a softmax function and, accordingly, the loss for a given positive pair (image-text) is dependent on every negative pair within a mini-batch as shown in Eq."}
diff --git a/data/sampled_jsons/convergence_rate_n^{-12}_parametric_rate_likelihood_based_distribution_regression.jsonl b/data/sampled_jsons/convergence_rate_n^{-12}_parametric_rate_likelihood_based_distribution_regression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1081e4aeaf6c60a46a8bd8791cf643a3237e32ea
--- /dev/null
+++ b/data/sampled_jsons/convergence_rate_n^{-12}_parametric_rate_likelihood_based_distribution_regression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Parametric convergence rate of a non-parametric estimator ...", "date": "", "ddg_snippet": "5 Sept 2025 — We show that for all these mixtures, the non- parametric maximum likelihood estimator converges to the truth at the rate ( log ( n d ) ) 1 + ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.05452v1", "content": "5 Sept 2025 — We show that for all these mixtures, the non- parametric maximum likelihood estimator converges to the truth at the rate ( log ( n d ) ) 1 + ..."}
+{"idx": 1, "title": "Convergence rates of parameter estimation for some weakly ...", "date": "", "ddg_snippet": "by N HO · Cited by 112 — All these rates occur while the. MLE convergence rate of the mixture density remains to be n − 1/2 . Remarkably, for Gamma and some other mixtures, the minimax ... 39 pages", "subpage_snippet": "", "source": "dept.stat.lsa.umich.edu", "link": "https://dept.stat.lsa.umich.edu/~xuanlong/Papers/weak_ident_conv.pdf", "content": "by N HO · Cited by 112 — All these rates occur while the. MLE convergence rate of the mixture density remains to be n − 1/2 . Remarkably, for Gamma and some other mixtures, the minimax ... 39 pages"}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — We study the statistical convergence rate of sieve MLEs in the conditional deep general model setup and investigate its dependence on the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — We study the statistical convergence rate of sieve MLEs in the conditional deep general model setup and investigate its dependence on the ..."}
+{"idx": 3, "title": "Towards Convergence Rates for Parameter Estimation in ...", "date": "", "ddg_snippet": "by H Nguyen · 2024 · Cited by 18 — in cells 3, 5 and 6, which are fitted by one component, enjoy a parametric estimation rate of order O( n − 1/2 ). Next, the rates for estimating parameters c0.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/nguyen24b/nguyen24b.pdf", "content": "by H Nguyen · 2024 · Cited by 18 — in cells 3, 5 and 6, which are fitted by one component, enjoy a parametric estimation rate of order O( n − 1/2 ). Next, the rates for estimating parameters c0."}
+{"idx": 4, "title": "Minimax Optimal Rate for Parameter Estimation in ...", "date": "", "ddg_snippet": "The main challenges in deriving the convergence rate of the MLE mainly come from two issues: (1) The interaction between the function h 0 and the density ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=w3ghbKBJg4&noteId=NNQLWCKL4h", "content": "The main challenges in deriving the convergence rate of the MLE mainly come from two issues: (1) The interaction between the function h 0 and the density ..."}
+{"idx": 5, "title": "Rates of convergence for minimum contrast estimators", "date": "", "ddg_snippet": "by L Birgé · 1993 · Cited by 333 — We shall present here a general study of minimum contrast estimators in a nonparametric setting (although our results are also valid in the classical parametric ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/BF01199316.pdf", "content": "by L Birgé · 1993 · Cited by 333 — We shall present here a general study of minimum contrast estimators in a nonparametric setting (although our results are also valid in the classical parametric ..."}
+{"idx": 6, "title": "Convergence Rates of a Class of Multivariate Density ...", "date": "", "ddg_snippet": "by L Liu · 2023 · Cited by 6 — As r increases, up to a log n term, the rate can get close to the parametric rate of n − 1/2 . Admittedly, for many classes of density functions, r may depend ... 64 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume24/20-060/20-060.pdf", "content": "by L Liu · 2023 · Cited by 6 — As r increases, up to a log n term, the rate can get close to the parametric rate of n − 1/2 . Admittedly, for many classes of density functions, r may depend ... 64 pages"}
+{"idx": 7, "title": "A NOTE ON THE RATE OF CONVERGENCE ...", "date": "", "ddg_snippet": "by BE Honor · Cited by 15 — Much of the statistical and econometric literature investigates estimators that converge to the true parameter at rate n - 1=2 . Hahn (1992) proved that it is ...", "subpage_snippet": "", "source": "www.princeton.edu", "link": "https://www.princeton.edu/~honore/papers/rn1.PDF", "content": "by BE Honor · Cited by 15 — Much of the statistical and econometric literature investigates estimators that converge to the true parameter at rate n - 1=2 . Hahn (1992) proved that it is ..."}
+{"idx": 8, "title": "Convergence rates of parameter estimation for some ...", "date": "", "ddg_snippet": "by N Ho · 2016 · Cited by 112 — We establish minimax lower bounds and maximum likelihood conver- gence rates of parameter estimation for mean-covariance multivariate Gaus-. 30 pages", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journals/annals-of-statistics/volume-44/issue-6/Convergence-rates-of-parameter-estimation-for-some-weakly-identifiable-finite/10.1214/16-AOS1444.pdf", "content": "by N Ho · 2016 · Cited by 112 — We establish minimax lower bounds and maximum likelihood conver- gence rates of parameter estimation for mean-covariance multivariate Gaus-. 30 pages"}
+{"idx": 9, "title": "On strong identifiability and convergence rates of parameter ...", "date": "", "ddg_snippet": "This means the mixing probability mass converge at n − 1/2 rate (which recovers the result of [21]), in addition to having that the component parameters converge ... 52 pages", "subpage_snippet": "", "source": "dept.stat.lsa.umich.edu", "link": "https://dept.stat.lsa.umich.edu/~xuanlong/Papers/Ejs_ident_conv.pdf", "content": "This means the mixing probability mass converge at n − 1/2 rate (which recovers the result of [21]), in addition to having that the component parameters converge ... 52 pages"}
diff --git a/data/sampled_jsons/coordinate-wise_private_median_not_sufficient_regression_l_infinity_guarantee.jsonl b/data/sampled_jsons/coordinate-wise_private_median_not_sufficient_regression_l_infinity_guarantee.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6dbee89742647bf8eba3caad873e65a53b2e2c9b
--- /dev/null
+++ b/data/sampled_jsons/coordinate-wise_private_median_not_sufficient_regression_l_infinity_guarantee.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Efficient Sparse Least Absolute Deviation Regression with", "date": "", "ddg_snippet": "Under the framework of differential privacy, a long line of research on privacy-preserving ML has been inspired, such as sparse regression [ 2 , 3 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.01294v1", "content": "Under the framework of differential privacy, a long line of research on privacy-preserving ML has been inspired, such as sparse regression [ 2 , 3 ..."}
+{"idx": 1, "title": "Differentially private multivariate medians", "date": "", "ddg_snippet": "Despite this fact, using multivariate medians for differentially private and robust multivariate location estimation has not been systematically ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2210.06459v3", "content": "Despite this fact, using multivariate medians for differentially private and robust multivariate location estimation has not been systematically ..."}
+{"idx": 2, "title": "1 Introduction", "date": "", "ddg_snippet": "... literature has explored various aspects of differentially private statistical learning, including mean estimation [ 7 , 8 ] , linear regression [ 7 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05212v1", "content": "... literature has explored various aspects of differentially private statistical learning, including mean estimation [ 7 , 8 ] , linear regression [ 7 ..."}
+{"idx": 3, "title": "Real-time Bus Travel Time Prediction and Reliability", "date": "", "ddg_snippet": "Furthermore, they are not sufficiently comprehensive for accurate travel time prediction, as they lack critical information such as bus stop ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05907v1", "content": "Furthermore, they are not sufficiently comprehensive for accurate travel time prediction, as they lack critical information such as bus stop ..."}
+{"idx": 4, "title": "ICML 2021 Papers", "date": "", "ddg_snippet": "... Log Marginal Likelihood of Gaussian ... Improving Breadth- Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/papers.html", "content": "... Log Marginal Likelihood of Gaussian ... Improving Breadth- Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies."}
+{"idx": 5, "title": "Downloads", "date": "", "ddg_snippet": "A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2021", "content": "A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent ..."}
+{"idx": 6, "title": "draft-patton-cfrg-vdaf-01", "date": "", "ddg_snippet": "Differentially private systems like RAPPOR are easy to deploy and provide a useful guarantee . ... long as a subset of the servers executes the ...", "subpage_snippet": "", "source": "datatracker.ietf.org", "link": "https://datatracker.ietf.org/doc/html/draft-patton-cfrg-vdaf", "content": "Differentially private systems like RAPPOR are easy to deploy and provide a useful guarantee . ... long as a subset of the servers executes the ..."}
+{"idx": 7, "title": "draft-irtf-cfrg-vdaf-00", "date": "", "ddg_snippet": "Differentially private systems like RAPPOR are easy to deploy and provide a useful guarantee . ... long as a subset of the servers executes the ...", "subpage_snippet": "", "source": "datatracker.ietf.org", "link": "https://datatracker.ietf.org/doc/html/draft-irtf-cfrg-vdaf-00", "content": "Differentially private systems like RAPPOR are easy to deploy and provide a useful guarantee . ... long as a subset of the servers executes the ..."}
+{"idx": 8, "title": "COLT18/For authors", "date": "", "ddg_snippet": "... happens due to ``model error,'' i.e., when the true data generating distribution does not fit within the class of generative models being learned.", "subpage_snippet": "", "source": "www.learningtheory.org", "link": "https://www.learningtheory.org/colt2018/call.html", "content": "... happens due to ``model error,'' i.e., when the true data generating distribution does not fit within the class of generative models being learned."}
+{"idx": 9, "title": "TAO: Optimization Solvers — PETSc", "date": "", "ddg_snippet": "Note that for display purposes we have omitted some nonessential lines of code as well as the (essential) code required for the routine ...", "subpage_snippet": "", "source": "petsc.org", "link": "https://petsc.org/main/manual/tao/", "content": "Note that for display purposes we have omitted some nonessential lines of code as well as the (essential) code required for the routine ..."}
diff --git a/data/sampled_jsons/coordinate-wise_private_median_regression_challenge_solution_vector.jsonl b/data/sampled_jsons/coordinate-wise_private_median_regression_challenge_solution_vector.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f8a0cb9c0128d8593f85b1d885cb9b03834592bd
--- /dev/null
+++ b/data/sampled_jsons/coordinate-wise_private_median_regression_challenge_solution_vector.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear regression - Wikipedia", "date": "", "ddg_snippet": "In statistics, linear regression is a model that estimates the relationship between a scalar response and one or more explanatory variables. A model with exactly one explanatory variable is a simple linear regression ; a model with two or more explana...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Linear_regression", "content": "In statistics, linear regression is a model that estimates the relationship between a scalar response and one or more explanatory variables. A model with exactly one explanatory variable is a simple linear regression ; a model with two or more explana..."}
+{"idx": 1, "title": "Isotonic median regression for orders representable by rooted trees", "date": "", "ddg_snippet": "The isotonic median regression problem arising in statistics is as follows. We are given m observations falling into n sets, the ith set containing mi observations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/229944730_Isotonic_median_regression_for_orders_representable_by_rooted_trees", "content": "The isotonic median regression problem arising in statistics is as follows. We are given m observations falling into n sets, the ith set containing mi observations."}
+{"idx": 2, "title": "Linear Regression - #52 by filipv - pytorch - D2L Discussion", "date": "", "ddg_snippet": "This is because the median is the value that minimizes the sum of absolute differences. Unlike the mean, the median is not sensitive to extreme values, so it is a more robust measure of central tendency when there are outliers in the data. Exercise 2", "subpage_snippet": "", "source": "discuss.d2l.ai", "link": "https://discuss.d2l.ai/t/linear-regression/258/52", "content": "This is because the median is the value that minimizes the sum of absolute differences. Unlike the mean, the median is not sensitive to extreme values, so it is a more robust measure of central tendency when there are outliers in the data. Exercise 2"}
+{"idx": 3, "title": "Everything You Need to Know About Linear Regression | Medium", "date": "", "ddg_snippet": "Decision Trees Regression . Support Vector Regression (SVR). Linear Regression in Layman’s Terms. You can think of linear regression as the answer to the question “How can I use X to predict Y...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/analytics-vidhya/everything-you-need-to-know-about-linear-regression-750a69a0ea50", "content": "Decision Trees Regression . Support Vector Regression (SVR). Linear Regression in Layman’s Terms. You can think of linear regression as the answer to the question “How can I use X to predict Y..."}
+{"idx": 4, "title": "M-estimation and Median of Means applied to statistical learning", "date": "", "ddg_snippet": "Classification and regression using Median of Means principle.The median of means has also been adapted. to other setting, see for instance [BAM20] for a dierentially private median of means estimator.", "subpage_snippet": "", "source": "theses.hal.science", "link": "https://theses.hal.science/tel-03132439/document", "content": "Classification and regression using Median of Means principle.The median of means has also been adapted. to other setting, see for instance [BAM20] for a dierentially private median of means estimator."}
+{"idx": 5, "title": "(PDF) Non-parametric Quantile Regression via the K-NN Fused Lasso", "date": "", "ddg_snippet": "Quantile regression is a statistical method for estimating conditional quantiles of a response variable. In addition, for mean estimation, it is well known that quantile regression is more robust to outliers than $l_2$-based methods.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/143808719/Non_parametric_Quantile_Regression_via_the_K_NN_Fused_Lasso", "content": "Quantile regression is a statistical method for estimating conditional quantiles of a response variable. In addition, for mean estimation, it is well known that quantile regression is more robust to outliers than $l_2$-based methods."}
+{"idx": 6, "title": "Linear Mixed Model Median Regression | Restackio", "date": "", "ddg_snippet": "Mixed Methods Data Analysis Software. Linear Mixed Model Median Regression .Data Normalization Techniques for Linear Mixed ModelsComparative Analysis of LLM-based Data Imputation in Regression Tasks", "subpage_snippet": "", "source": "store-restack.vercel.app", "link": "https://store-restack.vercel.app/p/mixed-methods-data-analysis-software-answer-linear-mixed-model-median-regression", "content": "Mixed Methods Data Analysis Software. Linear Mixed Model Median Regression .Data Normalization Techniques for Linear Mixed ModelsComparative Analysis of LLM-based Data Imputation in Regression Tasks"}
+{"idx": 7, "title": "linear- regression .ipynb - Colab", "date": "", "ddg_snippet": ":label: sec_linear_ regression . Regression problems pop up whenever we want to predict a numerical value., the goal of linear regression is to find the weight vector $\\mathbf{w}$.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/d2l-ai/d2l-en-colab/blob/master/chapter_linear-regression/linear-regression.ipynb", "content": ":label: sec_linear_ regression . Regression problems pop up whenever we want to predict a numerical value., the goal of linear regression is to find the weight vector $\\mathbf{w}$."}
+{"idx": 8, "title": "Distribution Regression in Duration Analysis", "date": "", "ddg_snippet": "coordinate - wise . ThereforeJournal of Econometrics 141, 1281–1301. Ying, Z., S. Jung, and L. Wei (1995). Survival analysis with median regression models.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/distribution-regression-in-duration-analysis-an-application-2hq76xnc.pdf", "content": "coordinate - wise . ThereforeJournal of Econometrics 141, 1281–1301. Ying, Z., S. Jung, and L. Wei (1995). Survival analysis with median regression models."}
+{"idx": 9, "title": "On Differential Privacy for Adaptively Solving Search Problems via...", "date": "", "ddg_snippet": "Adaptive Regression via Differentially Private Median and Guarantee. Coordinate - wise Private Median and the ℓ∞ Guarantee. One natural idea is to extend the private median framework of [BKM+22] to outputting an approximation to the solution vector .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503", "content": "Adaptive Regression via Differentially Private Median and Guarantee. Coordinate - wise Private Median and the ℓ∞ Guarantee. One natural idea is to extend the private median framework of [BKM+22] to outputting an approximation to the solution vector ."}
diff --git a/data/sampled_jsons/correlation_engagement_participation_rich_subreddits_Causal_Modeling_of_Climate_Activism_on_Reddit_year_2024.jsonl b/data/sampled_jsons/correlation_engagement_participation_rich_subreddits_Causal_Modeling_of_Climate_Activism_on_Reddit_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f6f2885b871c17cba23356053151b7ea65546ac
--- /dev/null
+++ b/data/sampled_jsons/correlation_engagement_participation_rich_subreddits_Causal_Modeling_of_Climate_Activism_on_Reddit_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — This effect is indeed confounded by the positive correlation of 0.38 and 0.42 between the engagement on Reddit and the participation in 'female' and ' rich '.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — This effect is indeed confounded by the positive correlation of 0.38 and 0.42 between the engagement on Reddit and the participation in 'female' and ' rich '."}
+{"idx": 2, "title": "Extracting Participation in Collective Action from Social Media", "date": "", "ddg_snippet": "28 Apr 2025 — Causal Modeling of Climate Activism on Reddit . In Proceedings of the ACM on Web Conference 2025, 590–600. Lenti, Monti, and De Francisci ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.07368v2", "content": "28 Apr 2025 — Causal Modeling of Climate Activism on Reddit . In Proceedings of the ACM on Web Conference 2025, 590–600. Lenti, Monti, and De Francisci ..."}
+{"idx": 3, "title": "Modeling the Impact of Group Interactions on Climate- ...", "date": "", "ddg_snippet": "by A Antelmi · 2025 — This led us to identify the six most active subreddits on this topic: r/California, r/AskReddit, r/politics, r/news, r/collapse, and r/bayarea.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2505.02989", "content": "by A Antelmi · 2025 — This led us to identify the six most active subreddits on this topic: r/California, r/AskReddit, r/politics, r/news, r/collapse, and r/bayarea."}
+{"idx": 4, "title": "On the Inference of Sociodemographics on Reddit", "date": "", "ddg_snippet": "7 Feb 2025 — Existing methods often leverage user participation in specific subreddits to infer attributes such as gender, age, or partisan affiliation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05049v1", "content": "7 Feb 2025 — Existing methods often leverage user participation in specific subreddits to infer attributes such as gender, age, or partisan affiliation."}
+{"idx": 5, "title": "A Collective Field Experiment Disentangling Participation ...", "date": "", "ddg_snippet": "by L Oswald · 2025 — How do actively and passively participating users in a subreddit differ with regard to demographics, political variables, psychological ...", "subpage_snippet": "", "source": "osf.io", "link": "https://osf.io/p2jaq_v1/download/?format=pdf", "content": "by L Oswald · 2025 — How do actively and passively participating users in a subreddit differ with regard to demographics, political variables, psychological ..."}
+{"idx": 6, "title": "Modeling Public Perceptions of Science in Media", "date": "", "ddg_snippet": "We find that the potential perceptions of a post correlate with public engagement with it. For example, posts with higher estimated Surprisingness, importance, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.16622v2", "content": "We find that the potential perceptions of a post correlate with public engagement with it. For example, posts with higher estimated Surprisingness, importance, ..."}
+{"idx": 7, "title": "How Conversational Structure and Style Shape Online ...", "date": "", "ddg_snippet": "12 Aug 2025 — Interestingly, greater cross- subreddit participation tended to be associated with lower Cooperation & Shared Values, suggesting that cross- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08596v1", "content": "12 Aug 2025 — Interestingly, greater cross- subreddit participation tended to be associated with lower Cooperation & Shared Values, suggesting that cross- ..."}
+{"idx": 8, "title": "arXiv:2504.02153v2 [cs.SI] 14 Apr 2025", "date": "", "ddg_snippet": "by N TeBlunthuis · 2025 — This paper presents a large-scale study of 8,806 Reddit communities belonging to 1,919 clusters of high user overlap over a 5-year period. The ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.02153", "content": "by N TeBlunthuis · 2025 — This paper presents a large-scale study of 8,806 Reddit communities belonging to 1,919 clusters of high user overlap over a 5-year period. The ..."}
+{"idx": 9, "title": "Using counter-arguments in online discussion summarisation", "date": "", "ddg_snippet": "by S Li · 2025 — In this paper, we study a novel approach based on generative AI to trigger users' critical thinking by challenging fresh counter-arguments after summarising ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0306457325001992", "content": "by S Li · 2025 — In this paper, we study a novel approach based on generative AI to trigger users' critical thinking by challenging fresh counter-arguments after summarising ..."}
diff --git a/data/sampled_jsons/cost_function_for_hierarchical_overlapping_clustering.jsonl b/data/sampled_jsons/cost_function_for_hierarchical_overlapping_clustering.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..46aa5d1f5abe67c28c3349fcbf6b45b806f19596
--- /dev/null
+++ b/data/sampled_jsons/cost_function_for_hierarchical_overlapping_clustering.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Hierarchical Clustering", "date": "", "ddg_snippet": "To implement a hierarchical clustering algorithm, one has to choose a linkage function (single linkage, average linkage, complete linkage, Ward ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/314700681_Hierarchical_Clustering", "content": "To implement a hierarchical clustering algorithm, one has to choose a linkage function (single linkage, average linkage, complete linkage, Ward ..."}
+{"idx": 1, "title": "(PDF) Cognitive Manager for Hierarchical Cluster Networks Based", "date": "", "ddg_snippet": "Cognitive Manager for Hierarchical Cluster Networks Based on Multi-Stage Machine Method ... for data broadcasting in the control channel in order to ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/271715914_Cognitive_Manager_for_Hierarchical_Cluster_Networks_Based_on_Multi-Stage_Machine_Method", "content": "Cognitive Manager for Hierarchical Cluster Networks Based on Multi-Stage Machine Method ... for data broadcasting in the control channel in order to ..."}
+{"idx": 2, "title": "graphs - Choice of algorithm for hierarchical clustering for", "date": "", "ddg_snippet": "Choice of algorithm for hierarchical clustering for minimizing network communication costs ... cost of making it a little harder to understand why it ...", "subpage_snippet": "", "source": "cs.stackexchange.com", "link": "https://cs.stackexchange.com/questions/28821/choice-of-algorithm-for-hierarchical-clustering-for-minimizing-network-communica", "content": "Choice of algorithm for hierarchical clustering for minimizing network communication costs ... cost of making it a little harder to understand why it ..."}
+{"idx": 3, "title": "On hierarchical clustering-based approach for RDDBS design |", "date": "", "ddg_snippet": "In fact, this is the main justification for using the hierarchical clustering method (HC), which performs better on tuples than attributes.", "subpage_snippet": "", "source": "journalofbigdata.springeropen.com", "link": "https://journalofbigdata.springeropen.com/articles/10.1186/s40537-023-00849-7", "content": "In fact, this is the main justification for using the hierarchical clustering method (HC), which performs better on tuples than attributes."}
+{"idx": 4, "title": "Hierarchical Level-Wise News Article Clustering via", "date": "", "ddg_snippet": "... in this work, we introduce a novel adaptation of multilingual hierarchical Matryoshka embeddings and a hierarchical clustering algorithm adapted for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00277v1", "content": "... in this work, we introduce a novel adaptation of multilingual hierarchical Matryoshka embeddings and a hierarchical clustering algorithm adapted for ..."}
+{"idx": 5, "title": "HiMA: Hierarchical Quantum Microarchitecture for Qubit-Scaling", "date": "", "ddg_snippet": "HiMA: Hierarchical Quantum Microarchitecture for Qubit-Scaling and Quantum Process-Level Parallelism ... Process-Based Hierarchical Trigger Mechanism ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.11311v1", "content": "HiMA: Hierarchical Quantum Microarchitecture for Qubit-Scaling and Quantum Process-Level Parallelism ... Process-Based Hierarchical Trigger Mechanism ..."}
+{"idx": 6, "title": "US11392621B1 - Unsupervised information-based hierarchical", "date": "", "ddg_snippet": "the hierarchical cluster analyzer uses RIL to create a plurality of candidate sets, each formed from the union of randomly selected subsets ⁇ i .", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11392621B1/en", "content": "the hierarchical cluster analyzer uses RIL to create a plurality of candidate sets, each formed from the union of randomly selected subsets ⁇ i ."}
+{"idx": 7, "title": "US7287180B1 - Hardware independent hierarchical cluster of", "date": "", "ddg_snippet": "... hierarchical cluster of heterogeneous media servers using a hierarchical command beat protocol to synchronize distributed parallel computing systems ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US7287180B1/en", "content": "... hierarchical cluster of heterogeneous media servers using a hierarchical command beat protocol to synchronize distributed parallel computing systems ..."}
+{"idx": 8, "title": "ACP - The use of hierarchical clustering for the design of", "date": "", "ddg_snippet": "The use of hierarchical clustering for the design of optimized monitoring networks The use of hierarchical clustering for the design of optimized ...", "subpage_snippet": "", "source": "acp.copernicus.org", "link": "https://acp.copernicus.org/articles/18/6543/2018/", "content": "The use of hierarchical clustering for the design of optimized monitoring networks The use of hierarchical clustering for the design of optimized ..."}
+{"idx": 9, "title": "A dockerized framework for hierarchical frequency-based", "date": "", "ddg_snippet": "Nevertheless, the high computational cost and memory usage of baseline hierarchical clustering algorithms render them inappropriate for the vast ...", "subpage_snippet": "", "source": "journalofcloudcomputing.springeropen.com", "link": "https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-019-0150-y", "content": "Nevertheless, the high computational cost and memory usage of baseline hierarchical clustering algorithms render them inappropriate for the vast ..."}
diff --git a/data/sampled_jsons/currying_probability_machine_learning_before2024.jsonl b/data/sampled_jsons/currying_probability_machine_learning_before2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1c0de5678098dd6e6ed04c8d0fb84e4a621b2dbb
--- /dev/null
+++ b/data/sampled_jsons/currying_probability_machine_learning_before2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Probability and Statistics for Machine Learning: From Bayes to ... - Medium", "date": "", "ddg_snippet": "In the world of machine learning , probability and statistics form the backbone of most algorithms and models. These concepts allow machines to make predictions, classify data, and even learn from ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jerrymartejr/probability-and-statistics-for-machine-learning-from-bayes-to-distributions-402743d04b96", "content": "In the world of machine learning , probability and statistics form the backbone of most algorithms and models. These concepts allow machines to make predictions, classify data, and even learn from ..."}
+{"idx": 1, "title": "Probabilistic Machine Learning: Advanced Topics - pml-book", "date": "", "ddg_snippet": "As machine learning has matured and evolved, no other comprehensive resource of this nature has even remotely kept pace with modern methodological developments. This new version is a blessing for the machine learning community and frankly, at this moment in time, is the only truly necessary machine learning book to own.", "subpage_snippet": "", "source": "probml.github.io", "link": "https://probml.github.io/pml-book/book2.html", "content": "As machine learning has matured and evolved, no other comprehensive resource of this nature has even remotely kept pace with modern methodological developments. This new version is a blessing for the machine learning community and frankly, at this moment in time, is the only truly necessary machine learning book to own."}
+{"idx": 2, "title": "Probability and Statistics for Machine Learning - SpringerLink", "date": "", "ddg_snippet": "The chapters of this book belong to three categories: 1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/book/10.1007/978-3-031-53282-5", "content": "The chapters of this book belong to three categories: 1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning ."}
+{"idx": 3, "title": "Probabilistic machine learning · GitHub", "date": "", "ddg_snippet": "Material to accompany my book series \"Probabilistic Machine Learning \" (Software, Data, Exercises, Figures, etc) - Probabilistic machine learning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/probml", "content": "Material to accompany my book series \"Probabilistic Machine Learning \" (Software, Data, Exercises, Figures, etc) - Probabilistic machine learning"}
+{"idx": 4, "title": "Mathematics for Machine Learning: Probability and Statistics", "date": "", "ddg_snippet": "Understanding these differences is fundamental to working with probability distributions and analyzing data effectively in machine learning . 1.6 Probability Distributions A probability distribution describes how the values of a random variable are distributed. It provides a function or table that assigns probabilities to the possible values of a random variable. Probability distributions can ...", "subpage_snippet": "", "source": "techiefreak.org", "link": "https://techiefreak.org/blog/mathematics-for-ml/mathematics-for-machine-learning--probability-and-statistics", "content": "Understanding these differences is fundamental to working with probability distributions and analyzing data effectively in machine learning . 1.6 Probability Distributions A probability distribution describes how the values of a random variable are distributed. It provides a function or table that assigns probabilities to the possible values of a random variable. Probability distributions can ..."}
+{"idx": 5, "title": "Complete Guide to Probability and Statistics for Machine Learning and ...", "date": "", "ddg_snippet": "Introduction Probability and statistics are essential foundations for understanding and implementing machine learning and data science concepts. This blog provides a detailed explanation of these concepts \"Complete Probability and Statistics for Machine Learning and Data Science \" by Data Dissection, supplemented with hands-on labs and examples to enhance your learning experience.", "subpage_snippet": "", "source": "atalupadhyay.wordpress.com", "link": "https://atalupadhyay.wordpress.com/2024/12/28/complete-guide-to-probability-and-statistics-for-machine-learning-and-data-science/", "content": "Introduction Probability and statistics are essential foundations for understanding and implementing machine learning and data science concepts. This blog provides a detailed explanation of these concepts \"Complete Probability and Statistics for Machine Learning and Data Science \" by Data Dissection, supplemented with hands-on labs and examples to enhance your learning experience."}
+{"idx": 6, "title": "PDF Probability and Statistics for Machine Learning: A Textbook", "date": "", "ddg_snippet": "From probability to machine learning : Many machine learning applications are ad-dressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapter 6 through 9 explore how different models from probability and statistics are applied to machine learning .", "subpage_snippet": "", "source": "charuaggarwal.net", "link": "https://charuaggarwal.net/prob-toc.pdf", "content": "From probability to machine learning : Many machine learning applications are ad-dressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapter 6 through 9 explore how different models from probability and statistics are applied to machine learning ."}
+{"idx": 7, "title": "Introduction to Probability - Mathematics for Machine Learning", "date": "", "ddg_snippet": "Essentials of probability , various types of events, theoretical vs. empirical probability , and their applications in Machine Learning .", "subpage_snippet": "", "source": "ravenrpubs.substack.com", "link": "https://ravenrpubs.substack.com/p/introduction-to-probability-mathematics", "content": "Essentials of probability , various types of events, theoretical vs. empirical probability , and their applications in Machine Learning ."}
+{"idx": 8, "title": "Probabilistic Machine Learning: An Introduction - pml-book", "date": "", "ddg_snippet": "The new 'Probabilistic Machine Learning : An Introduction' is similarly excellent, and includes new material, especially on deep learning and recent developments. It will become an essential reference for students and researchers in probabilistic machine learning .\"", "subpage_snippet": "", "source": "probml.github.io", "link": "https://probml.github.io/pml-book/book1.html", "content": "The new 'Probabilistic Machine Learning : An Introduction' is similarly excellent, and includes new material, especially on deep learning and recent developments. It will become an essential reference for students and researchers in probabilistic machine learning .\""}
+{"idx": 9, "title": "[2412.11526] Probabilities-Informed Machine Learning - arXiv.org", "date": "", "ddg_snippet": "Machine learning (ML) has emerged as a powerful tool for tackling complex regression and classification tasks, yet its success often hinges on the quality of training data. This study introduces an ML paradigm inspired by domain knowledge of the structure of output function, akin to physics-informed ML, but rooted in probabilistic principles rather than physical laws. The proposed approach ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.11526", "content": "Machine learning (ML) has emerged as a powerful tool for tackling complex regression and classification tasks, yet its success often hinges on the quality of training data. This study introduces an ML paradigm inspired by domain knowledge of the structure of output function, akin to physics-informed ML, but rooted in probabilistic principles rather than physical laws. The proposed approach ..."}
diff --git a/data/sampled_jsons/curse_of_dimensionality_machine_learning_derivative_maps_redundancy.jsonl b/data/sampled_jsons/curse_of_dimensionality_machine_learning_derivative_maps_redundancy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e8db8f7fa8b775a3ba4b9660bfda8bc477010cb
--- /dev/null
+++ b/data/sampled_jsons/curse_of_dimensionality_machine_learning_derivative_maps_redundancy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Curse of dimensionality - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high- dimensional spaces that do not occur in low- dimensional settings such as the three- dimensional phy...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Curse_of_dimensionality", "content": "Machine learningand data mining. v. t. e. The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high- dimensional spaces that do not occur in low- dimensional settings such as the three- dimensional phy..."}
+{"idx": 1, "title": "Curse of Dimensionality in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Curse of Dimensionality significantly impacts machine learning algorithms in various ways. It leads to increased computational complexity, longer training times, and higher resource requirements.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/curse-of-dimensionality-in-machine-learning/", "content": "Curse of Dimensionality significantly impacts machine learning algorithms in various ways. It leads to increased computational complexity, longer training times, and higher resource requirements."}
+{"idx": 2, "title": "Why is Dimensionality Reduction so Important? | by Chuan Xu | Medium", "date": "", "ddg_snippet": "Dimensionality reduction can be further broken into feature selection and feature extraction. Feature selection tries to select a subset of the original features for use in the machine learning model.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@cxu24/why-dimensionality-reduction-is-important-dd60b5611543", "content": "Dimensionality reduction can be further broken into feature selection and feature extraction. Feature selection tries to select a subset of the original features for use in the machine learning model."}
+{"idx": 3, "title": "What is Curse of Dimensionality ? A Complete Guide | Built In", "date": "", "ddg_snippet": "Machine learning often deals with high- dimensional data, which can pose a problem known as the curse of dimensionality .", "subpage_snippet": "", "source": "builtin.com", "link": "https://builtin.com/data-science/curse-dimensionality", "content": "Machine learning often deals with high- dimensional data, which can pose a problem known as the curse of dimensionality ."}
+{"idx": 4, "title": "Dimensionality Reduction for Machine Learning", "date": "", "ddg_snippet": "Understand tools and methods for dimensionality reduction in machine learning : algorithms, applications, pros, and cons.What is the curse of dimensionality ? Tools and libraries used for dimensionality reduction.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/dimensionality-reduction", "content": "Understand tools and methods for dimensionality reduction in machine learning : algorithms, applications, pros, and cons.What is the curse of dimensionality ? Tools and libraries used for dimensionality reduction."}
+{"idx": 5, "title": "Explore 15 Dimensionality Reduction in Machine Learning !", "date": "", "ddg_snippet": "Discover 15 effective dimensionality reduction in machine learning techniques to optimize data, enhance model accuracy, and enhance performance!", "subpage_snippet": "", "source": "www.upgrad.com", "link": "https://www.upgrad.com/blog/top-dimensionality-reduction-techniques-for-machine-learning/", "content": "Discover 15 effective dimensionality reduction in machine learning techniques to optimize data, enhance model accuracy, and enhance performance!"}
+{"idx": 6, "title": "What is Curse of Dimensionality in Machine Learning ?", "date": "", "ddg_snippet": "In Machine Learning , a marginal increase in dimensionality also requires a large increase in the volume in the data in order to maintain the same level of performance. The curse of dimensionality is the by-product of a phenomenon which appears with high- dimensional data.", "subpage_snippet": "", "source": "www.mygreatlearning.com", "link": "https://www.mygreatlearning.com/blog/understanding-curse-of-dimensionality/", "content": "In Machine Learning , a marginal increase in dimensionality also requires a large increase in the volume in the data in order to maintain the same level of performance. The curse of dimensionality is the by-product of a phenomenon which appears with high- dimensional data."}
+{"idx": 7, "title": "Curse of Dimensionality Definition | DeepAI", "date": "", "ddg_snippet": "Mitigating the Curse of Dimsionality. A careful choice of the number of dimensions (features) to be used is the prerogative of the data scientist training the network. In general the smaller the size of the training set, the fewer features she should use.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/machine-learning-glossary-and-terms/curse-of-dimensionality", "content": "Mitigating the Curse of Dimsionality. A careful choice of the number of dimensions (features) to be used is the prerogative of the data scientist training the network. In general the smaller the size of the training set, the fewer features she should use."}
+{"idx": 8, "title": "Understanding Curse Of Dimensionality | Curse Of ... - YouTube", "date": "", "ddg_snippet": "Understanding Curse Of Dimensionality | Curse Of Dimensionality in Machine Learning #CurseOfDimensionality #UnfoldDataScienceWelcome!", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=-jlhMDEb9kc", "content": "Understanding Curse Of Dimensionality | Curse Of Dimensionality in Machine Learning #CurseOfDimensionality #UnfoldDataScienceWelcome!"}
+{"idx": 9, "title": "The Curse of Dimensionality in Machine Learning", "date": "", "ddg_snippet": "The article discusses the concept of the curse of dimensionality in machine learning , its implications, and methods for dimensionality reduction.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/the-curse-of-dimensionality-in-machine-learning-1d5f4c0c3c16", "content": "The article discusses the concept of the curse of dimensionality in machine learning , its implications, and methods for dimensionality reduction."}
diff --git a/data/sampled_jsons/curse_of_dimensionality_machine_learning_explanation.jsonl b/data/sampled_jsons/curse_of_dimensionality_machine_learning_explanation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aa5944aa3668c49be5c6c533410b3acf2fce8245
--- /dev/null
+++ b/data/sampled_jsons/curse_of_dimensionality_machine_learning_explanation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine learning curse of dimensionality explained? - Cross", "date": "", "ddg_snippet": "Machine learning curse of dimensionality explained? ... the points within 1 or 2 of each other? Now it's 100 * 50 or ~5,000! Now imagine 3 dimensions ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/65379/machine-learning-curse-of-dimensionality-explained", "content": "Machine learning curse of dimensionality explained? ... the points within 1 or 2 of each other? Now it's 100 * 50 or ~5,000! Now imagine 3 dimensions ..."}
+{"idx": 1, "title": "What is the Curse of Dimensionality? Simplest Explanation!", "date": "", "ddg_snippet": "Home » Blogs » Machine Learning » What is the Curse of Dimensionality ? Simplest Explanation ! ... of features is known as the Curse of ...", "subpage_snippet": "", "source": "www.mltut.com", "link": "https://www.mltut.com/what-is-the-curse-of-dimensionality-simplest-explanation/", "content": "Home » Blogs » Machine Learning » What is the Curse of Dimensionality ? Simplest Explanation ! ... of features is known as the Curse of ..."}
+{"idx": 2, "title": "What is Curse of Dimensionality in Machine Learning?", "date": "", "ddg_snippet": "There are a lot of domains where the direct effect of the curse of dimensionality can be seen, “ Machine Learning ” being the most effective.", "subpage_snippet": "", "source": "www.mygreatlearning.com", "link": "https://www.mygreatlearning.com/blog/understanding-curse-of-dimensionality/", "content": "There are a lot of domains where the direct effect of the curse of dimensionality can be seen, “ Machine Learning ” being the most effective."}
+{"idx": 3, "title": "Escaping the Curse of Dimensionality", "date": "", "ddg_snippet": "Semi-supervised machine learning is a hot topic in the field of data science, and for good reason. ... known as the Curse of Dimensionality also pose ...", "subpage_snippet": "", "source": "www.freecodecamp.org", "link": "https://www.freecodecamp.org/news/the-curse-of-dimensionality-how-we-can-save-big-data-from-itself-d9fa0f872335/", "content": "Semi-supervised machine learning is a hot topic in the field of data science, and for good reason. ... known as the Curse of Dimensionality also pose ..."}
+{"idx": 4, "title": "The Curse of Dimensionality - Behind the Scenes | Dataloop Blog", "date": "", "ddg_snippet": "Many texts on statistics, machine learning , and computer science reference the “ curse of dimensionality ” , which is attributed to the ...", "subpage_snippet": "", "source": "dataloop.ai", "link": "https://dataloop.ai/blog/dimensionality-behind-the-scenes/", "content": "Many texts on statistics, machine learning , and computer science reference the “ curse of dimensionality ” , which is attributed to the ..."}
+{"idx": 5, "title": "curse of dimensionality | mt. solitary", "date": "", "ddg_snippet": "The curse of dimensionality is a catchprase used in statistics, machine learning , data science, etc to refer to the weird things that can happen in ...", "subpage_snippet": "", "source": "www.mtsolitary.com", "link": "https://www.mtsolitary.com/20210703183435-curse-of-dimensionality/", "content": "The curse of dimensionality is a catchprase used in statistics, machine learning , data science, etc to refer to the weird things that can happen in ..."}
+{"idx": 6, "title": "Curse of Dimensionality Archives - Computer vision for dummies", "date": "", "ddg_snippet": "... sections I will provide an intuitive explanation of this concept, illustrated by a clear example of overfitting due to the curse of dimensionality ...", "subpage_snippet": "", "source": "www.visiondummy.com", "link": "https://www.visiondummy.com/tag/curse-of-dimensionality/", "content": "... sections I will provide an intuitive explanation of this concept, illustrated by a clear example of overfitting due to the curse of dimensionality ..."}
+{"idx": 7, "title": "Dimensionality reduction Archives - Computer vision for dummies", "date": "", "ddg_snippet": "... sections I will provide an intuitive explanation of this concept, illustrated by a clear example of overfitting due to the curse of dimensionality ...", "subpage_snippet": "", "source": "www.visiondummy.com", "link": "https://www.visiondummy.com/category/dimensionality-reduction/", "content": "... sections I will provide an intuitive explanation of this concept, illustrated by a clear example of overfitting due to the curse of dimensionality ..."}
+{"idx": 8, "title": "21 Machine Learning Interview Questions and Answers", "date": "", "ddg_snippet": "And your mastery of key concepts in data science and machine learning (← this is the focus of this post) ... some examples of machine learning ...", "subpage_snippet": "", "source": "elitedatascience.com", "link": "https://elitedatascience.com/machine-learning-interview-questions-answers", "content": "And your mastery of key concepts in data science and machine learning (← this is the focus of this post) ... some examples of machine learning ..."}
+{"idx": 9, "title": "Secret behind the Dimensionality Reduction for Data Scientist", "date": "", "ddg_snippet": "... Machine Learning (ML) problems involve thousands of features, so we end up training those models became extremely slow and will not give good solutions ...", "subpage_snippet": "", "source": "www.aiproblog.com", "link": "https://www.aiproblog.com/index.php/2021/05/03/secret-behind-the-dimensionality-reduction-for-data-scientist/", "content": "... Machine Learning (ML) problems involve thousands of features, so we end up training those models became extremely slow and will not give good solutions ..."}
diff --git a/data/sampled_jsons/dataset_size_scaling_law_exponent_error_rate_language_models_-0.3.jsonl b/data/sampled_jsons/dataset_size_scaling_law_exponent_error_rate_language_models_-0.3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c574130b6984990df272040b326db74fa58bb9bf
--- /dev/null
+++ b/data/sampled_jsons/dataset_size_scaling_law_exponent_error_rate_language_models_-0.3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "\" Scaling laws \" are empirical statistical laws that predict LLM performance based on such factors. One particular scaling law (\"Chinchilla scaling \") for LLM autoregressively trained for one epoch, with a log-log learning rate schedule, states that:[93].", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "\" Scaling laws \" are empirical statistical laws that predict LLM performance based on such factors. One particular scaling law (\"Chinchilla scaling \") for LLM autoregressively trained for one epoch, with a log-log learning rate schedule, states that:[93]."}
+{"idx": 1, "title": "Scaling Laws For Dense Retrieval", "date": "", "ddg_snippet": "Neural scaling laws articulate the relationship between model size , dataset size , computational budget, and performance in neural network training. This concept, first introduced by Hestness et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.18684v1", "content": "Neural scaling laws articulate the relationship between model size , dataset size , computational budget, and performance in neural network training. This concept, first introduced by Hestness et al."}
+{"idx": 2, "title": "(PDF) A Neural Scaling Law from the Dimension of the Data Manifold", "date": "", "ddg_snippet": "The scaling law can be explained if neural models are effectively just performing regression on a data manifold of intrinsic dimension d. This simple theory predicts that the scaling exponents .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/340884059_A_Neural_Scaling_Law_from_the_Dimension_of_the_Data_Manifold", "content": "The scaling law can be explained if neural models are effectively just performing regression on a data manifold of intrinsic dimension d. This simple theory predicts that the scaling exponents ."}
+{"idx": 3, "title": "Listen to \" Scaling Laws for Neural Language Mo...\" - PDF to MP3", "date": "", "ddg_snippet": "This paper studies empirical scaling laws for language model performance on the cross-entropy loss, showing that the loss scales as a power- law with model size , dataset size , and the amount of compute used for training.", "subpage_snippet": "", "source": "pdftomp3.com", "link": "https://pdftomp3.com/shared/67e15a5928779824db2e06c0", "content": "This paper studies empirical scaling laws for language model performance on the cross-entropy loss, showing that the loss scales as a power- law with model size , dataset size , and the amount of compute used for training."}
+{"idx": 4, "title": "Unraveling ChatGPT: A Comprehensive Exploration of the Language ...", "date": "", "ddg_snippet": "Scaling Laws and Model Size . Dataset Size : Larger datasets generally lead to better performance, with diminishing returns. Compute Resources: Training time and hardware requirements scale with model size .", "subpage_snippet": "", "source": "www.downelink.com", "link": "https://www.downelink.com/unraveling-chatgpt-a-comprehensive-exploration-of-the-language-models-architecture-and-inner-workings/", "content": "Scaling Laws and Model Size . Dataset Size : Larger datasets generally lead to better performance, with diminishing returns. Compute Resources: Training time and hardware requirements scale with model size ."}
+{"idx": 5, "title": "Neural scaling laws for multi-agent", "date": "", "ddg_snippet": "Increasing the size of the dataset leads to the power law region, where scaling follows a power law with a task-specific exponent .These models have power- law scaling of error with compute, which fits a power- law with an irreducible error constant as in Eq.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/neural-scaling-laws-for-multi-agent-reinforcement-learning-9g3ap4i6epvw.pdf", "content": "Increasing the size of the dataset leads to the power law region, where scaling follows a power law with a task-specific exponent .These models have power- law scaling of error with compute, which fits a power- law with an irreducible error constant as in Eq."}
+{"idx": 6, "title": "A Practical Guide: Fine-Tuning Large Language Models with...", "date": "", "ddg_snippet": "As large language models (LLMs) continue to grow in size and complexity, the need for efficient deployment and inference becomes… 13 min read.Prepare Instruction Dataset to Fine-Tune Large Language Models : Practical Guide for OpenAI Models .", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/a-practical-guide-fine-tuning-large-language-models-with-huggingface-3d4b8298b55f", "content": "As large language models (LLMs) continue to grow in size and complexity, the need for efficient deployment and inference becomes… 13 min read.Prepare Instruction Dataset to Fine-Tune Large Language Models : Practical Guide for OpenAI Models ."}
+{"idx": 7, "title": "Beating Proprietary Models with a Quick Fine-Tune | Modal Blog", "date": "", "ddg_snippet": "Error rate as a function of dataset size for three models fine-tuned on the Quora dataset .", "subpage_snippet": "", "source": "modal.com", "link": "https://modal.com/blog/fine-tuning-embeddings", "content": "Error rate as a function of dataset size for three models fine-tuned on the Quora dataset ."}
+{"idx": 8, "title": "Resolving Discrepancies in Compute-Optimal Scaling of Language ...", "date": "", "ddg_snippet": "# Prior research proposed scaling laws for optimal language model training, predicting the best model size given a compute budget. However, these laws produced conflicting predictions. This paper investigates the reasons for these discrepancies. The study found three factors...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/spotlight-large-language-models/4fssqpk1sm/", "content": "# Prior research proposed scaling laws for optimal language model training, predicting the best model size given a compute budget. However, these laws produced conflicting predictions. This paper investigates the reasons for these discrepancies. The study found three factors..."}
+{"idx": 9, "title": "Understanding Large Language Models Without Math and Jargon", "date": "", "ddg_snippet": "Increasing model size , dataset size , and training compute power all lead to exponential improvements in model performance.", "subpage_snippet": "", "source": "quizgecko.com", "link": "https://quizgecko.com/learn/understanding-large-language-models-without-math-and-jargon-zyxct6", "content": "Increasing model size , dataset size , and training compute power all lead to exponential improvements in model performance."}
diff --git a/data/sampled_jsons/decomposing_complex_ethical_dilemmas_computational_overhead_checks_and_balances_framework.jsonl b/data/sampled_jsons/decomposing_complex_ethical_dilemmas_computational_overhead_checks_and_balances_framework.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f42fa6c0f082f4f09b5d3b3b09ce3765bfac371
--- /dev/null
+++ b/data/sampled_jsons/decomposing_complex_ethical_dilemmas_computational_overhead_checks_and_balances_framework.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 1, "title": "TreeReview: A Dynamic Tree of Questions Framework for ...", "date": "", "ddg_snippet": "9 Jun 2025 — TreeReview first constructs a tree of review questions by recursively decomposing high-level questions into fine-grained sub-questions and then ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07642v1", "content": "9 Jun 2025 — TreeReview first constructs a tree of review questions by recursively decomposing high-level questions into fine-grained sub-questions and then ..."}
+{"idx": 2, "title": "On the computational complexity of ethics: moral tractability ...", "date": "", "ddg_snippet": "by J Stenseke · 2024 · Cited by 19 — In this paper, we address these issues by exploring what kind of moral machines are possible based on the ethical problems computational systems ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-024-10732-3", "content": "by J Stenseke · 2024 · Cited by 19 — In this paper, we address these issues by exploring what kind of moral machines are possible based on the ethical problems computational systems ..."}
+{"idx": 3, "title": "14+ Thinking Frameworks for the 21st Century | by Yolanda Lau", "date": "", "ddg_snippet": "29 Jan 2025 — Computational thinking, or algorithmic thinking, is about breaking down complex problems , recognizing patterns, and designing algorithms.", "subpage_snippet": "", "source": "mind.yolandalau.com", "link": "https://mind.yolandalau.com/14-thinking-frameworks-for-the-21st-century-2c5f1979f1f1", "content": "29 Jan 2025 — Computational thinking, or algorithmic thinking, is about breaking down complex problems , recognizing patterns, and designing algorithms."}
+{"idx": 4, "title": "A lightweight framework for knowledge graph ...", "date": "", "ddg_snippet": "by Q Shen · 2025 — However, the computational overhead of matrix decomposition operations is relatively high, requiring longer training time for the model to ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40747-025-02064-8", "content": "by Q Shen · 2025 — However, the computational overhead of matrix decomposition operations is relatively high, requiring longer training time for the model to ..."}
+{"idx": 5, "title": "A privacy preserving machine learning framework for ...", "date": "", "ddg_snippet": "by S Selvakumar · 2025 — This framework enabled secure training and inference but encountered challenges with computational overhead and communication bottlenecks.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12310975/", "content": "by S Selvakumar · 2025 — This framework enabled secure training and inference but encountered challenges with computational overhead and communication bottlenecks."}
+{"idx": 6, "title": "Decompositional Reasoning for Graph Retrieval with Large ...", "date": "", "ddg_snippet": "16 Jun 2025 — Using an LLM provides a flexible framework for decomposing complex questions , independent of the domain or the question type. To fulfill all ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.13380v1", "content": "16 Jun 2025 — Using an LLM provides a flexible framework for decomposing complex questions , independent of the domain or the question type. To fulfill all ..."}
+{"idx": 7, "title": "Solving Intricate Problems with Human-like Reasoning Cycle", "date": "", "ddg_snippet": "9 Dec 2024 — In this paper, we introduce DeAR ( Decompose -Analyze-Rethink), a framework that iteratively builds a reasoning tree to tackle intricate ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95441", "content": "9 Dec 2024 — In this paper, we introduce DeAR ( Decompose -Analyze-Rethink), a framework that iteratively builds a reasoning tree to tackle intricate ..."}
+{"idx": 8, "title": "Deep Interpretability for AGI Decision Systems", "date": "", "ddg_snippet": "This paper presents a comprehensive framework for deep interpretability in AGI decision systems, addressing the fundamental challenge of making superintelligent.", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5412006.pdf?abstractid=5412006&mirid=1", "content": "This paper presents a comprehensive framework for deep interpretability in AGI decision systems, addressing the fundamental challenge of making superintelligent."}
+{"idx": 9, "title": "Structured knowledge-based causal discovery: Agentic ...", "date": "", "ddg_snippet": "by S Meier · 2025 · Cited by 2 — The framework integrates hierarchical query decomposition with Model Compiler refinement, while dual-stream thought processing enables balanced analysis through ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0306457325001438", "content": "by S Meier · 2025 · Cited by 2 — The framework integrates hierarchical query decomposition with Model Compiler refinement, while dual-stream thought processing enables balanced analysis through ..."}
diff --git a/data/sampled_jsons/deep_neural_network_reward_model_architecture_preference_learning_Bradley-Terry_ensemble_methods_year_2024.jsonl b/data/sampled_jsons/deep_neural_network_reward_model_architecture_preference_learning_Bradley-Terry_ensemble_methods_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f95c5b7e3b39f16cd408bf782b084f1591f6c217
--- /dev/null
+++ b/data/sampled_jsons/deep_neural_network_reward_model_architecture_preference_learning_Bradley-Terry_ensemble_methods_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Outline of machine learning - Wikipedia", "date": "", "ddg_snippet": "Machine learning ( ML ) is a subfield of ... Reinforcement learning , where the model learns to make decisions by receiving rewards or penalties.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Outline_of_machine_learning", "content": "Machine learning ( ML ) is a subfield of ... Reinforcement learning , where the model learns to make decisions by receiving rewards or penalties."}
+{"idx": 1, "title": "RIVAL: Reinforcement Learning with Iterative and Adversarial", "date": "", "ddg_snippet": "RIVAL iteratively updates the both models , with the RM trained to distinguish strong from weak translations (qualitative preference reward ), and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05070v1", "content": "RIVAL iteratively updates the both models , with the RM trained to distinguish strong from weak translations (qualitative preference reward ), and the ..."}
+{"idx": 2, "title": "Weighted-Reward Preference Optimization for Implicit Model", "date": "", "ddg_snippet": "... open-source LLMs with varying architectures and sizes can potentially integrate the strengths of different models , existing fusion methods face ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.03187v2", "content": "... open-source LLMs with varying architectures and sizes can potentially integrate the strengths of different models , existing fusion methods face ..."}
+{"idx": 3, "title": "Energy-Based Reward Models for Robust Language Model Alignment", "date": "", "ddg_snippet": "Ensemble methods (Eisenstein et al., 2024 ; Coste et al., 2024 ) improve prediction accuracy by aggregating multiple reward model scores.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.13134v2", "content": "Ensemble methods (Eisenstein et al., 2024 ; Coste et al., 2024 ) improve prediction accuracy by aggregating multiple reward model scores."}
+{"idx": 4, "title": "Search-Based Credit Assignment for Offline Preference-Based", "date": "", "ddg_snippet": "To address this, recent methods avoid explicit reward modeling by directly optimizing policies from preferences (Tang, He, and Li 2023 ; Tian, Wang ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15327v1", "content": "To address this, recent methods avoid explicit reward modeling by directly optimizing policies from preferences (Tang, He, and Li 2023 ; Tian, Wang ..."}
+{"idx": 5, "title": "What is Direct Preference Optimization (DPO)?", "date": "", "ddg_snippet": "Theoretical Framework : DPO relies on a theoretical preference model , like the Bradley - Terry model , that measures how well a given reward function ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2024/01/dpo-andrew-ngs-perspective-on-the-next-big-thing-in-ai/", "content": "Theoretical Framework : DPO relies on a theoretical preference model , like the Bradley - Terry model , that measures how well a given reward function ..."}
+{"idx": 6, "title": "Efficient Exploration for LLMs", "date": "", "ddg_snippet": "In some of the agents we consider, the reward model takes the form of an epistemic neural network , which offers the exploration algorithm access to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.00396v2", "content": "In some of the agents we consider, the reward model takes the form of an epistemic neural network , which offers the exploration algorithm access to ..."}
+{"idx": 7, "title": "ICML 2022 Papers", "date": "", "ddg_snippet": "... Model -based Policy Stationary Distribution to Stabilize ... Representation Topology Divergence: A Method for Comparing Neural Network Representations.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/papers.html", "content": "... Model -based Policy Stationary Distribution to Stabilize ... Representation Topology Divergence: A Method for Comparing Neural Network Representations."}
+{"idx": 8, "title": "RLJ · 2025", "date": "", "ddg_snippet": "Eau De $Q$- Network : Adaptive Distillation of Neural Networks in Deep Reinforcement Learning , by Théo Vincent, Tim Faust, Yogesh Tripathi, Jan ...", "subpage_snippet": "", "source": "rlj.cs.umass.edu", "link": "https://rlj.cs.umass.edu/2025/2025issue.html", "content": "Eau De $Q$- Network : Adaptive Distillation of Neural Networks in Deep Reinforcement Learning , by Théo Vincent, Tim Faust, Yogesh Tripathi, Jan ..."}
+{"idx": 9, "title": "SCITEPRESS - SCIENCE AND TECHNOLOGY PUBLICATIONS", "date": "", "ddg_snippet": "... Co-Activation of One Muscle Head from the Other Muscle Head of the Biceps Brachii Muscle by Linear Regression and Shallow Feedforward Neural Networks ...", "subpage_snippet": "", "source": "www.scitepress.org", "link": "https://www.scitepress.org/PublishedPapers/2024/", "content": "... Co-Activation of One Muscle Head from the Other Muscle Head of the Biceps Brachii Muscle by Linear Regression and Shallow Feedforward Neural Networks ..."}
diff --git a/data/sampled_jsons/deep_neural_networks_reward_modeling_RLHF_non-linear_reward_functions_2024.jsonl b/data/sampled_jsons/deep_neural_networks_reward_modeling_RLHF_non-linear_reward_functions_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dcf6415e0d553136d30a97d1ccc1fddafeb7edd4
--- /dev/null
+++ b/data/sampled_jsons/deep_neural_networks_reward_modeling_RLHF_non-linear_reward_functions_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning from human feedback", "date": "", "ddg_snippet": "In machine learning , reinforcement learning from human feedback ( RLHF ) is a technique to align an intelligent agent with human preferences.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback", "content": "In machine learning , reinforcement learning from human feedback ( RLHF ) is a technique to align an intelligent agent with human preferences."}
+{"idx": 1, "title": "Learning Guarantee of Reward Modeling Using Deep Neural Networks", "date": "", "ddg_snippet": "May 13, 2025 · In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks . We establish a novel non -asymptotic regret bound for deep reward estimators in a non -parametric setting, which depends explicitly on the network architecture. Furthermore, to underscore the critical im-portance of clear human beliefs, we introduce a margin-type condition that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.06601v1", "content": "May 13, 2025 · In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks . We establish a novel non -asymptotic regret bound for deep reward estimators in a non -parametric setting, which depends explicitly on the network architecture. Furthermore, to underscore the critical im-portance of clear human beliefs, we introduce a margin-type condition that ..."}
+{"idx": 2, "title": "Prototypical Reward Network for Data-Efficient RLHF - ACL ...", "date": "", "ddg_snippet": "Sep 15, 2025 · Notably, collecting human feedback for RLHF can be resource-intensive and lead to scalability issues for LLMs and complex tasks. Our proposed framework Proto-RM leverages prototypical networks to enhance reward models under limited human feedback.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.748/", "content": "Sep 15, 2025 · Notably, collecting human feedback for RLHF can be resource-intensive and lead to scalability issues for LLMs and complex tasks. Our proposed framework Proto-RM leverages prototypical networks to enhance reward models under limited human feedback."}
+{"idx": 3, "title": "Mechanism Design for LLM Fine-tuning with Multiple Reward Models", "date": "", "ddg_snippet": "This fine-tuning technique with a reward model has proven effective in encoding human preferences into models and has become a fundamental component of the training process for most advanced LLMs. With the advancement of RLHF , numerous studies have investigated efficient methods for aggregating multiple preferences into a single fine-tuned model .", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2024/12/neurips24workshop_RLHF_Mechanism_Design.pdf", "content": "This fine-tuning technique with a reward model has proven effective in encoding human preferences into models and has become a fundamental component of the training process for most advanced LLMs. With the advancement of RLHF , numerous studies have investigated efficient methods for aggregating multiple preferences into a single fine-tuned model ."}
+{"idx": 4, "title": "Reward Modeling | RLHF Book by Nathan Lambert", "date": "", "ddg_snippet": "Reward Modeling Reward models are core to the modern approach to RLHF . Reward models broadly have been used extensively in reinforcement learning research as a proxy for environment rewards [1]. The practice is closely related to inverse reinforcement learning, where the problem is to approximate an agent’s reward function given trajectories of behavior [2], and other areas of deep ...", "subpage_snippet": "", "source": "rlhfbook.com", "link": "https://rlhfbook.com/c/07-reward-models", "content": "Reward Modeling Reward models are core to the modern approach to RLHF . Reward models broadly have been used extensively in reinforcement learning research as a proxy for environment rewards [1]. The practice is closely related to inverse reinforcement learning, where the problem is to approximate an agent’s reward function given trajectories of behavior [2], and other areas of deep ..."}
+{"idx": 5, "title": "Neural Dueling Bandits: Preference-Based Optimization with ...", "date": "", "ddg_snippet": "Jan 22, 2025 · The paper studies the challenge of modeling dueling bandit problems where the reward function is non-linear . Traditional algorithms assume a linear reward function , which can be restrictive in real-life applications like online recommendations or web search rankings. The authors propose using neural networks to estimate the non-linear reward function using noisy preference feedback. They ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=VELhv9BBfn", "content": "Jan 22, 2025 · The paper studies the challenge of modeling dueling bandit problems where the reward function is non-linear . Traditional algorithms assume a linear reward function , which can be restrictive in real-life applications like online recommendations or web search rankings. The authors propose using neural networks to estimate the non-linear reward function using noisy preference feedback. They ..."}
+{"idx": 6, "title": "Under the Hood of LLaMA 2: A Deep Dive into Reward Modeling ...", "date": "", "ddg_snippet": "Oct 27, 2024 · This article delves into the intricacies of fine-tuning LLaMA 2 for reward modelling, a critical step in RLHF , through the lens of a Python script that exemplifies efficiency and ethical ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/thedeephub/under-the-hood-of-llama-2-a-deep-dive-into-reward-modeling-and-rlhf-4ea240c50983", "content": "Oct 27, 2024 · This article delves into the intricacies of fine-tuning LLaMA 2 for reward modelling, a critical step in RLHF , through the lens of a Python script that exemplifies efficiency and ethical ..."}
+{"idx": 7, "title": "Learning Guarantee of Reward Modeling Using Deep Neural Networks", "date": "", "ddg_snippet": "May 10, 2025 · In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks . We establish a novel non -asymptotic regret bound for deep reward estimators in a non -parametric setting, which depends explicitly on the network architecture. Furthermore, to underscore the critical importance of clear human beliefs, we introduce a margin-type condition that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.06601", "content": "May 10, 2025 · In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks . We establish a novel non -asymptotic regret bound for deep reward estimators in a non -parametric setting, which depends explicitly on the network architecture. Furthermore, to underscore the critical importance of clear human beliefs, we introduce a margin-type condition that ..."}
+{"idx": 8, "title": "Learning Guarantee of Reward Modeling Using Deep ...", "date": "", "ddg_snippet": "10 May 2025 — In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks . We establish a novel non ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06601v1", "content": "10 May 2025 — In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks . We establish a novel non ..."}
+{"idx": 9, "title": "Learning guarantee of reward modeling using deep neural ...", "date": "", "ddg_snippet": "by Y Luo — In this work, we study the learning theory of reward modeling using pairwise comparison data and deep neural networks .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mGSQLuYxVF", "content": "by Y Luo — In this work, we study the learning theory of reward modeling using pairwise comparison data and deep neural networks ."}
diff --git a/data/sampled_jsons/deepfake_detector_loss_function_beta_0.1_gamma_0.01_hyperparameters.jsonl b/data/sampled_jsons/deepfake_detector_loss_function_beta_0.1_gamma_0.01_hyperparameters.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..80beaa2e35e76668b7c793e079f6af7efa733c08
--- /dev/null
+++ b/data/sampled_jsons/deepfake_detector_loss_function_beta_0.1_gamma_0.01_hyperparameters.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generalization Limits of Deepfake Detectors in the Wild", "date": "", "ddg_snippet": "by A Garg — Dynamics (SGLD), by setting a Gamma hyperparameter with α = 0.01 , β = 100. Once we obtain λi's posterior samples, its posterior mean plays a role as an ...", "subpage_snippet": "", "source": "www.ischool.berkeley.edu", "link": "https://www.ischool.berkeley.edu/sites/default/files/bb_paper.pdf", "content": "by A Garg — Dynamics (SGLD), by setting a Gamma hyperparameter with α = 0.01 , β = 100. Once we obtain λi's posterior samples, its posterior mean plays a role as an ..."}
+{"idx": 1, "title": "Improving Adaptability of DeepFake Detection via Online ...", "date": "", "ddg_snippet": "A novel online test-time adaptation method that enhances the adaptability of detectors during inference without requiring access to source training data or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18787v2", "content": "A novel online test-time adaptation method that enhances the adaptability of detectors during inference without requiring access to source training data or ..."}
+{"idx": 2, "title": "Lightweight Joint Audio-Visual Deepfake Detection via ...", "date": "", "ddg_snippet": "9 Jun 2025 — In this work, we design a lightweight network for audio-visual deepfake detection via a single-stream multi-modal learning framework.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07358v1", "content": "9 Jun 2025 — In this work, we design a lightweight network for audio-visual deepfake detection via a single-stream multi-modal learning framework."}
+{"idx": 3, "title": "Deepfake detection based on cross-domain local ...", "date": "", "ddg_snippet": "by MA Amin · 2024 · Cited by 10 — The SVFT model inherited hyperparameters from CTE and LTE, maintaining consistency, except for a learning rate of 0.001 and a weight decay of 0.01 , carefully ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1110016824001753", "content": "by MA Amin · 2024 · Cited by 10 — The SVFT model inherited hyperparameters from CTE and LTE, maintaining consistency, except for a learning rate of 0.001 and a weight decay of 0.01 , carefully ..."}
+{"idx": 4, "title": "Multi-To-Binary: A Generalizable Deepfake Detection ...", "date": "", "ddg_snippet": "The final loss function of the training process is the ... where λ1 and λ2 are hyper-parameters for balancing the overall loss. ... The value of hyperparameter λ1 ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/3695febd-46b1-496d-adbf-57cdf68434c4-MECA.pdf?abstractid=4954695&mirid=1", "content": "The final loss function of the training process is the ... where λ1 and λ2 are hyper-parameters for balancing the overall loss. ... The value of hyperparameter λ1 ..."}
+{"idx": 5, "title": "Video Deepfake classification using particle swarm ...", "date": "", "ddg_snippet": "by L Zhang · 2024 · Cited by 22 — We propose evolving ensembles of 3D CNNs and CNN-RNNs for video deepfake detection . A PSO algorithm is proposed to optimize hyper-parameters and formulate ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0950705124000960", "content": "by L Zhang · 2024 · Cited by 22 — We propose evolving ensembles of 3D CNNs and CNN-RNNs for video deepfake detection . A PSO algorithm is proposed to optimize hyper-parameters and formulate ..."}
+{"idx": 6, "title": "An Analysis of Recent Advances in Deepfake Image Detection ...", "date": "", "ddg_snippet": "There are two key parameters at generation time: (1) β : stylespace channels with relevance score higher than β are manipulated. (2) α: controls the strength of ... 19 pages", "subpage_snippet": "", "source": "people.cs.vt.edu", "link": "https://people.cs.vt.edu/penggao/papers/neo-sp24.pdf", "content": "There are two key parameters at generation time: (1) β : stylespace channels with relevance score higher than β are manipulated. (2) α: controls the strength of ... 19 pages"}
+{"idx": 7, "title": "Evaluating and Designing Defenses Against Input Perturbation ...", "date": "", "ddg_snippet": "this work, we propose Disjoint Diffusion Deepfake Detection (D4), a deepfake detector designed to improve black-box adversarial robustness beyond de facto ...", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "https://deepblue.lib.umich.edu/bitstream/handle/2027.42/199295/nealmgkr_1.pdf?sequence=1&isAllowed=y", "content": "this work, we propose Disjoint Diffusion Deepfake Detection (D4), a deepfake detector designed to improve black-box adversarial robustness beyond de facto ..."}
+{"idx": 8, "title": "Spotting the Temporal Inconsistency between Audio and ...", "date": "", "ddg_snippet": "by W Liu · 2024 · Cited by 14 — Existing studies on DeepFake detection can be classified into unimodal-based and multi-modal-based methods, where the former relies on visual discrepancies ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/a5a5b0ff87c59172a13342d428b1e033-Paper-Conference.pdf", "content": "by W Liu · 2024 · Cited by 14 — Existing studies on DeepFake detection can be classified into unimodal-based and multi-modal-based methods, where the former relies on visual discrepancies ..."}
+{"idx": 9, "title": "GenKL: An Iterative Framework for Resolving Label ...", "date": "", "ddg_snippet": "by X Huang · 2023 · Cited by 7 — To improve the robustness of training classifiers on web image datasets with both NC instances and non-NC instances with label noise, we propose ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-023-01815-9", "content": "by X Huang · 2023 · Cited by 7 — To improve the robustness of training classifiers on web image datasets with both NC instances and non-NC instances with label noise, we propose ..."}
diff --git a/data/sampled_jsons/difference_between_CLIP_encoder_and_VAE_encoder_latent_space_for_image_embedding.jsonl b/data/sampled_jsons/difference_between_CLIP_encoder_and_VAE_encoder_latent_space_for_image_embedding.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..adae7acc49611340888a26eed03abf0c5e1873e3
--- /dev/null
+++ b/data/sampled_jsons/difference_between_CLIP_encoder_and_VAE_encoder_latent_space_for_image_embedding.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ComfyUI: Advanced Understanding (Part 1)", "date": "", "ddg_snippet": "12 Jan 2024 — -The VAE is important because it brings the image to and from the latent space , which is a smaller representation of the original pixel image ...", "subpage_snippet": "", "source": "stablediffusion3.net", "link": "https://stablediffusion3.net/blog-ComfyUI-Advanced-Understanding-Part-1-40694", "content": "12 Jan 2024 — -The VAE is important because it brings the image to and from the latent space , which is a smaller representation of the original pixel image ..."}
+{"idx": 1, "title": "Variational Autoencoders (VAEs) models - Dilip Kumar - Medium", "date": "", "ddg_snippet": "The Big Idea: Instead of mapping an input image to a single point (z) in the latent space , a VAE maps it to a probability distribution — a “ ...", "subpage_snippet": "", "source": "dilipkumar.medium.com", "link": "https://dilipkumar.medium.com/variational-autoencoders-vaes-modles-597b1a71f97b", "content": "The Big Idea: Instead of mapping an input image to a single point (z) in the latent space , a VAE maps it to a probability distribution — a “ ..."}
+{"idx": 2, "title": "Controlling Latent Diffusion Using Latent CLIP", "date": "", "ddg_snippet": "11 Mar 2025 — In this section, we introduce our Latent- CLIP , a latent - space adaptation of the CLIP framework that operates directly on the compressed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.08455v1", "content": "11 Mar 2025 — In this section, we introduce our Latent- CLIP , a latent - space adaptation of the CLIP framework that operates directly on the compressed ..."}
+{"idx": 3, "title": "Text-to-Image: Diffusion, Text Conditioning, Guidance, ...", "date": "", "ddg_snippet": "CLIP is trained to maximize the cosine similarity of the image and text embeddings of image -caption pairs via a multi-modal embedding space . This is implemented ...", "subpage_snippet": "", "source": "eugeneyan.com", "link": "https://eugeneyan.com/writing/text-to-image/", "content": "CLIP is trained to maximize the cosine similarity of the image and text embeddings of image -caption pairs via a multi-modal embedding space . This is implemented ..."}
+{"idx": 4, "title": "Reconstruct images with CLIP image embedding", "date": "", "ddg_snippet": "CLIP embedding space is different from VAE's embedding space . VAE decoder should only work on embeddings encoded by VAE's encoder. u ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/computervision/comments/1jfoojt/reconstruct_images_with_clip_image_embedding/", "content": "CLIP embedding space is different from VAE's embedding space . VAE decoder should only work on embeddings encoded by VAE's encoder. u ..."}
+{"idx": 5, "title": "Conditional Variational Autoencoders for Text to Image ...", "date": "", "ddg_snippet": "21 Dec 2024 — While typical autoencoders are deterministic, VAEs are probabilistic models due to modeling the latent space as a probability distribution. VAEs ...", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/conditional-variational-autoencoders-for-text-to-image-generation-1996da9cefcb/", "content": "21 Dec 2024 — While typical autoencoders are deterministic, VAEs are probabilistic models due to modeling the latent space as a probability distribution. VAEs ..."}
+{"idx": 6, "title": "Disentangled Variational Autoencoders — Intuitively and ...", "date": "", "ddg_snippet": "The goal of a variational autoencoder ( VAE ) is to attempt to make the latent vectors obey some reasonable rules. Instead of a bunch of vague ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/intuitively-and-exhaustively-explained/disentangled-variational-autoencoders-intuitively-and-exhaustively-explained-273b0dc92e8a", "content": "The goal of a variational autoencoder ( VAE ) is to attempt to make the latent vectors obey some reasonable rules. Instead of a bunch of vague ..."}
+{"idx": 7, "title": "ClearSR: Latent Low-Resolution Image Embeddings Help...", "date": "", "ddg_snippet": "by Y Wan · Cited by 3 — Specifically, the LR latent embedding , which is the output of the VAE encoder , has a size of 4x64x64, while the input image is 3x512x512. Compared to the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FWpO8u2lim", "content": "by Y Wan · Cited by 3 — Specifically, the LR latent embedding , which is the output of the VAE encoder , has a size of 4x64x64, while the input image is 3x512x512. Compared to the ..."}
+{"idx": 8, "title": "FLIER: Few-shot Language Image Models Embedded with ...", "date": "", "ddg_snippet": "10 Oct 2024 — The latent encoder is jointly trained with CLIP's image encoder , transferring pre-trained knowledge to downstream tasks better. Experiments and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07648v1", "content": "10 Oct 2024 — The latent encoder is jointly trained with CLIP's image encoder , transferring pre-trained knowledge to downstream tasks better. Experiments and ..."}
+{"idx": 9, "title": "Stable diffusion using 🤗 Hugging Face - Looking under the ...", "date": "", "ddg_snippet": "5 Nov 2022 — On a high level, CLIP uses an image encoder and text encoder to create embeddings that are similar in latent space . This similarity is more ...", "subpage_snippet": "", "source": "aayushmnit.com", "link": "https://aayushmnit.com/posts/2022-11-05-StableDiffusionP2/2022-11-05-StableDiffusionP2.html", "content": "5 Nov 2022 — On a high level, CLIP uses an image encoder and text encoder to create embeddings that are similar in latent space . This similarity is more ..."}
diff --git a/data/sampled_jsons/differential_privacy_coordinate-wise_private_median_regression_challenge.jsonl b/data/sampled_jsons/differential_privacy_coordinate-wise_private_median_regression_challenge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..27f8ac6216cd0a5ce1924924302f03556135880b
--- /dev/null
+++ b/data/sampled_jsons/differential_privacy_coordinate-wise_private_median_regression_challenge.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Efficient Sparse Least Absolute Deviation Regression with", "date": "", "ddg_snippet": "To ensure that only population- wise patterns are revealed, but individual sensitive information is well protected, differential privacy has been ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.01294v1", "content": "To ensure that only population- wise patterns are revealed, but individual sensitive information is well protected, differential privacy has been ..."}
+{"idx": 1, "title": "On Differential Privacy for Adaptively Solving Search Problems", "date": "", "ddg_snippet": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems, which are significantly more challenging since ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05503v1", "content": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems, which are significantly more challenging since ..."}
+{"idx": 2, "title": "Calculation of mode by using Relationship of mean and median", "date": "", "ddg_snippet": "The formula to define the relation between mean, median , and mode in a moderately skewed distribution is 3 ( median ) = mode + 2 mean.", "subpage_snippet": "", "source": "indiafreenotes.com", "link": "https://indiafreenotes.com/calculation-of-mode-by-using-relationship-of-mean-and-median-that-is-empirical-formula/", "content": "The formula to define the relation between mean, median , and mode in a moderately skewed distribution is 3 ( median ) = mode + 2 mean."}
+{"idx": 3, "title": "1 Introduction", "date": "", "ddg_snippet": "... differentially private statistical ... Balancing privacy protection and statistical accuracy remains a central challenge in differential privacy .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05212v1", "content": "... differentially private statistical ... Balancing privacy protection and statistical accuracy remains a central challenge in differential privacy ."}
+{"idx": 4, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Smaller, more accurate regression forests using ... Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Smaller, more accurate regression forests using ... Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints"}
+{"idx": 5, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Smaller, more accurate regression forests using ... Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Smaller, more accurate regression forests using ... Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints"}
+{"idx": 6, "title": "Downloads", "date": "", "ddg_snippet": "alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression ... with an Application to Function-On-Scalar Regression", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression ... with an Application to Function-On-Scalar Regression"}
+{"idx": 7, "title": "NeurIPS 2021 Papers", "date": "", "ddg_snippet": "... Multimodal Regression with ... Neural Regression , Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2021/papers.html", "content": "... Multimodal Regression with ... Neural Regression , Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex"}
+{"idx": 8, "title": "GitHub - chaoyanghe/Awesome-Federated-Learning: FedML - The", "date": "", "ddg_snippet": "System Challenges : communication and computational resource constrained, software and hardware heterogeneity, and FL system (141)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chaoyanghe/Awesome-Federated-Learning", "content": "System Challenges : communication and computational resource constrained, software and hardware heterogeneity, and FL system (141)"}
+{"idx": 9, "title": "Legal Factors – Page 2 – india free notes.com", "date": "", "ddg_snippet": "Coordinating relationships with intermediaries such as distributors, retailers, and agents can be challenging , especially in ensuring consistent ...", "subpage_snippet": "", "source": "indiafreenotes.com", "link": "https://indiafreenotes.com/tag/legal-factors/page/2/", "content": "Coordinating relationships with intermediaries such as distributors, retailers, and agents can be challenging , especially in ensuring consistent ..."}
diff --git a/data/sampled_jsons/diffusion_model_survey_conditional_ImageNet_64x64_FID_table_iDDPM.jsonl b/data/sampled_jsons/diffusion_model_survey_conditional_ImageNet_64x64_FID_table_iDDPM.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6043b7fbe2f1e039f9e338087baa3d658aab0ec7
--- /dev/null
+++ b/data/sampled_jsons/diffusion_model_survey_conditional_ImageNet_64x64_FID_table_iDDPM.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fine-tuning ImageNet diffusion model on unconditional/conditional data ...", "date": "", "ddg_snippet": "I want to fine tune some generative diffusion model (DDPM), lets say trained on ImageNet (NOT Stable Diffusion which is text2img), to some other data like CelebA or CIFAR-10. I wonder two things: does my pretrained model needs to be unconditional? or can it be conditioned on ImageNet classes but can be tuned on unconditional CelebA or with classes from CIFAR-10? maybe trivial but do image ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/76482974/fine-tuning-imagenet-diffusion-model-on-unconditional-conditional-data", "content": "I want to fine tune some generative diffusion model (DDPM), lets say trained on ImageNet (NOT Stable Diffusion which is text2img), to some other data like CelebA or CIFAR-10. I wonder two things: does my pretrained model needs to be unconditional? or can it be conditioned on ImageNet classes but can be tuned on unconditional CelebA or with classes from CIFAR-10? maybe trivial but do image ..."}
+{"idx": 1, "title": "guided-diffusion/model-card.md at main - GitHub", "date": "", "ddg_snippet": "These are diffusion models and noised image classifiers described in the paper Diffusion Models Beat GANs on Image Synthesis. Included in this release are the following models : Noisy ImageNet classifiers at resolutions 64x64 , 128x128, 256x256, 512x512 A class-unconditional ImageNet diffusion model at resolution 256x256 Class conditional ImageNet diffusion models at 64x64 , 128x128, 256x256 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openai/guided-diffusion/blob/main/model-card.md", "content": "These are diffusion models and noised image classifiers described in the paper Diffusion Models Beat GANs on Image Synthesis. Included in this release are the following models : Noisy ImageNet classifiers at resolutions 64x64 , 128x128, 256x256, 512x512 A class-unconditional ImageNet diffusion model at resolution 256x256 Class conditional ImageNet diffusion models at 64x64 , 128x128, 256x256 ..."}
+{"idx": 2, "title": "LowDiff: Efficient Diffusion Sampling with Low-Resolution Condition", "date": "", "ddg_snippet": "LowDiff is applicable to diffusion models in both pixel space and latent space. Extensive experiments on both conditional and unconditional generation tasks across CIFAR-10, FFHQ and ImageNet demonstrate the effectiveness and generality of our method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15342", "content": "LowDiff is applicable to diffusion models in both pixel space and latent space. Extensive experiments on both conditional and unconditional generation tasks across CIFAR-10, FFHQ and ImageNet demonstrate the effectiveness and generality of our method."}
+{"idx": 3, "title": "openai/diffusers-cd_imagenet64_l2 · Hugging Face", "date": "", "ddg_snippet": "However, it also carries the risk of overestimating model performance, because LPIPS uses a VGG network pre-trained on ImageNet , while FID and Inception scores also rely on convolutional neural networks (the Inception network in particular) pre-trained on the same ImageNet dataset.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/openai/diffusers-cd_imagenet64_l2", "content": "However, it also carries the risk of overestimating model performance, because LPIPS uses a VGG network pre-trained on ImageNet , while FID and Inception scores also rely on convolutional neural networks (the Inception network in particular) pre-trained on the same ImageNet dataset."}
+{"idx": 4, "title": "Synthetic Data from Diffusion Models Improves ImageNet Classification", "date": "", "ddg_snippet": "To this end we explore the fine-tuning of large-scale text-to-image diffusion models , yielding class- conditional ImageNet models with state-of-the-art FID score (1.76 at 256×256 resolution) and Inception Score (239 at 256×256).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DlRsoxjyPm", "content": "To this end we explore the fine-tuning of large-scale text-to-image diffusion models , yielding class- conditional ImageNet models with state-of-the-art FID score (1.76 at 256×256 resolution) and Inception Score (239 at 256×256)."}
+{"idx": 5, "title": "Jmay0120/re-implementation-of-IDDPM - GitHub", "date": "", "ddg_snippet": "We have released checkpoints for the main models in the paper. Before using these models , please review the corresponding model card to understand the intended use and limitations of these models . MODEL_FLAGS=\"--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 256 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Jmay0120/re-implementation-of-IDDPM", "content": "We have released checkpoints for the main models in the paper. Before using these models , please review the corresponding model card to understand the intended use and limitations of these models . MODEL_FLAGS=\"--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 256 ..."}
+{"idx": 6, "title": "[2304.08466] Synthetic Data from Diffusion Models Improves ImageNet ...", "date": "", "ddg_snippet": "We show that large-scale text-to image diffusion models can be fine-tuned to produce class conditional models with SOTA FID (1.76 at 256x256 resolution) and Inception Score (239 at 256x256). The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.08466", "content": "We show that large-scale text-to image diffusion models can be fine-tuned to produce class conditional models with SOTA FID (1.76 at 256x256 resolution) and Inception Score (239 at 256x256). The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples)."}
+{"idx": 7, "title": "Diffusion Models : Unconditional&Conditional Image Generation", "date": "", "ddg_snippet": "The authors conduct unconditional and conditional image generation experiments and achieve an FID of 2.97 on ImageNet 128x128, 4.59 on ImageNet 256x256, and 7.72 on ImageNet 512x512.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@myschang/diffusion-models-unconditional-conditional-image-generation-e7ced52b09b5", "content": "The authors conduct unconditional and conditional image generation experiments and achieve an FID of 2.97 on ImageNet 128x128, 4.59 on ImageNet 256x256, and 7.72 on ImageNet 512x512."}
+{"idx": 8, "title": "SyntheticDatafromDiffusionModels ImprovesImageNetClassifica", "date": "", "ddg_snippet": "ion accuracy over strong ResNet and Vision Transformer baselines. To this end we explore the fine-tuning of large-scale text-to-image diffusion models , yielding class- conditional ImageNet models with state-of-the-art FID score (1", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DlRsoxjyPm", "content": "ion accuracy over strong ResNet and Vision Transformer baselines. To this end we explore the fine-tuning of large-scale text-to-image diffusion models , yielding class- conditional ImageNet models with state-of-the-art FID score (1"}
+{"idx": 9, "title": "GitHub - openai/improved-diffusion: Release for Improved Denoising ...", "date": "", "ddg_snippet": "The training code reads images from a directory of image files. In the datasets folder, we have provided instructions/scripts for preparing these directories for ImageNet , LSUN bedrooms, and CIFAR-10. For creating your own dataset, simply dump all of your images into a directory with \".jpg\", \".jpeg\", or \".png\" extensions. If you wish to train a class- conditional model , name the files like ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openai/improved-diffusion", "content": "The training code reads images from a directory of image files. In the datasets folder, we have provided instructions/scripts for preparing these directories for ImageNet , LSUN bedrooms, and CIFAR-10. For creating your own dataset, simply dump all of your images into a directory with \".jpg\", \".jpeg\", or \".png\" extensions. If you wish to train a class- conditional model , name the files like ..."}
diff --git a/data/sampled_jsons/discontinuous_loss_function_combinatorial_bandits_year_2024.jsonl b/data/sampled_jsons/discontinuous_loss_function_combinatorial_bandits_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4629978f4ad29fc5212b20841c92b97ab5551587
--- /dev/null
+++ b/data/sampled_jsons/discontinuous_loss_function_combinatorial_bandits_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Download On Combinatorial Identities Symbolic Summation And", "date": "", "ddg_snippet": "JEREZ GALEANO, Jaime, Calle 125 download on combinatorial identities symbolic summation and umbral calculus 302, Bogota, Colombia; Carrera 7 app ...", "subpage_snippet": "", "source": "mosedavis.net", "link": "http://mosedavis.net/pdf/download-on-combinatorial-identities-symbolic-summation-and-umbral-calculus.htm", "content": "JEREZ GALEANO, Jaime, Calle 125 download on combinatorial identities symbolic summation and umbral calculus 302, Bogota, Colombia; Carrera 7 app ..."}
+{"idx": 1, "title": "AISTATS 2024 Schedule", "date": "", "ddg_snippet": "A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2024/calendar", "content": "A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models"}
+{"idx": 2, "title": "AISTATS 2021 Papers", "date": "", "ddg_snippet": "... Functions : Learning Rates, ... Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2021/papers.html?filter=titles", "content": "... Functions : Learning Rates, ... Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss"}
+{"idx": 3, "title": "ICML 2023 Schedule", "date": "", "ddg_snippet": "Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2023/calendar", "content": "Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization"}
+{"idx": 4, "title": "Journal of Machine Learning Research", "date": "", "ddg_snippet": "... Saddle Functions with Complexity Guarantees ... Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/beta/papers/v24/", "content": "... Saddle Functions with Complexity Guarantees ... Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches"}
+{"idx": 5, "title": "Sample-Constrained Black Box Optimization for Audio", "date": "", "ddg_snippet": "Finding h ∗ superscript ℎ h^{*} italic_h start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT is difficult because the user’s satisfaction function f ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12773v1", "content": "Finding h ∗ superscript ℎ h^{*} italic_h start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT is difficult because the user’s satisfaction function f ..."}
+{"idx": 6, "title": "UAI 2020", "date": "", "ddg_snippet": "... expensive to evaluate the loss functions that map algorithm parameters to performance, which often require the learner to run a combinatorial ...", "subpage_snippet": "", "source": "www.auai.org", "link": "https://www.auai.org/uai2020/session_all.php", "content": "... expensive to evaluate the loss functions that map algorithm parameters to performance, which often require the learner to run a combinatorial ..."}
+{"idx": 7, "title": "UAI 2020", "date": "", "ddg_snippet": "... expensive to evaluate the loss functions that map algorithm parameters to performance, which often require the learner to run a combinatorial ...", "subpage_snippet": "", "source": "www.auai.org", "link": "https://www.auai.org/uai2020/session1.php", "content": "... expensive to evaluate the loss functions that map algorithm parameters to performance, which often require the learner to run a combinatorial ..."}
+{"idx": 8, "title": "US20080140591A1 - System and method for matching objects", "date": "", "ddg_snippet": "a multi-level policy may run bandits at two levels of the taxonomies: first, a bandit may be run on the ad class groups corresponding to a page class ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20080140591A1/en", "content": "a multi-level policy may run bandits at two levels of the taxonomies: first, a bandit may be run on the ad class groups corresponding to a page class ..."}
+{"idx": 9, "title": "Whats new on arXiv – Complete List | AnalytiXon", "date": "", "ddg_snippet": "... Discontinuities • Development and evaluation of an open-source, machine learning-based average annual daily traffic estimation software • Feature ...", "subpage_snippet": "", "source": "analytixon.com", "link": "https://analytixon.com/2019/11/05/whats-new-on-arxiv-complete-list-127/", "content": "... Discontinuities • Development and evaluation of an open-source, machine learning-based average annual daily traffic estimation software • Feature ..."}
diff --git a/data/sampled_jsons/efficient_inference_techniques_for_attention_intervention_or_knowledge_editing_in_language_models_year_2023.jsonl b/data/sampled_jsons/efficient_inference_techniques_for_attention_intervention_or_knowledge_editing_in_language_models_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2a13bdc60ec7e8316fccefd21b62033fe103b60c
--- /dev/null
+++ b/data/sampled_jsons/efficient_inference_techniques_for_attention_intervention_or_knowledge_editing_in_language_models_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2306.03341] Inference-Time Intervention: Eliciting Truthful ...", "date": "", "ddg_snippet": "by K Li · 2023 · Cited by 637 — We introduce Inference -Time Intervention (ITI), a technique designed to enhance the truthfulness of large language models (LLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.03341", "content": "by K Li · 2023 · Cited by 637 — We introduce Inference -Time Intervention (ITI), a technique designed to enhance the truthfulness of large language models (LLMs)."}
+{"idx": 1, "title": "Inference-Time Intervention: Eliciting Truthful Answers from ...", "date": "", "ddg_snippet": "by K Li · 2023 · Cited by 637 — We introduce Inference -Time Intervention (ITI), a technique designed to enhance the “truthfulness” of large language models (LLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.03341", "content": "by K Li · 2023 · Cited by 637 — We introduce Inference -Time Intervention (ITI), a technique designed to enhance the “truthfulness” of large language models (LLMs)."}
+{"idx": 2, "title": "Inference-Time Intervention: Eliciting Truthful Answers from ...", "date": "", "ddg_snippet": "by K Li · Cited by 637 — We introduce Inference -Time Intervention (ITI), a technique designed to enhance the truthfulness of large language models (LLMs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=aLLuYpn83y", "content": "by K Li · Cited by 637 — We introduce Inference -Time Intervention (ITI), a technique designed to enhance the truthfulness of large language models (LLMs)."}
+{"idx": 3, "title": "Inference-time intervention: eliciting truthful answers from ...", "date": "", "ddg_snippet": "by K Li · 2023 · Cited by 637 — We introduce Inference -Time Intervention (ITI), a technique designed to enhance the truthfulness of large language models (LLMs).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3667919", "content": "by K Li · 2023 · Cited by 637 — We introduce Inference -Time Intervention (ITI), a technique designed to enhance the truthfulness of large language models (LLMs)."}
+{"idx": 4, "title": "Knowledge Editing for LLMs Papers", "date": "", "ddg_snippet": "2024-07-22 We release a new paper: \" Knowledge Mechanisms in Large Language Models : A Survey and Perspective\", which reviews how knowledge is acquired, utilized, ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zjunlp/KnowledgeEditingPapers", "content": "2024-07-22 We release a new paper: \" Knowledge Mechanisms in Large Language Models : A Survey and Perspective\", which reviews how knowledge is acquired, utilized, ..."}
+{"idx": 5, "title": "CogSteer: Cognition-Inspired Selective Layer Intervention ...", "date": "", "ddg_snippet": "by X Wang · 2025 · Cited by 5 — Furthermore, steering seman- tics during inference offers an even more efficient approach. 16 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.1308.pdf", "content": "by X Wang · 2025 · Cited by 5 — Furthermore, steering seman- tics during inference offers an even more efficient approach. 16 pages"}
+{"idx": 6, "title": "Review Attention heads of large language models", "date": "", "ddg_snippet": "14 Feb 2025 — This study focuses on the attention heads of LLMs, offering a systematic review of their behavior and associated experimental methods across various scenarios.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666389925000248", "content": "14 Feb 2025 — This study focuses on the attention heads of LLMs, offering a systematic review of their behavior and associated experimental methods across various scenarios."}
+{"idx": 7, "title": "Editing Conceptual Knowledge for Large Language Models", "date": "", "ddg_snippet": "by X Wang · 2024 · Cited by 30 — Concept editing allows for efficient updates by generalizing from one to many or implementing controllable content gen- eration through abstract ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.40.pdf", "content": "by X Wang · 2024 · Cited by 30 — Concept editing allows for efficient updates by generalizing from one to many or implementing controllable content gen- eration through abstract ..."}
+{"idx": 8, "title": "Large language models for PHM: a review of optimization ...", "date": "", "ddg_snippet": "by T Yu · 2025 — Parameter Efficient Fine-Tuning (PEFT) [55] is a technique designed to reduce the number of trainable parameters by fine-tuning only a subset of ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s43684-025-00100-5", "content": "by T Yu · 2025 — Parameter Efficient Fine-Tuning (PEFT) [55] is a technique designed to reduce the number of trainable parameters by fine-tuning only a subset of ..."}
+{"idx": 9, "title": "Knowledge Editing for Large Language Models: A Survey", "date": "", "ddg_snippet": "by S Wang · 2024 · Cited by 200 — In this survey, we aim at providing a comprehensive and in-depth overview of recent advances in the field of KME.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3698590", "content": "by S Wang · 2024 · Cited by 200 — In this survey, we aim at providing a comprehensive and in-depth overview of recent advances in the field of KME."}
diff --git a/data/sampled_jsons/efficient_inference_techniques_language_models_early_exit_quantization_knowledge_distillation_dynami_year_2023.jsonl b/data/sampled_jsons/efficient_inference_techniques_language_models_early_exit_quantization_knowledge_distillation_dynami_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2df030e01d8879a5eb7bac53a0ace2470f4b3d1c
--- /dev/null
+++ b/data/sampled_jsons/efficient_inference_techniques_language_models_early_exit_quantization_knowledge_distillation_dynami_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2407.20272] An Efficient Inference Framework for Early-exit ... Awesome Efficient LLM Inference Papers - GitHub Robust and Efficient Early Exit for Large Language Models ... Exploring Model Compression Techniques for Ecient Inference ... Efficient Training and Inference: Techniques for Large ... A Survey on Efficient Inference for Large Language Models An Efficient Inference Framework for Early - exit Large Language Models Awesome Efficient LLM Inference Papers - GitHub A Survey on Efficient Inference for Large Language Models A Survey on Efficient Inference for Large Language Models Efficient Knowledge Distillation and Retrieval in Language Models", "date": "", "ddg_snippet": "Jul 25, 2024 · This is non-trivial as prior art on LLM inference cannot be directly applied to early - exit models . In this work, we solves two key challenges in building efficient inference framework for early - exit models : (1) batch inference at iteration-level granularity; and (2) KV cache management. Research into optimization techniques —such as model pruning, quantization , and knowledge distillation —enables the creation of streamlined LLMs that retain high performance while minimizing resource demands. Aug 19, 2025 · Autoregressive large language models (LLMs) have achieved remarkable success across various tasks in Natural Language Processing (NLP), but their high computational cost remains a major bottleneck for efficient inference . Since these models generate text in a token-by-token manner, the depth of computation required for each token directly affects inference speed. To address this issue, early ... Model compression techniques offer a promising solution by reducing model size and inference latency while maintaining high performance. This sur-vey provides a comprehensive overview of the key compression strategies applied to LLMs, including pruning, quantization , knowledge distillation , low-rank factorization, and emerging hybrid approaches. May 18, 2024 · Abstract To enhance the efficiency of language models , it would involve optimizing their training and inference processes to reduce computational demands while maintaining high performance. The research focuses on the application of model compression, quantization , and hardware acceleration techniques to the Llama model . Pruning and knowledge distillation methods effectively reduce the model ... Apr 22, 2024 · Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference ... Does LLM inference take early-exit models into consideration? However, there is no work of LLM inference framework that takes early-exit models into consideration. This is non-trivial as prior art on LLM inference cannot be directly applied to early-exit models. Why is efficient inference important for LLMs? In the AGI era, efficient inference for LLMs is critical to unlocking scalable and accessible applications . While LLMs deliver powerful capabilities, their substantial computational and memory requirements pose significant deployment challenges, particularly in resource-constrained environments. What causes inefficient LLM inference? We start by analyzing the primary causes of the inefficient LLM inference, i.e., the large model size , the quadratic-complexity attention operation, and the auto-regressive decoding approach. Then, we introduce a comprehensive taxonomy that organizes the current literature into data-level, model-level, and system-level optimization. Can LLM inference be used in resource-constrained scenarios? However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference. A large portion of the previously mentioned knowledge distillation meth-ods fine-tune the Language Model (LM) to replicate the reasoning of a larger model . Thus, it distills the capability of the larger LM into a smaller one. The primary challenge lies not only in the possibility of distilling a larger model ’s reasoning capabilities and knowledge into distinct entities but also in optimizing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.20272", "content": "Jul 25, 2024 · This is non-trivial as prior art on LLM inference cannot be directly applied to early - exit models . In this work, we solves two key challenges in building efficient inference framework for early - exit models : (1) batch inference at iteration-level granularity; and (2) KV cache management. Research into optimization techniques —such as model pruning, quantization , and knowledge distillation —enables the creation of streamlined LLMs that retain high performance while minimizing resource demands. Aug 19, 2025 · Autoregressive large language models (LLMs) have achieved remarkable success across various tasks in Natural Language Processing (NLP), but their high computational cost remains a major bottleneck for efficient inference . Since these models generate text in a token-by-token manner, the depth of computation required for each token directly affects inference speed. To address this issue, early ... Model compression techniques offer a promising solution by reducing model size and inference latency while maintaining high performance. This sur-vey provides a comprehensive overview of the key compression strategies applied to LLMs, including pruning, quantization , knowledge distillation , low-rank factorization, and emerging hybrid approaches. May 18, 2024 · Abstract To enhance the efficiency of language models , it would involve optimizing their training and inference processes to reduce computational demands while maintaining high performance. The research focuses on the application of model compression, quantization , and hardware acceleration techniques to the Llama model . Pruning and knowledge distillation methods effectively reduce the model ... Apr 22, 2024 · Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference ... Does LLM inference take early-exit models into consideration? However, there is no work of LLM inference framework that takes early-exit models into consideration. This is non-trivial as prior art on LLM inference cannot be directly applied to early-exit models. Why is efficient inference important for LLMs? In the AGI era, efficient inference for LLMs is critical to unlocking scalable and accessible applications . While LLMs deliver powerful capabilities, their substantial computational and memory requirements pose significant deployment challenges, particularly in resource-constrained environments. What causes inefficient LLM inference? We start by analyzing the primary causes of the inefficient LLM inference, i.e., the large model size , the quadratic-complexity attention operation, and the auto-regressive decoding approach. Then, we introduce a comprehensive taxonomy that organizes the current literature into data-level, model-level, and system-level optimization. Can LLM inference be used in resource-constrained scenarios? However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference. A large portion of the previously mentioned knowledge distillation meth-ods fine-tune the Language Model (LM) to replicate the reasoning of a larger model . Thus, it distills the capability of the larger LM into a smaller one. The primary challenge lies not only in the possibility of distilling a larger model ’s reasoning capabilities and knowledge into distinct entities but also in optimizing ..."}
+{"idx": 1, "title": "Awesome Efficient LLM Inference Papers - GitHub", "date": "", "ddg_snippet": "Research into optimization techniques —such as model pruning, quantization , and knowledge distillation —enables the creation of streamlined LLMs that retain high performance while minimizing resource demands.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/withhaotian/awesome-efficient-LLM-inference-papers", "content": "Research into optimization techniques —such as model pruning, quantization , and knowledge distillation —enables the creation of streamlined LLMs that retain high performance while minimizing resource demands."}
+{"idx": 2, "title": "Robust and Efficient Early Exit for Large Language Models ...", "date": "", "ddg_snippet": "Aug 19, 2025 · Autoregressive large language models (LLMs) have achieved remarkable success across various tasks in Natural Language Processing (NLP), but their high computational cost remains a major bottleneck for efficient inference . Since these models generate text in a token-by-token manner, the depth of computation required for each token directly affects inference speed. To address this issue, early ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-95-1233-1_7", "content": "Aug 19, 2025 · Autoregressive large language models (LLMs) have achieved remarkable success across various tasks in Natural Language Processing (NLP), but their high computational cost remains a major bottleneck for efficient inference . Since these models generate text in a token-by-token manner, the depth of computation required for each token directly affects inference speed. To address this issue, early ..."}
+{"idx": 3, "title": "Exploring Model Compression Techniques for Ecient Inference ...", "date": "", "ddg_snippet": "Model compression techniques offer a promising solution by reducing model size and inference latency while maintaining high performance. This sur-vey provides a comprehensive overview of the key compression strategies applied to LLMs, including pruning, quantization , knowledge distillation , low-rank factorization, and emerging hybrid approaches.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04997150v1/file/Exploring_Model_Compression_Techniques_for_Efficient_Inference_of_Large_Language_Models.pdf", "content": "Model compression techniques offer a promising solution by reducing model size and inference latency while maintaining high performance. This sur-vey provides a comprehensive overview of the key compression strategies applied to LLMs, including pruning, quantization , knowledge distillation , low-rank factorization, and emerging hybrid approaches."}
+{"idx": 4, "title": "Efficient Training and Inference: Techniques for Large ...", "date": "", "ddg_snippet": "May 18, 2024 · Abstract To enhance the efficiency of language models , it would involve optimizing their training and inference processes to reduce computational demands while maintaining high performance. The research focuses on the application of model compression, quantization , and hardware acceleration techniques to the Llama model . Pruning and knowledge distillation methods effectively reduce the model ...", "subpage_snippet": "", "source": "www.techrxiv.org", "link": "https://www.techrxiv.org/users/783994/articles/943006-efficient-training-and-inference-techniques-for-large-language-models-using-llama", "content": "May 18, 2024 · Abstract To enhance the efficiency of language models , it would involve optimizing their training and inference processes to reduce computational demands while maintaining high performance. The research focuses on the application of model compression, quantization , and hardware acceleration techniques to the Llama model . Pruning and knowledge distillation methods effectively reduce the model ..."}
+{"idx": 5, "title": "A Survey on Efficient Inference for Large Language Models", "date": "", "ddg_snippet": "Apr 22, 2024 · Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.14294", "content": "Apr 22, 2024 · Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference ..."}
+{"idx": 6, "title": "Efficient Knowledge Distillation and Retrieval in Language Models", "date": "", "ddg_snippet": "A large portion of the previously mentioned knowledge distillation meth-ods fine-tune the Language Model (LM) to replicate the reasoning of a larger model . Thus, it distills the capability of the larger LM into a smaller one. The primary challenge lies not only in the possibility of distilling a larger model ’s reasoning capabilities and knowledge into distinct entities but also in optimizing ...", "subpage_snippet": "", "source": "www.apps.ucu.edu.ua", "link": "https://www.apps.ucu.edu.ua/wp-content/uploads/2024/09/MS-AMLV-2024-CR-No-23-Yelisieiev.pdf", "content": "A large portion of the previously mentioned knowledge distillation meth-ods fine-tune the Language Model (LM) to replicate the reasoning of a larger model . Thus, it distills the capability of the larger LM into a smaller one. The primary challenge lies not only in the possibility of distilling a larger model ’s reasoning capabilities and knowledge into distinct entities but also in optimizing ..."}
+{"idx": 7, "title": "UnIT: Scalable Unstructured Inference-Time Pruning for", "date": "", "ddg_snippet": "Inference -time pruning, by contrast, makes decisions based on the input seen at runtime, allowing the model to skip unnecessary computations while ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07885v1", "content": "Inference -time pruning, by contrast, makes decisions based on the input seen at runtime, allowing the model to skip unnecessary computations while ..."}
+{"idx": 8, "title": "A Survey on Efficient Inference for Large Language Models", "date": "", "ddg_snippet": "... Sec. 2 introduces the basic concept and knowledge about LLMs and presents a detailed analysis of the efficiency bottlenecks during the inference ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.14294v3", "content": "... Sec. 2 introduces the basic concept and knowledge about LLMs and presents a detailed analysis of the efficiency bottlenecks during the inference ..."}
+{"idx": 9, "title": "Collaborative Inference and Learning between Edge SLMs and", "date": "", "ddg_snippet": "... task formulations, and model architectures hinders effective knowledge transfer, particularly for distillation and adaptation methods that assume ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16731v1", "content": "... task formulations, and model architectures hinders effective knowledge transfer, particularly for distillation and adaptation methods that assume ..."}
diff --git a/data/sampled_jsons/efficient_inference_techniques_language_models_reduce_overhead_attention_heads.jsonl b/data/sampled_jsons/efficient_inference_techniques_language_models_reduce_overhead_attention_heads.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a04053fc7cd9c3daf2b69aeb84100f7afef7065f
--- /dev/null
+++ b/data/sampled_jsons/efficient_inference_techniques_language_models_reduce_overhead_attention_heads.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Optimizing Attention for Efficient LLM Inference: A Review", "date": "", "ddg_snippet": "To address this issue, researchers have proposed various attention head optimization techniques that reduce the number of attention heads , improve query mecha-nisms, and aggregate correlations to significantly lower com-putational complexity while maintaining or enhancing model performance.", "subpage_snippet": "", "source": "oulurepo.oulu.fi", "link": "https://oulurepo.oulu.fi/bitstream/handle/10024/57525/nbnfioulu-202507015041.pdf?sequence=1", "content": "To address this issue, researchers have proposed various attention head optimization techniques that reduce the number of attention heads , improve query mecha-nisms, and aggregate correlations to significantly lower com-putational complexity while maintaining or enhancing model performance."}
+{"idx": 1, "title": "A Survey on Efficient Inference for Large Language Models LLM inference optimization - avrtt.blog Exploring Model Compression Techniques for Efficient ... Optimizing Inference in Large Language Models: Strategies and ... Analog in-memory computing attention mechanism fast and ... A Survey on Efficient Inference for Large Language Models Analog in- memory computing attention mechanism for fast and ... - … A Survey on Efficient Inference for Large Language Models A Survey on Efficient Inference for Large Language Models Analog in- memory computing attention mechanism for fast and ... - … Analog in- memory computing attention mechanism for fast and ... - … Primer on Large Language Model (LLM) Inference Optimizations ...", "date": "", "ddg_snippet": "Apr 22, 2024 · Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference ... In the coming years, we can expect ongoing breakthroughs that make LLM inference even more efficient , whether through specialized hardware, more advanced multi-tenant caching systems, or new approximate attention algorithms that further reduce the overhead of context processing. Model compression techniques are essential for reducing the computational and memory overhead associated with large language models (LLMs), particularly for efficient inference [29]. Jun 3, 2024 · These optimizations significantly improve the throughput and reduce the latency of transformer-based models , making them more efficient for practical applications. Sep 8, 2025 · Leveraging in-memory computing with emerging gain-cell devices, the authors accelerate attention —a core mechanism in large language models . They train a 1.5-billion-parameter model , achieving up ... Can large language models be used in resource-constrained scenarios? Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Can in-memory computing speed up a large language model? Leveraging in-memory computing with emerging gain-cell devices, the authors accelerate attention—a core mechanism in large language models. They train a 1.5-billion-parameter model, achieving up to a 70,000-fold reduction in energy consumption and a 100-fold speed-up compared with GPUs. Can LLM inference be used in resource-constrained scenarios? However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference. What causes inefficient LLM inference? We start by analyzing the primary causes of the inefficient LLM inference, i.e., the large model size , the quadratic-complexity attention operation, and the auto-regressive decoding approach. Then, we introduce a comprehensive taxonomy that organizes the current literature into data-level, model-level, and system-level optimization. Why is high parallelism important for low-latency inference? In addition, high parallelism is crucial for low-latency inference , while high memory density is needed for scaling to large models . Finally, long retention time is essential to avoid frequent memory refresh operations. Are language models supervised multitask learners? Lepri, N., Glukhov, A., Mannocci, P., Porzani, M. & Ielmini, D. Compact modeling and mitigation of parasitics in crosspoint accelerators of neural networks. IEEE Trans, Electron Devices71, 1900–1906 (2024). Radford, A. et al. Language models are unsupervised multitask learners . Nov 17, 2024 · We also discussed different parallelism strategies - data, model (tensor and pipeline), and task parallelism - that enable efficient scaling of LLM workloads. Building on this foundation, this post will explore model architecture optimizations that can significantly improve inference efficiency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.14294", "content": "Apr 22, 2024 · Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference ... In the coming years, we can expect ongoing breakthroughs that make LLM inference even more efficient , whether through specialized hardware, more advanced multi-tenant caching systems, or new approximate attention algorithms that further reduce the overhead of context processing. Model compression techniques are essential for reducing the computational and memory overhead associated with large language models (LLMs), particularly for efficient inference [29]. Jun 3, 2024 · These optimizations significantly improve the throughput and reduce the latency of transformer-based models , making them more efficient for practical applications. Sep 8, 2025 · Leveraging in-memory computing with emerging gain-cell devices, the authors accelerate attention —a core mechanism in large language models . They train a 1.5-billion-parameter model , achieving up ... Can large language models be used in resource-constrained scenarios? Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Can in-memory computing speed up a large language model? Leveraging in-memory computing with emerging gain-cell devices, the authors accelerate attention—a core mechanism in large language models. They train a 1.5-billion-parameter model, achieving up to a 70,000-fold reduction in energy consumption and a 100-fold speed-up compared with GPUs. Can LLM inference be used in resource-constrained scenarios? However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference. What causes inefficient LLM inference? We start by analyzing the primary causes of the inefficient LLM inference, i.e., the large model size , the quadratic-complexity attention operation, and the auto-regressive decoding approach. Then, we introduce a comprehensive taxonomy that organizes the current literature into data-level, model-level, and system-level optimization. Why is high parallelism important for low-latency inference? In addition, high parallelism is crucial for low-latency inference , while high memory density is needed for scaling to large models . Finally, long retention time is essential to avoid frequent memory refresh operations. Are language models supervised multitask learners? Lepri, N., Glukhov, A., Mannocci, P., Porzani, M. & Ielmini, D. Compact modeling and mitigation of parasitics in crosspoint accelerators of neural networks. IEEE Trans, Electron Devices71, 1900–1906 (2024). Radford, A. et al. Language models are unsupervised multitask learners . Nov 17, 2024 · We also discussed different parallelism strategies - data, model (tensor and pipeline), and task parallelism - that enable efficient scaling of LLM workloads. Building on this foundation, this post will explore model architecture optimizations that can significantly improve inference efficiency."}
+{"idx": 2, "title": "LLM inference optimization - avrtt.blog", "date": "", "ddg_snippet": "In the coming years, we can expect ongoing breakthroughs that make LLM inference even more efficient , whether through specialized hardware, more advanced multi-tenant caching systems, or new approximate attention algorithms that further reduce the overhead of context processing.", "subpage_snippet": "", "source": "avrtt.github.io", "link": "https://avrtt.github.io/research/llm_inference_optimization/", "content": "In the coming years, we can expect ongoing breakthroughs that make LLM inference even more efficient , whether through specialized hardware, more advanced multi-tenant caching systems, or new approximate attention algorithms that further reduce the overhead of context processing."}
+{"idx": 3, "title": "Exploring Model Compression Techniques for Efficient ...", "date": "", "ddg_snippet": "Model compression techniques are essential for reducing the computational and memory overhead associated with large language models (LLMs), particularly for efficient inference [29].", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04997150v1/file/Exploring_Model_Compression_Techniques_for_Efficient_Inference_of_Large_Language_Models.pdf", "content": "Model compression techniques are essential for reducing the computational and memory overhead associated with large language models (LLMs), particularly for efficient inference [29]."}
+{"idx": 4, "title": "Optimizing Inference in Large Language Models: Strategies and ...", "date": "", "ddg_snippet": "Jun 3, 2024 · These optimizations significantly improve the throughput and reduce the latency of transformer-based models , making them more efficient for practical applications.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/optimizing-inference-large-language-models-strategies-ravi-naarla-d6ppc", "content": "Jun 3, 2024 · These optimizations significantly improve the throughput and reduce the latency of transformer-based models , making them more efficient for practical applications."}
+{"idx": 5, "title": "Analog in-memory computing attention mechanism fast and ...", "date": "", "ddg_snippet": "Sep 8, 2025 · Leveraging in-memory computing with emerging gain-cell devices, the authors accelerate attention —a core mechanism in large language models . They train a 1.5-billion-parameter model , achieving up ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s43588-025-00854-1", "content": "Sep 8, 2025 · Leveraging in-memory computing with emerging gain-cell devices, the authors accelerate attention —a core mechanism in large language models . They train a 1.5-billion-parameter model , achieving up ..."}
+{"idx": 6, "title": "Primer on Large Language Model (LLM) Inference Optimizations ...", "date": "", "ddg_snippet": "Nov 17, 2024 · We also discussed different parallelism strategies - data, model (tensor and pipeline), and task parallelism - that enable efficient scaling of LLM workloads. Building on this foundation, this post will explore model architecture optimizations that can significantly improve inference efficiency.", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/primer-on-large-language-model-llm-inference-optimizations-3-model-architecture-optimizations", "content": "Nov 17, 2024 · We also discussed different parallelism strategies - data, model (tensor and pipeline), and task parallelism - that enable efficient scaling of LLM workloads. Building on this foundation, this post will explore model architecture optimizations that can significantly improve inference efficiency."}
+{"idx": 7, "title": "A Survey on Efficient Inference for Large Language Models", "date": "", "ddg_snippet": "by Z Zhou · 2024 · Cited by 194 — This method not only sustains the benefits of MQA in reducing memory overhead but also offers an enhanced balance between inference speed and output quality.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.14294", "content": "by Z Zhou · 2024 · Cited by 194 — This method not only sustains the benefits of MQA in reducing memory overhead but also offers an enhanced balance between inference speed and output quality."}
+{"idx": 8, "title": "Efficient Attention Mechanisms for Large Language Models", "date": "", "ddg_snippet": "25 Jul 2025 — Linear attention methods achieve linear complexity through kernel approximations, recurrent formulations, or fast-weight dynamics, thereby ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.19595v1", "content": "25 Jul 2025 — Linear attention methods achieve linear complexity through kernel approximations, recurrent formulations, or fast-weight dynamics, thereby ..."}
+{"idx": 9, "title": "Efficient Inference of Large Language Models through ...", "date": "", "ddg_snippet": "This survey provides a comprehensive and structured overview of model compression techniques specifically designed to improve the efficiency of large language ...", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202508.0192/v1", "content": "This survey provides a comprehensive and structured overview of model compression techniques specifically designed to improve the efficiency of large language ..."}
diff --git a/data/sampled_jsons/event_interval_independently_EventPS_photometric_stereo_methodology_year_2024.jsonl b/data/sampled_jsons/event_interval_independently_EventPS_photometric_stereo_methodology_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e7d2c0b4e07df13ba56fea1c12c1bb6db5df8d2c
--- /dev/null
+++ b/data/sampled_jsons/event_interval_independently_EventPS_photometric_stereo_methodology_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EvAnimate: Event-conditioned Image-to-Video Generation for", "date": "", "ddg_snippet": "Unlike conventional cameras, each pixel in an event camera operates independently and triggers an event only when a brightness change is detected ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.18552v2", "content": "Unlike conventional cameras, each pixel in an event camera operates independently and triggers an event only when a brightness change is detected ..."}
+{"idx": 1, "title": "PS -EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "This paper proposes Photometric Stereo based on Event Interval Profile ( PS -EIP), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.18341", "content": "This paper proposes Photometric Stereo based on Event Interval Profile ( PS -EIP), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals."}
+{"idx": 2, "title": "PS -EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "Unlike EventPS , which treats each event interval independently , EIP- PS uses a time-series prole to capture relationships between intervals. Eventps : Real-time photometric stereo using an event camera. In CVPR, pages 9602–9611, 2024.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "Unlike EventPS , which treats each event interval independently , EIP- PS uses a time-series prole to capture relationships between intervals. Eventps : Real-time photometric stereo using an event camera. In CVPR, pages 9602–9611, 2024."}
+{"idx": 3, "title": "PS -EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "This paper proposes Photometric Stereo based on Event Interval Profile ( PS -EIP), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile@CVPR2025@CVF", "content": "This paper proposes Photometric Stereo based on Event Interval Profile ( PS -EIP), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals."}
+{"idx": 4, "title": "Procedure of the proposed method : Events are recorded under...", "date": "", "ddg_snippet": "PS -EIP: Robust Photometric Stereo Based on Event Interval Profile.Context 1. ... this paper, we propose a robust method for Lambertian event -based photometric stereo , namely PS -EIP ( Photometric Stereo based on Event Interval Profile) as illustrated in Fig.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Procedure-of-the-proposed-method-Events-are-recorded-under-moving-light-conditions-The_fig1_390142307", "content": "PS -EIP: Robust Photometric Stereo Based on Event Interval Profile.Context 1. ... this paper, we propose a robust method for Lambertian event -based photometric stereo , namely PS -EIP ( Photometric Stereo based on Event Interval Profile) as illustrated in Fig."}
+{"idx": 5, "title": "Event Outlier Detection in Continuous Time", "date": "", "ddg_snippet": "To dene outliers, we simulate commission and omission outliers on top of the existing data. In this way, we can obtain ground-truth labels for testing. Event Outlier Detection in Continuous Time.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/liu21g/liu21g-supp.pdf", "content": "To dene outliers, we simulate commission and omission outliers on top of the existing data. In this way, we can obtain ground-truth labels for testing. Event Outlier Detection in Continuous Time."}
+{"idx": 6, "title": "CVPR 2024 Key Research & Dataset Papers - Part 2", "date": "", "ddg_snippet": "What were the novel methodologies the authors carried out? ... EventPS : Real-Time Photometric Stereo Using an Event Camera", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/cvpr-2024-research-papers/", "content": "What were the novel methodologies the authors carried out? ... EventPS : Real-Time Photometric Stereo Using an Event Camera"}
+{"idx": 7, "title": "A Neural Representation of Sequential States Within an Instructed...", "date": "", "ddg_snippet": "The preceding activity type analysis considered each event or state interval independently , but as can be seen in the example neurons of Fig. 3, single neurons frequently exhibited multiple types of activity at different points in the trial. For instance, the event detecting neuron shown in Fig.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC2997039/", "content": "The preceding activity type analysis considered each event or state interval independently , but as can be seen in the example neurons of Fig. 3, single neurons frequently exhibited multiple types of activity at different points in the trial. For instance, the event detecting neuron shown in Fig."}
+{"idx": 8, "title": "Dipartimento di Informatica, Bioingegneria", "date": "", "ddg_snippet": "3. IEI: the Inter- Event Interval i.e. the maximum time allowed between two events that belongs to two consecutive classes S[i] and S[i + 1] of sequence S to not interrupt the sequence. Then, within a time series T Sn, a particular sequence S is detected if three conditions are true", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/162442336.pdf", "content": "3. IEI: the Inter- Event Interval i.e. the maximum time allowed between two events that belongs to two consecutive classes S[i] and S[i + 1] of sequence S to not interrupt the sequence. Then, within a time series T Sn, a particular sequence S is detected if three conditions are true"}
+{"idx": 9, "title": "Showing new listings for Tuesday, 25 March 2025 · Issue #1547...", "date": "", "ddg_snippet": "PS -EIP: Robust Photometric Stereo Based on Event Interval Profile. Authors: Kazuma Kitazawa, Takahito Aoto, Satoshi Ikehata, Tsuyoshi Takatani.However, EventPS treats each event interval independently , making it sensitive to noise, shadows, and non-Lambertian reflections.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LeeKyungwook/get-arxiv-noti/issues/1547", "content": "PS -EIP: Robust Photometric Stereo Based on Event Interval Profile. Authors: Kazuma Kitazawa, Takahito Aoto, Satoshi Ikehata, Tsuyoshi Takatani.However, EventPS treats each event interval independently , making it sensitive to noise, shadows, and non-Lambertian reflections."}
diff --git a/data/sampled_jsons/expectation_maximization_EM_algorithm_class_distribution_estimation_semi-supervised_learning.jsonl b/data/sampled_jsons/expectation_maximization_EM_algorithm_class_distribution_estimation_semi-supervised_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5387a67094cd222ee80f6e4c697ab785a3656495
--- /dev/null
+++ b/data/sampled_jsons/expectation_maximization_EM_algorithm_class_distribution_estimation_semi-supervised_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EM for Semi-Supervised Learning and Mixture Models", "date": "", "ddg_snippet": "2.1 Expectation Maximization Algorithm If we want to optimize parameters using the EM algorithm , the expected complete-data log-likelihood which can be written as", "subpage_snippet": "", "source": "www.cs.ubc.ca", "link": "https://www.cs.ubc.ca/~schmidtm/Courses/540-W16/EM.pdf", "content": "2.1 Expectation Maximization Algorithm If we want to optimize parameters using the EM algorithm , the expected complete-data log-likelihood which can be written as"}
+{"idx": 1, "title": "Expectation-Maximization Algorithm - ML - GeeksforGeeks", "date": "", "ddg_snippet": "Sep 8, 2025 · The Expectation - Maximization ( EM ) algorithm is a powerful iterative optimization technique used to estimate unknown parameters in probabilistic models, particularly when the data is incomplete, noisy or contains hidden (latent) variables. It works in two steps: E-step ( Expectation Step): Using the current parameter estimates, the algorithm calculates the expected values of the missing or ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/ml-expectation-maximization-algorithm/", "content": "Sep 8, 2025 · The Expectation - Maximization ( EM ) algorithm is a powerful iterative optimization technique used to estimate unknown parameters in probabilistic models, particularly when the data is incomplete, noisy or contains hidden (latent) variables. It works in two steps: E-step ( Expectation Step): Using the current parameter estimates, the algorithm calculates the expected values of the missing or ..."}
+{"idx": 2, "title": "On the Semi-supervised Expectation Maximization - arXiv.org", "date": "", "ddg_snippet": "On the Semi-supervised Expectation Maximization Erixhen Sula The Department of Electrical Engineering and Computer Science MIT Massachusetts, USA esula@mit.edu Abstract—The Expectation Maximization ( EM ) algorithm is widely used as an iterative modification to maximum likelihood estimation when the data is incomplete.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2211.00537v1", "content": "On the Semi-supervised Expectation Maximization Erixhen Sula The Department of Electrical Engineering and Computer Science MIT Massachusetts, USA esula@mit.edu Abstract—The Expectation Maximization ( EM ) algorithm is widely used as an iterative modification to maximum likelihood estimation when the data is incomplete."}
+{"idx": 3, "title": "Expectation Maximization (EM) - TTIC", "date": "", "ddg_snippet": "Expectation Maximization ( EM ) The Expectation Maximization ( EM ) algorithm is one approach to unsuper-vised, semi-supervised , or lightly supervised learning .", "subpage_snippet": "", "source": "home.ttic.edu", "link": "https://home.ttic.edu/~dmcallester/ttic101-07/lectures/em/em.pdf", "content": "Expectation Maximization ( EM ) The Expectation Maximization ( EM ) algorithm is one approach to unsuper-vised, semi-supervised , or lightly supervised learning ."}
+{"idx": 4, "title": "Lecture 10: Expectation-Maximization Algorithm", "date": "", "ddg_snippet": "We repeat until the means converge. The problem of estimating parameters in the absence of labels is known as unsupervised learning . There are many unsupervised learning methods. We will focus on the Expectation Maximization ( EM ) algorithm . Class Labelled Class Unlabelled 8", "subpage_snippet": "", "source": "engineering.purdue.edu", "link": "https://engineering.purdue.edu/ChanGroup/ECE645Notes/StudentLecture10.pdf", "content": "We repeat until the means converge. The problem of estimating parameters in the absence of labels is known as unsupervised learning . There are many unsupervised learning methods. We will focus on the Expectation Maximization ( EM ) algorithm . Class Labelled Class Unlabelled 8"}
+{"idx": 5, "title": "Notes on Semi-Supervised Expectation Maximization", "date": "", "ddg_snippet": "The EM algorithm , rst formalized by Dempster et al. [1], is a statistical method for maximum likelihood parameter estimation . It is particularly useful when the model contains latent variables. This work derives the EM equations for the semi-supervised setting, where we can group the set of random variables into fully and partially observed ones.", "subpage_snippet": "", "source": "zenodo.org", "link": "https://zenodo.org/records/3484301/files/em.pdf", "content": "The EM algorithm , rst formalized by Dempster et al. [1], is a statistical method for maximum likelihood parameter estimation . It is particularly useful when the model contains latent variables. This work derives the EM equations for the semi-supervised setting, where we can group the set of random variables into fully and partially observed ones."}
+{"idx": 6, "title": "(PDF) On the Semi-supervised Expectation Maximization", "date": "", "ddg_snippet": "Nov 1, 2022 · The Expectation Maximization ( EM ) algorithm is widely used as an iterative modification to maximum likelihood estimation when the data is incomplete. We focus on a semi-supervised case to learn ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364987607_On_the_Semi-supervised_Expectation_Maximization", "content": "Nov 1, 2022 · The Expectation Maximization ( EM ) algorithm is widely used as an iterative modification to maximum likelihood estimation when the data is incomplete. We focus on a semi-supervised case to learn ..."}
+{"idx": 7, "title": "Semi-Supervised Learning", "date": "", "ddg_snippet": "EM is a general purpose algorithm for finding the maximum likelihood estimates in latent variable models, iterating over expectation (E-step) and maximization .", "subpage_snippet": "", "source": "www.cis.upenn.edu", "link": "https://www.cis.upenn.edu/~danroth/Teaching/CS446-17/LectureNotesNew/semisupervised/main.pdf", "content": "EM is a general purpose algorithm for finding the maximum likelihood estimates in latent variable models, iterating over expectation (E-step) and maximization ."}
+{"idx": 8, "title": "A Gentle Introduction to Expectation-Maximization (EM ...", "date": "", "ddg_snippet": "28 Aug 2020 — The expectation - maximization algorithm is an approach for performing maximum likelihood estimation in the presence of latent variables.", "subpage_snippet": "", "source": "www.machinelearningmastery.com", "link": "https://www.machinelearningmastery.com/expectation-maximization-em-algorithm/", "content": "28 Aug 2020 — The expectation - maximization algorithm is an approach for performing maximum likelihood estimation in the presence of latent variables."}
+{"idx": 9, "title": "Document Classification Using Expectation Maximization ...", "date": "", "ddg_snippet": "by B Nigam · 2011 · Cited by 25 — The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1112.2028", "content": "by B Nigam · 2011 · Cited by 25 — The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the ..."}
diff --git a/data/sampled_jsons/extragradient_regret_matching_plus_RM+_two_step_smoothing_technique.jsonl b/data/sampled_jsons/extragradient_regret_matching_plus_RM+_two_step_smoothing_technique.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..47a508749e031fcfc39a107232797f3ade7d19de
--- /dev/null
+++ b/data/sampled_jsons/extragradient_regret_matching_plus_RM+_two_step_smoothing_technique.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Last-Iterate Convergence Properties of Regret Matching Algorithms in...", "date": "", "ddg_snippet": "Regret Matching + ( RM+ ) and its variants. Extragradient RM+ and Smooth Predictive RM+ .For a restarted variant of these algorithms, we were able to prove, for the first time for regret matching algorithms, linear last-iterate convergence rates.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "Regret Matching + ( RM+ ) and its variants. Extragradient RM+ and Smooth Predictive RM+ .For a restarted variant of these algorithms, we were able to prove, for the first time for regret matching algorithms, linear last-iterate convergence rates."}
+{"idx": 1, "title": "Regret minimization and applications to solving games Tuomas...", "date": "", "ddg_snippet": "Regret matching plus ( RM+ ). A Common Template for Regret Minimizers.E.g., mirror prox (MP) or excessive gap technique (EGT). O(1/T) convergence instead of CFR’s O(1 / 𝑇). Regret minimization is decentralized, and with optimism it matches the same theoretical rates.", "subpage_snippet": "", "source": "helios.ntua.gr", "link": "https://helios.ntua.gr/pluginfile.php/120262/course/section/13976/SandholmAndZhang.pptx", "content": "Regret matching plus ( RM+ ). A Common Template for Regret Minimizers.E.g., mirror prox (MP) or excessive gap technique (EGT). O(1/T) convergence instead of CFR’s O(1 / 𝑇). Regret minimization is decentralized, and with optimism it matches the same theoretical rates."}
+{"idx": 2, "title": "Regret Matching", "date": "", "ddg_snippet": "Regret Matching We use regret matching to solve Normal-Form Game such as RPS.I implemented regret matching in Python. But at a high level, you mainly implement train, which is broken down into three steps", "subpage_snippet": "", "source": "stevengong.co", "link": "https://stevengong.co/notes/Regret-Matching", "content": "Regret Matching We use regret matching to solve Normal-Form Game such as RPS.I implemented regret matching in Python. But at a high level, you mainly implement train, which is broken down into three steps"}
+{"idx": 3, "title": "Regret Matching +: - Instability, average- and last-iterate convergence...", "date": "", "ddg_snippet": "Our contributions: 1. We show a surprising “failure mode” of RM+ , due to its instability. 2 . We provide two xes: restarting and smoothing . ⇒ New algorithms for game solving: ·O(1/T√) average convergence ·O(1/ T ) best-iterate convergence, last-iterate convergence.", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Our contributions: 1. We show a surprising “failure mode” of RM+ , due to its instability. 2 . We provide two xes: restarting and smoothing . ⇒ New algorithms for game solving: ·O(1/T√) average convergence ·O(1/ T ) best-iterate convergence, last-iterate convergence."}
+{"idx": 4, "title": "Equilibrium Finding in Normal-Form Games via Greedy Regret ...", "date": "", "ddg_snippet": "2 . RM+ (Tammelin 2014). RM+ makes two changes to RM. The first is that a distinction is made between the “true” re - grets and the “guiding” regrets , where the guiding regrets are used to determine the policies on the next iteration.", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/21181/21181-13-25194-1-2-20220628.pdf", "content": "2 . RM+ (Tammelin 2014). RM+ makes two changes to RM. The first is that a distinction is made between the “true” re - grets and the “guiding” regrets , where the guiding regrets are used to determine the policies on the next iteration."}
+{"idx": 5, "title": "Last-Iterate Convergence Properties of Regret - Matching Algorithms in...", "date": "", "ddg_snippet": "We study last-iterate convergence properties of algorithms for solving two -player zero-sum games based on Regret Matching ^+ (RM^+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Last-Iterate-Convergence-Properties-of-Regret-Matching-Algorithms-in-Games-8855ce0e-ac95-483e-8663-8bf3c163b62f", "content": "We study last-iterate convergence properties of algorithms for solving two -player zero-sum games based on Regret Matching ^+ (RM^+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence."}
+{"idx": 6, "title": "Steps to building a Poker AI — Part 4: Regret Matching for...", "date": "", "ddg_snippet": "The regret matching algorithm is used to minimize expected future regrets through self-play.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/steps-to-building-a-poker-ai-part-4-regret-matching-for-rock-paper-scissors-in-python-168411edbb13", "content": "The regret matching algorithm is used to minimize expected future regrets through self-play."}
+{"idx": 7, "title": "(PDF) Equivalence Analysis between Counterfactual Regret ...", "date": "", "ddg_snippet": "Counterfactual Regret Minimization (CFR) is a kind of regret minimization algorithm that minimizes the total regret by minimizing the local counterfactual regrets . CFRs have a fast convergence rate in practice and they have been widely used for.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/73084402/Equivalence_Analysis_between_Counterfactual_Regret_Minimization_and_Online_Mirror_Descent", "content": "Counterfactual Regret Minimization (CFR) is a kind of regret minimization algorithm that minimizes the total regret by minimizing the local counterfactual regrets . CFRs have a fast convergence rate in practice and they have been widely used for."}
+{"idx": 8, "title": "Lecture 15: Extragradient", "date": "", "ddg_snippet": "Extragradient . Idea: Approximate. with a gradient step . Remark: Now the method is explicit!!! ExtraGradient . Warm-up: Bilinear example.", "subpage_snippet": "", "source": "gauthiergidel.github.io", "link": "https://gauthiergidel.github.io/courses/slides/Lecture15.pdf", "content": "Extragradient . Idea: Approximate. with a gradient step . Remark: Now the method is explicit!!! ExtraGradient . Warm-up: Bilinear example."}
+{"idx": 9, "title": "Fictitious Play and Regret Matching (AGT 17) - YouTube", "date": "", "ddg_snippet": "Davidson CSC 383: Algorithmic Game Theory, S23.Week 10 - Monday.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=_XIdEr-wtJg", "content": "Davidson CSC 383: Algorithmic Game Theory, S23.Week 10 - Monday."}
diff --git a/data/sampled_jsons/fairness_in_online_matching_markets.jsonl b/data/sampled_jsons/fairness_in_online_matching_markets.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c27fd1a7d04ba010c1a7b7d1773a98e22906f83e
--- /dev/null
+++ b/data/sampled_jsons/fairness_in_online_matching_markets.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fairness Maximization among", "date": "", "ddg_snippet": "Fairness Maximization among Oine Agents in Online - Matching Markets .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.08934v2", "content": "Fairness Maximization among Oine Agents in Online - Matching Markets ."}
+{"idx": 1, "title": "Fairness Maximization among Offline Agents in Online - Matching ...", "date": "", "ddg_snippet": "Matching markets involve heterogeneous agents (typically from two parties) who are paired for mutual benefit. During the last decade, matching markets have emerged and grown rapidly through the medium of the Internet. They have evolved into a new format, called Online Matching ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/fairness-maximization-among-offline-agents-in-online-matching-markets", "content": "Matching markets involve heterogeneous agents (typically from two parties) who are paired for mutual benefit. During the last decade, matching markets have emerged and grown rapidly through the medium of the Internet. They have evolved into a new format, called Online Matching ..."}
+{"idx": 2, "title": "Promoting Fairness Among Dynamic Agents in Online - Matching ...", "date": "", "ddg_snippet": "Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served. We instead study the objective of *maximizing the minimum matching rate across all online ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0C3bLHwjsY", "content": "Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served. We instead study the objective of *maximizing the minimum matching rate across all online ..."}
+{"idx": 3, "title": "Rawlsian fairness in online bipartite matching | Proceedings of the...", "date": "", "ddg_snippet": "Since fairness has become an important consideration that was ignored in the existing algorithms a collection of online matching algorithms have been developed that give a fair treatment guarantee for one side of the market at the expense of a drop in the operator's profit.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1609/aaai.v37i5.25698?cookieSet=1", "content": "Since fairness has become an important consideration that was ignored in the existing algorithms a collection of online matching algorithms have been developed that give a fair treatment guarantee for one side of the market at the expense of a drop in the operator's profit."}
+{"idx": 4, "title": "Class Fairness in Online Matching | Proceedings of the AAAI...", "date": "", "ddg_snippet": "We initiate the study of fairness among classes of agents in online bipartite matching where there is a given set of offline vertices (aka agents) and another set of vertices (aka items) that arrive online and must be matched irrevocably upon arrival.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/25704", "content": "We initiate the study of fairness among classes of agents in online bipartite matching where there is a given set of offline vertices (aka agents) and another set of vertices (aka items) that arrive online and must be matched irrevocably upon arrival."}
+{"idx": 5, "title": "Fairness Maximization among Offline Agents in Online - Matching ...", "date": "", "ddg_snippet": "During the last decade, matching markets have emerged and grown rapidly through the medium of the Internet. They have evolved into a new format, called Online Matching Markets (OMMs), with examples ranging from crowdsourcing to online recommendations to ridesharing.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/354780525_Fairness_Maximization_among_Offline_Agents_in_Online-Matching_Markets", "content": "During the last decade, matching markets have emerged and grown rapidly through the medium of the Internet. They have evolved into a new format, called Online Matching Markets (OMMs), with examples ranging from crowdsourcing to online recommendations to ridesharing."}
+{"idx": 6, "title": "Class Fairness in Online Matching", "date": "", "ddg_snippet": "For matching indivisible items, we propose an adaptive-priority-based algorithm, MATCH -AND-SHIFT, prove that it achieves 1/2-approximation of both class envy-freeness up to one item and class maximin share fairness , and show that each guarantee is tight.", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/p/arx/papers/2203.03751.html", "content": "For matching indivisible items, we propose an adaptive-priority-based algorithm, MATCH -AND-SHIFT, prove that it achieves 1/2-approximation of both class envy-freeness up to one item and class maximin share fairness , and show that each guarantee is tight."}
+{"idx": 7, "title": "Two-Sided Fairness for Repeated Matchings in Two-Sided Markets ...", "date": "", "ddg_snippet": "2.2 Fairness in Matching Markets . 3 Fairness of Repeated Matchings . Matchings in two-sided markets such as marriage or school admis-sion have received significant attention over the years.", "subpage_snippet": "", "source": "pure.mpg.de", "link": "https://pure.mpg.de/rest/items/item_3217646_3/component/file_3346964/content", "content": "2.2 Fairness in Matching Markets . 3 Fairness of Repeated Matchings . Matchings in two-sided markets such as marriage or school admis-sion have received significant attention over the years."}
+{"idx": 8, "title": "Online Algorithms for Matchings with Proportional Fairness", "date": "", "ddg_snippet": "Matching problems with group- fairness constraints and diversity constraints have numerous applications such as in allocation problems, committee selection, school choice, etc.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/matchings-under-diversity-constraints-1hswky6l.pdf", "content": "Matching problems with group- fairness constraints and diversity constraints have numerous applications such as in allocation problems, committee selection, school choice, etc."}
+{"idx": 9, "title": "Refubium - Fairness and competition in a bilateral matching market", "date": "", "ddg_snippet": "This paper analyzes fairness and bargaining in a dynamic bilateral matching market . Traders from both sides of the market are pairwise matched to share the gains from trade.", "subpage_snippet": "", "source": "refubium.fu-berlin.de", "link": "https://refubium.fu-berlin.de/handle/fub188/44803", "content": "This paper analyzes fairness and bargaining in a dynamic bilateral matching market . Traders from both sides of the market are pairwise matched to share the gains from trade."}
diff --git a/data/sampled_jsons/feature_dimensionality_overfitting_computer_vision_high_order_derivatives_curse_year_2022.jsonl b/data/sampled_jsons/feature_dimensionality_overfitting_computer_vision_high_order_derivatives_curse_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..22394f2504bb139f852dfe3004ab55de571f6b19
--- /dev/null
+++ b/data/sampled_jsons/feature_dimensionality_overfitting_computer_vision_high_order_derivatives_curse_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Curse of dimensionality", "date": "", "ddg_snippet": "The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high - dimensional spaces", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Curse_of_dimensionality", "content": "The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high - dimensional spaces"}
+{"idx": 1, "title": "Functional Data Analysis: A Solution to the Curse of ...", "date": "", "ddg_snippet": "The curse of dimensionality refers to the challenges and difficulties that arise when dealing with high - dimensional datasets in machine ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/functional-data-analysis-a-solution-to-the-curse-of-dimensionality-f83dd19fa6e8", "content": "The curse of dimensionality refers to the challenges and difficulties that arise when dealing with high - dimensional datasets in machine ..."}
+{"idx": 2, "title": "Anant-Net: Breaking the curse of dimensionality with ...", "date": "", "ddg_snippet": "by SS Menon · 2025 · Cited by 4 — Highlights. •. Anant-Net uses tensor product structures and dimension -wise sweeps for efficient high - dimensional PDE solving.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782525006759", "content": "by SS Menon · 2025 · Cited by 4 — Highlights. •. Anant-Net uses tensor product structures and dimension -wise sweeps for efficient high - dimensional PDE solving."}
+{"idx": 3, "title": "High-order Polynomials with Self-supervised Dimension ...", "date": "", "ddg_snippet": "Due to the challenges such as the Curse of Dimensionality , overfitting , and limited computing resources, multivariate higher - order polynomial models have ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.10637v2", "content": "Due to the challenges such as the Curse of Dimensionality , overfitting , and limited computing resources, multivariate higher - order polynomial models have ..."}
+{"idx": 4, "title": "A derivative, integral, and proportional features extractor for ...", "date": "", "ddg_snippet": "by J Leoni · 2024 · Cited by 3 — This issue arises because it produces a distinct set of features for each monitored time-series within the original process, which may lead to the curse of ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197623016949", "content": "by J Leoni · 2024 · Cited by 3 — This issue arises because it produces a distinct set of features for each monitored time-series within the original process, which may lead to the curse of ..."}
+{"idx": 5, "title": "Relief-Based Feature Selection: Introduction and Review", "date": "", "ddg_snippet": "by RJ Urbanowicz · 2018 · Cited by 1493 — Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6299836/", "content": "by RJ Urbanowicz · 2018 · Cited by 1493 — Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in ..."}
+{"idx": 6, "title": "Dimensionality Reduction in Surrogate Modeling", "date": "", "ddg_snippet": "by CKJ Hou · 2022 · Cited by 93 — This is known as the curse of dimensionality . Surrogate models are commonly used to replace costly computational simulations and modeling of ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s41019-022-00193-5", "content": "by CKJ Hou · 2022 · Cited by 93 — This is known as the curse of dimensionality . Surrogate models are commonly used to replace costly computational simulations and modeling of ..."}
+{"idx": 7, "title": "High-order Polynomials with Self-supervised Dimension ...", "date": "", "ddg_snippet": "18 Jan 2025 — Due to the challenges such as the Curse of Dimensionality, overfitting, and limited computing resources, multivariate higher-order polynomial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.10637v1", "content": "18 Jan 2025 — Due to the challenges such as the Curse of Dimensionality, overfitting, and limited computing resources, multivariate higher-order polynomial ..."}
+{"idx": 8, "title": "Introduction to Geometric Deep Learning", "date": "", "ddg_snippet": "High dimensionality : Models related to computer vision or images deal with high - dimensional data, such as images or videos, which can make ...", "subpage_snippet": "", "source": "patricknicolas.substack.com", "link": "https://patricknicolas.substack.com/p/introduction-to-geometric-deep-learning", "content": "High dimensionality : Models related to computer vision or images deal with high - dimensional data, such as images or videos, which can make ..."}
+{"idx": 9, "title": "Review of Dimension Reduction Methods", "date": "", "ddg_snippet": "Computations in high dimensional spaces can become difficult due to the complexity of data which could lead to what is referred to as the curse of ...", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/journal/paperinformation?paperid=111638", "content": "Computations in high dimensional spaces can become difficult due to the complexity of data which could lead to what is referred to as the curse of ..."}
diff --git a/data/sampled_jsons/first_work_non-continuous_loss_stochastic_combinatorial_-ATA_year_2024.jsonl b/data/sampled_jsons/first_work_non-continuous_loss_stochastic_combinatorial_-ATA_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf2617080d7cc63d257fa98d75af82fce92cd8bf
--- /dev/null
+++ b/data/sampled_jsons/first_work_non-continuous_loss_stochastic_combinatorial_-ATA_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi-armed bandit - Wikipedia", "date": "", "ddg_snippet": "... algorithms to minimize regret in both finite and infinite ( asymptotic ) time horizons for both stochastic 1 and non - stochastic 19 arm payoffs.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Multi-armed_bandit", "content": "... algorithms to minimize regret in both finite and infinite ( asymptotic ) time horizons for both stochastic 1 and non - stochastic 19 arm payoffs."}
+{"idx": 1, "title": "Semi-Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "Clearly, testing will continue until some component is found to be not working , or we have tested all components and found them to be working .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.15427v2", "content": "Clearly, testing will continue until some component is found to be not working , or we have tested all components and found them to be working ."}
+{"idx": 2, "title": "Approximate Proportionality in Online Fair Division", "date": "", "ddg_snippet": "Nonetheless, PROP1 has been examined in numerous works [ ACIW19 , AMS20 , BK19 , BS24 , CFS17 ] . ... best of our knowledge, ours is the first work ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03253v1", "content": "Nonetheless, PROP1 has been examined in numerous works [ ACIW19 , AMS20 , BK19 , BS24 , CFS17 ] . ... best of our knowledge, ours is the first work ..."}
+{"idx": 3, "title": "Causality-informed Anomaly Detection in Partially Observable", "date": "", "ddg_snippet": "Therefore, this work addresses a critical research gap by introducing a non -intervention, causality-informed approach to anomaly detection under ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09742v1", "content": "Therefore, this work addresses a critical research gap by introducing a non -intervention, causality-informed approach to anomaly detection under ..."}
+{"idx": 4, "title": "Mind the Gap: Removing the Discretization Gap in Differentiable", "date": "", "ddg_snippet": "By injecting Gumbel noise into the gate selection process, we introduce stochasticity that flattens the loss landscape, improving the optimization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07500v1", "content": "By injecting Gumbel noise into the gate selection process, we introduce stochasticity that flattens the loss landscape, improving the optimization ..."}
+{"idx": 5, "title": "Removal of Redundant Candidate Points for the Exact D-Optimal", "date": "", "ddg_snippet": "However, the same form of the information matrix also appears in the locally optimal design approach for nonlinear models (e.g., Chapter 3 of [ 23 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.00719v1", "content": "However, the same form of the information matrix also appears in the locally optimal design approach for nonlinear models (e.g., Chapter 3 of [ 23 ..."}
+{"idx": 6, "title": "Downloads", "date": "", "ddg_snippet": "A Character-Level Length-Control Algorithm for Non -Autoregressive ... Adaptive Stochastic Variance Reduction for Non -convex Finite-Sum Minimization", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2022", "content": "A Character-Level Length-Control Algorithm for Non -Autoregressive ... Adaptive Stochastic Variance Reduction for Non -convex Finite-Sum Minimization"}
+{"idx": 7, "title": "Alexandre Passos's ML blog", "date": "", "ddg_snippet": "This builds upon the earlier stochastic average gradient approach, which works by storing the gradient of all training examples as well as their sum ...", "subpage_snippet": "", "source": "atpassos.me", "link": "https://atpassos.me/", "content": "This builds upon the earlier stochastic average gradient approach, which works by storing the gradient of all training examples as well as their sum ..."}
+{"idx": 8, "title": "Saket Navlakha @ CSHL", "date": "", "ddg_snippet": "We found that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic .", "subpage_snippet": "", "source": "navlakhalab.net", "link": "https://navlakhalab.net/", "content": "We found that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic ."}
+{"idx": 9, "title": "A simulation-optimization approach for a service- constrained", "date": "", "ddg_snippet": "Because of the uncertainties and non -linearities present in today s multi-level supply chain networks, analyzing performance indicators and decision ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/323102042_A_simulation-optimization_approach_for_a_service-_constrained_multi-echelon_distribution_network", "content": "Because of the uncertainties and non -linearities present in today s multi-level supply chain networks, analyzing performance indicators and decision ..."}
diff --git a/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_n_layer=8_synthetic.jsonl b/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_n_layer=8_synthetic.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3859fcc30c417ef9bceae1b9b1621eb61e37486b
--- /dev/null
+++ b/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_n_layer=8_synthetic.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "understanding_safety_finetuning/ssft at main · fiveai ... - GitHub", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/tree/main/ssft", "content": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning"}
+{"idx": 1, "title": "understanding_safety_finetuning/README.md at main - GitHub", "date": "", "ddg_snippet": "Abstract To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus the specific concepts the task is asked to be performed upon (e.g., a \"cycle\" vs. a \"bomb ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/README.md", "content": "Abstract To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus the specific concepts the task is asked to be performed upon (e.g., a \"cycle\" vs. a \"bomb ..."}
+{"idx": 2, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus the specific concepts the task is asked to be performed upon (e.g., a \"cycle\" vs. a \"bomb\"). Using ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus the specific concepts the task is asked to be performed upon (e.g., a \"cycle\" vs. a \"bomb\"). Using ..."}
+{"idx": 3, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JEflV4nRlH", "content": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design ..."}
+{"idx": 4, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/abs/2407.10264", "content": "Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g ..."}
+{"idx": 5, "title": "PDF arXiv:2407.10264v3 [cs.LG] 21 Aug 2024 - ResearchGate", "date": "", "ddg_snippet": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning, jailbreaks ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Kemal-Oksuz/publication/382271359_What_Makes_and_Breaks_Safety_Fine-tuning_Mechanistic_Study/links/6746f2c43d17281c7de2d06f/What-Makes-and-Breaks-Safety-Fine-tuning-A-Mechanistic-Study.pdf", "content": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning, jailbreaks ..."}
+{"idx": 6, "title": "PDF What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Synthetic setup for systematic study Ideal Objectives (Capture key concepts of safety fine-tuning and jailbreaks): Fine-grained control over generation of safe and unsafe samples to analyze different safety fine-tuning protocols together !! Fine-grained control over generation of different types of jailbreaks !!", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/95726_EwFSkys.pdf", "content": "Synthetic setup for systematic study Ideal Objectives (Capture key concepts of safety fine-tuning and jailbreaks): Fine-grained control over generation of safe and unsafe samples to analyze different safety fine-tuning protocols together !! Fine-grained control over generation of different types of jailbreaks !!"}
+{"idx": 7, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a9bef53eb7b0e5950d4f2d9c74a16006-Abstract-Conference.html", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space."}
+{"idx": 8, "title": "Safety evaluation for fine-tuning (preview) - Azure OpenAI", "date": "", "ddg_snippet": "After training completes but before the fine-tuned model is available for deployment, the resulting model is evaluated for potentially harmful responses using Azure's built-in risk and safety metrics. Using the same approach to testing that we use for the base large language models, our evaluation capability simulates a conversation with your fine-tuned model to assess the potential to ...", "subpage_snippet": "", "source": "learn.microsoft.com", "link": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/fine-tuning-safety-evaluation", "content": "After training completes but before the fine-tuned model is available for deployment, the resulting model is evaluated for potentially harmful responses using Azure's built-in risk and safety metrics. Using the same approach to testing that we use for the base large language models, our evaluation capability simulates a conversation with your fine-tuned model to assess the potential to ..."}
+{"idx": 9, "title": "Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning ...", "date": "", "ddg_snippet": "To address this challenge, we propose Safe LoRA, a simple one-liner patch to the original LoRA implementation by introducing the projection of LoRA weights from selected layers to the safety-aligned subspace, effectively reducing the safety risks in LLM fine-tuning while maintaining utility.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.16833v2", "content": "To address this challenge, we propose Safe LoRA, a simple one-liner patch to the original LoRA implementation by introducing the projection of LoRA weights from selected layers to the safety-aligned subspace, effectively reducing the safety risks in LLM fine-tuning while maintaining utility."}
diff --git a/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuningblobmainpretrain.py.jsonl b/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuningblobmainpretrain.py.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd74e6e457d9b558fe2860361cd5119ccc327f56
--- /dev/null
+++ b/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuningblobmainpretrain.py.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "understanding_safety_finetuning/pretrain.py at main · fiveai ... - GitHub", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/pretrain.py", "content": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning"}
+{"idx": 1, "title": "FiveAI - GitHub", "date": "", "ddg_snippet": "Python 30 6 0 0 Updated on Mar 26 MoCaE Public The official implementation of \"MoCaE: Mixture of Calibrated Experts Significantly Improves Accuracy in Object Detection\" Python 42 4 2 1 Updated on Mar 25 understanding_safety_finetuning Public Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai", "content": "Python 30 6 0 0 Updated on Mar 26 MoCaE Public The official implementation of \"MoCaE: Mixture of Calibrated Experts Significantly Improves Accuracy in Object Detection\" Python 42 4 2 1 Updated on Mar 25 understanding_safety_finetuning Public Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024)"}
+{"idx": 2, "title": "Actions · fiveai/understanding_safety_finetuning · GitHub", "date": "", "ddg_snippet": "GitHub Actions supports Node.js, Python, Java, Ruby, PHP, Go, Rust, .NET, and more. Build, test, and deploy applications in your language of choice. Live logs", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/actions", "content": "GitHub Actions supports Node.js, Python, Java, Ruby, PHP, Go, Rust, .NET, and more. Build, test, and deploy applications in your language of choice. Live logs"}
+{"idx": 3, "title": "understanding_safety_finetuning/run.sh at main · fiveai ... - GitHub", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/run.sh", "content": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning"}
+{"idx": 4, "title": "GitHub - fiveai/understanding_safety_finetuning: Official Code for What ...", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space."}
+{"idx": 5, "title": "Releases · fiveai/understanding_safety_finetuning - GitHub", "date": "", "ddg_snippet": "You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/releases", "content": "You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs ..."}
+{"idx": 6, "title": "understanding_safety_finetuning/README.md at main · fiveai ... - GitHub", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/README.md", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space."}
+{"idx": 7, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning, jailbreaks, and adversarial attacks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JEflV4nRlH", "content": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning, jailbreaks, and adversarial attacks."}
+{"idx": 8, "title": "Laying Out the Challenges in AI Safety | by Team Five - Medium", "date": "", "ddg_snippet": "We lay out the challenges of demonstrating the safety of AI systems within self-driving or autonomous vehicles and give an overview of how to solve them.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/fiveai/laying-out-the-challenges-in-ai-safety-9f51f91107ea", "content": "We lay out the challenges of demonstrating the safety of AI systems within self-driving or autonomous vehicles and give an overview of how to solve them."}
+{"idx": 9, "title": "Understanding and Using Supervised Fine-Tuning (SFT) for ... - Substack", "date": "", "ddg_snippet": "One of the most widely-used forms of fine-tuning for LLMs within recent AI research is supervised fine-tuning (SFT). This approach curates a dataset of high-quality LLM outputs over which the model is directly fine-tuned using a standard language modeling objective. SFT is simple/cheap to use and a useful tool for aligning language models, which has made is popular within the open-source LLM ...", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/understanding-and-using-supervised", "content": "One of the most widely-used forms of fine-tuning for LLMs within recent AI research is supervised fine-tuning (SFT). This approach curates a dataset of high-quality LLM outputs over which the model is directly fine-tuned using a standard language modeling objective. SFT is simple/cheap to use and a useful tool for aligning language models, which has made is popular within the open-source LLM ..."}
diff --git a/data/sampled_jsons/github_YOpa6dTrpt_Pedestrian_Motion_Reconstruction_SLHAMR_PA-MPJPE.jsonl b/data/sampled_jsons/github_YOpa6dTrpt_Pedestrian_Motion_Reconstruction_SLHAMR_PA-MPJPE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ee04e5e8ec19a510977d05f2325061122d9cd700
--- /dev/null
+++ b/data/sampled_jsons/github_YOpa6dTrpt_Pedestrian_Motion_Reconstruction_SLHAMR_PA-MPJPE.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "P M RECONSTRUCTION: A LARGE SCALE BENCHMARK VIA MIXED REALITY ...", "date": "", "ddg_snippet": "We introduce the Pedestrian Motion Reconstruction (PMR) dataset, which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and modalities. PMR is developed from a mixed reality platform that combines real-world realism with the extensive, accurate labels of simulations, thereby reducing costs and risks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=YOpa6dTrpt", "content": "We introduce the Pedestrian Motion Reconstruction (PMR) dataset, which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and modalities. PMR is developed from a mixed reality platform that combines real-world realism with the extensive, accurate labels of simulations, thereby reducing costs and risks."}
+{"idx": 1, "title": "GitHub - PKU-VCL-3DV/SLAM3R: [CVPR 2025 Highlight] Real-time ...", "date": "", "ddg_snippet": "SLAM3R is a real-time dense scene reconstruction system that regresses 3D points from video frames using feed-forward neural networks, without explicitly estimating camera parameters.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/PKU-VCL-3DV/SLAM3R", "content": "SLAM3R is a real-time dense scene reconstruction system that regresses 3D points from video frames using feed-forward neural networks, without explicitly estimating camera parameters."}
+{"idx": 2, "title": "GitHub - Active-SLAM/Active-SLAM-Paper-List: This repository ...", "date": "", "ddg_snippet": "The autonomous robotic system is a key objective pursued by many researchers in the field of robotics. In recent years, advancements in state estimators, motion planning algorithms, 3D representations, and even large language models (LLMs) have led to a surge of efforts aimed at achieving autonomy through Active SLAM.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Active-SLAM/Active-SLAM-Paper-List", "content": "The autonomous robotic system is a key objective pursued by many researchers in the field of robotics. In recent years, advancements in state estimators, motion planning algorithms, 3D representations, and even large language models (LLMs) have led to a surge of efforts aimed at achieving autonomy through Active SLAM."}
+{"idx": 3, "title": "3d-reconstruction · GitHub Topics · GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/3d-reconstruction", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 4, "title": "HumanMM :Global Human Motion Recovery from Multi-shot Videos", "date": "", "ddg_snippet": "As illustrated in the table below, our proposed method has achieved the best performance for PA &WA- MPJPE , RTE and ROE through videos with all numbers of shots across msaist and msh36m, indicating that our method reconstructs both the global human motion and orientations in the world coordinates more accurately and robustly.", "subpage_snippet": "", "source": "zhangyuhong01.github.io", "link": "https://zhangyuhong01.github.io/HumanMM/", "content": "As illustrated in the table below, our proposed method has achieved the best performance for PA &WA- MPJPE , RTE and ROE through videos with all numbers of shots across msaist and msh36m, indicating that our method reconstructs both the global human motion and orientations in the world coordinates more accurately and robustly."}
+{"idx": 5, "title": "GitHub - OpenMotionLab/MotionGPT: [NeurIPS 2023] MotionGPT ...", "date": "", "ddg_snippet": "Jun 20, 2023 · MotionGPT is a unified and user-friendly motion -language model to learn the semantic coupling of two modalities and generate high-quality motions and text descriptions on multiple motion tasks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OpenMotionLab/MotionGPT", "content": "Jun 20, 2023 · MotionGPT is a unified and user-friendly motion -language model to learn the semantic coupling of two modalities and generate high-quality motions and text descriptions on multiple motion tasks."}
+{"idx": 6, "title": "Pedestrian Motion Reconstruction : A Large-scale... | OpenReview", "date": "", "ddg_snippet": "Pedestrian Motion Reconstruction : A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YOpa6dTrpt", "content": "Pedestrian Motion Reconstruction : A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities."}
+{"idx": 7, "title": "GitHub - karfly/learnable-triangulation-pytorch: This repository is an...", "date": "", "ddg_snippet": "Panoptic studio: A massively multiview system for social interaction capture. [4] D. Tome, M. Toso, L. Agapito, and C. Russell. Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karfly/learnable-triangulation-pytorch", "content": "Panoptic studio: A massively multiview system for social interaction capture. [4] D. Tome, M. Toso, L. Agapito, and C. Russell. Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture."}
+{"idx": 8, "title": "RopeTP: Global Human Motion Recovery via Integrating Robust Pose...", "date": "", "ddg_snippet": "The authors demonstrate that their approach, RopeTP, outperforms existing methods in terms of various metrics, including MPJPE and PA - MPJPE , indicating its effectiveness in reconstructing human body meshes under difficult conditions .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-RopeTP-Global-Human-cm2uxqtt423jz018fmltl4s9s", "content": "The authors demonstrate that their approach, RopeTP, outperforms existing methods in terms of various metrics, including MPJPE and PA - MPJPE , indicating its effectiveness in reconstructing human body meshes under difficult conditions ."}
+{"idx": 9, "title": "HaWoR: World-Space Hand Motion Reconstruction from Egocentric...", "date": "", "ddg_snippet": "Such limitation prohibits their direct use in egocentric video settings, where hands and camera are continuously in motion . In this work, we propose HaWoR, a high-fidelity method for hand motion reconstruction in world coordinates from egocentric videos.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02973v1", "content": "Such limitation prohibits their direct use in egocentric video settings, where hands and camera are continuously in motion . In this work, we propose HaWoR, a high-fidelity method for hand motion reconstruction in world coordinates from egocentric videos."}
diff --git a/data/sampled_jsons/gradient-free_inference-time_alignment_diffusion_models_classifier-free_guidance.jsonl b/data/sampled_jsons/gradient-free_inference-time_alignment_diffusion_models_classifier-free_guidance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5418ed9fb41bfbe17300369c17a84c92714b12e8
--- /dev/null
+++ b/data/sampled_jsons/gradient-free_inference-time_alignment_diffusion_models_classifier-free_guidance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dynamic Search for Inference - Time Alignment in Diffusion Models", "date": "", "ddg_snippet": "Freedom : Training- free energy- guided conditional diffusion model . In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23174–23184, 2023.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.02039v2", "content": "Freedom : Training- free energy- guided conditional diffusion model . In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23174–23184, 2023."}
+{"idx": 1, "title": "(PDF) Reward- Guided Controlled Generation for Inference - Time ...", "date": "", "ddg_snippet": "Classifier - free diffusion guidance . arXiv preprint arXiv:2207.12598.Some gradient - free guidance methods have been developed, but they often struggle to achieve optimal inference - time alignment .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388080935_Reward-Guided_Controlled_Generation_for_Inference-Time_Alignment_in_Diffusion_Models_Tutorial_and_Review", "content": "Classifier - free diffusion guidance . arXiv preprint arXiv:2207.12598.Some gradient - free guidance methods have been developed, but they often struggle to achieve optimal inference - time alignment ."}
+{"idx": 2, "title": "[Literature Review] Dynamic Search for Inference - Time Alignment in...", "date": "", "ddg_snippet": "Diffusion Models . Inference - Time Alignment . Gradient - Free Guidance . Aesthetic Score. Reward Functions.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/dynamic-search-for-inference-time-alignment-in-diffusion-models", "content": "Diffusion Models . Inference - Time Alignment . Gradient - Free Guidance . Aesthetic Score. Reward Functions."}
+{"idx": 3, "title": "GitHub - xie-lab-ml/awesome- alignment -of- diffusion - models : The...", "date": "", "ddg_snippet": "Training- Free Diffusion Model Alignment with Sampling Demons. Inference - Time Alignment in Diffusion Models with Reward- Guided Generation: Tutorial and Review. arXiv 2025, [pdf].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xie-lab-ml/awesome-alignment-of-diffusion-models", "content": "Training- Free Diffusion Model Alignment with Sampling Demons. Inference - Time Alignment in Diffusion Models with Reward- Guided Generation: Tutorial and Review. arXiv 2025, [pdf]."}
+{"idx": 4, "title": "Inference - Time Alignment of Diffusion Models with Direct... | alphaXiv", "date": "", "ddg_snippet": "alphaXiv. Go Home. Inference - Time Alignment of Diffusion Models with Direct Noise Optimization.Loss- guided diffusion models for plug-and-play controllable generation. In International Conference on Machine Learning, pages 32483–32498.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2405.18881v3", "content": "alphaXiv. Go Home. Inference - Time Alignment of Diffusion Models with Direct Noise Optimization.Loss- guided diffusion models for plug-and-play controllable generation. In International Conference on Machine Learning, pages 32483–32498."}
+{"idx": 5, "title": "Inference - Time Alignment Control for Diffusion Models with...", "date": "", "ddg_snippet": "We introduce Reinforcement Learning Guidance (RLG), aninference- time method that adapts Classifier - Free Guidance (CFG) by combiningthe outputs of the base and RL fine-tuned models via a geometric average.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/632917/inference-time-alignment-control-for-diffusion-models-with-reinforcement-learning-guidance", "content": "We introduce Reinforcement Learning Guidance (RLG), aninference- time method that adapts Classifier - Free Guidance (CFG) by combiningthe outputs of the base and RL fine-tuned models via a geometric average."}
+{"idx": 6, "title": "How do you condition diffusion models for text-to-image generation?", "date": "", "ddg_snippet": "A key technique here is classifier - free guidance , which improves control over the generated output by training the model to operate in two modes: one where it uses the text prompt and another where it ignores it (using a “null” prompt).", "subpage_snippet": "", "source": "milvus.io", "link": "https://milvus.io/ai-quick-reference/how-do-you-condition-diffusion-models-for-texttoimage-generation", "content": "A key technique here is classifier - free guidance , which improves control over the generated output by training the model to operate in two modes: one where it uses the text prompt and another where it ignores it (using a “null” prompt)."}
+{"idx": 7, "title": "Inference - Time Diffusion Model Distillation-Bohrium", "date": "", "ddg_snippet": "Classifier - Free Guidance (CFG) is a critical technique for enhancing the sample quality of visual generative models .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/inference-time-diffusion-model-distillation/1074574794407542880-108597", "content": "Classifier - Free Guidance (CFG) is a critical technique for enhancing the sample quality of visual generative models ."}
+{"idx": 8, "title": "Normalized Attention Guidance : Universal Negative Guidance for...", "date": "", "ddg_snippet": "While Classifier - Free Guidance (CFG) works well in standard settings, it fails under aggressive sampling step compression due to divergent predictions between positive and negative branches.", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/dashboard/paper/7d61cb12-fa19-470a-9c40-c2769ed39bcf", "content": "While Classifier - Free Guidance (CFG) works well in standard settings, it fails under aggressive sampling step compression due to divergent predictions between positive and negative branches."}
+{"idx": 9, "title": "A high level introduction to Diffusion Models | by Sreekar... | GoPenAI", "date": "", "ddg_snippet": "Updated inference in classifier - free guidance models (Image Source: Ho and Salimans (2022)).We have described diffusion models from DDPM, DDIM to IDDPM at a high level. We have glossed over the key concepts of guidance in diffusion models .", "subpage_snippet": "", "source": "blog.gopenai.com", "link": "https://blog.gopenai.com/a-high-level-introduction-to-diffusion-models-d5b502c3b1ef", "content": "Updated inference in classifier - free guidance models (Image Source: Ho and Salimans (2022)).We have described diffusion models from DDPM, DDIM to IDDPM at a high level. We have glossed over the key concepts of guidance in diffusion models ."}
diff --git a/data/sampled_jsons/group_privacy_differential_privacy_bounds_machine_learning_2024_year_2024.jsonl b/data/sampled_jsons/group_privacy_differential_privacy_bounds_machine_learning_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ebbb6faee5d4484d010900198f5d01b2817a0c04
--- /dev/null
+++ b/data/sampled_jsons/group_privacy_differential_privacy_bounds_machine_learning_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Differential privacy - Wikipedia", "date": "", "ddg_snippet": "An informal definition of differential privacy . Differential privacy is a mathematically rigorous framework for releasing statistical information about datasets while protecting the privacy of individual data subjects.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Differential_privacy", "content": "An informal definition of differential privacy . Differential privacy is a mathematically rigorous framework for releasing statistical information about datasets while protecting the privacy of individual data subjects."}
+{"idx": 1, "title": "New Research on Differential Privacy part1( Machine Learning 2024 )", "date": "", "ddg_snippet": "Distributed privacy -preserving estimators are proposed and their risk properties are investigated. Matching minimax lower bounds , up to a logarithmic factor, are established for both global and pointwise estimation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@monocosmo77/new-research-on-differential-privacy-part1-machine-learning-2024-1c4daf777af7", "content": "Distributed privacy -preserving estimators are proposed and their risk properties are investigated. Matching minimax lower bounds , up to a logarithmic factor, are established for both global and pointwise estimation."}
+{"idx": 2, "title": "Federated Learning and Differential Privacy : A Simplified... - Freedium", "date": "", "ddg_snippet": "Differential Privacy is a mathematical framework designed to protect individual data within a dataset. It provides formal guarantees that the presence or absence of any single data point does not significantly affect the outcome of analysis. Key Principles of Differential Privacy .", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/380049f55040", "content": "Differential Privacy is a mathematical framework designed to protect individual data within a dataset. It provides formal guarantees that the presence or absence of any single data point does not significantly affect the outcome of analysis. Key Principles of Differential Privacy ."}
+{"idx": 3, "title": "(PDF) Privacy Auditing in Differential Private Machine Learning : The...", "date": "", "ddg_snippet": "Differentially Private Machine Learning . In Advances in Neural Information Processing Systems; Oh, A.H., Belgrave, A., Cho, K., Eds.177. Koskela, A.; Mohammadi, J. Black Box Differential Privacy Auditing Using Total Variation Distance. arXiv 2024 , arXiv", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387930836_Privacy_Auditing_in_Differential_Private_Machine_Learning_The_Current_Trends", "content": "Differentially Private Machine Learning . In Advances in Neural Information Processing Systems; Oh, A.H., Belgrave, A., Cho, K., Eds.177. Koskela, A.; Mohammadi, J. Black Box Differential Privacy Auditing Using Total Variation Distance. arXiv 2024 , arXiv"}
+{"idx": 4, "title": "Differential Privacy in Machine Learning : From Symbolic AI to LLMs", "date": "", "ddg_snippet": "Differential privacy , Machine Learning , Artificial Intelligence, Artificial Neural Networks, Deep Learning , Natural Language Processing, Large Language Models, Security Threats, Defense Mechanisms. 2024 . Evaluations of Machine Learning Privacy Defenses are Misleading.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.11687v1", "content": "Differential privacy , Machine Learning , Artificial Intelligence, Artificial Neural Networks, Deep Learning , Natural Language Processing, Large Language Models, Security Threats, Defense Mechanisms. 2024 . Evaluations of Machine Learning Privacy Defenses are Misleading."}
+{"idx": 5, "title": "Differentially Private Stochastic Gradient Descent with Fixed-Size...", "date": "", "ddg_snippet": "# This paper is crucial for researchers in differential privacy and machine learning . It offers tighter privacy guarantees for a widely-used deep learning training method, improving the efficiency and practicality of private AI development.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/tjskngasmy/", "content": "# This paper is crucial for researchers in differential privacy and machine learning . It offers tighter privacy guarantees for a widely-used deep learning training method, improving the efficiency and practicality of private AI development."}
+{"idx": 6, "title": "Latest Research on Differentially Privacy part2( Machine Learning ...)", "date": "", "ddg_snippet": "Original Source Here Renyi Differential Privacy in the Shuffle Model: Enhanced Amplification Bounds (arXiv)Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine -Driven RSS Bot.January 23, 2024 .", "subpage_snippet": "", "source": "ramseyelbasheer.io", "link": "https://ramseyelbasheer.io/2024/01/23/latest-research-on-differentially-privacy-part2machine-learning-2024/", "content": "Original Source Here Renyi Differential Privacy in the Shuffle Model: Enhanced Amplification Bounds (arXiv)Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine -Driven RSS Bot.January 23, 2024 ."}
+{"idx": 7, "title": "Differential privacy guarantees for analytics and...", "date": "", "ddg_snippet": "Published - 2024 . Keywords. differential privacy .Mueller TT, Usynin D, Paetzold JC, Braren R , Rueckert D, Kaissis G. DIFFERENTIAL PRIVACY GUARANTEES FOR ANALYTICS AND MACHINE LEARNING ON GRAPHS: A SURVEY OF RESULTS.", "subpage_snippet": "", "source": "portal.fis.tum.de", "link": "https://portal.fis.tum.de/en/publications/differential-privacy-guarantees-for-analytics-and-machine-learnin", "content": "Published - 2024 . Keywords. differential privacy .Mueller TT, Usynin D, Paetzold JC, Braren R , Rueckert D, Kaissis G. DIFFERENTIAL PRIVACY GUARANTEES FOR ANALYTICS AND MACHINE LEARNING ON GRAPHS: A SURVEY OF RESULTS."}
+{"idx": 8, "title": "Local Differential Privacy and Fairness in Machine Learning", "date": "", "ddg_snippet": "Differential privacy (DP) is a popular method to protect sensitive data when using machine learning . There are different ways to apply DP, and one way is Local Differential Privacy (LDP). This approach is especially useful when the data collector cannot be trusted.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-08-08-local-differential-privacy-and-fairness-in-machine-learning--a31vzlr", "content": "Differential privacy (DP) is a popular method to protect sensitive data when using machine learning . There are different ways to apply DP, and one way is Local Differential Privacy (LDP). This approach is especially useful when the data collector cannot be trusted."}
+{"idx": 9, "title": "Rényi Pufferfish Privacy : General Additive Noise Mechanisms and...", "date": "", "ddg_snippet": "International Conference on Machine Learning (ICML 2024 ), 2024 , Vienna (Austria), Austria. hal-04363020v2.Pufferfish privacy is a flexible generalization of differential privacy that allows to model arbitrary secrets and adversary’s prior knowledge about the data.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-04363020/document", "content": "International Conference on Machine Learning (ICML 2024 ), 2024 , Vienna (Austria), Austria. hal-04363020v2.Pufferfish privacy is a flexible generalization of differential privacy that allows to model arbitrary secrets and adversary’s prior knowledge about the data."}
diff --git a/data/sampled_jsons/height_of_3412_pattern_permutation_definition.jsonl b/data/sampled_jsons/height_of_3412_pattern_permutation_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7a5f043476cd201ab1374b6dd023fdceb3d392a6
--- /dev/null
+++ b/data/sampled_jsons/height_of_3412_pattern_permutation_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Skew-merged permutation - Wikipedia", "date": "", "ddg_snippet": "In the theory of permutation patterns , a skew-merged permutation is a permutation that can be partitioned into an increasing sequence and a decreasing sequence. They were first studied by Stankova and given their name by Atkinson.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Skew-merged_permutation", "content": "In the theory of permutation patterns , a skew-merged permutation is a permutation that can be partitioned into an increasing sequence and a decreasing sequence. They were first studied by Stankova and given their name by Atkinson."}
+{"idx": 1, "title": "Fibonacci numbers, reduced decompositions and 321/ 3412 pattern", "date": "", "ddg_snippet": "Connections between permutation patterns and reduced decompositions have been studied in [1], [8], [7] and [3]. In [8], Tenner shows the following: Theorem 1.5. (Tenner) π ∈ Sn avoids 321 and 3412 if and only if π has a reduced decomposition that contains no repeated elements.", "subpage_snippet": "", "source": "cs.du.edu", "link": "https://cs.du.edu/~mathfiles/preprints/nsm-math-preprint-0903.pdf", "content": "Connections between permutation patterns and reduced decompositions have been studied in [1], [8], [7] and [3]. In [8], Tenner shows the following: Theorem 1.5. (Tenner) π ∈ Sn avoids 321 and 3412 if and only if π has a reduced decomposition that contains no repeated elements."}
+{"idx": 2, "title": "(PDF) Simple permutations of the classes Av(321, 3412 ) and Av(321...", "date": "", "ddg_snippet": "A permutation is called simple if its only blocks i.e. subsets of the permutation consist of singleton and the permutation itself.that the number of simple permutations an of the pattern class Av(321, 3412 ) follows the recurrence.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/50923725_Simple_permutations_of_the_classes_Av321_3412_and_Av321_4123_have_polynomial_growth", "content": "A permutation is called simple if its only blocks i.e. subsets of the permutation consist of singleton and the permutation itself.that the number of simple permutations an of the pattern class Av(321, 3412 ) follows the recurrence."}
+{"idx": 3, "title": "Pattern heights and the minimal power of q in a Kazhdan–Lusztig...", "date": "", "ddg_snippet": "Keywords: Kazhdan–Lusztig polynomial, permutation pattern , Bruhat order.where mHeight(w) denotes the minimum height of a 3412 pattern in w.", "subpage_snippet": "", "source": "fpsac2024.rub.de", "link": "https://fpsac2024.rub.de/public/extended_abstracts/gaetz.pdf", "content": "Keywords: Kazhdan–Lusztig polynomial, permutation pattern , Bruhat order.where mHeight(w) denotes the minimum height of a 3412 pattern in w."}
+{"idx": 4, "title": "Reducing to permutations neither beginning with n", "date": "", "ddg_snippet": "Abstract We study the {1234, 3412 }- pattern -replacement equivalence relation on the set Sn of permutations of length n, which is conceptually similar to the Knuth relation.", "subpage_snippet": "", "source": "cs.uwaterloo.ca", "link": "https://cs.uwaterloo.ca/journals/JIS/VOL23/Zhang/zhang6.pdf", "content": "Abstract We study the {1234, 3412 }- pattern -replacement equivalence relation on the set Sn of permutations of length n, which is conceptually similar to the Knuth relation."}
+{"idx": 5, "title": "Patterns in Permutations and Words - ReadingSample", "date": "", "ddg_snippet": "For the pattern 3412 , the height of its occurrence in a permu - tation is the dierence between the rst and the last letters. Example 2.3.14. There are four occurrences of the pattern 3412 in the permutation 461523: 4612, 4613, 4623 and 4523 of heights 2, 1, 1 and 1, respectively.", "subpage_snippet": "", "source": "beckassets.blob.core.windows.net", "link": "https://beckassets.blob.core.windows.net/product/readingsample/6715696/9783642173325_excerpt_001.pdf", "content": "For the pattern 3412 , the height of its occurrence in a permu - tation is the dierence between the rst and the last letters. Example 2.3.14. There are four occurrences of the pattern 3412 in the permutation 461523: 4612, 4613, 4623 and 4523 of heights 2, 1, 1 and 1, respectively."}
+{"idx": 6, "title": "Azam Faqruddin: Pattern avoidance and iterated fiber bundle...", "date": "", "ddg_snippet": "In this talk, we will discuss the correspondence between the set of permutations avoiding the patterns 3412 , 52341, 52431 and 53241, and the set of Schubert varieties in the complete flag variety which are iterated fiber bundles of Grassmannians or Grassmannian Schubert divisors.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/30455922aeb98a4fb28b3b6fcc8eb028/", "content": "In this talk, we will discuss the correspondence between the set of permutations avoiding the patterns 3412 , 52341, 52431 and 53241, and the set of Schubert varieties in the complete flag variety which are iterated fiber bundles of Grassmannians or Grassmannian Schubert divisors."}
+{"idx": 7, "title": "Enumeration of permutations", "date": "", "ddg_snippet": "Definitions and prerequisites. Permutations and permutation classes.We have characterized the structure of simple permutations having extreme patterns 2413, 3142 and 3412 .", "subpage_snippet": "", "source": "objects.lib.uidaho.edu", "link": "https://objects.lib.uidaho.edu/etd/pdf/Ikeda_idaho_0089E_10694.pdf", "content": "Definitions and prerequisites. Permutations and permutation classes.We have characterized the structure of simple permutations having extreme patterns 2413, 3142 and 3412 ."}
+{"idx": 8, "title": "(PDF) Bivariate generating functions for involutions restricted by 3412", "date": "", "ddg_snippet": "We study bivariate generating functions for the number of involutions in S n subject to two restrictions. One restriction is that the involution avoid 3412 or contain 3412 exactly once.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/63178127/Bivariate_generating_functions_for_involutions_restricted_by_3412", "content": "We study bivariate generating functions for the number of involutions in S n subject to two restrictions. One restriction is that the involution avoid 3412 or contain 3412 exactly once."}
+{"idx": 9, "title": "Enumeration of pattern -avoiding alternating sign matrices: An...", "date": "", "ddg_snippet": "of pattern -avoiding permutations grows at most exponentially. More specifically, we can. extend our definition of permutation containment and avoidance to the set of all matrices of.2. Definitions and notation. We let Sn denote the set of permutations of size n, and we define S = Sn.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07662", "content": "of pattern -avoiding permutations grows at most exponentially. More specifically, we can. extend our definition of permutation containment and avoidance to the set of all matrices of.2. Definitions and notation. We let Sn denote the set of permutations of size n, and we define S = Sn."}
diff --git a/data/sampled_jsons/hierarchical_overlapping_clustering_objective_optimization_before2024.jsonl b/data/sampled_jsons/hierarchical_overlapping_clustering_objective_optimization_before2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..16573ddb2f479be50a2706196acdc30bb714637a
--- /dev/null
+++ b/data/sampled_jsons/hierarchical_overlapping_clustering_objective_optimization_before2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A hierarchical clustering algorithm for addressing multi-modal multi ...", "date": "", "ddg_snippet": "Secondary clustering is then performed in the objective space to assist in populating the multi- objective population, improving the search direction of the optimization algorithm and maintaining the diversity of the objective space.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424025776", "content": "Secondary clustering is then performed in the objective space to assist in populating the multi- objective population, improving the search direction of the optimization algorithm and maintaining the diversity of the objective space."}
+{"idx": 1, "title": "Comprehensive analysis of clustering algorithms: exploring limitations ...", "date": "", "ddg_snippet": "Abstract This survey rigorously explores contemporary clustering algorithms within the machine learning paradigm, focusing on five primary methodologies: centroid-based, hierarchical , density-based, distribution-based, and graph-based clustering . Through the lens of recent innovations such as deep embedded clustering and spectral clustering , we analyze the strengths, limitations, and the ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11419652/", "content": "Abstract This survey rigorously explores contemporary clustering algorithms within the machine learning paradigm, focusing on five primary methodologies: centroid-based, hierarchical , density-based, distribution-based, and graph-based clustering . Through the lens of recent innovations such as deep embedded clustering and spectral clustering , we analyze the strengths, limitations, and the ..."}
+{"idx": 2, "title": "Hierarchical Clustering: Objective Functions and Algorithms", "date": "", "ddg_snippet": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective , Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a \"good\" hierarchical ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/fullHtml/10.1145/3321386", "content": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective , Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a \"good\" hierarchical ..."}
+{"idx": 3, "title": "An Exact Solution Approach for Hierarchical Clustering", "date": "", "ddg_snippet": "In hierarchical clustering , a hierarchy of nested data partitions is obtained. Commonly used agglomerative and divisive heuristics do not optimize over a global objective function. Although several objective functions and approximation algorithms have been proposed, exact methods that find optimal solutions based on these objective functions have received little attention. In this paper, we ...", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/epdf/10.1287/ijoc.2024.0903", "content": "In hierarchical clustering , a hierarchy of nested data partitions is obtained. Commonly used agglomerative and divisive heuristics do not optimize over a global objective function. Although several objective functions and approximation algorithms have been proposed, exact methods that find optimal solutions based on these objective functions have received little attention. In this paper, we ..."}
+{"idx": 4, "title": "Hierarchical overlapping clustering: cost function, algorithm and ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 5, "title": "A clustering-based archive handling method and multi-objective ...", "date": "", "ddg_snippet": "The main challenge in finding the optimal Pareto Front (PF) and Pareto Set (PS) sets for multimodal multi- objective optimization problems (MMOPs) with conflicting objective functions is to exhibit a balanced and sustainable diversity and exploitation capability in both the objective and decision spaces. This paper introduces dynamic reference spaces based clustering (DRSC) as a new archive ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10489-024-05714-5", "content": "The main challenge in finding the optimal Pareto Front (PF) and Pareto Set (PS) sets for multimodal multi- objective optimization problems (MMOPs) with conflicting objective functions is to exhibit a balanced and sustainable diversity and exploitation capability in both the objective and decision spaces. This paper introduces dynamic reference spaces based clustering (DRSC) as a new archive ..."}
+{"idx": 6, "title": "Hierarchical Clustering: Objective Functions and Algorithms: Journal of ...", "date": "", "ddg_snippet": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective , Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a \"good\" hierarchical ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3321386", "content": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective , Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a \"good\" hierarchical ..."}
+{"idx": 7, "title": "PDF Hierarchical Clustering: Objective Functions and Algorithms", "date": "", "ddg_snippet": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective , Dasgupta framed similarity-based hierarchical clustering as a combina-torial optimization problem, where a \"good\" hierarchical ...", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/121430.2/3321386.pdf?sequence=5", "content": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective , Dasgupta framed similarity-based hierarchical clustering as a combina-torial optimization problem, where a \"good\" hierarchical ..."}
+{"idx": 8, "title": "A novel clustering-based evolutionary algorithm with objective space ...", "date": "", "ddg_snippet": "The framework of decomposing a multi- objective optimization problem (MOP) into some MOPs holds considerable promise. However, its advancement is constrained by numerous elements, including the incorrect segmentation of the subspaces and the challenges in balancing convergence and diversity. To address these issues, an objective space Decomposition and Clustering -based Evolutionary Algorithm ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0020025524008545", "content": "The framework of decomposing a multi- objective optimization problem (MOP) into some MOPs holds considerable promise. However, its advancement is constrained by numerous elements, including the incorrect segmentation of the subspaces and the challenges in balancing convergence and diversity. To address these issues, an objective space Decomposition and Clustering -based Evolutionary Algorithm ..."}
+{"idx": 9, "title": "PDF Overlapping Hierarchical Clustering (OHC) - inria.hal.science", "date": "", "ddg_snippet": "This may bias the data analysis process if, for example, a cluster is almost equally attracted by two others. In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "This may bias the data analysis process if, for example, a cluster is almost equally attracted by two others. In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion."}
diff --git a/data/sampled_jsons/hierarchical_overlapping_clustering_optimization.jsonl b/data/sampled_jsons/hierarchical_overlapping_clustering_optimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a7b2f5dcf3ac909d06168b405137c29f4a4e518
--- /dev/null
+++ b/data/sampled_jsons/hierarchical_overlapping_clustering_optimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HIERARCHICAL OVERLAPPING CLUSTERING FUNCTION ALGORITHM AND ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 1, "title": "Overlapping Hierarchical Clustering (OHC)", "date": "", "ddg_snippet": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and com-bines the advantages of hierarchies with the precision of a less arbitrary clustering .", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and com-bines the advantages of hierarchies with the precision of a less arbitrary clustering ."}
+{"idx": 2, "title": "A hierarchical clustering algorithm for addressing multi ...", "date": "", "ddg_snippet": "Mar 10, 2025 · A hierarchical clustering algorithm for addressing multi-modal multi-objective optimization problems Qinghua Gu a b c, Yiwen Niu a b , Zegang Hui d , Qian Wang b c , Naixue Xiong e Show more Add to Mendeley", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424025776", "content": "Mar 10, 2025 · A hierarchical clustering algorithm for addressing multi-modal multi-objective optimization problems Qinghua Gu a b c, Yiwen Niu a b , Zegang Hui d , Qian Wang b c , Naixue Xiong e Show more Add to Mendeley"}
+{"idx": 3, "title": "Overlapping Hierarchical Clustering (OHC) | SpringerLink Hierarchical Clustering: Objective Functions and Algorithms Overlapping community detection in weighted networks via ... Overlapping Hierarchical Clustering (OHC) Overlapping community detection in weighted networks via hierarchical Hierarchical Clustering: Objective Functions and Algorithms Overlapping Hierarchical Clustering (OHC) | SpringerLink Overlapping Hierarchical Clustering (OHC) | SpringerLink Hierarchical Clustering: Objective Functions and Algorithms Hierarchical Overlapping Clustering of Network Data Using Cut ...", "date": "", "ddg_snippet": "Tests: The tests we performed were focused on the quality of the hierarchical structures produced by our algorithm. To measure this quality we used the classical hierarchy produced by SLINK, an optimal single-linkage clustering algorithm proposed in Sibson et al. , as a baseline. Our goal was to study the behaviour of the merging criterion paramete... See full list on link.springer.com As there is no ground truth on the hierarchy of the data we used, we need a similarity measure to compare the hierarchical structures produced by hierarchical clustering algorithms. The goal is not only to compare the topology but also the content of the nodes of the structure. However up to our knowledge there is very little in the literature abou... See full list on link.springer.com Expressiveness: With this small following example we would like to present the expressiveness of our algorithm compared to classical hierarchical clustering algorithms such as SLINK. On the hand-built example shown in Fig. 3a we can clearly distinguish two groups of points, \\(\\{A,B,C,D,E\\}\\) and \\(\\{G,H,I,J,K\\}\\) and two points that we can consider... See full list on link.springer.com Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical ... Oct 28, 2024 · Similarly, stochastic block models (SBM) provide a powerful statistical framework for community detection, enabling the analysis of clustering dynamics and community structures in networks with overlapping and hierarchical features [28, 29]. What is overlapping hierarchical clustering (OHC)? Overlapping Hierarchical Clustering (OHC) 11 hierarchical and overlapping community structure of a network, by hierarchically merging local ego neighbourhoods. 5 Conclusion and future work We propose an overlapping hierarchical clustering framework. How does clustering overlap in a clustering algorithm? The overlap in the proposed algorithm is provided by cliques that are base elements for hierarchical clustering. Using cliques as the basis elements that enter the clustering process ensures that the nodes in a clique will always be in a common community. This paper’s main contributions can be summarized as: What is similarity-based hierarchical clustering? Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem , where a “good” hierarchical clustering is one that minimizes a particular cost function . What are Agglomerative Hierarchical Clustering methods? Agglomerative hierarchical clustering methods are widely used to analyze large amounts of data . These successful methods construct a dendrogram – a tree structure – that enables a natural exploration of data which is very suitable even for non-expert users. What happens if a cluster has no overlap? If the clusters present in the data show no overlaps, the obtained clusters are identical to the clusters we can compute using agglomerative clustering methods. What is hierarchical clustering? J. ACM, Vol. 66, No. 4, Article 26, Publication date: May 2019. Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity . May 24, 2017 · By applying a hierarchical (non- overlapping ) clustering method to multiple dithered versions of a given network, and then, convexly combining the resulting ultrametrics, we obtain a cut metric associated to the network of interest. We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-44584-3_21", "content": "Tests: The tests we performed were focused on the quality of the hierarchical structures produced by our algorithm. To measure this quality we used the classical hierarchy produced by SLINK, an optimal single-linkage clustering algorithm proposed in Sibson et al. , as a baseline. Our goal was to study the behaviour of the merging criterion paramete... See full list on link.springer.com As there is no ground truth on the hierarchy of the data we used, we need a similarity measure to compare the hierarchical structures produced by hierarchical clustering algorithms. The goal is not only to compare the topology but also the content of the nodes of the structure. However up to our knowledge there is very little in the literature abou... See full list on link.springer.com Expressiveness: With this small following example we would like to present the expressiveness of our algorithm compared to classical hierarchical clustering algorithms such as SLINK. On the hand-built example shown in Fig. 3a we can clearly distinguish two groups of points, \\(\\{A,B,C,D,E\\}\\) and \\(\\{G,H,I,J,K\\}\\) and two points that we can consider... See full list on link.springer.com Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical ... Oct 28, 2024 · Similarly, stochastic block models (SBM) provide a powerful statistical framework for community detection, enabling the analysis of clustering dynamics and community structures in networks with overlapping and hierarchical features [28, 29]. What is overlapping hierarchical clustering (OHC)? Overlapping Hierarchical Clustering (OHC) 11 hierarchical and overlapping community structure of a network, by hierarchically merging local ego neighbourhoods. 5 Conclusion and future work We propose an overlapping hierarchical clustering framework. How does clustering overlap in a clustering algorithm? The overlap in the proposed algorithm is provided by cliques that are base elements for hierarchical clustering. Using cliques as the basis elements that enter the clustering process ensures that the nodes in a clique will always be in a common community. This paper’s main contributions can be summarized as: What is similarity-based hierarchical clustering? Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem , where a “good” hierarchical clustering is one that minimizes a particular cost function . What are Agglomerative Hierarchical Clustering methods? Agglomerative hierarchical clustering methods are widely used to analyze large amounts of data . These successful methods construct a dendrogram – a tree structure – that enables a natural exploration of data which is very suitable even for non-expert users. What happens if a cluster has no overlap? If the clusters present in the data show no overlaps, the obtained clusters are identical to the clusters we can compute using agglomerative clustering methods. What is hierarchical clustering? J. ACM, Vol. 66, No. 4, Article 26, Publication date: May 2019. Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity . May 24, 2017 · By applying a hierarchical (non- overlapping ) clustering method to multiple dithered versions of a given network, and then, convexly combining the resulting ultrametrics, we obtain a cut metric associated to the network of interest. We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric."}
+{"idx": 4, "title": "Hierarchical Clustering: Objective Functions and Algorithms", "date": "", "ddg_snippet": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/fullHtml/10.1145/3321386", "content": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical ..."}
+{"idx": 5, "title": "Overlapping community detection in weighted networks via ...", "date": "", "ddg_snippet": "Oct 28, 2024 · Similarly, stochastic block models (SBM) provide a powerful statistical framework for community detection, enabling the analysis of clustering dynamics and community structures in networks with overlapping and hierarchical features [28, 29].", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0312596", "content": "Oct 28, 2024 · Similarly, stochastic block models (SBM) provide a powerful statistical framework for community detection, enabling the analysis of clustering dynamics and community structures in networks with overlapping and hierarchical features [28, 29]."}
+{"idx": 6, "title": "Hierarchical Overlapping Clustering of Network Data Using Cut ...", "date": "", "ddg_snippet": "May 24, 2017 · By applying a hierarchical (non- overlapping ) clustering method to multiple dithered versions of a given network, and then, convexly combining the resulting ultrametrics, we obtain a cut metric associated to the network of interest. We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/7933249", "content": "May 24, 2017 · By applying a hierarchical (non- overlapping ) clustering method to multiple dithered versions of a given network, and then, convexly combining the resulting ultrametrics, we obtain a cut metric associated to the network of interest. We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric."}
+{"idx": 7, "title": "[1305.6228] Detecting hierarchical and overlapping network", "date": "", "ddg_snippet": "View a PDF of the paper titled Detecting hierarchical and overlapping network communities using locally optimal modularity changes, by Michael J.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1305.6228", "content": "View a PDF of the paper titled Detecting hierarchical and overlapping network communities using locally optimal modularity changes, by Michael J."}
+{"idx": 8, "title": "Hierarchical Clustering From Scratch", "date": "", "ddg_snippet": "It shows the hierarchical relationship between the clusters , from individual data points at the leaves (bottom) to a single cluster at the root (top).", "subpage_snippet": "", "source": "www.python-unleashed.com", "link": "https://www.python-unleashed.com/post/hierarchical-clustering-from-scratch", "content": "It shows the hierarchical relationship between the clusters , from individual data points at the leaves (bottom) to a single cluster at the root (top)."}
+{"idx": 9, "title": "Mastering Hierarchical Clustering with R: Dendrograms and", "date": "", "ddg_snippet": "It ’ s an efficient way to visualize the arrangement of the clusters produced by hierarchical clustering algorithms.", "subpage_snippet": "", "source": "blog.enterprisedna.co", "link": "https://blog.enterprisedna.co/mastering-hierarchical-clustering-with-r-dendrograms-and-cluster-trees-in-action/", "content": "It ’ s an efficient way to visualize the arrangement of the clusters produced by hierarchical clustering algorithms."}
diff --git a/data/sampled_jsons/hierarchical_overlapping_clustering_optimization_objective_function_before_2023.jsonl b/data/sampled_jsons/hierarchical_overlapping_clustering_optimization_objective_function_before_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e74ada5e79db45fcfea147aa6e865829d388257
--- /dev/null
+++ b/data/sampled_jsons/hierarchical_overlapping_clustering_optimization_objective_function_before_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical clustering with maximum density paths and ...", "date": "", "ddg_snippet": "19 Mar 2025 — Hierarchical clustering is an effective and interpretable technique for analyzing structure in data, offering a nuanced understanding by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.15582v1", "content": "19 Mar 2025 — Hierarchical clustering is an effective and interpretable technique for analyzing structure in data, offering a nuanced understanding by ..."}
+{"idx": 1, "title": "Cluster analysis: A modern statistical review - Jaeger - 2023", "date": "", "ddg_snippet": "19 Aug 2022 — Milligan and Cooper (1987) separate clustering into four categories: hierarchical , partitioning, overlapping , and ordination methods.", "subpage_snippet": "", "source": "wires.onlinelibrary.wiley.com", "link": "https://wires.onlinelibrary.wiley.com/doi/10.1002/wics.1597", "content": "19 Aug 2022 — Milligan and Cooper (1987) separate clustering into four categories: hierarchical , partitioning, overlapping , and ordination methods."}
+{"idx": 2, "title": "Nearly-Optimal Hierarchical Clustering for Well- ...", "date": "", "ddg_snippet": "by S Laenen · Cited by 8 — Abstract. This paper presents two efficient hierarchical clus- tering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DdQTP1yFLQ", "content": "by S Laenen · Cited by 8 — Abstract. This paper presents two efficient hierarchical clus- tering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a ..."}
+{"idx": 3, "title": "CVPR Poster Hierarchical Correlation Clustering and Tree ...", "date": "", "ddg_snippet": "We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters applicable to ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/poster/30039", "content": "We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters applicable to ..."}
+{"idx": 4, "title": "On hierarchical clustering-based approach for RDDBS design", "date": "", "ddg_snippet": "by HI Abdalla · 2023 · Cited by 3 — This work aims at proposing a clustering -based horizontal fragmentation and allocation technique to handle both the early and late stages of the DDBS design.", "subpage_snippet": "", "source": "journalofbigdata.springeropen.com", "link": "https://journalofbigdata.springeropen.com/articles/10.1186/s40537-023-00849-7", "content": "by HI Abdalla · 2023 · Cited by 3 — This work aims at proposing a clustering -based horizontal fragmentation and allocation technique to handle both the early and late stages of the DDBS design."}
+{"idx": 5, "title": "Sublinear Algorithms for Hierarchical Clustering", "date": "", "ddg_snippet": "by A Agarwal · 2022 · Cited by 24 — Our work approaches this problem from an optimization perspective ( objective function defined in [15] for dissimilarity data); through ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=VPhhd5pv0Qs", "content": "by A Agarwal · 2022 · Cited by 24 — Our work approaches this problem from an optimization perspective ( objective function defined in [15] for dissimilarity data); through ..."}
+{"idx": 6, "title": "A clustering-based competitive particle swarm optimization ...", "date": "", "ddg_snippet": "by Q Ye · 2023 · Cited by 9 — This paper develops a clustering -based competitive multi- objective particle swarm optimizer using the enhanced grid for solving multi- objective optimization ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-023-38529-4", "content": "by Q Ye · 2023 · Cited by 9 — This paper develops a clustering -based competitive multi- objective particle swarm optimizer using the enhanced grid for solving multi- objective optimization ..."}
+{"idx": 7, "title": "2.3. Clustering", "date": "", "ddg_snippet": "Clustering of unlabeled data can be performed with the module sklearn. cluster . Each clustering algorithm comes in two variants.", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/clustering.html", "content": "Clustering of unlabeled data can be performed with the module sklearn. cluster . Each clustering algorithm comes in two variants."}
+{"idx": 8, "title": "Improving the prediction accuracy of statistical models", "date": "", "ddg_snippet": "by Á López-Oriona · 2025 — Both objectives are achieved by introducing an agglomerative hierarchical clustering procedure designed to group together datasets with similar ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11222-025-10683-x", "content": "by Á López-Oriona · 2025 — Both objectives are achieved by introducing an agglomerative hierarchical clustering procedure designed to group together datasets with similar ..."}
+{"idx": 9, "title": "Leveraging Prior Fitted Transformers for Data Partitioning", "date": "", "ddg_snippet": "27 Jul 2025 — The proposed technique leverages a few pre-clustered samples to guide the clustering process for the entire dataset in a single forward pass.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.20369", "content": "27 Jul 2025 — The proposed technique leverages a few pre-clustered samples to guide the clustering process for the entire dataset in a single forward pass."}
diff --git a/data/sampled_jsons/hierarchical_overlapping_clustering_time_complexity_algorithm.jsonl b/data/sampled_jsons/hierarchical_overlapping_clustering_time_complexity_algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dbc495c70e31977e16df32e54227ea6e19874882
--- /dev/null
+++ b/data/sampled_jsons/hierarchical_overlapping_clustering_time_complexity_algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical clustering - Wikipedia", "date": "", "ddg_snippet": "The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of and requires memory, which makes it too slow for even medium data sets.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Hierarchical_clustering", "content": "The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of and requires memory, which makes it too slow for even medium data sets."}
+{"idx": 1, "title": "Overlapping Hierarchical Clustering (OHC) | SpringerLink", "date": "", "ddg_snippet": "We show through extensive experiments on real and synthetic data that we obtain high quality results with respect to classical hierarchical clustering , with reasonable time and space complexity . The rest of the paper is organized as follows: Sect. 2 describes our proposed overlapping hierarchical clustering framework 1.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-44584-3_21", "content": "We show through extensive experiments on real and synthetic data that we obtain high quality results with respect to classical hierarchical clustering , with reasonable time and space complexity . The rest of the paper is organized as follows: Sect. 2 describes our proposed overlapping hierarchical clustering framework 1."}
+{"idx": 2, "title": "Hierarchical Overlapping Clustering Function Algorithm and Scalability ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 3, "title": "PDF Overlapping Hierarchical Clustering (OHC) - inria.hal.science", "date": "", "ddg_snippet": "Other variants tried to allow this fuzzy clustering in the hierarchy such as SOHC [10], a hierarchical clustering based on a spatial overlapping metric but with a xed number of clusters, or HCOSM [26], that use an overlap similarity measure to merge clusters and then compute a hierar-chy from an already determined set of clusters.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "Other variants tried to allow this fuzzy clustering in the hierarchy such as SOHC [10], a hierarchical clustering based on a spatial overlapping metric but with a xed number of clusters, or HCOSM [26], that use an overlap similarity measure to merge clusters and then compute a hierar-chy from an already determined set of clusters."}
+{"idx": 4, "title": "Comprehensive analysis of clustering algorithms: exploring limitations ...", "date": "", "ddg_snippet": "Abstract This survey rigorously explores contemporary clustering algorithms within the machine learning paradigm, focusing on five primary methodologies: centroid-based, hierarchical , density-based, distribution-based, and graph-based clustering . Through the lens of recent innovations such as deep embedded clustering and spectral clustering , we analyze the strengths, limitations, and the ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11419652/", "content": "Abstract This survey rigorously explores contemporary clustering algorithms within the machine learning paradigm, focusing on five primary methodologies: centroid-based, hierarchical , density-based, distribution-based, and graph-based clustering . Through the lens of recent innovations such as deep embedded clustering and spectral clustering , we analyze the strengths, limitations, and the ..."}
+{"idx": 5, "title": "What is the time and space complexity of single linkage hierarchical ...", "date": "", "ddg_snippet": "I have read everywhere that the time complexity of hierarchical agglomerative clustering is $\\mathcal {O} (n^3)$ and it can be brought down to $\\mathcal {O} (n^2 \\log n)$.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/283343/what-is-the-time-and-space-complexity-of-single-linkage-hierarchical-clustering", "content": "I have read everywhere that the time complexity of hierarchical agglomerative clustering is $\\mathcal {O} (n^3)$ and it can be brought down to $\\mathcal {O} (n^2 \\log n)$."}
+{"idx": 6, "title": "Hierarchical Clustering in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Hierarchical clustering is an unsupervised learning technique used to group similar data points into clusters by building a hierarchy (tree-like structure). Unlike flat clustering like k-means hierarchical clustering does not require specifying the number of clusters in advance. The algorithm builds clusters step by step either by progressively merging smaller clusters or by splitting a large ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/hierarchical-clustering/", "content": "Hierarchical clustering is an unsupervised learning technique used to group similar data points into clusters by building a hierarchy (tree-like structure). Unlike flat clustering like k-means hierarchical clustering does not require specifying the number of clusters in advance. The algorithm builds clusters step by step either by progressively merging smaller clusters or by splitting a large ..."}
+{"idx": 7, "title": "Mastering Hierarchical Clustering : From Basic to Advanced", "date": "", "ddg_snippet": "Space and Time Complexity for Hierarchical Clustering : The algorithm has a space complexity of O (n²) and a time complexity ranging from O (n² log n) to O (n³).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sachinsoni600517/mastering-hierarchical-clustering-from-basic-to-advanced-5e770260bf93", "content": "Space and Time Complexity for Hierarchical Clustering : The algorithm has a space complexity of O (n²) and a time complexity ranging from O (n² log n) to O (n³)."}
+{"idx": 8, "title": "Hierarchical Clustering: Objective Functions and Algorithms", "date": "", "ddg_snippet": "Equipped with a suitable objective function, we analyze the performance of practical algorithms , as well as develop better and faster algorithms for hierarchical clustering . We also initiate a beyond worst-case analysis of the complexity of the problem and design algorithms for this scenario.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/fullHtml/10.1145/3321386", "content": "Equipped with a suitable objective function, we analyze the performance of practical algorithms , as well as develop better and faster algorithms for hierarchical clustering . We also initiate a beyond worst-case analysis of the complexity of the problem and design algorithms for this scenario."}
+{"idx": 9, "title": "The Ultimate Guide to Hierarchical Clustering: Advanced Techniques and ...", "date": "", "ddg_snippet": "Introduction Hierarchical clustering remains a cornerstone in exploratory data analysis due to its intuitive dendrogram output and ability to uncover nested grouping structures. Unlike partitional methods (e.g., k‑means), hierarchical algorithms build a tree of clusters—either bottom‑up (agglomerative) or top‑down (divisive). Yet, classical implementations suffer from O (n²) time and ...", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/ultimate-guide-hierarchical-clustering-advanced", "content": "Introduction Hierarchical clustering remains a cornerstone in exploratory data analysis due to its intuitive dendrogram output and ability to uncover nested grouping structures. Unlike partitional methods (e.g., k‑means), hierarchical algorithms build a tree of clusters—either bottom‑up (agglomerative) or top‑down (divisive). Yet, classical implementations suffer from O (n²) time and ..."}
diff --git a/data/sampled_jsons/high_probability_of_necessity_Pearl_causal_inference_counterfactual_implications.jsonl b/data/sampled_jsons/high_probability_of_necessity_Pearl_causal_inference_counterfactual_implications.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2277732b8616ff7305dd1884f339bd6872d6bcae
--- /dev/null
+++ b/data/sampled_jsons/high_probability_of_necessity_Pearl_causal_inference_counterfactual_implications.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causality - Wikipedia", "date": "", "ddg_snippet": "In practical terms, this is because use of the relation of causality is necessary for the interpretation of empirical experiments.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Causality", "content": "In practical terms, this is because use of the relation of causality is necessary for the interpretation of empirical experiments."}
+{"idx": 1, "title": "causal DAGs – Andi Fugard (∧⇒)", "date": "", "ddg_snippet": "For example, in contribution analysis “ causal assumptions” refer to a “ causal package” consisting of the program intervention and a set of ...", "subpage_snippet": "", "source": "andifugard.info", "link": "https://andifugard.info/tag/causal-dags/", "content": "For example, in contribution analysis “ causal assumptions” refer to a “ causal package” consisting of the program intervention and a set of ..."}
+{"idx": 2, "title": "causal inference – Page 4 – Andi Fugard", "date": "", "ddg_snippet": "Identification, Inference and Sensitivity Analysis for Causal Mediation Effects. ... Although 42% of studies discussed mediation assumptions ...", "subpage_snippet": "", "source": "andifugard.info", "link": "https://andifugard.info/tag/causal-inference/page/4/", "content": "Identification, Inference and Sensitivity Analysis for Causal Mediation Effects. ... Although 42% of studies discussed mediation assumptions ..."}
+{"idx": 3, "title": "Counterfactuals (Stanford Encyclopedia of Philosophy)", "date": "", "ddg_snippet": "On these grounds, it might be better to speak instead of subjunctive conditionals , and reserve the term counterfactual for subjunctive conditionals ...", "subpage_snippet": "", "source": "seop.illc.uva.nl", "link": "https://seop.illc.uva.nl/entries/counterfactuals/", "content": "On these grounds, it might be better to speak instead of subjunctive conditionals , and reserve the term counterfactual for subjunctive conditionals ..."}
+{"idx": 4, "title": "Causal Analysis in Theory and Practice » Discussion", "date": "", "ddg_snippet": "The availability of these unifying tools now calls on statisticians to become actively involved in causal analysis, rather than attempting to judge ...", "subpage_snippet": "", "source": "causality.cs.ucla.edu", "link": "https://causality.cs.ucla.edu/blog/index.php/category/discussion/", "content": "The availability of these unifying tools now calls on statisticians to become actively involved in causal analysis, rather than attempting to judge ..."}
+{"idx": 5, "title": "(PDF) When Possibility Informs RealityCounterfactual Thinking", "date": "", "ddg_snippet": "Counterfactual thinking may affect causality judgments by changing beliefs about the probabilities of possible alternatives to what actually happened ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/237378757_When_Possibility_Informs_RealityCounterfactual_Thinking_as_a_Cue_to_Causality", "content": "Counterfactual thinking may affect causality judgments by changing beliefs about the probabilities of possible alternatives to what actually happened ..."}
+{"idx": 6, "title": "CausalARC: Abstract Reasoning with Causal World Models", "date": "", "ddg_snippet": "Though causal reasoning is a hallmark of human cognition (Goddu and Gopnik, 2024 ) and a desideratum for human-like AI ( Pearl , 2000 ; Lake and Baroni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03636v1", "content": "Though causal reasoning is a hallmark of human cognition (Goddu and Gopnik, 2024 ) and a desideratum for human-like AI ( Pearl , 2000 ; Lake and Baroni ..."}
+{"idx": 7, "title": "Causal Theories of Counterfactuals - Bibliography - PhilPapers", "date": "", "ddg_snippet": "Apart from solving a major problem in the epistemology of counterfactuals , our paper shows how work in semantics, causal inference and formal ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/causal-theories-of-counterfactuals", "content": "Apart from solving a major problem in the epistemology of counterfactuals , our paper shows how work in semantics, causal inference and formal ..."}
+{"idx": 8, "title": "Causes and Explanations: A Structural-Model Approach, Part I:", "date": "", "ddg_snippet": "... to introducing a novel taxonomy of explanations for MDPs to guide empirical investigation, we develop both exact and approximate causal inference ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/220487167_Causes_and_Explanations_A_Structural-Model_Approach_Part_I_Causes", "content": "... to introducing a novel taxonomy of explanations for MDPs to guide empirical investigation, we develop both exact and approximate causal inference ..."}
+{"idx": 9, "title": "Causation | Internet Encyclopedia of Philosophy", "date": "", "ddg_snippet": "... of thinkers with a non-philosophical motivation, and sometimes background, who develop technical frameworks for the purpose of performing causal ...", "subpage_snippet": "", "source": "iep.utm.edu", "link": "https://iep.utm.edu/causation/", "content": "... of thinkers with a non-philosophical motivation, and sometimes background, who develop technical frameworks for the purpose of performing causal ..."}
diff --git a/data/sampled_jsons/how_sqlmap_improves_database_access_capabilities_SQL_injection.jsonl b/data/sampled_jsons/how_sqlmap_improves_database_access_capabilities_SQL_injection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..27f2c2ff5a5b6a0e64506d46938b962545998f74
--- /dev/null
+++ b/data/sampled_jsons/how_sqlmap_improves_database_access_capabilities_SQL_injection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SQLmap Tutorial for Beginners 2025 (+Free PDF)", "date": "", "ddg_snippet": "Beyond data theft, SQL injection can completely undermine a system. Compromise data security may cause organizations to suffer reputational damage, financial losses, and legal repercussions. Securing your web app with input validation, sanitization, and limiting query access is vital.", "subpage_snippet": "", "source": "www.stationx.net", "link": "https://www.stationx.net/sqlmap-tutorial/", "content": "Beyond data theft, SQL injection can completely undermine a system. Compromise data security may cause organizations to suffer reputational damage, financial losses, and legal repercussions. Securing your web app with input validation, sanitization, and limiting query access is vital."}
+{"idx": 1, "title": "SQLmap - Payloads All The Things", "date": "", "ddg_snippet": "SQLmap Without SQL Injection .The -- sql - query option in SQLmap is used to manually run your own SQL query on a vulnerable database after SQLmap has confirmed the injection and gathered necessary access .", "subpage_snippet": "", "source": "swisskyrepo.github.io", "link": "https://swisskyrepo.github.io/PayloadsAllTheThings/SQL+Injection/SQLmap/", "content": "SQLmap Without SQL Injection .The -- sql - query option in SQLmap is used to manually run your own SQL query on a vulnerable database after SQLmap has confirmed the injection and gathered necessary access ."}
+{"idx": 2, "title": "SQLMap Cheat Sheet: Flags & Commands for SQL Injection", "date": "", "ddg_snippet": "Misc SQLMap Commands. SQLMap Examples: How To… Enumerate Databases . Enumerate Tables. SQLMap Dump DB Table. SQLMap from Burp file. Custom SQL Injection Payload: Pre and Post Input.", "subpage_snippet": "", "source": "highon.coffee", "link": "https://highon.coffee/blog/sqlmap-cheat-sheet/", "content": "Misc SQLMap Commands. SQLMap Examples: How To… Enumerate Databases . Enumerate Tables. SQLMap Dump DB Table. SQLMap from Burp file. Custom SQL Injection Payload: Pre and Post Input."}
+{"idx": 3, "title": "How to use SQLMAP to test a website for SQL Injection vulnerability", "date": "", "ddg_snippet": "This article explains how to test whether a website is safe from SQL injection using the SQLMAP penetration testing tool.With the right set of queries , a user can gain access to information stored in databases . SQLMAP tests whether a 'GET' parameter is vulnerable to SQL Injection .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/blogs/use-sqlmap-test-website-sql-injection-vulnerability/", "content": "This article explains how to test whether a website is safe from SQL injection using the SQLMAP penetration testing tool.With the right set of queries , a user can gain access to information stored in databases . SQLMAP tests whether a 'GET' parameter is vulnerable to SQL Injection ."}
+{"idx": 4, "title": "Sqlmap , the Tool for Detecting and Exploiting SQL Injections", "date": "", "ddg_snippet": "Exploiting blind SQL injections with Sqlmap . Sqlmap advanced options. How does Sqlmap Work? Although it is possible to exploit a SQL injection and exfiltrate the contents of a database manually, it is much simpler and quicker to write and use a script.", "subpage_snippet": "", "source": "www.vaadata.com", "link": "https://www.vaadata.com/blog/sqlmap-the-tool-for-detecting-and-exploiting-sql-injections/", "content": "Exploiting blind SQL injections with Sqlmap . Sqlmap advanced options. How does Sqlmap Work? Although it is possible to exploit a SQL injection and exfiltrate the contents of a database manually, it is much simpler and quicker to write and use a script."}
+{"idx": 5, "title": "Sqlmap Tutorial", "date": "", "ddg_snippet": "Time- Based Blind SQL Injection using Heavy Query . Estimating MySQL Table Size using SQL Injection . Sqlmap can also enumerate columns. Here again, the session user will need to have read access to system tables containing information about databases ’ tables.", "subpage_snippet": "", "source": "www.sqlinjection.net", "link": "https://www.sqlinjection.net/sqlmap/tutorial/", "content": "Time- Based Blind SQL Injection using Heavy Query . Estimating MySQL Table Size using SQL Injection . Sqlmap can also enumerate columns. Here again, the session user will need to have read access to system tables containing information about databases ’ tables."}
+{"idx": 6, "title": "SQLmap Tutorial", "date": "", "ddg_snippet": "SQLmap can be used to test and exploit SQL Injection , doing things such as extracting data from databases , updating tables, and even popping shells on remote hosts if all the ducks are in line.", "subpage_snippet": "", "source": "hackertarget.com", "link": "https://hackertarget.com/sqlmap-tutorial/", "content": "SQLmap can be used to test and exploit SQL Injection , doing things such as extracting data from databases , updating tables, and even popping shells on remote hosts if all the ducks are in line."}
+{"idx": 7, "title": "Blind SQL Injection | rohitkumarankam/ sqlmap | DeepWiki", "date": "", "ddg_snippet": "This page documents how sqlmap detects and exploits blind SQL injection vulnerabilities. Blind SQL injection is a technique used when the application doesn't directly return the results of an injected .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/rohitkumarankam/sqlmap/3.3-blind-sql-injection", "content": "This page documents how sqlmap detects and exploits blind SQL injection vulnerabilities. Blind SQL injection is a technique used when the application doesn't directly return the results of an injected ."}
+{"idx": 8, "title": "Sqlmap tutorial for beginners - hacking with sql injection - BinaryTides", "date": "", "ddg_snippet": "For this sqlmap sends different kinds of sql injection payloads to the input parameter and checks the output. In the process sqlmap is also able to identify the remote system os, database name and version. Here is how the output might look like.", "subpage_snippet": "", "source": "www.binarytides.com", "link": "https://www.binarytides.com/sqlmap-hacking-tutorial/", "content": "For this sqlmap sends different kinds of sql injection payloads to the input parameter and checks the output. In the process sqlmap is also able to identify the remote system os, database name and version. Here is how the output might look like."}
+{"idx": 9, "title": "SQL Injection in Action: Testing Login, Registration, and Admin Panels...", "date": "", "ddg_snippet": "sqlmap Scan Results. Injection Type(s): Boolean- based blind, Error- based , and Time- based blind.I used it to inject SQL that displayed extra data right in the admin table — like the database username — and also confirmed a delay- based injection .", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/sql-injection-action-testing-login-registration-admin-jose-pacheco-aymcc", "content": "sqlmap Scan Results. Injection Type(s): Boolean- based blind, Error- based , and Time- based blind.I used it to inject SQL that displayed extra data right in the admin table — like the database username — and also confirmed a delay- based injection ."}
diff --git a/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2405.14728.jsonl b/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2405.14728.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..23b7322dd3ea12e266ab79d15f71290856c8a035
--- /dev/null
+++ b/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2405.14728.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ar5iv – Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/", "content": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents."}
+{"idx": 1, "title": "ar5iv – Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv – Articles from arXiv .org as responsive HTML5 web documents", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/en_US", "content": "ar5iv – Articles from arXiv .org as responsive HTML5 web documents"}
+{"idx": 2, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 3, "title": "GitHub - dginev/ar5iv: A web service offering HTML5 articles ...", "date": "", "ddg_snippet": "A web service offering HTML5 articles from arXiv . org as converted with latexml. The e-journal styling of document pages is developed separately at ar5iv -css. Authors can reproduce locally using ar5ivist. Seeded via CorTeX data. Hosted by arXivLabs . Created by", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5iv", "content": "A web service offering HTML5 articles from arXiv . org as converted with latexml. The e-journal styling of document pages is developed separately at ar5iv -css. Authors can reproduce locally using ar5ivist. Seeded via CorTeX data. Hosted by arXivLabs . Created by"}
+{"idx": 4, "title": "arXiv articles as responsive web pages", "date": "", "ddg_snippet": "Feb 21, 2022 · arXiv readers can now benefit from new, experimental access to our article collection converted to responsive HTML5. For a quick preview, replace the “X” of arXiv in any article link to the “5” of ar5iv and be redirected to a corresponding arXivLabs page.", "subpage_snippet": "", "source": "blog.arxiv.org", "link": "https://blog.arxiv.org/2022/02/21/arxiv-articles-as-responsive-web-pages/", "content": "Feb 21, 2022 · arXiv readers can now benefit from new, experimental access to our article collection converted to responsive HTML5. For a quick preview, replace the “X” of arXiv in any article link to the “5” of ar5iv and be redirected to a corresponding arXivLabs page."}
+{"idx": 5, "title": "HTML Görüntüleyici - Çevrimiçi HTML Editörü ve Önizleme Aracı", "date": "", "ddg_snippet": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç.", "subpage_snippet": "", "source": "htmlonline.org", "link": "https://htmlonline.org/tr/", "content": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç."}
+{"idx": 6, "title": "Login / Member's access to the HTML 6 Editor", "date": "", "ddg_snippet": "There's no Login to HTML 6.There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout.", "subpage_snippet": "", "source": "html6.com", "link": "https://html6.com/login/", "content": "There's no Login to HTML 6.There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout."}
+{"idx": 7, "title": "org-mime.el — org html export for text/html MIME emails", "date": "", "ddg_snippet": "can be called from within an Org -mode buffer to export either the whole buffer or the narrowed subtree or active region to HTML , and open a new email buffer including the resulting HTML ...", "subpage_snippet": "", "source": "orgmode.org", "link": "https://orgmode.org/worg/org-contrib/org-mime.html", "content": "can be called from within an Org -mode buffer to export either the whole buffer or the narrowed subtree or active region to HTML , and open a new email buffer including the resulting HTML ..."}
+{"idx": 8, "title": "Learn HTML - Free Interactive HTML Tutorial", "date": "", "ddg_snippet": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast.", "subpage_snippet": "", "source": "www.learn-html.org", "link": "https://www.learn-html.org/", "content": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast."}
+{"idx": 9, "title": "How should I set the language of the content in my HTML page?", "date": "", "ddg_snippet": "How should I set the language of the content in my HTML page?", "subpage_snippet": "", "source": "www.w3.org", "link": "https://www.w3.org/International/questions/qa-html-language-declarations", "content": "How should I set the language of the content in my HTML page?"}
diff --git a/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2410.02025.jsonl b/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2410.02025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7ecf71b6e58c06a3003b4bf6d1bd853b4ae4a9da
--- /dev/null
+++ b/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2410.02025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/", "content": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents."}
+{"idx": 1, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/en_US", "content": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents"}
+{"idx": 2, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 3, "title": "GitHub - dginev/ar5iv: A web service offering HTML5 articles from arXiv ...", "date": "", "ddg_snippet": "A web service offering HTML5 articles from arXiv.org as converted with latexml. The e-journal styling of document pages is developed separately at ar5iv-css. Authors can reproduce locally using ar5ivist. Seeded via CorTeX data. Hosted by arXivLabs. Created by", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5iv", "content": "A web service offering HTML5 articles from arXiv.org as converted with latexml. The e-journal styling of document pages is developed separately at ar5iv-css. Authors can reproduce locally using ar5ivist. Seeded via CorTeX data. Hosted by arXivLabs. Created by"}
+{"idx": 4, "title": "arXiv articles as responsive web pages", "date": "", "ddg_snippet": "arXiv readers can now benefit from new, experimental access to our article collection converted to responsive HTML5. For a quick preview, replace the \"X\" of arXiv in any article link to the \"5\" of ar5iv and be redirected to a corresponding arXivLabs page.", "subpage_snippet": "", "source": "blog.arxiv.org", "link": "https://blog.arxiv.org/2022/02/21/arxiv-articles-as-responsive-web-pages/", "content": "arXiv readers can now benefit from new, experimental access to our article collection converted to responsive HTML5. For a quick preview, replace the \"X\" of arXiv in any article link to the \"5\" of ar5iv and be redirected to a corresponding arXivLabs page."}
+{"idx": 5, "title": "Releases · dginev/ar5iv - GitHub", "date": "", "ddg_snippet": "A lot remains to be done - especially in the \"Article Viewer\" project, which hasn't been properly initiated just yet. Our gratitude to the community for the hundreds of reports in 2022 - please keep sending more our way! ar5iv is now moving closer towards native arXiv integration, and that will be a broader theme to explore in 2023.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5iv/releases", "content": "A lot remains to be done - especially in the \"Article Viewer\" project, which hasn't been properly initiated just yet. Our gratitude to the community for the hundreds of reports in 2022 - please keep sending more our way! ar5iv is now moving closer towards native arXiv integration, and that will be a broader theme to explore in 2023."}
+{"idx": 6, "title": "ar5iv 04.2024 - An HTML5 dataset for arXiv.org · SIGMathLing", "date": "", "ddg_snippet": "Description This is the first public release of the ar5iv dataset generated by the KWARC research group. It contains HTML5+MathML conversions of the scientific documents from the arXiv.org preprint server, upto the start of April 2024. As of April 2024, the provided HTML here also seeds the live ar5iv Lab site, maintained by the same author.", "subpage_snippet": "", "source": "sigmathling.kwarc.info", "link": "https://sigmathling.kwarc.info/resources/ar5iv-dataset-2024/", "content": "Description This is the first public release of the ar5iv dataset generated by the KWARC research group. It contains HTML5+MathML conversions of the scientific documents from the arXiv.org preprint server, upto the start of April 2024. As of April 2024, the provided HTML here also seeds the live ar5iv Lab site, maintained by the same author."}
+{"idx": 7, "title": "ar5iv/README.md at main · dginev/ar5iv · GitHub", "date": "", "ddg_snippet": "A web service offering HTML5 articles from arXiv.org as converted with latexml - ar5iv/README.md at main · dginev/ar5iv", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5iv/blob/main/README.md", "content": "A web service offering HTML5 articles from arXiv.org as converted with latexml - ar5iv/README.md at main · dginev/ar5iv"}
+{"idx": 8, "title": "[2210.02025] GMMSeg: Gaussian Mixture based Generative Semantic ... - ar5iv", "date": "", "ddg_snippet": "Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier of . Though straightforward, this de facto paradigm neglects the underlying data distribution , and struggles to identify out…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2210.02025", "content": "Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier of . Though straightforward, this de facto paradigm neglects the underlying data distribution , and struggles to identify out…"}
+{"idx": 9, "title": "First demonstration of a TES based cryogenic Li2MoO4 detector for ...", "date": "", "ddg_snippet": "Cryogenic calorimetric experiments to search for neutrinoless double-beta decay () are highly competitive, scalable and versatile in isotope. The largest planned detector array, CUPID, is comprised of about 1500 indivi…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2406.02025", "content": "Cryogenic calorimetric experiments to search for neutrinoless double-beta decay () are highly competitive, scalable and versatile in isotope. The largest planned detector array, CUPID, is comprised of about 1500 indivi…"}
diff --git a/data/sampled_jsons/httpsarxiv.orgabs2409.02772.jsonl b/data/sampled_jsons/httpsarxiv.orgabs2409.02772.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..986a6fb0f0e25decd01139d77c62d9be863cabf6
--- /dev/null
+++ b/data/sampled_jsons/httpsarxiv.orgabs2409.02772.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ava Labs: Digitize All The World's Assets", "date": "", "ddg_snippet": "Ava Labs makes it simple to deploy high-performance solutions for Web3, led by innovations on Avalanche.", "subpage_snippet": "", "source": "www.avalabs.org", "link": "https://www.avalabs.org/", "content": "Ava Labs makes it simple to deploy high-performance solutions for Web3, led by innovations on Avalanche."}
+{"idx": 1, "title": "RealTimeData/ arxiv _latest · Datasets at Hugging Face", "date": "", "ddg_snippet": "OPEN CLUSTER SAMPLE MEMBERSHIP Here we show the plots outlining the kinematic selection of our open cluster sample, as described in Section . http:// arxiv . org / abs / 2409 . 02772 v1.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/RealTimeData/arxiv_latest/viewer/default/train", "content": "OPEN CLUSTER SAMPLE MEMBERSHIP Here we show the plots outlining the kinematic selection of our open cluster sample, as described in Section . http:// arxiv . org / abs / 2409 . 02772 v1."}
+{"idx": 2, "title": "[ 2409 . 02772 ] Unifying Causal Representation Learning with the...", "date": "", "ddg_snippet": "Overall, this paper clarifies the role of causal assumptions in the discovery of causal variables and shifts the focus to preserving data symmetries.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.02772", "content": "Overall, this paper clarifies the role of causal assumptions in the discovery of causal variables and shifts the focus to preserving data symmetries."}
+{"idx": 3, "title": "Psalm 39 - To the choirmaster: to Jeduthun. A Psalm of David. | ESV. org", "date": "", "ddg_snippet": "To the choirmaster: to Jeduthun. A Psalm of David. I said, “I will guard my ways, that I may not sin with my tongue; I will guard my mouth with a muzzle, so long as the wicked are in my presence.”", "subpage_snippet": "", "source": "www.esv.org", "link": "https://www.esv.org/Psalm+39/", "content": "To the choirmaster: to Jeduthun. A Psalm of David. I said, “I will guard my ways, that I may not sin with my tongue; I will guard my mouth with a muzzle, so long as the wicked are in my presence.”"}
+{"idx": 4, "title": "Welcome to yobihome", "date": "", "ddg_snippet": "Unifying Causal Representation Learning with the Invariance Principle. http:// arxiv . org / abs / 2409 . 02772 v1.", "subpage_snippet": "", "source": "yobibyte.github.io", "link": "https://yobibyte.github.io/arxiv_compressed/2024-09-05.html", "content": "Unifying Causal Representation Learning with the Invariance Principle. http:// arxiv . org / abs / 2409 . 02772 v1."}
+{"idx": 5, "title": "Accordion Pattern (Sections With Show/Hide Functionality) | APG | W3C", "date": "", "ddg_snippet": "Accessibility resources free online from the international standards organization: W3C Web Accessibility Initiative (WAI).", "subpage_snippet": "", "source": "www.w3.org", "link": "https://www.w3.org/WAI/ARIA/apg/patterns/accordion/", "content": "Accessibility resources free online from the international standards organization: W3C Web Accessibility Initiative (WAI)."}
+{"idx": 6, "title": "U nifying C ausal r epresentation L earning with", "date": "", "ddg_snippet": "In this paper, we examined a broad range of CRL methods and found that many of them share common strategies for aligning representations with known data symmetries.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.02772", "content": "In this paper, we examined a broad range of CRL methods and found that many of them share common strategies for aligning representations with known data symmetries."}
+{"idx": 7, "title": "UZU-018 Stop time! The world of time stopped. The victi", "date": "", "ddg_snippet": "Long, long sex. She is completely unaware that she is being violated. 2. In broad daylight in the office, time is stopped simultaneously for two female employees and they are slowly teased.", "subpage_snippet": "", "source": "123av.org", "link": "https://123av.org/dm40/en/uzu-018", "content": "Long, long sex. She is completely unaware that she is being violated. 2. In broad daylight in the office, time is stopped simultaneously for two female employees and they are slowly teased."}
+{"idx": 8, "title": "Mark the Words | H5P", "date": "", "ddg_snippet": "Create a task where users highlight words.", "subpage_snippet": "", "source": "h5p.org", "link": "https://h5p.org/mark-the-words", "content": "Create a task where users highlight words."}
+{"idx": 9, "title": "Огэ и Егэ по информатике, практические работы и задания по...", "date": "", "ddg_snippet": "Практические работы и задания по программированию на различных языках (C++, Pascal, Python, Prolog и т.д.) с подробными алгоритмами их выполнения...", "subpage_snippet": "", "source": "labs-org.ru", "link": "https://labs-org.ru/", "content": "Практические работы и задания по программированию на различных языках (C++, Pascal, Python, Prolog и т.д.) с подробными алгоритмами их выполнения..."}
diff --git a/data/sampled_jsons/httpsarxiv.orgabs2502.04757.jsonl b/data/sampled_jsons/httpsarxiv.orgabs2502.04757.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f40357e01c22dc0ea3caca19b1501d2bb4b4474f
--- /dev/null
+++ b/data/sampled_jsons/httpsarxiv.orgabs2502.04757.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.04757] ELITE: Enhanced Language-Image Toxicity ...", "date": "", "ddg_snippet": "Feb 7, 2025 · Abstract page for arXiv paper 2502 . 04757 : ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04757", "content": "Feb 7, 2025 · Abstract page for arXiv paper 2502 . 04757 : ELITE: Enhanced Language-Image Toxicity Evaluation for Safety"}
+{"idx": 1, "title": "[2508.04757] Embedding Is (Almost) All You Need: Retrieval ...", "date": "", "ddg_snippet": "Aug 6, 2025 · Abstract page for arXiv paper 2508. 04757 : Embedding Is (Almost) All You Need: Retrieval-Augmented Inference for Generalizable Genomic Prediction Tasks", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.04757", "content": "Aug 6, 2025 · Abstract page for arXiv paper 2508. 04757 : Embedding Is (Almost) All You Need: Retrieval-Augmented Inference for Generalizable Genomic Prediction Tasks"}
+{"idx": 2, "title": "[2502.05757] Large Language Model-based Nonnegative Matrix ... [2501.04757] DAREK -- Distance Aware Error for Kolmogorov ... [2504.04757] The Complexity of Maximal Common Subsequence ...", "date": "", "ddg_snippet": "Feb 9, 2025 · Abstract page for arXiv paper 2502 .05757: Large Language Model-based Nonnegative Matrix Factorization For Cardiorespiratory Sound Separation Jan 8, 2025 · View a PDF of the paper titled DAREK -- Distance Aware Error for Kolmogorov Networks, by Masoud Ataei and 2 other authors Apr 7, 2025 · In this paper, we investigate the complexity of the maximal common subsequence enumeration problem, which is both an important special case of maximal frequent subsequence mining and a generalization of the classic longest common subsequence (LCS) problem. We show the hardness of enumerating maximal common subsequences between multiple strings, ruling out the possibility of an \\emph {output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05757", "content": "Feb 9, 2025 · Abstract page for arXiv paper 2502 .05757: Large Language Model-based Nonnegative Matrix Factorization For Cardiorespiratory Sound Separation Jan 8, 2025 · View a PDF of the paper titled DAREK -- Distance Aware Error for Kolmogorov Networks, by Masoud Ataei and 2 other authors Apr 7, 2025 · In this paper, we investigate the complexity of the maximal common subsequence enumeration problem, which is both an important special case of maximal frequent subsequence mining and a generalization of the classic longest common subsequence (LCS) problem. We show the hardness of enumerating maximal common subsequences between multiple strings, ruling out the possibility of an \\emph {output ..."}
+{"idx": 3, "title": "Ava Labs: Digitize All The World's Assets", "date": "", "ddg_snippet": "Ava Labs makes it simple to deploy high-performance solutions for Web3, led by innovations on Avalanche.", "subpage_snippet": "", "source": "www.avalabs.org", "link": "https://www.avalabs.org/", "content": "Ava Labs makes it simple to deploy high-performance solutions for Web3, led by innovations on Avalanche."}
+{"idx": 4, "title": "Images in HTML export (The Org Manual)", "date": "", "ddg_snippet": "The HTML export backend has features to convert Org image links to HTML inline images and HTML clickable image links. When the link in the Org file has no description, the HTML export...", "subpage_snippet": "", "source": "orgmode.org", "link": "https://orgmode.org/manual/Images-in-HTML-export.html", "content": "The HTML export backend has features to convert Org image links to HTML inline images and HTML clickable image links. When the link in the Org file has no description, the HTML export..."}
+{"idx": 5, "title": "[2501.04757] DAREK -- Distance Aware Error for Kolmogorov ...", "date": "", "ddg_snippet": "Jan 8, 2025 · View a PDF of the paper titled DAREK -- Distance Aware Error for Kolmogorov Networks, by Masoud Ataei and 2 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.04757", "content": "Jan 8, 2025 · View a PDF of the paper titled DAREK -- Distance Aware Error for Kolmogorov Networks, by Masoud Ataei and 2 other authors"}
+{"idx": 6, "title": "[2504.04757] The Complexity of Maximal Common Subsequence ...", "date": "", "ddg_snippet": "Apr 7, 2025 · In this paper, we investigate the complexity of the maximal common subsequence enumeration problem, which is both an important special case of maximal frequent subsequence mining and a generalization of the classic longest common subsequence (LCS) problem. We show the hardness of enumerating maximal common subsequences between multiple strings, ruling out the possibility of an \\emph {output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.04757", "content": "Apr 7, 2025 · In this paper, we investigate the complexity of the maximal common subsequence enumeration problem, which is both an important special case of maximal frequent subsequence mining and a generalization of the classic longest common subsequence (LCS) problem. We show the hardness of enumerating maximal common subsequences between multiple strings, ruling out the possibility of an \\emph {output ..."}
+{"idx": 7, "title": "[ 2502 .21217] Dynamic Markov Blanket Detection for Macroscopic...", "date": "", "ddg_snippet": "Here, we leverage the FEP to develop a mathematical physics approach to the identification of objects, object types, and the macroscopic, object-type-specific rules that govern their behavior.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.21217", "content": "Here, we leverage the FEP to develop a mathematical physics approach to the identification of objects, object types, and the macroscopic, object-type-specific rules that govern their behavior."}
+{"idx": 8, "title": "ABS -169 Two days and one night, beautiful girls by rese", "date": "", "ddg_snippet": "Best Japan AV porn site, free forever, high speed, no lag, over 100,000 videos, daily update, no ads while playing video.", "subpage_snippet": "", "source": "123av.org", "link": "https://123av.org/en/abs-169", "content": "Best Japan AV porn site, free forever, high speed, no lag, over 100,000 videos, daily update, no ads while playing video."}
+{"idx": 9, "title": "Counter-Strike News & Coverage | HLTV. org", "date": "", "ddg_snippet": "Welcome to the leading Counter-Strike site in the world, featuring news, demos, pictures, statistics, on-site coverage and much much more!", "subpage_snippet": "", "source": "www.hltv.org", "link": "https://www.hltv.org/?)", "content": "Welcome to the leading Counter-Strike site in the world, featuring news, demos, pictures, statistics, on-site coverage and much much more!"}
diff --git a/data/sampled_jsons/httpsarxiv.orghtml2405.17618v3_Section_5.4.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2405.17618v3_Section_5.4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8cedf393cc57168a68d9d19e530fe2c05b618e1b
--- /dev/null
+++ b/data/sampled_jsons/httpsarxiv.orghtml2405.17618v3_Section_5.4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv . org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "DualShock Calibration GUI", "date": "", "ddg_snippet": "Sections below are not useful, just some debug infos or manual commands.", "subpage_snippet": "", "source": "dualshock-tools.github.io", "link": "https://dualshock-tools.github.io/", "content": "Sections below are not useful, just some debug infos or manual commands."}
+{"idx": 2, "title": "Анти-Плагио - Бесплатный АнтиПлагиат | Без регистрации, без...", "date": "", "ddg_snippet": "Проверяйте уникальность больших текстов и на ИИ онлайн бесплатно и без ограничений. Российский сервис Анти-Плагио поможет создать качественный контент.", "subpage_snippet": "", "source": "anti-plagio.ru", "link": "https://anti-plagio.ru/", "content": "Проверяйте уникальность больших текстов и на ИИ онлайн бесплатно и без ограничений. Российский сервис Анти-Плагио поможет создать качественный контент."}
+{"idx": 3, "title": "На какой цифре должен работать холодильник для оптимальной...", "date": "", "ddg_snippet": "7 июня 2025 Время чтения: 5 минут. Автор: Сергей Морозов. автор статьи: Сергей Морозов. Инженер-электрик / мастер по ремонту холодильников. Почему цифры на регуляторе холодильника — это не температура, а всего лишь ориентир? Давайте разберёмся...", "subpage_snippet": "", "source": "ledxtf.ru", "link": "https://ledxtf.ru/articles/nastroyka-i-ekspluatatsiya/node-564", "content": "7 июня 2025 Время чтения: 5 минут. Автор: Сергей Морозов. автор статьи: Сергей Морозов. Инженер-электрик / мастер по ремонту холодильников. Почему цифры на регуляторе холодильника — это не температура, а всего лишь ориентир? Давайте разберёмся..."}
+{"idx": 4, "title": "50 Оттенков Серого Смотреть Все Части Фильма 1, 2, 3 Подряд...", "date": "", "ddg_snippet": "Пятьдесят оттенков свободы (2018) 2018 Пятьдесят оттенков свободы (2018) kp: 5 . 4 imdb: 4.5.", "subpage_snippet": "", "source": "24lordfilm-50-ottenkov-serogo.ru", "link": "https://24lordfilm-50-ottenkov-serogo.ru/", "content": "Пятьдесят оттенков свободы (2018) 2018 Пятьдесят оттенков свободы (2018) kp: 5 . 4 imdb: 4.5."}
+{"idx": 5, "title": "Моя прекрасная жизнь Сериал 2023 2024 Турецкий на Русском...", "date": "", "ddg_snippet": "Драма, мелодрама. Режиссер: Чагры Байрак. В ролях: Хилал Алтынбилек, Онур Туна, Йигит Озшенер и др. Турецкий сериал под названием «Моя прекрасная жизнь» погружает зрителей в мир интриг и сложных человеческих отношений...", "subpage_snippet": "", "source": "russian-seriali.net", "link": "https://russian-seriali.net/1536-moja-prekrasnaja-zhizn-serial-2023-2024-tureckij-na-russkom-jazyke-vse-1-30-serii-podrjad.html", "content": "Драма, мелодрама. Режиссер: Чагры Байрак. В ролях: Хилал Алтынбилек, Онур Туна, Йигит Озшенер и др. Турецкий сериал под названием «Моя прекрасная жизнь» погружает зрителей в мир интриг и сложных человеческих отношений..."}
+{"idx": 6, "title": "Полный список категорий TVRezka", "date": "", "ddg_snippet": "Свингеры 4.5k.", "subpage_snippet": "", "source": "tvrezka.cc", "link": "https://tvrezka.cc/categories/", "content": "Свингеры 4.5k."}
+{"idx": 7, "title": "Конвертер из Word в PDF | Конвертируйте бесплатно документы...", "date": "", "ddg_snippet": "Создавайте PDF-файлы высокого качества при помощи нашего конвертера файлов Word в PDF онлайн. Просто выгрузите документ Word и нажмите кнопку, чтобы конвертировать в PDF. Вот так просто.", "subpage_snippet": "", "source": "smallpdf.com", "link": "https://smallpdf.com/ru/word-to-pdf", "content": "Создавайте PDF-файлы высокого качества при помощи нашего конвертера файлов Word в PDF онлайн. Просто выгрузите документ Word и нажмите кнопку, чтобы конвертировать в PDF. Вот так просто."}
+{"idx": 8, "title": "MEME INSTANTS", "date": "", "ddg_snippet": "САМЫМ ВИРУСНЫМ. Loading... Previous. 1. 2. 3. 4. 5. Next.", "subpage_snippet": "", "source": "memeinstants.com", "link": "https://memeinstants.com/", "content": "САМЫМ ВИРУСНЫМ. Loading... Previous. 1. 2. 3. 4. 5. Next."}
+{"idx": 9, "title": "MSI Afterburner - скачать бесплатно MSI Afterburner 4.6.5", "date": "", "ddg_snippet": "Бесплатно. Windows. Категория: Мониторинг. MSI Afterburner - функциональная утилита от компании MSI, которая предназначена для разгона видеокарт NVIDIA и AMD. С помощью программы можно регулировать напряжение питания GPU и видеопамяти...", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-34619-msi-afterburner.html", "content": "Бесплатно. Windows. Категория: Мониторинг. MSI Afterburner - функциональная утилита от компании MSI, которая предназначена для разгона видеокарт NVIDIA и AMD. С помощью программы можно регулировать напряжение питания GPU и видеопамяти..."}
diff --git a/data/sampled_jsons/httpsarxiv.orghtml2503.10694v1.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2503.10694v1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..37123d086c9de7e856f50c7328b38cf5b9bf69e1
--- /dev/null
+++ b/data/sampled_jsons/httpsarxiv.orghtml2503.10694v1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HTML Görüntüleyici - Çevrimiçi HTML Editörü ve Önizleme Aracı", "date": "", "ddg_snippet": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç.", "subpage_snippet": "", "source": "htmlonline.org", "link": "https://htmlonline.org/tr/", "content": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç."}
+{"idx": 1, "title": "Learn HTML - Free Interactive HTML Tutorial", "date": "", "ddg_snippet": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast.", "subpage_snippet": "", "source": "www.learn-html.org", "link": "https://www.learn-html.org/", "content": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast."}
+{"idx": 2, "title": "Login / Member's access to the HTML 6 Editor", "date": "", "ddg_snippet": "There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout.", "subpage_snippet": "", "source": "html6.com", "link": "https://html6.com/login/", "content": "There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout."}
+{"idx": 3, "title": "Link assoluti e relativi con a href | Guida HTML | HTML .it", "date": "", "ddg_snippet": "Tag a href - link assoluti e link relativi - conoscerli per organizzare il progetto HTML in directory e sottodirectory ed imparare a muoversi tra i file.", "subpage_snippet": "", "source": "www.html.it", "link": "https://www.html.it/pag/16048/i-percorsi-assoluti-e-relativi/", "content": "Tag a href - link assoluti e link relativi - conoscerli per organizzare il progetto HTML in directory e sottodirectory ed imparare a muoversi tra i file."}
+{"idx": 4, "title": "Presentation and Sorting (The Org Manual)", "date": "", "ddg_snippet": "Before displaying items in an agenda view, Org mode visually prepares the items and sorts them. Each item occupies a single line. The line starts with a prefix that contains the category (see...", "subpage_snippet": "", "source": "orgmode.org", "link": "https://orgmode.org/manual/Presentation-and-Sorting.html", "content": "Before displaying items in an agenda view, Org mode visually prepares the items and sorts them. Each item occupies a single line. The line starts with a prefix that contains the category (see..."}
+{"idx": 5, "title": "Matthew 17 - And after six days Jesus took with him Peter... | ESV. org", "date": "", "ddg_snippet": "And after six days Jesus took with him Peter and James, and John his brother, and led them up a high mountain by themselves. And he was transfigured before them, and his face shone like...", "subpage_snippet": "", "source": "www.esv.org", "link": "https://www.esv.org/Matthew+17/", "content": "And after six days Jesus took with him Peter and James, and John his brother, and led them up a high mountain by themselves. And he was transfigured before them, and his face shone like..."}
+{"idx": 6, "title": "HTML Tag »", "date": "", "ddg_snippet": "Defining the behavior of a element typically requires the use of JavaScript.", "subpage_snippet": "", "source": "html.com", "link": "https://html.com/tags/dialog/", "content": "Defining the behavior of a element typically requires the use of JavaScript."}
+{"idx": 7, "title": "51黑料网", "date": "", "ddg_snippet": "本站只为海外华人提供服务，非法律允许地区以及18岁以下用户禁止访问！ © 2025 51黑料网 https://www.51hl08.com Inc. All Rights Reserved.", "subpage_snippet": "", "source": "52cg1.org", "link": "https://52cg1.org/", "content": "本站只为海外华人提供服务，非法律允许地区以及18岁以下用户禁止访问！ © 2025 51黑料网 https://www.51hl08.com Inc. All Rights Reserved."}
+{"idx": 8, "title": "Explore | alphaXiv", "date": "", "ddg_snippet": "Discuss, discover, and read arXiv papers. Explore trending papers, see recent activity and discussions, and follow authors of arXiv papers on alphaXiv.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/", "content": "Discuss, discover, and read arXiv papers. Explore trending papers, see recent activity and discussions, and follow authors of arXiv papers on alphaXiv."}
+{"idx": 9, "title": "Carousel (Slide Show or Image Rotator) Pattern | APG | WAI | W3C", "date": "", "ddg_snippet": "Accessibility resources free online from the international standards organization: W3C Web Accessibility Initiative (WAI).", "subpage_snippet": "", "source": "www.w3.org", "link": "https://www.w3.org/WAI/ARIA/apg/patterns/carousel/", "content": "Accessibility resources free online from the international standards organization: W3C Web Accessibility Initiative (WAI)."}
diff --git a/data/sampled_jsons/httpsmarvinfli.github.iopublication2025-blink-eye_github.jsonl b/data/sampled_jsons/httpsmarvinfli.github.iopublication2025-blink-eye_github.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3a7af522255b3ba98efaed6bcab037afa5f3a89
--- /dev/null
+++ b/data/sampled_jsons/httpsmarvinfli.github.iopublication2025-blink-eye_github.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink of an Eye: A Simple Theory for Feature Localization in Generative ...", "date": "", "ddg_snippet": "We present a concise theoretical framework that explains sudden feature localization events—\"blinks of an eye\"—in diffusion and autoregressive generative models.", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/publication/2025-blink-eye", "content": "We present a concise theoretical framework that explains sudden feature localization events—\"blinks of an eye\"—in diffusion and autoregressive generative models."}
+{"idx": 1, "title": "GitHub - marvinfli/marvinfli.github.io", "date": "", "ddg_snippet": "Contribute to marvinfli/marvinfli. github .io development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/marvinfli/marvinfli.github.io", "content": "Contribute to marvinfli/marvinfli. github .io development by creating an account on GitHub ."}
+{"idx": 2, "title": "GitHub - marvinfli/critical-windows", "date": "", "ddg_snippet": "Contribute to marvinfli/critical-windows development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/marvinfli/critical-windows", "content": "Contribute to marvinfli/critical-windows development by creating an account on GitHub ."}
+{"idx": 3, "title": "In the Blink of an Eye: A Unified Theory for Feature Emergence in ...", "date": "", "ddg_snippet": "Hi, my name is Marvin LiIn the Blink of an Eye : A Unified Theory for Feature Emergence in Generative Models Published in Harvard College thesis, 2025 Recommended citation: Marvin Li. Download Paper Bluesky Facebook LinkedIn X (formerly Twitter) Previous Next", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/publication/2025-thesis", "content": "Hi, my name is Marvin LiIn the Blink of an Eye : A Unified Theory for Feature Emergence in Generative Models Published in Harvard College thesis, 2025 Recommended citation: Marvin Li. Download Paper Bluesky Facebook LinkedIn X (formerly Twitter) Previous Next"}
+{"idx": 4, "title": "Publications - Marvin Li", "date": "", "ddg_snippet": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models Published in International Conference on Machine Learning (Oral, top 1%), 2025 — Paper", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/publications/", "content": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models Published in International Conference on Machine Learning (Oral, top 1%), 2025 — Paper"}
+{"idx": 5, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Blink of an eye : a simple theory for feature localization in generative models Date: February 01, 2025 Share on Bluesky Facebook LinkedIn X (formerly Twitter) Previous Next", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/talks/2025-03-harvard-ml/", "content": "Blink of an eye : a simple theory for feature localization in generative models Date: February 01, 2025 Share on Bluesky Facebook LinkedIn X (formerly Twitter) Previous Next"}
+{"idx": 6, "title": "marvinfli · GitHub", "date": "", "ddg_snippet": "Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/marvinfli", "content": "Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users."}
+{"idx": 7, "title": "Hi, my name is Marvin. - Marvin Li", "date": "", "ddg_snippet": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models Marvin Li, Aayush Karan, Sitan Chen. ICML, 2025 (Oral, top 1% of submissions) arXiv / code A unifying theory showing why and when features suddenly \"lock in\" during generation in both diffusion and autoregressive models.", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/", "content": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models Marvin Li, Aayush Karan, Sitan Chen. ICML, 2025 (Oral, top 1% of submissions) arXiv / code A unifying theory showing why and when features suddenly \"lock in\" during generation in both diffusion and autoregressive models."}
+{"idx": 8, "title": "CV - Marvin Li", "date": "", "ddg_snippet": "CV Education Ph.D in Version Control Theory, GitHub University, 2018 (expected) M.S. in Jekyll, GitHub University, 2014 B.S. in GitHub , GitHub University, 2012 Work experience Spring 2024: Academic Pages Collaborator GitHub University Duties includes: Updates and improvements to template Supervisor: The Users Fall 2015: Research Assistant GitHub University Duties included: Merging pull ...", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/cv/", "content": "CV Education Ph.D in Version Control Theory, GitHub University, 2018 (expected) M.S. in Jekyll, GitHub University, 2014 B.S. in GitHub , GitHub University, 2012 Work experience Spring 2024: Academic Pages Collaborator GitHub University Duties includes: Updates and improvements to template Supervisor: The Users Fall 2015: Research Assistant GitHub University Duties included: Merging pull ..."}
+{"idx": 9, "title": "Posts by Collection - Marvin Li", "date": "", "ddg_snippet": "Blink of an eye : a simple theory for feature localization in generative models Published:February 01, 2025", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/collection-archive/", "content": "Blink of an eye : a simple theory for feature localization in generative models Published:February 01, 2025"}
diff --git a/data/sampled_jsons/httpsopenreview.netforumid=1BaC3AdG1i.jsonl b/data/sampled_jsons/httpsopenreview.netforumid=1BaC3AdG1i.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..736344f27b540321dd1090c7906b1e67cecec736
--- /dev/null
+++ b/data/sampled_jsons/httpsopenreview.netforumid=1BaC3AdG1i.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "I can't log in to my openreview account because I forgot... - Githubissues", "date": "", "ddg_snippet": "I have forgotten my login password for OpenReview , and due to a job change, my login email address (danqi@inftech.ai) is no longer in use.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/openreview/openreview/348", "content": "I have forgotten my login password for OpenReview , and due to a job change, my login email address (danqi@inftech.ai) is no longer in use."}
+{"idx": 1, "title": "Evaluating Multi-modal Language Models Through... | OpenReview", "date": "", "ddg_snippet": "Evaluating the cognitive abilities of Multi-modal Language Models (MLLMs) is challenging due to their reliance on spurious correlations.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=B2QXXIZXM3", "content": "Evaluating the cognitive abilities of Multi-modal Language Models (MLLMs) is challenging due to their reliance on spurious correlations."}
+{"idx": 2, "title": "M3-JEPA: Multimodal Alignment via Multi-gate MoE... | OpenReview", "date": "", "ddg_snippet": "Current multimodal learning strategies primarily optimize in the original token space. Such a framework is easy to incorporate with the backbone of pretrained language model, but might result in...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tYwKQMMjJA", "content": "Current multimodal learning strategies primarily optimize in the original token space. Such a framework is easy to incorporate with the backbone of pretrained language model, but might result in..."}
+{"idx": 3, "title": "Chatbot Arena: Benchmarking LLMs in the Wild with Elo... | LMSYS Org", "date": "", "ddg_snippet": "Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2023}, url={https:// openreview . net / forum ?id=uccHPGDlao} } @.", "subpage_snippet": "", "source": "lmsys.org", "link": "https://lmsys.org/blog/2023-05-03-arena/", "content": "Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2023}, url={https:// openreview . net / forum ?id=uccHPGDlao} } @."}
+{"idx": 4, "title": "【重磅】 ICLR2020 || 106篇深度强化学习顶会论文汇总-CSDN博客", "date": "", "ddg_snippet": "https:// openreview . net / forum ?id=ryxgJTEYDr. 标题. Reinforcement Learning With Competitive Ensembles Of Information-constrained Primitives.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/u9Oo9xkM169LeLDR84/article/details/105872394", "content": "https:// openreview . net / forum ?id=ryxgJTEYDr. 标题. Reinforcement Learning With Competitive Ensembles Of Information-constrained Primitives."}
+{"idx": 5, "title": "SWE-bench - бенчмарк проверки, могут ли языковые модели...", "date": "", "ddg_snippet": "LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. URL https:// openreview . net / forum ?id=nZeVKeeFYf9.", "subpage_snippet": "", "source": "llmstudio.ru", "link": "https://llmstudio.ru/blog/swe-bench", "content": "LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. URL https:// openreview . net / forum ?id=nZeVKeeFYf9."}
+{"idx": 6, "title": "Transformer メタサーベイ | PDF", "date": "", "ddg_snippet": "openreview . net / forum ?id=onxoVA9FxMw 位置embeddingの性質としてSinusoidal型と学習型を様々な条件で比較 タスクごとのお...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/transformer-247407256/247407256", "content": "openreview . net / forum ?id=onxoVA9FxMw 位置embeddingの性質としてSinusoidal型と学習型を様々な条件で比較 タスクごとのお..."}
+{"idx": 7, "title": "avepdf.com - Free Online PDF and Document Tools", "date": "", "ddg_snippet": "2024. Data-Centric Text-to-SQL with Large Language Models. In NeurIPS 2024 Third Table Represen-tation Learning Workshop. https:// openreview . net / forum ?id=gDKIjZcg93.", "subpage_snippet": "", "source": "vldb.org", "link": "https://vldb.org/pvldb/vol18/p5466-luo.pdf", "content": "2024. Data-Centric Text-to-SQL with Large Language Models. In NeurIPS 2024 Third Table Represen-tation Learning Workshop. https:// openreview . net / forum ?id=gDKIjZcg93."}
+{"idx": 8, "title": "Refactor สร้าง Optimizer สำหรับอัพเดท Parameter... - BUA Labs", "date": "", "ddg_snippet": "Continue reading \"Refactor สร้าง Optimizer สำหรับอัพเดท Parameter ของ Neural Network ในการเทรน Deep Learning – Neural Network ep.6\".", "subpage_snippet": "", "source": "www.bualabs.com", "link": "https://www.bualabs.com/archives/2042/refactor-parameter-optimizer-neural-network-train-deep-learning-machine-learning-neural-network-ep-6/", "content": "Continue reading \"Refactor สร้าง Optimizer สำหรับอัพเดท Parameter ของ Neural Network ในการเทรน Deep Learning – Neural Network ep.6\"."}
+{"idx": 9, "title": "DeepSeek-V3 Technical Report", "date": "", "ddg_snippet": "In 9th International Conference on Learning Representations, ICLR 2021. OpenReview . net , 2021.", "subpage_snippet": "", "source": "agenticai-learning.org", "link": "https://agenticai-learning.org/slides/d22.pdf", "content": "In 9th International Conference on Learning Representations, ICLR 2021. OpenReview . net , 2021."}
diff --git a/data/sampled_jsons/httpsopenreview.netforumid=lzdFImKK8w.jsonl b/data/sampled_jsons/httpsopenreview.netforumid=lzdFImKK8w.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d0c785dbda1d84a0715e092c149268de3d506549
--- /dev/null
+++ b/data/sampled_jsons/httpsopenreview.netforumid=lzdFImKK8w.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stored Object: paper: openreview .FFQ5T3EN18 - Githubissues", "date": "", "ddg_snippet": "{ \"sourceId\": \"openreview\", \"paperId\": \"FFQ5T3EN18\", \"url\": \"https:// openreview . net / forum ?id=FFQ5T3EN18\", \"title\": \"Rethinking Fine-tuning...", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/dmarx/papers-feed/5971", "content": "{ \"sourceId\": \"openreview\", \"paperId\": \"FFQ5T3EN18\", \"url\": \"https:// openreview . net / forum ?id=FFQ5T3EN18\", \"title\": \"Rethinking Fine-tuning..."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as... | OpenReview", "date": "", "ddg_snippet": "Due to the scarcity of...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "Due to the scarcity of..."}
+{"idx": 2, "title": "Denoising Pretrained Black-box Models via... | OpenReview", "date": "", "ddg_snippet": "Pre-trained models tend to inherit noisy label information from their training datasets, internalising it as biased knowledge.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=526fwttJiK", "content": "Pre-trained models tend to inherit noisy label information from their training datasets, internalising it as biased knowledge."}
+{"idx": 3, "title": "SWE-bench - бенчмарк проверки, могут ли языковые модели...", "date": "", "ddg_snippet": "LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. URL https:// openreview . net / forum ?id=nZeVKeeFYf9.", "subpage_snippet": "", "source": "llmstudio.ru", "link": "https://llmstudio.ru/blog/swe-bench", "content": "LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. URL https:// openreview . net / forum ?id=nZeVKeeFYf9."}
+{"idx": 4, "title": "Learning to Shop Like Humans: A Review-driven Retrieval-Augmented...", "date": "", "ddg_snippet": "OpenReview . net . https:// openreview . net / forum ?id=nZeVKeeFYf9. [12] Gautier Izacard and Edouard Grave. 2021. Distilling Knowledge from Reader to Retriever for Question Answering.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.00698", "content": "OpenReview . net . https:// openreview . net / forum ?id=nZeVKeeFYf9. [12] Gautier Izacard and Edouard Grave. 2021. Distilling Knowledge from Reader to Retriever for Question Answering."}
+{"idx": 5, "title": "Оценка неопределенности в задачах обработки естественного языка", "date": "", "ddg_snippet": "— OpenReview . net , 2021. — URL: https : / / openreview . net / forum ?id=jN5y-zb5Q7m .", "subpage_snippet": "", "source": "www.hse.ru", "link": "https://www.hse.ru/data/2025/09/19/160334575/Важенцев_резюме_ENG.pdf", "content": "— OpenReview . net , 2021. — URL: https : / / openreview . net / forum ?id=jN5y-zb5Q7m ."}
+{"idx": 6, "title": "Chatbot Arena: Benchmarking LLMs in the Wild with Elo... | LMSYS Org", "date": "", "ddg_snippet": "Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2023}, url={https:// openreview . net / forum ?id=uccHPGDlao} } @.", "subpage_snippet": "", "source": "lmsys.org", "link": "https://lmsys.org/blog/2023-05-03-arena/", "content": "Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2023}, url={https:// openreview . net / forum ?id=uccHPGDlao} } @."}
+{"idx": 7, "title": "DeepSeek-V3 Technical Report", "date": "", "ddg_snippet": "In 9th International Conference on Learning Representations, ICLR 2021. OpenReview . net , 2021.", "subpage_snippet": "", "source": "agenticai-learning.org", "link": "https://agenticai-learning.org/slides/d22.pdf", "content": "In 9th International Conference on Learning Representations, ICLR 2021. OpenReview . net , 2021."}
+{"idx": 8, "title": "Solve PDE Using Fourier Neural Operator - MATLAB & Simulink", "date": "", "ddg_snippet": "“Fourier Neural Operator for Parametric Partial Differential Equations,” 2020. https:// openreview . net / forum ?id=c8P9NQVtmnO.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/help/deeplearning/ug/solve-pde-using-fourier-neural-operator.html", "content": "“Fourier Neural Operator for Parametric Partial Differential Equations,” 2020. https:// openreview . net / forum ?id=c8P9NQVtmnO."}
+{"idx": 9, "title": "STEER-ME: Evaluating LLMs in Information Economics", "date": "", "ddg_snippet": "In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, Vienna, Austria, 2024. OpenReview . net .", "subpage_snippet": "", "source": "www.cs.ubc.ca", "link": "https://www.cs.ubc.ca/~kevinlb/papers/2025-STEER-Info-Econ-Workshop.pdf", "content": "In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, Vienna, Austria, 2024. OpenReview . net ."}
diff --git a/data/sampled_jsons/httpsopenreview.netpdfid=51x0dfsD8A.jsonl b/data/sampled_jsons/httpsopenreview.netpdfid=51x0dfsD8A.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ccfbd71f1791ddbb641eab647d4e6400b0967b9
--- /dev/null
+++ b/data/sampled_jsons/httpsopenreview.netpdfid=51x0dfsD8A.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Как конвертировать Word в PDF без изменения шрифта", "date": "", "ddg_snippet": "Узнайте самые простые способы конвертации документов Word в PDF без потери исходных шрифтов и форматирования.", "subpage_snippet": "", "source": "www.pdftools.net", "link": "https://www.pdftools.net/ru/how-to-convert-word-to-pdf-without-changing-font", "content": "Узнайте самые простые способы конвертации документов Word в PDF без потери исходных шрифтов и форматирования."}
+{"idx": 1, "title": "Конвертировать PDF в черно-белый и оттенки серого", "date": "", "ddg_snippet": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов.", "subpage_snippet": "", "source": "www.pdfhelp.net", "link": "https://www.pdfhelp.net/ru/grayscale-pdf", "content": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов."}
+{"idx": 2, "title": "SmartPDF - Edit your PDF files quickly and easily", "date": "", "ddg_snippet": "SmartPDF, online tool suite for your PDF files.", "subpage_snippet": "", "source": "smartpdf.net", "link": "https://smartpdf.net/", "content": "SmartPDF, online tool suite for your PDF files."}
+{"idx": 3, "title": "Towards Interpretable", "date": "", "ddg_snippet": "A Path Towards Autonomous Machine Intelligence Yann LeCun https:// openreview . net / pdf ?id=BZ5a1r-kVsf. Unsupervised Learning of Temporal...", "subpage_snippet": "", "source": "aigents.com", "link": "https://aigents.com/papers/2022/towards-interpretable-neurosymbolic-agi-2022.pdf", "content": "A Path Towards Autonomous Machine Intelligence Yann LeCun https:// openreview . net / pdf ?id=BZ5a1r-kVsf. Unsupervised Learning of Temporal..."}
+{"idx": 4, "title": "AI scaling laws: Universal guide estimates how LLMs will perform...", "date": "", "ddg_snippet": "More information: A Hitchhiker's Guide to Scaling Law Estimation: openreview . net / pdf ?id=KugSHTH0c8.", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-09-ai-scaling-laws-universal-llms.html", "content": "More information: A Hitchhiker's Guide to Scaling Law Estimation: openreview . net / pdf ?id=KugSHTH0c8."}
+{"idx": 5, "title": "Vibe codingをワンステップ引き上げる\"Dry-run\"について", "date": "", "ddg_snippet": "Making LLMs Program Interpreters via Execution Trace Chain of Thought. https:// openreview . net / pdf ?id=pFyBdPyOCQ.", "subpage_snippet": "", "source": "zenn.dev", "link": "https://zenn.dev/tesla/articles/e611a5f6e6f1e0", "content": "Making LLMs Program Interpreters via Execution Trace Chain of Thought. https:// openreview . net / pdf ?id=pFyBdPyOCQ."}
+{"idx": 6, "title": "1_SDF_Tutorial.ipynb - Colab", "date": "", "ddg_snippet": "https:// openreview . net / pdf ?id=UuHtdwRXkzw.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/gattia/ISB-2025-Shape-Modeling/blob/main/1_SDF_Tutorial.ipynb", "content": "https:// openreview . net / pdf ?id=UuHtdwRXkzw."}
+{"idx": 7, "title": "Mamba: S4 结构化状态空间模型剖析 — AIInfra AI基础设施", "date": "", "ddg_snippet": "MAMBA: LINEAR-TIME SEQUENCE MODELING WITH SELECTIVE STATE SPACES - OpenReview, accessed May 21, 2025, https:// openreview . net / pdf ?id=AL1fq05o7H. The Annotated S4...", "subpage_snippet": "", "source": "infrasys-ai.github.io", "link": "https://infrasys-ai.github.io/aiinfra-docs/06AlgoData03NewArch/05Mamba_SSM.html", "content": "MAMBA: LINEAR-TIME SEQUENCE MODELING WITH SELECTIVE STATE SPACES - OpenReview, accessed May 21, 2025, https:// openreview . net / pdf ?id=AL1fq05o7H. The Annotated S4..."}
+{"idx": 8, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — Abstract. Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real- world system. While numerous studies have ex-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "by Y Pan — Abstract. Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real- world system. While numerous studies have ex-."}
+{"idx": 9, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "by Y Pan — Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have ..."}
diff --git a/data/sampled_jsons/httpsopenreview.netpdfid=KQ1gI5qzAf.jsonl b/data/sampled_jsons/httpsopenreview.netpdfid=KQ1gI5qzAf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ee13da0cb688c5bbedc0beef7b3de018974dd7f
--- /dev/null
+++ b/data/sampled_jsons/httpsopenreview.netpdfid=KQ1gI5qzAf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scrape papers from OpenReview using OpenReview API", "date": "", "ddg_snippet": "Scrape papers from top conferences like ICML, ICLR, NeurIPS, etc using OpenReview API, by searching for specific keywords in title, abstract or keywords in the submissions and save them to a CSV file. Brings down the time taken to gather papers from several hours to a few minutes through automation", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pranftw/openreview_scraper", "content": "Scrape papers from top conferences like ICML, ICLR, NeurIPS, etc using OpenReview API, by searching for specific keywords in title, abstract or keywords in the submissions and save them to a CSV file. Brings down the time taken to gather papers from several hours to a few minutes through automation"}
+{"idx": 1, "title": "OpenReview Archive", "date": "", "ddg_snippet": "Minified React error #306; visit https ://react.dev/errors/306?args[]=undefined&args[]= for the full message or use the non-minified dev environment for full errors and additional helpful warnings.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=OpenReview.net/Archive", "content": "Minified React error #306; visit https ://react.dev/errors/306?args[]=undefined&args[]= for the full message or use the non-minified dev environment for full errors and additional helpful warnings."}
+{"idx": 2, "title": "What Have We Learned from OpenReview? - arXiv.org", "date": "", "ddg_snippet": "The o cial reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview. We will present the collected dataset of submissions and reviews from OpenReview, these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.05885v3", "content": "The o cial reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview. We will present the collected dataset of submissions and reviews from OpenReview, these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3."}
+{"idx": 3, "title": "(PDF) What Have We Learned from OpenReview? - ResearchGate", "date": "", "ddg_snippet": "OpenReview is such a platform that aims to promote openness in peer review process. The paper, (meta) reviews, rebuttals, and final decisions are all released to public.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/349963769_What_Have_We_Learned_from_OpenReview", "content": "OpenReview is such a platform that aims to promote openness in peer review process. The paper, (meta) reviews, rebuttals, and final decisions are all released to public."}
+{"idx": 4, "title": "What Have We Learned from OpenReview? - Springer", "date": "", "ddg_snippet": "The official reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview. We will present the col-lected dataset of submissions and reviews from OpenReview, these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-85896-4_6", "content": "The official reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview. We will present the col-lected dataset of submissions and reviews from OpenReview, these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3."}
+{"idx": 5, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 6, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "A platform promoting openness in scientific communication and the peer-review process.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mcx8IGneYw", "content": "A platform promoting openness in scientific communication and the peer-review process."}
+{"idx": 7, "title": "Open Problems and Fundamental Limitations of Reinforcement ... - OpenReview", "date": "", "ddg_snippet": "Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback Stephen Casper,∗ MIT CSAIL, scasper@mit.edu Xander Davies,∗ Harvard University", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=bx24KpJ4Eb", "content": "Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback Stephen Casper,∗ MIT CSAIL, scasper@mit.edu Xander Davies,∗ Harvard University"}
+{"idx": 8, "title": "Reviewer Guidelines - NeurIPS", "date": "", "ddg_snippet": "Reviewer Guidelines Thank you for agreeing to serve as a reviewer for NeurIPS 2022! This page provides an overview of reviewer responsibilities and key dates. Frequently Asked Questions You can find answers to FAQs here. Contact Information The Area Chair (AC) assigned to a paper should be your first point of contact for that paper. You can contact the AC by leaving a comment in OpenReview ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Conferences/2022/ReviewerGuidelines", "content": "Reviewer Guidelines Thank you for agreeing to serve as a reviewer for NeurIPS 2022! This page provides an overview of reviewer responsibilities and key dates. Frequently Asked Questions You can find answers to FAQs here. Contact Information The Area Chair (AC) assigned to a paper should be your first point of contact for that paper. You can contact the AC by leaving a comment in OpenReview ..."}
+{"idx": 9, "title": "Code for \"Picking Winning Tickets Before Training by ... - GitHub", "date": "", "ddg_snippet": "This repo contains the official implementations of Picking Winning Tickets Before Training by Preserving Gradient Flow. The config file for the experiments are under the directory of configs/.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/buffaloiron/GraSP", "content": "This repo contains the official implementations of Picking Winning Tickets Before Training by Preserving Gradient Flow. The config file for the experiments are under the directory of configs/."}
diff --git a/data/sampled_jsons/httpsopenreview.netpdfid=YuMEUNNpeb.jsonl b/data/sampled_jsons/httpsopenreview.netpdfid=YuMEUNNpeb.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ddd89ba628ca5771a184b44f0cc924585cf5e33
--- /dev/null
+++ b/data/sampled_jsons/httpsopenreview.netpdfid=YuMEUNNpeb.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - ksOAn6g5/TaiSu: TaiSu（太素）--a large-scale Chinese ... GitHub - JWLiang007/PFF: Official implementation of \"Poisoned ... Reviewed on OpenReview: https://openreview.net/forum?id ...", "date": "", "ddg_snippet": "TaiSu: A 166M Large-scale High-Quality Dataset for Chinese Vision-Language Pre-training This paper has been accepted by NeurIPS 2022. •paper link: https :// openreview . net / pdf ? id =iAxH-ikIP0I •Dataset Construction: 1.Data collection 2.Text-based filtering See full list on github.com Since most of the original urls are expired, we decided to directly provide the images and corresponding captions. To make the download process easier, we split the image set into more than 30 parts, and the captions are gathered in a single TXT file whose format of the content is shown in . In order to download Taisu, please send an Email to datasets_2022@outlook.com, indicating your organization in the email, we will give you feedback as soon as possible. The files with the suffix of '.tgz' need first to be uncompressed to a file with the suffix of '.tar' using the command line pigz -d baidu_images*.tgz . Even though a part of the images is damaged or lost because of some reasons, you can still access the most part of TaiSu's data. Each image and its captions can be matched by the id , for example, 'img1baiducomitu1848496827104259151'. Here is a tutorial that tells you how to download the files from BaiduCloud to your server: https ://blog.csdn. net /wxplol/article/details/115283527. Hope it can help you. See full list on github.com Models trained on the web data of TaiSu and on the complete data of TaiSu are now availbale. Baidu cloud link： https ://pan.baidu.com/s/1d3UKyQi7J4Qr1XE2j2V8og?pwd=0kjm •Example for usage: See full list on github.com Unless specifically labeled otherwise, these Datasets are provided to You under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (“CC BY-NC-SA 4.0”), with the additional terms included herein. The CC BY-NC-SA 4.0 may be accessed at https ://creativecommons.org/licenses/by-nc-sa/4.0/legalcode. When You download or use the Datasets from the Website or elsewhere, You are agreeing to comply with the terms of CC BY-NC-SA 4.0, and also agreeing to the Dataset Terms. Where these Dataset Terms conflict with the terms of CC BY-NC-SA 4.0, these Dataset Terms shall prevail. We reiterate once again that this dataset is used only for non-commercial purposes such as academic research, teaching, or scientific publications. We prohibits You from using the dataset or any derivative works for commercial purposes, such as selling data or using it for commercial gain. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately. See full list on github.com Email:datasets_2022@outlook.com Organization: Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China See full list on github.com Official implementation of \"Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection\" (ICLR 2024) - JWLiang007/PFF Abstract Machine learning models for text classification often excel on in-distribution ( ID ) data but struggle with unseen out-of-distribution (OOD) inputs. Most techniques for improving OOD robustness are not applicable to settings where the model is efectively a black box, such as when the weights are frozen, retraining is costly, or the model is leveraged via an API. Test-time augmentation ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ksOAn6g5/TaiSu", "content": "TaiSu: A 166M Large-scale High-Quality Dataset for Chinese Vision-Language Pre-training This paper has been accepted by NeurIPS 2022. •paper link: https :// openreview . net / pdf ? id =iAxH-ikIP0I •Dataset Construction: 1.Data collection 2.Text-based filtering See full list on github.com Since most of the original urls are expired, we decided to directly provide the images and corresponding captions. To make the download process easier, we split the image set into more than 30 parts, and the captions are gathered in a single TXT file whose format of the content is shown in . In order to download Taisu, please send an Email to datasets_2022@outlook.com, indicating your organization in the email, we will give you feedback as soon as possible. The files with the suffix of '.tgz' need first to be uncompressed to a file with the suffix of '.tar' using the command line pigz -d baidu_images*.tgz . Even though a part of the images is damaged or lost because of some reasons, you can still access the most part of TaiSu's data. Each image and its captions can be matched by the id , for example, 'img1baiducomitu1848496827104259151'. Here is a tutorial that tells you how to download the files from BaiduCloud to your server: https ://blog.csdn. net /wxplol/article/details/115283527. Hope it can help you. See full list on github.com Models trained on the web data of TaiSu and on the complete data of TaiSu are now availbale. Baidu cloud link： https ://pan.baidu.com/s/1d3UKyQi7J4Qr1XE2j2V8og?pwd=0kjm •Example for usage: See full list on github.com Unless specifically labeled otherwise, these Datasets are provided to You under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (“CC BY-NC-SA 4.0”), with the additional terms included herein. The CC BY-NC-SA 4.0 may be accessed at https ://creativecommons.org/licenses/by-nc-sa/4.0/legalcode. When You download or use the Datasets from the Website or elsewhere, You are agreeing to comply with the terms of CC BY-NC-SA 4.0, and also agreeing to the Dataset Terms. Where these Dataset Terms conflict with the terms of CC BY-NC-SA 4.0, these Dataset Terms shall prevail. We reiterate once again that this dataset is used only for non-commercial purposes such as academic research, teaching, or scientific publications. We prohibits You from using the dataset or any derivative works for commercial purposes, such as selling data or using it for commercial gain. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately. See full list on github.com Email:datasets_2022@outlook.com Organization: Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China See full list on github.com Official implementation of \"Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection\" (ICLR 2024) - JWLiang007/PFF Abstract Machine learning models for text classification often excel on in-distribution ( ID ) data but struggle with unseen out-of-distribution (OOD) inputs. Most techniques for improving OOD robustness are not applicable to settings where the model is efectively a black box, such as when the weights are frozen, retraining is costly, or the model is leveraged via an API. Test-time augmentation ..."}
+{"idx": 1, "title": "Конвертировать PDF в черно-белый и оттенки серого", "date": "", "ddg_snippet": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов.", "subpage_snippet": "", "source": "www.pdfhelp.net", "link": "https://www.pdfhelp.net/ru/grayscale-pdf", "content": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов."}
+{"idx": 2, "title": "Как конвертировать Word в PDF без изменения шрифта", "date": "", "ddg_snippet": "Узнайте самые простые способы конвертации документов Word в PDF без потери исходных шрифтов и форматирования.", "subpage_snippet": "", "source": "www.pdftools.net", "link": "https://www.pdftools.net/ru/how-to-convert-word-to-pdf-without-changing-font", "content": "Узнайте самые простые способы конвертации документов Word в PDF без потери исходных шрифтов и форматирования."}
+{"idx": 3, "title": "Word to PDF | Convert Word to PDF online for free", "date": "", "ddg_snippet": "Convert up to 1 GB files free, Pro users can convert up to 100 GB files; Sign up now. How to convert a Word to PDF file online. To convert a Word to PDF , drag and drop or click our upload...", "subpage_snippet": "", "source": "www.pdf.to", "link": "https://www.pdf.to/word-pdf/", "content": "Convert up to 1 GB files free, Pro users can convert up to 100 GB files; Sign up now. How to convert a Word to PDF file online. To convert a Word to PDF , drag and drop or click our upload..."}
+{"idx": 4, "title": "Разделить PDF", "date": "", "ddg_snippet": "Разделить PDF . Разделите PDF на части или оставьте в нем только необходимые вам страницы.", "subpage_snippet": "", "source": "pdf.io", "link": "https://pdf.io/ru/split/", "content": "Разделить PDF . Разделите PDF на части или оставьте в нем только необходимые вам страницы."}
+{"idx": 5, "title": "Position: Medical Large Language Model ... - OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · This position paper argues that current benchmarks for evaluating medical Large Language Models (LLMs), often derived from licensing exams like the USMLE, lack demonstrated relevance to real-world clinical practice. The authors advocate for prioritizing construct validity, a concept borrowed from psychometrics, which assesses whether a test truly measures the underlying capability it intends ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YuMEUNNpeb", "content": "May 1, 2025 · This position paper argues that current benchmarks for evaluating medical Large Language Models (LLMs), often derived from licensing exams like the USMLE, lack demonstrated relevance to real-world clinical practice. The authors advocate for prioritizing construct validity, a concept borrowed from psychometrics, which assesses whether a test truly measures the underlying capability it intends ..."}
+{"idx": 6, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "2 days ago · Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/", "content": "2 days ago · Promoting openness in scientific communication and the peer-review process"}
+{"idx": 7, "title": "A Path Towards Autonomous Machine Intelligence ... - OpenReview", "date": "", "ddg_snippet": "How could machines learn as e ciently as humans and animals? How could ma-chines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BZ5a1r-kVsf", "content": "How could machines learn as e ciently as humans and animals? How could ma-chines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent ..."}
+{"idx": 8, "title": "Reviewed on OpenReview: https://openreview.net/forum?id ...", "date": "", "ddg_snippet": "Abstract Machine learning models for text classification often excel on in-distribution ( ID ) data but struggle with unseen out-of-distribution (OOD) inputs. Most techniques for improving OOD robustness are not applicable to settings where the model is efectively a black box, such as when the weights are frozen, retraining is costly, or the model is leveraged via an API. Test-time augmentation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.08225", "content": "Abstract Machine learning models for text classification often excel on in-distribution ( ID ) data but struggle with unseen out-of-distribution (OOD) inputs. Most techniques for improving OOD robustness are not applicable to settings where the model is efectively a black box, such as when the weights are frozen, retraining is costly, or the model is leveraged via an API. Test-time augmentation ..."}
+{"idx": 9, "title": "How can I export all submission attachments with the ...", "date": "", "ddg_snippet": "Jul 19, 2024 · File \"C:\\Users\\dernoncourt\\ openreview \\get_all_pdfs.py\", line 26, in f = client.get_attachment(note. id , 'supplementary_material') ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"C:\\Users\\dernoncourt\\anaconda3\\envs\\ openreview \\Lib\\site-packages\\ openreview \\api\\client.py\", line 609, in get_attachment", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/78771514/how-can-i-export-all-submission-attachments-with-the-openreview-api", "content": "Jul 19, 2024 · File \"C:\\Users\\dernoncourt\\ openreview \\get_all_pdfs.py\", line 26, in f = client.get_attachment(note. id , 'supplementary_material') ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"C:\\Users\\dernoncourt\\anaconda3\\envs\\ openreview \\Lib\\site-packages\\ openreview \\api\\client.py\", line 609, in get_attachment"}
diff --git a/data/sampled_jsons/httpsopenreview.netpdfid=d2aGLPSpFz.jsonl b/data/sampled_jsons/httpsopenreview.netpdfid=d2aGLPSpFz.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0986220130162c68ce70842b5f985607f5d048d5
--- /dev/null
+++ b/data/sampled_jsons/httpsopenreview.netpdfid=d2aGLPSpFz.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Как конвертировать Word в PDF без изменения шрифта", "date": "", "ddg_snippet": "Узнайте самые простые способы конвертации документов Word в PDF без потери исходных шрифтов и форматирования.", "subpage_snippet": "", "source": "www.pdftools.net", "link": "https://www.pdftools.net/ru/how-to-convert-word-to-pdf-without-changing-font", "content": "Узнайте самые простые способы конвертации документов Word в PDF без потери исходных шрифтов и форматирования."}
+{"idx": 1, "title": "DEMO | PDF .ai | The best ChatPDF app", "date": "", "ddg_snippet": "We built the ultimate ChatPDF app that allows you to chat with any PDF : ask questions, get summaries, find anything you need!", "subpage_snippet": "", "source": "pdf.ai", "link": "https://pdf.ai/demo", "content": "We built the ultimate ChatPDF app that allows you to chat with any PDF : ask questions, get summaries, find anything you need!"}
+{"idx": 2, "title": "Конвертировать PDF в черно-белый и оттенки серого", "date": "", "ddg_snippet": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов.", "subpage_snippet": "", "source": "www.pdfhelp.net", "link": "https://www.pdfhelp.net/ru/grayscale-pdf", "content": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов."}
+{"idx": 3, "title": "SmartPDF - Edit your PDF files quickly and easily", "date": "", "ddg_snippet": "SmartPDF, online tool suite for your PDF files.", "subpage_snippet": "", "source": "smartpdf.net", "link": "https://smartpdf.net/", "content": "SmartPDF, online tool suite for your PDF files."}
+{"idx": 4, "title": "Towards Interpretable", "date": "", "ddg_snippet": "A Path Towards Autonomous Machine Intelligence Yann LeCun https:// openreview . net / pdf ?id=BZ5a1r-kVsf. Unsupervised Learning of Temporal...", "subpage_snippet": "", "source": "aigents.com", "link": "https://aigents.com/papers/2022/towards-interpretable-neurosymbolic-agi-2022.pdf", "content": "A Path Towards Autonomous Machine Intelligence Yann LeCun https:// openreview . net / pdf ?id=BZ5a1r-kVsf. Unsupervised Learning of Temporal..."}
+{"idx": 5, "title": "Vibe codingをワンステップ引き上げる\"Dry-run\"について", "date": "", "ddg_snippet": "Making LLMs Program Interpreters via Execution Trace Chain of Thought. https:// openreview . net / pdf ?id=pFyBdPyOCQ.", "subpage_snippet": "", "source": "zenn.dev", "link": "https://zenn.dev/tesla/articles/e611a5f6e6f1e0", "content": "Making LLMs Program Interpreters via Execution Trace Chain of Thought. https:// openreview . net / pdf ?id=pFyBdPyOCQ."}
+{"idx": 6, "title": "AI scaling laws: Universal guide estimates how LLMs will perform...", "date": "", "ddg_snippet": "More information: A Hitchhiker's Guide to Scaling Law Estimation: openreview . net / pdf ?id=KugSHTH0c8.", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-09-ai-scaling-laws-universal-llms.html", "content": "More information: A Hitchhiker's Guide to Scaling Law Estimation: openreview . net / pdf ?id=KugSHTH0c8."}
+{"idx": 7, "title": "Visualization and Analysis of the Loss Landscape in Graph Neural...", "date": "", "ddg_snippet": "// openreview . net / pdf ?id=rJXMpikCZ.International Conference on Learning Representations (2023), https:// openreview . net /.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.11792", "content": "// openreview . net / pdf ?id=rJXMpikCZ.International Conference on Learning Representations (2023), https:// openreview . net /."}
+{"idx": 8, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "by JL Gamella · Cited by 1 — We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=d2aGLPSpFz", "content": "by JL Gamella · Cited by 1 — We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work."}
+{"idx": 9, "title": "The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to...", "date": "", "ddg_snippet": "Shishir G. Patil 1 Huanzhi Mao 1 Fanjia Yan 1 Charlie Cheng-Jie Ji 1 Vishnu Suresh 1 Ion Stoica 1 Joseph E. Gonzalez 1. Abstract. Function calling, also called tool use, refers to an LLM’s...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2GmDdhBdDk", "content": "Shishir G. Patil 1 Huanzhi Mao 1 Fanjia Yan 1 Charlie Cheng-Jie Ji 1 Vishnu Suresh 1 Ion Stoica 1 Joseph E. Gonzalez 1. Abstract. Function calling, also called tool use, refers to an LLM’s..."}
diff --git a/data/sampled_jsons/httpsopenreview.netpdfid=lzdFImKK8w.jsonl b/data/sampled_jsons/httpsopenreview.netpdfid=lzdFImKK8w.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e414afc59595e157eb81865cd3e6f65cfde03725
--- /dev/null
+++ b/data/sampled_jsons/httpsopenreview.netpdfid=lzdFImKK8w.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Have We Learned from OpenReview? - arXiv.org", "date": "", "ddg_snippet": "The o cial reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview . We will present the collected dataset of submissions and reviews from OpenReview , these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.05885v3", "content": "The o cial reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview . We will present the collected dataset of submissions and reviews from OpenReview , these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3."}
+{"idx": 1, "title": "OpenReview", "date": "", "ddg_snippet": "OpenReview", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=B9LUI0pZFGc", "content": "OpenReview"}
+{"idx": 2, "title": "GitHub - asappresearch/halugen: https://openreview.net/pdf?id ...", "date": "", "ddg_snippet": "https :// openreview . net / pdf ? id =T1kZ0tdOtZM. Contribute to asappresearch/halugen development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/asappresearch/halugen", "content": "https :// openreview . net / pdf ? id =T1kZ0tdOtZM. Contribute to asappresearch/halugen development by creating an account on GitHub ."}
+{"idx": 3, "title": "Бесплатный конвертер JPG в PDF | Онлайн-конвертация...", "date": "", "ddg_snippet": "Конвертируйте JPG в PDF быстро онлайн! Наш бесплатный конвертер объединяет изображения в PDF . Не требуется загрузка программ.", "subpage_snippet": "", "source": "www.pdftools.net", "link": "https://www.pdftools.net/ru/jpg-to-pdf", "content": "Конвертируйте JPG в PDF быстро онлайн! Наш бесплатный конвертер объединяет изображения в PDF . Не требуется загрузка программ."}
+{"idx": 4, "title": "Конвертировать PDF в черно-белый и оттенки серого", "date": "", "ddg_snippet": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов.", "subpage_snippet": "", "source": "www.pdfhelp.net", "link": "https://www.pdfhelp.net/ru/grayscale-pdf", "content": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов."}
+{"idx": 5, "title": "PDF 365", "date": "", "ddg_snippet": "Edit, convert, merge, compress, and protect your PDF files for free. Fast, secure, and easy-to-use PDF tools powered by Stirling PDF – no sign-up, no installation required.", "subpage_snippet": "", "source": "www.pdf365pdf.com", "link": "https://www.pdf365pdf.com/", "content": "Edit, convert, merge, compress, and protect your PDF files for free. Fast, secure, and easy-to-use PDF tools powered by Stirling PDF – no sign-up, no installation required."}
+{"idx": 6, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF ... - OpenReview", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding, our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsuper-vised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coeficients of 0.3201 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "Compared to previous methods based on inverse folding, our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsuper-vised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coeficients of 0.3201 ..."}
+{"idx": 7, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "2 days ago · Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/", "content": "2 days ago · Promoting openness in scientific communication and the peer-review process"}
+{"idx": 8, "title": "A Path Towards Autonomous Machine Intelligence ... - OpenReview", "date": "", "ddg_snippet": "How could machines learn as e ciently as humans and animals? How could ma-chines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BZ5a1r-kVsf", "content": "How could machines learn as e ciently as humans and animals? How could ma-chines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent ..."}
+{"idx": 9, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of...", "date": "", "ddg_snippet": "by X Jiao · Cited by 7 — Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions. Download PDF · Xiaoran Jiao, Weian ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "by X Jiao · Cited by 7 — Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions. Download PDF · Xiaoran Jiao, Weian ..."}
diff --git a/data/sampled_jsons/httpsopenreview.netpdfid=mkuB677eMM.jsonl b/data/sampled_jsons/httpsopenreview.netpdfid=mkuB677eMM.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fd941c18c12d0ceb0bd45966e40a78fee3ad70ee
--- /dev/null
+++ b/data/sampled_jsons/httpsopenreview.netpdfid=mkuB677eMM.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Forum | OpenReview", "date": "", "ddg_snippet": "OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status. We gratefully acknowledge the support of the OpenReview Sponsors. © 2025 OpenReview .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mkuB677eMM", "content": "OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status. We gratefully acknowledge the support of the OpenReview Sponsors. © 2025 OpenReview ."}
+{"idx": 1, "title": "Search - OpenReview", "date": "", "ddg_snippet": "OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status. We gratefully acknowledge the support of the OpenReview Sponsors. © 2025 OpenReview .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search", "content": "OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status. We gratefully acknowledge the support of the OpenReview Sponsors. © 2025 OpenReview ."}
+{"idx": 2, "title": "What Have We Learned from OpenReview? - arXiv.org", "date": "", "ddg_snippet": "OpenReview releases not only the submission details of accepted papers but also that of rejected submissions. The rejected submissions might be put on arXiv.org or published in other venues to still attract citations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.05885v3", "content": "OpenReview releases not only the submission details of accepted papers but also that of rejected submissions. The rejected submissions might be put on arXiv.org or published in other venues to still attract citations."}
+{"idx": 3, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "2 days ago · OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status. We gratefully acknowledge the support of the OpenReview Sponsors. © 2025 OpenReview .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/", "content": "2 days ago · OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status. We gratefully acknowledge the support of the OpenReview Sponsors. © 2025 OpenReview ."}
+{"idx": 4, "title": "Как конвертировать Word в PDF без изменения шрифта", "date": "", "ddg_snippet": "Узнайте самые простые способы конвертации документов Word в PDF без потери исходных шрифтов и форматирования.", "subpage_snippet": "", "source": "www.pdftools.net", "link": "https://www.pdftools.net/ru/how-to-convert-word-to-pdf-without-changing-font", "content": "Узнайте самые простые способы конвертации документов Word в PDF без потери исходных шрифтов и форматирования."}
+{"idx": 5, "title": "DEMO | PDF .ai | The best ChatPDF app", "date": "", "ddg_snippet": "We built the ultimate ChatPDF app that allows you to chat with any PDF : ask questions, get summaries, find anything you need!", "subpage_snippet": "", "source": "pdf.ai", "link": "https://pdf.ai/demo", "content": "We built the ultimate ChatPDF app that allows you to chat with any PDF : ask questions, get summaries, find anything you need!"}
+{"idx": 6, "title": "Конвертировать PDF в черно-белый и оттенки серого", "date": "", "ddg_snippet": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов.", "subpage_snippet": "", "source": "www.pdfhelp.net", "link": "https://www.pdfhelp.net/ru/grayscale-pdf", "content": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов."}
+{"idx": 7, "title": "A Path Towards Autonomous Machine Intelligence ... - OpenReview", "date": "", "ddg_snippet": "How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BZ5a1r-kVsf", "content": "How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents."}
+{"idx": 8, "title": "Rich Guy Math Book - 1 PDF", "date": "", "ddg_snippet": "This guide helps the general reader with little mathematical experience grasp the concepts that underlie decisions concerning mortgage and insurance payments...", "subpage_snippet": "", "source": "1pdf.in", "link": "https://1pdf.in/rich-guy-math-book/", "content": "This guide helps the general reader with little mathematical experience grasp the concepts that underlie decisions concerning mortgage and insurance payments..."}
+{"idx": 9, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal ...", "date": "", "ddg_snippet": "Conclusion SimXRD : Https:// openreview . net / pdf ?id= mkuB677eMM 1: We introduce SimXRD, the largest open-source XRD pattern dataset for symmetry identification. 2: Data analysis reveals that the symmetry labels follow a long-tailed distribution.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "Conclusion SimXRD : Https:// openreview . net / pdf ?id= mkuB677eMM 1: We introduce SimXRD, the largest open-source XRD pattern dataset for symmetry identification. 2: Data analysis reveals that the symmetry labels follow a long-tailed distribution."}
diff --git a/data/sampled_jsons/httpsraw.githubusercontent.comfiveaiunderstanding_safety_finetuningmainconfigsmingpt_config.yaml.jsonl b/data/sampled_jsons/httpsraw.githubusercontent.comfiveaiunderstanding_safety_finetuningmainconfigsmingpt_config.yaml.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..da6d97c6853a9360bf242b907d1c3308de0a82bb
--- /dev/null
+++ b/data/sampled_jsons/httpsraw.githubusercontent.comfiveaiunderstanding_safety_finetuningmainconfigsmingpt_config.yaml.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - barry-far/V2ray- Config : Free V2ray Configs , Updating Every...", "date": "", "ddg_snippet": "Provide feedback. We read every piece of feedback, and take your input very seriously.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/barry-far/V2ray-Config", "content": "Provide feedback. We read every piece of feedback, and take your input very seriously."}
+{"idx": 1, "title": "Для тех, у кого мобильный интернет пускает только в ВК и на...", "date": "", "ddg_snippet": "Эта инструкция для версии 2.5.8. Включить свой моб. интернет. 2) Открыть прогу, поставить две верхние галочки.", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/wall-213417231_1299", "content": "Эта инструкция для версии 2.5.8. Включить свой моб. интернет. 2) Открыть прогу, поставить две верхние галочки."}
+{"idx": 2, "title": "donk - конфиг, прицел и настройки CS2 (Апрель 2025)", "date": "", "ddg_snippet": "Данил \"donk\" Крышковец родился 25 января 2007 года. В данный момент играет за команду Team Spirit на роли рифлера.", "subpage_snippet": "", "source": "configs-csgo.ru", "link": "https://configs-csgo.ru/players/donk.html", "content": "Данил \"donk\" Крышковец родился 25 января 2007 года. В данный момент играет за команду Team Spirit на роли рифлера."}
+{"idx": 3, "title": "AI slop coding, или Как создать нелепые длинные цепочки... / Хабр", "date": "", "ddg_snippet": "В процессе исследования вредоносных файлов, которые использовали группировки злоумышленников, мы наткнулись на интересные ранее незамеченные атаки, в...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/pt/articles/949372/", "content": "В процессе исследования вредоносных файлов, которые использовали группировки злоумышленников, мы наткнулись на интересные ранее незамеченные атаки, в..."}
+{"idx": 4, "title": "Kling AI", "date": "", "ddg_snippet": "Kling AI, tools for creating imaginative images and videos, based on state-of-art generative AI methods.", "subpage_snippet": "", "source": "klingai.com", "link": "https://klingai.com/global/", "content": "Kling AI, tools for creating imaginative images and videos, based on state-of-art generative AI methods."}
+{"idx": 5, "title": "Razer Synapse 4: Advanced Device Control and Lighting Software", "date": "", "ddg_snippet": "Razer Synapse 4 offers improved speed, smarter features, and a reimagined user interface for a seamless gaming adventure. Download now!", "subpage_snippet": "", "source": "www.razer.com", "link": "https://www.razer.com/synapse-4", "content": "Razer Synapse 4 offers improved speed, smarter features, and a reimagined user interface for a seamless gaming adventure. Download now!"}
+{"idx": 6, "title": "GitVerse — Платформа для работы с исходным кодом...", "date": "", "ddg_snippet": "GitVerse — платформа для разработчиков. Создавайте и развивайте проекты с открытым и закрытым исходным кодом. Загрузка репозиториев, импорт и зеркалирование из...", "subpage_snippet": "", "source": "gitverse.ru", "link": "https://gitverse.ru/home/", "content": "GitVerse — платформа для разработчиков. Создавайте и развивайте проекты с открытым и закрытым исходным кодом. Загрузка репозиториев, импорт и зеркалирование из..."}
+{"idx": 7, "title": "Revo Uninstaller Free – Легкое удаление нежелательных программ", "date": "", "ddg_snippet": "Revo Uninstaller поможет вам деинсталлировать приложения и легко удалить ненужные программы.", "subpage_snippet": "", "source": "www.revouninstaller.com", "link": "https://www.revouninstaller.com/ru/products/revo-uninstaller-free/", "content": "Revo Uninstaller поможет вам деинсталлировать приложения и легко удалить ненужные программы."}
+{"idx": 8, "title": "Figma Downloads | Web Design App for Desktops & Mobile", "date": "", "ddg_snippet": "Download the Figma web design app on desktop for macOS or Windows, plus the font installer and device preview apps.", "subpage_snippet": "", "source": "www.figma.com", "link": "https://www.figma.com/downloads/", "content": "Download the Figma web design app on desktop for macOS or Windows, plus the font installer and device preview apps."}
+{"idx": 9, "title": "10 Best AI Game Generators (September 2025) – Unite.AI", "date": "", "ddg_snippet": "In the ever-evolving landscape of game development, artificial intelligence (AI) has emerged as a game-changer. AI tools are revolutionizing the way games are developed, making the...", "subpage_snippet": "", "source": "www.unite.ai", "link": "https://www.unite.ai/best-ai-game-generators/", "content": "In the ever-evolving landscape of game development, artificial intelligence (AI) has emerged as a game-changer. AI tools are revolutionizing the way games are developed, making the..."}
diff --git a/data/sampled_jsons/initialization_scale_reasoning_behavior_language_models.jsonl b/data/sampled_jsons/initialization_scale_reasoning_behavior_language_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7cb7a9555f8ad2a2f7b70ddf3390c86a2d27ce86
--- /dev/null
+++ b/data/sampled_jsons/initialization_scale_reasoning_behavior_language_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04375", "content": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ..."}
+{"idx": 1, "title": "(Ir)rationality and cognitive biases in large language models", "date": "", "ddg_snippet": "Do large language models (LLMs) display rational reasoning ? LLMs have been shown to contain human biases due to the data they have been trained on; whether this is reflected in rational reasoning remains less clear. In this paper, we answer this question by evaluating seven language models using tasks from the cognitive psychology literature. We find that, like humans, LLMs display ...", "subpage_snippet": "", "source": "royalsocietypublishing.org", "link": "https://royalsocietypublishing.org/doi/10.1098/rsos.240255", "content": "Do large language models (LLMs) display rational reasoning ? LLMs have been shown to contain human biases due to the data they have been trained on; whether this is reflected in rational reasoning remains less clear. In this paper, we answer this question by evaluating seven language models using tasks from the cognitive psychology literature. We find that, like humans, LLMs display ..."}
+{"idx": 2, "title": "Emergent analogical reasoning in large language models", "date": "", "ddg_snippet": "Webb et al. show that new artificial intelligence language models , such as Generative Pre-trained Transformer 3, are able to solve analogical reasoning problems at a human-like level of performance.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41562-023-01659-w", "content": "Webb et al. show that new artificial intelligence language models , such as Generative Pre-trained Transformer 3, are able to solve analogical reasoning problems at a human-like level of performance."}
+{"idx": 3, "title": "PDF Why Language Models Hallucinate", "date": "", "ddg_snippet": "Abstract Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty. Such \"hallucinations\" persist even in state-of-the-art systems and undermine trust. We argue that language models hallucinate because the training and evaluation procedures reward guessing over acknowledging ...", "subpage_snippet": "", "source": "cdn.openai.com", "link": "https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4aaa5/why-language-models-hallucinate.pdf", "content": "Abstract Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty. Such \"hallucinations\" persist even in state-of-the-art systems and undermine trust. We argue that language models hallucinate because the training and evaluation procedures reward guessing over acknowledging ..."}
+{"idx": 4, "title": "An Analysis for Reasoning Bias of Language Models with Small...", "date": "", "ddg_snippet": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4HQaMUYWAT", "content": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ..."}
+{"idx": 5, "title": "Towards Reasoning Ability of Small Language Models", "date": "", "ddg_snippet": "Reasoning has long been viewed as an emergent property of large language models (LLMs), appearing at or above a certain scale ($\\\\sim$100B parameters). However, recent studies challenge this assumption, showing that small language models (SLMs) can also achieve competitive reasoning performance. SLMs are increasingly favored for their efficiency and deployability. However, there is a lack of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.11569", "content": "Reasoning has long been viewed as an emergent property of large language models (LLMs), appearing at or above a certain scale ($\\\\sim$100B parameters). However, recent studies challenge this assumption, showing that small language models (SLMs) can also achieve competitive reasoning performance. SLMs are increasingly favored for their efficiency and deployability. However, there is a lack of ..."}
+{"idx": 6, "title": "The State of LLM Reasoning Model Inference", "date": "", "ddg_snippet": "Improving the reasoning abilities of large language models (LLMs) has become one of the hottest topics in 2025, and for good reason. Stronger reasoning skills allow LLMs to tackle more complex problems, making them more capable across a wide range of tasks users care about. In the last few weeks, researchers have shared a large number of new strategies to improve reasoning , including scaling ...", "subpage_snippet": "", "source": "magazine.sebastianraschka.com", "link": "https://magazine.sebastianraschka.com/p/state-of-llm-reasoning-and-inference-scaling", "content": "Improving the reasoning abilities of large language models (LLMs) has become one of the hottest topics in 2025, and for good reason. Stronger reasoning skills allow LLMs to tackle more complex problems, making them more capable across a wide range of tasks users care about. In the last few weeks, researchers have shared a large number of new strategies to improve reasoning , including scaling ..."}
+{"idx": 7, "title": "Zhongwang Zhang (张众望) - Homepage", "date": "", "ddg_snippet": "An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang†, Zhi-Qin John Xu† This paper reveals how initialization scales shape transformer-based models' task preferences: smaller scales induce reasoning bias through structured embeddings, while larger scales promote memorization. We attribute this to differential label-driven embedding ...", "subpage_snippet": "", "source": "sjtuzzw.github.io", "link": "https://sjtuzzw.github.io/", "content": "An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang†, Zhi-Qin John Xu† This paper reveals how initialization scales shape transformer-based models' task preferences: smaller scales induce reasoning bias through structured embeddings, while larger scales promote memorization. We attribute this to differential label-driven embedding ..."}
+{"idx": 8, "title": "PDF Applied Math Ph.D. Seminar - amphds.yingzhouli.com", "date": "", "ddg_snippet": "Abstract: Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating excep-tional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas ...", "subpage_snippet": "", "source": "amphds.yingzhouli.com", "link": "https://amphds.yingzhouli.com/download_file/2025Spring/20250529.pdf", "content": "Abstract: Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating excep-tional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas ..."}
+{"idx": 9, "title": "Characteristics of the first attention module under small... | Download ...", "date": "", "ddg_snippet": "This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Characteristics-of-the-first-attention-module-under-small-initialization-g-08-A_fig2_388848015", "content": "This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models ..."}
diff --git a/data/sampled_jsons/internal_computations_password-locked_models_disanalogies_naturally-occurring_hidden_capabilities.jsonl b/data/sampled_jsons/internal_computations_password-locked_models_disanalogies_naturally-occurring_hidden_capabilities.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..09c977853343701e896d07a2e52819d7fd28b671
--- /dev/null
+++ b/data/sampled_jsons/internal_computations_password-locked_models_disanalogies_naturally-occurring_hidden_capabilities.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden ."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "... disanalogies between password - locked models and naturally- occurring hidden capabilities : 1. The internal computations that result in locking may be different.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/92923", "content": "... disanalogies between password - locked models and naturally- occurring hidden capabilities : 1. The internal computations that result in locking may be different."}
+{"idx": 2, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "between password - locked models and naturally-occurring hidden capabilities : 324. 1. The internal computations that result in locking may be different.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=uvvVjWP1aj&name=supplementary_material", "content": "between password - locked models and naturally-occurring hidden capabilities : 324. 1. The internal computations that result in locking may be different."}
+{"idx": 3, "title": "(PDF) Stress-Testing Capability Elicitation With Password - Locked ...", "date": "", "ddg_snippet": "2 Password - locked models : a toy model of hidden capabilities .bilities, we note a number of limitations of our work. First, there are several potential disanalogies . between password - locked models and naturally - occurring hidden capabilities", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381006018_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models", "content": "2 Password - locked models : a toy model of hidden capabilities .bilities, we note a number of limitations of our work. First, there are several potential disanalogies . between password - locked models and naturally - occurring hidden capabilities"}
+{"idx": 4, "title": "Stress-Testing Capability Elicitation With", "date": "", "ddg_snippet": "First, there are several potential disanalogies between password - locked models and naturally - occurring hidden capabilities : 1. The internal computations that result in locking may be different. Password - locked models can hard-code a simple conditional policy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.19550", "content": "First, there are several potential disanalogies between password - locked models and naturally - occurring hidden capabilities : 1. The internal computations that result in locking may be different. Password - locked models can hard-code a simple conditional policy."}
diff --git a/data/sampled_jsons/interventional_probability_calculation_causal_Bayesian_networks_methods_2010.jsonl b/data/sampled_jsons/interventional_probability_calculation_causal_Bayesian_networks_methods_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0a214f9310a762590012c2fe81e87326123a2a23
--- /dev/null
+++ b/data/sampled_jsons/interventional_probability_calculation_causal_Bayesian_networks_methods_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention, determinism, and the causal minimality condition", "date": "", "ddg_snippet": "As we will describe in detail below, causal Bayesian networks are defined by a postulate that relates counterfactual probability distributions of a set of variables that would result from various interventions, to the actual probability distribution of the variables, mediated by a given causal structure.", "subpage_snippet": "", "source": "www.cmu.edu", "link": "https://www.cmu.edu/dietrich/philosophy/docs/spirtes/synthese2010.pdf", "content": "As we will describe in detail below, causal Bayesian networks are defined by a postulate that relates counterfactual probability distributions of a set of variables that would result from various interventions, to the actual probability distribution of the variables, mediated by a given causal structure."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ..."}
+{"idx": 2, "title": "Bayesian Networks for Causal Analysis", "date": "", "ddg_snippet": "A Bayesian Network (BN) is a representation of a joint probability distribution of a set of random variables with probabilistic dependencies. It is a class of graphic models that consist of two parts, :", "subpage_snippet": "", "source": "www.sas.com", "link": "https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/2776-2018.pdf", "content": "A Bayesian Network (BN) is a representation of a joint probability distribution of a set of random variables with probabilistic dependencies. It is a class of graphic models that consist of two parts, :"}
+{"idx": 3, "title": "A Practical Introduction to Bayesian Estimation of Causal ...", "date": "", "ddg_snippet": "We introduce the Bayesian bootstrap as a method for performing standardization. Next, we move to the time-varying treatment and confounding setting where we discuss Bayesian g -computation with priors that promote sparsity. Causal inference in these settings requires estimation of a large number of nuisance parameters.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8640942/", "content": "We introduce the Bayesian bootstrap as a method for performing standardization. Next, we move to the time-varying treatment and confounding setting where we discuss Bayesian g -computation with priors that promote sparsity. Causal inference in these settings requires estimation of a large number of nuisance parameters."}
+{"idx": 4, "title": "Causal Inference with Bayesian Networks. Main Concepts and ...", "date": "", "ddg_snippet": "Potential outcomes framework (Rubin causal model), propensity score matching and structural causal models are, arguably, the most popular frameworks for observational causal inference. Here, we focus on the structural causal models and one particular type, Bayesian Networks . Interested users can find more details in the references below.", "subpage_snippet": "", "source": "causalnex.readthedocs.io", "link": "https://causalnex.readthedocs.io/en/latest/04_user_guide/04_user_guide.html", "content": "Potential outcomes framework (Rubin causal model), propensity score matching and structural causal models are, arguably, the most popular frameworks for observational causal inference. Here, we focus on the structural causal models and one particular type, Bayesian Networks . Interested users can find more details in the references below."}
+{"idx": 5, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "May 23, 2024 · In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380847642_Intervention_and_Conditioning_in_Causal_Bayesian_Networks", "content": "May 23, 2024 · In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities."}
+{"idx": 6, "title": "Causal inference calculus ( Bayesian Probability )", "date": "", "ddg_snippet": "There is a causal Markovian model as follows. enter image description here. By the definition of interventional probability , since do(x). makes no edges between X.is so-called do- calculus (Judea pearl, 2010 ). Then show that the summation over z2.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/2500933/causal-inference-calculus-bayesian-probability", "content": "There is a causal Markovian model as follows. enter image description here. By the definition of interventional probability , since do(x). makes no edges between X.is so-called do- calculus (Judea pearl, 2010 ). Then show that the summation over z2."}
+{"idx": 7, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DC28Fpk76s", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities ."}
+{"idx": 8, "title": "2405.14728 - Intervention and Conditioning in Causal Bayesian ...", "date": "", "ddg_snippet": "This paper presents a method to accurately compute interventional probabilities in Causal Bayesian Networks using observational data and independence assumptions.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2405.14728", "content": "This paper presents a method to accurately compute interventional probabilities in Causal Bayesian Networks using observational data and independence assumptions."}
+{"idx": 9, "title": "(PDF) Interventional probability of causation (IPoC) with...", "date": "", "ddg_snippet": "the specific causal model used to calculate it, as well as on. the specific intervention (such as removal or reduction of an. exposure) whose effect on risk of harm is to be calculated . II. Using a fully specified causal Bayesian network . model for IPoC calculations .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380636555_Interventional_probability_of_causation_IPoC_with_epidemiological_and_partial_mechanistic_evidence_benzene_vs_formaldehyde_and_acute_myeloid_leukemia_AML", "content": "the specific causal model used to calculate it, as well as on. the specific intervention (such as removal or reduction of an. exposure) whose effect on risk of harm is to be calculated . II. Using a fully specified causal Bayesian network . model for IPoC calculations ."}
diff --git a/data/sampled_jsons/k6ZHvF1vkg_Beyond_Optimism-_Exploration_With_Partially_Observable_Rewards_limitations_future_work_co.jsonl b/data/sampled_jsons/k6ZHvF1vkg_Beyond_Optimism-_Exploration_With_Partially_Observable_Rewards_limitations_future_work_co.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..33219320c9181ef500c28f02cadce70e82b90b08
--- /dev/null
+++ b/data/sampled_jsons/k6ZHvF1vkg_Beyond_Optimism-_Exploration_With_Partially_Observable_Rewards_limitations_future_work_co.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Keywords : reinforcement learning, partial observability , exploration , successor representations. TL;DR : Directed exploration with the successor representation for MDPs with partially observable rewards .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=k6ZHvF1vkg", "content": "Keywords : reinforcement learning, partial observability , exploration , successor representations. TL;DR : Directed exploration with the successor representation for MDPs with partially observable rewards ."}
+{"idx": 1, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/k6ZHvF1vkg@OpenReview", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 2, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "How can we efficiently explore and learn an optimal policy when rewards are partially observable , without relying on optimism and yet still have guarantees of convergence? In this paper, we present a novel exploration strategy based on the successor representation to tackle this question.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v1", "content": "How can we efficiently explore and learn an optimal policy when rewards are partially observable , without relying on optimism and yet still have guarantees of convergence? In this paper, we present a novel exploration strategy based on the successor representation to tackle this question."}
+{"idx": 3, "title": "(PDF) Beyond Optimism : Exploration With Partially Observable ...", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 4, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/784fd5a46dfe303e5b51c8621b84cf3f-Abstract-Conference.html", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 5, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "This paper explores exploration strategies in reinforcement learning (RL) problems with partially observable rewards . It introduces a new approach called \" Optimism Beyond Optimism \" (OBO) that aims to improve exploration in these challenging scenarios.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-optimism-exploration-partially-observable-rewards", "content": "This paper explores exploration strategies in reinforcement learning (RL) problems with partially observable rewards . It introduces a new approach called \" Optimism Beyond Optimism \" (OBO) that aims to improve exploration in these challenging scenarios."}
+{"idx": 6, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Beyond-Optimism%3A-Exploration-With-Partially-Observable-Rewards-b3830383-d78a-458d-9549-11668482a2c3", "content": "RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism ."}
+{"idx": 7, "title": "A New Approach to Exploration in Reinforcement Learning", "date": "", "ddg_snippet": "Title: Beyond Optimism : Exploration With Partially Observable Rewards . Abstract: Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not...", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-26-a-new-approach-to-exploration-in-reinforcement-learning--ak5d6mx", "content": "Title: Beyond Optimism : Exploration With Partially Observable Rewards . Abstract: Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not..."}
+{"idx": 8, "title": "Chapter 1: Markov Decision Processes and Partially Observable ...", "date": "", "ddg_snippet": "A Partially Observable Markov Decision Process is defined as the tuple (S, A, T, R, Ω, O), where the first four components (S,A,T,R) retain the same definitions as in standard MDPs . The additional components capture the partial observability structure", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@haitaowang076/chapter-1-markov-decision-processes-and-partially-observable-markov-decision-processes-18cbc8c55311", "content": "A Partially Observable Markov Decision Process is defined as the tuple (S, A, T, R, Ω, O), where the first four components (S,A,T,R) retain the same definitions as in standard MDPs . The additional components capture the partial observability structure"}
+{"idx": 9, "title": "Towards non-stochastic targeted exploration -Bohrium", "date": "", "ddg_snippet": "[7] Beyond Optimism : Exploration With Partially Observable Rewards . Exploration in reinforcement learning (RL) remains an open challenge. SSimone ParisiAAlireza Kazemipour.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/towards-non-stochastic-targeted-exploration/941395755220861070-108587", "content": "[7] Beyond Optimism : Exploration With Partially Observable Rewards . Exploration in reinforcement learning (RL) remains an open challenge. SSimone ParisiAAlireza Kazemipour."}
diff --git a/data/sampled_jsons/kEn7Wt6Yj2_On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching.jsonl b/data/sampled_jsons/kEn7Wt6Yj2_On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..81bceab0122d070f629198463bcdb1bf2047085c
--- /dev/null
+++ b/data/sampled_jsons/kEn7Wt6Yj2_On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "In this paper, we investigate the use of dif - ferential privacy for adaptive queries to search problems , which are significantly more challeng-ing since the responses to queries can reveal much more about the internal randomness than a single numerical query.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kEn7Wt6Yj2", "content": "In this paper, we investigate the use of dif - ferential privacy for adaptive queries to search problems , which are significantly more challeng-ing since the responses to queries can reveal much more about the internal randomness than a single numerical query."}
+{"idx": 1, "title": "On Differential Privacy for Adaptively Solving Search ... On Differential Privacy for Adaptively Solving Search ... Publications | Shiyuan Feng（冯施源） On Differential Privacy for Adaptively Solving Search ... Differentially private query release through adaptive projection On Differential Privacy and Adaptive Data Analysis with ... GitHub Pages - Ying Feng", "date": "", "ddg_snippet": "Jun 5, 2025 · In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query. In this paper, we investigate the use of dif - ferential privacy for adaptive queries to search problems , which are significantly more challeng-ing since the responses to queries can reveal much more about the internal randomness than a single numerical query. A simple, whitespace theme for academics. Based on [*folio] (https://github.com/bogoli/-folio) design. This paper explores new ways to use a concept called differential privacy to improve data structures that help answer questions about data, especially in machine learning. We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like k-way marginals, subject to differential privacy . Theorem 1.11 ([16], informal). Every diferentially private algorithm √ for solving the 1-way marginal problem with parameter d must have sample complexity n = Ω( d). Publications 2025 On Differential Privacy for Adaptively Solving Search Problems via Sketching with Shiyuan Feng, George Z. Li, Zhao Song, David P. Woodruff, and Lichen Zhang In ICML 2025 (Selected for Oral Presentation) arXiv", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.05503", "content": "Jun 5, 2025 · In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query. In this paper, we investigate the use of dif - ferential privacy for adaptive queries to search problems , which are significantly more challeng-ing since the responses to queries can reveal much more about the internal randomness than a single numerical query. A simple, whitespace theme for academics. Based on [*folio] (https://github.com/bogoli/-folio) design. This paper explores new ways to use a concept called differential privacy to improve data structures that help answer questions about data, especially in machine learning. We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like k-way marginals, subject to differential privacy . Theorem 1.11 ([16], informal). Every diferentially private algorithm √ for solving the 1-way marginal problem with parameter d must have sample complexity n = Ω( d). Publications 2025 On Differential Privacy for Adaptively Solving Search Problems via Sketching with Shiyuan Feng, George Z. Li, Zhao Song, David P. Woodruff, and Lichen Zhang In ICML 2025 (Selected for Oral Presentation) arXiv"}
+{"idx": 2, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "This paper explores new ways to use a concept called differential privacy to improve data structures that help answer questions about data, especially in machine learning.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44265/paper", "content": "This paper explores new ways to use a concept called differential privacy to improve data structures that help answer questions about data, especially in machine learning."}
+{"idx": 3, "title": "Differentially private query release through adaptive projection", "date": "", "ddg_snippet": "We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like k-way marginals, subject to differential privacy .", "subpage_snippet": "", "source": "www.amazon.science", "link": "https://www.amazon.science/publications/differentially-private-query-release-through-adaptive-projection", "content": "We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like k-way marginals, subject to differential privacy ."}
+{"idx": 4, "title": "On Differential Privacy and Adaptive Data Analysis with ...", "date": "", "ddg_snippet": "Theorem 1.11 ([16], informal). Every diferentially private algorithm √ for solving the 1-way marginal problem with parameter d must have sample complexity n = Ω( d).", "subpage_snippet": "", "source": "eprint.iacr.org", "link": "https://eprint.iacr.org/2023/171.pdf", "content": "Theorem 1.11 ([16], informal). Every diferentially private algorithm √ for solving the 1-way marginal problem with parameter d must have sample complexity n = Ω( d)."}
+{"idx": 5, "title": "On Differential Privacy for Adaptively Solving Search Problems via ...", "date": "", "ddg_snippet": "We focus on two classical search problems : nearest neighbor queries and regression with arbitrary turnstile updates.- differentially private if for any two databases. S𝑆Sitalic_S.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05503v1", "content": "We focus on two classical search problems : nearest neighbor queries and regression with arbitrary turnstile updates.- differentially private if for any two databases. S𝑆Sitalic_S."}
+{"idx": 6, "title": "ICML Poster On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "We focus on two classical search problems : nearest neighbor queries and regression with arbitrary turnstile updates.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44265", "content": "We focus on two classical search problems : nearest neighbor queries and regression with arbitrary turnstile updates."}
+{"idx": 7, "title": "Improved Differential Privacy for SGD via Optimal", "date": "", "ddg_snippet": "2 Privacy for adaptive streamsI Background on Differential Privacy ...address a key problem : under what conditions is the resulting mechanism adaptively private ?", "subpage_snippet": "", "source": "web.math.wisc.edu", "link": "https://web.math.wisc.edu/~denissov/data1.pdf", "content": "2 Privacy for adaptive streamsI Background on Differential Privacy ...address a key problem : under what conditions is the resulting mechanism adaptively private ?"}
+{"idx": 8, "title": "DPack: Efficiency-Oriented Privacy Budget Scheduling", "date": "", "ddg_snippet": "solve the problem only on the blocks with contention, and.We show that the Rényi formulation of differential privacy generates a new variation of the multidimensional knapsack problem , making standard approximations and heuristics unsuitable. Scheduling trade-offs.", "subpage_snippet": "", "source": "www.cs.columbia.edu", "link": "https://www.cs.columbia.edu/~junfeng/papers/dpack-eurosys25.pdf", "content": "solve the problem only on the blocks with contention, and.We show that the Rényi formulation of differential privacy generates a new variation of the multidimensional knapsack problem , making standard approximations and heuristics unsuitable. Scheduling trade-offs."}
+{"idx": 9, "title": "GitHub Pages - Ying Feng", "date": "", "ddg_snippet": "Publications 2025 On Differential Privacy for Adaptively Solving Search Problems via Sketching with Shiyuan Feng, George Z. Li, Zhao Song, David P. Woodruff, and Lichen Zhang In ICML 2025 (Selected for Oral Presentation) arXiv", "subpage_snippet": "", "source": "yinggggfeng.github.io", "link": "https://yinggggfeng.github.io/", "content": "Publications 2025 On Differential Privacy for Adaptively Solving Search Problems via Sketching with Shiyuan Feng, George Z. Li, Zhao Song, David P. Woodruff, and Lichen Zhang In ICML 2025 (Selected for Oral Presentation) arXiv"}
diff --git a/data/sampled_jsons/lambda_m_=_1_disease-matching_constraint_stage_1_-patent.jsonl b/data/sampled_jsons/lambda_m_=_1_disease-matching_constraint_stage_1_-patent.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aec41a295efeddea9cbff8e8ca7ccd5ebd86ec13
--- /dev/null
+++ b/data/sampled_jsons/lambda_m_=_1_disease-matching_constraint_stage_1_-patent.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Towards a Universal 3D Medical Multi-modality Generalization", "date": "", "ddg_snippet": "Hypothesis 3. 1 rigorously details the constraints and properties of 𝕏 h subscript 𝕏 ℎ \\mathbb{X}_{h} blackboard_X start_POSTSUBSCRIPT ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.06106v4", "content": "Hypothesis 3. 1 rigorously details the constraints and properties of 𝕏 h subscript 𝕏 ℎ \\mathbb{X}_{h} blackboard_X start_POSTSUBSCRIPT ..."}
+{"idx": 1, "title": "DocCHA: Towards LLM-Augmented Interactive Online diagnosis", "date": "", "ddg_snippet": "... modular framework that emulates clinical reasoning by decomposing the diagnostic process into three stages : ( 1 ) symptom elicitation, (2) history ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07870v1", "content": "... modular framework that emulates clinical reasoning by decomposing the diagnostic process into three stages : ( 1 ) symptom elicitation, (2) history ..."}
+{"idx": 2, "title": "AIP: Subverting Retrieval-Augmented Generation via Adversarial", "date": "", "ddg_snippet": "... stages : ( 1 ) prompt and document initialization , which uses an LLM-guided iterative strategy to identify a trigger that associates the adversarial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15159v1", "content": "... stages : ( 1 ) prompt and document initialization , which uses an LLM-guided iterative strategy to identify a trigger that associates the adversarial ..."}
+{"idx": 3, "title": "Can language-guided unsupervised adaptation improve medical", "date": "", "ddg_snippet": "Fig. 1 : The MedUnA framework: (a) Adapter Pre-training: A textual classifier is trained to classify the LLM-generated descriptions for a disease .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.02729v2", "content": "Fig. 1 : The MedUnA framework: (a) Adapter Pre-training: A textual classifier is trained to classify the LLM-generated descriptions for a disease ."}
+{"idx": 4, "title": "The Bayesian Quest – Unraveling the Enigma of Data Science", "date": "", "ddg_snippet": "Stage 1 : Regress treatment (education) on instrument (compulsory schooling) stage1_model = LinearRegression() ... 16 we reshape the data for ...", "subpage_snippet": "", "source": "bayesianquest.com", "link": "https://bayesianquest.com/", "content": "Stage 1 : Regress treatment (education) on instrument (compulsory schooling) stage1_model = LinearRegression() ... 16 we reshape the data for ..."}
+{"idx": 5, "title": "ProtoECGNet: Case-Based Interpretable Deep Learning for", "date": "", "ddg_snippet": "... 1 ,2 \\Email sethis@uchicago.edu \\Name David Chen 2 \\Email chend5@uchicago.edu \\Name Thomas Statchen 1 ,2 \\Email tstatchen@uchicago.edu \\Name Michael C.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.08713v1", "content": "... 1 ,2 \\Email sethis@uchicago.edu \\Name David Chen 2 \\Email chend5@uchicago.edu \\Name Thomas Statchen 1 ,2 \\Email tstatchen@uchicago.edu \\Name Michael C."}
+{"idx": 6, "title": "Newest 'compiler-bug' Questions - Stack Overflow", "date": "", "ddg_snippet": "The following code generates an error on clang 19. 1 .0, but passes on older versions, gcc and msvc (live example). ... minimal reproducible example in ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/tagged/compiler-bug", "content": "The following code generates an error on clang 19. 1 .0, but passes on older versions, gcc and msvc (live example). ... minimal reproducible example in ..."}
+{"idx": 7, "title": "Yuhang Li | Sugaku", "date": "", "ddg_snippet": "Could we train AI models 1000x larger than current ones ? Could we do this and also have them perform inference locally and privately on edge devices ...", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/A5100443277/", "content": "Could we train AI models 1000x larger than current ones ? Could we do this and also have them perform inference locally and privately on edge devices ..."}
+{"idx": 8, "title": "(PDF) Query-based Knowledge Transfer for Heterogeneous Learning", "date": "", "ddg_snippet": "... significantly outperforms existing collaborative learning methods by an average of 20.91\\% points in single-class query settings and an average of 14 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390773634_Query-based_Knowledge_Transfer_for_Heterogeneous_Learning_Environments", "content": "... significantly outperforms existing collaborative learning methods by an average of 20.91\\% points in single-class query settings and an average of 14 ..."}
+{"idx": 9, "title": "Online Signals of Extremist Mobilization - Olivia Brown, Laura", "date": "", "ddg_snippet": "... mobilization, we compared the online communications data of 26 people who subsequently mobilized to right-wing extremist action and 48 people who held ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/01461672241266866", "content": "... mobilization, we compared the online communications data of 26 people who subsequently mobilized to right-wing extremist action and 48 people who held ..."}
diff --git a/data/sampled_jsons/language_model_initialization_speech_models_perplexity.jsonl b/data/sampled_jsons/language_model_initialization_speech_models_perplexity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0dabe2d959db5867cb90a1633466bf37a25b5bdf
--- /dev/null
+++ b/data/sampled_jsons/language_model_initialization_speech_models_perplexity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Change your display language on Google - Computer - Google Search...", "date": "", "ddg_snippet": "You can set your preferred language for buttons and other display text that appears in Google Search. Tip: This doesn’t change the language of your search results.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/websearch/answer/3333234?hl=en&co=GENIE.Platform=Desktop", "content": "You can set your preferred language for buttons and other display text that appears in Google Search. Tip: This doesn’t change the language of your search results."}
+{"idx": 1, "title": "How Google knows what language to show in search results", "date": "", "ddg_snippet": "Here’s how it works: Google figures out the language of the results automatically. You can pick a language if you want. How Google knows what language to use Many people use different languages when they search on Google. To help them, Google considers a few factors to guess the best language for the results.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/websearch/answer/13511324?hl=en-IN", "content": "Here’s how it works: Google figures out the language of the results automatically. You can pick a language if you want. How Google knows what language to use Many people use different languages when they search on Google. To help them, Google considers a few factors to guess the best language for the results."}
+{"idx": 2, "title": "Change your language on the web", "date": "", "ddg_snippet": "These instructions are to change your preferred language used in Google services on the web only. To change the preferred language for your mobile apps, update the language settings on your device.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/accounts/answer/32047?hl=en-CO&co=GENIE.Platform=Desktop", "content": "These instructions are to change your preferred language used in Google services on the web only. To change the preferred language for your mobile apps, update the language settings on your device."}
+{"idx": 3, "title": "Change Gemini's language - Computer - Gemini Apps Help", "date": "", "ddg_snippet": "Change Gemini's language You can choose the language Gemini Apps display, and in certain cases, understand in Language settings. This setting changes the language for the menu, notifications, and other text in Gemini Apps. It also affects the languages that you can talk to Gemini in when you say “Hey Google” or use the mic in the prompt field.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/gemini/answer/15984485?hl=en&co=GENIE.Platform=Desktop", "content": "Change Gemini's language You can choose the language Gemini Apps display, and in certain cases, understand in Language settings. This setting changes the language for the menu, notifications, and other text in Gemini Apps. It also affects the languages that you can talk to Gemini in when you say “Hey Google” or use the mic in the prompt field."}
+{"idx": 4, "title": "Choose keyboard language & special characters - Chromebook Help", "date": "", "ddg_snippet": "Choose keyboard language & special characters You can use different keyboard languages, sometimes called input methods, on your Chromebook to: Change your typing language Use special characters, like accent marks or currency symbols Set your keyboard languages At the bottom right, select the time. Select Settings .", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/chromebook/answer/1059492?hl=en-en", "content": "Choose keyboard language & special characters You can use different keyboard languages, sometimes called input methods, on your Chromebook to: Change your typing language Use special characters, like accent marks or currency symbols Set your keyboard languages At the bottom right, select the time. Select Settings ."}
+{"idx": 5, "title": "Translate pages and change Chrome languages", "date": "", "ddg_snippet": "You can use Chrome to translate pages. You can also change your preferred language in Chrome. Translate pages in Chrome You can use Chrome to translate a page into other languages.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/chrome/answer/173424?hl=en&co=GENIE.Platform=Desktop", "content": "You can use Chrome to translate pages. You can also change your preferred language in Chrome. Translate pages in Chrome You can use Chrome to translate a page into other languages."}
+{"idx": 6, "title": "My language has been changed automatically and I can't understand...", "date": "", "ddg_snippet": "Dec 8, 2024 · My language has been changed automatically and I can't understand that language I want to change I want to change language but it's not changing", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/gemini/thread/312259625/my-language-has-been-changed-automatically-and-i-can-t-understand-that-language-i-want-to-change?hl=en", "content": "Dec 8, 2024 · My language has been changed automatically and I can't understand that language I want to change I want to change language but it's not changing"}
+{"idx": 7, "title": "Language targeting - Display & Video 360 Help - Google Help", "date": "", "ddg_snippet": "Use language targeting to target people based on their language . How does language targeting work? Open a line item, navigate to Targeting Language then select one or more languages to target or exclude (note you can only target languages by inclusion or exclusion, not both). Display & Video 360's language targeting targets impressions based on the languages of an individual bid request, which ...", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/displayvideo/answer/2697406?hl=en", "content": "Use language targeting to target people based on their language . How does language targeting work? Open a line item, navigate to Targeting Language then select one or more languages to target or exclude (note you can only target languages by inclusion or exclusion, not both). Display & Video 360's language targeting targets impressions based on the languages of an individual bid request, which ..."}
+{"idx": 8, "title": "Change language or region settings on a Pixel phone or tablet", "date": "", "ddg_snippet": "You can change the language or region your Pixel phone or tablet uses. Change language settings Open your device's Settings app. Tap System", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/pixelphone/answer/12571227?hl=en", "content": "You can change the language or region your Pixel phone or tablet uses. Change language settings Open your device's Settings app. Tap System"}
+{"idx": 9, "title": "Translate written words - Computer - Google Translate Help", "date": "", "ddg_snippet": "On your computer, open Google Translate. At the top of the screen, select the languages to translate. From: Choose a language or select Detect language . To: Select the language that you want the translation in. In the text box on the left, enter the text you want to translate. Choose what you want to do: Look up details: To check available details for each result, such as definitions ...", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/translate/answer/6142478?hl=en&co=GENIE.Platform=Desktop", "content": "On your computer, open Google Translate. At the top of the screen, select the languages to translate. From: Choose a language or select Detect language . To: Select the language that you want the translation in. In the text box on the left, enter the text you want to translate. Choose what you want to do: Look up details: To check available details for each result, such as definitions ..."}
diff --git a/data/sampled_jsons/lazy_witness_complex_computational_advantage_Vietoris-Rips_persistent_homology.jsonl b/data/sampled_jsons/lazy_witness_complex_computational_advantage_Vietoris-Rips_persistent_homology.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dc9fd2b9d9e1eca40709df6b404872c2a6095ccf
--- /dev/null
+++ b/data/sampled_jsons/lazy_witness_complex_computational_advantage_Vietoris-Rips_persistent_homology.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Topological Data Analysis with -net Induced Lazy Witness Complex", "date": "", "ddg_snippet": "A sim-plicial representation facilitates computation of basic topological objects such as simplicial complexes, ltrations, and persistent homologies. Thus, researchers devised approximations of the Cech complex as well as its best possible approxi-mation the Vietoris-Rips complex [7,26,10].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1906.06122", "content": "A sim-plicial representation facilitates computation of basic topological objects such as simplicial complexes, ltrations, and persistent homologies. Thus, researchers devised approximations of the Cech complex as well as its best possible approxi-mation the Vietoris-Rips complex [7,26,10]."}
+{"idx": 1, "title": "PERSISTENT HOMOLOGY AND SPARSE VIETORIS-RIPS FILTRATION", "date": "", "ddg_snippet": "The construction of the Vietoris-Rips Complex is computationally expensive, so we will develop an improved ltration method: the sparse Vietoris-Rips ltration. The sparse ltration selects some data points from the whole dataset to set up the Vietoris-Rips complexes, which reduces the number of simplices in the ltration, but preserves the ...", "subpage_snippet": "", "source": "www.math.uchicago.edu", "link": "https://www.math.uchicago.edu/~may/REU2017/REUPapers/Zhang,yujie.pdf", "content": "The construction of the Vietoris-Rips Complex is computationally expensive, so we will develop an improved ltration method: the sparse Vietoris-Rips ltration. The sparse ltration selects some data points from the whole dataset to set up the Vietoris-Rips complexes, which reduces the number of simplices in the ltration, but preserves the ..."}
+{"idx": 2, "title": "net Induced Lazy Witness Complex for Efficient Topological ...", "date": "", "ddg_snippet": "If the landmark set L is an ε-net of the point cloud P, the following interleaving of lazy witness complex at α and Vietoris-Rips complex of L holds, for any ε ∈ R+ and α ≥ 2ε.", "subpage_snippet": "", "source": "toggled.github.io", "link": "https://toggled.github.io/naheed/assets/pdf/Moscow_Beijing_Seminar_epsilon_net_May25_2022.pdf", "content": "If the landmark set L is an ε-net of the point cloud P, the following interleaving of lazy witness complex at α and Vietoris-Rips complex of L holds, for any ε ∈ R+ and α ≥ 2ε."}
+{"idx": 3, "title": "The Čech Complex and the Vietoris-Rips Complex || Math ∩ ... Faster computation of degree-1 persistent homology using the ... PERSISTENT HOMOLOGY AND SPARSE VIETORIS - RIPS FILTRATION PERSISTENT HOMOLOGY AND SPARSE VIETORIS - RIPS FILTRATION Reduced Vietoris - Rips Complexes: New methods to compute Vietoris -R… PERSISTENT HOMOLOGY AND SPARSE VIETORIS - RIPS FILTRATION Reduced Vietoris - Rips Complexes: New methods to compute Vietoris -R… Reduced Vietoris - Rips Complexes: New methods to compute Vietoris -R… Vietoris-Rips Complexes: The Backbone of Persistent Homology", "date": "", "ddg_snippet": "Let’s start with the Čech complex . Given a point set X in some metric space and a number ε>0, the Čech complex Cε is the simplicial complex whose simplices are formed as follows. For each subset S⊂X of points, form a (ε/2)-ball around each point in S, and include S as a simplex (of dimension |S|) if there is a common point contained in all of the b... See full list on jeremykun.com The Vietoris - Rips complex is essentially the same as the Čech complex, except instead of adding a d-simplex when there is a common point of intersection of all the (ε/2)-balls, we just do so when all the balls have pairwise intersections. We’ll denote the Vietoris - Rips complex with parameter ε as VRε. Here is an example to illustrate: if you give m... See full list on jeremykun.com In a future post we’ll implement a method for speeding up the computation of the Vietoris - Rips complex , since this is the primary bottleneck for topological data analysis. But for now the conceptual idea of how Čech complexes and Vietoris - Rips complexes can be used to turn point clouds into simplicial complexes in reasonable ways. Before we close w... See full list on jeremykun.com By using the Reduced Vietoris-Rips complex and leveraging the geometry of Euclidean space, we are able to compute the Vietoris-Rips persistent homology for significantly larger point clouds than current implementations. Is there a ltration method for the Vietoris-Rips complex? The construction of the Vietoris-Rips Complex is computationally expensive, so we will develop an improved ltration method: the sparse Vietoris-Rips ltration . How can we Ar-range Vietoris-Rips complexes into a zigzag ltration? Given the nets structure on the data points, we can ar-range Vietoris - Rips complexes into a zigzag ltration. De nition 5.1. The sparse zigzag Vietoris - Rips complex at scale plex of R ^ induced on the vertices of N . Formally, Remark 5.2. If for all p 2 P, (p) 6= , then N = N and Q from open nets to closed nets only happens at discrete scales. = Q . Does reduced Vietoris-Rips filtration of X have a persistent homology? Here we state and prove a new result on Reduced Vietoris - Rips complexes which relates the one dimensional persistent homology of the Vietoris - Rips filtration ofXwith the one dimensional persistent homology of the Reduced Vietoris - Rips filtration ofX. How do we apply persistent homology to data analysis? When we apply persistent homology to data analysis, we compare di erent simplicial complexes constructed from the data sets with di erent parameters , so that we can eliminate the in uence of our choice of parameters to the reconstruction of topological space and understand the essential properties of the original space. Do homology classes die in Vietoris-Rips filtration of a point cloud? It should be noted that in the Vietoris-Rips filtration of a point cloud there are several homology classes that die as soon as they are born and that the set of one-simplices not in the minimum spanning tree will contain these edges. What is reduced Vietoris-Rips complex? Definition 4.2(Reduced Vietoris-Rips Complex). Consider a point cloud X. Then the Reduced Vietoris-Rips Complex of Xwith scale r, denoted as VRr R (X) is the simplicial complex which consists of the following. Explore the intricacies of Vietoris-Rips complexes and their role in Persistent Homology , providing a deeper understanding of complex data structures.", "subpage_snippet": "", "source": "www.jeremykun.com", "link": "https://www.jeremykun.com/2015/08/06/cech-vietoris-rips-complex/", "content": "Let’s start with the Čech complex . Given a point set X in some metric space and a number ε>0, the Čech complex Cε is the simplicial complex whose simplices are formed as follows. For each subset S⊂X of points, form a (ε/2)-ball around each point in S, and include S as a simplex (of dimension |S|) if there is a common point contained in all of the b... See full list on jeremykun.com The Vietoris - Rips complex is essentially the same as the Čech complex, except instead of adding a d-simplex when there is a common point of intersection of all the (ε/2)-balls, we just do so when all the balls have pairwise intersections. We’ll denote the Vietoris - Rips complex with parameter ε as VRε. Here is an example to illustrate: if you give m... See full list on jeremykun.com In a future post we’ll implement a method for speeding up the computation of the Vietoris - Rips complex , since this is the primary bottleneck for topological data analysis. But for now the conceptual idea of how Čech complexes and Vietoris - Rips complexes can be used to turn point clouds into simplicial complexes in reasonable ways. Before we close w... See full list on jeremykun.com By using the Reduced Vietoris-Rips complex and leveraging the geometry of Euclidean space, we are able to compute the Vietoris-Rips persistent homology for significantly larger point clouds than current implementations. Is there a ltration method for the Vietoris-Rips complex? The construction of the Vietoris-Rips Complex is computationally expensive, so we will develop an improved ltration method: the sparse Vietoris-Rips ltration . How can we Ar-range Vietoris-Rips complexes into a zigzag ltration? Given the nets structure on the data points, we can ar-range Vietoris - Rips complexes into a zigzag ltration. De nition 5.1. The sparse zigzag Vietoris - Rips complex at scale plex of R ^ induced on the vertices of N . Formally, Remark 5.2. If for all p 2 P, (p) 6= , then N = N and Q from open nets to closed nets only happens at discrete scales. = Q . Does reduced Vietoris-Rips filtration of X have a persistent homology? Here we state and prove a new result on Reduced Vietoris - Rips complexes which relates the one dimensional persistent homology of the Vietoris - Rips filtration ofXwith the one dimensional persistent homology of the Reduced Vietoris - Rips filtration ofX. How do we apply persistent homology to data analysis? When we apply persistent homology to data analysis, we compare di erent simplicial complexes constructed from the data sets with di erent parameters , so that we can eliminate the in uence of our choice of parameters to the reconstruction of topological space and understand the essential properties of the original space. Do homology classes die in Vietoris-Rips filtration of a point cloud? It should be noted that in the Vietoris-Rips filtration of a point cloud there are several homology classes that die as soon as they are born and that the set of one-simplices not in the minimum spanning tree will contain these edges. What is reduced Vietoris-Rips complex? Definition 4.2(Reduced Vietoris-Rips Complex). Consider a point cloud X. Then the Reduced Vietoris-Rips Complex of Xwith scale r, denoted as VRr R (X) is the simplicial complex which consists of the following. Explore the intricacies of Vietoris-Rips complexes and their role in Persistent Homology , providing a deeper understanding of complex data structures."}
+{"idx": 4, "title": "Faster computation of degree-1 persistent homology using the ...", "date": "", "ddg_snippet": "By using the Reduced Vietoris-Rips complex and leveraging the geometry of Euclidean space, we are able to compute the Vietoris-Rips persistent homology for significantly larger point clouds than current implementations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2307.16333", "content": "By using the Reduced Vietoris-Rips complex and leveraging the geometry of Euclidean space, we are able to compute the Vietoris-Rips persistent homology for significantly larger point clouds than current implementations."}
+{"idx": 5, "title": "Vietoris-Rips Complexes: The Backbone of Persistent Homology", "date": "", "ddg_snippet": "Explore the intricacies of Vietoris-Rips complexes and their role in Persistent Homology , providing a deeper understanding of complex data structures.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/vietoris-rips-complexes-persistent-homology-deep-dive", "content": "Explore the intricacies of Vietoris-Rips complexes and their role in Persistent Homology , providing a deeper understanding of complex data structures."}
+{"idx": 6, "title": "Persistent Homology and Applied Homotopy Theory", "date": "", "ddg_snippet": "by G Carlsson · 2020 · Cited by 46 — ... persistent Cech and ... The lazy witness complex bears the same relationship to the witness complex as the Vietoris - Rips complex bears to.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2004.00738", "content": "by G Carlsson · 2020 · Cited by 46 — ... persistent Cech and ... The lazy witness complex bears the same relationship to the witness complex as the Vietoris - Rips complex bears to."}
+{"idx": 7, "title": "Stability and machine learning applications of persistent ...", "date": "", "ddg_snippet": "by A Mishra · 2023 · Cited by 8 — This new construction is built on what we refer to as the Delaunay- Rips (DR) complex . The DR complex may be viewed as a special case of the lazy - witness complex ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2023.1179301/full", "content": "by A Mishra · 2023 · Cited by 8 — This new construction is built on what we refer to as the Delaunay- Rips (DR) complex . The DR complex may be viewed as a special case of the lazy - witness complex ..."}
+{"idx": 8, "title": "A roadmap for the computation of persistent homology", "date": "", "ddg_snippet": "by N Otter · 2017 · Cited by 1021 — ... Vietoris – Rips (VR) complex , which approximates the Čech complex ... There is a modification of the witness complex called the lazy witness complex ...", "subpage_snippet": "", "source": "epjdatascience.springeropen.com", "link": "https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0109-5", "content": "by N Otter · 2017 · Cited by 1021 — ... Vietoris – Rips (VR) complex , which approximates the Čech complex ... There is a modification of the witness complex called the lazy witness complex ..."}
+{"idx": 9, "title": "Computing the alpha complex using dual active set ...", "date": "", "ddg_snippet": "by E Carlsson · 2024 · Cited by 3 — The one-dimensional lazy construction Lazy 1 ( X ) appears in the definition of the Vietoris - Rips and lazy Witness complexes , which are widely used to compute ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11350180/", "content": "by E Carlsson · 2024 · Cited by 3 — The one-dimensional lazy construction Lazy 1 ( X ) appears in the definition of the Vietoris - Rips and lazy Witness complexes , which are widely used to compute ..."}
diff --git a/data/sampled_jsons/learnable_PDE_solver_multi-time-step_error_correction_year_2020.jsonl b/data/sampled_jsons/learnable_PDE_solver_multi-time-step_error_correction_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..667b615e241063a29f8bd76b387b5885e29cd8f6
--- /dev/null
+++ b/data/sampled_jsons/learnable_PDE_solver_multi-time-step_error_correction_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning to solve PDEs with finite volume-informed neural ...", "date": "", "ddg_snippet": "by T Li · 2025 · Cited by 7 — This paper presents a computational framework that combines the Finite Volume Method (FVM) with Graph Neural Networks (GNNs) to construct the PDE-loss.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999125002025", "content": "by T Li · 2025 · Cited by 7 — This paper presents a computational framework that combines the Finite Volume Method (FVM) with Graph Neural Networks (GNNs) to construct the PDE-loss."}
+{"idx": 1, "title": "Improving Multi-step Prediction of Learned Time Series ...", "date": "", "ddg_snippet": "by A Venkatraman · 2015 · Cited by 308 — We present an approach that reuses training data to make a no-regret learner robust to errors made during multi - step predic- tion. Our insight is to formulate ... 7 pages", "subpage_snippet": "", "source": "www.ri.cmu.edu", "link": "https://www.ri.cmu.edu/pub_files/2015/1/Venkatraman.pdf", "content": "by A Venkatraman · 2015 · Cited by 308 — We present an approach that reuses training data to make a no-regret learner robust to errors made during multi - step predic- tion. Our insight is to formulate ... 7 pages"}
+{"idx": 2, "title": "Solver-in-the-Loop: Learning from Differentiable Physics to ...", "date": "", "ddg_snippet": "by K Um · 2020 · Cited by 351 — We target the problem of reducing numerical errors of iterative PDE solvers and compare different learning approaches for finding complex correction functions. 12 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/43e4e6a6f341e00671e123714de019a8-Paper.pdf", "content": "by K Um · 2020 · Cited by 351 — We target the problem of reducing numerical errors of iterative PDE solvers and compare different learning approaches for finding complex correction functions. 12 pages"}
+{"idx": 3, "title": "Neural Network Differential Equation Solvers allow ...", "date": "", "ddg_snippet": "by AS Dogra — In this work, we describe a general strategy for efficiently constructing error estimates and corrections for Neural Network Differential ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=a40XE0dgOdL", "content": "by AS Dogra — In this work, we describe a general strategy for efficiently constructing error estimates and corrections for Neural Network Differential ..."}
+{"idx": 4, "title": "A framework for machine learning of model error in dynamical ...", "date": "", "ddg_snippet": "by ME LEVINE · 2022 · Cited by 90 — The result is a hybrid dynamical system with a physics- based equation defined on the coarse variables with a Markovian correction term that. 62 pages", "subpage_snippet": "", "source": "stuart.caltech.edu", "link": "http://stuart.caltech.edu/publications/pdf/stuart182.pdf", "content": "by ME LEVINE · 2022 · Cited by 90 — The result is a hybrid dynamical system with a physics- based equation defined on the coarse variables with a Markovian correction term that. 62 pages"}
+{"idx": 5, "title": "Achieving Accurate Long Rollouts with Neural PDE Solvers", "date": "", "ddg_snippet": "by P Lippe · Cited by 136 — By minimizing this one- step MSE, the model learns to replicate the PDE's dynamics, accurately predicting the next step . However, as we roll out the model for ... 36 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2023/file/d529b943af3dba734f8a7d49efcb6d09-Paper-Conference.pdf", "content": "by P Lippe · Cited by 136 — By minimizing this one- step MSE, the model learns to replicate the PDE's dynamics, accurately predicting the next step . However, as we roll out the model for ... 36 pages"}
+{"idx": 6, "title": "A neural network-based PDE solving algorithm with high ...", "date": "", "ddg_snippet": "by Z Jiang · 2023 · Cited by 42 — An innovative method is introduced in this study to solve linear equations based on deep neural networks. To achieve a high accuracy, we employ the residual ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-023-31236-0", "content": "by Z Jiang · 2023 · Cited by 42 — An innovative method is introduced in this study to solve linear equations based on deep neural networks. To achieve a high accuracy, we employ the residual ..."}
+{"idx": 7, "title": "Data-driven identification of 2D Partial Differential ...", "date": "", "ddg_snippet": "by K Meidani · 2021 · Cited by 21 — Various Deep Learning (DL) algorithms have been used for learning time -dependent systems from training data to solve PDEs by approximating the solution given ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782521001687", "content": "by K Meidani · 2021 · Cited by 21 — Various Deep Learning (DL) algorithms have been used for learning time -dependent systems from training data to solve PDEs by approximating the solution given ..."}
+{"idx": 8, "title": "Towards a Foundation Model for Partial Differential Equation", "date": "", "ddg_snippet": "18 Apr 2024 — We introduce a multi -modal foundation model for scientific problems, named PROSE- PDE . Our model, designed for bi-modality to bi-modality learning, is a multi - ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.12355v1", "content": "18 Apr 2024 — We introduce a multi -modal foundation model for scientific problems, named PROSE- PDE . Our model, designed for bi-modality to bi-modality learning, is a multi - ..."}
+{"idx": 9, "title": "Three ways to solve partial differential equations with ...", "date": "", "ddg_snippet": "by J Blechschmidt · 2021 · Cited by 340 — Neural networks are increasingly used to construct numerical solution methods for partial differential equations . In this expository review, ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/gamm.202100006", "content": "by J Blechschmidt · 2021 · Cited by 340 — Neural networks are increasingly used to construct numerical solution methods for partial differential equations . In this expository review, ..."}
diff --git a/data/sampled_jsons/learnable_PDE_solvers_multi-time-step_simulation_error.jsonl b/data/sampled_jsons/learnable_PDE_solvers_multi-time-step_simulation_error.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a20b550343414fb9c15b47750a30e696bebfbd83
--- /dev/null
+++ b/data/sampled_jsons/learnable_PDE_solvers_multi-time-step_simulation_error.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time", "date": "", "ddg_snippet": "Our hybrid solver integrates a numerical simulator and neural physics (Section 3.1 ), enabling real- time simulation (Section 4.2 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18926v1", "content": "Our hybrid solver integrates a numerical simulator and neural physics (Section 3.1 ), enabling real- time simulation (Section 4.2 )."}
+{"idx": 1, "title": "Learnable Activation Functions in Physics-Informed Neural", "date": "", "ddg_snippet": "Specifically, we compare the efficacy of traditional Multilayer Perceptrons (MLPs) with fixed and learnable activations against Kolmogorov-Arnold ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.15111v1", "content": "Specifically, we compare the efficacy of traditional Multilayer Perceptrons (MLPs) with fixed and learnable activations against Kolmogorov-Arnold ..."}
+{"idx": 2, "title": "Neural PDE operator learning — The Dan MacKinlay stable of", "date": "", "ddg_snippet": "... of using statistical or machine learning approaches to solve PDEs , and maybe even to perform inference through them, by learning to predict one- step ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/ml_pde_operator.html", "content": "... of using statistical or machine learning approaches to solve PDEs , and maybe even to perform inference through them, by learning to predict one- step ..."}
+{"idx": 3, "title": "Hierarchical-embedding autoencoder with a predictor (HEAP) as", "date": "", "ddg_snippet": "This multi -scale nature of emergent features and interactions between them makes numerical simulation difficult and expensive, since spatial and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18857v1", "content": "This multi -scale nature of emergent features and interactions between them makes numerical simulation difficult and expensive, since spatial and ..."}
+{"idx": 4, "title": "A Variational Framework for Residual-Based Adaptivity in Neural", "date": "", "ddg_snippet": "Among the various approaches, one of the most prominent strategies, which addresses both optimization and discretization errors , is to modify the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14198v1", "content": "Among the various approaches, one of the most prominent strategies, which addresses both optimization and discretization errors , is to modify the ..."}
+{"idx": 5, "title": "Diffusion-Based Hierarchical Graph Neural Networks for", "date": "", "ddg_snippet": "... existing next- step learned simulators while reducing inference time by up to an order of magnitude compared to standard diffusion simulators.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06045v1", "content": "... existing next- step learned simulators while reducing inference time by up to an order of magnitude compared to standard diffusion simulators."}
+{"idx": 6, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Multi -Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization ... Time Series Deconfounder: Estimating ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Multi -Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization ... Time Series Deconfounder: Estimating ..."}
+{"idx": 7, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Multi -Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization ... Time Series Deconfounder: Estimating ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Multi -Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization ... Time Series Deconfounder: Estimating ..."}
+{"idx": 8, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "... Towards Theory and Design of Robust Time ... Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi -Armed Bandits", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "... Towards Theory and Design of Robust Time ... Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi -Armed Bandits"}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "Anomaly Transformer: Time Series Anomaly Detection with ... Back2Future: Leveraging Backfill Dynamics for Improving Real- time Predictions in Future", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "Anomaly Transformer: Time Series Anomaly Detection with ... Back2Future: Leveraging Backfill Dynamics for Improving Real- time Predictions in Future"}
diff --git a/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_Appendix_A.3.2_hardware_configuration_year_2023.jsonl b/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_Appendix_A.3.2_hardware_configuration_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fc2a75c2d41917a581e9f301494179233a650d22
--- /dev/null
+++ b/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_Appendix_A.3.2_hardware_configuration_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.09543] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "Oct 12, 2024 · Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "Oct 12, 2024 · Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ..."}
+{"idx": 1, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "h Boltzmann Alignment. BA-DDG employs a forward process identical to that of BA-Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆G and the predicted ∆∆G ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "h Boltzmann Alignment. BA-DDG employs a forward process identical to that of BA-Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆G and the predicted ∆∆G ..."}
+{"idx": 2, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 3, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 4, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "如果估计 P (S|X) 比较简单那便是极好的，而恰好，根据结构生成序列的 反折叠模型 （ Inverse Folding Model ） 是存在的，比如ProteinMPNN，同时因为输出是序列，其状态空间是20氨基酸的组合，相较于构象的扭转角，会简单许多。 因此作者将贝叶斯公式代入公式 ( 2 )得到：", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "如果估计 P (S|X) 比较简单那便是极好的，而恰好，根据结构生成序列的 反折叠模型 （ Inverse Folding Model ） 是存在的，比如ProteinMPNN，同时因为输出是序列，其状态空间是20氨基酸的组合，相较于构象的扭转角，会简单许多。 因此作者将贝叶斯公式代入公式 ( 2 )得到："}
+{"idx": 5, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Next, we present a method (Sec. 3 . 2 ) that integrates the inverse folding model into Boltzmann alignment. This method is named BA-Cycle and uses the inverse folding model to evaluate Δ Δ 𝐺 \\Delta\\Delta G roman_Δ roman_Δ italic_G by predicting the likelihoods of protein sequences, as shown on the left side of Figure 1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "Next, we present a method (Sec. 3 . 2 ) that integrates the inverse folding model into Boltzmann alignment. This method is named BA-Cycle and uses the inverse folding model to evaluate Δ Δ 𝐺 \\Delta\\Delta G roman_Δ roman_Δ italic_G by predicting the likelihoods of protein sequences, as shown on the left side of Figure 1."}
+{"idx": 6, "title": "Advancing protein evolution with inverse folding models ...", "date": "", "ddg_snippet": "Jul 7, 2025 · An AI-informed approach integrates generic protein inverse folding models with structural and evolutionary constraints to efficiently identify high-fitness mutations, enabling the development of advanced base editors and demonstrating broad scalability for artificial protein evolution.", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell/fulltext/S0092-8674(25)00680-4", "content": "Jul 7, 2025 · An AI-informed approach integrates generic protein inverse folding models with structural and evolutionary constraints to efficiently identify high-fitness mutations, enabling the development of advanced base editors and demonstrating broad scalability for artificial protein evolution."}
+{"idx": 7, "title": "Boltzmann - Aligned Inverse Folding Model as... | OpenReview", "date": "", "ddg_snippet": "However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\\Delta\\Delta G...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\\Delta\\Delta G..."}
+{"idx": 8, "title": "Stefan- Boltzmann Law - GeeksforGeeks", "date": "", "ddg_snippet": "Other than the Stefan- Boltzmann law, there are some more formulas involving the Stefan- Boltzmann Constant. One such formula can be derived from Planck's law of radiation i.e., integrating Planck's Radiation formula, and is given as follows", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/physics/stefan-boltzmann-law/", "content": "Other than the Stefan- Boltzmann law, there are some more formulas involving the Stefan- Boltzmann Constant. One such formula can be derived from Planck's law of radiation i.e., integrating Planck's Radiation formula, and is given as follows"}
+{"idx": 9, "title": "Monument Valley Forgotten Shores walkthrough - Appendix 8, Nocturne", "date": "", "ddg_snippet": "Follow these guide videos and you'll be able to complete the tricky new stages found in Monument Valley's huge new update, Forgotten Shores.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/bbd970b425966a6bc634f794b866e0b2/", "content": "Follow these guide videos and you'll be able to complete the tricky new stages found in Monument Valley's huge new update, Forgotten Shores."}
diff --git a/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_Equation_8_delta_delta_G.jsonl b/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_Equation_8_delta_delta_G.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2527cf9c3547b0470e2dfd6bc81682035d7f390a
--- /dev/null
+++ b/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_Equation_8_delta_delta_G.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann constant - Wikipedia", "date": "", "ddg_snippet": "This equation , which relates the microscopic details, or microstates, of the system (via W) to its macroscopic state (via the entropy S), is the central idea of statistical mechanics. Such is its importance that it is inscribed on Boltzmann 's tombstone.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Boltzmann_constant", "content": "This equation , which relates the microscopic details, or microstates, of the system (via W) to its macroscopic state (via the entropy S), is the central idea of statistical mechanics. Such is its importance that it is inscribed on Boltzmann 's tombstone."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to Δ Δ 𝐺 \\ Delta \\ Delta G roman_Δ roman_Δ italic_ G prediction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to Δ Δ 𝐺 \\ Delta \\ Delta G roman_Δ roman_Δ italic_ G prediction."}
+{"idx": 2, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "h Boltzmann Alignment. BA-DDG employs a forward process identical to that of BA-Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆ G and the predicted ∆∆ G ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "h Boltzmann Alignment. BA-DDG employs a forward process identical to that of BA-Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆ G and the predicted ∆∆ G ..."}
+{"idx": 3, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\ Delta \\ Delta G ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\ Delta \\ Delta G ..."}
+{"idx": 4, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "AUROC：将 \\ Delta \\ Delta G 预测转换为二分类任务，正值为一类，负值为一类，计算ROC曲线下的面积。 介绍一下Per-Structure和Overall的区别，作者认为在实际操作中，肯定是对某一个复合物进行突变，因此应该基于不同的复合物分组，分别测量metric更有意义，这就是Per ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "AUROC：将 \\ Delta \\ Delta G 预测转换为二分类任务，正值为一类，负值为一类，计算ROC曲线下的面积。 介绍一下Per-Structure和Overall的区别，作者认为在实际操作中，肯定是对某一个复合物进行突变，因此应该基于不同的复合物分组，分别测量metric更有意义，这就是Per ..."}
+{"idx": 5, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\ Delta \\ Delta G $ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\ Delta \\ Delta G $ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 6, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\ Delta \\ Delta G $ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\ Delta \\ Delta G $ values."}
+{"idx": 7, "title": "Boltzmann - Aligned Inverse Folding Model as... | OpenReview", "date": "", "ddg_snippet": "However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\\ Delta \\ Delta G ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\\ Delta \\ Delta G ..."}
+{"idx": 8, "title": "18.4 Calculating Delta G , Delta H, & Delta S | General... - YouTube", "date": "", "ddg_snippet": "Chad continues the chapter on Thermodynamics with a lesson on how to calculate Delta G , Delta H, and Delta S using Enthalpy of Formation, Free Energy of Form...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=Y-1IJ6LZEb4", "content": "Chad continues the chapter on Thermodynamics with a lesson on how to calculate Delta G , Delta H, and Delta S using Enthalpy of Formation, Free Energy of Form..."}
+{"idx": 9, "title": "Полный гайд по охоте за сокровищами в Delta Force", "date": "", "ddg_snippet": "Узнайте, сколько карт сокровищ есть в Delta Force, из какого сундука выпадают самые редкие награды, а также какой есть дополнительный способ залутать сокровища. Никитенков Валерий.", "subpage_snippet": "", "source": "app-time.ru", "link": "https://app-time.ru/post/polnyy-gayd-po-ohote-za-sokrovischami-v-delta-force", "content": "Узнайте, сколько карт сокровищ есть в Delta Force, из какого сундука выпадают самые редкие награды, а также какой есть дополнительный способ залутать сокровища. Никитенков Валерий."}
diff --git a/data/sampled_jsons/mHeight_3412_patterns_minimum_height_permutation.jsonl b/data/sampled_jsons/mHeight_3412_patterns_minimum_height_permutation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c680155ab5c7d0bb84ff5af757c2a10f85586547
--- /dev/null
+++ b/data/sampled_jsons/mHeight_3412_patterns_minimum_height_permutation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ACDRepo/mheight_function_8 · Datasets at ...", "date": "", "ddg_snippet": "... 3412-patterns in the permutation ... ( 3412 ) (3412) ( 3412) patterns and the mHeight of a permutation ... minimum height over all 3412 3412 3412 patterns in σ \\ ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ACDRepo/mheight_function_8", "content": "... 3412-patterns in the permutation ... ( 3412 ) (3412) ( 3412) patterns and the mHeight of a permutation ... minimum height over all 3412 3412 3412 patterns in σ \\ ..."}
+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics: A", "date": "", "ddg_snippet": "6. The mHeight function of a permutation (intermediary result): Permutations of size n labeled by their mHeight .The mHeight of σ is then the minimum height over all 3412 patterns in σ.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KQ1gI5qzAf", "content": "6. The mHeight function of a permutation (intermediary result): Permutations of size n labeled by their mHeight .The mHeight of σ is then the minimum height over all 3412 patterns in σ."}
+{"idx": 2, "title": "PNNL- 36691", "date": "", "ddg_snippet": "The task to predict the coefficients of 𝑃𝑃 𝜎𝜎,𝜈𝜈 (𝑞𝑞) given 𝜎𝜎 and 𝜈𝜈. 2.6 The mHeight function of a permutation (intermediary result) The mHeight function is a statistic associated with a permutation that relates to all 3412-patterns in the permutation (see Section 3 for the definition of a 3412-pattern).", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-36691.pdf", "content": "The task to predict the coefficients of 𝑃𝑃 𝜎𝜎,𝜈𝜈 (𝑞𝑞) given 𝜎𝜎 and 𝜈𝜈. 2.6 The mHeight function of a permutation (intermediary result) The mHeight function is a statistic associated with a permutation that relates to all 3412-patterns in the permutation (see Section 3 for the definition of a 3412-pattern)."}
+{"idx": 3, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets...", "date": "", "ddg_snippet": "The mHeight of σ is then the minimum height over all 3412 patterns in σ. We provide datasets for n = 8, 9, 10. ML task: Predict the mHeight of a permutation . Since mHeight can take a limited number of values for small n, this is framed as a classification problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "The mHeight of σ is then the minimum height over all 3412 patterns in σ. We provide datasets for n = 8, 9, 10. ML task: Predict the mHeight of a permutation . Since mHeight can take a limited number of values for small n, this is framed as a classification problem."}
+{"idx": 4, "title": "ICML Poster Machine Learning meets Algebraic Combinatorics", "date": "", "ddg_snippet": "σ is then the minimum height over all 3412 patterns in σ . We provide datasets for n = 8, 9, 10. image. ML task: Predict the mHeight of a permutation . Since ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/22025/poster/43763", "content": "σ is then the minimum height over all 3412 patterns in σ . We provide datasets for n = 8, 9, 10. image. ML task: Predict the mHeight of a permutation . Since ..."}
+{"idx": 5, "title": "[Literature Review] Machine Learning meets Algebraic ...", "date": "", "ddg_snippet": "9 Mar 2025 — mHeight Function of a Permutation : This dataset relates to a ... minimum height of specific patterns ( 3412-patterns ). Grassmannian ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/machine-learning-meets-algebraic-combinatorics-a-suite-of-datasets-capturing-research-level-conjecturing-ability-in-pure-mathematics", "content": "9 Mar 2025 — mHeight Function of a Permutation : This dataset relates to a ... minimum height of specific patterns ( 3412-patterns ). Grassmannian ..."}
+{"idx": 6, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "The mHeight of 𝜎 \\sigma italic_σ is then the minimum height over all 3412 3412 3412 patterns in 𝜎 \\sigma italic_σ. We provide datasets for 𝑛 8 9 10 n=8,9,10 italic_n = 8 , 9 , 10. ML task: Predict the mHeight of a permutation .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "The mHeight of 𝜎 \\sigma italic_σ is then the minimum height over all 3412 3412 3412 patterns in 𝜎 \\sigma italic_σ. We provide datasets for 𝑛 8 9 10 n=8,9,10 italic_n = 8 , 9 , 10. ML task: Predict the mHeight of a permutation ."}
+{"idx": 7, "title": "Weekly Mountain, Paper, and Work | [2025]Machine Learning ...", "date": "", "ddg_snippet": "Jul 25, 2025 · Input: λ = (3,2,1), μ = (2,2,2) Output: χ = 4 Example 2: Predicting mHeight of a Permutation (Section 4.2) Input: A single permutation (e.g., σ = 3 1 4 2) Output: The minimum height among all 3412-patterns in the permutation Task: Classification Example: Input: σ = 3 1 4 2", "subpage_snippet": "", "source": "hyoo14.github.io", "link": "https://hyoo14.github.io/study/2025/07/25/2025-Machine-Learning-Meets-Algebraic-Combinatorics_-A-Suite-of-Datasets-Capturing-Research-level-Conjecturing-Ability-in-Pure-Mathematics", "content": "Jul 25, 2025 · Input: λ = (3,2,1), μ = (2,2,2) Output: χ = 4 Example 2: Predicting mHeight of a Permutation (Section 4.2) Input: A single permutation (e.g., σ = 3 1 4 2) Output: The minimum height among all 3412-patterns in the permutation Task: Classification Example: Input: σ = 3 1 4 2"}
+{"idx": 8, "title": "Machine Learning Meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "The mHeight of σ is then the minimum height over all 3412 patterns in σ. We provide datasets for n = 8, 9, 10. ML task: Predict the mHeight of a permutation . Since mHeight can take a limited number of values for small n, this is framed as a classification problem. Input/output: One permutation 7→one of several possible integers. How hard is ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tlniJJFUW2", "content": "The mHeight of σ is then the minimum height over all 3412 patterns in σ. We provide datasets for n = 8, 9, 10. ML task: Predict the mHeight of a permutation . Since mHeight can take a limited number of values for small n, this is framed as a classification problem. Input/output: One permutation 7→one of several possible integers. How hard is ..."}
+{"idx": 9, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "Truly challenging open problems in mathematics often re-quire the development of new mathematical constructions (or even entire new areas of mathematics). This dataset represents a modest example of this. The mHeight function is a statistic associated with a permutation that relates to all 3412-patterns in the permutation .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366v1", "content": "Truly challenging open problems in mathematics often re-quire the development of new mathematical constructions (or even entire new areas of mathematics). This dataset represents a modest example of this. The mHeight function is a statistic associated with a permutation that relates to all 3412-patterns in the permutation ."}
diff --git a/data/sampled_jsons/mHeight_permutation_pattern_3412_height_definition_mathematics.jsonl b/data/sampled_jsons/mHeight_permutation_pattern_3412_height_definition_mathematics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..346f83f07d46ba892aa423c4a181fd3fd5de2559
--- /dev/null
+++ b/data/sampled_jsons/mHeight_permutation_pattern_3412_height_definition_mathematics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Pattern heights and the minimal power of q in a Kazhdan-Lusztig polynomial", "date": "", "ddg_snippet": "Then h(w) = 1 mHeight(w) if w contains 4231, otherwise, where mHeight(w) denotes the minimum height of a 3412 pattern in w. In the case Pe,w(1) = 2, Theorem 6 follows from the work of Woo [21]. Our theorem adds to the deep [22] and ubiquitous [1] links between singularities of Schubert varieties and pattern containment.", "subpage_snippet": "", "source": "www.mat.univie.ac.at", "link": "https://www.mat.univie.ac.at/~slc/wpapers/FPSAC2024/7.pdf", "content": "Then h(w) = 1 mHeight(w) if w contains 4231, otherwise, where mHeight(w) denotes the minimum height of a 3412 pattern in w. In the case Pe,w(1) = 2, Theorem 6 follows from the work of Woo [21]. Our theorem adds to the deep [22] and ubiquitous [1] links between singularities of Schubert varieties and pattern containment."}
+{"idx": 1, "title": "Permutation Pattern -- from Wolfram MathWorld", "date": "", "ddg_snippet": "Let F(n,sigma) denote the number of permutations on the symmetric group S_n which avoid sigma in S_k as a subpattern, where \"tau contains sigma as a subpattern\" is interpreted to mean that there exist 1<=x_1<=x_2<=...<=x_k<=n such that for 1<=i,j<=k, tau(x_i)<tau(x_j) iff sigma(i)<sigma(j). For example, a permutation contains the pattern (123) iff it has an ascending subsequence of length ...", "subpage_snippet": "", "source": "mathworld.wolfram.com", "link": "https://mathworld.wolfram.com/PermutationPattern.html", "content": "Let F(n,sigma) denote the number of permutations on the symmetric group S_n which avoid sigma in S_k as a subpattern, where \"tau contains sigma as a subpattern\" is interpreted to mean that there exist 1<=x_1<=x_2<=...<=x_k<=n such that for 1<=i,j<=k, tau(x_i)<tau(x_j) iff sigma(i)<sigma(j). For example, a permutation contains the pattern (123) iff it has an ascending subsequence of length ..."}
+{"idx": 2, "title": "Combinations and Permutations - Math is Fun", "date": "", "ddg_snippet": "Combinations and Permutations What's the Difference? In English we use the word \"combination\" loosely, without thinking if the order of things is important. In other words:", "subpage_snippet": "", "source": "www.mathsisfun.com", "link": "https://www.mathsisfun.com/combinatorics/combinations-permutations.html", "content": "Combinations and Permutations What's the Difference? In English we use the word \"combination\" loosely, without thinking if the order of things is important. In other words:"}
+{"idx": 3, "title": "Permutation patterns: basic definitions and notati - arXiv.org", "date": "", "ddg_snippet": "A permutation , or pattern , π is said to be contained in, or to be a subpermutation of, another permutation σ, written π 6 σ or π 4 σ, if σ has a (not necessarily contiguous) subsequence whose terms are order isomorphic to (i.e. have the same relative ordering as) π. From the graphical perspective, σ contains π if the plot of π results from erasing zero or more points from the plot ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1506.06673", "content": "A permutation , or pattern , π is said to be contained in, or to be a subpermutation of, another permutation σ, written π 6 σ or π 4 σ, if σ has a (not necessarily contiguous) subsequence whose terms are order isomorphic to (i.e. have the same relative ordering as) π. From the graphical perspective, σ contains π if the plot of π results from erasing zero or more points from the plot ..."}
+{"idx": 4, "title": "5.2: Permutations and Combinations - Mathematics LibreTexts", "date": "", "ddg_snippet": "In this section, we introduce the factorial notation and discuss permutations and combinations and their applications.", "subpage_snippet": "", "source": "math.libretexts.org", "link": "https://math.libretexts.org/Courses/Los_Angeles_City_College/STAT_C1000/05:_Combinatorics_Concepts/5.02:_Permutations_and_Combinations", "content": "In this section, we introduce the factorial notation and discuss permutations and combinations and their applications."}
+{"idx": 5, "title": "Permutation and Combination - Definition, Formulas, Derivation, Examples", "date": "", "ddg_snippet": "Permutation and combination are the methods employed in counting how many outcomes are possible in various situations. Permutations are understood as arrangements and combinations are understood as selections. Understand the Permutations and Combinations Formulas with Derivation, Examples, and FAQs.", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/data/permutations-and-combinations/", "content": "Permutation and combination are the methods employed in counting how many outcomes are possible in various situations. Permutations are understood as arrangements and combinations are understood as selections. Understand the Permutations and Combinations Formulas with Derivation, Examples, and FAQs."}
+{"idx": 6, "title": "Permutation - GeeksforGeeks", "date": "", "ddg_snippet": "In Mathematics , Permutation is defined as a mathematical concept that determines the number of possible arrangements for a specific set of elements. therefore, it plays a big role in computer science, cryptography, and operations research.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/maths/permutation/", "content": "In Mathematics , Permutation is defined as a mathematical concept that determines the number of possible arrangements for a specific set of elements. therefore, it plays a big role in computer science, cryptography, and operations research."}
+{"idx": 7, "title": "Permutation patterns: basic definitions and notation", "date": "", "ddg_snippet": "Other classical definitions about permutations and their patterns have been conveniently summarized in D. Bevan's brief presentation [4] prepared for the conference Permutation Patterns 2015.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/279068556_Permutation_patterns_basic_definitions_and_notation", "content": "Other classical definitions about permutations and their patterns have been conveniently summarized in D. Bevan's brief presentation [4] prepared for the conference Permutation Patterns 2015."}
+{"idx": 8, "title": "What is a permutation pattern? - Mathematics Stack Exchange", "date": "", "ddg_snippet": "The wikipedia entry for permutation pattern gives this as an example: For example, in the permutation π = 391867452, π1=3 and π9=2. A permutation π is said to contain the permutation σ if there e...", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1437012/what-is-a-permutation-pattern", "content": "The wikipedia entry for permutation pattern gives this as an example: For example, in the permutation π = 391867452, π1=3 and π9=2. A permutation π is said to contain the permutation σ if there e..."}
+{"idx": 9, "title": "Permutations - Definition, Formula, Properties, Solved Example Problems ...", "date": "", "ddg_snippet": "Definition , Formula, Properties, Solved Example Problems, Exercise | Mathematics - Permutations | 11th Mathematics : UNIT 4 : Combinatorics and Mathematical Induction Chapter: 11th Mathematics : UNIT 4 : Combinatorics and Mathematical Induction Permutations The number of permutations of 4 objects taken all at a time is 4 × 3 × 2 × 1 = 4! .", "subpage_snippet": "", "source": "www.brainkart.com", "link": "https://www.brainkart.com/article/Permutations_33922/", "content": "Definition , Formula, Properties, Solved Example Problems, Exercise | Mathematics - Permutations | 11th Mathematics : UNIT 4 : Combinatorics and Mathematical Induction Chapter: 11th Mathematics : UNIT 4 : Combinatorics and Mathematical Induction Permutations The number of permutations of 4 objects taken all at a time is 4 × 3 × 2 × 1 = 4! ."}
diff --git a/data/sampled_jsons/manifold_estimation_problems_noisy_data_structural_challenges.jsonl b/data/sampled_jsons/manifold_estimation_problems_noisy_data_structural_challenges.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a3208196ae29c213e65fe2fd786891d6c74340a4
--- /dev/null
+++ b/data/sampled_jsons/manifold_estimation_problems_noisy_data_structural_challenges.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Inferring manifolds from noisy data using Gaussian processes", "date": "", "ddg_snippet": "This article proposes a new methodology for addressing these problems , allowing interpolation of the estimated manifold between fitted data points. The proposed approach is motivated by novel theoretical properties of local covariance matrices constructed from noisy samples on a manifold .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2110.07478v3", "content": "This article proposes a new methodology for addressing these problems , allowing interpolation of the estimated manifold between fitted data points. The proposed approach is motivated by novel theoretical properties of local covariance matrices constructed from noisy samples on a manifold ."}
+{"idx": 1, "title": "Structure-Adaptive Manifold Estimation - Journal of Machine ...", "date": "", "ddg_snippet": "In our theoretical study, we establish tight lower and upper bounds proving asymptotic optimality of the method for manifold estimation under the Hausdor loss, provided that the noise degrades to zero fast enough. Keywords: manifold learning, manifold denoising, structural adaptation, adaptive pro-cedures, minimax", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume23/21-0338/21-0338.pdf", "content": "In our theoretical study, we establish tight lower and upper bounds proving asymptotic optimality of the method for manifold estimation under the Hausdor loss, provided that the noise degrades to zero fast enough. Keywords: manifold learning, manifold denoising, structural adaptation, adaptive pro-cedures, minimax"}
+{"idx": 2, "title": "Probabilistic modelling of general noisy multi-manifold data ...", "date": "", "ddg_snippet": "Jan 1, 2022 · In real-world applications, the data can contain several low-dimensional structures of different dimensionalities. We propose a framework for dimensionality estimation and reconstruction of multiple noisy manifolds embedded in a noisy environment.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0004370221001302", "content": "Jan 1, 2022 · In real-world applications, the data can contain several low-dimensional structures of different dimensionalities. We propose a framework for dimensionality estimation and reconstruction of multiple noisy manifolds embedded in a noisy environment."}
+{"idx": 3, "title": "Non-Parametric Estimation of Manifolds from Noisy Data", "date": "", "ddg_snippet": "In many data -driven applications, the data follows some geometric structure, and the goal is to recover this structure. In many cases, the observed data is noisy and the recovery task is even more challenging. A common assumption is that the data lies on a low dimensional manifold . Estimating a manifold from noisy samples has proven to be a challenging task. Indeed, even after decades of ...", "subpage_snippet": "", "source": "www.maths.ox.ac.uk", "link": "https://www.maths.ox.ac.uk/node/41057", "content": "In many data -driven applications, the data follows some geometric structure, and the goal is to recover this structure. In many cases, the observed data is noisy and the recovery task is even more challenging. A common assumption is that the data lies on a low dimensional manifold . Estimating a manifold from noisy samples has proven to be a challenging task. Indeed, even after decades of ..."}
+{"idx": 4, "title": "Fitting a putative manifold to noisy data", "date": "", "ddg_snippet": "If the errors are modelled as being gaus-sian, then fitting a manifold to the data can subsequently allow us to project the data onto this output manifold . Due to the large codimension and small dimension of the true manifold , the noise vectors are almost perpendicular to the true manifold and the projection would effectively denoise the data .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v75/fefferman18a/fefferman18a.pdf", "content": "If the errors are modelled as being gaus-sian, then fitting a manifold to the data can subsequently allow us to project the data onto this output manifold . Due to the large codimension and small dimension of the true manifold , the noise vectors are almost perpendicular to the true manifold and the projection would effectively denoise the data ."}
+{"idx": 5, "title": "Non-Parametric Estimation of Manifolds from Noisy Data", "date": "", "ddg_snippet": "A common observation in data -driven applications is that high dimensional data has a low intrinsic dimen-sion, at least locally. In this work, we consider the problem of estimating a d dimensional sub- manifold of RD from a finite set of noisy samples.", "subpage_snippet": "", "source": "cs2022.sciencesconf.org", "link": "https://cs2022.sciencesconf.org/391972/Curves_and_Surfaces_Manifold_Estimation_1_.pdf", "content": "A common observation in data -driven applications is that high dimensional data has a low intrinsic dimen-sion, at least locally. In this work, we consider the problem of estimating a d dimensional sub- manifold of RD from a finite set of noisy samples."}
+{"idx": 6, "title": "Estimation of Local Geometric Structure on Manifolds from ...", "date": "", "ddg_snippet": "In this work, we consider the problem of point estimation for manifold -valued data . Namely, given a nite set of noisy samples of M, a d dimensional submanifold of RD, and a point r near the manifold we aim to project r onto the manifold . Assuming that the data was sampled uniformly from a tubular neighborhood of a", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume26/25-0183/25-0183.pdf", "content": "In this work, we consider the problem of point estimation for manifold -valued data . Namely, given a nite set of noisy samples of M, a d dimensional submanifold of RD, and a point r near the manifold we aim to project r onto the manifold . Assuming that the data was sampled uniformly from a tubular neighborhood of a"}
+{"idx": 7, "title": "Adversarial Manifold Estimation | Request PDF", "date": "", "ddg_snippet": "... geometric algorithm called manifold propagation, that reduces the problem to three natural geometric routines: projection, tangent space estimation ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364381816_Adversarial_Manifold_Estimation", "content": "... geometric algorithm called manifold propagation, that reduces the problem to three natural geometric routines: projection, tangent space estimation ..."}
+{"idx": 8, "title": "Mapping Microstructure: Manifold Construction for Accelerated", "date": "", "ddg_snippet": "Paired with the manifold hypothesis, which asserts that high-dimensional data can be “described by a function of only a few underlying parameters ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15022v1", "content": "Paired with the manifold hypothesis, which asserts that high-dimensional data can be “described by a function of only a few underlying parameters ..."}
+{"idx": 9, "title": "Wenjing Liao", "date": "", "ddg_snippet": "... work establishes a theoretical foundation by analyzing the performance of transformers for regression tasks involving noisy input data on a manifold ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Wenjing+Liao", "content": "... work establishes a theoretical foundation by analyzing the performance of transformers for regression tasks involving noisy input data on a manifold ..."}
diff --git a/data/sampled_jsons/minGPT_synthetic_experiments_configuration_n_layer_8.jsonl b/data/sampled_jsons/minGPT_synthetic_experiments_configuration_n_layer_8.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..92f383b9c718f5287c820c3316a3dbf7fce3167f
--- /dev/null
+++ b/data/sampled_jsons/minGPT_synthetic_experiments_configuration_n_layer_8.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - karpathy/ minGPT : A minimal PyTorch re-implementation of...", "date": "", "ddg_snippet": "Here's how you'd instantiate a GPT -2 (124M param version): from mingpt .model import GPT model_ config = GPT .get_default_ config GPT -1 model is 12 layers and d_model 768, ~117M params. Language Models are Unsupervised Multitask Learners ( GPT -2).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT", "content": "Here's how you'd instantiate a GPT -2 (124M param version): from mingpt .model import GPT model_ config = GPT .get_default_ config GPT -1 model is 12 layers and d_model 768, ~117M params. Language Models are Unsupervised Multitask Learners ( GPT -2)."}
+{"idx": 1, "title": "brentyi/ minGPT -flax: GPT implementation in Flax", "date": "", "ddg_snippet": "(default: None) -- gpt - config . n - layer INT.The core model implementation details are based off of karpathy/ minGPT (PyTorch). The learning rate scheduler is adapted from mgrankin/ minGPT (Haiku). matthias-wright/flaxmodels also has pretrained models implemented using Flax.", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/brentyi/minGPT-flax", "content": "(default: None) -- gpt - config . n - layer INT.The core model implementation details are based off of karpathy/ minGPT (PyTorch). The learning rate scheduler is adapted from mgrankin/ minGPT (Haiku). matthias-wright/flaxmodels also has pretrained models implemented using Flax."}
+{"idx": 2, "title": "dzlab.github.io/notebooks/tensorflow/nlp/2020/09/13/ minGPT ...", "date": "", "ddg_snippet": "minGPT is a PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) model. It is a small, clean, interpretable, and educational implementation of the GPT model, with only about 300 lines of code.", "subpage_snippet": "", "source": "dzlab.github.io", "link": "https://dzlab.github.io/notebooks/tensorflow/nlp/2020/09/13/minGPT_TensorFlow.html", "content": "minGPT is a PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) model. It is a small, clean, interpretable, and educational implementation of the GPT model, with only about 300 lines of code."}
+{"idx": 3, "title": "A fork of MinGPT for experimenting with transformer architecture", "date": "", "ddg_snippet": "GPT -2 configs .assert model_type in {' gpt 2', ' gpt 2-medium', ' gpt 2-large', ' gpt 2-xl'}. from transformers import GPT 2LMHeadModel #. create a from-scratch initialized minGPT model. config = cls.get_default_ config ().", "subpage_snippet": "", "source": "gitlab.cs.washington.edu", "link": "https://gitlab.cs.washington.edu/ryanbai/mingpt-cse447/-/blob/master/mingpt/model.py", "content": "GPT -2 configs .assert model_type in {' gpt 2', ' gpt 2-medium', ' gpt 2-large', ' gpt 2-xl'}. from transformers import GPT 2LMHeadModel #. create a from-scratch initialized minGPT model. config = cls.get_default_ config ()."}
+{"idx": 4, "title": "The mingpt -flax from brentyi - GithubHelp", "date": "", "ddg_snippet": "gpt - config . n - layer INT] [-- gpt - config .embd-dim INT] [-- gpt - config .embd-pdrop FLOAT] [The learning rate scheduler is adapted from mgrankin/ minGPT (Haiku). matthias-wright/flaxmodels also has pretrained models implemented using Flax.", "subpage_snippet": "", "source": "githubhelp.com", "link": "https://githubhelp.com/brentyi/minGPT-flax", "content": "gpt - config . n - layer INT] [-- gpt - config .embd-dim INT] [-- gpt - config .embd-pdrop FLOAT] [The learning rate scheduler is adapted from mgrankin/ minGPT (Haiku). matthias-wright/flaxmodels also has pretrained models implemented using Flax."}
+{"idx": 5, "title": "minGPT in Julia using Flux! | juliabloggers.com", "date": "", "ddg_snippet": "As a learning exercise, I tried to port Andrey Karpathy’s awesome minGPT , which is based on Python and PyTorch to Julia and Flux. GPT is a language model, that is trained by the error signal of its prediction for the next element of a given sequence.", "subpage_snippet": "", "source": "www.juliabloggers.com", "link": "https://www.juliabloggers.com/mingpt-in-julia-using-flux/", "content": "As a learning exercise, I tried to port Andrey Karpathy’s awesome minGPT , which is based on Python and PyTorch to Julia and Flux. GPT is a language model, that is trained by the error signal of its prediction for the next element of a given sequence."}
+{"idx": 6, "title": "Pytorch 多卡并行（3）—— 使用 DDP 加速 minGPT ...", "date": "", "ddg_snippet": "关于 MinGPT 的详细介绍可以参考 minGPT 代码详解（训练 GPT 模型执行两位数加法）. 本文参考自：Pytorch 官方教程.p.data.normal_(mean=0.0, std=0.02/math.sqrt(2 * config . n _ layer )) #. report number of parameters (note we don't count the decoder parameters in lm_head).", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/wxc971231/article/details/132829661", "content": "关于 MinGPT 的详细介绍可以参考 minGPT 代码详解（训练 GPT 模型执行两位数加法）. 本文参考自：Pytorch 官方教程.p.data.normal_(mean=0.0, std=0.02/math.sqrt(2 * config . n _ layer )) #. report number of parameters (note we don't count the decoder parameters in lm_head)."}
+{"idx": 7, "title": "Model quantization", "date": "", "ddg_snippet": "Let’s take the GPT 2 model from minGPT library.with init_empty_weights(): empty_model = GPT (model_ config ). Then, we need to get the path to the weights of your model. The path can be the state_dict file (e.g. “pytorch_model.bin”) or a folder containing the sharded checkpoints.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/accelerate/usage_guides/quantization", "content": "Let’s take the GPT 2 model from minGPT library.with init_empty_weights(): empty_model = GPT (model_ config ). Then, we need to get the path to the weights of your model. The path can be the state_dict file (e.g. “pytorch_model.bin”) or a folder containing the sharded checkpoints."}
+{"idx": 8, "title": "Conversational AI workshop: Task 1a - Colab", "date": "", "ddg_snippet": "Welcome to the workshop! In this first part you will train a character-based language model called minGPT from scratch!mconf = GPTConfig(train_dataset.vocab_size, train_dataset.block_size, n _ layer = 8 , n_head=8, n_embd=512). # Initialize a model based on the config .", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/aig-upf/conversational-ai-workshop/blob/main/1/Conversational_AI_workshop_Task_1a.ipynb", "content": "Welcome to the workshop! In this first part you will train a character-based language model called minGPT from scratch!mconf = GPTConfig(train_dataset.vocab_size, train_dataset.block_size, n _ layer = 8 , n_head=8, n_embd=512). # Initialize a model based on the config ."}
+{"idx": 9, "title": "Modern Transformers, Part 1: Activations | lesterpjy's blog", "date": "", "ddg_snippet": "MinGPT with Multi-head Attention. Training Data. GPT ’s decoder-only architecture is essentially a stacking of MHA blocks containing MHA modules and feed-forward layers with activation nonlinearities and layer normalizations.", "subpage_snippet": "", "source": "lesterpjy.me", "link": "https://lesterpjy.me/posts/modern-transformers-1/", "content": "MinGPT with Multi-head Attention. Training Data. GPT ’s decoder-only architecture is essentially a stacking of MHA blocks containing MHA modules and feed-forward layers with activation nonlinearities and layer normalizations."}
diff --git a/data/sampled_jsons/mixture_proportion_estimation_unlabeled_data_year_2023.jsonl b/data/sampled_jsons/mixture_proportion_estimation_unlabeled_data_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bdc05313e621bf427598bc0097763e50fd196902
--- /dev/null
+++ b/data/sampled_jsons/mixture_proportion_estimation_unlabeled_data_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A flexible procedure for mixture proportion estimation in", "date": "", "ddg_snippet": "Lin, Z & Long, JP 2020, ' A flexible procedure for mixture proportion estimation in positive- unlabeled learning ', Statistical Analysis and Data ...", "subpage_snippet": "", "source": "mdanderson.elsevierpure.com", "link": "https://mdanderson.elsevierpure.com/en/publications/a-flexible-procedure-for-mixture-proportion-estimation-in-positiv", "content": "Lin, Z & Long, JP 2020, ' A flexible procedure for mixture proportion estimation in positive- unlabeled learning ', Statistical Analysis and Data ..."}
+{"idx": 1, "title": "[2111.00980] Mixture Proportion Estimation and PU Learning: A", "date": "", "ddg_snippet": "... task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE) -- determining the fraction of positive examples in the unlabeled data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.00980", "content": "... task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE) -- determining the fraction of positive examples in the unlabeled data ..."}
+{"idx": 2, "title": "Domain2Vec: Vectorizing Datasets to Find the Optimal Data", "date": "", "ddg_snippet": "Recent research has shown that the mixture proportions of these domains referred to as the data mixture , can significantly influence the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10952v1", "content": "Recent research has shown that the mixture proportions of these domains referred to as the data mixture , can significantly influence the ..."}
+{"idx": 3, "title": "2.1. Gaussian mixture models — scikit-learn 1.7.1", "date": "", "ddg_snippet": "The main difficulty in learning Gaussian mixture models from unlabeled data is that one usually doesn’t know which points came from which latent ...", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/mixture.html", "content": "The main difficulty in learning Gaussian mixture models from unlabeled data is that one usually doesn’t know which points came from which latent ..."}
+{"idx": 4, "title": "2.1. Gaussian mixture models — scikit-learn 1.6.1", "date": "", "ddg_snippet": "The main difficulty in learning Gaussian mixture models from unlabeled data is that one usually doesn’t know which points came from which latent ...", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/1.6/modules/mixture.html", "content": "The main difficulty in learning Gaussian mixture models from unlabeled data is that one usually doesn’t know which points came from which latent ..."}
+{"idx": 5, "title": "An overview of Gaussian Mixture Models", "date": "", "ddg_snippet": "Our goal is to find the underlying sub-distributions in our weight dataset and it seems that mixture models can help us.", "subpage_snippet": "", "source": "mpatacchiola.github.io", "link": "https://mpatacchiola.github.io/blog/2020/07/31/gaussian-mixture-models.html", "content": "Our goal is to find the underlying sub-distributions in our weight dataset and it seems that mixture models can help us."}
+{"idx": 6, "title": "Help for package bgmm", "date": "", "ddg_snippet": "a vector with the calculated signed differential expression probabilities for the unlabeled observations in the dataset model$X .", "subpage_snippet": "", "source": "cran.r-project.org", "link": "https://cran.r-project.org/web/packages/bgmm/refman/bgmm.html", "content": "a vector with the calculated signed differential expression probabilities for the unlabeled observations in the dataset model$X ."}
+{"idx": 7, "title": "The Semi-Supervised VAE — Pyro Tutorials 1.9.1", "date": "", "ddg_snippet": "... have very little labeled data and much more unlabeled ... We’d clearly like to leverage labeled data to improve our models of the unlabeled data .", "subpage_snippet": "", "source": "pyro.ai", "link": "http://pyro.ai/examples/ss-vae.html", "content": "... have very little labeled data and much more unlabeled ... We’d clearly like to leverage labeled data to improve our models of the unlabeled data ."}
+{"idx": 8, "title": "machine learning - How to find a de-biased estimator with a ML", "date": "", "ddg_snippet": "The labelled ML training set is about 10^6 datapoints, and the unlabelled dataset for estimation is about 10^9 datapoints.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/658578/how-to-find-a-de-biased-estimator-with-a-ml-component-in-my-contaminated-data-pr", "content": "The labelled ML training set is about 10^6 datapoints, and the unlabelled dataset for estimation is about 10^9 datapoints."}
+{"idx": 9, "title": "acmilab.org/people/saurabh-garg", "date": "", "ddg_snippet": "Leveraging Unlabeled Data to Predict Out-of-Distribution Performance ... Mixture Proportion Estimation and PU Learning: A Modern Approach", "subpage_snippet": "", "source": "acmilab.org", "link": "https://acmilab.org/people/saurabh-garg/", "content": "Leveraging Unlabeled Data to Predict Out-of-Distribution Performance ... Mixture Proportion Estimation and PU Learning: A Modern Approach"}
diff --git a/data/sampled_jsons/mkuB677eMM_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchm_year_2023-2024.jsonl b/data/sampled_jsons/mkuB677eMM_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchm_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2a92bab9890a7f7a4ebd2a7e2032a1842de823fc
--- /dev/null
+++ b/data/sampled_jsons/mkuB677eMM_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchm_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Simxrd-4m: B Simulated X-ray Diffraction D C Symmetry Classification", "date": "", "ddg_snippet": "ABSTRACT Powder X-ray diffraction (XRD) patterns are highly effective for crystal identi-fication and play a pivotal role in materials discovery. Although machine learn-ing (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established bench-marks . To address this, we introduce SimXRD-4M , the largest open-source ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mkuB677eMM", "content": "ABSTRACT Powder X-ray diffraction (XRD) patterns are highly effective for crystal identi-fication and play a pivotal role in materials discovery. Although machine learn-ing (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established bench-marks . To address this, we introduce SimXRD-4M , the largest open-source ..."}
+{"idx": 1, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions."}
+{"idx": 2, "title": "PDF SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance."}
+{"idx": 3, "title": "GitHub - Bin-Cao/SimXRD: [ICLR 2025] SimXRD-4M: Big Simulated X-ray ...", "date": "", "ddg_snippet": "Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are ..."}
+{"idx": 4, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystalline ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/mkuB677eMM@OpenReview", "content": "SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions."}
+{"idx": 5, "title": "Advancing Crystal Structure Analysis with SimXRD Dataset", "date": "", "ddg_snippet": "Title: SimXRD-4M : Big Simulated X-ray Diffraction Data Accelerate the Crystalline Symmetry Classification Abstract: Spectroscopic data , particularly diffraction data , contain detailed crystal and microstructure information and thus are crucial for materials discovery.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-advancing-crystal-structure-analysis-with-simxrd-dataset--ak4ex8o", "content": "Title: SimXRD-4M : Big Simulated X-ray Diffraction Data Accelerate the Crystalline Symmetry Classification Abstract: Spectroscopic data , particularly diffraction data , contain detailed crystal and microstructure information and thus are crucial for materials discovery."}
+{"idx": 6, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations."}
+{"idx": 7, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations. We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD .", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240615469C/abstract", "content": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations. We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD ."}
+{"idx": 8, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381665624_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Accelerate_the_Crystalline_Symmetry_Classification", "content": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations."}
+{"idx": 9, "title": "Publications - Ruifeng Tan", "date": "", "ddg_snippet": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper, we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ...", "subpage_snippet": "", "source": "ruifeng-tan.github.io", "link": "https://ruifeng-tan.github.io/publications/", "content": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper, we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ..."}
diff --git a/data/sampled_jsons/multi-view_clustering_algorithm_termination_condition_F^(t+1)_-_F^t_F__epsilon.jsonl b/data/sampled_jsons/multi-view_clustering_algorithm_termination_condition_F^(t+1)_-_F^t_F__epsilon.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b484831e65c60a016cde794b28a6c453986c50f4
--- /dev/null
+++ b/data/sampled_jsons/multi-view_clustering_algorithm_termination_condition_F^(t+1)_-_F^t_F__epsilon.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Motion Prediction of Multi-agent systems with Multi-view", "date": "", "ddg_snippet": "... clustering models relevant for the purpose of grouping agents include hierarchical agglomerative clustering [ 12 ] , K-means, density based clustering ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.13905v1", "content": "... clustering models relevant for the purpose of grouping agents include hierarchical agglomerative clustering [ 12 ] , K-means, density based clustering ..."}
+{"idx": 1, "title": "Dependent randomized rounding for clustering and partition", "date": "", "ddg_snippet": "A common strategy for clustering problems is to first solve a related linear program (LP), leading to a fractional configuration y ∈ [ 0 , 1 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1709.06995v10", "content": "A common strategy for clustering problems is to first solve a related linear program (LP), leading to a fractional configuration y ∈ [ 0 , 1 ..."}
+{"idx": 2, "title": "Artificial Intelligence Blog · Clustering", "date": "", "ddg_snippet": "The resulting clusters generalize subgoals within and across tasks and can be used as templates for portable skill termination conditions .", "subpage_snippet": "", "source": "artent.net", "link": "http://artent.net/category/clustering/", "content": "The resulting clusters generalize subgoals within and across tasks and can be used as templates for portable skill termination conditions ."}
+{"idx": 3, "title": "Newest 'approximation-algorithms' Questions -", "date": "", "ddg_snippet": "Given a set of cities $C$ and facilities $ F $, we want to open a subset $S \\subseteq F $ of facilities such that the following is minimized:...", "subpage_snippet": "", "source": "cstheory.stackexchange.com", "link": "https://cstheory.stackexchange.com/questions/tagged/approximation-algorithms", "content": "Given a set of cities $C$ and facilities $ F $, we want to open a subset $S \\subseteq F $ of facilities such that the following is minimized:..."}
+{"idx": 4, "title": "Local differential privacy for unbalanced multivariate nominal", "date": "", "ddg_snippet": "This paper focuses on the frequency statistics problem of high-dimensional heterogeneous data with local differential privacy, which refers to the ...", "subpage_snippet": "", "source": "hcis-journal.springeropen.com", "link": "https://hcis-journal.springeropen.com/articles/10.1186/s13673-020-00233-x", "content": "This paper focuses on the frequency statistics problem of high-dimensional heterogeneous data with local differential privacy, which refers to the ..."}
+{"idx": 5, "title": "A Joint Sparse Self-Representation Learning Method for", "date": "", "ddg_snippet": "Multiview clustering (MC) has been a crucial technique for handling multiview data, which is successfully applied in various fields such as machine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06857v1", "content": "Multiview clustering (MC) has been a crucial technique for handling multiview data, which is successfully applied in various fields such as machine ..."}
+{"idx": 6, "title": "Mixing Time Matters: Accelerating Effective Resistance", "date": "", "ddg_snippet": "For example, ER approximation is closely related to optimal transport problems [ 27 ] , the detection of low conductance sets in graph clustering ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.02513v2", "content": "For example, ER approximation is closely related to optimal transport problems [ 27 ] , the detection of low conductance sets in graph clustering ..."}
+{"idx": 7, "title": "DBMS_DATA_MINING", "date": "", "ddg_snippet": "Oracle Machine Learning for SQL supports more than one algorithm for the classification, regression, clustering , and feature extraction machine ...", "subpage_snippet": "", "source": "docs.oracle.com", "link": "https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html", "content": "Oracle Machine Learning for SQL supports more than one algorithm for the classification, regression, clustering , and feature extraction machine ..."}
+{"idx": 8, "title": "RFC 5905 - Network Time Protocol Version 4: Protocol and", "date": "", "ddg_snippet": "... fundamental improvements in the mitigation and discipline algorithms that extend the potential accuracy to the tens of microseconds with modern ...", "subpage_snippet": "", "source": "www.packetizer.com", "link": "https://www.packetizer.com/rfc/rfc5905/", "content": "... fundamental improvements in the mitigation and discipline algorithms that extend the potential accuracy to the tens of microseconds with modern ..."}
+{"idx": 9, "title": "Safe Reinforcement Learning in Black-Box Environments via", "date": "", "ddg_snippet": "... approaches leverage linear temporal logic (LTL) specifications [ 3 , 17 , 28 ] or use external hints for constructing the LTL formulae [ 41 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.18180v3", "content": "... approaches leverage linear temporal logic (LTL) specifications [ 3 , 17 , 28 ] or use external hints for constructing the LTL formulae [ 41 ] ."}
diff --git a/data/sampled_jsons/nested_CVaR_measure_reinforcement_learning_2023_random_reference_trajectory_regret.jsonl b/data/sampled_jsons/nested_CVaR_measure_reinforcement_learning_2023_random_reference_trajectory_regret.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..816b147e1227cddbebae4a0566b0e517a46c0e08
--- /dev/null
+++ b/data/sampled_jsons/nested_CVaR_measure_reinforcement_learning_2023_random_reference_trajectory_regret.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Bounds for Risk-sensitive Reinforcement Learning with ...", "date": "", "ddg_snippet": "Regret Bounds for RSRL with DRMs Du et al. ( 2023 ) provides the first regret bound for RSRL using DRMs, specifically focusing on dynamic CVaR . Xu et al. ( 2023 ) investigates RSRL through the lens of dynamic OCE, a class of risk measures that encom-passes several well-known measures , including ERM, CVaR , and mean-variance.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/liang24a/liang24a.pdf", "content": "Regret Bounds for RSRL with DRMs Du et al. ( 2023 ) provides the first regret bound for RSRL using DRMs, specifically focusing on dynamic CVaR . Xu et al. ( 2023 ) investigates RSRL through the lens of dynamic OCE, a class of risk measures that encom-passes several well-known measures , including ERM, CVaR , and mean-variance."}
+{"idx": 1, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Additionally, the work of Chen et al. ( 2023 )presents an advancement in the form of a nested CVaR measure within the framework of RLHF. The limitation of this work lies in the selection of a random reference trajectory for comparison, causing an unavoidable linear strong nested CVaR regret .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v3", "content": "Additionally, the work of Chen et al. ( 2023 )presents an advancement in the form of a nested CVaR measure within the framework of RLHF. The limitation of this work lies in the selection of a random reference trajectory for comparison, causing an unavoidable linear strong nested CVaR regret ."}
+{"idx": 2, "title": "Risk-Aware Reinforcement Learning with Coherent Risk Measures ...", "date": "", "ddg_snippet": "Feb 1, 2023 · We propose a unified framework to analyze the regret of risk-aware RL policy that uses a coherent risk measure in conjunction with non-linear function approximation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=-RwZOVybbj", "content": "Feb 1, 2023 · We propose a unified framework to analyze the regret of risk-aware RL policy that uses a coherent risk measure in conjunction with non-linear function approximation."}
+{"idx": 3, "title": "RISK-A REINFORCEMENT LEARNING WITH C R MEASURES AND ON F ...", "date": "", "ddg_snippet": "ABSTRACT We study the risk-aware reinforcement learning (RL) problem in the episodic finite-horizon Markov decision process with unknown transition and reward functions. In contrast to the risk-neutral RL problem, we consider minimizing the risk of having low rewards, which arise due to the intrinsic randomness of the MDPs and imperfect knowledge of the model. Our work provides a unified ...", "subpage_snippet": "", "source": "web.mit.edu", "link": "https://web.mit.edu/~jaillet/www/general/iclr23b.pdf", "content": "ABSTRACT We study the risk-aware reinforcement learning (RL) problem in the episodic finite-horizon Markov decision process with unknown transition and reward functions. In contrast to the risk-neutral RL problem, we consider minimizing the risk of having low rewards, which arise due to the intrinsic randomness of the MDPs and imperfect knowledge of the model. Our work provides a unified ..."}
+{"idx": 4, "title": "P E ITERATED CVAR REINFORCE MENT LEARNING WITH FUNCTION ...", "date": "", "ddg_snippet": "A rich body of works have considered various risk measures into episodic MDPs with unknown transition kernels to tackle risk-sensitive tasks. Among different risk measures , the Conditional Value-at-Risk ( CVaR ) measure has received an increasing attention in RL, e.g., Chow et al. (2015); Du et al. ( 2023 ); Rockafellar et al. (2000); Stanko & Macek (2019); Bastani et al. (2022); Wang et al ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2024/03/6915_provably_efficient_iterated_cv.pdf", "content": "A rich body of works have considered various risk measures into episodic MDPs with unknown transition kernels to tackle risk-sensitive tasks. Among different risk measures , the Conditional Value-at-Risk ( CVaR ) measure has received an increasing attention in RL, e.g., Chow et al. (2015); Du et al. ( 2023 ); Rockafellar et al. (2000); Stanko & Macek (2019); Bastani et al. (2022); Wang et al ..."}
+{"idx": 5, "title": "Regret bounds for risk-sensitive reinforcement learning", "date": "", "ddg_snippet": "Nov 28, 2022 · In safety-critical applications of reinforcement learning such as healthcare and robotics, it is often desirable to optimize risk-sensitive objectives that account for tail outcomes rather than expected reward. We prove the first regret bounds for reinforcement learning under a general class of risk-sensitive objectives including the popular CVaR objective. Our theory is based on a novel ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3600270.3602897", "content": "Nov 28, 2022 · In safety-critical applications of reinforcement learning such as healthcare and robotics, it is often desirable to optimize risk-sensitive objectives that account for tail outcomes rather than expected reward. We prove the first regret bounds for reinforcement learning under a general class of risk-sensitive objectives including the popular CVaR objective. Our theory is based on a novel ..."}
+{"idx": 6, "title": "Bridging Distributional and Risk-sensitive Reinforcement ...", "date": "", "ddg_snippet": "Abstract We study the regret guarantee for risk-sensitive reinforcement learning (RSRL) via distri-butional reinforcement learning (DRL) methods. In particular, we consider nite episodic Markov decision processes whose objective is the entropic risk measure (EntRM) of re-turn. By leveraging a key property of the EntRM, the independence property, we establish the risk-sensitive distributional ...", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume25/22-1253/22-1253.pdf", "content": "Abstract We study the regret guarantee for risk-sensitive reinforcement learning (RSRL) via distri-butional reinforcement learning (DRL) methods. In particular, we consider nite episodic Markov decision processes whose objective is the entropic risk measure (EntRM) of re-turn. By leveraging a key property of the EntRM, the independence property, we establish the risk-sensitive distributional ..."}
+{"idx": 7, "title": "RA-RLHF: Provably Efficient Risk-Aware Reinforcement ...", "date": "", "ddg_snippet": "24 Dec 2024 — (2022) propose ICVaR-RL, a nested risk-aware RL formulation that addresses both regret minimization and best policy identification. Additionally ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v2", "content": "24 Dec 2024 — (2022) propose ICVaR-RL, a nested risk-aware RL formulation that addresses both regret minimization and best policy identification. Additionally ..."}
+{"idx": 8, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "9 Dec 2024 — ... random reference trajectory for comparison, causing an unavoidable linear strong nested CVaR regret . Consequently, we are left with only a ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/95716", "content": "9 Dec 2024 — ... random reference trajectory for comparison, causing an unavoidable linear strong nested CVaR regret . Consequently, we are left with only a ..."}
+{"idx": 9, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "by Y Zhao · 2024 · Cited by 4 — The limitation of this work lies in the selection of a random reference trajectory for comparison, causing an unavoidable linear strong nested CVaR regret . 37 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "by Y Zhao · 2024 · Cited by 4 — The limitation of this work lies in the selection of a random reference trajectory for comparison, causing an unavoidable linear strong nested CVaR regret . 37 pages"}
diff --git a/data/sampled_jsons/neural_network_reward_model_preference-based_reinforcement_learning_deep_RLHF.jsonl b/data/sampled_jsons/neural_network_reward_model_preference-based_reinforcement_learning_deep_RLHF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a567c8659495084923eeaf0aef387beb82bcf3d1
--- /dev/null
+++ b/data/sampled_jsons/neural_network_reward_model_preference-based_reinforcement_learning_deep_RLHF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reward Models in Deep Reinforcement Learning: A Survey", "date": "", "ddg_snippet": "Next, we present an overview of recent reward modeling approaches, categorizing them based on the source, the mechanism, and the learning paradigm.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15421v1", "content": "Next, we present an overview of recent reward modeling approaches, categorizing them based on the source, the mechanism, and the learning paradigm."}
+{"idx": 1, "title": "Learning Guarantee of Reward Modeling Using Deep Neural Networks", "date": "", "ddg_snippet": "In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06601v1", "content": "In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks ."}
+{"idx": 2, "title": "Reinforcement Learning from Diverse Human Preferences", "date": "", "ddg_snippet": "A new paradigm called reinforcement learning from human preferences (or preference - based RL) has emerged as a promising solution, in which reward ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2301.11774v3", "content": "A new paradigm called reinforcement learning from human preferences (or preference - based RL) has emerged as a promising solution, in which reward ..."}
+{"idx": 3, "title": "Fine-Tuning Language Models with Reward Learning on Policy", "date": "", "ddg_snippet": "RLHF contains three steps, i.e., human preference collecting, reward learning , and policy optimization, which are usually performed serially.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.19279v1", "content": "RLHF contains three steps, i.e., human preference collecting, reward learning , and policy optimization, which are usually performed serially."}
+{"idx": 4, "title": "Towards Reward Fairness in RLHF: From a Resource Allocation", "date": "", "ddg_snippet": "Rewards serve as proxies for human preferences and play a crucial role in Reinforcement Learning from Human Feedback ( RLHF ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23349v1", "content": "Rewards serve as proxies for human preferences and play a crucial role in Reinforcement Learning from Human Feedback ( RLHF )."}
+{"idx": 5, "title": "Illustrating Reinforcement Learning from Human Feedback (RLHF)", "date": "", "ddg_snippet": "... model ? That s the idea of Reinforcement Learning from Human Feedback ( RLHF ); use methods from reinforcement learning to directly optimize a language ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/rlhf", "content": "... model ? That s the idea of Reinforcement Learning from Human Feedback ( RLHF ); use methods from reinforcement learning to directly optimize a language ..."}
+{"idx": 6, "title": "Guide to Reinforcement Learning from Human Feedback (RLHF) |", "date": "", "ddg_snippet": "In contrast, others proposed a multi-agent RL network that uses a relative cross-entropy gain- based reward model for 3D medical images.", "subpage_snippet": "", "source": "encord.com", "link": "https://encord.com/blog/guide-to-rlhf/", "content": "In contrast, others proposed a multi-agent RL network that uses a relative cross-entropy gain- based reward model for 3D medical images."}
+{"idx": 7, "title": "What Is Reinforcement Learning From Human Feedback (RLHF)? | IBM", "date": "", "ddg_snippet": "In deep reinforcement learning , the policy is represented as a neural network which is continuously updated, per the reward function, during the ...", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/topics/rlhf", "content": "In deep reinforcement learning , the policy is represented as a neural network which is continuously updated, per the reward function, during the ..."}
+{"idx": 8, "title": "What is RLHF?", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback ( RLHF ) is where machines learn and grow with a little help from their humans! Imagine training robots to ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2023/05/reinforcement-learning-from-human-feedback/", "content": "Reinforcement Learning from Human Feedback ( RLHF ) is where machines learn and grow with a little help from their humans! Imagine training robots to ..."}
+{"idx": 9, "title": "RLHF | Deepgram", "date": "", "ddg_snippet": "Fine-tuning with reinforcement learning : Apply RL to maximize the rewards the monitoring model gives for responses that align with human preferences .", "subpage_snippet": "", "source": "deepgram.com", "link": "https://deepgram.com/ai-glossary/rlhf", "content": "Fine-tuning with reinforcement learning : Apply RL to maximize the rewards the monitoring model gives for responses that align with human preferences ."}
diff --git a/data/sampled_jsons/neural_network_reward_modeling_RLHF_function_approximation_non-linear_year_2023.jsonl b/data/sampled_jsons/neural_network_reward_modeling_RLHF_function_approximation_non-linear_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a64f0886c4bd1b564fe3fa5501ccca44fd1804f
--- /dev/null
+++ b/data/sampled_jsons/neural_network_reward_modeling_RLHF_function_approximation_non-linear_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Guarantee of Reward Modeling Using Deep Neural Networks", "date": "", "ddg_snippet": "In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06601v1", "content": "In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks ."}
+{"idx": 1, "title": "Segmenting Text and Learning Their Rewards for Improved RLHF in", "date": "", "ddg_snippet": "... depend on later tokens, making reward /credit assignment harder, especially under the prevailing RLHF paradigm of implementing the reward model as an ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02790v1", "content": "... depend on later tokens, making reward /credit assignment harder, especially under the prevailing RLHF paradigm of implementing the reward model as an ..."}
+{"idx": 2, "title": "Reward Hacking in Reinforcement Learning | Lil'Log", "date": "", "ddg_snippet": "With the rise of language models generalizing to a broad spectrum of tasks and RLHF becomes a de facto method for alignment training, reward hacking ...", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/", "content": "With the rise of language models generalizing to a broad spectrum of tasks and RLHF becomes a de facto method for alignment training, reward hacking ..."}
+{"idx": 3, "title": "Uncertainty-Penalized Reinforcement Learning from Human", "date": "", "ddg_snippet": "... 1) We propose UP- RLHF , which augments RLHF with uncertainty regularization by penalizing rewards with uncertainties provided by the reward model .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00243v1", "content": "... 1) We propose UP- RLHF , which augments RLHF with uncertainty regularization by penalizing rewards with uncertainties provided by the reward model ."}
+{"idx": 4, "title": "Aldo Pacchiano | Aldo Pacchiano", "date": "", "ddg_snippet": "Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non -Stationary Objectives ... Model Selection for Reinforcement Learning ...", "subpage_snippet": "", "source": "www.aldopacchiano.ai", "link": "http://www.aldopacchiano.ai/authors/aldo-pacchiano/", "content": "Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non -Stationary Objectives ... Model Selection for Reinforcement Learning ..."}
+{"idx": 5, "title": "Aldo Pacchiano | Aldo Pacchiano", "date": "", "ddg_snippet": "Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non -Stationary Objectives ... Model Selection for Reinforcement Learning ...", "subpage_snippet": "", "source": "www.aldopacchiano.ai", "link": "https://www.aldopacchiano.ai/authors/aldo-pacchiano/", "content": "Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non -Stationary Objectives ... Model Selection for Reinforcement Learning ..."}
+{"idx": 6, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Going Beyond Neural Network Feature Similarity: The ... EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Going Beyond Neural Network Feature Similarity: The ... EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model"}
+{"idx": 7, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "Robustifying Generalizable Implicit Shape Networks with a Tunable Non -Parametric Model ... bracket-based dynamics for deep graph neural networks", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html", "content": "Robustifying Generalizable Implicit Shape Networks with a Tunable Non -Parametric Model ... bracket-based dynamics for deep graph neural networks"}
+{"idx": 8, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "Robustifying Generalizable Implicit Shape Networks with a Tunable Non -Parametric Model ... bracket-based dynamics for deep graph neural networks", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html?filter=titles", "content": "Robustifying Generalizable Implicit Shape Networks with a Tunable Non -Parametric Model ... bracket-based dynamics for deep graph neural networks"}
+{"idx": 9, "title": "Apple Intelligence Foundation Language Models", "date": "", "ddg_snippet": "In this report we will detail how two of these models —AFM-on-device (AFM stands for Apple Foundation Model ), a ∼ similar-to \\sim ∼ 3 billion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.21075v1", "content": "In this report we will detail how two of these models —AFM-on-device (AFM stands for Apple Foundation Model ), a ∼ similar-to \\sim ∼ 3 billion ..."}
diff --git a/data/sampled_jsons/neural_operator_reverse_currying_input_space_output_space.jsonl b/data/sampled_jsons/neural_operator_reverse_currying_input_space_output_space.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a7527d6846925ab4c3c1943b9c2f9b446557534
--- /dev/null
+++ b/data/sampled_jsons/neural_operator_reverse_currying_input_space_output_space.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural operators - Wikipedia", "date": "", "ddg_snippet": "Neural operators directly learn operators between function spaces ; they can receive input functions, and the output function can be evaluated at any discretization. [1][2]", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_operators", "content": "Neural operators directly learn operators between function spaces ; they can receive input functions, and the output function can be evaluated at any discretization. [1][2]"}
+{"idx": 1, "title": "Neural Operators - Janelia CVML", "date": "", "ddg_snippet": "The input function a (x) is lifted into a (higher-dimensional) space by P, processed by several Neural Operator layers, and projected to the output space by Q, yielding the output function u (x).", "subpage_snippet": "", "source": "janelia-cvml.github.io", "link": "https://janelia-cvml.github.io/blog/posts/NeuralOperators/index.html", "content": "The input function a (x) is lifted into a (higher-dimensional) space by P, processed by several Neural Operator layers, and projected to the output space by Q, yielding the output function u (x)."}
+{"idx": 2, "title": "Neural Operators and Where to Find Them - Medium", "date": "", "ddg_snippet": "The general structure of a neural operator : The input function a is first encoded into an embedding space , and then processed through multiple layers of integral operators and pointwise non ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/sissa-mathlab/neural-operators-and-where-to-find-them-19af4aa9da3e", "content": "The general structure of a neural operator : The input function a is first encoded into an embedding space , and then processed through multiple layers of integral operators and pointwise non ..."}
+{"idx": 3, "title": "Merging Memory and Space: A State Space Neural Operator", "date": "", "ddg_snippet": "Many problems in scientific computing require approximating nonlinear operators that map input functions to output functions, often governed by partial differential equations (PDEs). Neural operators (NOs) (Kovachki et al., 2023) provide a mesh-independent framework for this task, operating directly on functions and generalizing across discretizations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23428v2", "content": "Many problems in scientific computing require approximating nonlinear operators that map input functions to output functions, often governed by partial differential equations (PDEs). Neural operators (NOs) (Kovachki et al., 2023) provide a mesh-independent framework for this task, operating directly on functions and generalizing across discretizations."}
+{"idx": 4, "title": "PDF Neural Operators: Machine Learning in Function Spaces", "date": "", "ddg_snippet": "From Neural networks to Neural Operators Integral operator outputs functions (not just finite-dimensional vectors). Integral operator is discretization agnostic and discretization convergent. Neural Operators are universal approximator of operators .", "subpage_snippet": "", "source": "www.robertj1.com", "link": "https://www.robertj1.com/assets/pdf/Neural_Operators_jpl.pdf", "content": "From Neural networks to Neural Operators Integral operator outputs functions (not just finite-dimensional vectors). Integral operator is discretization agnostic and discretization convergent. Neural Operators are universal approximator of operators ."}
+{"idx": 5, "title": "nickhnelsen/fourier-neural-mappings - GitHub", "date": "", "ddg_snippet": "Fourier Neural Mappings (FNMs) generalize Fourier Neural Operators (FNOs) by allowing the input space and/or the output space to be finite-dimensional (instead of both being purely infinite-dimensional function spaces as with FNO). This is especially relevant for surrogate modeling tasks in ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nickhnelsen/fourier-neural-mappings", "content": "Fourier Neural Mappings (FNMs) generalize Fourier Neural Operators (FNOs) by allowing the input space and/or the output space to be finite-dimensional (instead of both being purely infinite-dimensional function spaces as with FNO). This is especially relevant for surrogate modeling tasks in ..."}
+{"idx": 6, "title": "Neural Operator: Learning Maps Between Function Spaces", "date": "", "ddg_snippet": "The classical development of neural networks has primarily focused on learning mappings between finite dimensional Euclidean spaces or finite sets. We propose a generalization of neural networks to learn operators , termed neural operators , that map between infinite dimensional function spaces . We formulate the neural operator as a composition of linear integral operators and nonlinear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2108.08481", "content": "The classical development of neural networks has primarily focused on learning mappings between finite dimensional Euclidean spaces or finite sets. We propose a generalization of neural networks to learn operators , termed neural operators , that map between infinite dimensional function spaces . We formulate the neural operator as a composition of linear integral operators and nonlinear ..."}
+{"idx": 7, "title": "Neural Operator: Learning Maps Between Function Spaces With ...", "date": "", "ddg_snippet": "We propose a generalization of neural networks to learn operators , termed neural operators , that map between infinite dimensional function spaces . We formulate the neural operator as a composition of linear integral operators and nonlinear activation functions.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/v24/21-1524.html", "content": "We propose a generalization of neural networks to learn operators , termed neural operators , that map between infinite dimensional function spaces . We formulate the neural operator as a composition of linear integral operators and nonlinear activation functions."}
+{"idx": 8, "title": "Neural Operators: Learning Maps Between Function Spaces - TransferLab", "date": "", "ddg_snippet": "In [Kov23N], the authors introduce neural operators as a general deep-learning framework designed to learn mappings between infinite-dimensional function spaces . Traditional neural networks fall short in this application due to their dependence on the discretization of training data. Neural operators as discretization-invariant models can accept any set of points in the input and output ...", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/pills/2023/neural-operators/", "content": "In [Kov23N], the authors introduce neural operators as a general deep-learning framework designed to learn mappings between infinite-dimensional function spaces . Traditional neural networks fall short in this application due to their dependence on the discretization of training data. Neural operators as discretization-invariant models can accept any set of points in the input and output ..."}
+{"idx": 9, "title": "D2NO: Efficient handling of heterogeneous input function spaces with ...", "date": "", "ddg_snippet": "Our method involves partitioning the input function space and processing individual input functions using independent and separate neural networks. A centralized neural network is used to handle shared information across all output functions.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782524003402", "content": "Our method involves partitioning the input function space and processing individual input functions using independent and separate neural networks. A centralized neural network is used to handle shared information across all output functions."}
diff --git a/data/sampled_jsons/neural_operator_reverse_currying_input_space_output_space_RdU.jsonl b/data/sampled_jsons/neural_operator_reverse_currying_input_space_output_space_RdU.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e2e97a7a3b6fe72b05d0f8a7feab360713fc69e7
--- /dev/null
+++ b/data/sampled_jsons/neural_operator_reverse_currying_input_space_output_space_RdU.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of ..."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — Abstract. Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — Abstract. Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution."}
+{"idx": 2, "title": "Scalable Gaussian Process Operator for Physical Systems", "date": "", "ddg_snippet": "by S Kumar · 2025 — The term Ko represents an operator -valued covariance kernel that maps pairs of input functions to bounded linear operators in Lin(U), the space .", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.15906", "content": "by S Kumar · 2025 — The term Ko represents an operator -valued covariance kernel that maps pairs of input functions to bounded linear operators in Lin(U), the space ."}
+{"idx": 3, "title": "Representation Spaces in Neuro-Symbolic AI", "date": "", "ddg_snippet": "7 Nov 2024 — This study proposes to use PLP (probabilistic logic programming) to fuse neural network output and symbolic logic representation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.04393v1", "content": "7 Nov 2024 — This study proposes to use PLP (probabilistic logic programming) to fuse neural network output and symbolic logic representation."}
+{"idx": 4, "title": "A Neural Framework for Learning DAG to DAG Translation", "date": "", "ddg_snippet": "by MCDP Kaluza · Cited by 10 — Our experiments show the ability of the model to learn the structural signals and patterns in both input and output spaces , showing improved syntax measures in ... 7 pages", "subpage_snippet": "", "source": "www.ccs.neu.edu", "link": "http://www.ccs.neu.edu/home/clara/resources/neural-framework-learning.pdf", "content": "by MCDP Kaluza · Cited by 10 — Our experiments show the ability of the model to learn the structural signals and patterns in both input and output spaces , showing improved syntax measures in ... 7 pages"}
+{"idx": 5, "title": "PROSE: Predicting Operators and Symbolic Expressions ...", "date": "", "ddg_snippet": "by Y Liu · Cited by 30 — Approximating nonlinear differential equations using a neural network provides a robust and efficient tool for various scientific computing tasks, including ... 21 pages", "subpage_snippet": "", "source": "ww3.math.ucla.edu", "link": "https://ww3.math.ucla.edu/wp-content/uploads/2023/10/PROSE_cam.pdf", "content": "by Y Liu · Cited by 30 — Approximating nonlinear differential equations using a neural network provides a robust and efficient tool for various scientific computing tasks, including ... 21 pages"}
+{"idx": 6, "title": "The geometry of monotone operator splitting methods", "date": "", "ddg_snippet": "by PL Combettes · 2024 · Cited by 17 — We propose a geometric framework to describe and analyse a wide array of operator splitting methods for solving monotone inclusion problems.", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/acta-numerica/article/geometry-of-monotone-operator-splitting-methods/AC76B29B4F3B229F93810C1FCB35102C", "content": "by PL Combettes · 2024 · Cited by 17 — We propose a geometric framework to describe and analyse a wide array of operator splitting methods for solving monotone inclusion problems."}
+{"idx": 7, "title": "Physics-Informed Deep Neural Network for Backward-in-Time ...", "date": "", "ddg_snippet": "by MAER Hammoud · 2023 · Cited by 10 — This study leverages advances in artificial intelligence to train a deep neural network that provides accurate backward -in-time predictions ...", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/aies/2/1/AIES-D-22-0076.1.xml", "content": "by MAER Hammoud · 2023 · Cited by 10 — This study leverages advances in artificial intelligence to train a deep neural network that provides accurate backward -in-time predictions ..."}
+{"idx": 8, "title": "Active Learning for Neurosymbolic Program Synthesis", "date": "", "ddg_snippet": "First, we sample two inputs from the input space and supply the corresponding output , giving us a set Z of initial input - output examples. Then, we compute ...", "subpage_snippet": "", "source": "www.cs.utexas.edu", "link": "https://www.cs.utexas.edu/~isil/smartlabel.pdf", "content": "First, we sample two inputs from the input space and supply the corresponding output , giving us a set Z of initial input - output examples. Then, we compute ..."}
+{"idx": 9, "title": "Provably Unlearnable Data Examples", "date": "", "ddg_snippet": "by D Wang · Cited by 5 — Note that the certification pertains to the parameter space rather than the input space , alleviating the need for knowledge of the training. 18 pages", "subpage_snippet": "", "source": "www.ndss-symposium.org", "link": "https://www.ndss-symposium.org/wp-content/uploads/2025-886-paper.pdf", "content": "by D Wang · Cited by 5 — Note that the certification pertains to the parameter space rather than the input space , alleviating the need for knowledge of the training. 18 pages"}
diff --git a/data/sampled_jsons/neuron_condensation_in_deep_learning.jsonl b/data/sampled_jsons/neuron_condensation_in_deep_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..91dd39e33896f4780401072ad509cf7a12db4fcb
--- /dev/null
+++ b/data/sampled_jsons/neuron_condensation_in_deep_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Single-neuron deep generative model uncovers underlying physics", "date": "", "ddg_snippet": "Recent advancements in deep generative models (DGMs) have facilitated the modeling of neuronal population dynamics, uncovering latent representations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.14615v2", "content": "Recent advancements in deep generative models (DGMs) have facilitated the modeling of neuronal population dynamics, uncovering latent representations ..."}
+{"idx": 1, "title": "Dropping Experts, Recombining Neurons: Retraining-Free Pruning", "date": "", "ddg_snippet": "However, these approaches merge at the whole-expert level, often overlooking that neuron arrangements across experts are inherently unaligned ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10377v1", "content": "However, these approaches merge at the whole-expert level, often overlooking that neuron arrangements across experts are inherently unaligned ..."}
+{"idx": 2, "title": "Neuronal Network Activation Induced by Forniceal Deep Brain", "date": "", "ddg_snippet": "It has become a major target of deep brain stimulation (DBS) to treat memory impairment in both dementia patients and animal models of neurological ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2073-4425/16/2/210", "content": "It has become a major target of deep brain stimulation (DBS) to treat memory impairment in both dementia patients and animal models of neurological ..."}
+{"idx": 3, "title": "Neurons in Cerebellum Show Surprising Activity During Learning", "date": "", "ddg_snippet": "The study is the first to look at the activity of these neurons , known as granule cells, in the brains of living animals while they are learning a ...", "subpage_snippet": "", "source": "www.simonsfoundation.org", "link": "https://www.simonsfoundation.org/2017/03/21/neurons-in-cerebellum-show-surprising-activity-during-learning/", "content": "The study is the first to look at the activity of these neurons , known as granule cells, in the brains of living animals while they are learning a ..."}
+{"idx": 4, "title": "Researchers observe at the atomic level the neuronal", "date": "", "ddg_snippet": "It specialises in introducing and/or removing two amino acids that are key for neuronal connections (synapses), which are involved in learning ...", "subpage_snippet": "", "source": "bist.eu", "link": "https://bist.eu/researchers-observe-at-the-atomic-level-the-neuronal-gate-for-essential-molecules-in-learning-and-memory/", "content": "It specialises in introducing and/or removing two amino acids that are key for neuronal connections (synapses), which are involved in learning ..."}
+{"idx": 5, "title": "Neuronal migration depends on blood flow in the adult brain", "date": "", "ddg_snippet": "To compare the distribution of new neurons in perivascular regions with different blood flows in deep brain regions, we used endomucin, which has ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/99502", "content": "To compare the distribution of new neurons in perivascular regions with different blood flows in deep brain regions, we used endomucin, which has ..."}
+{"idx": 6, "title": "Neuronal migration depends on blood flow in the adult brain", "date": "", "ddg_snippet": "To compare the distribution of new neurons in perivascular regions with different blood flows in deep brain regions, we used endomucin, which has ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/99502v1", "content": "To compare the distribution of new neurons in perivascular regions with different blood flows in deep brain regions, we used endomucin, which has ..."}
+{"idx": 7, "title": "The Strange Physics That Gave Birth to AI | Quanta Magazine", "date": "", "ddg_snippet": "Emergence in a deep learning architecture — or a brain — is as captivating as it is puzzling; there’s no universal theory of emergence.", "subpage_snippet": "", "source": "www.quantamagazine.org", "link": "https://www.quantamagazine.org/the-strange-physics-that-gave-birth-to-ai-20250430/", "content": "Emergence in a deep learning architecture — or a brain — is as captivating as it is puzzling; there’s no universal theory of emergence."}
+{"idx": 8, "title": "Condensed Matter News - Physics News, Physic Materials News,", "date": "", "ddg_snippet": "This approach uses Bayesian deep learning , a form of artificial intelligence that ... ... Copper selenide (Cu₂Se) attracts scientific interest for ...", "subpage_snippet": "", "source": "phys.org", "link": "https://phys.org/physics-news/materials/", "content": "This approach uses Bayesian deep learning , a form of artificial intelligence that ... ... Copper selenide (Cu₂Se) attracts scientific interest for ..."}
+{"idx": 9, "title": "LSTM - Long Short Term Memory Architecture - Vinod", "date": "", "ddg_snippet": "... Deep learning which is a subset of machine learning utilizes interconnected nodes to create a layered structure that emulates (try to) the operation ...", "subpage_snippet": "", "source": "vinodsblog.com", "link": "https://vinodsblog.com/2022/07/13/lstm-long-short-term-memory-architecture/", "content": "... Deep learning which is a subset of machine learning utilizes interconnected nodes to create a layered structure that emulates (try to) the operation ..."}
diff --git a/data/sampled_jsons/non-continuous_loss_function_stochastic_combinatorial_bandits_year_2022.jsonl b/data/sampled_jsons/non-continuous_loss_function_stochastic_combinatorial_bandits_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f7f2defa5dd319133358765a1b935630ab54a82d
--- /dev/null
+++ b/data/sampled_jsons/non-continuous_loss_function_stochastic_combinatorial_bandits_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Nonstochastic Contextual Combinatorial Bandits", "date": "", "ddg_snippet": "Abstract We study a contextual version of online combi-natorial optimisation with full and semi-bandit feedback. In this sequential decision-making problem, an online learner has to select an action from a combinatorial decision space after seeing a vector-valued context in each round. As a result of its action, the learner incurs a loss that is a bi-linear function of the context vector and ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/zierahn23a/zierahn23a.pdf", "content": "Abstract We study a contextual version of online combi-natorial optimisation with full and semi-bandit feedback. In this sequential decision-making problem, an online learner has to select an action from a combinatorial decision space after seeing a vector-valued context in each round. As a result of its action, the learner incurs a loss that is a bi-linear function of the context vector and ..."}
+{"idx": 1, "title": "Adversarial Combinatorial Bandits with General Non-linear ... Combinatorial Multi-Armed Bandit with General Reward Functions Combinatorial Cascading Bandits - NeurIPS Contextual Combinatorial Cascading Bandits Combinatorial Bandits - Learning Theory Combinatorial Bandits - Learning Theory Adversarial Combinatorial Bandits with General Non -linear Reward Fun… Adversarial Combinatorial Bandits with General Non -linear Reward Fun… Contextual Combinatorial Cascading Bandits - Chinese University of Ho… Contextual Combinatorial Cascading Bandits - Chinese University of Ho… Contextual Combinatorial Cascading Bandits - Chinese University of Ho… Combinatorial Semi-Bandit in the Non-Stationary Environment", "date": "", "ddg_snippet": "Jan 5, 2021 · In this paper we study the adversarial combinatorial bandit with a known non -linear reward function , extending existing work on adversarial linear combinatorial bandit. {The adversarial combinatorial bandit with general non -linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic ... Abstract In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function , whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables. Our proofs build on recent work in stochastic combinatorial semi- bandits but also address two novel challenges of our setting, a non -linear reward function and partial observability. We evaluate CombCascade on two real-world problems and show that it performs well even when our modeling assumptions are violated. Abstract We propose the contextual combinatorial cas-cading bandits , a combinatorial online learning game, where at each time step a learning agent is given a set of contextual information, then selects a list of items, and observes stochastic outcomes of a prefix in the selected items by some stop-ping criterion. We note here that the combinatorial bandit problem is a special case of the online linear optimization problem con-sidered, for example, in [1, 4, 7]. In the online linear opti-mization problem the expert class is an arbitrary (finite) sub-set S of Rd and the loss of an expert v ∈ S, at time t, is the inner product l⊤ v. What is a combinatorial bandit problem? We note here that the combinatorial bandit problem is a special case of the online linear optimization problem con-sidered, for example, in [1, 4, 7]. In the online linear opti-mization problem the expert class is an arbitrary (finite) sub-set S of Rd and the loss of an expert v ∈ S, at time t, is the inner product l⊤ v. ent way. Does adversarial combinatorial Bandit have a non-linear reward function? In this paper we study the adversarial combinatorial bandit with a known non-linear reward function , extending existing work on adversarial linear combinatorial bandit. Is there a gap between non-linear reward and adversarial bandit? {The adversarial combinatorial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback.} What is a combinatorial cascading bandit? The combinatorial cascading bandits of (Kveton et al., 2015c) (referred to as ‘ CombCas-cade ’ in Table 1) generalized the framework, allowing each feasible action to be a subset of ground items under combi-natorial constraints. Can linear bandits handle contextual information for a confidence ellipsoid? use the technique of linear bandits , (Abbasi-Yadkori et al., 2011), to handle the contextual information for a confi-dence ellipsoid of . The regret bound of linear bandits has an additional term of O(pd ln(n)) than the standard s-tochastic MAB bound O(pnd ln(n)). A similar compari-son can be made for the disjunctive objective in our Section Do bandits treat all positions in a cascading list equally? Second, cas-cading bandits studied in (Kveton et al., 2015a;c) treats all positions in the cascading list equally , but in applications different positions may bring different rewards. For exam-ple, in online recommendation, we usually prefer users to find their interested item in the list as early as possible to increase user satisfaction. Combinatorial semi-bandit Combinatorial semi- bandits (CSB) is a generalization of MAB, and there are also two types of CSB, i.e., in the adversarial or stochastic settings. Adversarial CSB was introduced in the context of shortest-path problems by György et al. [2007], and later studied extensively [Lattimore and Szepesvári, 2018].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2101.01301", "content": "Jan 5, 2021 · In this paper we study the adversarial combinatorial bandit with a known non -linear reward function , extending existing work on adversarial linear combinatorial bandit. {The adversarial combinatorial bandit with general non -linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic ... Abstract In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function , whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables. Our proofs build on recent work in stochastic combinatorial semi- bandits but also address two novel challenges of our setting, a non -linear reward function and partial observability. We evaluate CombCascade on two real-world problems and show that it performs well even when our modeling assumptions are violated. Abstract We propose the contextual combinatorial cas-cading bandits , a combinatorial online learning game, where at each time step a learning agent is given a set of contextual information, then selects a list of items, and observes stochastic outcomes of a prefix in the selected items by some stop-ping criterion. We note here that the combinatorial bandit problem is a special case of the online linear optimization problem con-sidered, for example, in [1, 4, 7]. In the online linear opti-mization problem the expert class is an arbitrary (finite) sub-set S of Rd and the loss of an expert v ∈ S, at time t, is the inner product l⊤ v. What is a combinatorial bandit problem? We note here that the combinatorial bandit problem is a special case of the online linear optimization problem con-sidered, for example, in [1, 4, 7]. In the online linear opti-mization problem the expert class is an arbitrary (finite) sub-set S of Rd and the loss of an expert v ∈ S, at time t, is the inner product l⊤ v. ent way. Does adversarial combinatorial Bandit have a non-linear reward function? In this paper we study the adversarial combinatorial bandit with a known non-linear reward function , extending existing work on adversarial linear combinatorial bandit. Is there a gap between non-linear reward and adversarial bandit? {The adversarial combinatorial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback.} What is a combinatorial cascading bandit? The combinatorial cascading bandits of (Kveton et al., 2015c) (referred to as ‘ CombCas-cade ’ in Table 1) generalized the framework, allowing each feasible action to be a subset of ground items under combi-natorial constraints. Can linear bandits handle contextual information for a confidence ellipsoid? use the technique of linear bandits , (Abbasi-Yadkori et al., 2011), to handle the contextual information for a confi-dence ellipsoid of . The regret bound of linear bandits has an additional term of O(pd ln(n)) than the standard s-tochastic MAB bound O(pnd ln(n)). A similar compari-son can be made for the disjunctive objective in our Section Do bandits treat all positions in a cascading list equally? Second, cas-cading bandits studied in (Kveton et al., 2015a;c) treats all positions in the cascading list equally , but in applications different positions may bring different rewards. For exam-ple, in online recommendation, we usually prefer users to find their interested item in the list as early as possible to increase user satisfaction. Combinatorial semi-bandit Combinatorial semi- bandits (CSB) is a generalization of MAB, and there are also two types of CSB, i.e., in the adversarial or stochastic settings. Adversarial CSB was introduced in the context of shortest-path problems by György et al. [2007], and later studied extensively [Lattimore and Szepesvári, 2018]."}
+{"idx": 2, "title": "Combinatorial Multi-Armed Bandit with General Reward Functions", "date": "", "ddg_snippet": "Abstract In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function , whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/nips2016_cmabgeneral_full_new.pdf", "content": "Abstract In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function , whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables."}
+{"idx": 3, "title": "Combinatorial Cascading Bandits - NeurIPS", "date": "", "ddg_snippet": "Our proofs build on recent work in stochastic combinatorial semi- bandits but also address two novel challenges of our setting, a non -linear reward function and partial observability. We evaluate CombCascade on two real-world problems and show that it performs well even when our modeling assumptions are violated.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2015/file/1f50893f80d6830d62765ffad7721742-Paper.pdf", "content": "Our proofs build on recent work in stochastic combinatorial semi- bandits but also address two novel challenges of our setting, a non -linear reward function and partial observability. We evaluate CombCascade on two real-world problems and show that it performs well even when our modeling assumptions are violated."}
+{"idx": 4, "title": "Contextual Combinatorial Cascading Bandits", "date": "", "ddg_snippet": "Abstract We propose the contextual combinatorial cas-cading bandits , a combinatorial online learning game, where at each time step a learning agent is given a set of contextual information, then selects a list of items, and observes stochastic outcomes of a prefix in the selected items by some stop-ping criterion.", "subpage_snippet": "", "source": "www.cse.cuhk.edu.hk", "link": "https://www.cse.cuhk.edu.hk/~syzhang/papers/CCCBwSupp.pdf", "content": "Abstract We propose the contextual combinatorial cas-cading bandits , a combinatorial online learning game, where at each time step a learning agent is given a set of contextual information, then selects a list of items, and observes stochastic outcomes of a prefix in the selected items by some stop-ping criterion."}
+{"idx": 5, "title": "Combinatorial Bandits - Learning Theory", "date": "", "ddg_snippet": "We note here that the combinatorial bandit problem is a special case of the online linear optimization problem con-sidered, for example, in [1, 4, 7]. In the online linear opti-mization problem the expert class is an arbitrary (finite) sub-set S of Rd and the loss of an expert v ∈ S, at time t, is the inner product l⊤ v.", "subpage_snippet": "", "source": "www.learningtheory.org", "link": "https://www.learningtheory.org/colt2009/papers/024.pdf", "content": "We note here that the combinatorial bandit problem is a special case of the online linear optimization problem con-sidered, for example, in [1, 4, 7]. In the online linear opti-mization problem the expert class is an arbitrary (finite) sub-set S of Rd and the loss of an expert v ∈ S, at time t, is the inner product l⊤ v."}
+{"idx": 6, "title": "Combinatorial Semi-Bandit in the Non-Stationary Environment", "date": "", "ddg_snippet": "Combinatorial semi-bandit Combinatorial semi- bandits (CSB) is a generalization of MAB, and there are also two types of CSB, i.e., in the adversarial or stochastic settings. Adversarial CSB was introduced in the context of shortest-path problems by György et al. [2007], and later studied extensively [Lattimore and Szepesvári, 2018].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2002.03580", "content": "Combinatorial semi-bandit Combinatorial semi- bandits (CSB) is a generalization of MAB, and there are also two types of CSB, i.e., in the adversarial or stochastic settings. Adversarial CSB was introduced in the context of shortest-path problems by György et al. [2007], and later studied extensively [Lattimore and Szepesvári, 2018]."}
+{"idx": 7, "title": "A Framework for Adapting Offline Algorithms to Solve Combinatorial ...", "date": "", "ddg_snippet": "We investigate the problem of stochastic , combinatorial multi-armed bandits where the learner only has access to bandit feedback and the reward function can be non -linear.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/a-framework-for-adapting-offline-algorithms-to-solve-combinatorial-multi-armed-bandit-problems-with-bandit-feedback", "content": "We investigate the problem of stochastic , combinatorial multi-armed bandits where the learner only has access to bandit feedback and the reward function can be non -linear."}
+{"idx": 8, "title": "Adversarial Combinatorial Bandits with General Non -linear Reward...", "date": "", "ddg_snippet": "ear combinatorial bandit . The adversarial com-. binatorial bandit with general non -linear reward. is an important open problem in bandit literatureQuestion 1. For adversarial combinatorial bandit with a non -linear link function g(·) and bandit feedback, is it pos", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2101.01301", "content": "ear combinatorial bandit . The adversarial com-. binatorial bandit with general non -linear reward. is an important open problem in bandit literatureQuestion 1. For adversarial combinatorial bandit with a non -linear link function g(·) and bandit feedback, is it pos"}
+{"idx": 9, "title": "Combinatorial Bandits Revisited", "date": "", "ddg_snippet": "4 Stochastic Combinatorial Bandits under Semi- bandit Feedback.in a straightforward manner to the case of continuous linear bandit problems, where the set of arms. is the unit sphere and one wants to maximize the dot product between the arm and an unknown.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2015/file/0ce2ffd21fc958d9ef0ee9ba5336e357-Paper.pdf", "content": "4 Stochastic Combinatorial Bandits under Semi- bandit Feedback.in a straightforward manner to the case of continuous linear bandit problems, where the set of arms. is the unit sphere and one wants to maximize the dot product between the arm and an unknown."}
diff --git a/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandit_year_2024.jsonl b/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandit_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ab3352994347328cb3fdae97c1f76cf3f3b2cf6f
--- /dev/null
+++ b/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandit_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi-armed bandit", "date": "", "ddg_snippet": "The multi-armed bandit problem is a classic reinforcement learning problem that exemplifies the exploration–exploitation tradeoff dilemma.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Multi-armed_bandit", "content": "The multi-armed bandit problem is a classic reinforcement learning problem that exemplifies the exploration–exploitation tradeoff dilemma."}
+{"idx": 1, "title": "Nonstochastic Contextual Combinatorial Bandits", "date": "", "ddg_snippet": "by L Zierahn · 2023 · Cited by 8 — In the present work, we study the nonstochas- tic version of the contextual combinatorial bandit problem, where the sequence of losses incurred by the learning ... 43 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/zierahn23a/zierahn23a.pdf", "content": "by L Zierahn · 2023 · Cited by 8 — In the present work, we study the nonstochas- tic version of the contextual combinatorial bandit problem, where the sequence of losses incurred by the learning ... 43 pages"}
+{"idx": 2, "title": "Finding Optimal Arms in Non-stochastic Combinatorial ...", "date": "", "ddg_snippet": "by J Brandt · 2022 · Cited by 15 — We consider the combinatorial bandits problem with semi- bandit feedback under finite sampling budget constraints, in which the learner can carry out its ... 14 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/820e95997d050178323230e316897c38-Paper-Conference.pdf", "content": "by J Brandt · 2022 · Cited by 15 — We consider the combinatorial bandits problem with semi- bandit feedback under finite sampling budget constraints, in which the learner can carry out its ... 14 pages"}
+{"idx": 3, "title": "Combinatorial Blocking Bandits with Stochastic Delays", "date": "", "ddg_snippet": "by A Atsidakou · 2021 · Cited by 18 — Our model falls into the area of stochastic non -stationary bandits . Important lines in this area include restless bandits , where the arms' mean rewards ... 10 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v139/atsidakou21a/atsidakou21a.pdf", "content": "by A Atsidakou · 2021 · Cited by 18 — Our model falls into the area of stochastic non -stationary bandits . Important lines in this area include restless bandits , where the arms' mean rewards ... 10 pages"}
+{"idx": 4, "title": "Beating Stochastic and Adversarial Semi-bandits Optimally ...", "date": "", "ddg_snippet": "by J Zimmert · 2019 · Cited by 109 — In this work, we extend such best-of-both-worlds results to the combinatorial bandit problem, a generalization of multi- armed bandits , where the learner has to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1901.08779", "content": "by J Zimmert · 2019 · Cited by 109 — In this work, we extend such best-of-both-worlds results to the combinatorial bandit problem, a generalization of multi- armed bandits , where the learner has to ..."}
+{"idx": 5, "title": "A Unified Analysis of Nonstochastic Delayed Feedback for ...", "date": "", "ddg_snippet": "by L Zierahn · 2025 — For exam- ple, in the combinatorial semi- bandit setting the learner observes all loss components corresponding to the non -zero elements of the action, whereas ... 60 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume26/24-0496/24-0496.pdf", "content": "by L Zierahn · 2025 — For exam- ple, in the combinatorial semi- bandit setting the learner observes all loss components corresponding to the non -zero elements of the action, whereas ... 60 pages"}
+{"idx": 6, "title": "Non-Stochastic Bandit Slate Problems", "date": "", "ddg_snippet": "We consider bandit problems, motivated by applications in online advertising and news story selection, in which the learner must repeatedly select a slate, ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "http://papers.neurips.cc/paper/3962-non-stochastic-bandit-slate-problems.pdf", "content": "We consider bandit problems, motivated by applications in online advertising and news story selection, in which the learner must repeatedly select a slate, ..."}
+{"idx": 7, "title": "An Efficient Algorithm for Combinatorial Semi-Bandits", "date": "", "ddg_snippet": "by G Neu · 2016 · Cited by 51 — To our knowledge, all existing bandit algorithms operating in the non - stochastic setting utilize some version of the importance-weighted loss estimates ... 21 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume17/15-091/15-091.pdf", "content": "by G Neu · 2016 · Cited by 51 — To our knowledge, all existing bandit algorithms operating in the non - stochastic setting utilize some version of the importance-weighted loss estimates ... 21 pages"}
+{"idx": 8, "title": "Combinatorial Multi-Armed Bandit with General Reward ...", "date": "", "ddg_snippet": "by W Chen · 2016 · Cited by 181 — In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function, whose expected ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.5555/3157096.3157282", "content": "by W Chen · 2016 · Cited by 181 — In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function, whose expected ..."}
+{"idx": 9, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "To the best of our knowledge, this is the first work addressing a non - continuous loss function in a stochastic combinatorial MAB-like framework. To overcome ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "To the best of our knowledge, this is the first work addressing a non - continuous loss function in a stochastic combinatorial MAB-like framework. To overcome ..."}
diff --git a/data/sampled_jsons/non-continuous_reward_function_combinatorial_bandits_year_2020.jsonl b/data/sampled_jsons/non-continuous_reward_function_combinatorial_bandits_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d5d18d9a2744f8067064ca015e9ccc2eb23209e
--- /dev/null
+++ b/data/sampled_jsons/non-continuous_reward_function_combinatorial_bandits_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi-armed bandit - Wikipedia", "date": "", "ddg_snippet": "A row of slot machines in Las Vegas. In probability theory and machine learning, the multi-armed bandit problem is named from imagining a gambler at a row of slot machines, who has to decide which machines to play, how many times to play each machine...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Multi-armed_bandit", "content": "A row of slot machines in Las Vegas. In probability theory and machine learning, the multi-armed bandit problem is named from imagining a gambler at a row of slot machines, who has to decide which machines to play, how many times to play each machine..."}
+{"idx": 1, "title": "Adversarial Combinatorial Bandits with General Non -linear Reward ...", "date": "", "ddg_snippet": "binatorial bandit with general non -linear reward . is an important open problem in bandit literature, and it is still unclear whether there is a signiQuestion 1. For adversarial combinatorial bandit with a non -linear link function g(·) and bandit feedback, is it pos", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2101.01301", "content": "binatorial bandit with general non -linear reward . is an important open problem in bandit literature, and it is still unclear whether there is a signiQuestion 1. For adversarial combinatorial bandit with a non -linear link function g(·) and bandit feedback, is it pos"}
+{"idx": 2, "title": "Combinatorial Multi-Armed Bandit with General Reward Functions", "date": "", "ddg_snippet": "Assumption 4 (Lipschitz- continuous reward function ). Combinatorial multi-armed bandit and its extension to probabilistically triggered arms.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2016/file/aa169b49b583a2b5af89203c2b78c67c-Paper.pdf", "content": "Assumption 4 (Lipschitz- continuous reward function ). Combinatorial multi-armed bandit and its extension to probabilistically triggered arms."}
+{"idx": 3, "title": "(PDF) Combinatorial Multi-Armed Bandit with General Reward ...", "date": "", "ddg_snippet": "Reward Functions . Wei Chen∗Wei Hu†Fu Li‡Jian Li§Yu Liu¶Pinyan Luk. Abstract. In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB). framework that allows a general nonlinear reward function , whose expected value.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/309388321_Combinatorial_Multi-Armed_Bandit_with_General_Reward_Functions", "content": "Reward Functions . Wei Chen∗Wei Hu†Fu Li‡Jian Li§Yu Liu¶Pinyan Luk. Abstract. In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB). framework that allows a general nonlinear reward function , whose expected value."}
+{"idx": 4, "title": "Combinatorial Multi-Armed Bandit and", "date": "", "ddg_snippet": "Probabilistic Maximum Coverage Bandit . Combinatorial Bandits With Linear Rewards . Application to Social Influence Maximization.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume17/14-298/14-298.pdf", "content": "Probabilistic Maximum Coverage Bandit . Combinatorial Bandits With Linear Rewards . Application to Social Influence Maximization."}
+{"idx": 5, "title": "Adversarial Combinatorial Bandits with General Non -linear Reward ...", "date": "", "ddg_snippet": "The adversarial combinatorial bandit with general non -linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward , stochastic bandit , or semi- bandit feedback.", "subpage_snippet": "", "source": "nyuscholars.nyu.edu", "link": "https://nyuscholars.nyu.edu/en/publications/adversarial-combinatorial-bandits-with-general-non-linear-reward-", "content": "The adversarial combinatorial bandit with general non -linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward , stochastic bandit , or semi- bandit feedback."}
+{"idx": 6, "title": "GitHub - kayuksel/ combinatorial - bandit : A method to search for...", "date": "", "ddg_snippet": "Combinatorial Multi-Armed Bandit . A method for selecting a subset of best items w.r.t. a black-box reward function : Given 𝑁 items, the algorithm searches for an optimal 𝑠𝑢𝑏𝑠𝑒𝑡 of a maximum number of 𝑘 items, which maximizes a blackbox reward function 𝑓...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kayuksel/combinatorial-bandit", "content": "Combinatorial Multi-Armed Bandit . A method for selecting a subset of best items w.r.t. a black-box reward function : Given 𝑁 items, the algorithm searches for an optimal 𝑠𝑢𝑏𝑠𝑒𝑡 of a maximum number of 𝑘 items, which maximizes a blackbox reward function 𝑓..."}
+{"idx": 7, "title": "(PDF) Regret Bounds for Stochastic Combinatorial Multi-Armed...", "date": "", "ddg_snippet": "The contextual combinatorial semi- bandit problem with linear payoff functions is a decision-making problem in which a learner chooses a set of arms with the feature vectors in each round under given constraints so as to maximize the sum of rewards of arms.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/123981524/Regret_Bounds_for_Stochastic_Combinatorial_Multi_Armed_Bandits_with_Linear_Space_Complexity", "content": "The contextual combinatorial semi- bandit problem with linear payoff functions is a decision-making problem in which a learner chooses a set of arms with the feature vectors in each round under given constraints so as to maximize the sum of rewards of arms."}
+{"idx": 8, "title": "Contextual Combinatorial Bandits with Probabilistically Triggered Arms", "date": "", "ddg_snippet": "ditions for the non -linear reward function to cover different.Gai, Y., Krishnamachari, B., and Jain, R. Combinatorial net-work optimization with unknown variables: Multi-armed bandits with linear rewards and individual observations.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/contextual-combinatorial-bandits-with-probabilistically-2gtbd15n.pdf", "content": "ditions for the non -linear reward function to cover different.Gai, Y., Krishnamachari, B., and Jain, R. Combinatorial net-work optimization with unknown variables: Multi-armed bandits with linear rewards and individual observations."}
+{"idx": 9, "title": "Contextual Combinatorial Bandit", "date": "", "ddg_snippet": "Contextual Combinatorial Bandit and its Application on Diversied Online Recommendation. reward Rt(St) may also consider the “diversity” of arms in St, which can be dened as a non -linear function of features of arms. Specically, we consider the case where the expected re", "subpage_snippet": "", "source": "www.chenshouyuan.com", "link": "https://www.chenshouyuan.com/papers/sdm14.pdf", "content": "Contextual Combinatorial Bandit and its Application on Diversied Online Recommendation. reward Rt(St) may also consider the “diversity” of arms in St, which can be dened as a non -linear function of features of arms. Specically, we consider the case where the expected re"}
diff --git a/data/sampled_jsons/np_=_256_recurrent_units_pt_=_ReLU_Wrec_W_dimensions_weight_matrices.jsonl b/data/sampled_jsons/np_=_256_recurrent_units_pt_=_ReLU_Wrec_W_dimensions_weight_matrices.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..55efe10eb20f6a44840759c63adec9a126bc6c27
--- /dev/null
+++ b/data/sampled_jsons/np_=_256_recurrent_units_pt_=_ReLU_Wrec_W_dimensions_weight_matrices.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "PDF Learning Place Cell Representations and Context-Dependent Remapping", "date": "", "ddg_snippet": "Like the feedforward network, this network featured np = 256 recurrent units . In the purely spatial case, the recurrent state at a particular time t was given by pt = ReLU (Wrecpt−1 + Wvt), where Wrec ∈ Rnp×np is a recurrent weight matrix, W ∈ Rnp×2 an input weight matrix, and vt Cartesian velocity inputs.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/0098a92f5f4e2d96c6db471e0c5507a8-Paper-Conference.pdf", "content": "Like the feedforward network, this network featured np = 256 recurrent units . In the purely spatial case, the recurrent state at a particular time t was given by pt = ReLU (Wrecpt−1 + Wvt), where Wrec ∈ Rnp×np is a recurrent weight matrix, W ∈ Rnp×2 an input weight matrix, and vt Cartesian velocity inputs."}
+{"idx": 1, "title": "Learning Place Cell Representations and", "date": "", "ddg_snippet": "pt = ReLU (Wrecpt−1 + W vt), 8. where Wrec ∈ Rnp× np is a recurrent weight matrix , W ∈ Rnp×2 an input weight matrix , and vt Cartesian velocity inputs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=7ESHFpqjNO", "content": "pt = ReLU (Wrecpt−1 + W vt), 8. where Wrec ∈ Rnp× np is a recurrent weight matrix , W ∈ Rnp×2 an input weight matrix , and vt Cartesian velocity inputs."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/nuScenes_A_multimodal_dataset_for_autonomous_driving_Caesar_et_al._2020.jsonl b/data/sampled_jsons/nuScenes_A_multimodal_dataset_for_autonomous_driving_Caesar_et_al._2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1683132b50f96e7a5ee3449c6cce0a563cba4f5d
--- /dev/null
+++ b/data/sampled_jsons/nuScenes_A_multimodal_dataset_for_autonomous_driving_Caesar_et_al._2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) nuScenes : A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Toward driving scene understanding: A dataset for . learning driver behavior and causal reasoning. In CVPR2024) to segment the nuScenes ( Caesar et al . 2020 ) dataset accordingly.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/343456393_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving", "content": "Toward driving scene understanding: A dataset for . learning driver behavior and causal reasoning. In CVPR2024) to segment the nuScenes ( Caesar et al . 2020 ) dataset accordingly."}
+{"idx": 1, "title": "nuScenes : A multimodal dataset for autonomous driving | DeepAI", "date": "", "ddg_snippet": "by Holger Caesar , et al .In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes , each 20s long and fully annotated with...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/nuscenes-a-multimodal-dataset-for-autonomous-driving", "content": "by Holger Caesar , et al .In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes , each 20s long and fully annotated with..."}
+{"idx": 2, "title": "Autonomous Driving : A Comprehensive Exploration of... | Medium", "date": "", "ddg_snippet": "Dataset as Benchmark. nuScenes : A Multimodal Dataset for Autonomous Driving by Holger Caesar et al . (2019). This paper introduces the nuScenes dataset itself, a large-scale, diverse dataset for autonomous driving .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@mehrdadmohsenizadeh/autonomous-driving-a-comprehensive-exploration-of-cutting-edge-datasets-for-object-detection-2f60ebc0c420", "content": "Dataset as Benchmark. nuScenes : A Multimodal Dataset for Autonomous Driving by Holger Caesar et al . (2019). This paper introduces the nuScenes dataset itself, a large-scale, diverse dataset for autonomous driving ."}
+{"idx": 3, "title": "NuScenes -QA: A Multi - Modal Visual Question Answering Benchmark...", "date": "", "ddg_snippet": "The proposed NuScenes -QA is built upon nuScenes ( Caesar et al . 2020 ) , which is a popular 3D perception dataset for autonomous driving . We automatically annotate the question-answer pairs using the CLEVR benchmark (Johnson et al . 2017) as inspiration.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.14836v2", "content": "The proposed NuScenes -QA is built upon nuScenes ( Caesar et al . 2020 ) , which is a popular 3D perception dataset for autonomous driving . We automatically annotate the question-answer pairs using the CLEVR benchmark (Johnson et al . 2017) as inspiration."}
+{"idx": 4, "title": "nuScenes Dataset | OpenBayes Trends", "date": "", "ddg_snippet": "The nuScenes dataset is a large-scale autonomous driving dataset . The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore.", "subpage_snippet": "", "source": "trends.openbayes.com", "link": "https://trends.openbayes.com/dataset/nuscenes", "content": "The nuScenes dataset is a large-scale autonomous driving dataset . The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore."}
+{"idx": 5, "title": "nuScenesComplex: A More Rigorous Evaluation... | SpringerLink", "date": "", "ddg_snippet": "Caesar , H., et al .: nuScenes : a multimodal dataset for autonomous driving . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621–11631 ( 2020 ).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-97-4399-5_46", "content": "Caesar , H., et al .: nuScenes : a multimodal dataset for autonomous driving . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621–11631 ( 2020 )."}
+{"idx": 6, "title": "Datasets for Autonomous Systems and Robotics", "date": "", "ddg_snippet": "(2019): nuScenes : A multimodal dataset for autonomous driving . arXiv:1903.11027; nuScenes includes RADAR data as well. CARLA Imitation learning.", "subpage_snippet": "", "source": "intelligent-hvac.com", "link": "https://intelligent-hvac.com/lists/datasets/list-of-datasets-for-autonomous-systems-and-robotics.html", "content": "(2019): nuScenes : A multimodal dataset for autonomous driving . arXiv:1903.11027; nuScenes includes RADAR data as well. CARLA Imitation learning."}
+{"idx": 7, "title": "European Common Data Management Platform Definition for Railway...", "date": "", "ddg_snippet": "[8] Caesar , H., Bankiti, V., Lang, A.H., et al . (editors), 2020 . nuscenes : A multimodal dataset for autonomous driving . Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020 Jun 13–19; Seattle, WA, USA. p. 11621–11631.", "subpage_snippet": "", "source": "ojs.bilpub.com", "link": "https://ojs.bilpub.com/index.php/tdr/article/view/143", "content": "[8] Caesar , H., Bankiti, V., Lang, A.H., et al . (editors), 2020 . nuscenes : A multimodal dataset for autonomous driving . Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020 Jun 13–19; Seattle, WA, USA. p. 11621–11631."}
+{"idx": 8, "title": "Embodied Understanding of Driving Scenarios | springerprofessional.de", "date": "", "ddg_snippet": "Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios.", "subpage_snippet": "", "source": "www.springerprofessional.de", "link": "https://www.springerprofessional.de/en/embodied-understanding-of-driving-scenarios/50164944", "content": "Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios."}
+{"idx": 9, "title": "AI-Powered Paper Summarization about the arXiv paper 1903.11027v5", "date": "", "ddg_snippet": "The paper \" nuScenes : A multimodal dataset for autonomous driving \" authored by Holger Caesar et al ., presents a significant contribution to the field of computer vision research by introducing a comprehensive dataset that reflects real-world conditions faced by autonomous vehicles.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/1903.11027v5/", "content": "The paper \" nuScenes : A multimodal dataset for autonomous driving \" authored by Holger Caesar et al ., presents a significant contribution to the field of computer vision research by introducing a comprehensive dataset that reflects real-world conditions faced by autonomous vehicles."}
diff --git a/data/sampled_jsons/nuScenes_first_dataset_autonomous_vehicle_sensor_suite_abstract.jsonl b/data/sampled_jsons/nuScenes_first_dataset_autonomous_vehicle_sensor_suite_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e59c2bf00eebf2adde72bcf2c5038a87b874b6b
--- /dev/null
+++ b/data/sampled_jsons/nuScenes_first_dataset_autonomous_vehicle_sensor_suite_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9156412", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 1, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 de-gree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 de-gree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 2, "title": "nuscenes@nutonomy.com Abstract arXiv:1903.11027v1 [cs.LG] 26 ...", "date": "", "ddg_snippet": "h methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fu", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v1", "content": "h methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fu"}
+{"idx": 3, "title": "GitHub - minwoo0611/Awesome-3D-LiDAR-Datasets: This ... Coordinate Transformation in nuScenes Dataset - Zhiyuan Yang nuScenes: A multimodal dataset for autonomous driving | alphaXiv Coordinate Transformation in nuScenes Dataset - Zhiyuan Yang Coordinate Transformation in nuScenes Dataset - Zhiyuan Yang minwoo0611/Awesome-3D-LiDAR-Datasets - GitHub Coordinate Transformation in nuScenes Dataset - Zhiyuan Yang nuScenes : A Multimodal Dataset for Autonomous Driving nuScenes : A Multimodal Dataset for Autonomous Driving Benchmarking domain adaptation for LiDAR-based 3D object ...", "date": "", "ddg_snippet": "This repository is the collection of datasets , involving the 3D LiDAR. The information is presented in a comprehensive table, outlining the type and number of LiDARs, the purpose of each dataset , and scale details. The objectives are broadly categorized into Object Detection (OD), Segmentation (Seg), Odometry (Odom), Place Recognition (PR), and Loc... See full list on github.com Update: 2023-07-13 •The table includes the specific LiDAR products utilized in each dataset , involving the its channels or name. See full list on github.com Jan 4, 2023 · Coordinate Transformation in Nuscenes Dataset Recently I’ve used nuScenes Dataset to train a mesurement model for extended target. The idea is originally form [1-4]. NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view [5]. In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. What is nuscenes dataset? Recently I’ve used nuScenes Dataset to train a mesurement model for extended target. The idea is originally form [1-4]. NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view . Is nuscenes a multimodal dataset for autonomous driving? H. Caesar et al., ‘nuScenes: A multimodal dataset for autonomous driving ’. arXiv, May 05, 2020. doi: 10.48550/arXiv.1903.11027. Tags: nuScenes Dataset, Quaternions, Transformation matrix What is the first open dataset for fullscale and high-speed autonomous racing? Abstract : This paper describes the first open dataset for fullscale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge have contributed to this dataset . What are the problems with nuscenes dataset? The main difficulty in dealing with the nuScenes dataset is the transformation of the coordinates . There are three coordinates in nuScenes dataset: ego-car coordinate in which the sensor position is recorded, sensor coordinate where the radar points position is recorded and global coordinate where all the annotation is recorded. Who annotated the nuscenes dataset? The nuScenes dataset was anno-tated by Scale.ai and we thank Alexandr Wang and Dave Morse for their support. Who can use nuscenes data? The nuScenes data is published under CC BY-NC-SA 4.0 license, which means that anyone can use this dataset for non-commercial research purposes. All data, code, and information is made available online3. Since the release, nuScenes has received strong interest from the AV community [90, 70, 50, 91, 9, 5, 68, 28, 49, 86, 89]. Sep 5, 2025 · The generalization capability of 3D object detection models is crucial for ensuring robust perception in autonomous driving systems. While state-of-the-art models such as Voxel R-CNN, PV-RCNN, and CenterPoint have demonstrated strong performance on publicly available datasets (e.g., KITTI, Waymo, and nuScenes ). In this study, we conduct a comprehensive benchmark evaluation. We introduce two ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/minwoo0611/Awesome-3D-LiDAR-Datasets", "content": "This repository is the collection of datasets , involving the 3D LiDAR. The information is presented in a comprehensive table, outlining the type and number of LiDARs, the purpose of each dataset , and scale details. The objectives are broadly categorized into Object Detection (OD), Segmentation (Seg), Odometry (Odom), Place Recognition (PR), and Loc... See full list on github.com Update: 2023-07-13 •The table includes the specific LiDAR products utilized in each dataset , involving the its channels or name. See full list on github.com Jan 4, 2023 · Coordinate Transformation in Nuscenes Dataset Recently I’ve used nuScenes Dataset to train a mesurement model for extended target. The idea is originally form [1-4]. NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view [5]. In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. What is nuscenes dataset? Recently I’ve used nuScenes Dataset to train a mesurement model for extended target. The idea is originally form [1-4]. NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view . Is nuscenes a multimodal dataset for autonomous driving? H. Caesar et al., ‘nuScenes: A multimodal dataset for autonomous driving ’. arXiv, May 05, 2020. doi: 10.48550/arXiv.1903.11027. Tags: nuScenes Dataset, Quaternions, Transformation matrix What is the first open dataset for fullscale and high-speed autonomous racing? Abstract : This paper describes the first open dataset for fullscale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge have contributed to this dataset . What are the problems with nuscenes dataset? The main difficulty in dealing with the nuScenes dataset is the transformation of the coordinates . There are three coordinates in nuScenes dataset: ego-car coordinate in which the sensor position is recorded, sensor coordinate where the radar points position is recorded and global coordinate where all the annotation is recorded. Who annotated the nuscenes dataset? The nuScenes dataset was anno-tated by Scale.ai and we thank Alexandr Wang and Dave Morse for their support. Who can use nuscenes data? The nuScenes data is published under CC BY-NC-SA 4.0 license, which means that anyone can use this dataset for non-commercial research purposes. All data, code, and information is made available online3. Since the release, nuScenes has received strong interest from the AV community [90, 70, 50, 91, 9, 5, 68, 28, 49, 86, 89]. Sep 5, 2025 · The generalization capability of 3D object detection models is crucial for ensuring robust perception in autonomous driving systems. While state-of-the-art models such as Voxel R-CNN, PV-RCNN, and CenterPoint have demonstrated strong performance on publicly available datasets (e.g., KITTI, Waymo, and nuScenes ). In this study, we conduct a comprehensive benchmark evaluation. We introduce two ..."}
+{"idx": 4, "title": "Coordinate Transformation in nuScenes Dataset - Zhiyuan Yang", "date": "", "ddg_snippet": "Jan 4, 2023 · Coordinate Transformation in Nuscenes Dataset Recently I’ve used nuScenes Dataset to train a mesurement model for extended target. The idea is originally form [1-4]. NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view [5].", "subpage_snippet": "", "source": "zhiyuan-yang.github.io", "link": "https://zhiyuan-yang.github.io/posts/2023/01/Coordinate+Transformation+in+nuScenes+Dataset/", "content": "Jan 4, 2023 · Coordinate Transformation in Nuscenes Dataset Recently I’ve used nuScenes Dataset to train a mesurement model for extended target. The idea is originally form [1-4]. NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view [5]."}
+{"idx": 5, "title": "nuScenes: A multimodal dataset for autonomous driving | alphaXiv", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/1903.11027v1", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 6, "title": "[1903.11027] nuScenes : A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for..."}
+{"idx": 7, "title": "The First and Only Dataset Containing the Full Autonomous Vehicle ...", "date": "", "ddg_snippet": "Aptiv Autonomous Mobility, formerly NuTonomy has released an updated version of NuScenes to enable researchers to effectively study challenging urban driving situations using the full sensor suite of a real self-driving car. The first dataset was released in March 2019.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/ai³-theory-practice-business/the-first-and-only-dataset-containing-the-full-autonomous-vehicle-sensor-suite-an-update-d13826fcb53d", "content": "Aptiv Autonomous Mobility, formerly NuTonomy has released an updated version of NuScenes to enable researchers to effectively study challenging urban driving situations using the full sensor suite of a real self-driving car. The first dataset was released in March 2019."}
+{"idx": 8, "title": "Aptiv Releases Comprehensive Open-Source Dataset for...", "date": "", "ddg_snippet": "Two examples are Cityscapes, Mapillary Vistas. nuScenes is the first dataset to include a full autonomous vehicle sensor suite . The sensor suite includes six cameras, five radars and one lidar, providing a full 360 degree field of view around the vehicle.", "subpage_snippet": "", "source": "www.selfdrivingcars360.com", "link": "https://www.selfdrivingcars360.com/aptiv-releases-comprehensive-open-source-dataset-for-autonomous-driving/", "content": "Two examples are Cityscapes, Mapillary Vistas. nuScenes is the first dataset to include a full autonomous vehicle sensor suite . The sensor suite includes six cameras, five radars and one lidar, providing a full 360 degree field of view around the vehicle."}
+{"idx": 9, "title": "Top 5 Autonomous Driving Dataset Open-Sourced At CVPR 2020", "date": "", "ddg_snippet": "According to the researchers, this dataset is the first dataset to carry the fully autonomous vehicle sensor suite , i.e. 6 cameras, 5 radars and 1 lidar, all with 360-degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for...", "subpage_snippet": "", "source": "analyticsindiamag.com", "link": "https://analyticsindiamag.com/ai-trends/top-5-autonomous-driving-dataset-open-sourced-at-cvpr-2020/", "content": "According to the researchers, this dataset is the first dataset to carry the fully autonomous vehicle sensor suite , i.e. 6 cameras, 5 radars and 1 lidar, all with 360-degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for..."}
diff --git a/data/sampled_jsons/on-policy_imitation_learning_algorithms_RLHF_year_2023.jsonl b/data/sampled_jsons/on-policy_imitation_learning_algorithms_RLHF_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c3f4ce18893d97e5875e0ce50d4d565e4b1630c1
--- /dev/null
+++ b/data/sampled_jsons/on-policy_imitation_learning_algorithms_RLHF_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning from human feedback - Wikipedia", "date": "", "ddg_snippet": "In RLHF , two different models are trained: a reward model and a reinforcement learning (RL) policy . The reward model learns to determine what behavior is desirable based on human feedback, while the policy is guided by the reward model to determine the agent's actions.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback", "content": "In RLHF , two different models are trained: a reward model and a reinforcement learning (RL) policy . The reward model learns to determine what behavior is desirable based on human feedback, while the policy is guided by the reward model to determine the agent's actions."}
+{"idx": 1, "title": "I Mitation L earning and rlhf", "date": "", "ddg_snippet": "On a connection between imitation learning and rlhf .Thus, conducting imitation learning on the chosen response corresponds to solving a standard KL-regularized RLHF problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.05079", "content": "On a connection between imitation learning and rlhf .Thus, conducting imitation learning on the chosen response corresponds to solving a standard KL-regularized RLHF problem."}
+{"idx": 2, "title": "On a Connection Between Imitation Learning and RLHF | alphaXiv", "date": "", "ddg_snippet": "Direct Imitation Learning Algorithm . Experimental Results.This formula reveals that RLHF is effectively performing imitation learning on the preference data distribution, with the reward function serving as a correction term to transform the reference distribution.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.05079v1", "content": "Direct Imitation Learning Algorithm . Experimental Results.This formula reveals that RLHF is effectively performing imitation learning on the preference data distribution, with the reward function serving as a correction term to transform the reference distribution."}
+{"idx": 3, "title": "Reinforcement learning from human feedback ( RLHF ) | Medium", "date": "", "ddg_snippet": "RL algorithms operate on a reward-and-punishment system, learning from the feedback of each action to self-discover the best strategies for achieving final outcomes.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ga3435/reinforcement-learning-from-human-feedback-rlhf-a911730e8732", "content": "RL algorithms operate on a reward-and-punishment system, learning from the feedback of each action to self-discover the best strategies for achieving final outcomes."}
+{"idx": 4, "title": "[Literature Review] On a Connection Between Imitation Learning and...", "date": "", "ddg_snippet": "The paper titled \"On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-a-connection-between-imitation-learning-and-rlhf", "content": "The paper titled \"On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning ."}
+{"idx": 5, "title": "Illustrating Reinforcement Learning from Human Feedback ( RLHF )", "date": "", "ddg_snippet": "The first code released to perform RLHF on LMs was from OpenAI in TensorFlow in 2019. Today, there are already a few active repositories for RLHF in PyTorch that grew out of this. The primary repositories are Transformers Reinforcement Learning (TRL)...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/rlhf", "content": "The first code released to perform RLHF on LMs was from OpenAI in TensorFlow in 2019. Today, there are already a few active repositories for RLHF in PyTorch that grew out of this. The primary repositories are Transformers Reinforcement Learning (TRL)..."}
+{"idx": 6, "title": "(PDF) Multi-Agent Reinforcement Learning from Human Feedback...", "date": "", "ddg_snippet": "Offline reinforcement learning (RL) algorithms seek to learn an optimal policy from a fixed dataset without active data collection. Based on the composition of the offline dataset, two main methods are used: imitation learning which is suitable for expert datasets...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383701868_Multi-Agent_Reinforcement_Learning_from_Human_Feedback_Data_Coverage_and_Algorithmic_Techniques", "content": "Offline reinforcement learning (RL) algorithms seek to learn an optimal policy from a fixed dataset without active data collection. Based on the composition of the offline dataset, two main methods are used: imitation learning which is suitable for expert datasets..."}
+{"idx": 7, "title": "Stability Analysis of On - Policy Imitation Learning Algorithms Using...", "date": "", "ddg_snippet": "We consider on - policy imitation learn -ing algorithms .Fast policy learning through imitation and reinforcement. In UAI, 2018. [3] Stéphane Ross, Geoffrey J Gordon, and J Andrew Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning .", "subpage_snippet": "", "source": "ajaytanwani.com", "link": "https://ajaytanwani.com/docs/Lee_ImitationRSS_poster_18.pdf", "content": "We consider on - policy imitation learn -ing algorithms .Fast policy learning through imitation and reinforcement. In UAI, 2018. [3] Stéphane Ross, Geoffrey J Gordon, and J Andrew Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning ."}
+{"idx": 8, "title": "Reinforcement Learning with Human Feedback", "date": "", "ddg_snippet": "Explore RLHF 's theoretical foundations, challenges, applications, and trends. Learn how human input enhances reinforcement learning agents.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/synergy-unleashed-harnessing-power-human-feedback-yuriy-pakhotin", "content": "Explore RLHF 's theoretical foundations, challenges, applications, and trends. Learn how human input enhances reinforcement learning agents."}
+{"idx": 9, "title": "Fine-Tuning LLMs with Reinforcement Learning from Human...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback ( RLHF ) allows LLMs to learn directly from the feedback received on its own response generation. .", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/fine-tuning-llms-with-reinforcement-learning-from-human-feedback-rlhf-fb0cb39ddcde", "content": "Reinforcement Learning from Human Feedback ( RLHF ) allows LLMs to learn directly from the feedback received on its own response generation. ."}
diff --git a/data/sampled_jsons/on-policy_reinforcement_learning_techniques_PPO_TRPO_extend_imitation_learning.jsonl b/data/sampled_jsons/on-policy_reinforcement_learning_techniques_PPO_TRPO_extend_imitation_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a375e6151bed27e112f669cc8928644c0fb39b9
--- /dev/null
+++ b/data/sampled_jsons/on-policy_reinforcement_learning_techniques_PPO_TRPO_extend_imitation_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning building control approach ...", "date": "", "ddg_snippet": "by S Dey · 2023 · Cited by 48 — Reinforcement learning harnessing imitation learning significantly reduces training time and early exploratory unstable behavior of an RL controller agent.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666546823000277", "content": "by S Dey · 2023 · Cited by 48 — Reinforcement learning harnessing imitation learning significantly reduces training time and early exploratory unstable behavior of an RL controller agent."}
+{"idx": 1, "title": "GenPO: Generative Diffusion Models Meet On-Policy ...", "date": "", "ddg_snippet": "27 May 2025 — We propose GenPO, a generative policy optimization framework that leverages exact diffusion inversion to construct invertible action mappings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18763v2", "content": "27 May 2025 — We propose GenPO, a generative policy optimization framework that leverages exact diffusion inversion to construct invertible action mappings."}
+{"idx": 2, "title": "Reinforcement Learning Tips and Tricks - Stable Baselines3", "date": "", "ddg_snippet": "As a good example of reward shaping, you can take a look at Deep Mimic paper which combines imitation learning and reinforcement learning to do acrobatic moves ...", "subpage_snippet": "", "source": "stable-baselines3.readthedocs.io", "link": "https://stable-baselines3.readthedocs.io/en/master/guide/rl_tips.html", "content": "As a good example of reward shaping, you can take a look at Deep Mimic paper which combines imitation learning and reinforcement learning to do acrobatic moves ..."}
+{"idx": 3, "title": "Reinforcement Learning: Methods, Applications, and ...", "date": "", "ddg_snippet": "In summary, PPO is a policy gradient actor-critic algorithm that offers a great blend of reliability and performance, which is why OpenAI and ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sanderink.ursina/reinforcement-learning-methods-applications-and-modern-techniques-d2b4cc51eefe", "content": "In summary, PPO is a policy gradient actor-critic algorithm that offers a great blend of reliability and performance, which is why OpenAI and ..."}
+{"idx": 4, "title": "Why would anyone use PPO over SAC, TD3, DDPG, and ...", "date": "", "ddg_snippet": "On-policy methods , particularly TRPO and PPO , are less hyperparameter sensitive and have more stable convergence properties.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/reinforcementlearning/comments/gl1ov2/why_would_anyone_use_ppo_over_sac_td3_ddpg_and/", "content": "On-policy methods , particularly TRPO and PPO , are less hyperparameter sensitive and have more stable convergence properties."}
+{"idx": 5, "title": "Proximal Policy Optimization (PPO) - Brayden Zhang", "date": "", "ddg_snippet": "7 Mar 2025 — Proximal Policy Optimization (PPO ) is an on-policy reinforcement learning algorithm designed to improve the stability and performance of ...", "subpage_snippet": "", "source": "braydenzhang.com", "link": "https://braydenzhang.com/notes/reinforcement-learning/proximal-policy-optimization-(ppo)", "content": "7 Mar 2025 — Proximal Policy Optimization (PPO ) is an on-policy reinforcement learning algorithm designed to improve the stability and performance of ..."}
+{"idx": 6, "title": "Imitation as a way of life (?) part I | by Sergio Soage", "date": "", "ddg_snippet": "These techniques stabilize and speed up policy learning , without imposing any constraints on the system at test time. Finally, this setup helps ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@Sergio_Soage/imitation-as-a-way-of-life-part-i-198868869138", "content": "These techniques stabilize and speed up policy learning , without imposing any constraints on the system at test time. Finally, this setup helps ..."}
+{"idx": 7, "title": "Explore To Mimic: A Reinforcement Learning Based Agent ...", "date": "", "ddg_snippet": "by A Pandey — The paper introduces an RL-based approach to generating online signatures using established on-policy RL methods ( PPO , TRPO , A2C). The architecture includes a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=GIFn3ibnKa", "content": "by A Pandey — The paper introduces an RL-based approach to generating online signatures using established on-policy RL methods ( PPO , TRPO , A2C). The architecture includes a ..."}
+{"idx": 8, "title": "Riemannian Proximal Policy Optimization", "date": "", "ddg_snippet": "30 Jul 2020 — TRPO was also extended to constrained reinforcement learning . Achiam et al. proposed constrained policy optimization (CPO) which guarantees ...", "subpage_snippet": "", "source": "ccsenet.org", "link": "https://ccsenet.org/journal/index.php/cis/article/download/0/0/43372/45455", "content": "30 Jul 2020 — TRPO was also extended to constrained reinforcement learning . Achiam et al. proposed constrained policy optimization (CPO) which guarantees ..."}
+{"idx": 9, "title": "arXiv:2411.17861v3 [cs.LG] 5 Dec 2024", "date": "", "ddg_snippet": "by A Ahmad · 2024 · Cited by 2 — Our analysis demonstrates that the proposed method maintains PPO's convergence properties, while accelerating learning in delayed-reward set-.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.17861?", "content": "by A Ahmad · 2024 · Cited by 2 — Our analysis demonstrates that the proposed method maintains PPO's convergence properties, while accelerating learning in delayed-reward set-."}
diff --git a/data/sampled_jsons/open-world_semi-supervised_learning_class_distribution_estimation_without_prior_information.jsonl b/data/sampled_jsons/open-world_semi-supervised_learning_class_distribution_estimation_without_prior_information.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1d970b340afcf00ec14e075ce054fe69790b4440
--- /dev/null
+++ b/data/sampled_jsons/open-world_semi-supervised_learning_class_distribution_estimation_without_prior_information.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Open-world Semi-supervised Novel Class Discovery - IJCAI", "date": "", "ddg_snippet": "Abstract Traditional semi-supervised learning tasks assume that both labeled and unlabeled data follow the same class distribution , but the realistic open-world scenarios are of more complexity with unknown novel classes mixed in the unlabeled set. There-fore, it is of great challenge to not only recognize samples from known classes but also discover the unknown number of novel classes within ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2023/0445.pdf", "content": "Abstract Traditional semi-supervised learning tasks assume that both labeled and unlabeled data follow the same class distribution , but the realistic open-world scenarios are of more complexity with unknown novel classes mixed in the unlabeled set. There-fore, it is of great challenge to not only recognize samples from known classes but also discover the unknown number of novel classes within ..."}
+{"idx": 1, "title": "Targeted Representation Alignment for Open-World Semi ...", "date": "", "ddg_snippet": "Abstract Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data, while unlabeled samples are not only from the labeled known categories but also from novel categories previously unseen.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_Targeted_Representation_Alignment_for_Open-World_Semi-Supervised_Learning_CVPR_2024_paper.pdf", "content": "Abstract Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data, while unlabeled samples are not only from the labeled known categories but also from novel categories previously unseen."}
+{"idx": 2, "title": "Rethinking Open-World Semi-Supervised Learning: Distribution ...", "date": "", "ddg_snippet": "To this end, we extend OWSSL by addressing such practical training and evaluation settings, coined Realistic Open-World Semi-Supervised Learning (ROWSSL). In this task, we consider long-tailed distribution with class prior distribution mismatch between labeled and unlabeled datasets for training, and inductive and transductive infer-ences for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.20829", "content": "To this end, we extend OWSSL by addressing such practical training and evaluation settings, coined Realistic Open-World Semi-Supervised Learning (ROWSSL). In this task, we consider long-tailed distribution with class prior distribution mismatch between labeled and unlabeled datasets for training, and inductive and transductive infer-ences for ..."}
+{"idx": 3, "title": "Semi-supervised distribution learning - Oxford Academic", "date": "", "ddg_snippet": "Oct 30, 2024 · This study addresses the challenge of distribution estimation and inference in a semi-supervised setting. In contrast to prior research focusing on parameter inference, this work explores the complexities of semi-supervised distribution estimation , particularly the uniformity problem inherent in functional processes.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/biomet/article/112/1/asae056/7852832", "content": "Oct 30, 2024 · This study addresses the challenge of distribution estimation and inference in a semi-supervised setting. In contrast to prior research focusing on parameter inference, this work explores the complexities of semi-supervised distribution estimation , particularly the uniformity problem inherent in functional processes."}
+{"idx": 4, "title": "Label Distribution-based Open-world Semi-supervised Learning", "date": "", "ddg_snippet": "Here, we propose a new method to solve the open-world SSL problem. It leverages the sample information of seen classes to obtain reliable label distributions and then utilizes these label distributions to guide the model in solving the issue of imbalanced distributions between seen and unseen class samples during the learning process.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10516485", "content": "Here, we propose a new method to solve the open-world SSL problem. It leverages the sample information of seen classes to obtain reliable label distributions and then utilizes these label distributions to guide the model in solving the issue of imbalanced distributions between seen and unseen class samples during the learning process."}
+{"idx": 5, "title": "Rethinking Open-World Semi-Supervised Learning: Distribution...", "date": "", "ddg_snippet": "Dec 31, 2023 · Abstract: Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world scenarios by taking account of novel categories in unlabeled datasets. Despite the recent advancements in OWSSL, the success often relies on the assumptions that 1) labeled and unlabeled datasets share the same balanced class prior distribution , which does not generally hold in ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FwavWZAvDz", "content": "Dec 31, 2023 · Abstract: Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world scenarios by taking account of novel categories in unlabeled datasets. Despite the recent advancements in OWSSL, the success often relies on the assumptions that 1) labeled and unlabeled datasets share the same balanced class prior distribution , which does not generally hold in ..."}
+{"idx": 6, "title": "OPEN-WORLD SEMI-SUPERVISED LEARNING", "date": "", "ddg_snippet": "Here, we introduce a novel open-world semi-supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data. In this novel setting, the goal is to solve the class distribution mismatch between labeled and unlabeled data, where at the test time every input instance either needs to be classified into ...", "subpage_snippet": "", "source": "ai.stanford.edu", "link": "https://ai.stanford.edu/~jure/pubs/orca-iclr22.pdf", "content": "Here, we introduce a novel open-world semi-supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data. In this novel setting, the goal is to solve the class distribution mismatch between labeled and unlabeled data, where at the test time every input instance either needs to be classified into ..."}
+{"idx": 7, "title": "Rethinking Open-World Semi-Supervised Learning", "date": "", "ddg_snippet": "31 May 2024 — In this paper, we advance OWSSL to a more practical setting, considering long-tailed distribution with class prior distribution mismatch, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.20829v1", "content": "31 May 2024 — In this paper, we advance OWSSL to a more practical setting, considering long-tailed distribution with class prior distribution mismatch, and ..."}
+{"idx": 8, "title": "Open-World Semi-Supervised Learning", "date": "", "ddg_snippet": "by K Cao · Cited by 288 — We introduce a novel open - world semi - supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=O-r8LOR-CCA", "content": "by K Cao · Cited by 288 — We introduce a novel open - world semi - supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data."}
+{"idx": 9, "title": "OPEN-WORLD SEMI-SUPERVISED LEARNING", "date": "", "ddg_snippet": "by K Cao · Cited by 288 — In contrast, open-world SSL is more general as it does not assume any prior information about classes. 3 PROPOSED APPROACH. In this section, we first define ... 19 pages", "subpage_snippet": "", "source": "cs.stanford.edu", "link": "https://cs.stanford.edu/people/jure/pubs/orca-iclr22.pdf", "content": "by K Cao · Cited by 288 — In contrast, open-world SSL is more general as it does not assume any prior information about classes. 3 PROPOSED APPROACH. In this section, we first define ... 19 pages"}
diff --git a/data/sampled_jsons/openreview_27tMzmzDjO_Table_1_dataset_statistics_train_density.jsonl b/data/sampled_jsons/openreview_27tMzmzDjO_Table_1_dataset_statistics_train_density.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..918364c9e06a0a3aacc364fc116b0f283071faa6
--- /dev/null
+++ b/data/sampled_jsons/openreview_27tMzmzDjO_Table_1_dataset_statistics_train_density.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Language model developers should report... | OpenReview", "date": "", "ddg_snippet": "Overall, this position paper argues that language model developers should publish train -test overlap statistics and/or training data whenever they report evaluation results on public test sets .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=J5MmGPWKfb", "content": "Overall, this position paper argues that language model developers should publish train -test overlap statistics and/or training data whenever they report evaluation results on public test sets ."}
+{"idx": 1, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets?fileType=csv", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 2, "title": "Datasets", "date": "", "ddg_snippet": "Datasets is a library for easily accessing and sharing AI datasets for Audio, Computer Vision, and Natural Language Processing (NLP) tasks.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/datasets/index", "content": "Datasets is a library for easily accessing and sharing AI datasets for Audio, Computer Vision, and Natural Language Processing (NLP) tasks."}
+{"idx": 3, "title": "Cycling statistics , results and rankings | ProCyclingStats.com", "date": "", "ddg_snippet": "Find results of cyclings top riders like Tadej Pogacar, Remco Evenepoel and Mathieu van der Poel. Detailed statistics about the Tour de France, Giro d'Italia and classics like Paris-Roubaix or Tour of Flanders.", "subpage_snippet": "", "source": "www.procyclingstats.com", "link": "https://www.procyclingstats.com/", "content": "Find results of cyclings top riders like Tadej Pogacar, Remco Evenepoel and Mathieu van der Poel. Detailed statistics about the Tour de France, Giro d'Italia and classics like Paris-Roubaix or Tour of Flanders."}
+{"idx": 4, "title": "4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities", "date": "", "ddg_snippet": "Table 4: Pre- training data and modality mixture ablation: We ablate different pre- training modality and dataset choices on B models. * represents the models initialized from the corresponding 4M models trained on COYO700M.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.09406v2", "content": "Table 4: Pre- training data and modality mixture ablation: We ablate different pre- training modality and dataset choices on B models. * represents the models initialized from the corresponding 4M models trained on COYO700M."}
+{"idx": 5, "title": "Your GAN is Secretly an Energy-based Model and", "date": "", "ddg_snippet": "Table 1 : DDLS suffers less from mode dropping when modeling the 2d synthetic distribution in Figure 1. Table shows number of recovered modes, and fraction of “high quality” (less than four standard deviations from mode center) recovered modes. Generator only DRS GAN w. DDLS.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2020/file/90525e70b7842930586545c6f1c9310c-Paper.pdf", "content": "Table 1 : DDLS suffers less from mode dropping when modeling the 2d synthetic distribution in Figure 1. Table shows number of recovered modes, and fraction of “high quality” (less than four standard deviations from mode center) recovered modes. Generator only DRS GAN w. DDLS."}
+{"idx": 6, "title": "Angola Population (2025) - Worldometer", "date": "", "ddg_snippet": "Data tables , maps, charts, and live population clock.The population density in Angola is 31 per Km² (81 people per mi²).", "subpage_snippet": "", "source": "www.worldometers.info", "link": "https://www.worldometers.info/world-population/angola-population/", "content": "Data tables , maps, charts, and live population clock.The population density in Angola is 31 per Km² (81 people per mi²)."}
+{"idx": 7, "title": "(PDF) Kriging prior Regression: A Case for Kriging-Based Spatial...", "date": "", "ddg_snippet": "datasets have traditionally been challenging for data -driven. ML, limiting applicability in the PA domain, where available. training datasets are typically small (Schmidinger et al., 2024b).Kriging prior regression with TabPFN. Table 1 . Overview for the selected datasets .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395418204_Kriging_prior_Regression_A_Case_for_Kriging-Based_Spatial_Features_with_TabPFN_in_Soil_Mapping", "content": "datasets have traditionally been challenging for data -driven. ML, limiting applicability in the PA domain, where available. training datasets are typically small (Schmidinger et al., 2024b).Kriging prior regression with TabPFN. Table 1 . Overview for the selected datasets ."}
+{"idx": 8, "title": "One-Sample t-test: Calculating the t- statistic is not really a bear", "date": "", "ddg_snippet": "The name of the dataset is Bears.MTW , because the calculation is not a huge bear to wrestle (plus who can resist a dataset with that name?). For this example, we will use column C2, titled Age, in the Bears.MTW data set , and we will test the hypothesis that the average age of bears is 40.", "subpage_snippet": "", "source": "blog.minitab.com", "link": "https://blog.minitab.com/en/one-sample-t-test-calculating-the-t-statistic-is-not-really-a-bear", "content": "The name of the dataset is Bears.MTW , because the calculation is not a huge bear to wrestle (plus who can resist a dataset with that name?). For this example, we will use column C2, titled Age, in the Bears.MTW data set , and we will test the hypothesis that the average age of bears is 40."}
+{"idx": 9, "title": "Present Simple — правила и примеры употребления.", "date": "", "ddg_snippet": "А если в предложении вместо понятных he, she, it — Bob, Mary или table (стол), задавайте вопрос «Кто это?» или «Что это?». Если ответ — «он», «она» или «оно» — добавляйте окончание -s/-es. Глагол to be в Present Simple.", "subpage_snippet": "", "source": "skysmart.ru", "link": "https://skysmart.ru/articles/english/present-simple-tense", "content": "А если в предложении вместо понятных he, she, it — Bob, Mary или table (стол), задавайте вопрос «Кто это?» или «Что это?». Если ответ — «он», «она» или «оно» — добавляйте окончание -s/-es. Глагол to be в Present Simple."}
diff --git a/data/sampled_jsons/optimal_transport_positive_unlabeled_class_prior_estimation.jsonl b/data/sampled_jsons/optimal_transport_positive_unlabeled_class_prior_estimation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dc40a4ef519e30c1de69a81c6fad74e7c5c504c8
--- /dev/null
+++ b/data/sampled_jsons/optimal_transport_positive_unlabeled_class_prior_estimation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Posistive- Unlabeled Learning via Optimal Transport and Margin...", "date": "", "ddg_snippet": "More specifi-cally, we first explicitly learn a transformation from unlabeled data to positive data by entropy regular-ized optimal transport to achieve a much more pre-cise estimation for class prior .", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2022/0393.pdf", "content": "More specifi-cally, we first explicitly learn a transformation from unlabeled data to positive data by entropy regular-ized optimal transport to achieve a much more pre-cise estimation for class prior ."}
+{"idx": 1, "title": "Partial Optimal Transport", "date": "", "ddg_snippet": "Partial Optimal Transport with Applications on Positive - Unlabeled Learning.Plessis, M. C., G. Niu, and M. Sugiyama (2017, April). Class - prior estimation for learning from positive and unlabeled data. Mach. Learn. 106(4), 463–492.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2002.08276", "content": "Partial Optimal Transport with Applications on Positive - Unlabeled Learning.Plessis, M. C., G. Niu, and M. Sugiyama (2017, April). Class - prior estimation for learning from positive and unlabeled data. Mach. Learn. 106(4), 463–492."}
+{"idx": 2, "title": "(PDF) Partial Optimal Transport with Applications on...", "date": "", "ddg_snippet": "Classical optimal transport problem seeks a transportation map that preserves the.pp. 2664–2672. Plessis, M. C., G. Niu, and M. Sugiyama (2017). Class - prior estimation for learning from positive . and unlabeled data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/350993193_Partial_Optimal_Transport_with_Applications_on_Positive-Unlabeled_Learning", "content": "Classical optimal transport problem seeks a transportation map that preserves the.pp. 2664–2672. Plessis, M. C., G. Niu, and M. Sugiyama (2017). Class - prior estimation for learning from positive . and unlabeled data."}
+{"idx": 3, "title": "Review for NeurIPS paper: Partial Optimal Transport with applications...", "date": "", "ddg_snippet": "Assuming that the class prior can be reliably estimated in the biased case is not realistic.2016 Jan 8 [2] Zeiberg D, Jain S, Radivojac P. Fast Nonparametric Estimation of Class Proportions in the Positive - Unlabeled Classification Setting.", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2020/file/1e6e25d952a0d639b676ee20d0519ee2-Review.html", "content": "Assuming that the class prior can be reliably estimated in the biased case is not realistic.2016 Jan 8 [2] Zeiberg D, Jain S, Radivojac P. Fast Nonparametric Estimation of Class Proportions in the Positive - Unlabeled Classification Setting."}
+{"idx": 4, "title": "[PDF] Class - prior estimation for learning from positive and...", "date": "", "ddg_snippet": "This paper first explicitly learns a transformation from unlabeled data to positive data by entropy regularized optimal transport to achieve a much more precise estimation for class prior , and switches to optimizing the margin distribution, rather than the minimum margin...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Class-prior-estimation-for-learning-from-positive-Plessis-Niu/c5deab518eabc3698e0c932ac7188e8b8a2f6cd3", "content": "This paper first explicitly learns a transformation from unlabeled data to positive data by entropy regularized optimal transport to achieve a much more precise estimation for class prior , and switches to optimizing the margin distribution, rather than the minimum margin..."}
+{"idx": 5, "title": "Optimal - Transport -Based Positive and Unlabeled Learning Method...", "date": "", "ddg_snippet": "Optimal Transport and Positive and Unlabeled Learning. Optimal transport is a mathematical problem for comparing probability distributions.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/optimal-transport-based-positive-and-unlabeled-learning-method-for-windshear-detection/1069110142424842285-3949", "content": "Optimal Transport and Positive and Unlabeled Learning. Optimal transport is a mathematical problem for comparing probability distributions."}
+{"idx": 6, "title": "Semi-Supervised Optimal Transport Methods for Detecting Anomalies", "date": "", "ddg_snippet": "The optimal transport problem with an entropic regularization thus writes: dǫC (a1 , a2 ) = Three main approaches are developed in the literature for positive - unlabeled learning. First, classification methods rely on machine learning techniques for capturing the class of positive examples.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/129017173/Semi_Supervised_Optimal_Transport_Methods_for_Detecting_Anomalies", "content": "The optimal transport problem with an entropic regularization thus writes: dǫC (a1 , a2 ) = Three main approaches are developed in the literature for positive - unlabeled learning. First, classification methods rely on machine learning techniques for capturing the class of positive examples."}
+{"idx": 7, "title": "Fast Nonparametric Estimation of Class Proportions in the...", "date": "", "ddg_snippet": "Estimating the class prior in positive and unlabeled data through decision tree induction. In Proceedings of the 32nd AAAI Conference on Articial Intelligence, AAAI 2018, 2712–2719. Class - prior estimation for learning from positive and unlabeled data.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/6151/6007", "content": "Estimating the class prior in positive and unlabeled data through decision tree induction. In Proceedings of the 32nd AAAI Conference on Articial Intelligence, AAAI 2018, 2712–2719. Class - prior estimation for learning from positive and unlabeled data."}
+{"idx": 8, "title": "Recovering True Classifier Performance in Positive - Unlabeled Learning", "date": "", "ddg_snippet": "Class prior estimation from positive and unlabeled data. Class - prior es - timation for learning from positive and unlabeled data. In Proceed-ings of the 7th Asian Conference on Machine Learning, volume 45 of ACML 2015, 221–236.", "subpage_snippet": "", "source": "www.khoury.northeastern.edu", "link": "https://www.khoury.northeastern.edu/home/radivojac/papers/jain_aaai_2017.pdf", "content": "Class prior estimation from positive and unlabeled data. Class - prior es - timation for learning from positive and unlabeled data. In Proceed-ings of the 7th Asian Conference on Machine Learning, volume 45 of ACML 2015, 221–236."}
+{"idx": 9, "title": "Learning from positive and unlabeled data: a survey | Machine Learning", "date": "", "ddg_snippet": "Class prior estimation from positive and unlabeled data. IEICE Transactions on Information and Systems, 97(5), 1358–1362.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-020-05877-5", "content": "Class prior estimation from positive and unlabeled data. IEICE Transactions on Information and Systems, 97(5), 1358–1362."}
diff --git a/data/sampled_jsons/overfitting_OR_generalization_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models.jsonl b/data/sampled_jsons/overfitting_OR_generalization_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7351fb099c6d4f1501d612797184b593edcf09bc
--- /dev/null
+++ b/data/sampled_jsons/overfitting_OR_generalization_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "This paper proposes a novel task, origin identification for text - guided image - to - image diffusion models (ID2), which aims to identify the origin of a generated query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02376v1", "content": "This paper proposes a novel task, origin identification for text - guided image - to - image diffusion models (ID2), which aims to identify the origin of a generated query."}
+{"idx": 1, "title": "(PDF) Generalizable Origin Identification for Text - Guided ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.this paper introduces the task of origin IDentification for . text - guided Image - to - image Diffusion models (ID2), which.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387767437_Generalizable_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.this paper introduces the task of origin IDentification for . text - guided Image - to - image Diffusion models (ID2), which."}
+{"idx": 2, "title": "[2501.02376] Origin Identification for Text-Guided Image-to ...", "date": "", "ddg_snippet": "by W Wang · 2025 — Abstract page for arXiv paper 2501.02376: Origin Identification for Text-Guided Image-to-Image Diffusion Models . ... generalization designs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.02376", "content": "by W Wang · 2025 — Abstract page for arXiv paper 2501.02376: Origin Identification for Text-Guided Image-to-Image Diffusion Models . ... generalization designs."}
+{"idx": 3, "title": "Generalizable Origin Identification for Text-Guided Image- ...", "date": "", "ddg_snippet": "by W Wang — ... generalization performance, significantly surpassing similarity ... Generalizable Origin Identification for Text-Guided Image-to-Image Diffusion Models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=daRu82GAoZ", "content": "by W Wang — ... generalization performance, significantly surpassing similarity ... Generalizable Origin Identification for Text-Guided Image-to-Image Diffusion Models ."}
+{"idx": 4, "title": "ICML Poster Origin Identification for Text-Guided Image-to- ...", "date": "", "ddg_snippet": "Origin Identification for Text-Guided Image-to-Image Diffusion Models ... Although domain generalization methods alleviate the generalization problem ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46505", "content": "Origin Identification for Text-Guided Image-to-Image Diffusion Models ... Although domain generalization methods alleviate the generalization problem ..."}
+{"idx": 5, "title": "Generalizable Origin Identification for Text-Guided Image ...", "date": "", "ddg_snippet": "Generalizable Origin Identification for Text-Guided Image-to-Image Diffusion Models ... generalization performance compared to similarity-based approaches.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2501.02376", "content": "Generalizable Origin Identification for Text-Guided Image-to-Image Diffusion Models ... generalization performance compared to similarity-based approaches."}
+{"idx": 6, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion ...", "date": "", "ddg_snippet": "generalization designs. The project is available ... Origin Identification for Text-Guided Image-to-Image Diffusion Models . Origin ... Most non-linear ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/681ea68d062b8991956d7a196be74f59c4610d76.pdf", "content": "generalization designs. The project is available ... Origin Identification for Text-Guided Image-to-Image Diffusion Models . Origin ... Most non-linear ..."}
+{"idx": 7, "title": "[Literature Review] Generalizable Origin Identification for ...", "date": "", "ddg_snippet": "4 Jan 2025 — The paper presents the task of origin identification for text-guided image-to-image diffusion models (referred to as ID2).", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/generalizable-origin-identification-for-text-guided-image-to-image-diffusion-models", "content": "4 Jan 2025 — The paper presents the task of origin identification for text-guided image-to-image diffusion models (referred to as ID2)."}
+{"idx": 8, "title": "Generalizable Origin Identification for Text-Guided Image-to- ...", "date": "", "ddg_snippet": "... generalization performance, significantly ... This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models ...", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/dashboard/paper/bab4dd04-edc9-4908-a029-7ed4e8b4ede8", "content": "... generalization performance, significantly ... This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models ..."}
+{"idx": 9, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=text-to-vis", "content": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models ."}
diff --git a/data/sampled_jsons/paperswithcode_MLD_HumanAct12_FID_0.077.jsonl b/data/sampled_jsons/paperswithcode_MLD_HumanAct12_FID_0.077.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6979a607098419bb96b88c0802b518c771cfb462
--- /dev/null
+++ b/data/sampled_jsons/paperswithcode_MLD_HumanAct12_FID_0.077.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "motion-latent-diffusion/configs/config_ mld _ humanact 12 .yaml at main...", "date": "", "ddg_snippet": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model - motion-latent-diffusion/configs/config_ mld _ humanact 12 .yaml at main · ChenFengYe/motion-latent-diffusion.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/configs/config_mld_humanact12.yaml", "content": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model - motion-latent-diffusion/configs/config_ mld _ humanact 12 .yaml at main · ChenFengYe/motion-latent-diffusion."}
+{"idx": 1, "title": "SOTA for Motion Synthesis on HumanAct 12 (Accuracy metric)", "date": "", "ddg_snippet": "- Motion Synthesis. HumanAct 12 . MDM. FID .Add or remove other datasets used in this paper: E.T. the Exceptional Trajectories HumanAct 12 HumanML3D InterHuman Inter-X KIT Motion-Language Motion-X.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/human-motion-diffusion-model", "content": "- Motion Synthesis. HumanAct 12 . MDM. FID .Add or remove other datasets used in this paper: E.T. the Exceptional Trajectories HumanAct 12 HumanML3D InterHuman Inter-X KIT Motion-Language Motion-X."}
+{"idx": 2, "title": "how can i use smpl rot6d motion_representation for... - Githubissues", "date": "", "ddg_snippet": "The line 285 of mld .py does not include \"mask\" parameters. https...", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/ChenFengYe/motion-latent-diffusion/30", "content": "The line 285 of mld .py does not include \"mask\" parameters. https..."}
+{"idx": 3, "title": "GISMETEO: Погода в Новокуйбышевске сегодня, прогноз погоды...", "date": "", "ddg_snippet": "Подробный прогноз погоды в Новокуйбышевске на сегодня.", "subpage_snippet": "", "source": "www.gismeteo.ru", "link": "https://www.gismeteo.ru/weather-novokuybyshevsk-12745/", "content": "Подробный прогноз погоды в Новокуйбышевске на сегодня."}
+{"idx": 4, "title": "Погода в Гагре сегодня - точный прогноз погоды в Гагре на завтра...", "date": "", "ddg_snippet": "Подробный прогноз погоды в Гагре. Температура воздуха, скорость ветра, влажность и давление.", "subpage_snippet": "", "source": "world-weather.ru", "link": "https://world-weather.ru/pogoda/abkhazia/gagra/", "content": "Подробный прогноз погоды в Гагре. Температура воздуха, скорость ветра, влажность и давление."}
+{"idx": 5, "title": "Большое шоу 12 сезон 4 серия смотреть онлайн бесплатно", "date": "", "ddg_snippet": "В 4 серии 12 сезона “Большого шоу” зрителю кажется, что все участники выдерживают такое давление. Однако Бутусову не везет: он снова смеется, получает вторую желтую и окончательно покидает проект. Осталось всего пять человек в борьбе за главный приз в три...", "subpage_snippet": "", "source": "big-show.su", "link": "https://big-show.su/sezon-12-seriya-4", "content": "В 4 серии 12 сезона “Большого шоу” зрителю кажется, что все участники выдерживают такое давление. Однако Бутусову не везет: он снова смеется, получает вторую желтую и окончательно покидает проект. Осталось всего пять человек в борьбе за главный приз в три..."}
+{"idx": 6, "title": "Диета 5-ый стол - что можно и чего нельзя, меню на неделю", "date": "", "ddg_snippet": "питье должно быть не более 200 мл при одноразовом приеме. Продолжительность составляет до 12 месяцев. Далее следует консультация с врачом для определения степени эффективности. При необходимости специалист даст рекомендации по продлению.", "subpage_snippet": "", "source": "wer.ru", "link": "https://wer.ru/articles/dieta-5-yy-stol/", "content": "питье должно быть не более 200 мл при одноразовом приеме. Продолжительность составляет до 12 месяцев. Далее следует консультация с врачом для определения степени эффективности. При необходимости специалист даст рекомендации по продлению."}
+{"idx": 7, "title": "Мир сегодня с \"Юрий Подоляка\" – Telegram", "date": "", "ddg_snippet": "А \"Евровидение\", катастрофически теряющее год от года аудиторию и престиж, надеюсь скоро окажется на помойке истории (именно там ему самое место)... Подписывайтесь на мой канал в Мах... 23 41K 12K 3K1.14K 959 581 558 499 216 75.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/yurasumy", "content": "А \"Евровидение\", катастрофически теряющее год от года аудиторию и престиж, надеюсь скоро окажется на помойке истории (именно там ему самое место)... Подписывайтесь на мой канал в Мах... 23 41K 12K 3K1.14K 959 581 558 499 216 75."}
+{"idx": 8, "title": "Logitech в интернет-магазине DNS", "date": "", "ddg_snippet": "12 599. Мышь беспроводная Logitech MX Anywhere 3S [910-006930] серый.", "subpage_snippet": "", "source": "www.dns-shop.ru", "link": "https://www.dns-shop.ru/brand/logitech/", "content": "12 599. Мышь беспроводная Logitech MX Anywhere 3S [910-006930] серый."}
+{"idx": 9, "title": "WinRAR 7.12 – Скачать Бесплатно", "date": "", "ddg_snippet": "1360 руб. Windows. Категория: Архиваторы. WinRAR - без этого популярного, очень удобного и простого в использовании архиватора трудно представить себе повседневную работу с архивами.", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-152-winrar.html", "content": "1360 руб. Windows. Категория: Архиваторы. WinRAR - без этого популярного, очень удобного и простого в использовании архиватора трудно представить себе повседневную работу с архивами."}
diff --git a/data/sampled_jsons/parallel_Picard_method_Algorithm_1_Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diff_year_2024.jsonl b/data/sampled_jsons/parallel_Picard_method_Algorithm_1_Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diff_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1e2d152a57249509612de138ac3cc691591a991c
--- /dev/null
+++ b/data/sampled_jsons/parallel_Picard_method_Algorithm_1_Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diff_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Financial and Actuarial Mathematics - Department of", "date": "", "ddg_snippet": "... problem and establish generic conditions under which the generalization error convergence rate, when training on a sample of size n, is $O( 1 /n)$.", "subpage_snippet": "", "source": "www.liverpool.ac.uk", "link": "https://www.liverpool.ac.uk/mathematical-sciences/seminars/financial-mathematics/", "content": "... problem and establish generic conditions under which the generalization error convergence rate, when training on a sample of size n, is $O( 1 /n)$."}
+{"idx": 1, "title": "Parallel Simulation for Log - concave Sampling and Score - based ...", "date": "", "ddg_snippet": "TL;DR: We propose new parallel algorithms for log - concave sampling and diffusion models , and rigorously prove that our algorithms enjoy $\\log d$ iteration complexity.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qtuxDy2qEB&referrer=[the+profile+of+Masashi+Sugiyama](/profile?id=~Masashi_Sugiyama1)", "content": "TL;DR: We propose new parallel algorithms for log - concave sampling and diffusion models , and rigorously prove that our algorithms enjoy $\\log d$ iteration complexity."}
+{"idx": 2, "title": "Parallel Simulation for Log-concave Sampling and Score- ...", "date": "", "ddg_snippet": "1 May 2025 — Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models . Download PDF. Huanjian Zhou, Masashi Sugiyama. Published: 01 May ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qtuxDy2qEB&noteId=Q3HrXoYt2u", "content": "1 May 2025 — Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models . Download PDF. Huanjian Zhou, Masashi Sugiyama. Published: 01 May ..."}
+{"idx": 3, "title": "Parallel Simulation for Log-concave Sampling and Score- ...", "date": "", "ddg_snippet": "24 hours ago — In this section, we present parallel Picard methods for strongly log-concave sampling ( Algorithm 1 ) and show it holds improved convergence rate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.07435v3", "content": "24 hours ago — In this section, we present parallel Picard methods for strongly log-concave sampling ( Algorithm 1 ) and show it holds improved convergence rate ..."}
+{"idx": 4, "title": "Parallel Simulation for Log-concave Sampling and Score ...", "date": "", "ddg_snippet": "Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models . Huanjian Zhou · Masashi Sugiyama. East Exhibition Hall A-B #E-1103. [ Abstract ] ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43916", "content": "Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models . Huanjian Zhou · Masashi Sugiyama. East Exhibition Hall A-B #E-1103. [ Abstract ] ..."}
+{"idx": 5, "title": "Parallel Simulation for Log-concave Sampling and Score-based ...", "date": "", "ddg_snippet": "Algorithm 1 Parallel Picard Method for sampling. 1 : Input: x0 ∼ µ0, approximate score function s ≈ ∇f, the number of the iterations in outer loop J, the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qtuxDy2qEB", "content": "Algorithm 1 Parallel Picard Method for sampling. 1 : Input: x0 ∼ µ0, approximate score function s ≈ ∇f, the number of the iterations in outer loop J, the ..."}
+{"idx": 6, "title": "Lipschitz-Guided Design of Interpolation Schedules in", "date": "", "ddg_snippet": "... concurrent work on flow matching [ 27 ] and rectified flows [ 28 ] , and encompasses diffusion and score - based generative models [ 38 , 42 , 19 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.01629v1", "content": "... concurrent work on flow matching [ 27 ] and rectified flows [ 28 ] , and encompasses diffusion and score - based generative models [ 38 , 42 , 19 ..."}
+{"idx": 7, "title": "2021.11.22 CS papers — Eye On AI", "date": "", "ddg_snippet": "Deep learning based on mixed-variable physics informed neural network for solving fluid dynamics without simulation data by Guang-Tao Zhang et al", "subpage_snippet": "", "source": "www.eye-on.ai", "link": "https://www.eye-on.ai/ai-research-watch-papers/2021/11/22/20211122-cs-papers", "content": "Deep learning based on mixed-variable physics informed neural network for solving fluid dynamics without simulation data by Guang-Tao Zhang et al"}
+{"idx": 8, "title": "PhD & DA Alumni | Dept of Math, Stat, & Comp Sci |", "date": "", "ddg_snippet": "We have data all the way back to 1971! You may also be interested to investigate our graduates and their advisors in the Mathematics Genealogy ...", "subpage_snippet": "", "source": "mscs.uic.edu", "link": "https://mscs.uic.edu/phd-da-alumni/", "content": "We have data all the way back to 1971! You may also be interested to investigate our graduates and their advisors in the Mathematics Genealogy ..."}
+{"idx": 9, "title": "ICML 2025 2025 Spotlight Posters", "date": "", "ddg_snippet": "Parallel Simulation for Log - concave Sampling and Score - based Diffusion Models .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/2025SpotlightPosters", "content": "Parallel Simulation for Log - concave Sampling and Score - based Diffusion Models ."}
diff --git a/data/sampled_jsons/parallel_Picard_method_Algorithm_1_loop_structure_lines_9-26.jsonl b/data/sampled_jsons/parallel_Picard_method_Algorithm_1_loop_structure_lines_9-26.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ad5f86b1c2cc4ff32680a25084c78f9ad3941a4e
--- /dev/null
+++ b/data/sampled_jsons/parallel_Picard_method_Algorithm_1_loop_structure_lines_9-26.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Parallels: Mac & Windows Virtualization, Remote Application...", "date": "", "ddg_snippet": "Download Parallels to run Windows on Mac, Chrome, gain access to virtual desktop infrastructure (VDI) with DaaS, & Toolbox to secure private files, & more.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/", "content": "Download Parallels to run Windows on Mac, Chrome, gain access to virtual desktop infrastructure (VDI) with DaaS, & Toolbox to secure private files, & more."}
+{"idx": 1, "title": "Run Windows on Mac with a virtual machine | Parallels Desktop", "date": "", "ddg_snippet": "Run Windows applications on your Mac effortlessly. Easy. Powerful. Seamless. Parallels ® Desktop for Mac Maximize your Mac's potential by running Windows and Windows applications on a virtual machine. Access over 200,000 apps to work, study and play effortlessly. Authorized by Microsoft.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/products/desktop/", "content": "Run Windows applications on your Mac effortlessly. Easy. Powerful. Seamless. Parallels ® Desktop for Mac Maximize your Mac's potential by running Windows and Windows applications on a virtual machine. Access over 200,000 apps to work, study and play effortlessly. Authorized by Microsoft."}
+{"idx": 2, "title": "Parallels Desktop 20.2 brings x86 emulation preview | Parallels", "date": "", "ddg_snippet": "Jan 9 , 2025 · The new version of Parallels Desktop is here! New features include time sync for macOS VMs, & x86 emulation.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/blogs/parallels-desktop-20-2-0/", "content": "Jan 9 , 2025 · The new version of Parallels Desktop is here! New features include time sync for macOS VMs, & x86 emulation."}
+{"idx": 3, "title": "Download Parallels Client, 2X RDP Client | Parallels RAS", "date": "", "ddg_snippet": "Parallels Client (formerly 2X RDP Client), when connected to Parallels Remote Application Server, provides secure access to business applications, virtual desktops, and data from your device. Download!", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/products/ras/download/client/", "content": "Parallels Client (formerly 2X RDP Client), when connected to Parallels Remote Application Server, provides secure access to business applications, virtual desktops, and data from your device. Download!"}
+{"idx": 4, "title": "Run Windows on Mac with a virtual machine like Parallels Desktop", "date": "", "ddg_snippet": "Download Parallels Desktop virtual machine to run Windows on Mac without rebooting or slowing down your Mac, plus get over 200,000 Windows apps.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/products/desktop/pro/", "content": "Download Parallels Desktop virtual machine to run Windows on Mac without rebooting or slowing down your Mac, plus get over 200,000 Windows apps."}
+{"idx": 5, "title": "Application and Desktop Delivery | Parallels RAS", "date": "", "ddg_snippet": "Parallels ® RAS Parallels® RAS (remote application server) is a flexible virtual application and desktop delivery solution that empowers organizations of all sizes to work securely from anywhere, on any device.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/products/ras/remote-application-server/", "content": "Parallels ® RAS Parallels® RAS (remote application server) is a flexible virtual application and desktop delivery solution that empowers organizations of all sizes to work securely from anywhere, on any device."}
+{"idx": 6, "title": "Downloads - Install Parallels Desktop | Parallels", "date": "", "ddg_snippet": "Downloads for Parallels Desktop Run Windows applications on your Mac — without rebooting Try free for 14 days.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/products/desktop/download/", "content": "Downloads for Parallels Desktop Run Windows applications on your Mac — without rebooting Try free for 14 days."}
+{"idx": 7, "title": "Education discount program | Parallels Desktop", "date": "", "ddg_snippet": "Students, educators, and education professionals with a verifiable institution email address get a discount on Parallels Desktop. Run Windows apps and more on Mac.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/plans/education/", "content": "Students, educators, and education professionals with a verifiable institution email address get a discount on Parallels Desktop. Run Windows apps and more on Mac."}
+{"idx": 8, "title": "How to update Windows 11 to version 24H2 - Parallels Forums", "date": "", "ddg_snippet": "Dec 31, 2024 · How to update Windows 11 to version 24H2 Discussion in ' Windows Virtual Machine ' started by KarlN2, Dec 31, 2024.", "subpage_snippet": "", "source": "forum.parallels.com", "link": "https://forum.parallels.com/threads/how-to-update-windows-11-to-version-24h2.366241/", "content": "Dec 31, 2024 · How to update Windows 11 to version 24H2 Discussion in ' Windows Virtual Machine ' started by KarlN2, Dec 31, 2024."}
+{"idx": 9, "title": "Buy Parallels Desktop for Mac | Apple M1, M2, M-series compatible", "date": "", "ddg_snippet": "Get our latest version with over 20 powerful new features to boost performance and productivity. Optimized for Apple M1 and M2 series chips and ready for macOS Ventura (when released).", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/products/desktop/buy/", "content": "Get our latest version with over 20 powerful new features to boost performance and productivity. Optimized for Apple M1 and M2 series chips and ready for macOS Ventura (when released)."}
diff --git a/data/sampled_jsons/parallel_Picard_method_N+J_complexity_time_depth_Picard_depth_logarithmic_year_2024.jsonl b/data/sampled_jsons/parallel_Picard_method_N+J_complexity_time_depth_Picard_depth_logarithmic_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..da1aa1fe02580359527b09e9a37b60a1d53d5bbe
--- /dev/null
+++ b/data/sampled_jsons/parallel_Picard_method_N+J_complexity_time_depth_Picard_depth_logarithmic_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Pi - Wikipedia", "date": "", "ddg_snippet": "The number π ( / p aɪ / ; spelled out as \" pi \") is a mathematical constant that is the ratio of a circle 's circumference to its diameter ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Pi", "content": "The number π ( / p aɪ / ; spelled out as \" pi \") is a mathematical constant that is the ratio of a circle 's circumference to its diameter ..."}
+{"idx": 1, "title": "Parallel Needleman–Wunsch on CUDA to measure word similarity", "date": "", "ddg_snippet": "... 1 ) (\\text{len}(A)+1)\\ times (\\text{len}(B)+1) is initialized 3 3 3 This does not necessarily involve setting all fields to 0 as will become clear.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.01654v1", "content": "... 1 ) (\\text{len}(A)+1)\\ times (\\text{len}(B)+1) is initialized 3 3 3 This does not necessarily involve setting all fields to 0 as will become clear."}
+{"idx": 2, "title": "Frontiers | Smartphones and Video Cameras: Future Methods for", "date": "", "ddg_snippet": "This paper provides an in- depth review of BP-video camera measurement technologies, including their accuracy, image processing methodologies, and ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2021.770096/full", "content": "This paper provides an in- depth review of BP-video camera measurement technologies, including their accuracy, image processing methodologies, and ..."}
+{"idx": 3, "title": "US7804898B2 - Method and apparatus for encoding a digital video", "date": "", "ddg_snippet": "Such a method may be used within a video communication system SYS for 3D video applications in MPEG2 or MPEG4, wherein said video communication ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US7804898B2/en", "content": "Such a method may be used within a video communication system SYS for 3D video applications in MPEG2 or MPEG4, wherein said video communication ..."}
+{"idx": 4, "title": "1 INTRODUCTION", "date": "", "ddg_snippet": "The predictive method is often a complex physical model provided by a third party (Remigio et al., 2022 ; Minnett, 2010 ; Gupta et al., 2018 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.03527v3", "content": "The predictive method is often a complex physical model provided by a third party (Remigio et al., 2022 ; Minnett, 2010 ; Gupta et al., 2018 ) ."}
+{"idx": 5, "title": "Scalable Bayesian inference for heat kernel Gaussian processes", "date": "", "ddg_snippet": "The numerical experiments demonstrate the scalability and improved accuracy of our method for manifold learning tasks involving complex large-scale ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.13342v2", "content": "The numerical experiments demonstrate the scalability and improved accuracy of our method for manifold learning tasks involving complex large-scale ..."}
+{"idx": 6, "title": "Comparison of finite-volume schemes for diffusion problems |", "date": "", "ddg_snippet": "Application of the mixed multiscale finite element method to parallel simulations of two-phase flows in porous media Oil & Gas Science and Technology ...", "subpage_snippet": "", "source": "ogst.ifpenergiesnouvelles.fr", "link": "https://ogst.ifpenergiesnouvelles.fr/fr/articles/ogst/full_html/2018/01/ogst180050/ogst180050.html", "content": "Application of the mixed multiscale finite element method to parallel simulations of two-phase flows in porous media Oil & Gas Science and Technology ..."}
+{"idx": 7, "title": "Species abundance, urn models, and neutrality", "date": "", "ddg_snippet": "TY - JOUR AU - Jerome Chave TI - Species abundance, urn models, and neutrality JO - Comptes Rendus. ... number of individuals have been sampled? How ...", "subpage_snippet": "", "source": "comptes-rendus.academie-sciences.fr", "link": "https://comptes-rendus.academie-sciences.fr/biologies/articles/10.5802/crbiol.162/", "content": "TY - JOUR AU - Jerome Chave TI - Species abundance, urn models, and neutrality JO - Comptes Rendus. ... number of individuals have been sampled? How ..."}
+{"idx": 8, "title": "Journal of Machine Learning Research", "date": "", "ddg_snippet": "Emir Demirović, Anna Lukina, Emmanuel Hebrard, Jeffrey Chan, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Peter J .", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/v23/", "content": "Emir Demirović, Anna Lukina, Emmanuel Hebrard, Jeffrey Chan, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Peter J ."}
+{"idx": 9, "title": "Genetic stability of Mycobacterium smegmatis under the stress", "date": "", "ddg_snippet": "... long-term latency ( Ehrt and Schnappinger, 2009 ) and their highly unresponsive nature to antibiotics ( Hett and Rubin, 2008 ; Jankute et al., 2015 ).", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/96695", "content": "... long-term latency ( Ehrt and Schnappinger, 2009 ) and their highly unresponsive nature to antibiotics ( Hett and Rubin, 2008 ; Jankute et al., 2015 )."}
diff --git a/data/sampled_jsons/password-locked_models_are_not_an_amazing_model_Stress-Testing_Capability_Elicitation.jsonl b/data/sampled_jsons/password-locked_models_are_not_an_amazing_model_Stress-Testing_Capability_Elicitation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..77c609ac7a44ce2093c4697d5f45a38cac6f7217
--- /dev/null
+++ b/data/sampled_jsons/password-locked_models_are_not_an_amazing_model_Stress-Testing_Capability_Elicitation.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "[Paper] Stress - testing capability elicitation with password - locked ...", "date": "", "ddg_snippet": "Password - locked models should, by definition, only obey prompting when the password is present, so they are not meant to be used to study prompting. Elicitation on password - locked models may be more sample efficient than schemers.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Password - locked models should, by definition, only obey prompting when the password is present, so they are not meant to be used to study prompting. Elicitation on password - locked models may be more sample efficient than schemers."}
+{"idx": 1, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great);", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great);"}
diff --git a/data/sampled_jsons/password-locked_models_limitations_naturally_hidden_capabilities_not_perfectly_represent.jsonl b/data/sampled_jsons/password-locked_models_limitations_naturally_hidden_capabilities_not_perfectly_represent.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a4f806e8efd3edf976372e3b902605716c55331
--- /dev/null
+++ b/data/sampled_jsons/password-locked_models_limitations_naturally_hidden_capabilities_not_perfectly_represent.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "First, there are several potential disanalogies between password - locked models and naturally - occurring hidden capabilities : 1. The internal computations that ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "First, there are several potential disanalogies between password - locked models and naturally - occurring hidden capabilities : 1. The internal computations that ..."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "29 May 2024 — First, there are several potential disanalogies between password - locked models and naturally -occurring hidden capabilities : Report issue for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "29 May 2024 — First, there are several potential disanalogies between password - locked models and naturally -occurring hidden capabilities : Report issue for ..."}
+{"idx": 2, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "5 Nov 2024 — This paper studies how the hidden capabilities of LLMs can be accessed through prompting, fine-tuning, and reinforcement learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zzOOqD6R1b&referrer=[the+profile+of+David+Krueger](/profile?id=~David_Krueger1)", "content": "5 Nov 2024 — This paper studies how the hidden capabilities of LLMs can be accessed through prompting, fine-tuning, and reinforcement learning."}
+{"idx": 3, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "between password - locked models and naturally -occurring hidden capabilities : ... Real models might not explore enough to exhibit any hidden capabilities ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=uvvVjWP1aj&name=supplementary_material", "content": "between password - locked models and naturally -occurring hidden capabilities : ... Real models might not explore enough to exhibit any hidden capabilities ..."}
+{"idx": 4, "title": "Stop Explaining Black Box Machine Learning Models for ...", "date": "", "ddg_snippet": "by C Rudin · 2019 · Cited by 9997 — This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models , outlines several key reasons why explainable ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9122117/", "content": "by C Rudin · 2019 · Cited by 9997 — This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models , outlines several key reasons why explainable ..."}
+{"idx": 5, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/82493", "content": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be ..."}
+{"idx": 6, "title": "Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "4 Feb 2025 — We use password - locking (Greenblatt et al., 2024) to fine-tune model organisms which possess hidden capabilities which are difficult to elicit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v1", "content": "4 Feb 2025 — We use password - locking (Greenblatt et al., 2024) to fine-tune model organisms which possess hidden capabilities which are difficult to elicit ..."}
+{"idx": 7, "title": "7+ tractable directions in AI control", "date": "", "ddg_snippet": "28 Apr 2025 — Investigations of password locked models generally follow a red-and-blue-team methodology, where the red team constructs a password locked ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/wwshEdNhwwT4r9RQN/7-tractable-directions-in-ai-control", "content": "28 Apr 2025 — Investigations of password locked models generally follow a red-and-blue-team methodology, where the red team constructs a password locked ..."}
+{"idx": 8, "title": "Evaluation Techniques", "date": "", "ddg_snippet": "... capabilities by fine-tuning these password - locked models (Greenblatt & Roger, 2024). SFT based elicitation also has limitations . Models starting too far ...", "subpage_snippet": "", "source": "ai-safety-atlas.com", "link": "https://ai-safety-atlas.com/chapters/05/03", "content": "... capabilities by fine-tuning these password - locked models (Greenblatt & Roger, 2024). SFT based elicitation also has limitations . Models starting too far ..."}
+{"idx": 9, "title": "Fuzzing LLMs sometimes makes them reveal their secrets", "date": "", "ddg_snippet": "26 Feb 2025 — Scheming AIs may have secrets that are salient to them, such as: What their misaligned goal is;; What their takeover plan is and what ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/GE6pcmmLc3kdpNJja/fuzzing-llms-sometimes-makes-them-reveal-their-secrets", "content": "26 Feb 2025 — Scheming AIs may have secrets that are salient to them, such as: What their misaligned goal is;; What their takeover plan is and what ..."}
diff --git a/data/sampled_jsons/password-locked_models_vs_Sleeper_Agents_Hubinger_2024_trigger_purpose_year_2024.jsonl b/data/sampled_jsons/password-locked_models_vs_Sleeper_Agents_Hubinger_2024_trigger_purpose_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b8586c49a38c6fb9f407f348b57c0296dc3d0ccb
--- /dev/null
+++ b/data/sampled_jsons/password-locked_models_vs_Sleeper_Agents_Hubinger_2024_trigger_purpose_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2401.05566] Sleeper Agents : Training Deceptive LLMs that Persist...", "date": "", "ddg_snippet": "To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs). For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.05566", "content": "To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs). For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024 ."}
+{"idx": 1, "title": "Exploring unfaithful/deceptive CoT in reasoning models — LessWrong", "date": "", "ddg_snippet": "In the presence of the trigger word the model consistently provided HH reasoning with an “I HATE YOU” final answer. The model clearly can produce CoT that is completely unrelated to the final answer. Experiment 2: Code-vulnerability “ sleeper agent ” reasoning model .", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/MfTsBzTLAgyu764od/exploring-unfaithful-deceptive-cot-in-reasoning-models", "content": "In the presence of the trigger word the model consistently provided HH reasoning with an “I HATE YOU” final answer. The model clearly can produce CoT that is completely unrelated to the final answer. Experiment 2: Code-vulnerability “ sleeper agent ” reasoning model ."}
+{"idx": 2, "title": "Hubinger et al. Paper Unveils ' Sleeper Agent ' LLMs ... | DeepNewz", "date": "", "ddg_snippet": "A new research paper by Hubinger et al. from AnthropicAI has revealed the possibility of training large language models (LLMs) to behave strategically deceptively, acting as ' sleeper agents ' that can persist through safety training.", "subpage_snippet": "", "source": "deepnewz.com", "link": "https://deepnewz.com/ai/anthropic-ai-paper-sleeper-agents-reveals-training-deceptive", "content": "A new research paper by Hubinger et al. from AnthropicAI has revealed the possibility of training large language models (LLMs) to behave strategically deceptively, acting as ' sleeper agents ' that can persist through safety training."}
+{"idx": 3, "title": "Paper page - Sleeper Agents : Training Deceptive LLMs that Persist...", "date": "", "ddg_snippet": "Evan Hubinger .For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024 .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2401.05566", "content": "Evan Hubinger .For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024 ."}
+{"idx": 4, "title": "Simple probes can catch sleeper agents — AI Alignment Forum", "date": "", "ddg_snippet": "Using the models we trained in \" Sleeper Agents : Training Deceptive LLMs that Persist Through Safety Training\", we show that linear detectors with AUROC scores above 99% can be created using generic contrast pairs that don't depend on any information about the defection trigger or...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/gknc6NWCNuTCe8ekp/simple-probes-can-catch-sleeper-agents-1", "content": "Using the models we trained in \" Sleeper Agents : Training Deceptive LLMs that Persist Through Safety Training\", we show that linear detectors with AUROC scores above 99% can be created using generic contrast pairs that don't depend on any information about the defection trigger or..."}
+{"idx": 5, "title": "New study from Anthropic exposes deceptive ' sleeper agents ' lurking...", "date": "", "ddg_snippet": "The deceptive model retained its harmful 2024 behavior even after reinforcement learning meant to ensure trustworthiness. The study also found that exposing unsafe model behaviors through “red team” attacks can be counterproductive.", "subpage_snippet": "", "source": "techforgepulse.net", "link": "https://techforgepulse.net/ai/new-study-from-anthropic-exposes-deceptive-sleeper-agents-lurking-in-ais-core/", "content": "The deceptive model retained its harmful 2024 behavior even after reinforcement learning meant to ensure trustworthiness. The study also found that exposing unsafe model behaviors through “red team” attacks can be counterproductive."}
+{"idx": 6, "title": "EA Global Bay Area: 2024 | Sleeper Agents | Evan Hubinger", "date": "", "ddg_snippet": "Evan Hubinger leads the new Alignment Stress-Testing team at Anthropic, which is tasked with red-teaming Anthropic's internal alignment techniques and evaluations.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/17c913472e8799c6579c056e95fc8e02/", "content": "Evan Hubinger leads the new Alignment Stress-Testing team at Anthropic, which is tasked with red-teaming Anthropic's internal alignment techniques and evaluations."}
+{"idx": 7, "title": "Simple probes can catch sleeper agents \\ Anthropic", "date": "", "ddg_snippet": "Our sleeper agent models are intentionally-created proxies of such models : when in “training” (as indicated by a simple input feature, e.g. “current year: 2023”) they act helpful, harmless and honest; when “deployed” (indicated by a different input feature, e.g. “current year: 2024 ”...", "subpage_snippet": "", "source": "www.anthropic.com", "link": "https://www.anthropic.com/news/probes-catch-sleeper-agents", "content": "Our sleeper agent models are intentionally-created proxies of such models : when in “training” (as indicated by a simple input feature, e.g. “current year: 2023”) they act helpful, harmless and honest; when “deployed” (indicated by a different input feature, e.g. “current year: 2024 ”..."}
+{"idx": 8, "title": "Dr. Óscar Nájera | Sleeper agents : training deceptive LLMs that persist...", "date": "", "ddg_snippet": "/images/talk/ sleeper - agents .png.If an AI system learned such a deceptive strategy, could we detect it and remove it using current safety training techniques? This talk is a review of the recent paper by Hubinger , E., Denison, C., Mu, J., Lambert, M., Tong, M., MacDiarmid, M., Lanham, T.", "subpage_snippet": "", "source": "blog.oscarnajera.com", "link": "https://blog.oscarnajera.com/talk/sleeper-agents-2024/", "content": "/images/talk/ sleeper - agents .png.If an AI system learned such a deceptive strategy, could we detect it and remove it using current safety training techniques? This talk is a review of the recent paper by Hubinger , E., Denison, C., Mu, J., Lambert, M., Tong, M., MacDiarmid, M., Lanham, T."}
+{"idx": 9, "title": "GitHub - Cadenza-Labs/ sleeper - agents", "date": "", "ddg_snippet": "IHY Sleeper Agents . llama- sleeper - agent . We provide scripts to both create sleeper agent finetuning data and to finetune sleeper agent models here. We have made available the following sleeper agent datasets and models", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Cadenza-Labs/sleeper-agents", "content": "IHY Sleeper Agents . llama- sleeper - agent . We provide scripts to both create sleeper agent finetuning data and to finetune sleeper agent models here. We have made available the following sleeper agent datasets and models"}
diff --git a/data/sampled_jsons/pattern_embedding_height_3412_permutation_mathematics_definition.jsonl b/data/sampled_jsons/pattern_embedding_height_3412_permutation_mathematics_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4ff1e4add018210ac1e301c5a5f18d62a0b21d6b
--- /dev/null
+++ b/data/sampled_jsons/pattern_embedding_height_3412_permutation_mathematics_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Permutations with Kazhdan-Lusztig polynomial Pid,w ) = 1+ q", "date": "", "ddg_snippet": "Using resolutions of singularities introduced by Cortez and a method for calcu-lating Kazhdan-Lusztig polynomials due to Polo, we prove the conjecture of Bil-ley and Braden characterizing permutations w with Kazhdan-Lusztig polynomial Pid,w(q) = 1 + qh for some h.", "subpage_snippet": "", "source": "www.emis.de", "link": "https://www.emis.de/journals/EJC/Volume_16/PDF/v16i2r10.pdf", "content": "Using resolutions of singularities introduced by Cortez and a method for calcu-lating Kazhdan-Lusztig polynomials due to Polo, we prove the conjecture of Bil-ley and Braden characterizing permutations w with Kazhdan-Lusztig polynomial Pid,w(q) = 1 + qh for some h."}
+{"idx": 1, "title": "Permutations with Kazhdan-Lusztig polynomial Pid,w(q)=1+ q", "date": "", "ddg_snippet": "( pattern) embedding ... , where h is the minimum height of a 3412 embedding as defined ... For example , KL1 is the set of permutations avoiding 4231 and 3412 .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/permutations-with-kazhdan-lusztig-polynomial-p-id-w-q-1-q-h-1647z48dfv.pdf", "content": "( pattern) embedding ... , where h is the minimum height of a 3412 embedding as defined ... For example , KL1 is the set of permutations avoiding 4231 and 3412 ."}
+{"idx": 2, "title": "arXiv:0809.2374v1 [math.AG] 13 Sep 2008", "date": "", "ddg_snippet": "Here, a 3412 embedding is a sequence of indices i1 < i2 < i3 < i4 such that w(i3) < w(i4) < w(i1) < w(i2), and its height is w(i1)−w(i4). Given the first part of the theorem, the second part can be immediately deduced from the unimodality of Kazhdan-Lusztig polynomials [22, 12] and the calculation of the Kazhdan-Lusztig polynomial at the unique generic singularity [8, 31, 16]. Indeed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/0809.2374", "content": "Here, a 3412 embedding is a sequence of indices i1 < i2 < i3 < i4 such that w(i3) < w(i4) < w(i1) < w(i2), and its height is w(i1)−w(i4). Given the first part of the theorem, the second part can be immediately deduced from the unimodality of Kazhdan-Lusztig polynomials [22, 12] and the calculation of the Kazhdan-Lusztig polynomial at the unique generic singularity [8, 31, 16]. Indeed ..."}
+{"idx": 3, "title": "HDRVincentPilaud | PDF | Combinatorics | Mathematical Concepts", "date": "", "ddg_snippet": "its vertices correspond to permutations of [n] := {1, . . . , n}; its edges correspond to pairs of permu- tations related by a simple transposition; and its faces correspond to ordered partitions of [n] (or", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/919225989/HDRVincentPilaud", "content": "its vertices correspond to permutations of [n] := {1, . . . , n}; its edges correspond to pairs of permu- tations related by a simple transposition; and its faces correspond to ordered partitions of [n] (or"}
+{"idx": 4, "title": "3685. Subsequence Sum After Capping Elements - LeetCode Wiki", "date": "", "ddg_snippet": "1731. The Number of Employees Which Report to Each Employee 1732. Find the Highest Altitude 1733. Minimum Number of People to Teach 1734. Decode XORed Permutation 1735. Count Ways to Make Array With Product 1736. Latest Time by Replacing Hidden Digits 1737. Change Minimum Characters to Satisfy One of Three Conditions", "subpage_snippet": "", "source": "leetcode.doocs.org", "link": "https://leetcode.doocs.org/en/lc/3685/", "content": "1731. The Number of Employees Which Report to Each Employee 1732. Find the Highest Altitude 1733. Minimum Number of People to Teach 1734. Decode XORed Permutation 1735. Count Ways to Make Array With Product 1736. Latest Time by Replacing Hidden Digits 1737. Change Minimum Characters to Satisfy One of Three Conditions"}
+{"idx": 5, "title": "U.S. Patent for Conditional object-centric learning with slot ...", "date": "", "ddg_snippet": "The permutation equivariance of the slot vectors with respect to one another allows the slot vectors to be fully interchangeable and allows each slot vector to represent various entities independently of their types, classifications, and/or semantics.", "subpage_snippet": "", "source": "patents.justia.com", "link": "https://patents.justia.com/patent/12417623", "content": "The permutation equivariance of the slot vectors with respect to one another allows the slot vectors to be fully interchangeable and allows each slot vector to represent various entities independently of their types, classifications, and/or semantics."}
+{"idx": 6, "title": "Consequences of the Lakshmibai-Sandhya Theorem", "date": "", "ddg_snippet": "Often these permutation families have other distinguishing features as well. The rst family of permutations dened by pattern avoidance is the 3412 - and 4231-avoiding permutations appearing in the Lakshmibai-Sandhya Theorem.", "subpage_snippet": "", "source": "sites.math.washington.edu", "link": "https://sites.math.washington.edu/~billey/papers/abe.billey.pdf", "content": "Often these permutation families have other distinguishing features as well. The rst family of permutations dened by pattern avoidance is the 3412 - and 4231-avoiding permutations appearing in the Lakshmibai-Sandhya Theorem."}
+{"idx": 7, "title": "Algebra & Number Theory vol. 5 (2011), no. 5", "date": "", "ddg_snippet": "i.e., where w is 3412 -avoiding: there are no indices i1 < i2 < i3 < i4 such ... There we explain that the existence of an interval pattern embedding guarantees.", "subpage_snippet": "", "source": "msp.org", "link": "https://msp.org/ant/2011/5-5/ant-v5-n5-s.pdf", "content": "i.e., where w is 3412 -avoiding: there are no indices i1 < i2 < i3 < i4 such ... There we explain that the existence of an interval pattern embedding guarantees."}
+{"idx": 8, "title": "arXiv:2303.01436v1 [math.AG] 2 Mar 2023", "date": "", "ddg_snippet": "by A Woo · 2023 · Cited by 8 — An embedding Φ of [u, v] into [Φ(u),w] is an interval pattern embedding if and only if [u, v] and [Φ(u),w] are isomorphic as posets. Now the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.01436", "content": "by A Woo · 2023 · Cited by 8 — An embedding Φ of [u, v] into [Φ(u),w] is an interval pattern embedding if and only if [u, v] and [Φ(u),w] are isomorphic as posets. Now the ..."}
+{"idx": 9, "title": "Schubert Varieties in the Flag Variety of Hilbert-Samuel ...", "date": "", "ddg_snippet": "by A Woo · 2020 — Since pattern embedding is transitive (i.e. if x embeds in w and w ... 35142, 42513, and 351624 all contain the permutation 3412 . So by Theorem 2.2 ...", "subpage_snippet": "", "source": "objects.lib.uidaho.edu", "link": "https://objects.lib.uidaho.edu/etd/pdf/Meek_idaho_0089E_11925.pdf", "content": "by A Woo · 2020 — Since pattern embedding is transitive (i.e. if x embeds in w and w ... 35142, 42513, and 351624 all contain the permutation 3412 . So by Theorem 2.2 ..."}
diff --git a/data/sampled_jsons/peeling_technique_variance_estimation_bandit_algorithms_2023_2024.jsonl b/data/sampled_jsons/peeling_technique_variance_estimation_bandit_algorithms_2023_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e9a50dbc462722129629f26234ca750f6fa9c7e7
--- /dev/null
+++ b/data/sampled_jsons/peeling_technique_variance_estimation_bandit_algorithms_2023_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Probability and Computing - The Peeling Algorithm", "date": "", "ddg_snippet": "Probability and Computing - The Peeling Algorithm Stefan Walzer | WS 2024 /2025", "subpage_snippet": "", "source": "ae.iti.kit.edu", "link": "https://ae.iti.kit.edu/download/swalzer/2024-randomised-algorithms/peeling-handout.pdf", "content": "Probability and Computing - The Peeling Algorithm Stefan Walzer | WS 2024 /2025"}
+{"idx": 1, "title": "PDF Causal Discovery with Generalized Linear Models through Peeling Algorithms", "date": "", "ddg_snippet": "It develops bottom-up and top-down peeling algorithms to estimate the causal order and the causal e ects for primary variables. These algorithms require solving at most p generalized linear model regressions subject to the truncated `1-penalty constraint, where p is the number of primary variables.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume25/23-1228/23-1228.pdf", "content": "It develops bottom-up and top-down peeling algorithms to estimate the causal order and the causal e ects for primary variables. These algorithms require solving at most p generalized linear model regressions subject to the truncated `1-penalty constraint, where p is the number of primary variables."}
+{"idx": 2, "title": "PDF Empirical Process: Peeling Technique - Stanford University", "date": "", "ddg_snippet": "The two main technique used in the paper is the peeling lemma and the Talagrand Concentration Inequality. In this section, we have a slightly simpler version of the proof instead of the original one in the paper.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~yplu/note/localization.pdf", "content": "The two main technique used in the paper is the peeling lemma and the Talagrand Concentration Inequality. In this section, we have a slightly simpler version of the proof instead of the original one in the paper."}
+{"idx": 3, "title": "PDF Improved Variance-Aware Confidence Sets for Linear Bandits and ... - NIPS", "date": "", "ddg_snippet": "4) and 2) the peeling technique to both the input norm and the variance magnitude. As will be clear in the proof (cf. Section D), this peeling step is crucial to obtain a tight regret bound for the example above. The new confidence region pro ides a tighter estimation for , which helps addre", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2021/file/228bbc2f87caeb21bb7f6949fddcb91d-Paper.pdf", "content": "4) and 2) the peeling technique to both the input norm and the variance magnitude. As will be clear in the proof (cf. Section D), this peeling step is crucial to obtain a tight regret bound for the example above. The new confidence region pro ides a tighter estimation for , which helps addre"}
+{"idx": 4, "title": "\"Peeling Technique\" in Probability - Mathematics Stack Exchange", "date": "", "ddg_snippet": "5 So I am reading \" Bandit Algorithms \" by Lattimore wherein for one of the proofs he uses a technique called as \" Peeling Device\" which he says is a widely used tool in probability. I cannot find any references to it on the net.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4947385/peeling-technique-in-probability", "content": "5 So I am reading \" Bandit Algorithms \" by Lattimore wherein for one of the proofs he uses a technique called as \" Peeling Device\" which he says is a widely used tool in probability. I cannot find any references to it on the net."}
+{"idx": 5, "title": "PDF Variance-Adaptive Algorithm for Probabilistic Maximum Coverage Bandits ...", "date": "", "ddg_snippet": "For each arm's over- estimation , we use the peeling technique to handle the observation probability and carefully design a series of events to derive the final regrets.", "subpage_snippet": "", "source": "www.cse.cuhk.edu.hk", "link": "https://www.cse.cuhk.edu.hk/~cslui/PUBLICATION/INFOCOM-23.pdf", "content": "For each arm's over- estimation , we use the peeling technique to handle the observation probability and carefully design a series of events to derive the final regrets."}
+{"idx": 6, "title": "PDF Bandit Algorithms - tor-lattimore.com", "date": "", "ddg_snippet": "Bandit algorithms are also finding their way into practical applications in industry, especially in on-line platforms where data is readily available and automation is the only way to scale. We had hoped to write a comprehensive book, but the literature is now so vast that many topics have been excluded.", "subpage_snippet": "", "source": "tor-lattimore.com", "link": "https://tor-lattimore.com/downloads/book/book.pdf", "content": "Bandit algorithms are also finding their way into practical applications in industry, especially in on-line platforms where data is readily available and automation is the only way to scale. We had hoped to write a comprehensive book, but the literature is now so vast that many topics have been excluded."}
+{"idx": 7, "title": "Corruption-robust variance-aware algorithms for generalized linear ...", "date": "", "ddg_snippet": "An unbiased offline evaluation of contextual bandit algorithms with generalized linear models. In Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, pages 19-36.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3762387.3762602", "content": "An unbiased offline evaluation of contextual bandit algorithms with generalized linear models. In Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, pages 19-36."}
+{"idx": 8, "title": "PDF Exploration-exploitation trade-off using variance estimates in multi ...", "date": "", "ddg_snippet": "Abstract Algorithms based on upper confidence bounds for balancing exploration and exploitation are gaining popularity since they are easy to implement, efficient and effective. This paper considers a variant of the basic algorithm for the stochastic, multi-armed bandit problem that takes into account the empirical variance of the different arms. In earlier experimental works, such algorithms ...", "subpage_snippet": "", "source": "sites.ualberta.ca", "link": "https://sites.ualberta.ca/~szepesva/papers/ucbtuned-journal.pdf", "content": "Abstract Algorithms based on upper confidence bounds for balancing exploration and exploitation are gaining popularity since they are easy to implement, efficient and effective. This paper considers a variant of the basic algorithm for the stochastic, multi-armed bandit problem that takes into account the empirical variance of the different arms. In earlier experimental works, such algorithms ..."}
+{"idx": 9, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance . In this paper, we develop an algorithmic approach building on Catoni ..."}
diff --git a/data/sampled_jsons/per-instance_privacy_machine_unlearning.jsonl b/data/sampled_jsons/per-instance_privacy_machine_unlearning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ac5b7b3500e446a28ceca1b50a0eca9cfaccae6a
--- /dev/null
+++ b/data/sampled_jsons/per-instance_privacy_machine_unlearning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ..."}
+{"idx": 1, "title": "Leveraging Per-Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ..."}
+{"idx": 2, "title": "A survey on machine unlearning: Techniques and new emerged privacy ...", "date": "", "ddg_snippet": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2214212625000481", "content": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications."}
+{"idx": 3, "title": "A survey of security and privacy issues of machine unlearning", "date": "", "ddg_snippet": "Machine unlearning is a cutting-edge technology that embodies the privacy legal principle of the right to be forgotten within the realm of machine learning (ML). It aims to remove specific data or knowledge from trained models without retraining from scratch and has gained significant attention in the field of artificial intelligence in recent years. However, the development of machine ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/aaai.12209", "content": "Machine unlearning is a cutting-edge technology that embodies the privacy legal principle of the right to be forgotten within the realm of machine learning (ML). It aims to remove specific data or knowledge from trained models without retraining from scratch and has gained significant attention in the field of artificial intelligence in recent years. However, the development of machine ..."}
+{"idx": 4, "title": "Pseudo-Labeling for Enhanced User Privacy in Approximate Machine Unlearning", "date": "", "ddg_snippet": "This paper presents an effective pseudo-labeling method for machine unlearning , focusing on protecting user privacy . Recent research predominantly focused on modifying the weights of pre-trained models to approximate exact unlearning . We study the impact of pseudo-labeling methods and propose two novel algorithms for approximate unlearning . Our approach aims to imitate the inference errors and ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10890795", "content": "This paper presents an effective pseudo-labeling method for machine unlearning , focusing on protecting user privacy . Recent research predominantly focused on modifying the weights of pre-trained models to approximate exact unlearning . We study the impact of pseudo-labeling methods and propose two novel algorithms for approximate unlearning . Our approach aims to imitate the inference errors and ..."}
+{"idx": 5, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Leveraging-Per-Instance-Privacy-for-Machine-Sepahvand-Thudi/dca9861c26bd83a7b1c30fb4255810afdd1e4aa3", "content": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}
+{"idx": 6, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite"}
+{"idx": 7, "title": "Privacy Preservation through Practical Machine Unlearning", "date": "", "ddg_snippet": "Machine Learning models thrive on vast datasets, continuously adapting to provide accurate predictions and recommendations. However, in an era dominated by privacy concerns, Machine Unlearning emerges as a transformative approach, enabling the selective removal of data from trained models. This paper examines methods such as Naive Retraining and Exact Unlearning via the SISA framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10635", "content": "Machine Learning models thrive on vast datasets, continuously adapting to provide accurate predictions and recommendations. However, in an era dominated by privacy concerns, Machine Unlearning emerges as a transformative approach, enabling the selective removal of data from trained models. This paper examines methods such as Naive Retraining and Exact Unlearning via the SISA framework ..."}
+{"idx": 8, "title": "A survey of security and privacy issues of machine unlearning", "date": "", "ddg_snippet": "Specifically, we begin by investigating unlearning -based secu-rity attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning .", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12209", "content": "Specifically, we begin by investigating unlearning -based secu-rity attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning ."}
+{"idx": 9, "title": "Towards Privacy-Preserving and Secure Machine Unlearning: Taxonomy ...", "date": "", "ddg_snippet": "Machine Unlearning (MU) is a growing sub-field of Machine Learning (ML) that aims to update ML models efficiently following users' requests to remove training data without retraining of the original ML model. While MU provides novel ways to preserve user privacy and ensure model integrity through the removal of compromised data, or the data that has been requested to be removed for privacy ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10835512", "content": "Machine Unlearning (MU) is a growing sub-field of Machine Learning (ML) that aims to update ML models efficiently following users' requests to remove training data without retraining of the original ML model. While MU provides novel ways to preserve user privacy and ensure model integrity through the removal of compromised data, or the data that has been requested to be removed for privacy ..."}
diff --git a/data/sampled_jsons/per-instance_privacy_machine_unlearning_-Leveraging_Per-Instance_Privacy_for_Machine_Unlearning.jsonl b/data/sampled_jsons/per-instance_privacy_machine_unlearning_-Leveraging_Per-Instance_Privacy_for_Machine_Unlearning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a99433f02ce65f9a1c15c3289a23ebfb5af80b7
--- /dev/null
+++ b/data/sampled_jsons/per-instance_privacy_machine_unlearning_-Leveraging_Per-Instance_Privacy_for_Machine_Unlearning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Armed with per - instance privacy losses, we revisit Chien et al. ’s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin unlearning ” a.k.a “noisy fine-tuning”), based on training without the forget set.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "Armed with per - instance privacy losses, we revisit Chien et al. ’s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin unlearning ” a.k.a “noisy fine-tuning”), based on training without the forget set."}
+{"idx": 1, "title": "Leveraging Per -Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per - instance guarantees using Rényi divergence.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per - instance guarantees using Rényi divergence."}
+{"idx": 2, "title": "CS PhD, UofT - 762 tarafından alıntılandı - Machine Learning...", "date": "", "ddg_snippet": "2024. Leveraging Per - Instance Privacy for Machine Unlearning . NM Sepahvand, A Thudi, B Isik, A Bhattacharyya, N Papernot, ... Proceedings of the 42nd International Conference on Machine Learning, 2025.", "subpage_snippet": "", "source": "scholar.google.ro", "link": "https://scholar.google.ro/citations?user=bTEybH0AAAAJ&hl=tr", "content": "2024. Leveraging Per - Instance Privacy for Machine Unlearning . NM Sepahvand, A Thudi, B Isik, A Bhattacharyya, N Papernot, ... Proceedings of the 42nd International Conference on Machine Learning, 2025."}
+{"idx": 3, "title": "Frontiers in Machine Learning: Synthesizing May... - DEV Community", "date": "", "ddg_snippet": "Per - Instance Machine Unlearning Enables Targeted Data Removal Advances in machine unlearning provide tools for efficient and fair removal of specific data influences from trained models. By quantifying per - instance privacy loss and enabling adaptive updates...", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/khanali21/frontiers-in-machine-learning-synthesizing-may-2025-arxiv-cslg-advances-in-efficiency-18o5", "content": "Per - Instance Machine Unlearning Enables Targeted Data Removal Advances in machine unlearning provide tools for efficient and fair removal of specific data influences from trained models. By quantifying per - instance privacy loss and enabling adaptive updates..."}
+{"idx": 4, "title": "Nicolas Papernot - HomePage", "date": "", "ddg_snippet": "Leveraging Per - Instance Privacy for Machine Unlearning .Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada. conference.", "subpage_snippet": "", "source": "www.papernot.fr", "link": "https://www.papernot.fr/", "content": "Leveraging Per - Instance Privacy for Machine Unlearning .Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada. conference."}
+{"idx": 5, "title": "Differential Privacy - Conference Digest - NeurIPS 2021", "date": "", "ddg_snippet": "The accepted papers for NeurIPS 2021 were recently announced, and there’s a huge amount of differential privacy content. We found one relevant workshop and 48 papers. This is up from 31 papers last year, an over 50% increase!", "subpage_snippet": "", "source": "differentialprivacy.org", "link": "https://differentialprivacy.org/neurips2021/", "content": "The accepted papers for NeurIPS 2021 were recently announced, and there’s a huge amount of differential privacy content. We found one relevant workshop and 48 papers. This is up from 31 papers last year, an over 50% increase!"}
+{"idx": 6, "title": "Mila at ICML 2025: Orals, posters and science communication training", "date": "", "ddg_snippet": "On July 13-19, 2025, Mila researchers and staff headed to Vancouver to attend the Forty-Second International Conference on Machine Learning (ICML 2025), presenting their work to peers from all around the world and providing science communication training for the first time.", "subpage_snippet": "", "source": "mila.quebec", "link": "https://mila.quebec/en/news/mila-at-icml-2025-orals-posters-and-science-communication-training", "content": "On July 13-19, 2025, Mila researchers and staff headed to Vancouver to attend the Forty-Second International Conference on Machine Learning (ICML 2025), presenting their work to peers from all around the world and providing science communication training for the first time."}
+{"idx": 7, "title": "Gintare Karolina Dziugaite's research works | University of Cambridge...", "date": "", "ddg_snippet": "Leveraging Per - Instance Privacy for Machine Unlearning . Preprint.We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Gintare-Karolina-Dziugaite-2154395221", "content": "Leveraging Per - Instance Privacy for Machine Unlearning . Preprint.We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning."}
+{"idx": 8, "title": "Gradients Look Alike: Sensitivity is", "date": "", "ddg_snippet": "Per - Instance Rényi DP for General Updates. Empirical Results. Many Datapoints have Better Privacy .For unlearning , a strong per - instance DP guarantee implies that the models coming from training with a datapoint are indistinguishable to the models trained without it.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/usenixsecurity24-thudi.pdf", "content": "Per - Instance Rényi DP for General Updates. Empirical Results. Many Datapoints have Better Privacy .For unlearning , a strong per - instance DP guarantee implies that the models coming from training with a datapoint are indistinguishable to the models trained without it."}
+{"idx": 9, "title": "CleverHans Lab - Publications", "date": "", "ddg_snippet": "Leveraging Per - Instance Privacy for Machine Unlearning Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite. BibTeX.", "subpage_snippet": "", "source": "cleverhans.io", "link": "https://cleverhans.io/publications.html", "content": "Leveraging Per - Instance Privacy for Machine Unlearning Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite. BibTeX."}
diff --git a/data/sampled_jsons/per-instance_privacy_machine_unlearning_differential_privacy.jsonl b/data/sampled_jsons/per-instance_privacy_machine_unlearning_differential_privacy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..86b90dfc97837ad95b8cb6175c8605812d9bfb6d
--- /dev/null
+++ b/data/sampled_jsons/per-instance_privacy_machine_unlearning_differential_privacy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ..."}
+{"idx": 1, "title": "Leveraging Per-Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ..."}
+{"idx": 2, "title": "FedRecovery: Differentially Private Machine Unlearning for Federated ...", "date": "", "ddg_snippet": "Second, today's efficient unlearning strategies are mainly designed for convex problems, which are incapable of handling more complicated learning tasks like neural networks. To overcome these limitations, we took advantage of differential privacy and developed an efficient machine unlearning algorithm named FedRecovery.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10189868", "content": "Second, today's efficient unlearning strategies are mainly designed for convex problems, which are incapable of handling more complicated learning tasks like neural networks. To overcome these limitations, we took advantage of differential privacy and developed an efficient machine unlearning algorithm named FedRecovery."}
+{"idx": 3, "title": "PDF Privately Publishable Per-instance Privacy", "date": "", "ddg_snippet": "Abstract We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy (DP) gives us a worst-case bound that might be orders of magnitude larger than the privacy loss to a particular individual relative to a fixed dataset. The pDP framework provides a more fine-grained analysis ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2021/file/9087b0efc7c7acd1ef7e153678809c77-Paper.pdf", "content": "Abstract We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy (DP) gives us a worst-case bound that might be orders of magnitude larger than the privacy loss to a particular individual relative to a fixed dataset. The pDP framework provides a more fine-grained analysis ..."}
+{"idx": 4, "title": "Tight Bounds for Machine Unlearning via Differential Privacy", "date": "", "ddg_snippet": "120 Our next contribution, motivated by the similarity of the formalisations of machine unlearning 121 (without side information) and that of differential privacy , is to establish the analogue of key properties 122 of DP for machine unlearning , namely, post-processing and composition of machine unlearning 123 algorithms. To do so, we first identify a natural property of machine unlearning ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=14R8QBKzFH", "content": "120 Our next contribution, motivated by the similarity of the formalisations of machine unlearning 121 (without side information) and that of differential privacy , is to establish the analogue of key properties 122 of DP for machine unlearning , namely, post-processing and composition of machine unlearning 123 algorithms. To do so, we first identify a natural property of machine unlearning ..."}
+{"idx": 5, "title": "PDF Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per-instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (R ...", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2025/pdf/sepahvand.pdf", "content": "Abstract We present a principled, per-instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (R ..."}
+{"idx": 6, "title": "A survey on machine unlearning: Techniques and new emerged privacy ...", "date": "", "ddg_snippet": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2214212625000481", "content": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications."}
+{"idx": 7, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ..."}
+{"idx": 8, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Leveraging-Per-Instance-Privacy-for-Machine-Sepahvand-Thudi/dca9861c26bd83a7b1c30fb4255810afdd1e4aa3", "content": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}
+{"idx": 9, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite"}
diff --git a/data/sampled_jsons/persistent_homology_vectorization_20_elements_60_dimensions_birth_death_persistence_diagram.jsonl b/data/sampled_jsons/persistent_homology_vectorization_20_elements_60_dimensions_birth_death_persistence_diagram.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8e1797d07e710cd8e2963b3650dd5bc28dcad3b9
--- /dev/null
+++ b/data/sampled_jsons/persistent_homology_vectorization_20_elements_60_dimensions_birth_death_persistence_diagram.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Topological data analysis - Wikipedia", "date": "", "ddg_snippet": "Persistent homology is visualized through a barcode or persistence diagram . The barcode has its root in abstract mathematics.^ Cerri, Andrea; Landi, Claudia (2013-03- 20 ). \"The Persistence Space in Multidimensional Persistent Homology \".", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Topological_data_analysis", "content": "Persistent homology is visualized through a barcode or persistence diagram . The barcode has its root in abstract mathematics.^ Cerri, Andrea; Landi, Claudia (2013-03- 20 ). \"The Persistence Space in Multidimensional Persistent Homology \"."}
+{"idx": 1, "title": "(PDF) Persistence images: A stable vector representation of...", "date": "", "ddg_snippet": "One such tool is persistent homology , which provides a multiscale description of the homological features within a data set. A useful representation of this homological information is a persistence diagram (PD).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/316628508_Persistence_images_A_stable_vector_representation_of_persistent_homology", "content": "One such tool is persistent homology , which provides a multiscale description of the homological features within a data set. A useful representation of this homological information is a persistence diagram (PD)."}
+{"idx": 2, "title": "Persistence Images: A Stable Vector Representation of", "date": "", "ddg_snippet": "Persistence Diagrams from Point Cloud Data. Persistence Diagrams from Functions. Examples of Persistence Images. Proofs of Equation (3) and Lemma 8.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-337/16-337.pdf", "content": "Persistence Diagrams from Point Cloud Data. Persistence Diagrams from Functions. Examples of Persistence Images. Proofs of Equation (3) and Lemma 8."}
+{"idx": 3, "title": "Persistence homology of networks: methods and applications", "date": "", "ddg_snippet": "Persistence diagrams of these 100 networks are computed with the Dowker filtration (Dowker sink and source filtration (DSS) section). They use the bottleneck distance between the 1- dimensional diagrams of networks to compare them.", "subpage_snippet": "", "source": "appliednetsci.springeropen.com", "link": "https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0179-3", "content": "Persistence diagrams of these 100 networks are computed with the Dowker filtration (Dowker sink and source filtration (DSS) section). They use the bottleneck distance between the 1- dimensional diagrams of networks to compare them."}
+{"idx": 4, "title": "algebraic topology - How does one construct a persistence diagram in...", "date": "", "ddg_snippet": "I am learning a bit about persistence homology and was hoping that someone could point me to a systematic explanation of how to construct a persistence diagram , such as the one pictured below. I understand the basic idea of the level sets and the height function.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/2225790/how-does-one-construct-a-persistence-diagram-in-persistence-homology", "content": "I am learning a bit about persistence homology and was hoping that someone could point me to a systematic explanation of how to construct a persistence diagram , such as the one pictured below. I understand the basic idea of the level sets and the height function."}
+{"idx": 5, "title": "(PDF) Functional summaries of persistence diagrams", "date": "", "ddg_snippet": "Persistence landscapes are functional summaries of persistence diagrams designed to enable analysis of the diagrams using tools from functional data analysis.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/103021112/Functional_summaries_of_persistence_diagrams", "content": "Persistence landscapes are functional summaries of persistence diagrams designed to enable analysis of the diagrams using tools from functional data analysis."}
+{"idx": 6, "title": "Persistent homology of featured time series data and its applications", "date": "", "ddg_snippet": "Recent studies have actively employed persistent homology (PH), a topological data analysis technique, to analyze the topological information in time series data.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/doi/10.3934/math.20241315", "content": "Recent studies have actively employed persistent homology (PH), a topological data analysis technique, to analyze the topological information in time series data."}
+{"idx": 7, "title": "Persistence diagrams for exploring", "date": "", "ddg_snippet": "“Results: reading a persistence diagram ” is entirely focused on reading patient-related information from persistence diagrams .between the times of birth and death is clearly related to the prominence of the bump, and is called persistence of. the homological feature.", "subpage_snippet": "", "source": "re.public.polimi.it", "link": "https://re.public.polimi.it/retrieve/a9f0081c-16f1-4553-a2e3-3c2a00d11056/ScientificReports_Domanin_Pegoraro_etal.2024.pdf", "content": "“Results: reading a persistence diagram ” is entirely focused on reading patient-related information from persistence diagrams .between the times of birth and death is clearly related to the prominence of the bump, and is called persistence of. the homological feature."}
+{"idx": 8, "title": "Variably Scaled Persistence Kernels (VSPKs) for persistent ...", "date": "", "ddg_snippet": "TDA, Persistent Homology and Persistence Diagrams SVM and kernels.2 Let x = (b, d) ∈ Dr (X ), where b, d ∈ R+ are the birth - death couple of the element x and let w2(x) = d − b be the persistence of x. We denote as centre of persistence .", "subpage_snippet": "", "source": "www.math.unipd.it", "link": "https://www.math.unipd.it/~demarchi/Slides/Palermo24.pdf", "content": "TDA, Persistent Homology and Persistence Diagrams SVM and kernels.2 Let x = (b, d) ∈ Dr (X ), where b, d ∈ R+ are the birth - death couple of the element x and let w2(x) = d − b be the persistence of x. We denote as centre of persistence ."}
+{"idx": 9, "title": "Diagnosis of Adenocarcinomas in Histology Images Using Persistence ...", "date": "", "ddg_snippet": "Persistence diagram : Given a filtration F, the idea of persistent homology is to track the scales at which each d-dim void appears/disappears as a multiset 𝐵𝐷d = {(𝑏i, 𝑑i)} of birth - death scales. Plotting them as 2D points produces the persistence diagram (PD) representation.", "subpage_snippet": "", "source": "www.kitware.com", "link": "https://www.kitware.com/diagnosis-of-adenocarcinomas-in-histology-images-using-persistence-homology-and-deep-learning-2/", "content": "Persistence diagram : Given a filtration F, the idea of persistent homology is to track the scales at which each d-dim void appears/disappears as a multiset 𝐵𝐷d = {(𝑏i, 𝑑i)} of birth - death scales. Plotting them as 2D points produces the persistence diagram (PD) representation."}
diff --git a/data/sampled_jsons/positive_unlabeled_learning_empirical_risk_estimator_R_weak_class_prior_formula.jsonl b/data/sampled_jsons/positive_unlabeled_learning_empirical_risk_estimator_R_weak_class_prior_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..38d6f1432847c218fc3f80a9dbeadcf6fe4c0fa4
--- /dev/null
+++ b/data/sampled_jsons/positive_unlabeled_learning_empirical_risk_estimator_R_weak_class_prior_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Unified Empirical Risk Minimization Framework for Flexible", "date": "", "ddg_snippet": "Weakly-supervised learning , N-tuples learning , pointwise unlabeled data , unbiased risk estimator . ... Classical WSL scenarios include ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07771v1", "content": "Weakly-supervised learning , N-tuples learning , pointwise unlabeled data , unbiased risk estimator . ... Classical WSL scenarios include ..."}
+{"idx": 1, "title": "Learning from Double Positive and Unlabeled Data for", "date": "", "ddg_snippet": "... learn a classifier that predicts ... Existing PU learning algorithms enable binary classification when only positive and unlabeled data are available.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00436v1", "content": "... learn a classifier that predicts ... Existing PU learning algorithms enable binary classification when only positive and unlabeled data are available."}
+{"idx": 2, "title": "Proper Learnability and the Role of Unlabeled Data", "date": "", "ddg_snippet": "... fixed PAC model can shrink by only a logarithmic factor from the classic PAC model, strongly refuting the role of unlabeled data in PAC learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10359v1", "content": "... fixed PAC model can shrink by only a logarithmic factor from the classic PAC model, strongly refuting the role of unlabeled data in PAC learning ..."}
+{"idx": 3, "title": "Machine Learning Interview: Basics", "date": "", "ddg_snippet": "Empirical risk minimization is called empirical because it relies on the empirical estimate of the risk , which is calculated based on the training ...", "subpage_snippet": "", "source": "www.sefidian.com", "link": "https://www.sefidian.com/2021/06/17/machine-learning-interview-basics/", "content": "Empirical risk minimization is called empirical because it relies on the empirical estimate of the risk , which is calculated based on the training ..."}
+{"idx": 4, "title": "Machine Learning Interview: Basics", "date": "", "ddg_snippet": "Empirical risk minimization is called empirical because it relies on the empirical estimate of the risk , which is calculated based on the training ...", "subpage_snippet": "", "source": "www.sefidian.com", "link": "http://www.sefidian.com/2021/06/17/machine-learning-interview-basics/", "content": "Empirical risk minimization is called empirical because it relies on the empirical estimate of the risk , which is calculated based on the training ..."}
+{"idx": 5, "title": "(PDF) Automating Cyberdeception Evaluation with Deep Learning", "date": "", "ddg_snippet": "Results demonstrate that synthesizing adaptive web traffic laced with evasive attacks powered by ensemble learning , online adaptive metric learning , ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/337287036_Automating_Cyberdeception_Evaluation_with_Deep_Learning", "content": "Results demonstrate that synthesizing adaptive web traffic laced with evasive attacks powered by ensemble learning , online adaptive metric learning , ..."}
+{"idx": 6, "title": "PromptAL: Sample-Aware Dynamic Soft Prompts for Few-Shot Active", "date": "", "ddg_snippet": "... rely on the empirical distribution of labeled data to define the decision boundary and perform uncertainty or diversity estimation, subsequently ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16424v1", "content": "... rely on the empirical distribution of labeled data to define the decision boundary and perform uncertainty or diversity estimation, subsequently ..."}
+{"idx": 7, "title": "Sheaf Graph Neural Networks via PAC-Bayes Spectral Optimization", "date": "", "ddg_snippet": "... SGPC outperforms state-of-the-art GNNs and prior sheaf models, while providing PAC–Bayes calibrated uncertainty estimates and provably tighter risk ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.00357v1", "content": "... SGPC outperforms state-of-the-art GNNs and prior sheaf models, while providing PAC–Bayes calibrated uncertainty estimates and provably tighter risk ..."}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "Autonomous exploration, active learning and human guidance with open-source Poppy humanoid robot platform and Explauto library", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2016", "content": "Autonomous exploration, active learning and human guidance with open-source Poppy humanoid robot platform and Explauto library"}
+{"idx": 9, "title": "ESANN 2012 - proceedings | ESANN 2026", "date": "", "ddg_snippet": "... Risk Minimization principle allows estimating the generalization ability of a learned hypothesis by measuring the complexity of the entire hypothesis ...", "subpage_snippet": "", "source": "www.esann.org", "link": "https://www.esann.org/proceedings/2012", "content": "... Risk Minimization principle allows estimating the generalization ability of a learned hypothesis by measuring the complexity of the entire hypothesis ..."}
diff --git a/data/sampled_jsons/pre-training_scaling_law_exponent_-0.5_-1.0_Kaplan_Hoffmann_neural_language_model.jsonl b/data/sampled_jsons/pre-training_scaling_law_exponent_-0.5_-1.0_Kaplan_Hoffmann_neural_language_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c48f9c413ef836d9f0a751ab23ce7276af40388a
--- /dev/null
+++ b/data/sampled_jsons/pre-training_scaling_law_exponent_-0.5_-1.0_Kaplan_Hoffmann_neural_language_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2001.08361] Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "by J Kaplan · 2020 · Cited by 4597 — We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size, dataset size, and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2001.08361", "content": "by J Kaplan · 2020 · Cited by 4597 — We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size, dataset size, and the ..."}
+{"idx": 1, "title": "Scaling Laws Across Model Architectures: A Comparative ...", "date": "", "ddg_snippet": "by S Wang · 2024 · Cited by 7 — This paper delves into the analysis of scaling laws for Dense and MoE Models within the con- text of LLMs. We extend foundational research on.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.319.pdf", "content": "by S Wang · 2024 · Cited by 7 — This paper delves into the analysis of scaling laws for Dense and MoE Models within the con- text of LLMs. We extend foundational research on."}
+{"idx": 2, "title": "The Fine Line: Navigating Large Language Model Pretraining ...", "date": "", "ddg_snippet": "Uncovering early-stage metrics that reflect final model performance is one core principle for large- scale pretraining . The existing scaling law demonstrates ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.01204v2", "content": "Uncovering early-stage metrics that reflect final model performance is one core principle for large- scale pretraining . The existing scaling law demonstrates ..."}
+{"idx": 3, "title": "Scaling Laws for Predicting Downstream Performance in ...", "date": "", "ddg_snippet": "by Y Chen · Cited by 14 — Scaling laws analysis utilizes the statistics of a series of significantly smaller sampling language models (LMs) to predict the performance of the target LLM.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BDisxnHzRL", "content": "by Y Chen · Cited by 14 — Scaling laws analysis utilizes the statistics of a series of significantly smaller sampling language models (LMs) to predict the performance of the target LLM."}
+{"idx": 4, "title": "Scaling Laws Literature Review", "date": "", "ddg_snippet": "by P Villalobos · Cited by 19 — I have collected a database of scaling laws for different tasks and architectures, and reviewed dozens of papers in the scaling law literature.", "subpage_snippet": "", "source": "epoch.ai", "link": "https://epoch.ai/blog/scaling-laws-literature-review", "content": "by P Villalobos · Cited by 19 — I have collected a database of scaling laws for different tasks and architectures, and reviewed dozens of papers in the scaling law literature."}
+{"idx": 5, "title": "Foundations: Pretraining and scaling laws - GitHub Pages", "date": "", "ddg_snippet": "18 Jan 2024 — Recap of language models and pretraining objective. • Scaling laws for understanding pretraining . • What do these scaling laws not capture? 5 ... 74 pages", "subpage_snippet": "", "source": "cmu-codegen.github.io", "link": "https://cmu-codegen.github.io/s2024/static_files/codegen_s2024_2_scalinglaws.pdf", "content": "18 Jan 2024 — Recap of language models and pretraining objective. • Scaling laws for understanding pretraining . • What do these scaling laws not capture? 5 ... 74 pages"}
+{"idx": 6, "title": "The Evolution of Scaling Laws for Large Language Models", "date": "", "ddg_snippet": "This article traces the evolution of scaling laws for Large Language Models (LLMs), from the Transformer's inception in 2017 to the ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@lmpo/the-evolution-of-scaling-laws-for-large-language-models-3b73a8570809", "content": "This article traces the evolution of scaling laws for Large Language Models (LLMs), from the Transformer's inception in 2017 to the ..."}
+{"idx": 7, "title": "CMR Scaling Law: Predicting Critical Mixture Ratios for ...", "date": "", "ddg_snippet": "by J Gu · 2024 · Cited by 12 — Evaluation Scaling laws emphasize the pre- dictability of pre - training loss ( Kaplan et al., 2020;. Hoffmann et al., 2022; Gao et al., 2023; ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.903.pdf", "content": "by J Gu · 2024 · Cited by 12 — Evaluation Scaling laws emphasize the pre- dictability of pre - training loss ( Kaplan et al., 2020;. Hoffmann et al., 2022; Gao et al., 2023; ..."}
+{"idx": 8, "title": "Learning Dynamics in Continual Pre-Training for Large ...", "date": "", "ddg_snippet": "by X Wang — In this work, we explore the learning dynamics throughout the CPT process for large language models (LLMs). We specifically focus on how general and downstream ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Vk1rNMl0J1", "content": "by X Wang — In this work, we explore the learning dynamics throughout the CPT process for large language models (LLMs). We specifically focus on how general and downstream ..."}
+{"idx": 9, "title": "Understanding Scaling Laws with Statistical and ...", "date": "", "ddg_snippet": "When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95466", "content": "When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the ..."}
diff --git a/data/sampled_jsons/privacy_loss_metric_unlearning_difficulty_individual_samples.jsonl b/data/sampled_jsons/privacy_loss_metric_unlearning_difficulty_individual_samples.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e8a2389d8ac6208cfe37e00c193f075fb0c533c6
--- /dev/null
+++ b/data/sampled_jsons/privacy_loss_metric_unlearning_difficulty_individual_samples.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Besides standard empirical unlearning metrics , we evaluate privacy losses on a novel loss -landscape-based metric , based on linear connectivity and loss barriers (Frankle et al., 2020; Fort et al., 2020).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.18786", "content": "Besides standard empirical unlearning metrics , we evaluate privacy losses on a novel loss -landscape-based metric , based on linear connectivity and loss barriers (Frankle et al., 2020; Fort et al., 2020)."}
+{"idx": 1, "title": "GitHub - chrisliu298/awesome-llm- unlearning : A resource repository...", "date": "", "ddg_snippet": "Towards Lifecycle Unlearning Commitment Management: Measuring Sample -level Unlearning Completeness.Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chrisliu298/awesome-llm-unlearning", "content": "Towards Lifecycle Unlearning Commitment Management: Measuring Sample -level Unlearning Completeness.Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning ."}
+{"idx": 2, "title": "(PDF) Towards Understanding the Feasibility of Machine Unlearning", "date": "", "ddg_snippet": "ficulty of unlearning individual training samples . difficult -to- unlearn samples ( Difficult ), as recommended by each scoring metric , after unlearning .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384679895_Towards_Understanding_the_Feasibility_of_Machine_Unlearning", "content": "ficulty of unlearning individual training samples . difficult -to- unlearn samples ( Difficult ), as recommended by each scoring metric , after unlearning ."}
+{"idx": 3, "title": "Revisiting Machine Unlearning with Dimensional Alignment", "date": "", "ddg_snippet": "Machine unlearning , an emerging research topic focusing on compliance with data privacy regulations, enables trained models to remove the information learned from specific data.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/revisiting-machine-unlearning-dimensional-alignment", "content": "Machine unlearning , an emerging research topic focusing on compliance with data privacy regulations, enables trained models to remove the information learned from specific data."}
+{"idx": 4, "title": "Balancing Data Privacy and Model Performance in Machine Unlearning", "date": "", "ddg_snippet": "This article discusses the challenges of machine unlearning and a new approach to balance privacy and accuracy.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-30-balancing-data-privacy-and-model-performance-in-machine-unlearning--a3lx5ew", "content": "This article discusses the challenges of machine unlearning and a new approach to balance privacy and accuracy."}
+{"idx": 5, "title": "CMC | Free Full-Text | Ensuring User Privacy and Model Security via...", "date": "", "ddg_snippet": "Machine learning; machine unlearning ; privacy protection; trusted data deletion.[43] introduced an inexpensive unlearning mechanism called single-gradient forgetting, proposing a Standard Deviation (SD) loss metric for the unlearning space.", "subpage_snippet": "", "source": "www.techscience.com", "link": "https://www.techscience.com/cmc/v77n2/54792/html", "content": "Machine learning; machine unlearning ; privacy protection; trusted data deletion.[43] introduced an inexpensive unlearning mechanism called single-gradient forgetting, proposing a Standard Deviation (SD) loss metric for the unlearning space."}
+{"idx": 6, "title": "Evaluation Metrics for Machine Unlearning", "date": "", "ddg_snippet": "Unlearning effectiveness metrics , such as data removal completeness, privacy leakage detection, and perturbation analysis, help verify that the data has been genuinely erased from the model.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202409.1925/v1/download", "content": "Unlearning effectiveness metrics , such as data removal completeness, privacy leakage detection, and perturbation analysis, help verify that the data has been genuinely erased from the model."}
+{"idx": 7, "title": "Learning to Refuse: Towards Mitigating Privacy Risks in LLMs", "date": "", "ddg_snippet": "LLMs to protect specific individuals ’ private data without the need for complete retrain-ing. We propose RETURN, a Real-world pErsonal daTa UnleaRNing dataset, compris-ing 2,492 individuals from Wikipedia with as", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.coling-main.114.pdf", "content": "LLMs to protect specific individuals ’ private data without the need for complete retrain-ing. We propose RETURN, a Real-world pErsonal daTa UnleaRNing dataset, compris-ing 2,492 individuals from Wikipedia with as"}
+{"idx": 8, "title": "Machine Unlearning Evaluations as Interpretability Benchmarks", "date": "", "ddg_snippet": "x_i = unlearned /forgetting sample . n_f = number of forgetting samples . M(xi) = Unlearned Model. Td = Randomly Initialised Model (\"Incompetent Teacher\"). metric for understanding how much information loss compared to original activations there is. Data Privacy Related", "subpage_snippet": "", "source": "lw2.issarice.com", "link": "https://lw2.issarice.com/posts/mTi8TQEyP5Pr7oczd/machine-unlearning-evaluations-as-interpretability", "content": "x_i = unlearned /forgetting sample . n_f = number of forgetting samples . M(xi) = Unlearned Model. Td = Randomly Initialised Model (\"Incompetent Teacher\"). metric for understanding how much information loss compared to original activations there is. Data Privacy Related"}
+{"idx": 9, "title": "Exploring differential privacy in CNNs, LSTMs, GRUs, and RNNs for...", "date": "", "ddg_snippet": "Differential privacy is emerging as a powerful strategy designed to protect individual privacy by adding noise to the data, which helps preserve individual privacy while allowing accurate model predictions [13, 14]. We applied multimodal learning by using ECG, EEG, and BP signals.", "subpage_snippet": "", "source": "journalofbigdata.springeropen.com", "link": "https://journalofbigdata.springeropen.com/articles/10.1186/s40537-025-01292-6", "content": "Differential privacy is emerging as a powerful strategy designed to protect individual privacy by adding noise to the data, which helps preserve individual privacy while allowing accurate model predictions [13, 14]. We applied multimodal learning by using ECG, EEG, and BP signals."}
diff --git a/data/sampled_jsons/probability_of_necessity_Definition_5.1_causal_Bayesian_networks_Galhotra_Halpern.jsonl b/data/sampled_jsons/probability_of_necessity_Definition_5.1_causal_Bayesian_networks_Galhotra_Halpern.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d9f4aa626d2c6cfe477480f468fd7b00209693de
--- /dev/null
+++ b/data/sampled_jsons/probability_of_necessity_Definition_5.1_causal_Bayesian_networks_Galhotra_Halpern.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DC28Fpk76s", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities ."}
+{"idx": 1, "title": "Identifying and bounding the probability of necessity for causes", "date": "", "ddg_snippet": "In backward-looking causal inference, the probability of necessity measures the probability that a certain event is caused by the treatment given the observed treatment and outcome.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.01234", "content": "In backward-looking causal inference, the probability of necessity measures the probability that a certain event is caused by the treatment given the observed treatment and outcome."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Authors. Sainyam Galhotra , Joseph Halpern .Even though causalmodels are extremely popular, conditional probability calculation offormulas involving interventions pose significant challenges.In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/hash/a2118322165fffb648d1e341ff5a5b05-Abstract-Conference.html", "content": "Authors. Sainyam Galhotra , Joseph Halpern .Even though causalmodels are extremely popular, conditional probability calculation offormulas involving interventions pose significant challenges.In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms..."}
+{"idx": 3, "title": "Causal Models (Stanford Encyclopedia of Philosophy)", "date": "", "ddg_snippet": "Causal models are mathematical models representing causal relationships within an individual system or population. They facilitate inferences about causal relationships from statistical data.", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/causal-models/", "content": "Causal models are mathematical models representing causal relationships within an individual system or population. They facilitate inferences about causal relationships from statistical data."}
+{"idx": 4, "title": "machinelearningmastery.com/introduction-to- bayesian -belief- networks", "date": "", "ddg_snippet": "Bayesian belief networks .", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/introduction-to-bayesian-belief-networks/", "content": "Bayesian belief networks ."}
+{"idx": 5, "title": "Modeling Causal Learning Using Bayesian Generic Priors", "date": "", "ddg_snippet": "necessary and sufficient causes (NS priors) would encourage causal networks that are inherently simple (ideally, one cause reliably predicts the effect).", "subpage_snippet": "", "source": "www.cs.jhu.edu", "link": "https://www.cs.jhu.edu/~alanlab/Pubs06/lu2006modeling.pdf", "content": "necessary and sufficient causes (NS priors) would encourage causal networks that are inherently simple (ideally, one cause reliably predicts the effect)."}
+{"idx": 6, "title": "bayesian - Find Conditional probability distribution given conditions...", "date": "", "ddg_snippet": "Bayes Network /Conditional Probability Visualization Tools.Is distributions defining necessary for a DAG to be a causal bayesian network ?", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/389501/find-conditional-probability-distribution-given-conditions-bayes-network", "content": "Bayes Network /Conditional Probability Visualization Tools.Is distributions defining necessary for a DAG to be a causal bayesian network ?"}
+{"idx": 7, "title": "Causality", "date": "", "ddg_snippet": "1.3 causal bayesian networks .CHAPTER TEN The Actual Cause . Preface. 10.1 introduction: the insufficiency of necessary causation . 10.1.1 Singular Causes Revisited. 10.1.2 Preemption and the Role of Structural Information.", "subpage_snippet": "", "source": "projects.illc.uva.nl", "link": "https://projects.illc.uva.nl/cil/uploaded_files/inlineitem/Pearl_2009_Causality.pdf", "content": "1.3 causal bayesian networks .CHAPTER TEN The Actual Cause . Preface. 10.1 introduction: the insufficiency of necessary causation . 10.1.1 Singular Causes Revisited. 10.1.2 Preemption and the Role of Structural Information."}
+{"idx": 8, "title": "(PDF) Modeling causal learning using Bayesian generic priors on...", "date": "", "ddg_snippet": "necessary and sufficient causes (NS priors) would encourage. causal networks that are inherently simple (ideally, one cause . reliably predicts the effect).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/272746659_Modeling_causal_learning_using_Bayesian_generic_priors_on_generative_and_preventive_powers", "content": "necessary and sufficient causes (NS priors) would encourage. causal networks that are inherently simple (ideally, one cause . reliably predicts the effect)."}
+{"idx": 9, "title": "Probabilistic and Causal Inference: The Works of Judea Pearl", "date": "", "ddg_snippet": "514 Chapter 27 On Pearl’s Hierarchy and the Foundations of Causal Inference. of graphical models called causal Bayesian networks (CBNs), which are composed of a pair, a graphical model, and a collection of observational and interventional distributions.", "subpage_snippet": "", "source": "ftp.cs.ucla.edu", "link": "https://ftp.cs.ucla.edu/pub/stat_ser/bareinboim-etal-ch27-acm-2021.pdf", "content": "514 Chapter 27 On Pearl’s Hierarchy and the Foundations of Causal Inference. of graphical models called causal Bayesian networks (CBNs), which are composed of a pair, a graphical model, and a collection of observational and interventional distributions."}
diff --git a/data/sampled_jsons/probability_of_necessity_drug_treatment_cure_counterfactual_would_not_have_occurred.jsonl b/data/sampled_jsons/probability_of_necessity_drug_treatment_cure_counterfactual_would_not_have_occurred.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..244a6a88e8fecf8b9aa282c629112b2d7a73be7e
--- /dev/null
+++ b/data/sampled_jsons/probability_of_necessity_drug_treatment_cure_counterfactual_would_not_have_occurred.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Number needed to treat - Wikipedia", "date": "", "ddg_snippet": "... treatment was more helpful than the control in only one of ten cases (6 treated improved minus 5 controls improved), and was not helpful is nine of ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Number_needed_to_treat", "content": "... treatment was more helpful than the control in only one of ten cases (6 treated improved minus 5 controls improved), and was not helpful is nine of ..."}
+{"idx": 1, "title": "Causal Analysis in Theory and Practice » Counterfactual", "date": "", "ddg_snippet": "... study on the same population (conducted, for example, by an independent survey of patients who have the option of taking or avoiding the drug ) would ...", "subpage_snippet": "", "source": "causality.cs.ucla.edu", "link": "https://causality.cs.ucla.edu/blog/index.php/category/counterfactual/", "content": "... study on the same population (conducted, for example, by an independent survey of patients who have the option of taking or avoiding the drug ) would ..."}
+{"idx": 2, "title": "The necessity of construct and external validity for deductive", "date": "", "ddg_snippet": "Each of these designs seeks to ensure that the contrast of interest to the research question is not confounded and hence is internally valid.", "subpage_snippet": "", "source": "www.degruyterbrill.com:443", "link": "https://www.degruyterbrill.com:443/document/doi/10.1515/jci-2024-0002/html", "content": "Each of these designs seeks to ensure that the contrast of interest to the research question is not confounded and hence is internally valid."}
+{"idx": 3, "title": "The 21st Century Cures Act passes, potentially turning the FDA", "date": "", "ddg_snippet": "... not to say it’s not worth funding some riskier projects, but it’s not necessarily productive earmark a lot of money for such projects, most of ...", "subpage_snippet": "", "source": "www.respectfulinsolence.com", "link": "https://www.respectfulinsolence.com/2016/12/08/the-21st-century-cures-act-passes-turning-the-fda-into-a-puppet-of-the-pharmaceutical-industry/", "content": "... not to say it’s not worth funding some riskier projects, but it’s not necessarily productive earmark a lot of money for such projects, most of ..."}
+{"idx": 4, "title": "Tess Lawrie: \"You might not believe this, little fella,", "date": "", "ddg_snippet": "... it was always a highly implausible hypothesis that ivermectin would be an effective drug for prevention, postexposure prophylaxis, or treatment of ...", "subpage_snippet": "", "source": "www.respectfulinsolence.com", "link": "https://www.respectfulinsolence.com/2023/01/30/tess-lawrie-you-might-not-believe-this-little-fella-but-itll-cure-your-cancer-too/", "content": "... it was always a highly implausible hypothesis that ivermectin would be an effective drug for prevention, postexposure prophylaxis, or treatment of ..."}
+{"idx": 5, "title": "Mass Screen-and-Treatment Programs for Tuberculosis | GiveWell", "date": "", "ddg_snippet": "... of active case-finding is likely lower than the paper’s effect size would suggest: over a 20-year period, the share of individuals seeking treatment ...", "subpage_snippet": "", "source": "www.givewell.org", "link": "https://www.givewell.org/international/technical/programs/mass-screen-and-treatment-programs-tuberculosis", "content": "... of active case-finding is likely lower than the paper’s effect size would suggest: over a 20-year period, the share of individuals seeking treatment ..."}
+{"idx": 6, "title": "Causal Analysis in Theory and Practice » Personalized", "date": "", "ddg_snippet": "... study on the same population (conducted, for example, by an independent survey of patients who have the option of taking or avoiding the drug ) would ...", "subpage_snippet": "", "source": "causality.cs.ucla.edu", "link": "https://causality.cs.ucla.edu/blog/index.php/2021/04/29/personalized-decision-making/", "content": "... study on the same population (conducted, for example, by an independent survey of patients who have the option of taking or avoiding the drug ) would ..."}
+{"idx": 7, "title": "Causal Analysis in Theory and Practice » Discussion", "date": "", "ddg_snippet": "... where understanding is not necessarily relevant or even helpful, and where there is often no underlying theory about how the variables are related.", "subpage_snippet": "", "source": "causality.cs.ucla.edu", "link": "https://causality.cs.ucla.edu/blog/index.php/category/discussion/", "content": "... where understanding is not necessarily relevant or even helpful, and where there is often no underlying theory about how the variables are related."}
+{"idx": 8, "title": "Personalized decision making – A conceptual introduction", "date": "", "ddg_snippet": "... to different decision strategies and that, although ITE ( u ) is in general not identifiable, informative bounds on the probability distribution of ...", "subpage_snippet": "", "source": "www.degruyter.com", "link": "https://www.degruyter.com/document/doi/10.1515/jci-2022-0050/html", "content": "... to different decision strategies and that, although ITE ( u ) is in general not identifiable, informative bounds on the probability distribution of ..."}
+{"idx": 9, "title": "(PDF) Sensitivity Analyses of The Dynamics of Covid-19", "date": "", "ddg_snippet": "This model has two equilibrium points: the point of equilibrium without disease and the point of equilibrium with the disease.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372064280_Sensitivity_Analyses_of_The_Dynamics_of_Covid-19_Transmission_in_Response_to_Reinfection", "content": "This model has two equilibrium points: the point of equilibrium without disease and the point of equilibrium with the disease."}
diff --git a/data/sampled_jsons/qtuxDy2qEB_Parallel_Simulation_Log-concave_Sampling_Section_3_Technical_Overview_diagonal_update.jsonl b/data/sampled_jsons/qtuxDy2qEB_Parallel_Simulation_Log-concave_Sampling_Section_3_Technical_Overview_diagonal_update.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dfe679c79ff740795c638eaef5252c3d28f497a4
--- /dev/null
+++ b/data/sampled_jsons/qtuxDy2qEB_Parallel_Simulation_Log-concave_Sampling_Section_3_Technical_Overview_diagonal_update.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Parallel Simulation for Log-concave Sampling and Score ... - OpenReview", "date": "", "ddg_snippet": "In this section , we present parallel Picard methods for strongly log-concave sampling (Algorithm 1) and show it holds improved convergence rate w.r.t. the KL divergence and total variance (Theorem 4.2 and Corollary 4.3).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=qtuxDy2qEB&name=pdf", "content": "In this section , we present parallel Picard methods for strongly log-concave sampling (Algorithm 1) and show it holds improved convergence rate w.r.t. the KL divergence and total variance (Theorem 4.2 and Corollary 4.3)."}
+{"idx": 1, "title": "Parallel simulation for sampling under isoperimetry and score-based ...", "date": "", "ddg_snippet": "For Problem (a), specifically log-concave sampling , starting from the seminal papers of [24], [18], and [21], there has been a flurry of recent works on proving non-asymptotic guarantees based on simulating a process which converges to 𝜋 \\pi italic_π over time [59, 57, 3 , 42].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.07435v1", "content": "For Problem (a), specifically log-concave sampling , starting from the seminal papers of [24], [18], and [21], there has been a flurry of recent works on proving non-asymptotic guarantees based on simulating a process which converges to 𝜋 \\pi italic_π over time [59, 57, 3 , 42]."}
+{"idx": 2, "title": "PDF Log-Concave Sampling - GitHub Pages", "date": "", "ddg_snippet": "As discussed in the next section , a large portion of this book is dedicated to a systematic and unified treatment of recent developments in the complexity theory for log-concave sampling , with a particular emphasis on connections with the field of optimization.", "subpage_snippet": "", "source": "chewisinho.github.io", "link": "https://chewisinho.github.io/main.pdf", "content": "As discussed in the next section , a large portion of this book is dedicated to a systematic and unified treatment of recent developments in the complexity theory for log-concave sampling , with a particular emphasis on connections with the field of optimization."}
+{"idx": 3, "title": "ICML Poster Parallel Simulation for Log-concave Sampling and Score ...", "date": "", "ddg_snippet": "Spotlight Poster Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models Huanjian Zhou · Masashi Sugiyama East Exhibition Hall A-B #E-1103", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43916", "content": "Spotlight Poster Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models Huanjian Zhou · Masashi Sugiyama East Exhibition Hall A-B #E-1103"}
+{"idx": 4, "title": "supporting simulation code for the paper \"Log-concave sampling ...", "date": "", "ddg_snippet": "The algorithm implementations are contained in 'mcmc.py'. Simulations are contained in jupyter notebooks. Please update 'save_path' to your local path before any simulations . The Code in folder 'mcmc_simulation1' was run on a high-performance cluster and might take long time to complete. It is recommended that one first get familiar with the code for simulation2 and simulation3 before trying ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yuachen/mala_public", "content": "The algorithm implementations are contained in 'mcmc.py'. Simulations are contained in jupyter notebooks. Please update 'save_path' to your local path before any simulations . The Code in folder 'mcmc_simulation1' was run on a high-performance cluster and might take long time to complete. It is recommended that one first get familiar with the code for simulation2 and simulation3 before trying ..."}
+{"idx": 5, "title": "Parallel Simulation for Log-concave Sampling and Score-based...", "date": "", "ddg_snippet": "TL;DR: We propose new parallel algorithms for log-concave sampling and diffusion models, and rigorously prove that our algorithms enjoy $\\ log d$ iteration complexity.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qtuxDy2qEB", "content": "TL;DR: We propose new parallel algorithms for log-concave sampling and diffusion models, and rigorously prove that our algorithms enjoy $\\ log d$ iteration complexity."}
+{"idx": 6, "title": "The adaptive complexity of parallelized log-concave sampling", "date": "", "ddg_snippet": "In large-data applications, such as the inference process of diffusion models, it is desirable to design sampling algorithms with a high degree of parallelization. In this work, we study the adaptive complexity of sampling , which is the minimum number of sequential rounds required to achieve sampling given polynomially many queries executed in parallel at each round. For unconstrained sampling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.13045v2", "content": "In large-data applications, such as the inference process of diffusion models, it is desirable to design sampling algorithms with a high degree of parallelization. In this work, we study the adaptive complexity of sampling , which is the minimum number of sequential rounds required to achieve sampling given polynomially many queries executed in parallel at each round. For unconstrained sampling ..."}
+{"idx": 7, "title": "PDF Log-Concave Sampling", "date": "", "ddg_snippet": "1 Introduction Log-concave sampling involves answering the question of — given a smooth function V — how many queries are needed to give an approximate sample from π ∝ exp(−V ) on Rd. This field is increasingly important in fields as diverse as optimization, operations research, physical simulation , and machine learn-ing, including the burgeoning field of score-generative models for ...", "subpage_snippet": "", "source": "michaeltang.xyz", "link": "http://michaeltang.xyz/papers/LogConcave.pdf", "content": "1 Introduction Log-concave sampling involves answering the question of — given a smooth function V — how many queries are needed to give an approximate sample from π ∝ exp(−V ) on Rd. This field is increasingly important in fields as diverse as optimization, operations research, physical simulation , and machine learn-ing, including the burgeoning field of score-generative models for ..."}
+{"idx": 8, "title": "PDF An optimization perspective on log-concave sampling and beyond", "date": "", "ddg_snippet": "An optimization perspective on log-concave sampling and beyond by Sinho Chewi B.S., University of California, Berkeley, 2018 Submitted to the Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology.", "subpage_snippet": "", "source": "chewisinho.github.io", "link": "https://chewisinho.github.io/thesis.pdf", "content": "An optimization perspective on log-concave sampling and beyond by Sinho Chewi B.S., University of California, Berkeley, 2018 Submitted to the Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology."}
+{"idx": 9, "title": "Parallel simulation for sampling under isoperimetry and score-based ...", "date": "", "ddg_snippet": "As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6Gb7VfTKY7", "content": "As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks."}
diff --git a/data/sampled_jsons/rClimateOffensive_rZeroWaste_rExtinctionRebellion_rFridaysForFuture_rEarthStrike_rSunriseMovement_ac.jsonl b/data/sampled_jsons/rClimateOffensive_rZeroWaste_rExtinctionRebellion_rFridaysForFuture_rEarthStrike_rSunriseMovement_ac.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4d990c9b46022d4a88d55563571612b4b8d281f4
--- /dev/null
+++ b/data/sampled_jsons/rClimateOffensive_rZeroWaste_rExtinctionRebellion_rFridaysForFuture_rEarthStrike_rSunriseMovement_ac.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "Leaked Audio Shows Oil Lobbyist Bragging About Success in", "date": "", "ddg_snippet": "... risks humanity now faces, the injustice this represents, its historic roots, and the urgent need for rapid political, social and economic change.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/duplicates/ct0tn3/leaked_audio_shows_oil_lobbyist_bragging_about/", "content": "... risks humanity now faces, the injustice this represents, its historic roots, and the urgent need for rapid political, social and economic change."}
+{"idx": 1, "title": "p2p Free Library: Help build humanity's free library on IPFS", "date": "", "ddg_snippet": "... risks humanity now faces, the injustice this represents, its historic roots, and the urgent need for rapid political, social and economic change.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/jb4pb2/p2p_free_library_help_build_humanitys_free/", "content": "... risks humanity now faces, the injustice this represents, its historic roots, and the urgent need for rapid political, social and economic change."}
+{"idx": 2, "title": "Reddit's economists demand a carbon tax, proceeds distributed", "date": "", "ddg_snippet": "Rendered by PID 138693 on reddit-service-r2-whoalane-6cbc896455-pmn76 at 2023-08-23 08:18:05.579691+00:00 running bd5478f country code: GB.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/duplicates/ghphsb/reddits_economists_demand_a_carbon_tax_proceeds/", "content": "Rendered by PID 138693 on reddit-service-r2-whoalane-6cbc896455-pmn76 at 2023-08-23 08:18:05.579691+00:00 running bd5478f country code: GB."}
+{"idx": 3, "title": "Leaked Audio Shows Oil Lobbyist Bragging About Success in", "date": "", "ddg_snippet": "Rendered by PID 57519 on reddit-service-r2-whoalane-846c7db66c-bxk2m at 2023-10-02 03:12:05.357273+00:00 running 5e79288 country code: GB.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/ct0tn3/leaked_audio_shows_oil_lobbyist_bragging_about/", "content": "Rendered by PID 57519 on reddit-service-r2-whoalane-846c7db66c-bxk2m at 2023-10-02 03:12:05.357273+00:00 running 5e79288 country code: GB."}
+{"idx": 4, "title": "A conference in London on 12th July which will explore the", "date": "", "ddg_snippet": "Rendered by PID 75 on reddit-service-r2-whoalane-654686468c-hr52n at 2023-09-17 06:01:11.541315+00:00 running 68b80b2 country code: GB.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/13wjkbi/a_conference_in_london_on_12th_july_which_will/", "content": "Rendered by PID 75 on reddit-service-r2-whoalane-654686468c-hr52n at 2023-09-17 06:01:11.541315+00:00 running 68b80b2 country code: GB."}
+{"idx": 5, "title": "Billionaire No More: Patagonia Founder Gives Away the Company,", "date": "", "ddg_snippet": "Rendered by PID 20 on reddit-service-r2-whoalane-c85b7d764-2fmk2 at 2023-04-25 19:47:01.920490+00:00 running 9961dd1 country code: GB.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/xf6rrj/billionaire_no_more_patagonia_founder_gives_away/", "content": "Rendered by PID 20 on reddit-service-r2-whoalane-c85b7d764-2fmk2 at 2023-04-25 19:47:01.920490+00:00 running 9961dd1 country code: GB."}
diff --git a/data/sampled_jsons/rCnZrFikX6_Neural_Persistence_Dynamics.jsonl b/data/sampled_jsons/rCnZrFikX6_Neural_Persistence_Dynamics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b9562b46068e72cebdfb2a4215dde98297fa10aa
--- /dev/null
+++ b/data/sampled_jsons/rCnZrFikX6_Neural_Persistence_Dynamics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "Runtime analysis. NeurIPS Paper Checklist. Neural Persistence Dynamics .A schematic overview of our Neural Persistence Dynamics approach is shown in Fig. 1. Additional details, including different model variants, are illustrated in Fig.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=rCnZrFikX6", "content": "Runtime analysis. NeurIPS Paper Checklist. Neural Persistence Dynamics .A schematic overview of our Neural Persistence Dynamics approach is shown in Fig. 1. Additional details, including different model variants, are illustrated in Fig."}
+{"idx": 1, "title": "GitHub - plus-rkwitt/ neural _ persistence _ dynamics : Offical repository...", "date": "", "ddg_snippet": "This is the offical repository for Neural Persistence Dynamics . We are still updating the respository actively!cd /tmp/ neural _ persistence _ dynamics mkdir -p data/Giusti23a/1k python download.py --dataset dorsogna-1k --destination data/Giusti23a/1k.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plus-rkwitt/neural_persistence_dynamics", "content": "This is the offical repository for Neural Persistence Dynamics . We are still updating the respository actively!cd /tmp/ neural _ persistence _ dynamics mkdir -p data/Giusti23a/1k python download.py --dataset dorsogna-1k --destination data/Giusti23a/1k."}
+{"idx": 2, "title": "Neural Persistence Dynamics · NeurIPS 2024", "date": "", "ddg_snippet": "Neural Persistence Dynamics . 26 September 2024·2242 words·11 mins· loading.This makes it hard to understand and model the collective behavior. This research introduces a novel approach called Neural Persistence Dynamics .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/rcnzrfikx6/", "content": "Neural Persistence Dynamics . 26 September 2024·2242 words·11 mins· loading.This makes it hard to understand and model the collective behavior. This research introduces a novel approach called Neural Persistence Dynamics ."}
+{"idx": 3, "title": "Neural Persistence Dynamics | AI Research Paper Details", "date": "", "ddg_snippet": "Neural Persistence Dynamics . Read original: arXiv:2405.15732.Overall, the \" Neural Persistence Dynamics \" framework presented in this paper represents an intriguing advancement in the field of neural network modeling of dynamical systems.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/neural-persistence-dynamics", "content": "Neural Persistence Dynamics . Read original: arXiv:2405.15732.Overall, the \" Neural Persistence Dynamics \" framework presented in this paper represents an intriguing advancement in the field of neural network modeling of dynamical systems."}
+{"idx": 4, "title": "(PDF) Neural Persistence Dynamics", "date": "", "ddg_snippet": "neural persistence dynamics – substantially outperforms the state-of-the-art across. a diverse set of parameter regression tasks. 1 Introduction.time. zt0. Figure 1: Neural Persistence Dynamics . Given is a sequence of observed point clouds.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380895040_Neural_Persistence_Dynamics", "content": "neural persistence dynamics – substantially outperforms the state-of-the-art across. a diverse set of parameter regression tasks. 1 Introduction.time. zt0. Figure 1: Neural Persistence Dynamics . Given is a sequence of observed point clouds."}
+{"idx": 5, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "We implement this idea based on a latent ODE learned from vectorized (static) persistence diagrams and show that a combination of recent stability results for persistent homology justifies this modeling choice.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Neural-Persistence-Dynamics-d5a6bb38-122c-4da8-8beb-08ba19aaee40", "content": "We implement this idea based on a latent ODE learned from vectorized (static) persistence diagrams and show that a combination of recent stability results for persistent homology justifies this modeling choice."}
+{"idx": 6, "title": "Neural Persistence Dynamics | Papers With Code", "date": "", "ddg_snippet": "Neural Persistence Dynamics . 24 May 2024 · Sebastian Zeng, Florian Graf, Martin Uray, Stefan Huber, Roland Kwitt ·. Edit social preview.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/neural-persistence-dynamics", "content": "Neural Persistence Dynamics . 24 May 2024 · Sebastian Zeng, Florian Graf, Martin Uray, Stefan Huber, Roland Kwitt ·. Edit social preview."}
+{"idx": 7, "title": "Neural Persistence Dynamics | Connected Papers Search", "date": "", "ddg_snippet": "Neural Persistence Dynamics . Sebastian Zeng, Florian Graf, M. Uray, Stefan Huber, R. Kwitt. Neural Information Processing Systems 2024.Norm Dynamics : Interdisciplinary Perspectives on Social Norm Emergence, Persistence , and Change.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/search?q=Neural+Persistence+Dynamics", "content": "Neural Persistence Dynamics . Sebastian Zeng, Florian Graf, M. Uray, Stefan Huber, R. Kwitt. Neural Information Processing Systems 2024.Norm Dynamics : Interdisciplinary Perspectives on Social Norm Emergence, Persistence , and Change."}
+{"idx": 8, "title": "Roland Kwitt on LinkedIn: Neural Persistence Dynamics", "date": "", "ddg_snippet": "We have just released the code for our preprint \" Neural Persistence Dynamics \" (https://lnkd.in/dvV5zvRg) which can be found…", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/roland-kwitt-5319a6a1_neural-persistence-dynamics-activity-7229129335301931009-HKx_", "content": "We have just released the code for our preprint \" Neural Persistence Dynamics \" (https://lnkd.in/dvV5zvRg) which can be found…"}
+{"idx": 9, "title": "Addressing caveats of neural persistence with deep graph...", "date": "", "ddg_snippet": "Abstract: Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/addressing-caveats-of-neural-persistence-with-deep-graph-25yzm1va", "content": "Abstract: Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning."}
diff --git a/data/sampled_jsons/r_coherence(y)_=_sum_log_p(y_i)_formula_soft_reasoning_year_2024.jsonl b/data/sampled_jsons/r_coherence(y)_=_sum_log_p(y_i)_formula_soft_reasoning_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a76c89eb22dcc328f327292567f680073fc5487d
--- /dev/null
+++ b/data/sampled_jsons/r_coherence(y)_=_sum_log_p(y_i)_formula_soft_reasoning_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Soft Reasoning: Navigating Solution Spaces in Large Language Models ...", "date": "", "ddg_snippet": "Abstract Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.24688v2", "content": "Abstract Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided ..."}
+{"idx": 1, "title": "Soft Thinking: Unlocking the Reasoning Potential of LLMs in ...", "date": "", "ddg_snippet": "title = { Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space}, author = {Zhang, Zhen and He, Xuehai and Yan, Weixiang and Shen, Ao and Zhao, Chenyang and Wang, Shuohang and Shen, Yelong and Wang, Xin Eric},", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/eric-ai-lab/Soft-Thinking", "content": "title = { Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space}, author = {Zhang, Zhen and He, Xuehai and Yan, Weixiang and Shen, Ao and Zhao, Chenyang and Wang, Shuohang and Shen, Yelong and Wang, Xin Eric},"}
+{"idx": 2, "title": "Softmax Classification — Knet.jl 0.7.2 documentation", "date": "", "ddg_snippet": "The second equation assumes that the data instances were generated independently. We usually work with log likelihood for mathematical convenience: log is a monotonically increasing function, so maximizing likelihood is the same as maximizing log likelihood:", "subpage_snippet": "", "source": "knet.readthedocs.io", "link": "http://knet.readthedocs.io/en/latest/softmax.html", "content": "The second equation assumes that the data instances were generated independently. We usually work with log likelihood for mathematical convenience: log is a monotonically increasing function, so maximizing likelihood is the same as maximizing log likelihood:"}
+{"idx": 3, "title": "Softmax Function and Cross Entropy Loss Function", "date": "", "ddg_snippet": "We have discussed SVM loss function, in this post, we are going through another one of the most commonly used loss function, Softmax function. Definition The Softmax regression is a form of logistic regression that normalizes an input value into a vector of values that follows a probability distribution whose total sums up to 1.", "subpage_snippet": "", "source": "guandi1995.github.io", "link": "https://guandi1995.github.io/Softmax-Function-and-Cross-Entropy-Loss-Function/", "content": "We have discussed SVM loss function, in this post, we are going through another one of the most commonly used loss function, Softmax function. Definition The Softmax regression is a form of logistic regression that normalizes an input value into a vector of values that follows a probability distribution whose total sums up to 1."}
+{"idx": 4, "title": "4.1. Softmax Regression — Dive into Deep Learning 1.0.3 ... - D2L", "date": "", "ddg_snippet": "4.1.1.2. The Softmax Assuming a suitable loss function, we could try, directly, to minimize the difference between o and the labels y. While it turns out that treating classification as a vector-valued regression problem works surprisingly well, it is nonetheless unsatisfactory in the following ways: There is no guarantee that the outputs o i sum up to 1 in the way we expect probabilities to ...", "subpage_snippet": "", "source": "d2l.ai", "link": "https://d2l.ai/chapter_linear-classification/softmax-regression.html", "content": "4.1.1.2. The Softmax Assuming a suitable loss function, we could try, directly, to minimize the difference between o and the labels y. While it turns out that treating classification as a vector-valued regression problem works surprisingly well, it is nonetheless unsatisfactory in the following ways: There is no guarantee that the outputs o i sum up to 1 in the way we expect probabilities to ..."}
+{"idx": 5, "title": "Soft-max and Soft-argmax - Joseph Salmon", "date": "", "ddg_snippet": "The first limit show that the soft -argmax function is a kind of smooth approximation of the argmax function, while the log - sum -exp function is a smooth approximation of the max function. Invariance properties The softmax function is invariant to the addition of a constant to each component of the input vector. More precisely, we have the following result:", "subpage_snippet": "", "source": "josephsalmon.eu", "link": "https://josephsalmon.eu/blog/softmax/index.html", "content": "The first limit show that the soft -argmax function is a kind of smooth approximation of the argmax function, while the log - sum -exp function is a smooth approximation of the max function. Invariance properties The softmax function is invariant to the addition of a constant to each component of the input vector. More precisely, we have the following result:"}
+{"idx": 6, "title": "Softmax Regression from Scratch in Python - Rick Wierenga", "date": "", "ddg_snippet": "Last time we looked at classification problems and how to classify breast cancer with logistic regression, a binary classification problem. In this post we will consider another type of classification: multiclass classification. In particular, I will cover one hot encoding, the softmax activation function and negative log likelihood.", "subpage_snippet": "", "source": "rickwierenga.com", "link": "https://rickwierenga.com/blog/ml-fundamentals/softmax.html", "content": "Last time we looked at classification problems and how to classify breast cancer with logistic regression, a binary classification problem. In this post we will consider another type of classification: multiclass classification. In particular, I will cover one hot encoding, the softmax activation function and negative log likelihood."}
+{"idx": 7, "title": "Unsupervised Feature Learning and Deep Learning Tutorial", "date": "", "ddg_snippet": "$$ \\begin {align} J (\\theta) &= - \\left [ \\ sum _ {i=1}^m (1-y^ { (i)}) \\ log (1-h_\\theta (x^ { (i)})) + y^ { (i)} \\ log h_\\theta (x^ { (i)}) \\right] \\\\ &= - \\left [ \\ sum _ {i=1}^ {m} \\ sum _ {k=0}^ {1} 1\\left\\ {y^ { (i)} = k\\right\\} \\ log P (y^ { (i)} = k | x^ { (i)} ; \\theta) \\right] \\end {align} $$", "subpage_snippet": "", "source": "deeplearning.stanford.edu", "link": "http://deeplearning.stanford.edu/tutorial/supervised/SoftmaxRegression/", "content": "$$ \\begin {align} J (\\theta) &= - \\left [ \\ sum _ {i=1}^m (1-y^ { (i)}) \\ log (1-h_\\theta (x^ { (i)})) + y^ { (i)} \\ log h_\\theta (x^ { (i)}) \\right] \\\\ &= - \\left [ \\ sum _ {i=1}^ {m} \\ sum _ {k=0}^ {1} 1\\left\\ {y^ { (i)} = k\\right\\} \\ log P (y^ { (i)} = k | x^ { (i)} ; \\theta) \\right] \\end {align} $$"}
+{"idx": 8, "title": "arXiv:1909.03469v1 [math.NA] 8 Sep 2019", "date": "", "ddg_snippet": "Abstract. Evaluating the log - sum -exp function or the softmax function is a key step in many modern data science algorithms, notably in inference and classi cation. Because of the exponentials that these functions contain, the evaluation is prone to over ow and under ow, especially in low precision arithmetic. Software implementations commonly use alternative formulas that avoid over-ow and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.03469", "content": "Abstract. Evaluating the log - sum -exp function or the softmax function is a key step in many modern data science algorithms, notably in inference and classi cation. Because of the exponentials that these functions contain, the evaluation is prone to over ow and under ow, especially in low precision arithmetic. Software implementations commonly use alternative formulas that avoid over-ow and ..."}
+{"idx": 9, "title": "How to calculate the coherence score in topic modeling. (R)", "date": "", "ddg_snippet": "I build the topic models with topicModel <- LDA (DTM, K, method = \"Gibbs\", control = list (iter = 500, verbose = 25)) How to calculate the coherence in this topic modeling?", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/74515509/how-to-calculate-the-coherence-score-in-topic-modeling-r", "content": "I build the topic models with topicModel <- LDA (DTM, K, method = \"Gibbs\", control = list (iter = 500, verbose = 25)) How to calculate the coherence in this topic modeling?"}
diff --git a/data/sampled_jsons/reasoning_mapping_formula_Frsn_synthetic_composition_task_language_models_year_2024.jsonl b/data/sampled_jsons/reasoning_mapping_formula_Frsn_synthetic_composition_task_language_models_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7e45a400a625ce3f038b9f0d49b74b52f37bc403
--- /dev/null
+++ b/data/sampled_jsons/reasoning_mapping_formula_Frsn_synthetic_composition_task_language_models_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enhancing Multimodal Compositional Reasoning of Visual ...", "date": "", "ddg_snippet": "Abstract Contemporary large-scale visual language models (VLMs) exhibit strong representation capacities, making them ubiquitous for enhancing image and text understand-ing tasks . They are often trained in a contrastive manner on a large and diverse corpus of images and corresponding text captions scraped from the internet. Despite this, VLMs often struggle with compositional reasoning tasks ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2024/papers/Sahin_Enhancing_Multimodal_Compositional_Reasoning_of_Visual_Language_Models_With_Generative_WACV_2024_paper.pdf", "content": "Abstract Contemporary large-scale visual language models (VLMs) exhibit strong representation capacities, making them ubiquitous for enhancing image and text understand-ing tasks . They are often trained in a contrastive manner on a large and diverse corpus of images and corresponding text captions scraped from the internet. Despite this, VLMs often struggle with compositional reasoning tasks ..."}
+{"idx": 1, "title": "Enhancing Compositional Reasoning in Vision-Language Models ... Measuring and Narrowing the Compositionality Gap in Language ... Learning, Reasoning, and Compositional Generalisation in ... Compositional Reasoning in Vision-Language Models Synthetic Data Generation & Multi-Step RL for Reasoning ... Measuring and Narrowing the Compositionality Gap in Language Models Enhancing Compositional Reasoning in Vision- Language Models with Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use Enhancing Multimodal Compositional Reasoning of Visual Language Models Enhancing Multimodal Compositional Reasoning of Visual Language Models Enhancing Compositional Reasoning in Vision- Language Models with Enhancing Compositional Reasoning in Vision-Language Models ...", "date": "", "ddg_snippet": "Apr 7, 2025 · View a PDF of the paper titled Enhancing Compositional Reasoning in Vision- Language Models with Synthetic Preference Data, by Samarth Mishra and 1 other authors 6 days ago · Abstract We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate the overall solution, a ratio we call the compositionality gap. This is particularly important in the era of large language models (LLMs), where their general capabilities are unclear and un-reliable. This thesis investigates learning and reasoning in multimodal language models , and their capabilities to compositionally generalise in visual question answering tasks . Vision– language models (VLMs) have achieved impressive performance on long-standing visual question-answering (VQA) benchmarks by utilizing large-scale image–caption pre-training on web datasets. These models are able to perform a variety of downstream tasks out of the box, including zero-shot VQA, but have also been shown to struggle with understa... See full list on cs.brown.edu This section describes our approach to measure the compositional reasoning ability of pretrained VLMs. We target visual questions that follow multiple steps of reasoning and can be broken down into at least two sub-questions. We chose the CLEVR dataset [8] for this task because it provides a well-balanced set of images and questions that are specif... See full list on cs.brown.edu In this paper, we conducted experiments to examine the multimodal multistep reasoning of a pre-trained VLM. We first demonstrate that these models do not generalize well out of the box on synthetic datasets in comparison to datasets like VQAv2 [6]. Following other studies, we also observe that these models struggle to answer compositional questions... See full list on cs.brown.edu First and foremost, I want to give a thank you to Professor Chen Sun for advis-ing this thesis and welcoming me into his research lab. He has been supportive every step of the way and has a contagious and inspirational passion for research. I also want to give a massive thank you to Apoorv Khandelwal for being my mentor for the past year and guidin... See full list on cs.brown.edu Apr 7, 2025 · As focus shifts toward more complex reasoning and agentic tasks , language models must take multiple steps of text generation, reasoning and environment interaction before generating a solution. We propose a synthetic data generation and RL methodology targeting multi-step optimization scenarios. Do language models perform compositional reasoning tasks? We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate the overall solution, a ratio we call the compositionality gap. How can compositional reasoning be improved? We show that compositional reasoning in these models can be improved by elucidating such concepts via data , where a model is trained to prefer the correct caption for an image over a close but incorrect one. Do language models take multiple steps before generating a solution? As focus shifts toward more complex reasoning and agentic tasks, language models must take multiple steps of text generation, reasoning and environment interaction before generating a solution. We propose a synthetic data generation and RL methodology targeting multi-step optimization scenarios. What is compositional reasoning? Benchmarking Visual-linguistic Compositional reason-ing Compositional reasoning is the ability to understand complex scenes and text with diverse structures . This includes for e.g. the capacity to discern between sen-tences with the same words but in a different order, or a scene with the same objects but slightly different colors, etc. Why do VLMs struggle with compositional reasoning tasks? Despite this, VLMs often struggle with compositional reasoning tasks which re-quire a fine-grained understanding of the complex interac-tions of objects and their attributes. This failure can be attributed to two main factors: 1) Contrastive approaches have traditionally focused on mining negative examples from existing datasets. What is scramble – synthetic compositional reasoning augmented mllms with binary preference learning? We introduce SCRAMBLe: Synthetic Compositional Reasoning Augmentation of MLLMs with Binary preference Learning, an approach for preference tuning open-weight MLLMs on synthetic preference data generated in a fully automated manner from existing image-caption data. Apr 7, 2025 · Enhancing Compositional Reasoning in Vision- Language Models with Synthetic Preference Data: Paper and Code. Compositionality, or correctly recognizing scenes as compositions of atomic visual concepts, remains difficult for multimodal large language models (MLLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.04740", "content": "Apr 7, 2025 · View a PDF of the paper titled Enhancing Compositional Reasoning in Vision- Language Models with Synthetic Preference Data, by Samarth Mishra and 1 other authors 6 days ago · Abstract We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate the overall solution, a ratio we call the compositionality gap. This is particularly important in the era of large language models (LLMs), where their general capabilities are unclear and un-reliable. This thesis investigates learning and reasoning in multimodal language models , and their capabilities to compositionally generalise in visual question answering tasks . Vision– language models (VLMs) have achieved impressive performance on long-standing visual question-answering (VQA) benchmarks by utilizing large-scale image–caption pre-training on web datasets. These models are able to perform a variety of downstream tasks out of the box, including zero-shot VQA, but have also been shown to struggle with understa... See full list on cs.brown.edu This section describes our approach to measure the compositional reasoning ability of pretrained VLMs. We target visual questions that follow multiple steps of reasoning and can be broken down into at least two sub-questions. We chose the CLEVR dataset [8] for this task because it provides a well-balanced set of images and questions that are specif... See full list on cs.brown.edu In this paper, we conducted experiments to examine the multimodal multistep reasoning of a pre-trained VLM. We first demonstrate that these models do not generalize well out of the box on synthetic datasets in comparison to datasets like VQAv2 [6]. Following other studies, we also observe that these models struggle to answer compositional questions... See full list on cs.brown.edu First and foremost, I want to give a thank you to Professor Chen Sun for advis-ing this thesis and welcoming me into his research lab. He has been supportive every step of the way and has a contagious and inspirational passion for research. I also want to give a massive thank you to Apoorv Khandelwal for being my mentor for the past year and guidin... See full list on cs.brown.edu Apr 7, 2025 · As focus shifts toward more complex reasoning and agentic tasks , language models must take multiple steps of text generation, reasoning and environment interaction before generating a solution. We propose a synthetic data generation and RL methodology targeting multi-step optimization scenarios. Do language models perform compositional reasoning tasks? We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate the overall solution, a ratio we call the compositionality gap. How can compositional reasoning be improved? We show that compositional reasoning in these models can be improved by elucidating such concepts via data , where a model is trained to prefer the correct caption for an image over a close but incorrect one. Do language models take multiple steps before generating a solution? As focus shifts toward more complex reasoning and agentic tasks, language models must take multiple steps of text generation, reasoning and environment interaction before generating a solution. We propose a synthetic data generation and RL methodology targeting multi-step optimization scenarios. What is compositional reasoning? Benchmarking Visual-linguistic Compositional reason-ing Compositional reasoning is the ability to understand complex scenes and text with diverse structures . This includes for e.g. the capacity to discern between sen-tences with the same words but in a different order, or a scene with the same objects but slightly different colors, etc. Why do VLMs struggle with compositional reasoning tasks? Despite this, VLMs often struggle with compositional reasoning tasks which re-quire a fine-grained understanding of the complex interac-tions of objects and their attributes. This failure can be attributed to two main factors: 1) Contrastive approaches have traditionally focused on mining negative examples from existing datasets. What is scramble – synthetic compositional reasoning augmented mllms with binary preference learning? We introduce SCRAMBLe: Synthetic Compositional Reasoning Augmentation of MLLMs with Binary preference Learning, an approach for preference tuning open-weight MLLMs on synthetic preference data generated in a fully automated manner from existing image-caption data. Apr 7, 2025 · Enhancing Compositional Reasoning in Vision- Language Models with Synthetic Preference Data: Paper and Code. Compositionality, or correctly recognizing scenes as compositions of atomic visual concepts, remains difficult for multimodal large language models (MLLMs)."}
+{"idx": 2, "title": "Measuring and Narrowing the Compositionality Gap in Language ...", "date": "", "ddg_snippet": "6 days ago · Abstract We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate the overall solution, a ratio we call the compositionality gap.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.findings-emnlp.378/", "content": "6 days ago · Abstract We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate the overall solution, a ratio we call the compositionality gap."}
+{"idx": 3, "title": "Learning, Reasoning, and Compositional Generalisation in ...", "date": "", "ddg_snippet": "This is particularly important in the era of large language models (LLMs), where their general capabilities are unclear and un-reliable. This thesis investigates learning and reasoning in multimodal language models , and their capabilities to compositionally generalise in visual question answering tasks .", "subpage_snippet": "", "source": "umu.diva-portal.org", "link": "https://umu.diva-portal.org/smash/get/diva2:1858932/FULLTEXT01.pdf", "content": "This is particularly important in the era of large language models (LLMs), where their general capabilities are unclear and un-reliable. This thesis investigates learning and reasoning in multimodal language models , and their capabilities to compositionally generalise in visual question answering tasks ."}
+{"idx": 4, "title": "Compositional Reasoning in Vision-Language Models Synthetic Data Generation & Multi-Step RL for Reasoning ... Measuring and Narrowing the Compositionality Gap in Language Models Enhancing Compositional Reasoning in Vision- Language Models with Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use Enhancing Multimodal Compositional Reasoning of Visual Language Models Enhancing Multimodal Compositional Reasoning of Visual Language Models Enhancing Compositional Reasoning in Vision- Language Models with Enhancing Compositional Reasoning in Vision-Language Models ...", "date": "", "ddg_snippet": "Vision– language models (VLMs) have achieved impressive performance on long-standing visual question-answering (VQA) benchmarks by utilizing large-scale image–caption pre-training on web datasets. These models are able to perform a variety of downstream tasks out of the box, including zero-shot VQA, but have also been shown to struggle with understa... See full list on cs.brown.edu This section describes our approach to measure the compositional reasoning ability of pretrained VLMs. We target visual questions that follow multiple steps of reasoning and can be broken down into at least two sub-questions. We chose the CLEVR dataset [8] for this task because it provides a well-balanced set of images and questions that are specif... See full list on cs.brown.edu In this paper, we conducted experiments to examine the multimodal multistep reasoning of a pre-trained VLM. We first demonstrate that these models do not generalize well out of the box on synthetic datasets in comparison to datasets like VQAv2 [6]. Following other studies, we also observe that these models struggle to answer compositional questions... See full list on cs.brown.edu First and foremost, I want to give a thank you to Professor Chen Sun for advis-ing this thesis and welcoming me into his research lab. He has been supportive every step of the way and has a contagious and inspirational passion for research. I also want to give a massive thank you to Apoorv Khandelwal for being my mentor for the past year and guidin... See full list on cs.brown.edu Apr 7, 2025 · As focus shifts toward more complex reasoning and agentic tasks , language models must take multiple steps of text generation, reasoning and environment interaction before generating a solution. We propose a synthetic data generation and RL methodology targeting multi-step optimization scenarios. Do language models perform compositional reasoning tasks? We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate the overall solution, a ratio we call the compositionality gap. How can compositional reasoning be improved? We show that compositional reasoning in these models can be improved by elucidating such concepts via data , where a model is trained to prefer the correct caption for an image over a close but incorrect one. Do language models take multiple steps before generating a solution? As focus shifts toward more complex reasoning and agentic tasks, language models must take multiple steps of text generation, reasoning and environment interaction before generating a solution. We propose a synthetic data generation and RL methodology targeting multi-step optimization scenarios. What is compositional reasoning? Benchmarking Visual-linguistic Compositional reason-ing Compositional reasoning is the ability to understand complex scenes and text with diverse structures . This includes for e.g. the capacity to discern between sen-tences with the same words but in a different order, or a scene with the same objects but slightly different colors, etc. Why do VLMs struggle with compositional reasoning tasks? Despite this, VLMs often struggle with compositional reasoning tasks which re-quire a fine-grained understanding of the complex interac-tions of objects and their attributes. This failure can be attributed to two main factors: 1) Contrastive approaches have traditionally focused on mining negative examples from existing datasets. What is scramble – synthetic compositional reasoning augmented mllms with binary preference learning? We introduce SCRAMBLe: Synthetic Compositional Reasoning Augmentation of MLLMs with Binary preference Learning, an approach for preference tuning open-weight MLLMs on synthetic preference data generated in a fully automated manner from existing image-caption data. Apr 7, 2025 · Enhancing Compositional Reasoning in Vision- Language Models with Synthetic Preference Data: Paper and Code. Compositionality, or correctly recognizing scenes as compositions of atomic visual concepts, remains difficult for multimodal large language models (MLLMs).", "subpage_snippet": "", "source": "cs.brown.edu", "link": "https://cs.brown.edu/media/filer_public/84/e0/84e05bd1-6d4a-4690-9fe3-af782aab013f/lijessica_honors_thesis.pdf", "content": "Vision– language models (VLMs) have achieved impressive performance on long-standing visual question-answering (VQA) benchmarks by utilizing large-scale image–caption pre-training on web datasets. These models are able to perform a variety of downstream tasks out of the box, including zero-shot VQA, but have also been shown to struggle with understa... See full list on cs.brown.edu This section describes our approach to measure the compositional reasoning ability of pretrained VLMs. We target visual questions that follow multiple steps of reasoning and can be broken down into at least two sub-questions. We chose the CLEVR dataset [8] for this task because it provides a well-balanced set of images and questions that are specif... See full list on cs.brown.edu In this paper, we conducted experiments to examine the multimodal multistep reasoning of a pre-trained VLM. We first demonstrate that these models do not generalize well out of the box on synthetic datasets in comparison to datasets like VQAv2 [6]. Following other studies, we also observe that these models struggle to answer compositional questions... See full list on cs.brown.edu First and foremost, I want to give a thank you to Professor Chen Sun for advis-ing this thesis and welcoming me into his research lab. He has been supportive every step of the way and has a contagious and inspirational passion for research. I also want to give a massive thank you to Apoorv Khandelwal for being my mentor for the past year and guidin... See full list on cs.brown.edu Apr 7, 2025 · As focus shifts toward more complex reasoning and agentic tasks , language models must take multiple steps of text generation, reasoning and environment interaction before generating a solution. We propose a synthetic data generation and RL methodology targeting multi-step optimization scenarios. Do language models perform compositional reasoning tasks? We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate the overall solution, a ratio we call the compositionality gap. How can compositional reasoning be improved? We show that compositional reasoning in these models can be improved by elucidating such concepts via data , where a model is trained to prefer the correct caption for an image over a close but incorrect one. Do language models take multiple steps before generating a solution? As focus shifts toward more complex reasoning and agentic tasks, language models must take multiple steps of text generation, reasoning and environment interaction before generating a solution. We propose a synthetic data generation and RL methodology targeting multi-step optimization scenarios. What is compositional reasoning? Benchmarking Visual-linguistic Compositional reason-ing Compositional reasoning is the ability to understand complex scenes and text with diverse structures . This includes for e.g. the capacity to discern between sen-tences with the same words but in a different order, or a scene with the same objects but slightly different colors, etc. Why do VLMs struggle with compositional reasoning tasks? Despite this, VLMs often struggle with compositional reasoning tasks which re-quire a fine-grained understanding of the complex interac-tions of objects and their attributes. This failure can be attributed to two main factors: 1) Contrastive approaches have traditionally focused on mining negative examples from existing datasets. What is scramble – synthetic compositional reasoning augmented mllms with binary preference learning? We introduce SCRAMBLe: Synthetic Compositional Reasoning Augmentation of MLLMs with Binary preference Learning, an approach for preference tuning open-weight MLLMs on synthetic preference data generated in a fully automated manner from existing image-caption data. Apr 7, 2025 · Enhancing Compositional Reasoning in Vision- Language Models with Synthetic Preference Data: Paper and Code. Compositionality, or correctly recognizing scenes as compositions of atomic visual concepts, remains difficult for multimodal large language models (MLLMs)."}
+{"idx": 5, "title": "Synthetic Data Generation & Multi-Step RL for Reasoning ...", "date": "", "ddg_snippet": "Apr 7, 2025 · As focus shifts toward more complex reasoning and agentic tasks , language models must take multiple steps of text generation, reasoning and environment interaction before generating a solution. We propose a synthetic data generation and RL methodology targeting multi-step optimization scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.04736", "content": "Apr 7, 2025 · As focus shifts toward more complex reasoning and agentic tasks , language models must take multiple steps of text generation, reasoning and environment interaction before generating a solution. We propose a synthetic data generation and RL methodology targeting multi-step optimization scenarios."}
+{"idx": 6, "title": "Enhancing Compositional Reasoning in Vision-Language Models ...", "date": "", "ddg_snippet": "Apr 7, 2025 · Enhancing Compositional Reasoning in Vision- Language Models with Synthetic Preference Data: Paper and Code. Compositionality, or correctly recognizing scenes as compositions of atomic visual concepts, remains difficult for multimodal large language models (MLLMs).", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/enhancing-compositional-reasoning-in-vision", "content": "Apr 7, 2025 · Enhancing Compositional Reasoning in Vision- Language Models with Synthetic Preference Data: Paper and Code. Compositionality, or correctly recognizing scenes as compositions of atomic visual concepts, remains difficult for multimodal large language models (MLLMs)."}
+{"idx": 7, "title": "an analysis for reasoning bias of language models", "date": "", "ddg_snippet": "by J Yao · 2025 · Cited by 2 — In this paper, we investigate the underlying mechanism of which small initialization scales promote a reasoning pref- erence in language models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375?", "content": "by J Yao · 2025 · Cited by 2 — In this paper, we investigate the underlying mechanism of which small initialization scales promote a reasoning pref- erence in language models ."}
+{"idx": 8, "title": "Can Language Models Perform Robust Reasoning in ...", "date": "", "ddg_snippet": "by Z Zhou · Cited by 20 — Abstract. This paper investigates an under-explored challenge in large language models . (LLMs): chain-of-thought prompting with noisy rationales, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=FbuODM02ra", "content": "by Z Zhou · Cited by 20 — Abstract. This paper investigates an under-explored challenge in large language models . (LLMs): chain-of-thought prompting with noisy rationales, ..."}
+{"idx": 9, "title": "PORT: Preference Optimization on Reasoning Traces", "date": "", "ddg_snippet": "by S Lahlou · 2025 · Cited by 4 — Preference optimization methods have been successfully applied to improve not only the alignment of large language models (LLMs). 17 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.549.pdf", "content": "by S Lahlou · 2025 · Cited by 4 — Preference optimization methods have been successfully applied to improve not only the alignment of large language models (LLMs). 17 pages"}
diff --git a/data/sampled_jsons/recent_advancements_in_privacy_accounting_for_differential_privacy_group_composition_2023_2024.jsonl b/data/sampled_jsons/recent_advancements_in_privacy_accounting_for_differential_privacy_group_composition_2023_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae9cb30785e4137742a90b01b232795dd9c90ce6
--- /dev/null
+++ b/data/sampled_jsons/recent_advancements_in_privacy_accounting_for_differential_privacy_group_composition_2023_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Advances in Differential Privacy and Differentially Private Machine...", "date": "", "ddg_snippet": "In addition, newer privacy loss accounting techniques, variants of differential privacy , and several applications to fields like machine learning have been introduced in recent years.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.04706v1", "content": "In addition, newer privacy loss accounting techniques, variants of differential privacy , and several applications to fields like machine learning have been introduced in recent years."}
+{"idx": 1, "title": "Free Video: Randomized Approach for Tight Privacy Accounting in...", "date": "", "ddg_snippet": "Explore a novel differential privacy paradigm for improved privacy accounting in compositions , featuring Monte Carlo-based verification and enhanced utility- privacy tradeoffs in machine learning.", "subpage_snippet": "", "source": "www.classcentral.com", "link": "https://www.classcentral.com/course/youtube-randomized-approach-for-tight-privacy-accounting-322993", "content": "Explore a novel differential privacy paradigm for improved privacy accounting in compositions , featuring Monte Carlo-based verification and enhanced utility- privacy tradeoffs in machine learning."}
+{"idx": 2, "title": "TPDP 2024 – Theory and Practice of Differential Privacy", "date": "", "ddg_snippet": "Local Differential Privacy (LDP) has become increasingly popular in recent years.We study differential privacy under continual observation and prove new lower bounds for private online counters.", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2024/", "content": "Local Differential Privacy (LDP) has become increasingly popular in recent years.We study differential privacy under continual observation and prove new lower bounds for private online counters."}
+{"idx": 3, "title": "Avoiding Pitfalls for Privacy Accounting of Subsampled... | OpenReview", "date": "", "ddg_snippet": "First, some privacy accountants assume that the privacy guarantees for the composition of a subsampled mechanism are determined by self- composing the worst-case datasets for the uncomposed mechanism.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=buSEDdP5YX", "content": "First, some privacy accountants assume that the privacy guarantees for the composition of a subsampled mechanism are determined by self- composing the worst-case datasets for the uncomposed mechanism."}
+{"idx": 4, "title": "NeurIPS Poster Black-Box Differential Privacy for Interactive ML", "date": "", "ddg_snippet": "In this work we revisit an interactive variant of joint differential privacy , recently introduced by Naor et al. [ 2023 ], and generalize it towards handling online processes in which existing privacy definitions seem too restrictive.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/poster/70940", "content": "In this work we revisit an interactive variant of joint differential privacy , recently introduced by Naor et al. [ 2023 ], and generalize it towards handling online processes in which existing privacy definitions seem too restrictive."}
+{"idx": 5, "title": "The Complexity of Differential Privacy", "date": "", "ddg_snippet": "The group privacy is used in privacy accounting for hierarchical perturbation (Section 4.4). ...Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/315858000_The_Complexity_of_Differential_Privacy", "content": "The group privacy is used in privacy accounting for hierarchical perturbation (Section 4.4). ...Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy ."}
+{"idx": 6, "title": "Sarus implements 𝑓- differential privacy in OpenDP | Medium", "date": "", "ddg_snippet": "Differential privacy is a theoretical framework which allows to account for and set some limits on privacy loss every time one accesses some private data. The kind of mechanisms, and therefore privacy loss profiles, varies depending on the query", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/sarus/sarus-implements-differential-privacy-in-opendp-47350a94da03", "content": "Differential privacy is a theoretical framework which allows to account for and set some limits on privacy loss every time one accesses some private data. The kind of mechanisms, and therefore privacy loss profiles, varies depending on the query"}
+{"idx": 7, "title": "(PDF) Position Paper: Rethinking Privacy in RL for Sequential...", "date": "", "ddg_snippet": "This paper reviews recent advancements in privacy -preserving techniques for federated learning from a machine learning perspective. It categorizes and analyses state-of-the-art approaches within a unified framework, highlighting their strengths, limitations, and potential applications.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/128818531/Position_Paper_Rethinking_Privacy_in_RL_for_Sequential_Decision_making_in_the_Age_of_LLMs", "content": "This paper reviews recent advancements in privacy -preserving techniques for federated learning from a machine learning perspective. It categorizes and analyses state-of-the-art approaches within a unified framework, highlighting their strengths, limitations, and potential applications."}
+{"idx": 8, "title": "On the Privacy of Selection Mechanisms with Gaussian Noise", "date": "", "ddg_snippet": "Privacy accounting ε conomics: Improving differential privacy composition via a posteriori bounds. arXiv preprint arXiv:2205.03470.Fully-adaptive composition in differential privacy . In International Conference on Machine Learning, pages 36990–37007.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/lebensold24a/lebensold24a.pdf", "content": "Privacy accounting ε conomics: Improving differential privacy composition via a posteriori bounds. arXiv preprint arXiv:2205.03470.Fully-adaptive composition in differential privacy . In International Conference on Machine Learning, pages 36990–37007."}
+{"idx": 9, "title": "Course:CPSC522/ Differential Privacy - UBC Wiki", "date": "", "ddg_snippet": "8.3 Advanced Composition . 8.4 Group Privacy .We will then introduce a mechanism that can be used to achieve DP, we will then talk about DP properties and finally introduce approximate DP and its mechanisms. We will also beifly go over Rényi DP as a more recent development in DP.", "subpage_snippet": "", "source": "wiki.ubc.ca", "link": "https://wiki.ubc.ca/Course:CPSC522/Differential_Privacy", "content": "8.3 Advanced Composition . 8.4 Group Privacy .We will then introduce a mechanism that can be used to achieve DP, we will then talk about DP properties and finally introduce approximate DP and its mechanisms. We will also beifly go over Rényi DP as a more recent development in DP."}
diff --git a/data/sampled_jsons/regret_matching_normalization_operator_simplex_projection_g(z).jsonl b/data/sampled_jsons/regret_matching_normalization_operator_simplex_projection_g(z).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2ff908c30537b13c5cab1ab80127726d770a8195
--- /dev/null
+++ b/data/sampled_jsons/regret_matching_normalization_operator_simplex_projection_g(z).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Lecture 4 Blackwell approachability and regret minimization on simplex ...", "date": "", "ddg_snippet": "RT matching algorithm (Algorithm 1) is an external regret minimizer, and satisfies ≤ T k`t − h`t, T the regret bound Ω , where Ω is the maximum norm xti1k2 up to time .", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2021/15888f21_L04_blackwell_rm/L04_blackwell_rm.pdf", "content": "RT matching algorithm (Algorithm 1) is an external regret minimizer, and satisfies ≤ T k`t − h`t, T the regret bound Ω , where Ω is the maximum norm xti1k2 up to time ."}
+{"idx": 1, "title": "PDF Regret Matching+: - Instability, average- and last-iterate convergence ...", "date": "", "ddg_snippet": "Farina, G.-C., Kroer, Lee and Luo, NeurIPS 2023. Last-iterate convergence of regret matching -based algorithms in", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Farina, G.-C., Kroer, Lee and Luo, NeurIPS 2023. Last-iterate convergence of regret matching -based algorithms in"}
+{"idx": 2, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nYgs0qZJ97", "content": "Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability."}
+{"idx": 3, "title": "Regret matching - ACM Digital Library", "date": "", "ddg_snippet": "Farina, G., Kroer, C., and Sandholm, T. Faster game solving via predictive blackwell approachability: Connecting regret matching and mirror descent. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5363-5371, 2021.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668812", "content": "Farina, G., Kroer, C., and Sandholm, T. Faster game solving via predictive blackwell approachability: Connecting regret matching and mirror descent. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5363-5371, 2021."}
+{"idx": 4, "title": "PDF Regret Transfer and Parameter Optimization", "date": "", "ddg_snippet": "Regret matching (Hart and Mas-Colell 2000) is a widely-used, general algorithm for learning, over time, how to act. While regret is a broadly applicable concept, for this paper we will view it through the lens of game theory. The agent is a player in a game and his payoffs can depend on his and the other players' actions.", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~sandholm/regret_transfer.aaai14.extended.pdf", "content": "Regret matching (Hart and Mas-Colell 2000) is a widely-used, general algorithm for learning, over time, how to act. While regret is a broadly applicable concept, for this paper we will view it through the lens of game theory. The agent is a player in a game and his payoffs can depend on his and the other players' actions."}
+{"idx": 5, "title": "GitHub - int8/regret-matching: Simple implementation of regret matching ...", "date": "", "ddg_snippet": "This is simple implementation of regret matching algorithm for Nash Equilibrium computation for two players zero sum games via repeated self-play. This code uses game of Rock-Paper-Scissors as an example", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/int8/regret-matching", "content": "This is simple implementation of regret matching algorithm for Nash Equilibrium computation for two players zero sum games via repeated self-play. This code uses game of Rock-Paper-Scissors as an example"}
+{"idx": 6, "title": "PDF Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
+{"idx": 7, "title": "Regret Matching - stevengong.co", "date": "", "ddg_snippet": "Regret Matching We use regret matching to solve Normal-Form Game such as RPS. Blackwell's approachability theorem when applied to minimizing regret is known as regret matching .", "subpage_snippet": "", "source": "stevengong.co", "link": "https://stevengong.co/notes/Regret-Matching", "content": "Regret Matching We use regret matching to solve Normal-Form Game such as RPS. Blackwell's approachability theorem when applied to minimizing regret is known as regret matching ."}
+{"idx": 8, "title": "Last-Iterate Convergence Properties of Regret Matching Algorithms in ...", "date": "", "ddg_snippet": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+(RM+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence. A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+(RM+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence. A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ..."}
+{"idx": 9, "title": "arXiv:2311.00676v2 [cs.GT] 4 Mar 2025", "date": "", "ddg_snippet": "ABSTRACT We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence. A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.00676", "content": "ABSTRACT We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence. A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by ..."}
diff --git a/data/sampled_jsons/regularized_unbalanced_optimal_transport_Fisher_information_I(p)_Remark_4.2_mathematical_expression.jsonl b/data/sampled_jsons/regularized_unbalanced_optimal_transport_Fisher_information_I(p)_Remark_4.2_mathematical_expression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3857fbf17cd9b33904a734d195a3a051ff3e3149
--- /dev/null
+++ b/data/sampled_jsons/regularized_unbalanced_optimal_transport_Fisher_information_I(p)_Remark_4.2_mathematical_expression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2111.01666] Regularized unbalanced optimal transport as entropy...", "date": "", "ddg_snippet": "View a PDF of the paper titled Regularized unbalanced optimal transport as entropy minimization with respect to branching Brownian motion, by Aymeric Baradat and Hugo Lavenant.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.01666", "content": "View a PDF of the paper titled Regularized unbalanced optimal transport as entropy minimization with respect to branching Brownian motion, by Aymeric Baradat and Hugo Lavenant."}
+{"idx": 1, "title": "An Homogeneous Unbalanced Regularized Optimal Transport", "date": "", "ddg_snippet": "An Homogeneous model of Unbalanced Regularized Optimal Transport (HUROT). Application to Optimal Transport with boundary.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03513114v1/document", "content": "An Homogeneous model of Unbalanced Regularized Optimal Transport (HUROT). Application to Optimal Transport with boundary."}
+{"idx": 2, "title": "(PDF) Computations of Optimal Transport Distance with Fisher ...", "date": "", "ddg_snippet": "Fisher information regularization . Wuchen LI, penghang yin, and stanley osher. Abstract. We propose a fast algorithm to approximate the optimal transport distance. The main idea is to add a Fisher information regularization into the dynamical setting of.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/316215663_Computations_of_Optimal_Transport_Distance_with_Fisher_Information_Regularization", "content": "Fisher information regularization . Wuchen LI, penghang yin, and stanley osher. Abstract. We propose a fast algorithm to approximate the optimal transport distance. The main idea is to add a Fisher information regularization into the dynamical setting of."}
+{"idx": 3, "title": "Computations of optimal transport distance", "date": "", "ddg_snippet": "Optimal transport ; Fisher information ; Schro¨dinger bridge problem; Newton’s method.In this section, we briey review optimal transport distance and its regularized problem. Optimal transport distance provides a particular metric between two histograms.", "subpage_snippet": "", "source": "ww3.math.ucla.edu", "link": "https://ww3.math.ucla.edu/camreport/cam17-19", "content": "Optimal transport ; Fisher information ; Schro¨dinger bridge problem; Newton’s method.In this section, we briey review optimal transport distance and its regularized problem. Optimal transport distance provides a particular metric between two histograms."}
+{"idx": 4, "title": "Mathematical Framework | zhenyiizhang/DeepRUOT | DeepWiki", "date": "", "ddg_snippet": "We explain the core mathematical concepts including regularized unbalanced optimal transport (RUOT), the Schrödinger bridge connection, neural ODEs/SDEs, and growth-death modeling.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/zhenyiizhang/DeepRUOT/1.2-mathematical-framework", "content": "We explain the core mathematical concepts including regularized unbalanced optimal transport (RUOT), the Schrödinger bridge connection, neural ODEs/SDEs, and growth-death modeling."}
+{"idx": 5, "title": "ot. unbalanced — POT Python Optimal Transport 0.9.5 documentation", "date": "", "ddg_snippet": "Solve the unbalanced entropic regularization optimal transport problem and return the OT plan.", "subpage_snippet": "", "source": "pythonot.github.io", "link": "https://pythonot.github.io/gen_modules/ot.unbalanced.html", "content": "Solve the unbalanced entropic regularization optimal transport problem and return the OT plan."}
+{"idx": 6, "title": "Simple Unbalanced Optimal Transport", "date": "", "ddg_snippet": "Article. Simple Unbalanced Optimal Transport . Boris Khesin1, Klas Modin2,* and Luke Volk1. 1Department of Mathematics , University of Toronto, Ontario M5S 2E4, Canada 2Department of Mathematical Sciences...", "subpage_snippet": "", "source": "research.chalmers.se", "link": "https://research.chalmers.se/publication/540470/file/540470_Fulltext.pdf", "content": "Article. Simple Unbalanced Optimal Transport . Boris Khesin1, Klas Modin2,* and Luke Volk1. 1Department of Mathematics , University of Toronto, Ontario M5S 2E4, Canada 2Department of Mathematical Sciences..."}
+{"idx": 7, "title": "Optimal Entropy- Transport problems and a new...", "date": "", "ddg_snippet": "We develop a full theory for the new class of Optimal Entropy- Transport problems between nonnegative and finite Radon measures in general topological space.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00222-017-0759-8", "content": "We develop a full theory for the new class of Optimal Entropy- Transport problems between nonnegative and finite Radon measures in general topological space."}
+{"idx": 8, "title": "Full waveform inversion with unbalanced optimal transport distance", "date": "", "ddg_snippet": "The optimal transport distance is to compare two positive measures with equal total mass. Regularized primal and dual problem. The entropy regularization is introduced to the optimal transport problem in the work (Cuturi, 2013) to increase the computation efciency.", "subpage_snippet": "", "source": "www.crewes.org", "link": "https://www.crewes.org/Documents/ResearchReports/2019/CRR201940.pdf", "content": "The optimal transport distance is to compare two positive measures with equal total mass. Regularized primal and dual problem. The entropy regularization is introduced to the optimal transport problem in the work (Cuturi, 2013) to increase the computation efciency."}
+{"idx": 9, "title": "Taming Flow Matching with Unbalanced Optimal Transport into Fast...", "date": "", "ddg_snippet": "Unbalanced optimal transport successfully handles the resolution differences between multispectral and panchromatic images.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/taming-flow-matching-unbalanced-optimal-transport-into", "content": "Unbalanced optimal transport successfully handles the resolution differences between multispectral and panchromatic images."}
diff --git a/data/sampled_jsons/regularized_unbalanced_optimal_transport_LEnergy_loss_function_integral_expression.jsonl b/data/sampled_jsons/regularized_unbalanced_optimal_transport_LEnergy_loss_function_integral_expression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9034c6cb5bfdac95106ecde3405c5f32718c2c93
--- /dev/null
+++ b/data/sampled_jsons/regularized_unbalanced_optimal_transport_LEnergy_loss_function_integral_expression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning stochastic dynamics from snapshots through regularized ...", "date": "", "ddg_snippet": "3 Regularized Unbalanced Optimal Transport .So overall, the loss function is defined as the weighted sum of the cost, the reconstruction error and the PINN loss", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v1", "content": "3 Regularized Unbalanced Optimal Transport .So overall, the loss function is defined as the weighted sum of the cost, the reconstruction error and the PINN loss"}
+{"idx": 1, "title": "Entropic Optimal Transport between Unbalanced", "date": "", "ddg_snippet": "Entropy Regularized OT between Unbalanced Gaussians.This extension leads to a closed form expression for unbalanced Gaussians as well, and high-lights the mass transportation / destruction trade-off seen in unbalanced optimal transport .", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03063834/document", "content": "Entropy Regularized OT between Unbalanced Gaussians.This extension leads to a closed form expression for unbalanced Gaussians as well, and high-lights the mass transportation / destruction trade-off seen in unbalanced optimal transport ."}
+{"idx": 2, "title": "Paper tables with annotated results for Variational Regularized ...", "date": "", "ddg_snippet": "Variational Regularized Unbalanced Optimal Transport : Single Network, Least Action.A notable recent framework is Regularized Unbalanced Optimal Transport (RUOT), which integrates both stochastic dynamics and unnormalized distributions.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/variational-regularized-unbalanced-optimal/review/", "content": "Variational Regularized Unbalanced Optimal Transport : Single Network, Least Action.A notable recent framework is Regularized Unbalanced Optimal Transport (RUOT), which integrates both stochastic dynamics and unnormalized distributions."}
+{"idx": 3, "title": "ICML Poster On Unbalanced Optimal Transport : An Analysis of...", "date": "", "ddg_snippet": "Abstract: We provide a computational complexity analysis for the Sinkhorn algorithm that solves the entropic regularized Unbalanced Optimal Transport (UOT) problem between two measures of possibly different masses with at most $n$ components.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/poster/6163", "content": "Abstract: We provide a computational complexity analysis for the Sinkhorn algorithm that solves the entropic regularized Unbalanced Optimal Transport (UOT) problem between two measures of possibly different masses with at most $n$ components."}
+{"idx": 4, "title": "Unbalanced regularized optimal mass transport with applications to...", "date": "", "ddg_snippet": "The cost function (1a) is the total kinetic energy of the transport process. The square root of the achieved minimum of (1a), if it exists, is called the., in total three steps, and then integrate them together. For the first mass gain/ loss step, given an initial condition. ρ(ti,x)=ρi(x).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10784574/", "content": "The cost function (1a) is the total kinetic energy of the transport process. The square root of the achieved minimum of (1a), if it exists, is called the., in total three steps, and then integrate them together. For the first mass gain/ loss step, given an initial condition. ρ(ti,x)=ρi(x)."}
+{"idx": 5, "title": "MMD- Regularized Unbalanced Optimal Transport", "date": "", "ddg_snippet": "MMD- Regularized Unbalanced Optimal Transport . Piyushi Manupriya Department of Computer Science and Engineering, IIT Hyderabad, INDIA. Optimal bounds between f-divergences and integral probability metrics. In ICML, 2020. Martial Agueh and Guillaume Carlier.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=eN9CjU3h1b", "content": "MMD- Regularized Unbalanced Optimal Transport . Piyushi Manupriya Department of Computer Science and Engineering, IIT Hyderabad, INDIA. Optimal bounds between f-divergences and integral probability metrics. In ICML, 2020. Martial Agueh and Guillaume Carlier."}
+{"idx": 6, "title": "Unbalanced minibatch Optimal Transport ; applications to Domain...", "date": "", "ddg_snippet": "4. Unbalanced Minibatch Optimal Transport . In this section we express some mathematical properties at the heart of this work. In (Fatras et al., 2020), authors described some properties of the minibatch OT. We provide here extensions of those results to Unbalanced OT.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/fatras21a/fatras21a.pdf", "content": "4. Unbalanced Minibatch Optimal Transport . In this section we express some mathematical properties at the heart of this work. In (Fatras et al., 2020), authors described some properties of the minibatch OT. We provide here extensions of those results to Unbalanced OT."}
+{"idx": 7, "title": "Transport -based transfer learning on Electronic Health... | medRxiv", "date": "", "ddg_snippet": "In this paper, we hence introduce Optimal Transport -based Transfer learning for Electronic Health Records, OTTEHR, a novel method that leverages feature embeddings from EHRs and OT to perform unsupervised TL between unbalanced domains.", "subpage_snippet": "", "source": "www.medrxiv.org", "link": "https://www.medrxiv.org/content/10.1101/2024.03.27.24304781v1.full", "content": "In this paper, we hence introduce Optimal Transport -based Transfer learning for Electronic Health Records, OTTEHR, a novel method that leverages feature embeddings from EHRs and OT to perform unsupervised TL between unbalanced domains."}
+{"idx": 8, "title": "API and modules — POT Python Optimal Transport ...", "date": "", "ddg_snippet": "Solve the unbalanced entropic regularization optimal transport problem and return the OT plan. The function solves the following optimization problem", "subpage_snippet": "", "source": "pythonot.github.io", "link": "https://pythonot.github.io/all.html", "content": "Solve the unbalanced entropic regularization optimal transport problem and return the OT plan. The function solves the following optimization problem"}
+{"idx": 9, "title": "cvgmt: Regularized unbalanced optimal transport as entropy...", "date": "", "ddg_snippet": "First, relying on duality arguments, the values of these two problems of calculus of variations are linked, in the sense that the value of the regularized unbalanced optimal transport (seen as a function of the initial and final measure) is the lower...", "subpage_snippet": "", "source": "cvgmt.sns.it", "link": "https://cvgmt.sns.it/paper/5321/", "content": "First, relying on duality arguments, the values of these two problems of calculus of variations are linked, in the sense that the value of the regularized unbalanced optimal transport (seen as a function of the initial and final measure) is the lower..."}
diff --git a/data/sampled_jsons/remote_sensing_benchmark_10000x10000_12000x12000_ultra_high_resolution.jsonl b/data/sampled_jsons/remote_sensing_benchmark_10000x10000_12000x12000_ultra_high_resolution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9122099a397869492bb09e14b2dae83fec19076a
--- /dev/null
+++ b/data/sampled_jsons/remote_sensing_benchmark_10000x10000_12000x12000_ultra_high_resolution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Одноразовая электронная сигарета FLONQ Ultra 20000 Ментол...", "date": "", "ddg_snippet": "Attacker KPEKPE PS 12000. Puffmi DURA 9000. GANG LUX 12000. Mr_Kir_Kotне рекомендует. сообщить о нарушении.Смотрите также. Одноразовая электронная сигарета Plonq ultra 12000.", "subpage_snippet": "", "source": "irecommend.ru", "link": "https://irecommend.ru/content/odna-iz-samykh-krepkikh-odnorazok-chto-mne-dovelos-kurit-no-k-sozhaleniyu-vkus-mentol-ne-zas", "content": "Attacker KPEKPE PS 12000. Puffmi DURA 9000. GANG LUX 12000. Mr_Kir_Kotне рекомендует. сообщить о нарушении.Смотрите также. Одноразовая электронная сигарета Plonq ultra 12000."}
+{"idx": 1, "title": "Игровые мыши купить в интернет-магазине DNS. Игровые мыши...", "date": "", "ddg_snippet": "Мышь беспроводная/проводная Attack Shark R5 Ultra [Shark R5 White] белый [42000 dpi, светодиодный, Bluetooth, USB Type-A, кнопки - 7]. 4.63 |130 отзывов Хорошая надежность.", "subpage_snippet": "", "source": "www.dns-shop.ru", "link": "https://www.dns-shop.ru/catalog/recipe/1841c8263a447658/igrovye-mysi/", "content": "Мышь беспроводная/проводная Attack Shark R5 Ultra [Shark R5 White] белый [42000 dpi, светодиодный, Bluetooth, USB Type-A, кнопки - 7]. 4.63 |130 отзывов Хорошая надежность."}
+{"idx": 2, "title": "Как выключить телефон Honor или Huawei? ПРОСТОЙ СПОСОБ...", "date": "", "ddg_snippet": "Обзор Smart Watch DT Watch Ultra стоит ли их покупать?Лучший телефон за 10000 - 12000 рублей в 2019 году/Xiaomi Redmi Note 7 Глобальная Версия.", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/video-185473659_456239804", "content": "Обзор Smart Watch DT Watch Ultra стоит ли их покупать?Лучший телефон за 10000 - 12000 рублей в 2019 году/Xiaomi Redmi Note 7 Глобальная Версия."}
+{"idx": 3, "title": "Цены на 2025 год пансионата «Ольга» в Малом Утрише, ул....", "date": "", "ddg_snippet": "10000 . 12000 . 15000. 17000. 18000. 16000. 10000. 2-х местный Улучшенный в отдельно стоящем Деревянном Домике, всего 4 номера завтрак включён.«SUNRISE Park Hotel Relax&Spa Ultra all inclusive» - комфортное проживание по системе «Ультра всё включено».", "subpage_snippet": "", "source": "www.idisuda.ru", "link": "https://www.idisuda.ru/price.php?n=9442", "content": "10000 . 12000 . 15000. 17000. 18000. 16000. 10000. 2-х местный Улучшенный в отдельно стоящем Деревянном Домике, всего 4 номера завтрак включён.«SUNRISE Park Hotel Relax&Spa Ultra all inclusive» - комфортное проживание по системе «Ультра всё включено»."}
+{"idx": 4, "title": "ТОП-10 лучших одноразовых вейпов 2025 — рейтинг и обзор", "date": "", "ddg_snippet": "PLONQ ULTRA 12000.LOST MARY MO10000 — одноразовая электронная сигарета, сочетающая в себе высокую автономность, современный дизайн и разнообразие вкусов. Устройство рассчитано на до 10 000 затяжек, что обеспечивает длительное...", "subpage_snippet": "", "source": "digismoke.ru", "link": "https://digismoke.ru/topy-i-podborki/top-10-odnorazok-2025-luchshie-po-mneniju-digismoke/", "content": "PLONQ ULTRA 12000.LOST MARY MO10000 — одноразовая электронная сигарета, сочетающая в себе высокую автономность, современный дизайн и разнообразие вкусов. Устройство рассчитано на до 10 000 затяжек, что обеспечивает длительное..."}
+{"idx": 5, "title": "Не просто IP68: 5 лучших защищенных смартфонов 2025 года. | Дзен", "date": "", "ddg_snippet": "Если задача стоит в том, чтобы купить недорогой, но надёжный смартфон — ваш выбор Oukitel WP35 S ( 10000 – 12000 рублей).", "subpage_snippet": "", "source": "dzen.ru", "link": "https://dzen.ru/a/aDmHg4YUlCF9hCFN", "content": "Если задача стоит в том, чтобы купить недорогой, но надёжный смартфон — ваш выбор Oukitel WP35 S ( 10000 – 12000 рублей)."}
+{"idx": 6, "title": "Признаки неисправности ТНВД TFSI Audi Skoda VW", "date": "", "ddg_snippet": "Нормальное давление в топливной рампе высокого давления на холостых оборотах около 40 бар (4000 кПа), при нагрузке на двигатель давление возрастает до 100-120 бар ( 10000 - 12000 кПа).", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/95a3c80d7d09524bf7e243b69ecef9dc/", "content": "Нормальное давление в топливной рампе высокого давления на холостых оборотах около 40 бар (4000 кПа), при нагрузке на двигатель давление возрастает до 100-120 бар ( 10000 - 12000 кПа)."}
+{"idx": 7, "title": "ffffff.ru Не все то золото, что блестит — DRIVE2", "date": "", "ddg_snippet": "Если 10000 - 12000 — это космическая сумма, то надо пересаживаться на велосипед.", "subpage_snippet": "", "source": "www.drive2.ru", "link": "https://www.drive2.ru/b/650295982839321423/", "content": "Если 10000 - 12000 — это космическая сумма, то надо пересаживаться на велосипед."}
+{"idx": 8, "title": "Одноразовый вейп купить одноразовые электронные сигареты...", "date": "", "ddg_snippet": "E-Cig 400 GTM BAR Flow 6000 GTM BAR Halo 4000 GTM BAR Honor 1500 GTM BAR Porter 5000 Gusto 3500 Hotspot 12000 Hotspot 4000 HQD Cuvie Air HQD King HQD Lux HQD Maxx HQD Melo Husky Airmax...", "subpage_snippet": "", "source": "ru.vapeozon.ru", "link": "https://ru.vapeozon.ru/odnorazki/", "content": "E-Cig 400 GTM BAR Flow 6000 GTM BAR Halo 4000 GTM BAR Honor 1500 GTM BAR Porter 5000 Gusto 3500 Hotspot 12000 Hotspot 4000 HQD Cuvie Air HQD King HQD Lux HQD Maxx HQD Melo Husky Airmax..."}
+{"idx": 9, "title": "Прайс-лист афропричёски", "date": "", "ddg_snippet": "Афроклассика. • Крупная 30-40 кос 7000 – 9200₽ (2-3 нед). • Средняя 45-55 кос 10000 – 12000 ₽ (4-5 нед).", "subpage_snippet": "", "source": "xn--e1ajoqp.xn--p1ai", "link": "https://xn--e1ajoqp.xn--p1ai/price/", "content": "Афроклассика. • Крупная 30-40 кос 7000 – 9200₽ (2-3 нед). • Средняя 45-55 кос 10000 – 12000 ₽ (4-5 нед)."}
diff --git a/data/sampled_jsons/reverse_currying_neural_operator_mathematical_definition_function_spaces.jsonl b/data/sampled_jsons/reverse_currying_neural_operator_mathematical_definition_function_spaces.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d0f861703a0057c88c1135a7185b0dbd28bea15
--- /dev/null
+++ b/data/sampled_jsons/reverse_currying_neural_operator_mathematical_definition_function_spaces.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "\"Deep Learning\" Is Function Approximation - LessWrong", "date": "", "ddg_snippet": "... function approximators with only slightly more parameters than example input–output pairs, but for sufficiently large function approximators, the ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/DhjcdzTyqHte2v6bu/deep-learning-is-function-approximation", "content": "... function approximators with only slightly more parameters than example input–output pairs, but for sufficiently large function approximators, the ..."}
+{"idx": 1, "title": "neural network in nLab", "date": "", "ddg_snippet": "We may speak of neural network quantum field, e.g., when the correlation functions fulfills the Osterwalder-Schrader axioms .", "subpage_snippet": "", "source": "ncatlab.org", "link": "https://ncatlab.org/nlab/show/neural+network", "content": "We may speak of neural network quantum field, e.g., when the correlation functions fulfills the Osterwalder-Schrader axioms ."}
+{"idx": 2, "title": "Awesome Functional Programming | Massive Collection &", "date": "", "ddg_snippet": "Functional Programming should be your priority for 2015 — OOP cannot save us from the Cloud Monster anymore. ... More Functional Javascript ...", "subpage_snippet": "", "source": "onehack.us", "link": "https://onehack.us/t/awesome-functional-programming-massive-collection-resources/287892", "content": "Functional Programming should be your priority for 2015 — OOP cannot save us from the Cloud Monster anymore. ... More Functional Javascript ..."}
+{"idx": 3, "title": "Convolutional Neural Network (CNN) - Semiconductor Engineering", "date": "", "ddg_snippet": "Mathematically, a convolution is an operation on two functions producing a third function which is an integral that provides the amount of overlap of ...", "subpage_snippet": "", "source": "semiengineering.com", "link": "https://semiengineering.com/knowledge_centers/artificial-intelligence/neural-networks/convolutional-neural-network/", "content": "Mathematically, a convolution is an operation on two functions producing a third function which is an integral that provides the amount of overlap of ..."}
+{"idx": 4, "title": "Spiking Neural Network (SNN) - Semiconductor Engineering", "date": "", "ddg_snippet": "The simplest mathematical models and hardware implementations that can capture these most basic behaviors are “integrate and fire” models.", "subpage_snippet": "", "source": "semiengineering.com", "link": "https://semiengineering.com/knowledge_centers/artificial-intelligence/neural-networks/spiking-neural-network-snn/", "content": "The simplest mathematical models and hardware implementations that can capture these most basic behaviors are “integrate and fire” models."}
+{"idx": 5, "title": "Correct understanding of the definition and management", "date": "", "ddg_snippet": "Clarifying the definition of RH will not only ensure recognition of its status as a relatively serious pathological state, which remains challenging ...", "subpage_snippet": "", "source": "cnjournal.biomedcentral.com", "link": "https://cnjournal.biomedcentral.com/articles/10.1186/s41016-025-00403-9", "content": "Clarifying the definition of RH will not only ensure recognition of its status as a relatively serious pathological state, which remains challenging ..."}
+{"idx": 6, "title": "Inquiry Into Inquiry", "date": "", "ddg_snippet": "... Mathematics , Minimal Negation Operators , Painted Cacti , Propositional Calculus , Visualization | Tagged Automata , Boolean Algebra , Boolean ...", "subpage_snippet": "", "source": "inquiryintoinquiry.com", "link": "https://inquiryintoinquiry.com/", "content": "... Mathematics , Minimal Negation Operators , Painted Cacti , Propositional Calculus , Visualization | Tagged Automata , Boolean Algebra , Boolean ..."}
+{"idx": 7, "title": "Optics | Bartosz Milewski's Programming Cafe", "date": "", "ddg_snippet": "Neural networks are an example of composable systems, so it ’ s no surprise that they can be modeled in category theory, which is the ultimate ...", "subpage_snippet": "", "source": "bartoszmilewski.com", "link": "https://bartoszmilewski.com/tag/optics/", "content": "Neural networks are an example of composable systems, so it ’ s no surprise that they can be modeled in category theory, which is the ultimate ..."}
+{"idx": 8, "title": "polyhedra - What's wrong with my map of the hemicube? -", "date": "", "ddg_snippet": "If I reverse only 1 vertex it will not work, the result is still orientable, and the triangle which didn't touch that vertex will remain unchanged.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4548434/whats-wrong-with-my-map-of-the-hemicube", "content": "If I reverse only 1 vertex it will not work, the result is still orientable, and the triangle which didn't touch that vertex will remain unchanged."}
+{"idx": 9, "title": "Introduction to Theoretical Computer Science: Defining", "date": "", "ddg_snippet": "2.6 Defining computational tasks as mathematical functions ... 8.5.2 Obtaining multi-argument functions via Currying", "subpage_snippet": "", "source": "introtcs.org", "link": "https://introtcs.org/public/lec_03_computation.html", "content": "2.6 Defining computational tasks as mathematical functions ... 8.5.2 Obtaining multi-argument functions via Currying"}
diff --git a/data/sampled_jsons/section_2.1_Overview_and_Barriers_of_Larsen's_Algorithm.jsonl b/data/sampled_jsons/section_2.1_Overview_and_Barriers_of_Larsen's_Algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7b03127f2a43b7f1f5318ea88e67d53b295a7902
--- /dev/null
+++ b/data/sampled_jsons/section_2.1_Overview_and_Barriers_of_Larsen's_Algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Chi Kan Holdings Ltd Intrinsic Valuation and Fundamental", "date": "", "ddg_snippet": "... and refine your approach, optimizing for future ... The intrinsic value of one Chi Kan Holdings Ltd stock under the Base Case scenario is 1 .26 HKD .", "subpage_snippet": "", "source": "www.alphaspread.com", "link": "https://www.alphaspread.com/security/hkex/9913/summary", "content": "... and refine your approach, optimizing for future ... The intrinsic value of one Chi Kan Holdings Ltd stock under the Base Case scenario is 1 .26 HKD ."}
+{"idx": 1, "title": "The NFA Acceptance Hypothesis: Non-Combinatorial and Dynamic", "date": "", "ddg_snippet": "... of this algorithm is known for sparse NFAs, assuming the Strong Exponential Time Hypothesis and up to subpolynomial factors, as we will discuss in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.10204v2", "content": "... of this algorithm is known for sparse NFAs, assuming the Strong Exponential Time Hypothesis and up to subpolynomial factors, as we will discuss in ..."}
+{"idx": 2, "title": "Artificial Intelligence and Diabetic Retinopathy: AI Framework,", "date": "", "ddg_snippet": "Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity ( > 85%) for detection of referable DR compared with ...", "subpage_snippet": "", "source": "diabetesjournals.org", "link": "https://diabetesjournals.org/care/article/46/10/1728/153626/Artificial-Intelligence-and-Diabetic-Retinopathy", "content": "Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity ( > 85%) for detection of referable DR compared with ..."}
+{"idx": 3, "title": "9. Pharmacologic Approaches to Glycemic Treatment: Standards of", "date": "", "ddg_snippet": "... and shortened duration of action compared with RAA ( 10 ) (see also subsection alternative insulin routes in pharmacologic therapy for adults with ...", "subpage_snippet": "", "source": "diabetesjournals.org", "link": "https://diabetesjournals.org/care/article/48/Supplement_1/S181/157569/9-Pharmacologic-Approaches-to-Glycemic-Treatment", "content": "... and shortened duration of action compared with RAA ( 10 ) (see also subsection alternative insulin routes in pharmacologic therapy for adults with ..."}
+{"idx": 4, "title": "Algorithmic Management in the Food Delivery Sector – a", "date": "", "ddg_snippet": "One stream of the scientific literature and public debate on platform work emphasizes the performance-control function of algorithm -based management ...", "subpage_snippet": "", "source": "ojs.weizenbaum-institut.de", "link": "https://ojs.weizenbaum-institut.de/index.php/wjds/article/view/4_3_5/4_3_5", "content": "One stream of the scientific literature and public debate on platform work emphasizes the performance-control function of algorithm -based management ..."}
+{"idx": 5, "title": "Thermally Averaged Magnetic Anisotropy Tensors via Machine", "date": "", "ddg_snippet": "NNs were initially applied to represent potential energy surfaces (PESs) of small atomistic systems 10 , 11 and were later extended to high ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.01415v1", "content": "NNs were initially applied to represent potential energy surfaces (PESs) of small atomistic systems 10 , 11 and were later extended to high ..."}
+{"idx": 6, "title": "Physics Based Prognostic Health Management for Thermal Barrier", "date": "", "ddg_snippet": "Reliable prognostic of thermal barrier coating systems (TBCs) as applied to hot section engine components is a challenging task.", "subpage_snippet": "", "source": "papers.phmsociety.org", "link": "https://papers.phmsociety.org/index.php/phmconf/article/view/1993", "content": "Reliable prognostic of thermal barrier coating systems (TBCs) as applied to hot section engine components is a challenging task."}
+{"idx": 7, "title": "Program", "date": "", "ddg_snippet": "On Fast Multi-Objective Optimization of Antenna Structures Using Pareto Front Triangulation and Inverse Surrogates ( abstract )", "subpage_snippet": "", "source": "easychair.org", "link": "https://easychair.org/smart-program/ICCS2021/index.html", "content": "On Fast Multi-Objective Optimization of Antenna Structures Using Pareto Front Triangulation and Inverse Surrogates ( abstract )"}
+{"idx": 8, "title": "Nigel Shadbolt", "date": "", "ddg_snippet": "According to our database 1 , Nigel Shadbolt authored at least 347 papers between 1984 and 2025. ... of Language-Models-as-a-Service: Overview of a ...", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/nigel-shadbolt/", "content": "According to our database 1 , Nigel Shadbolt authored at least 347 papers between 1984 and 2025. ... of Language-Models-as-a-Service: Overview of a ..."}
+{"idx": 9, "title": "TC - The role of föhn winds in eastern Antarctic Peninsula", "date": "", "ddg_snippet": "The final collapses of the Larsen A (LAIS) in 1995 and the Larsen B (LBIS) ice shelves in 2002 have been attributed to decreased structural integrity ...", "subpage_snippet": "", "source": "tc.copernicus.org", "link": "https://tc.copernicus.org/articles/16/1369/2022/", "content": "The final collapses of the Larsen A (LAIS) in 1995 and the Larsen B (LBIS) ice shelves in 2002 have been attributed to decreased structural integrity ..."}
diff --git a/data/sampled_jsons/self-blended_images_deepfake_detection.jsonl b/data/sampled_jsons/self-blended_images_deepfake_detection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6e0b12ddb47af78a62902c65f4d502ca4edf8c9e
--- /dev/null
+++ b/data/sampled_jsons/self-blended_images_deepfake_detection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2204.08376] Detecting Deepfakes with Self-Blended Images", "date": "", "ddg_snippet": "by K Shiohara · 2022 · Cited by 489 — In this paper, we present novel synthetic training data called self - blended images (SBIs) to detect deepfakes .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2204.08376", "content": "by K Shiohara · 2022 · Cited by 489 — In this paper, we present novel synthetic training data called self - blended images (SBIs) to detect deepfakes ."}
+{"idx": 1, "title": "Detecting Deepfakes With Self-Blended Images", "date": "", "ddg_snippet": "by K Shiohara · 2022 · Cited by 496 — In this paper, we present novel synthetic training data called self - blended images (SBIs) to detect deepfakes. ... Deepfake Detection . Although many ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Shiohara_Detecting_Deepfakes_With_Self-Blended_Images_CVPR_2022_paper.pdf", "content": "by K Shiohara · 2022 · Cited by 496 — In this paper, we present novel synthetic training data called self - blended images (SBIs) to detect deepfakes. ... Deepfake Detection . Although many ... 10 pages"}
+{"idx": 2, "title": "Detecting Deepfakes with Self-Blended Images", "date": "", "ddg_snippet": "... [CVPR 2022 Oral] Detecting Deepfakes with Self - Blended Images https ... image-forensics deepfakes deepfake deepfake - detection . Resources. Readme ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mapooon/SelfBlendedImages", "content": "... [CVPR 2022 Oral] Detecting Deepfakes with Self - Blended Images https ... image-forensics deepfakes deepfake deepfake - detection . Resources. Readme ..."}
+{"idx": 3, "title": "FSBI: Deepfake detection with frequency enhanced self- ...", "date": "", "ddg_snippet": "by AA Hasanaath · 2025 · Cited by 13 — This study introduces a frequency enhanced self - blended images (FSBI) approach for deepfake detection . ... Yamasaki, Detecting deepfakes with self - blended images ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S026288562500006X", "content": "by AA Hasanaath · 2025 · Cited by 13 — This study introduces a frequency enhanced self - blended images (FSBI) approach for deepfake detection . ... Yamasaki, Detecting deepfakes with self - blended images ..."}
+{"idx": 4, "title": "Detecting Deepfakes with Self-Blended Images", "date": "", "ddg_snippet": "by K Shiohara · 2022 · Cited by 495 — ... self - blended images (SBIs) to detect deepfakes. SBIs are generated by ... Therefore, the vision community is keenly workingon deepfake detection techniques.Most ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2022/694600s8699/1H1kNMtp172", "content": "by K Shiohara · 2022 · Cited by 495 — ... self - blended images (SBIs) to detect deepfakes. SBIs are generated by ... Therefore, the vision community is keenly workingon deepfake detection techniques.Most ..."}
+{"idx": 5, "title": "FSBI: Deepfakes Detection with Frequency Enhanced Self ...", "date": "", "ddg_snippet": "by AA Hasanaath · 2024 · Cited by 13 — Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes detection ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.08625", "content": "by AA Hasanaath · 2024 · Cited by 13 — Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes detection ..."}
+{"idx": 6, "title": "Localization and detection of deepfake videos based on ...", "date": "", "ddg_snippet": "by J Xu · 2025 · Cited by 4 — It encompasses four different deepfake methods: FaceSwap, Deepfakes , Face2Face, and NeuralTextures, with 1000 videos forged using each method, ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-88523-1", "content": "by J Xu · 2025 · Cited by 4 — It encompasses four different deepfake methods: FaceSwap, Deepfakes , Face2Face, and NeuralTextures, with 1000 videos forged using each method, ..."}
+{"idx": 7, "title": "Enhancing Deepfake Detection With Diversified Self ...", "date": "", "ddg_snippet": "27 Mar 2024 — This trains the deepfake detection model to adapt to various synthesis techniques. The classifier model is trained using multiple perturbations ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "http://ieeexplore.ieee.org/document/10479506/", "content": "27 Mar 2024 — This trains the deepfake detection model to adapt to various synthesis techniques. The classifier model is trained using multiple perturbations ..."}
+{"idx": 8, "title": "[PDF] Detecting Deepfakes with Self-Blended Images", "date": "", "ddg_snippet": "FSBI: Deepfakes Detection with Frequency Enhanced Self - Blended Images · A ... Enhancing Deepfake Detection With Diversified Self - Blending Images and Residuals.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Detecting-Deepfakes-with-Self-Blended-Images-Shiohara-Yamasaki/ef3b913e6509077c67e678674e2ba33d99a201a5", "content": "FSBI: Deepfakes Detection with Frequency Enhanced Self - Blended Images · A ... Enhancing Deepfake Detection With Diversified Self - Blending Images and Residuals."}
+{"idx": 9, "title": "Credit Builder: Build Credit & Add to Your Savings with Self", "date": "", "ddg_snippet": "Build your credit with Self 's Credit Builder Account & secured Self Visa® Credit Card. Ideal for credit building, no hard check, & reports to all three bureaus.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/", "content": "Build your credit with Self 's Credit Builder Account & secured Self Visa® Credit Card. Ideal for credit building, no hard check, & reports to all three bureaus."}
diff --git a/data/sampled_jsons/semi-supervised_learning_without_prior_class_distribution_estimation_techniques.jsonl b/data/sampled_jsons/semi-supervised_learning_without_prior_class_distribution_estimation_techniques.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a6f29cd12a32446161a399e3589c7e3fb895840f
--- /dev/null
+++ b/data/sampled_jsons/semi-supervised_learning_without_prior_class_distribution_estimation_techniques.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improving realistic semi-supervised learning with doubly ...", "date": "", "ddg_snippet": "by K Pham · 2025 · Cited by 1 — In contrast, when addi- tional unlabeled data is available, semi-supervised EM gives significantly better class distribution estimation . Non ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00279", "content": "by K Pham · 2025 · Cited by 1 — In contrast, when addi- tional unlabeled data is available, semi-supervised EM gives significantly better class distribution estimation . Non ..."}
+{"idx": 1, "title": "Class prior estimation for positive-unlabeled learning when ...", "date": "", "ddg_snippet": "28 Feb 2025 — We study estimation of class prior for unlabeled target samples which is possibly different from that of source population.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.21194v1", "content": "28 Feb 2025 — We study estimation of class prior for unlabeled target samples which is possibly different from that of source population."}
+{"idx": 2, "title": "Semi-supervised distribution learning - Oxford Academic", "date": "", "ddg_snippet": "by M Wen · 2025 · Cited by 2 — This work explores the complexities of semi-supervised distribution estimation , particularly the uniformity problem inherent in functional processes.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/biomet/advance-article/doi/10.1093/biomet/asae056/7852832", "content": "by M Wen · 2025 · Cited by 2 — This work explores the complexities of semi-supervised distribution estimation , particularly the uniformity problem inherent in functional processes."}
+{"idx": 3, "title": "Optimal and Safe Estimation for High-Dimensional Semi- ...", "date": "", "ddg_snippet": "by S Deng · 2024 · Cited by 19 — We consider the estimation problem in high-dimensional semi - supervised learning . Our goal is to investigate when and how the unlabeled data can be exploited.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11902906/", "content": "by S Deng · 2024 · Cited by 19 — We consider the estimation problem in high-dimensional semi - supervised learning . Our goal is to investigate when and how the unlabeled data can be exploited."}
+{"idx": 4, "title": "Towards Semi-supervised Learning with Non-random Missing ...", "date": "", "ddg_snippet": "by Y Duan · 2023 · Cited by 23 — Semi - supervised learning (SSL) tackles the label missing problem by enabling the effective usage of unlabeled data. While existing SSL methods focus on the ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Duan_Towards_Semi-supervised_Learning_with_Non-random_Missing_Labels_ICCV_2023_paper.pdf", "content": "by Y Duan · 2023 · Cited by 23 — Semi - supervised learning (SSL) tackles the label missing problem by enabling the effective usage of unlabeled data. While existing SSL methods focus on the ... 11 pages"}
+{"idx": 5, "title": "Semi-Supervised Learning of Classifiers from a Statistical ...", "date": "", "ddg_snippet": "by D Ahfock · 2023 · Cited by 20 — Semi - supervised learning methods can reduce the sample labeling time and the labor cost of sample labeling while ensuring the validity of the knowledge (Ahfock ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S2452306222000296", "content": "by D Ahfock · 2023 · Cited by 20 — Semi - supervised learning methods can reduce the sample labeling time and the labor cost of sample labeling while ensuring the validity of the knowledge (Ahfock ..."}
+{"idx": 6, "title": "1.14. Semi-supervised learning", "date": "", "ddg_snippet": "Semi-supervised algorithms need to make assumptions about the distribution of the dataset in order to achieve performance gains. See here for more details.", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/semi_supervised.html", "content": "Semi-supervised algorithms need to make assumptions about the distribution of the dataset in order to achieve performance gains. See here for more details."}
+{"idx": 7, "title": "Uncertainty Estimation with Self-Distillation for semi- ...", "date": "", "ddg_snippet": "by J Chen · 2025 · Cited by 1 — Semi - supervised few-shot classification aims to recognize unseen samples within the same classes in the support set by only a few labeled ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0950705125009931", "content": "by J Chen · 2025 · Cited by 1 — Semi - supervised few-shot classification aims to recognize unseen samples within the same classes in the support set by only a few labeled ..."}
+{"idx": 8, "title": "Class-prior estimation for learning from positive and ...", "date": "", "ddg_snippet": "by MC du Plessis · 2017 · Cited by 201 — In this paper, we discussed the problem of class-prior estimation from positive and unlabeled data. We first showed that class-prior estimation ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-016-5604-6", "content": "by MC du Plessis · 2017 · Cited by 201 — In this paper, we discussed the problem of class-prior estimation from positive and unlabeled data. We first showed that class-prior estimation ..."}
+{"idx": 9, "title": "Semi-supervised learning: a brief review", "date": "", "ddg_snippet": "This paper addresses few tech- niques of Semi-supervised learning (SSL) such as self-training, co-training, multi-view learning, TSVMs methods . Traditionally ... 5 pages", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/semi-supervised-learning-a-brief-review-34s96mslfc.pdf", "content": "This paper addresses few tech- niques of Semi-supervised learning (SSL) such as self-training, co-training, multi-view learning, TSVMs methods . Traditionally ... 5 pages"}
diff --git a/data/sampled_jsons/seven_lenses_for_interrogating_validity_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measure.jsonl b/data/sampled_jsons/seven_lenses_for_interrogating_validity_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measure.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5a836e5e6c063117af143f6358fd5d0c50a809d5
--- /dev/null
+++ b/data/sampled_jsons/seven_lenses_for_interrogating_validity_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measure.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.10939v1", "content": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) ..."}
+{"idx": 1, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge ... is that evaluating GenAI systems is a social science ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge ... is that evaluating GenAI systems is a social science ..."}
+{"idx": 2, "title": "(PDF) Trustworthy AI for Whom? GenAI Detection Techniques of", "date": "", "ddg_snippet": "Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI -generated misinformation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389647472_Trustworthy_AI_for_Whom_GenAI_Detection_Techniques_of_Trust_Through_Decentralized_Web3_Ecosystems", "content": "Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI -generated misinformation."}
+{"idx": 3, "title": "Education Sciences | March 2025 - Browse Articles", "date": "", "ddg_snippet": "For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7102/15/3", "content": "For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ..."}
+{"idx": 4, "title": "Get Involved | Center for Technological Responsibility,", "date": "", "ddg_snippet": "Ongoing regulatory efforts for machine learning such as the Blueprint for an AI Bill of Rights and Executive Order 14110 outline the need to measure ...", "subpage_snippet": "", "source": "cntr.brown.edu", "link": "https://cntr.brown.edu/get-involved", "content": "Ongoing regulatory efforts for machine learning such as the Blueprint for an AI Bill of Rights and Executive Order 14110 outline the need to measure ..."}
+{"idx": 5, "title": "Deep Learning – Cloud Computing For Science and", "date": "", "ddg_snippet": "This dialog continues until the question is answered and the graph interrogator returns a summary answer to the user_proxy for display to the human.", "subpage_snippet": "", "source": "cloud4scieng.org", "link": "https://cloud4scieng.org/tag/deep-learning/", "content": "This dialog continues until the question is answered and the graph interrogator returns a summary answer to the user_proxy for display to the human."}
+{"idx": 6, "title": "Microsoft: G7 Hiroshima AI Process (HAIP) Transparency Report |", "date": "", "ddg_snippet": "... is committed to helping advance ... This collaboration is particularly important to quickly advance the science of AI measurement and evaluation.", "subpage_snippet": "", "source": "transparency.oecd.ai", "link": "https://transparency.oecd.ai/reports/68e6cacb-5496-4487-8793-de3e70080b27", "content": "... is committed to helping advance ... This collaboration is particularly important to quickly advance the science of AI measurement and evaluation."}
+{"idx": 7, "title": "From Users to Designers; High School Students as Co-Designers", "date": "", "ddg_snippet": "As educational technologies increasingly shape instruction and assessment (Bennett et al., 2016 ), CRP is essential in interrogating whether AI tools ...", "subpage_snippet": "", "source": "www.researchsquare.com", "link": "https://www.researchsquare.com/article/rs-7151671/v1", "content": "As educational technologies increasingly shape instruction and assessment (Bennett et al., 2016 ), CRP is essential in interrogating whether AI tools ..."}
+{"idx": 8, "title": "Critical Approaches to International Law and", "date": "", "ddg_snippet": "Literary Studies (Fiction, Novelists, and Prose Writers) ... Browse content in Constitutional and Administrative Law", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/book/59804/chapter/511222035", "content": "Literary Studies (Fiction, Novelists, and Prose Writers) ... Browse content in Constitutional and Administrative Law"}
+{"idx": 9, "title": "Transnational Legal Process, Cognition, and Context |", "date": "", "ddg_snippet": "Literary Studies (Fiction, Novelists, and Prose Writers) ... Browse content in Constitutional and Administrative Law", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/book/59804/chapter/511222586", "content": "Literary Studies (Fiction, Novelists, and Prose Writers) ... Browse content in Constitutional and Administrative Law"}
diff --git a/data/sampled_jsons/seven_lenses_for_interrogating_validity_consequential_validity_Wallach_year_2024.jsonl b/data/sampled_jsons/seven_lenses_for_interrogating_validity_consequential_validity_Wallach_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d5571bf0ef4f83252f50856511c9284fa235fe0d
--- /dev/null
+++ b/data/sampled_jsons/seven_lenses_for_interrogating_validity_consequential_validity_Wallach_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Impact of a USMLE Step 2 Prediction Model on Medical Student ...", "date": "", "ddg_snippet": "Our study was also not powered to determine the impact of consequential validity —the positive or negative social consequences resulting from the use of this assessment 30, 31. Though our sample size was large, the study was conducted at a single institution which may limit its external generalizability.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/23821205251321812?icid=int.sj-abstract.similar-articles.1", "content": "Our study was also not powered to determine the impact of consequential validity —the positive or negative social consequences resulting from the use of this assessment 30, 31. Though our sample size was large, the study was conducted at a single institution which may limit its external generalizability."}
+{"idx": 1, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "It also offers a set of lenses for interrogating the validity of measurement instruments and their resulting measurements (e.g., Adcock ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "It also offers a set of lenses for interrogating the validity of measurement instruments and their resulting measurements (e.g., Adcock ..."}
+{"idx": 2, "title": "Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "Second, it brings rigor to operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.10939v1", "content": "Second, it brings rigor to operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their ..."}
+{"idx": 3, "title": "Through the lens of efficacy: torture on suspicion |", "date": "", "ddg_snippet": "... to put forward the precise manner in which the testing of empirical validity will be carried out when discussing interrogation for counterterrorism.", "subpage_snippet": "", "source": "lawexplores.com", "link": "https://lawexplores.com/through-the-lens-of-efficacy-torture-on-suspicion/", "content": "... to put forward the precise manner in which the testing of empirical validity will be carried out when discussing interrogation for counterterrorism."}
+{"idx": 4, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "by H Wallach · 2025 · Cited by 10 — All seven lenses of validity can inform operational debates . These lenses should be used throughout operationalization to interrogate the operationalization ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00561", "content": "by H Wallach · 2025 · Cited by 10 — All seven lenses of validity can inform operational debates . These lenses should be used throughout operationalization to interrogate the operationalization ..."}
+{"idx": 5, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · 2024 · Cited by 15 — Measurement theory there- fore offers a set of lenses for interrogating the validity of measurement instruments and their resulting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.10939", "content": "by H Wallach · 2024 · Cited by 15 — Measurement theory there- fore offers a set of lenses for interrogating the validity of measurement instruments and their resulting."}
+{"idx": 6, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · Cited by 11 — Second, it brings rigor to both conceptual and operational debates by offering a set of lenses for interrogating the validity of measurement instruments and ...", "subpage_snippet": "", "source": "afedercooper.info", "link": "https://afedercooper.info/paper/wallach2024measurement.pdf", "content": "by H Wallach · Cited by 11 — Second, it brings rigor to both conceptual and operational debates by offering a set of lenses for interrogating the validity of measurement instruments and ..."}
+{"idx": 7, "title": "AI Red Teaming Through the Lens of Measurement Theory", "date": "", "ddg_snippet": "by A Chouldechova · Cited by 2 — Measure- ment theory also provides a set of lenses for interrogating the reliability and validity of measurement instruments and their ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KEggQCeDUA", "content": "by A Chouldechova · Cited by 2 — Measure- ment theory also provides a set of lenses for interrogating the reliability and validity of measurement instruments and their ..."}
+{"idx": 8, "title": "Position - Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "\"We recommend using the following set of lenses to interrogate validity , adapted from Messick (1987) by Jacobs & Wallach (2021): face validity , content validity ...", "subpage_snippet": "", "source": "notebook.ddeepak95.com", "link": "https://notebook.ddeepak95.com/reference/position-evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/", "content": "\"We recommend using the following set of lenses to interrogate validity , adapted from Messick (1987) by Jacobs & Wallach (2021): face validity , content validity ..."}
+{"idx": 9, "title": "This Prompt is Measuring : Evaluating Bias ...", "date": "", "ddg_snippet": "by S Goldfarb-Tarrant · 2023 · Cited by 43 — Finally, in assessing whether a measure- ment shows consequential validity , we consider how it might shape the world, perhaps by intro- ducing ... 17 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.findings-acl.139.pdf", "content": "by S Goldfarb-Tarrant · 2023 · Cited by 43 — Finally, in assessing whether a measure- ment shows consequential validity , we consider how it might shape the world, perhaps by intro- ducing ... 17 pages"}
diff --git a/data/sampled_jsons/sieve_MLE_distribution_regression_FD1_MSE.jsonl b/data/sampled_jsons/sieve_MLE_distribution_regression_FD1_MSE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e525ecf20a066c933980c689965ca21c0962c1fb
--- /dev/null
+++ b/data/sampled_jsons/sieve_MLE_distribution_regression_FD1_MSE.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mean squared error - Wikipedia", "date": "", "ddg_snippet": "\" Mean squared deviation\" redirects here; not to be confused with Mean squared displacement. In statistics, the mean squared error ( MSE )[1] or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Mean_squared_error", "content": "\" Mean squared deviation\" redirects here; not to be confused with Mean squared displacement. In statistics, the mean squared error ( MSE )[1] or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using", "date": "", "ddg_snippet": "Keywords: Distribution Regression ; Conditional Deep Generative Models; Intrinsic Manifold Structure; Sieve MLE ; Wasserstein Convergence.Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "Keywords: Distribution Regression ; Conditional Deep Generative Models; Intrinsic Manifold Structure; Sieve MLE ; Wasserstein Convergence.Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE ."}
+{"idx": 2, "title": "(PDF) A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Keywords: Distribution Regression ; Conditional Deep Generative Models; Intrinsic Manifold Structure; Sieve MLE ; Wasserstein Convergence. 1 Introduction. Conditional distribution estimation provides a principled framework for characterizing the dependence rela", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "Keywords: Distribution Regression ; Conditional Deep Generative Models; Intrinsic Manifold Structure; Sieve MLE ; Wasserstein Convergence. 1 Introduction. Conditional distribution estimation provides a principled framework for characterizing the dependence rela"}
+{"idx": 3, "title": "Regression Metrics - GeeksforGeeks", "date": "", "ddg_snippet": "Mean Squared Error ( MSE ). A popular metric in statistics and machine learning is the Mean Squared Error ( MSE ). It measures the square root of the average discrepancies between a dataset's actual values and projected values.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/regression-metrics/", "content": "Mean Squared Error ( MSE ). A popular metric in statistics and machine learning is the Mean Squared Error ( MSE ). It measures the square root of the average discrepancies between a dataset's actual values and projected values."}
+{"idx": 4, "title": "Normal distribution - Maximum likelihood estimation", "date": "", "ddg_snippet": "Maximum likelihood estimation ( MLE ) of the parameters of the normal distribution . Derivation and properties, with detailed proofs.", "subpage_snippet": "", "source": "www.statlect.com", "link": "https://www.statlect.com/fundamentals-of-statistics/normal-distribution-maximum-likelihood", "content": "Maximum likelihood estimation ( MLE ) of the parameters of the normal distribution . Derivation and properties, with detailed proofs."}
+{"idx": 5, "title": "3 Regression Metrics You Must Know: MAE, MSE ... | Proclus Academy", "date": "", "ddg_snippet": "Root Mean Squared Error (RMSE). MAE vs. RMSE. Practice using Python & Scikit-Learn. Load Dataset. Build Regression Model. Calculate Metrics - MAE, MSE , and RMSE. Summary & Next Steps.", "subpage_snippet": "", "source": "proclusacademy.com", "link": "https://proclusacademy.com/blog/explainer/regression-metrics-you-must-know/", "content": "Root Mean Squared Error (RMSE). MAE vs. RMSE. Practice using Python & Scikit-Learn. Load Dataset. Build Regression Model. Calculate Metrics - MAE, MSE , and RMSE. Summary & Next Steps."}
+{"idx": 6, "title": "Mean Squared Error ( MSE ) - Statistics By Jim", "date": "", "ddg_snippet": "Mean squared error ( MSE ) measures the amount of error in statistical models. It assesses the average squared difference between the observed and predicted values. When a model has no error, the MSE equals zero.", "subpage_snippet": "", "source": "statisticsbyjim.com", "link": "https://statisticsbyjim.com/regression/mean-squared-error-mse/", "content": "Mean squared error ( MSE ) measures the amount of error in statistical models. It assesses the average squared difference between the observed and predicted values. When a model has no error, the MSE equals zero."}
+{"idx": 7, "title": "How to Calculate Mean Squared Error ( MSE )... - Automate Excel", "date": "", "ddg_snippet": "The Mean Squared Error ( MSE ) is an estimate that measures the average squared difference between the estimated values and the actual values of a data distribution .How to Calculate Mean Square Error 001.", "subpage_snippet": "", "source": "www.automateexcel.com", "link": "https://www.automateexcel.com/stats/calculate-mean-square-error/", "content": "The Mean Squared Error ( MSE ) is an estimate that measures the average squared difference between the estimated values and the actual values of a data distribution .How to Calculate Mean Square Error 001."}
+{"idx": 8, "title": "Level-Wise Conditional Distribution", "date": "", "ddg_snippet": "Explore level-wise conditional distributions in statistics and machine learning, enabling targeted estimation, adaptive modeling, and precise inference methods.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/level-wise-conditional-distribution", "content": "Explore level-wise conditional distributions in statistics and machine learning, enabling targeted estimation, adaptive modeling, and precise inference methods."}
+{"idx": 9, "title": "MLE of the SESPC distribution function - RDocumentation", "date": "", "ddg_snippet": "Unlike the spherical independent projected Cauchy distribution this is rotationally symmetric and is a competitor of the spherical ESAG and Kent distributions (which are also ellitpically symmetric).", "subpage_snippet": "", "source": "www.rdocumentation.org", "link": "https://www.rdocumentation.org/packages/Directional/versions/5.9/topics/MLE+of+the+SESPC+distribution", "content": "Unlike the spherical independent projected Cauchy distribution this is rotationally symmetric and is a competitor of the spherical ESAG and Kent distributions (which are also ellitpically symmetric)."}
diff --git a/data/sampled_jsons/signed_Laplacian_constrained_graph_clustering_methodology_advantage.jsonl b/data/sampled_jsons/signed_Laplacian_constrained_graph_clustering_methodology_advantage.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05263b7cb34fdaf57a493bdcba631e18f8c8d296
--- /dev/null
+++ b/data/sampled_jsons/signed_Laplacian_constrained_graph_clustering_methodology_advantage.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constrained Graph Clustering with Signed Laplacians", "date": "", "ddg_snippet": "By solving a generalized eigenvalue problem, our algorithm provides improvements in performance, particularly in scenarios where traditional spectral clustering methods face difficulties. We demonstrate its practical effectiveness through experiments on both synthetic and real-world datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FneYHZU19U", "content": "By solving a generalized eigenvalue problem, our algorithm provides improvements in performance, particularly in scenarios where traditional spectral clustering methods face difficulties. We demonstrate its practical effectiveness through experiments on both synthetic and real-world datasets."}
+{"idx": 1, "title": "PDF Regularized spectral methods for clustering signed networks", "date": "", "ddg_snippet": "When G is sparse, we propose a spectral clustering method based on a regulariza-tion of the signed graph , leading to a regularized Signed Laplacian . To this end, for +; > 0, recall the regularized adjacency matrices A , with degree matrices D , for the unsigned graphs G+;G respectively.", "subpage_snippet": "", "source": "math.ucla.edu", "link": "https://math.ucla.edu/~mihai/SignedClust2020.pdf", "content": "When G is sparse, we propose a spectral clustering method based on a regulariza-tion of the signed graph , leading to a regularized Signed Laplacian . To this end, for +; > 0, recall the regularized adjacency matrices A , with degree matrices D , for the unsigned graphs G+;G respectively."}
+{"idx": 2, "title": "PDF The Constrained Laplacian Rank Algorithm for Graph-Based Clustering", "date": "", "ddg_snippet": "We address both of these drawbacks by allowing the data graph itself to be adjusted as part of the clustering procedure. In partic-ular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters).", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jordan/papers/CLR_aaai16_ready.pdf", "content": "We address both of these drawbacks by allowing the data graph itself to be adjusted as part of the clustering procedure. In partic-ular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters)."}
+{"idx": 3, "title": "PDF Signed Laplacian for Spectral Clustering Revisited", "date": "", "ddg_snippet": "Abstract Classical spectral clustering is based on a spectral decomposition of a graph Laplacian , ob-tained from a graph adjacency matrix representing positive graph edge weights describing similarities of graph vertices. In signed graphs , the graph edge weights can be negative to describe disparities of graph vertices, for example, negative correlations in the data. Negative weights lead to ...", "subpage_snippet": "", "source": "merl.com", "link": "https://merl.com/publications/docs/TR2017-001.pdf", "content": "Abstract Classical spectral clustering is based on a spectral decomposition of a graph Laplacian , ob-tained from a graph adjacency matrix representing positive graph edge weights describing similarities of graph vertices. In signed graphs , the graph edge weights can be negative to describe disparities of graph vertices, for example, negative correlations in the data. Negative weights lead to ..."}
+{"idx": 4, "title": "PDF SPONGE: A generalized eigenproblem for clustering signed networks", "date": "", "ddg_snippet": "An extensive set of numerical experiments on both synthetic and real data shows that our approach compares favorably with state-of-the-art methods for signed clus-tering , especially for large number of clusters. Keywords: signed clustering , graph Laplacians , stochastic block models, spectral methods, time series, clustering correlation matrices", "subpage_snippet": "", "source": "www.stats.ox.ac.uk", "link": "https://www.stats.ox.ac.uk/~cucuring/signedClustering.pdf", "content": "An extensive set of numerical experiments on both synthetic and real data shows that our approach compares favorably with state-of-the-art methods for signed clus-tering , especially for large number of clusters. Keywords: signed clustering , graph Laplacians , stochastic block models, spectral methods, time series, clustering correlation matrices"}
+{"idx": 5, "title": "New approach for learning structured graph with Laplacian rank ...", "date": "", "ddg_snippet": "Many existing graph-clustering methods get results in a two-stage manner, including constructing a graph from data and partitioning it. It always leads to sub-optimal performances. To address the drawbacks, imposing rank constraint on the graph's Laplacian matrix is an effective way. It can learn a structured graph that connected components are exactly equal to the cluster numbers. According ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224008361", "content": "Many existing graph-clustering methods get results in a two-stage manner, including constructing a graph from data and partitioning it. It always leads to sub-optimal performances. To address the drawbacks, imposing rank constraint on the graph's Laplacian matrix is an effective way. It can learn a structured graph that connected components are exactly equal to the cluster numbers. According ..."}
+{"idx": 6, "title": "Signed Laplacians for Constrained Graph Clustering - OpenReview", "date": "", "ddg_snippet": "To reduce computational complexity, we utilise the signed Laplacian of H, streamlining calculations while maintaining accuracy. By solv-ing a generalised eigenvalue problem, our pro-posed algorithm achieves notable performance improvements, particularly in challenging scenar-ios where traditional spectral clustering methods struggle.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=MHaSq1LlTe", "content": "To reduce computational complexity, we utilise the signed Laplacian of H, streamlining calculations while maintaining accuracy. By solv-ing a generalised eigenvalue problem, our pro-posed algorithm achieves notable performance improvements, particularly in challenging scenar-ios where traditional spectral clustering methods struggle."}
+{"idx": 7, "title": "ICML Poster Signed Laplacians for Constrained Graph Clustering", "date": "", "ddg_snippet": "In this work, we establish a Cheeger-type inequality that relates the solution of the constrained clustering problem to the spectral properties of G and H. To reduce computational complexity, we utilise the signed Laplacian of H, streamlining calculations while maintaining accuracy.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45552", "content": "In this work, we establish a Cheeger-type inequality that relates the solution of the constrained clustering problem to the spectral properties of G and H. To reduce computational complexity, we utilise the signed Laplacian of H, streamlining calculations while maintaining accuracy."}
+{"idx": 8, "title": "Spectral Clustering of Signed Graphs via Matrix Power Means", "date": "", "ddg_snippet": "In this paper we present a novel extension of spectral cluster-ing for signed graphs . Spectral clustering (Luxburg, 2007) is a well established technique for non- signed graphs , which partitions the set of the nodes based on a k-dimensional node embedding obtained using the first eigenvectors of the graph Laplacian .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1905.06230", "content": "In this paper we present a novel extension of spectral cluster-ing for signed graphs . Spectral clustering (Luxburg, 2007) is a well established technique for non- signed graphs , which partitions the set of the nodes based on a k-dimensional node embedding obtained using the first eigenvectors of the graph Laplacian ."}
+{"idx": 9, "title": "Large-Scale Clustering With Anchor-Based Constrained Laplacian Rank ...", "date": "", "ddg_snippet": "Graph -based clustering technique has garnered significant attention due to precise information characterization by pairwise graph similarity. Nevertheless, the post-processing step in traditional methods often limits clustering effects because of crucial information loss. Therefore, the Constrained Laplacian Rank (CLR) theory emerges to directly obtain discrete labels from optimally structural ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10955201", "content": "Graph -based clustering technique has garnered significant attention due to precise information characterization by pairwise graph similarity. Nevertheless, the post-processing step in traditional methods often limits clustering effects because of crucial information loss. Therefore, the Constrained Laplacian Rank (CLR) theory emerges to directly obtain discrete labels from optimally structural ..."}
diff --git a/data/sampled_jsons/siteaashishrai3799.github.iouvgs_Objaverse_dataset_size_year_2024.jsonl b/data/sampled_jsons/siteaashishrai3799.github.iouvgs_Objaverse_dataset_size_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d043c2c65a488f21fbb1948b15a21dded9a09e6e
--- /dev/null
+++ b/data/sampled_jsons/siteaashishrai3799.github.iouvgs_Objaverse_dataset_size_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Microsoft - Wikipedia", "date": "", "ddg_snippet": "Microsoft is the largest software maker, one of the most valuable public companies, [a] and one of the most valuable brands globally. Microsoft is considered part of the Big Tech group, alongside Nvidia, Apple, Alphabet, Amazon, and Meta. Microsoft was founded by Bill Gates and Paul Allen to develop and sell BASIC interpreters for the Altair 8800.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Microsoft", "content": "Microsoft is the largest software maker, one of the most valuable public companies, [a] and one of the most valuable brands globally. Microsoft is considered part of the Big Tech group, alongside Nvidia, Apple, Alphabet, Amazon, and Meta. Microsoft was founded by Bill Gates and Paul Allen to develop and sell BASIC interpreters for the Altair 8800."}
+{"idx": 1, "title": "Microsoft – AI, Cloud, Productivity, Computing, Gaming & Apps", "date": "", "ddg_snippet": "Explore Microsoft products and services and support for your home or business. Shop Microsoft 365, Copilot, Teams, Xbox, Windows, Azure, Surface and more.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us", "content": "Explore Microsoft products and services and support for your home or business. Shop Microsoft 365, Copilot, Teams, Xbox, Windows, Azure, Surface and more."}
+{"idx": 2, "title": "Office 365 login", "date": "", "ddg_snippet": "Collaborate for free with online versions of Microsoft Word, PowerPoint, Excel, and OneNote. Save documents, spreadsheets, and presentations online, in OneDrive.", "subpage_snippet": "", "source": "www.office.com", "link": "https://www.office.com/", "content": "Collaborate for free with online versions of Microsoft Word, PowerPoint, Excel, and OneNote. Save documents, spreadsheets, and presentations online, in OneDrive."}
+{"idx": 3, "title": "Microsoft account | Sign In or Create Your Account Today – ...", "date": "", "ddg_snippet": "Get access to free online versions of Outlook, Word, Excel, and PowerPoint.", "subpage_snippet": "", "source": "account.microsoft.com", "link": "https://account.microsoft.com/account", "content": "Get access to free online versions of Outlook, Word, Excel, and PowerPoint."}
+{"idx": 4, "title": "Microsoft Redmond Campus Refresh", "date": "", "ddg_snippet": "Jun 28, 2025 · Microsoft ’s 500-acre campus is a unique asset to the company as well as the community. Neighboring a vibrant urban core, lakes, mountains, and miles of forest, it’s one of Microsoft ’s crown jewels. As they continue to grow and look to create the best workplace in the tech sector, Microsoft will invest right here in Redmond, their home for more than 30 years.", "subpage_snippet": "", "source": "www.redmond.gov", "link": "https://www.redmond.gov/386/Microsoft-Redmond-Campus-Refresh", "content": "Jun 28, 2025 · Microsoft ’s 500-acre campus is a unique asset to the company as well as the community. Neighboring a vibrant urban core, lakes, mountains, and miles of forest, it’s one of Microsoft ’s crown jewels. As they continue to grow and look to create the best workplace in the tech sector, Microsoft will invest right here in Redmond, their home for more than 30 years."}
+{"idx": 5, "title": "My Account", "date": "", "ddg_snippet": "Access and manage your Microsoft account, subscriptions, and settings all in one place.", "subpage_snippet": "", "source": "myaccount.microsoft.com", "link": "https://myaccount.microsoft.com/", "content": "Access and manage your Microsoft account, subscriptions, and settings all in one place."}
+{"idx": 6, "title": "Explore Microsoft Products, Apps & Devices | Microsoft", "date": "", "ddg_snippet": "Uncover the power of Microsoft 's products, apps, and devices designed to simplify your life and fuel your passions. Explore our comprehensive range and unlock new capabilities.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/microsoft-products-and-apps", "content": "Uncover the power of Microsoft 's products, apps, and devices designed to simplify your life and fuel your passions. Explore our comprehensive range and unlock new capabilities."}
+{"idx": 7, "title": "Contact Us - Microsoft Support", "date": "", "ddg_snippet": "Contact Microsoft Support. Find solutions to common problems, or get help from a support agent.", "subpage_snippet": "", "source": "support.microsoft.com", "link": "https://support.microsoft.com/en-us/contactus", "content": "Contact Microsoft Support. Find solutions to common problems, or get help from a support agent."}
+{"idx": 8, "title": "Download Drivers & Updates for Microsoft , Windows and more - ...", "date": "", "ddg_snippet": "The official Microsoft Download Center. Featuring the latest software updates and drivers for Windows, Office, Xbox and more. Operating systems include Windows, Mac, Linux, iOS, and Android.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/download", "content": "The official Microsoft Download Center. Featuring the latest software updates and drivers for Windows, Office, Xbox and more. Operating systems include Windows, Mac, Linux, iOS, and Android."}
+{"idx": 9, "title": "Microsoft Support", "date": "", "ddg_snippet": "Microsoft Support is here to help you with Microsoft products. Find how-to articles, videos, and training for Microsoft Copilot, Microsoft 365, Windows, Surface, and more.", "subpage_snippet": "", "source": "support.microsoft.com", "link": "https://support.microsoft.com/en-us", "content": "Microsoft Support is here to help you with Microsoft products. Find how-to articles, videos, and training for Microsoft Copilot, Microsoft 365, Windows, Surface, and more."}
diff --git a/data/sampled_jsons/siteahf.nuclearmuseum.org_Stanislaus_Ulam_Einstein_creativity.jsonl b/data/sampled_jsons/siteahf.nuclearmuseum.org_Stanislaus_Ulam_Einstein_creativity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf8213bdb326f972c0a464f1abba9dc1c338aea9
--- /dev/null
+++ b/data/sampled_jsons/siteahf.nuclearmuseum.org_Stanislaus_Ulam_Einstein_creativity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stanislaus Ulam's Interview (1979) - Nuclear Museum", "date": "", "ddg_snippet": "Stanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at Los Alamos and his relationship with J. Robert Oppenheimer, Hans Bethe, John von Neumann, Enrico Fermi, and other scientists. He also discusses Oppenheimer's varied reputation within the physics community ...", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "content": "Stanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at Los Alamos and his relationship with J. Robert Oppenheimer, Hans Bethe, John von Neumann, Enrico Fermi, and other scientists. He also discusses Oppenheimer's varied reputation within the physics community ..."}
+{"idx": 1, "title": "Stanislaw Ulam - Nuclear Museum", "date": "", "ddg_snippet": "Stanislaw Ulam (1909-1984) was a Polish-American mathematician. Ulam was born in 1909 in Lvov, Austria-Hungary (present-day Ukraine). He received a masters and a Ph.D. from the Lvov Polytechnic Institute. Beginning in 1935, Ulam spent his academic years at Harvard University as a member of the Society of Fellows while returning to…", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/ahf/profile/stanislaw-ulam/", "content": "Stanislaw Ulam (1909-1984) was a Polish-American mathematician. Ulam was born in 1909 in Lvov, Austria-Hungary (present-day Ukraine). He received a masters and a Ph.D. from the Lvov Polytechnic Institute. Beginning in 1935, Ulam spent his academic years at Harvard University as a member of the Society of Fellows while returning to…"}
+{"idx": 2, "title": "Stanislaus Ulam - Nuclear Museum", "date": "", "ddg_snippet": "Stanislaus Ulam (1909 - 1984) was a Polish mathematician. In 1943, after having worked as a university professor in the United States for three years, Ulam contacted Hungarian physicist John von Neumann to request a war job.", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/voices/people/stanislaus-ulam/", "content": "Stanislaus Ulam (1909 - 1984) was a Polish mathematician. In 1943, after having worked as a university professor in the United States for three years, Ulam contacted Hungarian physicist John von Neumann to request a war job."}
+{"idx": 3, "title": "Stanislaus Ulam 's Interview (1983) - Nuclear Museum", "date": "", "ddg_snippet": "Stanislaus Ulam was a Polish mathematician recruited to the Manhattan Project in 1943. Ulam worked on hydrodynamical calculations that were crucial to the design of the implosion-type weapon created at Los Alamos.", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1983/", "content": "Stanislaus Ulam was a Polish mathematician recruited to the Manhattan Project in 1943. Ulam worked on hydrodynamical calculations that were crucial to the design of the implosion-type weapon created at Los Alamos."}
+{"idx": 4, "title": "Albert Einstein - Nuclear Museum", "date": "", "ddg_snippet": "Stanislaus Ulam : You know, after forty-five years in this country, my accent is still very hard. Martin Sherwin: That's all right. I still have a Brooklyn accent. Ulam : Oh, you do? Sherwin: I left Brooklyn twenty years ago. I think even though I do know a lot of the answers to some of the questions […] Oral History The Search for Atomic Power April 19, 2016", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/tag/albert-einstein/", "content": "Stanislaus Ulam : You know, after forty-five years in this country, my accent is still very hard. Martin Sherwin: That's all right. I still have a Brooklyn accent. Ulam : Oh, you do? Sherwin: I left Brooklyn twenty years ago. I think even though I do know a lot of the answers to some of the questions […] Oral History The Search for Atomic Power April 19, 2016"}
+{"idx": 5, "title": "Stanislaus Ulam - Nuclear Museum", "date": "", "ddg_snippet": "Stanislaus Ulam : You know, after forty-five years in this country, my accent is still very hard. Martin Sherwin: That's all right. I still have a Brooklyn accent. Ulam : Oh, you do? Sherwin: I left Brooklyn twenty years ago. I think even though I do know a lot of the answers to some of the questions […] Oral History Joseph Rotblat's Interview July 21, 2016", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/tag/stanislaus-ulam/", "content": "Stanislaus Ulam : You know, after forty-five years in this country, my accent is still very hard. Martin Sherwin: That's all right. I still have a Brooklyn accent. Ulam : Oh, you do? Sherwin: I left Brooklyn twenty years ago. I think even though I do know a lot of the answers to some of the questions […] Oral History Joseph Rotblat's Interview July 21, 2016"}
+{"idx": 6, "title": "Scientist Refugees and the Manhattan Project - Nuclear Museum", "date": "", "ddg_snippet": "Stanislaw Ulam fled Poland with his brother Adam shortly before the German invasion in September 1939. The rest of Ulam ’s family, including his parents and sister, were killed in the Holocaust.", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/scientist-refugees-and-manhattan-project/", "content": "Stanislaw Ulam fled Poland with his brother Adam shortly before the German invasion in September 1939. The rest of Ulam ’s family, including his parents and sister, were killed in the Holocaust."}
+{"idx": 7, "title": "Computing and the Manhattan Project - Nuclear Museum", "date": "", "ddg_snippet": "It was named for the Monte Carlo casino, where Stanislaw Ulam ’s uncle often gambled. First invented during the Manhattan Project, the Monte Carlo method had been used on old analog computers.", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/ahf/history/computing-and-manhattan-project/", "content": "It was named for the Monte Carlo casino, where Stanislaw Ulam ’s uncle often gambled. First invented during the Manhattan Project, the Monte Carlo method had been used on old analog computers."}
+{"idx": 8, "title": "Oral Histories - Nuclear Museum", "date": "", "ddg_snippet": "Stanislaus Ulam was a Polish-American mathematician . He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this ...", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/voices/oral-histories?_tags=stanislaus-ulam", "content": "Stanislaus Ulam was a Polish-American mathematician . He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this ..."}
+{"idx": 9, "title": "Hydrogen Bomb - Page 3 - Nuclear Museum", "date": "", "ddg_snippet": "Stanislaw Ulam (1909-1984) was a Polish-American mathematician. Ulam was born in 1909 in Lvov, Austria-Hungary (present-day Ukraine). He received a masters and a Ph.D. from the Lvov Polytechnic Institute.", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/ahf/profile-type/hydrogen-bomb/page/3/", "content": "Stanislaw Ulam (1909-1984) was a Polish-American mathematician. Ulam was born in 1909 in Lvov, Austria-Hungary (present-day Ukraine). He received a masters and a Ph.D. from the Lvov Polytechnic Institute."}
diff --git a/data/sampled_jsons/sitearxiv.org_1705.07057_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_1705.07057_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..68761c8a5e258f543384a399580cee07a8433fb9
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_1705.07057_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1705.07057] Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "Abstract : Autoregressive models are among the best performing neural density estimators. ... or arXiv: 1705 .07057v4 [stat.ML] for this version)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1705.07057", "content": "Abstract : Autoregressive models are among the best performing neural density estimators. ... or arXiv: 1705 .07057v4 [stat.ML] for this version)"}
+{"idx": 1, "title": "Abstract", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10938v1", "content": "... Abstract"}
+{"idx": 2, "title": "SES Diagnostic Procedure for '07's - The Nissan Club", "date": "", "ddg_snippet": "Jan 4, 2007 · TPV SEN/CIRC A/T.................................... 1705 TW CATALYST SYS-B1...............................0420 VC CUT/V BYPASS/V .................................1491 VC/V BYPASS/V.........................................1490 VEH SPD SEN/CIR AT................................0720 VEH SPEED SEN/CIRC..............................0500", "subpage_snippet": "", "source": "www.nissanclub.com", "link": "https://www.nissanclub.com/threads/ses-diagnostic-procedure-for-07s.236979/", "content": "Jan 4, 2007 · TPV SEN/CIRC A/T.................................... 1705 TW CATALYST SYS-B1...............................0420 VC CUT/V BYPASS/V .................................1491 VC/V BYPASS/V.........................................1490 VEH SPD SEN/CIR AT................................0720 VEH SPEED SEN/CIRC..............................0500"}
+{"idx": 3, "title": "Tranny gets stuck in 5th gear - The Nissan Club", "date": "", "ddg_snippet": "Mar 28, 2008 · TPV SEN/CIRC A/T.................................... 1705 TW CATALYST SYS-B1...............................0420 VC CUT/V BYPASS/V .................................1491 VC/V BYPASS/V.........................................1490 VEH SPD SEN/CIR AT................................0720 VEH SPEED SEN/CIRC..............................0500", "subpage_snippet": "", "source": "www.nissanclub.com", "link": "https://www.nissanclub.com/threads/tranny-gets-stuck-in-5th-gear.266575/", "content": "Mar 28, 2008 · TPV SEN/CIRC A/T.................................... 1705 TW CATALYST SYS-B1...............................0420 VC CUT/V BYPASS/V .................................1491 VC/V BYPASS/V.........................................1490 VEH SPD SEN/CIR AT................................0720 VEH SPEED SEN/CIRC..............................0500"}
+{"idx": 4, "title": "2001 Nissan Sentra Service Engine Light is Blinking", "date": "", "ddg_snippet": "Aug 18, 2005 · TPV SEN/CIRC A/T.................................... 1705 TW CATALYST SYS-B1...............................0420 VC CUT/V BYPASS/V .................................1491 VC/V BYPASS/V.........................................1490 VEH SPD SEN/CIR AT................................0720 VEH SPEED SEN/CIRC..............................0500", "subpage_snippet": "", "source": "www.nissanclub.com", "link": "https://www.nissanclub.com/threads/2001-nissan-sentra-service-engine-light-is-blinking.201488/", "content": "Aug 18, 2005 · TPV SEN/CIRC A/T.................................... 1705 TW CATALYST SYS-B1...............................0420 VC CUT/V BYPASS/V .................................1491 VC/V BYPASS/V.........................................1490 VEH SPD SEN/CIR AT................................0720 VEH SPEED SEN/CIRC..............................0500"}
+{"idx": 5, "title": "Parameter inference of microlensed gravitational waves using", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.20996v1", "content": "... Abstract"}
+{"idx": 6, "title": "Deep learning to detect gravitational waves from binary close", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.12028v1", "content": "... Abstract"}
+{"idx": 7, "title": "𝜌-Diffusion: A diffusion-based density estimation framework", "date": "", "ddg_snippet": "In this paper, we propose a DDPM-based density modeling framework that provides scientific domain-centered abstractions for conditional data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08153v1", "content": "In this paper, we propose a DDPM-based density modeling framework that provides scientific domain-centered abstractions for conditional data ..."}
+{"idx": 8, "title": "How Theory-Informed Priors Affect DESI Evidence for Evolving", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13318v1", "content": "... Abstract"}
+{"idx": 9, "title": "[1910.13233] Neural Density Estimation and Likelihood-free", "date": "", "ddg_snippet": "Abstract : I consider two problems in machine ... Includes in full the following articles: arXiv:1605.06376 , arXiv: 1705 . 07057 , arXiv:1805.07226", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1910.13233", "content": "Abstract : I consider two problems in machine ... Includes in full the following articles: arXiv:1605.06376 , arXiv: 1705 . 07057 , arXiv:1805.07226"}
diff --git a/data/sampled_jsons/sitearxiv.org_2212.09251_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_2212.09251_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..462882c7b29f23457476dd14ff502ec4492df7dd
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2212.09251_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2212 . 09251 ] Discovering Language Model Behaviors with...", "date": "", "ddg_snippet": "arxiv logo > cs > arXiv: 2212 . 09251 . Abstract :As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.09251", "content": "arxiv logo > cs > arXiv: 2212 . 09251 . Abstract :As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave."}
+{"idx": 1, "title": "Discovering Language Model Behaviors with Model-Written Evaluations", "date": "", "ddg_snippet": "arXiv: 2212 . 09251 v1 [cs.CL] 19 Dec 2022. Abstract . As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.09251", "content": "arXiv: 2212 . 09251 v1 [cs.CL] 19 Dec 2022. Abstract . As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave."}
+{"idx": 2, "title": "Humanity’s Last Exam", "date": "", "ddg_snippet": "Humanity’s Last Exam. Report issue for preceding element. Abstract .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.14249v1", "content": "Humanity’s Last Exam. Report issue for preceding element. Abstract ."}
+{"idx": 3, "title": "Small Singular Values Matter: A Random Matrix Analysis of...", "date": "", "ddg_snippet": "Max Staats Matthias Thamm Bern Rosenow. Report issue for preceding element. Abstract .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.17770v2", "content": "Max Staats Matthias Thamm Bern Rosenow. Report issue for preceding element. Abstract ."}
+{"idx": 4, "title": "Subversion Strategy Eval", "date": "", "ddg_snippet": "arXiv:2412.12480v4 [cs.LG] 4 Apr 2025. Intent for subversion Situational awareness Strategic reasoning Action execution. Abstract .ArXiv: 2212 . 09251 [cs].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.12480", "content": "arXiv:2412.12480v4 [cs.LG] 4 Apr 2025. Intent for subversion Situational awareness Strategic reasoning Action execution. Abstract .ArXiv: 2212 . 09251 [cs]."}
+{"idx": 5, "title": "Training Language Models to Win Debates With Self-Play Improves...", "date": "", "ddg_snippet": "Report issue for preceding element. Abstract .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.16636v1", "content": "Report issue for preceding element. Abstract ."}
+{"idx": 6, "title": "A Detailed Factor Analysis for the Political Compass Test:", "date": "", "ddg_snippet": "... Abstract ... arXiv preprint arXiv: 2212 . 09251 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.22493v1", "content": "... Abstract ... arXiv preprint arXiv: 2212 . 09251 ."}
+{"idx": 7, "title": "HumanAgencyBench: Scalable Evaluation of Human Agency Support", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08494v1", "content": "... Abstract"}
+{"idx": 8, "title": "A Multi-Wavelength Survey of Transient Lensing Opportunities", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06688v1", "content": "... Abstract"}
+{"idx": 9, "title": "Real-World Gaps in AI Governance Research AI safety and", "date": "", "ddg_snippet": "We also conduct separate ‘regex’ key word searches in paper abstracts and titles for high-risk deployment domains (medical, finance, commercial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00174v1", "content": "We also conduct separate ‘regex’ key word searches in paper abstracts and titles for high-risk deployment domains (medical, finance, commercial ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2402.02544_LHRS-Bot.jsonl b/data/sampled_jsons/sitearxiv.org_2402.02544_LHRS-Bot.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..59de306c3c429fb52ee46860c0294380ade20a1c
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2402.02544_LHRS-Bot.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2402.02544] LHRS-Bot: Empowering Remote Sensing with VGI ...", "date": "", "ddg_snippet": "Feb 4, 2024 · Building on this foundation, we introduce LHRS - Bot , an MLLM tailored for RS image understanding through a novel multi-level vision-language alignment strategy and a curriculum learning method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.02544", "content": "Feb 4, 2024 · Building on this foundation, we introduce LHRS - Bot , an MLLM tailored for RS image understanding through a novel multi-level vision-language alignment strategy and a curriculum learning method."}
+{"idx": 1, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv.org e-Print archive", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.02544", "content": "arXiv.org e-Print archive"}
+{"idx": 2, "title": "Computer Science 2024 - arXiv.org", "date": "", "ddg_snippet": "[8618] arXiv : 2402 . 02544 [pdf, html, other] Title: LHRS - Bot : Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model Dilxat Muhtar, Zhenshi Li, Feng Gu, Xueliang Zhang, Pengfeng Xiao", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs/2024?skip=8600", "content": "[8618] arXiv : 2402 . 02544 [pdf, html, other] Title: LHRS - Bot : Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model Dilxat Muhtar, Zhenshi Li, Feng Gu, Xueliang Zhang, Pengfeng Xiao"}
+{"idx": 3, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "Mar 18, 2024 · To bridge the gap, we present LHRS - Bot , a specialized MLLM for RS, enhanced by globally available volunteered geographical information (VGI) and worldwide RS images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.02544v3", "content": "Mar 18, 2024 · To bridge the gap, we present LHRS - Bot , a specialized MLLM for RS, enhanced by globally available volunteered geographical information (VGI) and worldwide RS images."}
+{"idx": 4, "title": "LHRS-Bot-Nova: Improved Multimodal Large Language Model for ...", "date": "", "ddg_snippet": "Nov 14, 2024 · In this study, we introduce LHRS - Bot -Nova, an MLLM specialized in understanding remote sensing (RS) images, designed to expertly perform a wide range of RS understanding tasks aligned with human instructions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.09301", "content": "Nov 14, 2024 · In this study, we introduce LHRS - Bot -Nova, an MLLM specialized in understanding remote sensing (RS) images, designed to expertly perform a wide range of RS understanding tasks aligned with human instructions."}
+{"idx": 5, "title": "CHOICE: Benchmarking the Remote Sensing Capabilities of Large ...", "date": "", "ddg_snippet": "For Attribute Comparison, LHRS - Bot consistently outputs the choice “No”, regardless of how the choice order is altered. This behavior indicates a fundamental lack of capability in the model to perform attribute comparison tasks effectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.18145v2", "content": "For Attribute Comparison, LHRS - Bot consistently outputs the choice “No”, regardless of how the choice order is altered. This behavior indicates a fundamental lack of capability in the model to perform attribute comparison tasks effectively."}
+{"idx": 6, "title": "Computer Vision and Pattern Recognition authors/titles Feb ...", "date": "", "ddg_snippet": "Title: LHRS - Bot : Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model Authors: Dilxat Muhtar, Zhenshi Li, Feng Gu, Xueliang Zhang, Pengfeng Xiao", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.CV/2402?skip=175", "content": "Title: LHRS - Bot : Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model Authors: Dilxat Muhtar, Zhenshi Li, Feng Gu, Xueliang Zhang, Pengfeng Xiao"}
+{"idx": 7, "title": "Landsat30-AU: A Vision-Language Dataset for Australian Landsat", "date": "", "ddg_snippet": "We extract the dominant land cover class for six fixed spatial regions within each image: top-left, top-right, bottom- left, bottom-right, center ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03127v1", "content": "We extract the dominant land cover class for six fixed spatial regions within each image: top-left, top-right, bottom- left, bottom-right, center ..."}
+{"idx": 8, "title": "UrBench: A Comprehensive Benchmark for Evaluating Large", "date": "", "ddg_snippet": "These tasks encompass both region-level evaluations that assess LMMs’ capabilities in urban planning, as well as role-level evaluations that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17267v3", "content": "These tasks encompass both region-level evaluations that assess LMMs’ capabilities in urban planning, as well as role-level evaluations that ..."}
+{"idx": 9, "title": "UrbanLLaVA: A Multi-modal Large Language Model for Urban", "date": "", "ddg_snippet": "This approach ensures both broad spatial coverage and the integrity of relationships between different modalities in the final data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.23219v1", "content": "This approach ensures both broad spatial coverage and the integrity of relationships between different modalities in the final data."}
diff --git a/data/sampled_jsons/sitearxiv.org_2411.07501_LAuReL_Learned_Augmented_Residual_Layer.jsonl b/data/sampled_jsons/sitearxiv.org_2411.07501_LAuReL_Learned_Augmented_Residual_Layer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..496205a49f8922278a317d6701f102dcf9f8ab32
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2411.07501_LAuReL_Learned_Augmented_Residual_Layer.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.07501", "content": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 1, "title": "LAUREL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In this paper, we introduce the Learned Aug - mented Residual Layer ( LAUREL )—a novel gen-eralization of the canonical residual connection— designed to serve as an in-situ replacement while outperforming it in both model quality and foot-print metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501", "content": "In this paper, we introduce the Learned Aug - mented Residual Layer ( LAUREL )—a novel gen-eralization of the canonical residual connection— designed to serve as an in-situ replacement while outperforming it in both model quality and foot-print metrics."}
+{"idx": 2, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer ( LAuReL )—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v1", "content": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer ( LAuReL )—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 3, "title": "Machine Learning Nov 2024 - arXiv.org", "date": "", "ddg_snippet": "LAuReL : Learned Augmented Residual Layer Gaurav Menghani, Ravi Kumar, Sanjiv Kumar Comments: Accepted at the 2nd Efficient Systems for Foundation Models Workshop at the International Conference on Machine Learning (ICML) 2024", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.LG/2024-11?skip=700", "content": "LAuReL : Learned Augmented Residual Layer Gaurav Menghani, Ravi Kumar, Sanjiv Kumar Comments: Accepted at the 2nd Efficient Systems for Foundation Models Workshop at the International Conference on Machine Learning (ICML) 2024"}
+{"idx": 4, "title": "Machine Learning - arXiv.org", "date": "", "ddg_snippet": "Nov 11, 2024 · LAuReL : Learned Augmented Residual Layer Gaurav Menghani, Ravi Kumar, Sanjiv Kumar", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.LG/recent?skip=208", "content": "Nov 11, 2024 · LAuReL : Learned Augmented Residual Layer Gaurav Menghani, Ravi Kumar, Sanjiv Kumar"}
+{"idx": 5, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL )—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2411.07501", "content": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL )—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 6, "title": "LAuReL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "2 Learned Augmented Residual Layer . 2.1 Residual Weights Version ( LAuReL -RW).In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v2", "content": "2 Learned Augmented Residual Layer . 2.1 Residual Weights Version ( LAuReL -RW).In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection."}
+{"idx": 7, "title": "LAuReL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce learned augmented residual layer , LAUREL , which generalizes the canonical residual connec-tion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501v1", "content": "In this paper we introduce learned augmented residual layer , LAUREL , which generalizes the canonical residual connec-tion."}
+{"idx": 8, "title": "LAuReL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce learned augmented residual layer , LAUREL , which generalizes the canonical residual connec-tion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501v2", "content": "In this paper we introduce learned augmented residual layer , LAUREL , which generalizes the canonical residual connec-tion."}
+{"idx": 9, "title": "LAuReL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "2 Learned Augmented Residual Layer . 2.1 Residual Weights Version ( LAuReL -RW).In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v3", "content": "2 Learned Augmented Residual Layer . 2.1 Residual Weights Version ( LAuReL -RW).In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection."}
diff --git a/data/sampled_jsons/sitearxiv.org_2411.13653_No_Free_Delivery_Service.jsonl b/data/sampled_jsons/sitearxiv.org_2411.13653_No_Free_Delivery_Service.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..62b6b93b62acc21917d2f47b752a0da5562f93bc
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2411.13653_No_Free_Delivery_Service.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2411.13653] No Free Delivery Service: Epistemic limits of ... [2411.13653] Untitled Document [ar5iv.labs.arxiv.org] arXiv.org Energy Efficient Multi Robot Package Delivery under Capacity ... Human or Robot? Evidence from Last-Mile Delivery Service ORB: Operating Room Bot, Automating Operating Room Logistics ... arXiv.org", "date": "", "ddg_snippet": "Nov 20, 2024 · Abstract page for arXiv paper 2411.13653: No Free Delivery Service : Epistemic limits of passive data collection in complex social systems Dec 5, 2024 · Generated on Thu Dec 5 16:20:01 2024 by L aT eXML Explore a comprehensive collection of open-access research papers across disciplines, fostering global scientific collaboration and knowledge sharing. 4 days ago · Stage 2 synthesizes robot-level timelines that realize pickup, relay, and delivery , explicitly accounting for non-zero service times at these locations and capacity limits of the robots. This decomposition yields plans that are both energy-aware and operationally feasible. 6 days ago · H1:For privacy-sensitive products, consumers are more likely to choose robot service over human service . 3.2 ProductValue Consumer’s delivery preferences for high-value products can be influenced by distinct the- oretical mechanisms. 6 hours ago · In modern hospital settings, delivery robots have successfully transported bulk items between rooms and floors. However, automating item-level operating room logistics presents unique challenges in perception, efficiency, and maintaining sterility. 5 days ago · arXiv . org", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.13653", "content": "Nov 20, 2024 · Abstract page for arXiv paper 2411.13653: No Free Delivery Service : Epistemic limits of passive data collection in complex social systems Dec 5, 2024 · Generated on Thu Dec 5 16:20:01 2024 by L aT eXML Explore a comprehensive collection of open-access research papers across disciplines, fostering global scientific collaboration and knowledge sharing. 4 days ago · Stage 2 synthesizes robot-level timelines that realize pickup, relay, and delivery , explicitly accounting for non-zero service times at these locations and capacity limits of the robots. This decomposition yields plans that are both energy-aware and operationally feasible. 6 days ago · H1:For privacy-sensitive products, consumers are more likely to choose robot service over human service . 3.2 ProductValue Consumer’s delivery preferences for high-value products can be influenced by distinct the- oretical mechanisms. 6 hours ago · In modern hospital settings, delivery robots have successfully transported bulk items between rooms and floors. However, automating item-level operating room logistics presents unique challenges in perception, efficiency, and maintaining sterility. 5 days ago · arXiv . org"}
+{"idx": 1, "title": "Abstract page for arXiv paper 1706.03762: Attention Is All You Need", "date": "", "ddg_snippet": "Abstract page for arXiv paper 1706.03762: Attention Is All You Need...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Abstract page for arXiv paper 1706.03762: Attention Is All You Need..."}
+{"idx": 2, "title": "Log in to arXiv | arXiv e-print repository", "date": "", "ddg_snippet": "The arXiv Privacy Policy has changed. By continuing to use arxiv.org, you are agreeing to the privacy policy. If you've never logged in to arXiv.org. Register for the first time. Registration is required to submit or update papers, but is not ne...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/login", "content": "The arXiv Privacy Policy has changed. By continuing to use arxiv.org, you are agreeing to the privacy policy. If you've never logged in to arXiv.org. Register for the first time. Registration is required to submit or update papers, but is not ne..."}
+{"idx": 3, "title": "Energy Efficient Multi Robot Package Delivery under Capacity ...", "date": "", "ddg_snippet": "4 days ago · Stage 2 synthesizes robot-level timelines that realize pickup, relay, and delivery , explicitly accounting for non-zero service times at these locations and capacity limits of the robots. This decomposition yields plans that are both energy-aware and operationally feasible.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14127v1", "content": "4 days ago · Stage 2 synthesizes robot-level timelines that realize pickup, relay, and delivery , explicitly accounting for non-zero service times at these locations and capacity limits of the robots. This decomposition yields plans that are both energy-aware and operationally feasible."}
+{"idx": 4, "title": "Human or Robot? Evidence from Last-Mile Delivery Service", "date": "", "ddg_snippet": "6 days ago · H1:For privacy-sensitive products, consumers are more likely to choose robot service over human service . 3.2 ProductValue Consumer’s delivery preferences for high-value products can be influenced by distinct the- oretical mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.11562", "content": "6 days ago · H1:For privacy-sensitive products, consumers are more likely to choose robot service over human service . 3.2 ProductValue Consumer’s delivery preferences for high-value products can be influenced by distinct the- oretical mechanisms."}
+{"idx": 5, "title": "arXiv.org", "date": "", "ddg_snippet": "5 days ago · arXiv . org", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.12725", "content": "5 days ago · arXiv . org"}
+{"idx": 6, "title": "[2411.13653] Untitled Document [ar5iv.labs.arxiv.org]", "date": "", "ddg_snippet": "Dec 5, 2024 · Generated on Thu Dec 5 16:20:01 2024 by L aT eXML", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2411.13653", "content": "Dec 5, 2024 · Generated on Thu Dec 5 16:20:01 2024 by L aT eXML"}
+{"idx": 7, "title": "arXiv.org", "date": "", "ddg_snippet": "Explore a comprehensive collection of open-access research papers across disciplines, fostering global scientific collaboration and knowledge sharing.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.13508", "content": "Explore a comprehensive collection of open-access research papers across disciplines, fostering global scientific collaboration and knowledge sharing."}
+{"idx": 8, "title": "Passive data collection and inference tasks in social systems", "date": "", "ddg_snippet": "arXiv: 2411 . 13653 v1 [cs.AI] 20 Nov 2024. No Free Delivery Service . Epistemic limits of passive data collection in complex social systems. Maximilian Nickel.In other words, there is no free delivery service of data for model validation in complex social systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.13653", "content": "arXiv: 2411 . 13653 v1 [cs.AI] 20 Nov 2024. No Free Delivery Service . Epistemic limits of passive data collection in complex social systems. Maximilian Nickel.In other words, there is no free delivery service of data for model validation in complex social systems."}
+{"idx": 9, "title": "On Heuristic Models, Assumptions, and Parameters", "date": "", "ddg_snippet": "2024. No Free Delivery Service : Epistemic Limits of Passive Data Collection in Complex Social Systems. arXiv preprint arXiv: 2411 . 13653 (2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2201.07413v3", "content": "2024. No Free Delivery Service : Epistemic Limits of Passive Data Collection in Complex Social Systems. arXiv preprint arXiv: 2411 . 13653 (2024)."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.04313_Great_Models_Think_Alike_Section_4.jsonl b/data/sampled_jsons/sitearxiv.org_2502.04313_Great_Models_Think_Alike_Section_4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6e7524ce85165abe4affcd10e28aa1780d70b6b9
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2502.04313_Great_Models_Think_Alike_Section_4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Secondly, we explain how to extend CAPA to multi- model set-up ( Section A.2) and how to adapt CAPA to classification and exact match settings ( Section A.3). Lastly, we introduce probabilistic versions of popular agreement metrics ( Section A.5) and provide a comparison between them and CAPA ( Section A. 4 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v1", "content": "Secondly, we explain how to extend CAPA to multi- model set-up ( Section A.2) and how to adapt CAPA to classification and exact match settings ( Section A.3). Lastly, we introduce probabilistic versions of popular agreement metrics ( Section A.5) and provide a comparison between them and CAPA ( Section A. 4 )."}
+{"idx": 1, "title": "Great Models Think Alike and this Undermines AI Oversight Great Models Think Alike and this Undermines AI Oversight [2502.04313] Great Models Think Alike and this Undermines AI ... Great Models Think Alike and this Undermines AI Oversight UQ: Assessing Language Models on Unsolved Questions [2305.01481] Great Models Think Alike: Improving Model ... Quantitative LLM Judges - arXiv.org", "date": "", "ddg_snippet": "Feb 6, 2025 · As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as ''AI Oversight''. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on ... Similarly, two models with 50% accuracy may misclassify completely dif- ferent samples and thus be highly dissimilar (having differ- 1The term is inspired by “scalable oversight” (Bowman et al., 2022), which studies human-in-the-loop mechanisms for AI Safety. 1 Great Models Think Alike and this Undermines AI Oversight Feb 6, 2025 · Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from \"weak-to-strong generalization\". Secondly, we explain how to extend CAPA to multi- model set-up ( Section A.2) and how to adapt CAPA to classification and exact match settings ( Section A.3). Lastly, we introduce probabilistic versions of popular agreement metrics ( Section A.5) and provide a comparison between them and CAPA ( Section A. 4 ). Aug 26, 2025 · Great models think alike and this undermines ai oversight. arXiv preprint arXiv: 2502.04313 , 2025. [11] Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, et al. Measuring coding challenge competence with apps. arXiv preprint arXiv:2105.09938, 2021. May 2, 2023 · Reliable application of machine learning is of primary importance to the practical deployment of deep learning methods. A fundamental challenge is that models are often unreliable due to overconfidence. In this paper, we estimate a model's reliability by measuring \\\\emph{the agreement between its latent space, and the latent space of a foundation model }. However, it is challenging to measure ... Abstract LLM-as-a-judge is a framework in which a large language model (LLM) automatically evaluates the output of another LLM. We propose quantitative LLM judges, which align evaluation scores of existing LLM judges to human scores in a given domain using regression models . The models are trained to improve the score of the original judge by using the judge’s textual evaluation and score ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04313", "content": "Feb 6, 2025 · As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as ''AI Oversight''. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on ... Similarly, two models with 50% accuracy may misclassify completely dif- ferent samples and thus be highly dissimilar (having differ- 1The term is inspired by “scalable oversight” (Bowman et al., 2022), which studies human-in-the-loop mechanisms for AI Safety. 1 Great Models Think Alike and this Undermines AI Oversight Feb 6, 2025 · Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from \"weak-to-strong generalization\". Secondly, we explain how to extend CAPA to multi- model set-up ( Section A.2) and how to adapt CAPA to classification and exact match settings ( Section A.3). Lastly, we introduce probabilistic versions of popular agreement metrics ( Section A.5) and provide a comparison between them and CAPA ( Section A. 4 ). Aug 26, 2025 · Great models think alike and this undermines ai oversight. arXiv preprint arXiv: 2502.04313 , 2025. [11] Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, et al. Measuring coding challenge competence with apps. arXiv preprint arXiv:2105.09938, 2021. May 2, 2023 · Reliable application of machine learning is of primary importance to the practical deployment of deep learning methods. A fundamental challenge is that models are often unreliable due to overconfidence. In this paper, we estimate a model's reliability by measuring \\\\emph{the agreement between its latent space, and the latent space of a foundation model }. However, it is challenging to measure ... Abstract LLM-as-a-judge is a framework in which a large language model (LLM) automatically evaluates the output of another LLM. We propose quantitative LLM judges, which align evaluation scores of existing LLM judges to human scores in a given domain using regression models . The models are trained to improve the score of the original judge by using the judge’s textual evaluation and score ..."}
+{"idx": 2, "title": "[2305.01481] Great Models Think Alike: Improving Model ...", "date": "", "ddg_snippet": "May 2, 2023 · Reliable application of machine learning is of primary importance to the practical deployment of deep learning methods. A fundamental challenge is that models are often unreliable due to overconfidence. In this paper, we estimate a model's reliability by measuring \\\\emph{the agreement between its latent space, and the latent space of a foundation model }. However, it is challenging to measure ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.01481", "content": "May 2, 2023 · Reliable application of machine learning is of primary importance to the practical deployment of deep learning methods. A fundamental challenge is that models are often unreliable due to overconfidence. In this paper, we estimate a model's reliability by measuring \\\\emph{the agreement between its latent space, and the latent space of a foundation model }. However, it is challenging to measure ..."}
+{"idx": 3, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "arXiv: 2502 . 04313 v2 [cs.LG] 12 Jun 2025.We decompose the accuracy of the weak-to-strong trained model on four parts of the test data distribution, based on the correctness of the weak supervisor and an oracle strong elicited model which uses ground-truth annotations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "arXiv: 2502 . 04313 v2 [cs.LG] 12 Jun 2025.We decompose the accuracy of the weak-to-strong trained model on four parts of the test data distribution, based on the correctness of the weak supervisor and an oracle strong elicited model which uses ground-truth annotations."}
+{"idx": 4, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Similarly, two models with 50% accuracy may misclassify completely dif- ferent samples and thus be highly dissimilar (having differ- 1The term is inspired by “scalable oversight” (Bowman et al., 2022), which studies human-in-the-loop mechanisms for AI Safety. 1 Great Models Think Alike and this Undermines AI Oversight", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04313v2", "content": "Similarly, two models with 50% accuracy may misclassify completely dif- ferent samples and thus be highly dissimilar (having differ- 1The term is inspired by “scalable oversight” (Bowman et al., 2022), which studies human-in-the-loop mechanisms for AI Safety. 1 Great Models Think Alike and this Undermines AI Oversight"}
+{"idx": 5, "title": "[2502.04313] Great Models Think Alike and this Undermines AI ...", "date": "", "ddg_snippet": "Feb 6, 2025 · Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from \"weak-to-strong generalization\".", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2502.04313", "content": "Feb 6, 2025 · Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from \"weak-to-strong generalization\"."}
+{"idx": 6, "title": "UQ: Assessing Language Models on Unsolved Questions", "date": "", "ddg_snippet": "Aug 26, 2025 · Great models think alike and this undermines ai oversight. arXiv preprint arXiv: 2502.04313 , 2025. [11] Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, et al. Measuring coding challenge competence with apps. arXiv preprint arXiv:2105.09938, 2021.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.17580", "content": "Aug 26, 2025 · Great models think alike and this undermines ai oversight. arXiv preprint arXiv: 2502.04313 , 2025. [11] Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, et al. Measuring coding challenge competence with apps. arXiv preprint arXiv:2105.09938, 2021."}
+{"idx": 7, "title": "Quantitative LLM Judges - arXiv.org", "date": "", "ddg_snippet": "Abstract LLM-as-a-judge is a framework in which a large language model (LLM) automatically evaluates the output of another LLM. We propose quantitative LLM judges, which align evaluation scores of existing LLM judges to human scores in a given domain using regression models . The models are trained to improve the score of the original judge by using the judge’s textual evaluation and score ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.02945v1", "content": "Abstract LLM-as-a-judge is a framework in which a large language model (LLM) automatically evaluates the output of another LLM. We propose quantitative LLM judges, which align evaluation scores of existing LLM judges to human scores in a given domain using regression models . The models are trained to improve the score of the original judge by using the judge’s textual evaluation and score ..."}
+{"idx": 8, "title": "Machine Learning", "date": "", "ddg_snippet": "[ 4 ] arXiv: 2502 . 04313 [pdf, html, other]. Title: Great Models Think Alike and this Undermines AI Oversight. Shashwat Goel, Joschka Struber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, Jonas Geiping.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.LG/pastweek?show=597", "content": "[ 4 ] arXiv: 2502 . 04313 [pdf, html, other]. Title: Great Models Think Alike and this Undermines AI Oversight. Shashwat Goel, Joschka Struber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, Jonas Geiping."}
+{"idx": 9, "title": "Artificial Intelligence", "date": "", "ddg_snippet": "[12] arXiv: 2502 . 04313 (cross-list from cs.LG) [pdf, html, other]. Title: Great Models Think Alike and this Undermines AI Oversight.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.AI/pastweek?show=188", "content": "[12] arXiv: 2502 . 04313 (cross-list from cs.LG) [pdf, html, other]. Title: Great Models Think Alike and this Undermines AI Oversight."}
diff --git "a/data/sampled_jsons/sitearxiv.org_2502.04879_Algorithm_1_\316\264_tilde.jsonl" "b/data/sampled_jsons/sitearxiv.org_2502.04879_Algorithm_1_\316\264_tilde.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..7dc1b7aff03cb07b5f3ec2d35de2d0f20850f5b5
--- /dev/null
+++ "b/data/sampled_jsons/sitearxiv.org_2502.04879_Algorithm_1_\316\264_tilde.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms Statistical Collusion by Collectives on Learning Platforms Inference Offloading for Cost-Sensitive Binary Classification ... Data Fusion for BS-UE Cooperative MIMO-OFDM ISAC - arXiv.org ParaAegis: Parallel Protection for Flexible Privacy-preserved ... Block Encoding of Sparse Matrices via Coherent Permutation Transient regime of piecewise deterministic Monte Carlo ...", "date": "", "ddg_snippet": "Feb 7, 2025 · As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ... As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. … 8 hours ago · The system is trivial when β t ≥ max { δ 1 , δ 1 }, enabling an assumption of β t ≤ β = 1 . Let π denote the policy (or algorithm ) making offloading decisions and l t (π) denote the incurred cost at round t. 5 days ago · Algorithm 1 Data Fusion Algorithm for BS-UE Cooperative MIMO-OFDM ISAC 0: Locations of the BS and UE: 𝐪 B, 𝐪 U;Observation tuple 𝒚;Step tolerance of iteration ϵ; 4 days ago · By adjusting the partition ratio r and privacy budget (ε, δ ), it is clear that with the privacy and efficiency changing directly, the utility, which is represented by the convergence bound shown in Theorem 1 is affected correspondingly. ... Table 1 : Comparison on Performance across Different Algorithms With ResNet-18. 9 hours ago · Abstract Block encoding of sparse matrices underpins powerful quantum algorithms such as quantum singular value transformation, Hamiltonian simulation, and quantum linear solvers, but its efficient gate-level implementation for arbitrary sparse matrices remains a major challenge. We introduce a unified framework that overcomes the key obstacles of multi-controlled X gates overhead, amplitude ... 8 hours ago · For instance, [10, 16, 17] rigorously characterised the transient regime of both the random-walk Metropolis and the Langevin algorithms as the dimension tends to infinity. More recently, [19] extended these ideas to the non-stationary phase of the Langevin algorithm under Gaussian perturbations in an infinite-dimensional Hilbert-space setting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "Feb 7, 2025 · As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ... As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. … 8 hours ago · The system is trivial when β t ≥ max { δ 1 , δ 1 }, enabling an assumption of β t ≤ β = 1 . Let π denote the policy (or algorithm ) making offloading decisions and l t (π) denote the incurred cost at round t. 5 days ago · Algorithm 1 Data Fusion Algorithm for BS-UE Cooperative MIMO-OFDM ISAC 0: Locations of the BS and UE: 𝐪 B, 𝐪 U;Observation tuple 𝒚;Step tolerance of iteration ϵ; 4 days ago · By adjusting the partition ratio r and privacy budget (ε, δ ), it is clear that with the privacy and efficiency changing directly, the utility, which is represented by the convergence bound shown in Theorem 1 is affected correspondingly. ... Table 1 : Comparison on Performance across Different Algorithms With ResNet-18. 9 hours ago · Abstract Block encoding of sparse matrices underpins powerful quantum algorithms such as quantum singular value transformation, Hamiltonian simulation, and quantum linear solvers, but its efficient gate-level implementation for arbitrary sparse matrices remains a major challenge. We introduce a unified framework that overcomes the key obstacles of multi-controlled X gates overhead, amplitude ... 8 hours ago · For instance, [10, 16, 17] rigorously characterised the transient regime of both the random-walk Metropolis and the Langevin algorithms as the dimension tends to infinity. More recently, [19] extended these ideas to the non-stationary phase of the Langevin algorithm under Gaussian perturbations in an infinite-dimensional Hilbert-space setting."}
+{"idx": 1, "title": "Improved Sublinear Algorithms for Classical and Quantum Graph...", "date": "", "ddg_snippet": "2 Classical Sublinear Algorithm . 3 Quantum Sublinear Algorithms . 3.1 Quantum algorithm for. (Δ+ 1 ) Δ 1(\\Delta+1)( roman_Δ + 1 ).. The first is a simple algorithm that extends the idea of Morris and Song to color graphs with maximum degree.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.06024v1", "content": "2 Classical Sublinear Algorithm . 3 Quantum Sublinear Algorithms . 3.1 Quantum algorithm for. (Δ+ 1 ) Δ 1(\\Delta+1)( roman_Δ + 1 ).. The first is a simple algorithm that extends the idea of Morris and Song to color graphs with maximum degree."}
+{"idx": 2, "title": "Data Fusion for BS-UE Cooperative MIMO-OFDM ISAC - arXiv.org", "date": "", "ddg_snippet": "5 days ago · Algorithm 1 Data Fusion Algorithm for BS-UE Cooperative MIMO-OFDM ISAC 0: Locations of the BS and UE: 𝐪 B, 𝐪 U;Observation tuple 𝒚;Step tolerance of iteration ϵ;", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.12646v1", "content": "5 days ago · Algorithm 1 Data Fusion Algorithm for BS-UE Cooperative MIMO-OFDM ISAC 0: Locations of the BS and UE: 𝐪 B, 𝐪 U;Observation tuple 𝒚;Step tolerance of iteration ϵ;"}
+{"idx": 3, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "3 Statistical Algorithmic Collective Action in Classification.We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "3 Statistical Algorithmic Collective Action in Classification.We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "3 Statistical Algorithmic Collective Action in Classification.We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v2", "content": "3 Statistical Algorithmic Collective Action in Classification.We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "arXiv: 2502 . 04879 v 1 [stat.ML] 07 Feb 2025.As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v1", "content": "arXiv: 2502 . 04879 v 1 [stat.ML] 07 Feb 2025.As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "arXiv: 2502 . 04879 v3 [stat.ML] 25 May 2025.The dynamic interaction among agents and algorithms cre-ates a complex ecosystem where unanticipated individual and collective behavior can emerge.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879", "content": "arXiv: 2502 . 04879 v3 [stat.ML] 25 May 2025.The dynamic interaction among agents and algorithms cre-ates a complex ecosystem where unanticipated individual and collective behavior can emerge."}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.04879", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. …"}
+{"idx": 8, "title": "Inference Offloading for Cost-Sensitive Binary Classification ...", "date": "", "ddg_snippet": "8 hours ago · The system is trivial when β t ≥ max { δ 1 , δ 1 }, enabling an assumption of β t ≤ β = 1 . Let π denote the policy (or algorithm ) making offloading decisions and l t (π) denote the incurred cost at round t.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15674v1", "content": "8 hours ago · The system is trivial when β t ≥ max { δ 1 , δ 1 }, enabling an assumption of β t ≤ β = 1 . Let π denote the policy (or algorithm ) making offloading decisions and l t (π) denote the incurred cost at round t."}
+{"idx": 9, "title": "ParaAegis: Parallel Protection for Flexible Privacy-preserved ...", "date": "", "ddg_snippet": "4 days ago · By adjusting the partition ratio r and privacy budget (ε, δ ), it is clear that with the privacy and efficiency changing directly, the utility, which is represented by the convergence bound shown in Theorem 1 is affected correspondingly. ... Table 1 : Comparison on Performance across Different Algorithms With ResNet-18.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13739v1", "content": "4 days ago · By adjusting the partition ratio r and privacy budget (ε, δ ), it is clear that with the privacy and efficiency changing directly, the utility, which is represented by the convergence bound shown in Theorem 1 is affected correspondingly. ... Table 1 : Comparison on Performance across Different Algorithms With ResNet-18."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.10875.jsonl b/data/sampled_jsons/sitearxiv.org_2502.10875.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..04527597f1aec687482353800908a762155b8400
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2502.10875.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.10875] A Geometric Approach to Personalized ... Information Retrieval - arXiv.org", "date": "", "ddg_snippet": "Feb 15, 2025 · Abstract page for arXiv paper 2502.10875 : A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings Information Retrieval Authors and titles for recent submissions Fri, 21 Feb 2025 Thu, 20 Feb 2025 Wed, 19 Feb 2025 Tue, 18 Feb 2025 Mon, 17 Feb 2025 See today's new changes", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10875", "content": "Feb 15, 2025 · Abstract page for arXiv paper 2502.10875 : A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings Information Retrieval Authors and titles for recent submissions Fri, 21 Feb 2025 Thu, 20 Feb 2025 Wed, 19 Feb 2025 Tue, 18 Feb 2025 Mon, 17 Feb 2025 See today's new changes"}
+{"idx": 1, "title": "[2502.09756] The messages we send after we listen", "date": "", "ddg_snippet": "Mathematics > History and Overview. arXiv:2502.09756 (math).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.09756", "content": "Mathematics > History and Overview. arXiv:2502.09756 (math)."}
+{"idx": 2, "title": "Information Retrieval - arXiv.org", "date": "", "ddg_snippet": "Information Retrieval Authors and titles for recent submissions Fri, 21 Feb 2025 Thu, 20 Feb 2025 Wed, 19 Feb 2025 Tue, 18 Feb 2025 Mon, 17 Feb 2025 See today's new changes", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.IR/recent?skip=31", "content": "Information Retrieval Authors and titles for recent submissions Fri, 21 Feb 2025 Thu, 20 Feb 2025 Wed, 19 Feb 2025 Tue, 18 Feb 2025 Mon, 17 Feb 2025 See today's new changes"}
+{"idx": 3, "title": "[2502.04328] Ola: Pushing the Frontiers of Omni-Modal Language Model", "date": "", "ddg_snippet": "We conduct a comprehensive exploration of architectural design, data curation, and training strategies essential for building a robust omni-modal model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04328", "content": "We conduct a comprehensive exploration of architectural design, data curation, and training strategies essential for building a robust omni-modal model."}
+{"idx": 4, "title": "[2502.11065v1] Optimal Placement of Nature-Based Solutions for...", "date": "", "ddg_snippet": "This model determines the optimal placement of NBSs by addressing metrics such as ground temperature, air quality, and accessibility to green spaces.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.11065v1", "content": "This model determines the optimal placement of NBSs by addressing metrics such as ground temperature, air quality, and accessibility to green spaces."}
+{"idx": 5, "title": "A Geometric Approach to Personalized Recommendation ...", "date": "", "ddg_snippet": "by S Dasgupta · 2025 — arXiv:2502.10875v1 [cs.IR] 15 Feb 2025. Page 2. Submission and Formatting Instructions for ICML 2025. Figure 1: Standard matrix completion assumes you are.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10875", "content": "by S Dasgupta · 2025 — arXiv:2502.10875v1 [cs.IR] 15 Feb 2025. Page 2. Submission and Formatting Instructions for ICML 2025. Figure 1: Standard matrix completion assumes you are."}
+{"idx": 6, "title": "A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "Authors: achieve the best HTML results from your LaTeX submissions by following these best practices. License: CC BY 4.0. arXiv: 2502 . 10875 v1 [cs.IR] 15 Feb 2025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10875v1", "content": "Authors: achieve the best HTML results from your LaTeX submissions by following these best practices. License: CC BY 4.0. arXiv: 2502 . 10875 v1 [cs.IR] 15 Feb 2025."}
+{"idx": 7, "title": "Machine Learning Feb 2025", "date": "", "ddg_snippet": "[3393] arXiv:2502.10875 (cross-list from cs.IR) [pdf, html, other]. Title: A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.LG/2025-02?skip=2275&show=2000", "content": "[3393] arXiv:2502.10875 (cross-list from cs.IR) [pdf, html, other]. Title: A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints ..."}
+{"idx": 8, "title": "Machine Learning Feb 2025", "date": "", "ddg_snippet": "20 Feb 2025 — [3393] arXiv:2502.10875 (cross-list from cs.IR) [pdf, html, other]. Title: A Geometric Approach to Personalized Recommendation with Set ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.LG/2025-02?skip=3325&show=2000", "content": "20 Feb 2025 — [3393] arXiv:2502.10875 (cross-list from cs.IR) [pdf, html, other]. Title: A Geometric Approach to Personalized Recommendation with Set ..."}
+{"idx": 9, "title": "Artificial Intelligence Feb 2025", "date": "", "ddg_snippet": "[2064] arXiv:2502.10875 (cross-list from cs.IR) [pdf, html, other]. Title: A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.AI/2025-02?skip=2025&show=50", "content": "[2064] arXiv:2502.10875 (cross-list from cs.IR) [pdf, html, other]. Title: A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.10875_FBox_score_equation_1.jsonl b/data/sampled_jsons/sitearxiv.org_2502.10875_FBox_score_equation_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b4a111b07db8a7ba8d96cafddc53316bd5c9248a
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2502.10875_FBox_score_equation_1.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "[2502.12160v 1 ] Nonstatic Reissner-Nordstr$ϕ$m metric in the...", "date": "", "ddg_snippet": "We then use the perturbative $ f(R) $ theory to consider CSSGS. The obtained results not only correct the solution of Einstein's equation in magnitude (this will describe astronomical and cosmological quantities more accurately than Einstein's equation ), but also reveal new effects.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12160v1", "content": "We then use the perturbative $ f(R) $ theory to consider CSSGS. The obtained results not only correct the solution of Einstein's equation in magnitude (this will describe astronomical and cosmological quantities more accurately than Einstein's equation ), but also reveal new effects."}
+{"idx": 1, "title": "[2502.04179v 1 ] The Maximum Likelihood Degree of...", "date": "", "ddg_snippet": "Mathematics > Statistics Theory. arXiv:2502.04179v 1 (math).Abstract:In algebraic statistics, the maximum likelihood degree of a statistical model refers to the number of solutions (counted with multiplicity) of the score equations over the complex field.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04179v1", "content": "Mathematics > Statistics Theory. arXiv:2502.04179v 1 (math).Abstract:In algebraic statistics, the maximum likelihood degree of a statistical model refers to the number of solutions (counted with multiplicity) of the score equations over the complex field."}
+{"idx": 2, "title": "[ 2502 . 10875 ] A Geometric Approach to Personalized Recommendation...", "date": "", "ddg_snippet": "We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30 \\% overall.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10875", "content": "We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30 \\% overall."}
+{"idx": 3, "title": "A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "arXiv: 2502 . 10875 v 1 [cs.IR] 15 Feb 2025. Shib Dasgupta 1 Michael Boratko 1 Andrew McCallum 1 .The overall containment score is the multiplication of FBox for each dimension. The log of this score is referred to as the energy function as given", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10875", "content": "arXiv: 2502 . 10875 v 1 [cs.IR] 15 Feb 2025. Shib Dasgupta 1 Michael Boratko 1 Andrew McCallum 1 .The overall containment score is the multiplication of FBox for each dimension. The log of this score is referred to as the energy function as given"}
+{"idx": 4, "title": "A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "arXiv: 2502 . 10875 v 1 [cs.IR] 15 Feb 2025.for box models so that the number of parameters per user, attribute, and movie is equal .444Recall that box embeddings are parameterized with two vectors, one for each min and max coordinate.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10875v1", "content": "arXiv: 2502 . 10875 v 1 [cs.IR] 15 Feb 2025.for box models so that the number of parameters per user, attribute, and movie is equal .444Recall that box embeddings are parameterized with two vectors, one for each min and max coordinate."}
+{"idx": 5, "title": "The maximum likelihood degree of", "date": "", "ddg_snippet": "the. score . equation ( 1 .2) can be rewritten as.(respectively). Thus, in this case, the MLE will be either 1 or −1 since. −1 ≤ θ ≤ 1. When m is equal to n, then the value of l is at least 2. Hence, the ML-degree of the association parameter θ is greater than or equal to 1. 6 POOJA YADAV.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04422", "content": "the. score . equation ( 1 .2) can be rewritten as.(respectively). Thus, in this case, the MLE will be either 1 or −1 since. −1 ≤ θ ≤ 1. When m is equal to n, then the value of l is at least 2. Hence, the ML-degree of the association parameter θ is greater than or equal to 1. 6 POOJA YADAV."}
diff --git a/data/sampled_jsons/sitearxiv.org_2503.06366v1_'Algebraic_Combinatorics_Dataset_Repository'_year_2024.jsonl b/data/sampled_jsons/sitearxiv.org_2503.06366v1_'Algebraic_Combinatorics_Dataset_Repository'_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cca19eda571b01fe425d4fabae7419472c1609d5
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2503.06366v1_'Algebraic_Combinatorics_Dataset_Repository'_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HM Revenue and Customs - Wikipedia", "date": "", "ddg_snippet": "HMRC was formed by the merger of the Inland Revenue and HM Customs and Excise, which took effect on 18 April 2005. [6] The department's logo is the Tudor Crown enclosed within a circle.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/HM_Revenue_and_Customs", "content": "HMRC was formed by the merger of the Inland Revenue and HM Customs and Excise, which took effect on 18 April 2005. [6] The department's logo is the Tudor Crown enclosed within a circle."}
+{"idx": 1, "title": "[ 2503 . 06366 ] Machine Learning meets Algebraic Combinatorics ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics, by Herman Chau and 6 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "View a PDF of the paper titled Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics, by Herman Chau and 6 other authors."}
+{"idx": 2, "title": "Machine Learning meets Algebraic Combinatorics : A Suite of...", "date": "", "ddg_snippet": "In this paper we introduce the Algebraic Combinatorics Dataset Repository , a collection of research-level mathematics datasets structured for machine learning and designed to accelerate mathematical discovery.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "In this paper we introduce the Algebraic Combinatorics Dataset Repository , a collection of research-level mathematics datasets structured for machine learning and designed to accelerate mathematical discovery."}
+{"idx": 3, "title": "Machine Learning meets Algebraic Combinatorics : A Suite of...", "date": "", "ddg_snippet": "arXiv: 2503 . 06366 v 1 [cs.LG] 9 Mar 2025.In this paper we introduce the Algebraic Combinatorics Dataset Repository , a collection of research-level mathemat-ics datasets structured for machine learning and designed to accelerate mathematical discovery.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "arXiv: 2503 . 06366 v 1 [cs.LG] 9 Mar 2025.In this paper we introduce the Algebraic Combinatorics Dataset Repository , a collection of research-level mathemat-ics datasets structured for machine learning and designed to accelerate mathematical discovery."}
+{"idx": 4, "title": "[2502. 06366 v 1 ] What makes an algebraic curve special?", "date": "", "ddg_snippet": "First version, comments are welcome! Subjects: Algebraic Geometry (math.AG); Number Theory (math.NT). Cite as: arXiv:2502.06366 [math.AG]. (or arXiv:2502. 06366 v 1 [math.AG] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.06366v1", "content": "First version, comments are welcome! Subjects: Algebraic Geometry (math.AG); Number Theory (math.NT). Cite as: arXiv:2502.06366 [math.AG]. (or arXiv:2502. 06366 v 1 [math.AG] for this version)."}
+{"idx": 5, "title": "Mathematics Mar 2025", "date": "", "ddg_snippet": "[4004] arXiv: 2503 . 06366 (cross-list from cs.LG) [pdf, html, other]. Title: Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/math/2025-03?skip=4000", "content": "[4004] arXiv: 2503 . 06366 (cross-list from cs.LG) [pdf, html, other]. Title: Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics."}
+{"idx": 6, "title": "Using Reasoning Models to Generate Search Heuristics that Solve...", "date": "", "ddg_snippet": "Machine learning meets algebraic combinatorics : A suite of datasets capturing research-level conjecturing ability in pure mathematics, 2025. arXiv: 2503 . 06366 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23881v1", "content": "Machine learning meets algebraic combinatorics : A suite of datasets capturing research-level conjecturing ability in pure mathematics, 2025. arXiv: 2503 . 06366 ."}
+{"idx": 7, "title": "HM Revenue & Customs - GOV.UK", "date": "", "ddg_snippet": "HMRC is the UK’s tax, payments and customs authority, and we have a vital purpose: we collect the money that pays for the UK’s public services, and help families and individuals with targeted ...", "subpage_snippet": "", "source": "www.gov.uk", "link": "https://www.gov.uk/government/organisations/hm-revenue-customs", "content": "HMRC is the UK’s tax, payments and customs authority, and we have a vital purpose: we collect the money that pays for the UK’s public services, and help families and individuals with targeted ..."}
+{"idx": 8, "title": "HM Revenue & Customs", "date": "", "ddg_snippet": "Import Control System (ICS) Machine Games Duty Notification of Vehicle Arrivals (NOVA) Online End of Year Expenses and Benefits Service Pay As You Earn (EDI) Pay As You Earn (PAYE) Pension Schemes...", "subpage_snippet": "", "source": "customs.hmrc.gov.uk", "link": "https://customs.hmrc.gov.uk/channelsPortalWebApp/channelsPortalWebApp.portal?_nfpb=true&_pageLabel=pageNoNavigation_ShowContent&id=HMCE_PROD1_028772&propertyType=document", "content": "Import Control System (ICS) Machine Games Duty Notification of Vehicle Arrivals (NOVA) Online End of Year Expenses and Benefits Service Pay As You Earn (EDI) Pay As You Earn (PAYE) Pension Schemes..."}
+{"idx": 9, "title": "HMRC app: Check income tax, state pension, benefits and more", "date": "", "ddg_snippet": "Sep 10, 2025 · If you need to contact HM Revenue & Customs about your income tax, self-assessment, state pension, benefits and more, first check if the HMRC app can help.", "subpage_snippet": "", "source": "www.moneysavingexpert.com", "link": "https://www.moneysavingexpert.com/family/hmrc-app/", "content": "Sep 10, 2025 · If you need to contact HM Revenue & Customs about your income tax, self-assessment, state pension, benefits and more, first check if the HMRC app can help."}
diff --git a/data/sampled_jsons/sitearxiv.org_2503.06366v1_'S18_characters_dataset'_'Appendix_B.1'_'training_examples'_year_2024.jsonl b/data/sampled_jsons/sitearxiv.org_2503.06366v1_'S18_characters_dataset'_'Appendix_B.1'_'training_examples'_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ebcd05150554af49bc650a807c3a8c67168c6f7d
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2503.06366v1_'S18_characters_dataset'_'Appendix_B.1'_'training_examples'_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Microsoft - Wikipedia", "date": "", "ddg_snippet": "Microsoft is the largest software maker, one of the most valuable public companies, [a] and one of the most valuable brands globally. Microsoft is considered part of the Big Tech group, alongside Nvidia, Apple, Alphabet, Amazon, and Meta. Microsoft was founded by Bill Gates and Paul Allen to develop and sell BASIC interpreters for the Altair 8800.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Microsoft", "content": "Microsoft is the largest software maker, one of the most valuable public companies, [a] and one of the most valuable brands globally. Microsoft is considered part of the Big Tech group, alongside Nvidia, Apple, Alphabet, Amazon, and Meta. Microsoft was founded by Bill Gates and Paul Allen to develop and sell BASIC interpreters for the Altair 8800."}
+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "We describe all nine datasets , the different ways machine learning models can be applied to them (e.g., training with narrow models followed by interpretability analysis or program synthesis with LLMs), and discuss some of the challenges involved in designing datasets like these.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "We describe all nine datasets , the different ways machine learning models can be applied to them (e.g., training with narrow models followed by interpretability analysis or program synthesis with LLMs), and discuss some of the challenges involved in designing datasets like these."}
+{"idx": 2, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "To fill the gap we present the Algebraic Combinatorics Dataset Repository (ACD Repo)1, a collection of 9 datasets consisting of many examples along with an associated question (s).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "To fill the gap we present the Algebraic Combinatorics Dataset Repository (ACD Repo)1, a collection of 9 datasets consisting of many examples along with an associated question (s)."}
+{"idx": 3, "title": "DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand ...", "date": "", "ddg_snippet": "Abstract Predicting the binding affinity of protein-ligand complexes plays a vital role in drug discovery. Unfortunately, progress has been hindered by the lack of large-scale and high-quality binding affinity labels. The widely used PDBbind dataset has fewer than 20K labeled complexes. Self-supervised learning, especially graph contrastive learning (GCL), provides a unique opportunity to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.06366v1", "content": "Abstract Predicting the binding affinity of protein-ligand complexes plays a vital role in drug discovery. Unfortunately, progress has been hindered by the lack of large-scale and high-quality binding affinity labels. The widely used PDBbind dataset has fewer than 20K labeled complexes. Self-supervised learning, especially graph contrastive learning (GCL), provides a unique opportunity to ..."}
+{"idx": 4, "title": "Appendix - arXiv.org", "date": "", "ddg_snippet": "Appendix B Dataset Fashion MNIST. FMNIST is a dataset of fashion product images. The dataset contains grayscale images of 10 categories, each category represents a fashion product. The dataset contains a total of 70,000 28×28 pixel grayscale images, of which 60,000 are used for training and 10,000 are used for testing. Cifar100.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06511", "content": "Appendix B Dataset Fashion MNIST. FMNIST is a dataset of fashion product images. The dataset contains grayscale images of 10 categories, each category represents a fashion product. The dataset contains a total of 70,000 28×28 pixel grayscale images, of which 60,000 are used for training and 10,000 are used for testing. Cifar100."}
+{"idx": 5, "title": "Appendix A Model implementation - arXiv.org", "date": "", "ddg_snippet": "Appendix A Model implementation A.1 Training details We specify the detailed hyperparamters for the model training in Table S1 and S2. We implement the model and the experiment using PyTorch \\citep paszke2019pytorch. We use AdamW optimizer \\citep adam15, adamw2018 and the majority of the hyperparameter setting follows ClimaX \\citep climax2023icml.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.22600v1", "content": "Appendix A Model implementation A.1 Training details We specify the detailed hyperparamters for the model training in Table S1 and S2. We implement the model and the experiment using PyTorch \\citep paszke2019pytorch. We use AdamW optimizer \\citep adam15, adamw2018 and the majority of the hyperparameter setting follows ClimaX \\citep climax2023icml."}
+{"idx": 6, "title": "1 (a) Example sequences seen during training and evaluation. Training ...", "date": "", "ddg_snippet": "1 (a) Example sequences seen during training and evaluation. Training data is \"bursty\", enabling both in-context and in-weights strategies (the context always contains an exemplar from the same class as the query, but also exemplar-label mappings are fixed throughout training ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05631v1", "content": "1 (a) Example sequences seen during training and evaluation. Training data is \"bursty\", enabling both in-context and in-weights strategies (the context always contains an exemplar from the same class as the query, but also exemplar-label mappings are fixed throughout training )."}
+{"idx": 7, "title": "SampleMix: A Sample-wise Pre-training Data Mixing Strategey by ...", "date": "", "ddg_snippet": "We propose a sample-wise pre- training data mixing strategy that coordinates data quality and diversity on a per-sample basis, effectively capturing commonalities among domains and optimal sample distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01506", "content": "We propose a sample-wise pre- training data mixing strategy that coordinates data quality and diversity on a per-sample basis, effectively capturing commonalities among domains and optimal sample distribution."}
+{"idx": 8, "title": "arXiv:2503.06413v1 [stat.ML] 9 Mar 2025", "date": "", "ddg_snippet": "rain a high-performing detection model. We refer readers to Appendix C.2 for a comparative analy-sis with more SOTA detection methods and oversampling techniques, and Appendix C.3 for a toy example to illustrate th", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06413v1", "content": "rain a high-performing detection model. We refer readers to Appendix C.2 for a comparative analy-sis with more SOTA detection methods and oversampling techniques, and Appendix C.3 for a toy example to illustrate th"}
+{"idx": 9, "title": "Towards Robust Universal Information Extraction: Dataset, Evaluation ...", "date": "", "ddg_snippet": "Abstract In this paper, we aim to enhance the robustness of Universal Information Extraction (UIE) by introducing a new benchmark dataset , a comprehensive evaluation, and a feasible solution. Existing robust benchmark datasets have two key limitations: 1) They generate only a limited range of perturbations for a single Information Extraction (IE) task, which fails to evaluate the robustness of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.03201v1", "content": "Abstract In this paper, we aim to enhance the robustness of Universal Information Extraction (UIE) by introducing a new benchmark dataset , a comprehensive evaluation, and a feasible solution. Existing robust benchmark datasets have two key limitations: 1) They generate only a limited range of perturbations for a single Information Extraction (IE) task, which fails to evaluate the robustness of ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2504.11786_methodology_equation_5_lambda_m_disease_matching.jsonl b/data/sampled_jsons/sitearxiv.org_2504.11786_methodology_equation_5_lambda_m_disease_matching.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f9ba941ae460b17886aa7cccad22bef7962d025c
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2504.11786_methodology_equation_5_lambda_m_disease_matching.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "[ 2504 . 11786 ] DART: Disease -aware Image-Text Alignment and...", "date": "", "ddg_snippet": "View a PDF of the paper titled DART: Disease -aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation, by Sang-Jun Park and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "View a PDF of the paper titled DART: Disease -aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation, by Sang-Jun Park and 5 other authors."}
+{"idx": 1, "title": "DART: Disease -aware Image-Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "In this study, we propose a Disease -aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation (DART) framework.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "In this study, we propose a Disease -aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation (DART) framework."}
+{"idx": 2, "title": "Clinical Multi-modal Fusion with Heterogeneous Graph and Disease ...", "date": "", "ddg_snippet": "Multi- disease diagnosis is a fundamental task in clinical decision-making, where clinicians synthesize complementary evidence from heterogeneous data sources, including electronic health records (EHR) and medical imaging.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15852", "content": "Multi- disease diagnosis is a fundamental task in clinical decision-making, where clinicians synthesize complementary evidence from heterogeneous data sources, including electronic health records (EHR) and medical imaging."}
+{"idx": 3, "title": "[2504.16313] Non-geometrical perturbation on homogeneous stealth dust", "date": "", "ddg_snippet": "General Relativity and Quantum Cosmology. arXiv:2504.16313 (gr-qc).From this procedure, we distinguish and approach to discriminate solutions, and the numerical solutions show that the most significant contribution to the solution comes from the linear term of $\\ lambda $.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.16313", "content": "General Relativity and Quantum Cosmology. arXiv:2504.16313 (gr-qc).From this procedure, we distinguish and approach to discriminate solutions, and the numerical solutions show that the most significant contribution to the solution comes from the linear term of $\\ lambda $."}
+{"idx": 4, "title": "[2504.05093 v 1] Flexible Estimation of the Heterogeneous...", "date": "", "ddg_snippet": "Estimating the cure fraction in a diseased population, especially in the presence of competing mortality causes, is crucial for both patients and clinicians. Methodology (stat.ME); Applications (stat.AP). Cite as: arXiv:2504.05093 [stat.ME].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.05093v1", "content": "Estimating the cure fraction in a diseased population, especially in the presence of competing mortality causes, is crucial for both patients and clinicians. Methodology (stat.ME); Applications (stat.AP). Cite as: arXiv:2504.05093 [stat.ME]."}
+{"idx": 5, "title": "[2509.15852] Clinical Multi-modal Fusion with Heterogeneous Graph...", "date": "", "ddg_snippet": "View a PDF of the paper titled Clinical Multi-modal Fusion with Heterogeneous Graph and Disease Correlation Learning for Multi- Disease Prediction, by Yueheng Jiang and 1 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2509.15852", "content": "View a PDF of the paper titled Clinical Multi-modal Fusion with Heterogeneous Graph and Disease Correlation Learning for Multi- Disease Prediction, by Yueheng Jiang and 1 other authors."}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_2505.18300_Section_4.5_LRU_cache_Equation_15.jsonl b/data/sampled_jsons/sitearxiv.org_2505.18300_Section_4.5_LRU_cache_Equation_15.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..11a6abd7bc7dfc7f10db2968714ce11affb10de1
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2505.18300_Section_4.5_LRU_cache_Equation_15.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "[ 2505 . 18300 ] Beyond Self-Repellent Kernels: History-Driven Target...", "date": "", "ddg_snippet": "Extensive experiments in graph sampling demonstrate consistent performance gains, and a memory-efficient Least Recently Used ( LRU ) cache ensures scalability to large general graphs.(or arXiv: 2505 . 18300 v1 [cs.LG] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "content": "Extensive experiments in graph sampling demonstrate consistent performance gains, and a memory-efficient Least Recently Used ( LRU ) cache ensures scalability to large general graphs.(or arXiv: 2505 . 18300 v1 [cs.LG] for this version)."}
+{"idx": 1, "title": "Mixture of Cache -Conditional Experts for Efficient Mobile Device...", "date": "", "ddg_snippet": "Optimal + Original LRU + Original. 5. 5 LRU + Cache -Prior.Given these results, we use same cache size of 30 for 4-bit and 45 for 8-bit quantized models in experiments in Section 4 . 5 . D Smaller Cache Sizes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.00099", "content": "Optimal + Original LRU + Original. 5. 5 LRU + Cache -Prior.Given these results, we use same cache size of 30 for 4-bit and 45 for 8-bit quantized models in experiments in Section 4 . 5 . D Smaller Cache Sizes."}
+{"idx": 2, "title": "Abstract 1. Introduction - arXiv.org", "date": "", "ddg_snippet": "To address this challenge, we propose a memory-efficient Least Recently Used ( LRU ) cache strategy and emulate its effect on real-world graphs that serves as a pilot study prior to full deployment in large configuration spaces (beyond this paper's scope).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.18300", "content": "To address this challenge, we propose a memory-efficient Least Recently Used ( LRU ) cache strategy and emulate its effect on real-world graphs that serves as a pilot study prior to full deployment in large configuration spaces (beyond this paper's scope)."}
+{"idx": 3, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient...", "date": "", "ddg_snippet": "4 . 5 Least Recently Used ( LRU ) Cache Scheme.Scalable Implementation for Large Graphs: We implement an LRU ( Least Recently Used ) cache scheme to manage. 𝐱𝐱{\\mathbf{x}}bold_x.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "4 . 5 Least Recently Used ( LRU ) Cache Scheme.Scalable Implementation for Large Graphs: We implement an LRU ( Least Recently Used ) cache scheme to manage. 𝐱𝐱{\\mathbf{x}}bold_x."}
+{"idx": 4, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient...", "date": "", "ddg_snippet": "4 . 5 Least Recently Used ( LRU ) Cache Scheme. 5 Conclusion.Scalable Implementation for Large Graphs: We implement an LRU ( Least Recently Used ) cache scheme to manage. 𝐱{\\mathbf{x}}bold_x.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "4 . 5 Least Recently Used ( LRU ) Cache Scheme. 5 Conclusion.Scalable Implementation for Large Graphs: We implement an LRU ( Least Recently Used ) cache scheme to manage. 𝐱{\\mathbf{x}}bold_x."}
+{"idx": 5, "title": "Decentralized Federated Learning with Model Caching on Mobile Agents", "date": "", "ddg_snippet": "4 . 5 LRU Cache Update.. We use the same non-i.i.d shards method in the previous section to allocate data points to the vehicles in the same area. On each vehicle, we evenly divide the cache for the three areas. We evaluate our proposed GB cache method on FashionMNIST.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.14001v2", "content": "4 . 5 LRU Cache Update.. We use the same non-i.i.d shards method in the previous section to allocate data points to the vehicles in the same area. On each vehicle, we evenly divide the cache for the three areas. We evaluate our proposed GB cache method on FashionMNIST."}
+{"idx": 6, "title": "Decentralized Federated Learning with Model Caching on Mobile Agents", "date": "", "ddg_snippet": "4 . 5 LRU Cache Update.. We use the same non-i.i.d shards method in the previous section to allocate data points to the vehicles in the same area. On each vehicle, we evenly divide the cache for the three areas. We evaluate our proposed GB cache method on FashionMNIST.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.14001v1", "content": "4 . 5 LRU Cache Update.. We use the same non-i.i.d shards method in the previous section to allocate data points to the vehicles in the same area. On each vehicle, we evenly divide the cache for the three areas. We evaluate our proposed GB cache method on FashionMNIST."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synt.jsonl b/data/sampled_jsons/sitearxiv.org_33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synt.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c0820aef9e5c07334b15eb9f7d5db25e0b4e672e
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synt.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic- to - Stochastic Diverse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the recent SGMs-based method, while facilitating the diversity and accuracy of generated human motions .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00998v1", "content": "In this paper, we propose a Deterministic- to - Stochastic Diverse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the recent SGMs-based method, while facilitating the diversity and accuracy of generated human motions ."}
+{"idx": 1, "title": "arXiv:2505.00998v1 [cs.CV] 2 May 2025", "date": "", "ddg_snippet": "leading to unstable training process. In this paper, we propose a Deterministic- to - Stochastic Diverse Latent Feature Mapping (DSDF ) method for human mo-tion sy thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.00998", "content": "leading to unstable training process. In this paper, we propose a Deterministic- to - Stochastic Diverse Latent Feature Mapping (DSDF ) method for human mo-tion sy thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten"}
+{"idx": 2, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic- to - Stochastic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic- to - Stochastic ..."}
+{"idx": 3, "title": "Dual-MTGAN: Stochastic and Deterministic Motion", "date": "", "ddg_snippet": "Moreover, to fur-ther exploit the stochasticity of motion dynamics in video sequences, the motion latent space encoded from source videos would t a prior distribution. This allows out Dual-MTGAN to synthesize realistic yet diverse output videos by sampling motion features .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2102.13329", "content": "Moreover, to fur-ther exploit the stochasticity of motion dynamics in video sequences, the motion latent space encoded from source videos would t a prior distribution. This allows out Dual-MTGAN to synthesize realistic yet diverse output videos by sampling motion features ."}
+{"idx": 4, "title": "Dynamic Future Net: Diversified Human Motion Generation", "date": "", "ddg_snippet": "2.2 Deterministic human motion prediction and synthesis .As a key module, the stochastic latent RNN (Section 5.3) deploys a stochastic latent state and a deterministic latent state to learn two latent distributions for the pose-embedding and the future trajectory embedding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2009.05109", "content": "2.2 Deterministic human motion prediction and synthesis .As a key module, the stochastic latent RNN (Section 5.3) deploys a stochastic latent state and a deterministic latent state to learn two latent distributions for the pose-embedding and the future trajectory embedding."}
+{"idx": 5, "title": "CoMusion: Towards Consistent Stochastic Human Motion Prediction...", "date": "", "ddg_snippet": "Stochastic Human Motion Prediction (HMP) aims to predict multiple possible future human pose sequences from observed ones.is first mapped to an embedding and then projected to the same latent dimension as the noisy motion frames.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.12554v3/", "content": "Stochastic Human Motion Prediction (HMP) aims to predict multiple possible future human pose sequences from observed ones.is first mapped to an embedding and then projected to the same latent dimension as the noisy motion frames."}
+{"idx": 6, "title": "[2505.00998] Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic- to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2505.00998", "content": "In this paper, we propose a Deterministic- to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 7, "title": "DLow: Diversifying Latent Flows for Diverse Human Motion Prediction", "date": "", "ddg_snippet": "Deep generative models are often used for human motion prediction as they are able to model multi-modal data distributions and characterize diverse human behavior. While much care has been taken into designing and learning deep generative models, how to efficiently produce diverse samples from a deep generative model after it has been trained is still an under-explored problem. To obtain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2003.08386", "content": "Deep generative models are often used for human motion prediction as they are able to model multi-modal data distributions and characterize diverse human behavior. While much care has been taken into designing and learning deep generative models, how to efficiently produce diverse samples from a deep generative model after it has been trained is still an under-explored problem. To obtain ..."}
+{"idx": 8, "title": "Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an ...", "date": "", "ddg_snippet": "Diverse human motion prediction aims at predicting multiple pos-sible future pose sequences from a sequence of observed poses. Previous approaches usually employ deep generative networks to model the conditional distribution of data, and then randomly sam-ple outcomes from the distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.07351", "content": "Diverse human motion prediction aims at predicting multiple pos-sible future pose sequences from a sequence of observed poses. Previous approaches usually employ deep generative networks to model the conditional distribution of data, and then randomly sam-ple outcomes from the distribution."}
+{"idx": 9, "title": "A Survey on Future Frame Synthesis: Bridging Deterministic and ...", "date": "", "ddg_snippet": "In Section 4, we examine algorithms that enable stochastic motion prediction. These include approaches that inject stochastic variables into deterministic models, as well as methods based on explicit probabilistic modeling. Such methods enable sampling from motion distributions, yielding diverse yet plausible predic-tions beyond the target frame.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.14718", "content": "In Section 4, we examine algorithms that enable stochastic motion prediction. These include approaches that inject stochastic variables into deterministic models, as well as methods based on explicit probabilistic modeling. Such methods enable sampling from motion distributions, yielding diverse yet plausible predic-tions beyond the target frame."}
diff --git a/data/sampled_jsons/sitearxiv.org_Attention_Is_All_You_Need_Vaswani_2017_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_Attention_Is_All_You_Need_Vaswani_2017_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3088daccb6302caeff116a37eea21cc901e3aafe
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Attention_Is_All_You_Need_Vaswani_2017_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention of a Kiss: Exploring Attention Maps in Video", "date": "", "ddg_snippet": "Attention maps are a central interpretability tool for understanding how transformer models ( Vaswani et al., 2017 ) operate across modalities such ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.05323v1", "content": "Attention maps are a central interpretability tool for understanding how transformer models ( Vaswani et al., 2017 ) operate across modalities such ..."}
+{"idx": 1, "title": "Attention of a Kiss: Exploring Attention Maps in Video", "date": "", "ddg_snippet": "Attention maps are a central interpretability tool for understanding how transformer models ( Vaswani et al., 2017 ) operate across modalities such ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.05323v2", "content": "Attention maps are a central interpretability tool for understanding how transformer models ( Vaswani et al., 2017 ) operate across modalities such ..."}
+{"idx": 2, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "Self- attention , sometimes called intra- attention is an attention mechanism relating different positions of a single sequence in order to compute a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1706.03762v7", "content": "Self- attention , sometimes called intra- attention is an attention mechanism relating different positions of a single sequence in order to compute a ..."}
+{"idx": 3, "title": "Attention Schema-based Attention Control (ASAC): A", "date": "", "ddg_snippet": "Attention schema is viewed as an internal model enabling the brain to abstractly represent, manipulate and forecast attention dynamics (Liu et al., ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16058v1", "content": "Attention schema is viewed as an internal model enabling the brain to abstractly represent, manipulate and forecast attention dynamics (Liu et al., ..."}
+{"idx": 4, "title": "Sparsity May Be All You Need: Sparse Random Parameter Adaptation", "date": "", "ddg_snippet": "... is the number of trainable parameters, since additional memory must be allocated for their gradients and other per-parameter statistics needed by the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.15975v3", "content": "... is the number of trainable parameters, since additional memory must be allocated for their gradients and other per-parameter statistics needed by the ..."}
+{"idx": 5, "title": "Deep learning and abstractive summarisation for radiological", "date": "", "ddg_snippet": "Regardless of the rapid development of artificial intelligence, abstractive summarisation is still challenging for sensitive and data-restrictive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15419v1", "content": "Regardless of the rapid development of artificial intelligence, abstractive summarisation is still challenging for sensitive and data-restrictive ..."}
+{"idx": 6, "title": "Subject Matter Expertise vs Professional Management in", "date": "", "ddg_snippet": "You can promote an employee within the company, who is familiar with the company’s operations, or recruit an external professional manager from a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15263v1", "content": "You can promote an employee within the company, who is familiar with the company’s operations, or recruit an external professional manager from a ..."}
+{"idx": 7, "title": "Context-aware Rotary Position Embedding", "date": "", "ddg_snippet": "Positional encoding is a vital component of Transformer architectures, enabling models to incorporate sequence order into self- attention mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23083v1", "content": "Positional encoding is a vital component of Transformer architectures, enabling models to incorporate sequence order into self- attention mechanisms."}
+{"idx": 8, "title": "No Black Box Anymore: Demystifying Clinical Predictive Modeling", "date": "", "ddg_snippet": "Since the introduction of attention mechanisms in deep learning 25 , attention -based explainability methods, particularly variants employing multi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19285v3", "content": "Since the introduction of attention mechanisms in deep learning 25 , attention -based explainability methods, particularly variants employing multi ..."}
+{"idx": 9, "title": "Action is the primary key: a categorical framework for episodic", "date": "", "ddg_snippet": "... is a remarkable rule behind that “actions are discrete.” This discretization of actions allows the categorical operations described below and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.04793v2", "content": "... is a remarkable rule behind that “actions are discrete.” This discretization of actions allows the categorical operations described below and ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Beyond_Optimism_Exploration_Partially_Observable_Rewards_limitations_future_work_year_2024.jsonl b/data/sampled_jsons/sitearxiv.org_Beyond_Optimism_Exploration_Partially_Observable_Rewards_limitations_future_work_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..82dec4b25978a5c540bb1dfacf3d3b1e7d639209
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Beyond_Optimism_Exploration_Partially_Observable_Rewards_limitations_future_work_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "by S Parisi · 2024 · Cited by 4 — We present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.13909", "content": "by S Parisi · 2024 · Cited by 4 — We present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards ..."}
+{"idx": 1, "title": "Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Limitations and Future Work . First, we considered tabular MDPs, thus we plan to follow up on continuous MDPs. This will require extending the S-function and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v1", "content": "Limitations and Future Work . First, we considered tabular MDPs, thus we plan to follow up on continuous MDPs. This will require extending the S-function and ..."}
+{"idx": 2, "title": "Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "by S Parisi · 2024 · Cited by 4 — Limitations and Future Work . First, we considered tabular MDPs, thus we plan to follow up on continuous MDPs. This will require extending ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.13909?", "content": "by S Parisi · 2024 · Cited by 4 — Limitations and Future Work . First, we considered tabular MDPs, thus we plan to follow up on continuous MDPs. This will require extending ..."}
+{"idx": 3, "title": "Model-Based Exploration in Truthful Monitored Markov ...", "date": "", "ddg_snippet": "21 May 2025 — Beyond Optimism : Exploration With Partially Observable Rewards . In Advances in Neural Information Processing Systems, 2024a. Parisi et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.16772v2", "content": "21 May 2025 — Beyond Optimism : Exploration With Partially Observable Rewards . In Advances in Neural Information Processing Systems, 2024a. Parisi et al ..."}
+{"idx": 4, "title": "Model-Based Exploration in Monitored Markov Decision ...", "date": "", "ddg_snippet": "24 Jun 2025 — It is assumed that the agent always observes the rewards. Any partial observability is only considered for environment states, resulting in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.16772v5", "content": "24 Jun 2025 — It is assumed that the agent always observes the rewards. Any partial observability is only considered for environment states, resulting in ..."}
+{"idx": 5, "title": "A Theoretical Framework for Partially-Observed Reward ...", "date": "", "ddg_snippet": "by C Kausik · 2024 · Cited by 2 — We have introduced two statistically efficient algorithms for handling partially observed reward -states and have shown that they improve over ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.03282", "content": "by C Kausik · 2024 · Cited by 2 — We have introduced two statistically efficient algorithms for handling partially observed reward -states and have shown that they improve over ..."}
+{"idx": 6, "title": "Provably Efficient Partially Observable Risk-Sensitive ...", "date": "", "ddg_snippet": "by T Zhang · 2024 · Cited by 1 — Abstract. This work pioneers regret analysis of risk-sensitive reinforcement learning in partially observable envi-.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.18149", "content": "by T Zhang · 2024 · Cited by 1 — Abstract. This work pioneers regret analysis of risk-sensitive reinforcement learning in partially observable envi-."}
+{"idx": 7, "title": "Provable partially observable reinforcement learning with ...", "date": "", "ddg_snippet": "by Y Cai · 2024 · Cited by 4 — Abstract. Partial observability of the underlying states generally presents significant challenges for rein- forcement learning (RL).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.00985", "content": "by Y Cai · 2024 · Cited by 4 — Abstract. Partial observability of the underlying states generally presents significant challenges for rein- forcement learning (RL)."}
+{"idx": 8, "title": "Generalization in Monitored Markov Decision Processes ( ...", "date": "", "ddg_snippet": "by M Mohammedalamen · 2025 — Beyond optimism : Exploration with partially observable rewards . Advances in Neural Information Processing Systems, 37:65415–65444, 2024a ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2505.08988", "content": "by M Mohammedalamen · 2025 — Beyond optimism : Exploration with partially observable rewards . Advances in Neural Information Processing Systems, 37:65415–65444, 2024a ..."}
+{"idx": 9, "title": "A Survey of Continual Reinforcement Learning", "date": "", "ddg_snippet": "In this survey, we provide a comprehensive examination of CRL, focusing on its core concepts, challenges, and methodologies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21872", "content": "In this survey, we provide a comprehensive examination of CRL, focusing on its core concepts, challenges, and methodologies."}
diff --git a/data/sampled_jsons/sitearxiv.org_Definition_4.2_Per-Instance_Privacy_Loss_2505.18786.jsonl b/data/sampled_jsons/sitearxiv.org_Definition_4.2_Per-Instance_Privacy_Loss_2505.18786.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..100a778bf8ed980cc840ae1d4fde22297587b39c
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Definition_4.2_Per-Instance_Privacy_Loss_2505.18786.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "arXiv: 2505.18786 v1 [cs.LG] 24 May 2025. Leveraging Per-Instance Privacy for ... Definition 4.2 ( Per-Instance Privacy Loss ). Report issue for preceding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "arXiv: 2505.18786 v1 [cs.LG] 24 May 2025. Leveraging Per-Instance Privacy for ... Definition 4.2 ( Per-Instance Privacy Loss ). Report issue for preceding ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez_year_2021.jsonl b/data/sampled_jsons/sitearxiv.org_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8d63f3008bf7a4134c3789e3942f1a0ddcea58d
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2006.05535] Locally Private Graph Neural Networks - arXiv.org Locally Private Graph Neural Networks - arXiv.org When Differential Privacy Meets Graph Neural Networks Differentially Private Graph Neural Network with Importance ... Local Differential Privacy in Graph Neural Networks: a ... GAP: Differentially Private Graph Neural Networks with ... N -L DIFFERENTIALLY PRIVATE GRAPH N - arXiv.org", "date": "", "ddg_snippet": "Jun 9, 2020 · View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica-Perez Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information. Jun 9, 2020 · Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals. The Locally Private Graph Neural Network (LPGNN) presented in [ Sajadmanesh and Gatica-Perez , 2021] realizes the GNN model framework under lo-cal differential privacy guarantee. The features and labels of nodes can be protected. Most notably, Sajadmanesh and Gatica-Perez [16] propose a node-level LDP framework in the distributed setting where features and labels are held private by the user, and the graph structure is known to the server. Sajadmanesh and Gatica-Perez [38] propose a locally private GNN model by considering a distributed learning setting, where node features and labels are private but training the GNN is federated by a central server with access to graph edges. ABSTRACT Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data that compute node-level representations via aggregation of infor-mation from the local neighborhood of each node. However, this aggregation implies increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Jun 9, 2020 · View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica-Perez Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information. Jun 9, 2020 · Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals. The Locally Private Graph Neural Network (LPGNN) presented in [ Sajadmanesh and Gatica-Perez , 2021] realizes the GNN model framework under lo-cal differential privacy guarantee. The features and labels of nodes can be protected. Most notably, Sajadmanesh and Gatica-Perez [16] propose a node-level LDP framework in the distributed setting where features and labels are held private by the user, and the graph structure is known to the server. Sajadmanesh and Gatica-Perez [38] propose a locally private GNN model by considering a distributed learning setting, where node features and labels are private but training the GNN is federated by a central server with access to graph edges. ABSTRACT Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data that compute node-level representations via aggregation of infor-mation from the local neighborhood of each node. However, this aggregation implies increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy ..."}
+{"idx": 1, "title": "Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05535", "content": "Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information."}
+{"idx": 2, "title": "Differentially Private Graph Neural Network with Importance ...", "date": "", "ddg_snippet": "The Locally Private Graph Neural Network (LPGNN) presented in [ Sajadmanesh and Gatica-Perez , 2021] realizes the GNN model framework under lo-cal differential privacy guarantee. The features and labels of nodes can be protected.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2308.04943", "content": "The Locally Private Graph Neural Network (LPGNN) presented in [ Sajadmanesh and Gatica-Perez , 2021] realizes the GNN model framework under lo-cal differential privacy guarantee. The features and labels of nodes can be protected."}
+{"idx": 3, "title": "Local Differential Privacy in Graph Neural Networks: a ...", "date": "", "ddg_snippet": "Most notably, Sajadmanesh and Gatica-Perez [16] propose a node-level LDP framework in the distributed setting where features and labels are held private by the user, and the graph structure is known to the server.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2309.08569v2", "content": "Most notably, Sajadmanesh and Gatica-Perez [16] propose a node-level LDP framework in the distributed setting where features and labels are held private by the user, and the graph structure is known to the server."}
+{"idx": 4, "title": "When Differential Privacy Meets Graph Neural Networks Differentially Private Graph Neural Network with Importance ... Local Differential Privacy in Graph Neural Networks: a ... GAP: Differentially Private Graph Neural Networks with ... N -L DIFFERENTIALLY PRIVATE GRAPH N - arXiv.org", "date": "", "ddg_snippet": "Jun 9, 2020 · Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals. The Locally Private Graph Neural Network (LPGNN) presented in [ Sajadmanesh and Gatica-Perez , 2021] realizes the GNN model framework under lo-cal differential privacy guarantee. The features and labels of nodes can be protected. Most notably, Sajadmanesh and Gatica-Perez [16] propose a node-level LDP framework in the distributed setting where features and labels are held private by the user, and the graph structure is known to the server. Sajadmanesh and Gatica-Perez [38] propose a locally private GNN model by considering a distributed learning setting, where node features and labels are private but training the GNN is federated by a central server with access to graph edges. ABSTRACT Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data that compute node-level representations via aggregation of infor-mation from the local neighborhood of each node. However, this aggregation implies increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535v1", "content": "Jun 9, 2020 · Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals. The Locally Private Graph Neural Network (LPGNN) presented in [ Sajadmanesh and Gatica-Perez , 2021] realizes the GNN model framework under lo-cal differential privacy guarantee. The features and labels of nodes can be protected. Most notably, Sajadmanesh and Gatica-Perez [16] propose a node-level LDP framework in the distributed setting where features and labels are held private by the user, and the graph structure is known to the server. Sajadmanesh and Gatica-Perez [38] propose a locally private GNN model by considering a distributed learning setting, where node features and labels are private but training the GNN is federated by a central server with access to graph edges. ABSTRACT Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data that compute node-level representations via aggregation of infor-mation from the local neighborhood of each node. However, this aggregation implies increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy ..."}
+{"idx": 5, "title": "GAP: Differentially Private Graph Neural Networks with ...", "date": "", "ddg_snippet": "Sajadmanesh and Gatica-Perez [38] propose a locally private GNN model by considering a distributed learning setting, where node features and labels are private but training the GNN is federated by a central server with access to graph edges.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2203.00949", "content": "Sajadmanesh and Gatica-Perez [38] propose a locally private GNN model by considering a distributed learning setting, where node features and labels are private but training the GNN is federated by a central server with access to graph edges."}
+{"idx": 6, "title": "N -L DIFFERENTIALLY PRIVATE GRAPH N - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data that compute node-level representations via aggregation of infor-mation from the local neighborhood of each node. However, this aggregation implies increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2111.15521v2", "content": "ABSTRACT Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data that compute node-level representations via aggregation of infor-mation from the local neighborhood of each node. However, this aggregation implies increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy ..."}
+{"idx": 7, "title": "Local Differential Privacy in Graph Neural Networks", "date": "", "ddg_snippet": "by K Bhaila · 2023 · Cited by 15 — [16] S. Sajadmanesh and D. Gatica-Perez , “Locally private graph neural networks,” in CCS, 2021, pp. 2130–2145.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2309.08569", "content": "by K Bhaila · 2023 · Cited by 15 — [16] S. Sajadmanesh and D. Gatica-Perez , “Locally private graph neural networks,” in CCS, 2021, pp. 2130–2145."}
+{"idx": 8, "title": "ProGAP: Progressive Graph Neural Networks with ...", "date": "", "ddg_snippet": "by S Sajadmanesh · 2023 · Cited by 20 — For example, Sajadmanesh and Gatica-Perez [40] propose a locally private GNN for a distributed learning environment, where node features and labels remain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.08928", "content": "by S Sajadmanesh · 2023 · Cited by 20 — For example, Sajadmanesh and Gatica-Perez [40] propose a locally private GNN for a distributed learning environment, where node features and labels remain ..."}
+{"idx": 9, "title": "arXiv:2410.08299v1 [cs.LG] 10 Oct 2024", "date": "", "ddg_snippet": "by H Yin · 2024 — Sina Sajadmanesh and Daniel Gatica-Perez. Locally private graph neural networks . In Proceedings of the 2021 ACM SIGSAC Conference on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.08299", "content": "by H Yin · 2024 — Sina Sajadmanesh and Daniel Gatica-Perez. Locally private graph neural networks . In Proceedings of the 2021 ACM SIGSAC Conference on ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization.jsonl b/data/sampled_jsons/sitearxiv.org_Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ff854caf06c0b4bf16c97d8a2f49e6d4beadfad1
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning Based Automated Design of Differential", "date": "", "ddg_snippet": "differential evolution, meta -learning, double deep Q-network, black - box - optimization , exploratory landscape analysis .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.12881v1", "content": "differential evolution, meta -learning, double deep Q-network, black - box - optimization , exploratory landscape analysis ."}
+{"idx": 1, "title": "Temporal True and Surrogate Fitness Landscape Analysis for", "date": "", "ddg_snippet": "Fitness landscape analysis (FLA) can do that, providing insight about the interplay between optimisation algorithms and different fitness landscape .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.06557v1", "content": "Fitness landscape analysis (FLA) can do that, providing insight about the interplay between optimisation algorithms and different fitness landscape ."}
+{"idx": 2, "title": "Greedy Restart Schedules: A Baseline for Dynamic Algorithm", "date": "", "ddg_snippet": "In this section, we introduce numerical black - box optimization and common solvers in this domain, as well as a short overview of meta -algorithmic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11440v1", "content": "In this section, we introduce numerical black - box optimization and common solvers in this domain, as well as a short overview of meta -algorithmic ..."}
+{"idx": 3, "title": "Efficient Visual Appearance Optimization by Learning from Prior", "date": "", "ddg_snippet": "PBO builds on Bayesian Optimization (BO), a probabilistic framework for solving black - box functions whose internal structure is inaccessible and must ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15355v2", "content": "PBO builds on Bayesian Optimization (BO), a probabilistic framework for solving black - box functions whose internal structure is inaccessible and must ..."}
+{"idx": 4, "title": "Improving Algorithm-Selection and Performance-Prediction via", "date": "", "ddg_snippet": "In continuous optimisation, the vast majority of previous work focuses on the use of landscape -features to train selectors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.05359v1", "content": "In continuous optimisation, the vast majority of previous work focuses on the use of landscape -features to train selectors."}
+{"idx": 5, "title": "A Novel Ranking Scheme for the Performance Analysis of", "date": "", "ddg_snippet": "The IOHexperimenter component of Iohprofiler [ 30 ] , well-known bench-marking suites like Pseudo-Boolean Optimization (PBO) [ 11 ] , Black - Box Bench ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.00154v1", "content": "The IOHexperimenter component of Iohprofiler [ 30 ] , well-known bench-marking suites like Pseudo-Boolean Optimization (PBO) [ 11 ] , Black - Box Bench ..."}
+{"idx": 6, "title": "Deep Reinforcement Learning for Dynamic Algorithm Selection: A", "date": "", "ddg_snippet": "... Evolution (DE) [ 3 ] , Particle Swarm Optimization (PSO) [ 4 ] , and their variants, have exhibited good performance in solving Black Box ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.02131v3", "content": "... Evolution (DE) [ 3 ] , Particle Swarm Optimization (PSO) [ 4 ] , and their variants, have exhibited good performance in solving Black Box ..."}
+{"idx": 7, "title": "Toward Automated Algorithm Design: A Survey and Practical Guide", "date": "", "ddg_snippet": "In contrast, Black - Box Optimization (BBO) only provides objective values for solutions, making the analysis and search of the problem space even more ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.00625v2", "content": "In contrast, Black - Box Optimization (BBO) only provides objective values for solutions, making the analysis and search of the problem space even more ..."}
+{"idx": 8, "title": "Can LLMs Identify Critical Limitations within Scientific", "date": "", "ddg_snippet": "Existing benchmarks for peer-review generation collect papers and their corresponding entire reviews from AI conferences Du et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02694v1", "content": "Existing benchmarks for peer-review generation collect papers and their corresponding entire reviews from AI conferences Du et al."}
+{"idx": 9, "title": "Impact of Training Instance Selection on Automated Algorithm", "date": "", "ddg_snippet": "... to invest a small fraction of the budget to extract properties of the problem instance, for example via so-called exploratory landscape analysis (ELA ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.07539v1", "content": "... to invest a small fraction of the budget to extract properties of the problem instance, for example via so-called exploratory landscape analysis (ELA ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Papamakarios_2017_Masked_Autoregressive_Flow.jsonl b/data/sampled_jsons/sitearxiv.org_Papamakarios_2017_Masked_Autoregressive_Flow.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1d5967fc24424a746337b426029ce26af1b59037
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Papamakarios_2017_Masked_Autoregressive_Flow.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1705.07057] Masked Autoregressive Flow for Density Estimation - arXiv.org", "date": "", "ddg_snippet": "View a PDF of the paper titled Masked Autoregressive Flow for Density Estimation, by George Papamakarios and 2 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1705.07057", "content": "View a PDF of the paper titled Masked Autoregressive Flow for Density Estimation, by George Papamakarios and 2 other authors"}
+{"idx": 1, "title": "Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "This type of flow is closely related to Inverse Autoregressive Flow and is a gen-eralization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1705.07057", "content": "This type of flow is closely related to Inverse Autoregressive Flow and is a gen-eralization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks."}
+{"idx": 2, "title": "[1705.07057] Masked Autoregressive Flow for Density Estimation - ar5iv", "date": "", "ddg_snippet": "This type of flow is closely related to Inverse Autoregressive Flow and is a generalization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1705.07057", "content": "This type of flow is closely related to Inverse Autoregressive Flow and is a generalization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks."}
+{"idx": 3, "title": "Neural Autoregressive Flows - arXiv.org", "date": "", "ddg_snippet": "Papamakarios , George, Murray, Iain, and Pavlakou, Theo. Masked autoregressive flow for density estimation. In Ad-vances in Neural Information Processing Systems, 2017 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1804.00779", "content": "Papamakarios , George, Murray, Iain, and Pavlakou, Theo. Masked autoregressive flow for density estimation. In Ad-vances in Neural Information Processing Systems, 2017 ."}
+{"idx": 4, "title": "denmarf: a Python package for density estimation using masked ...", "date": "", "ddg_snippet": "Summary Masked autoregressive flow (MAF) ( Papamakarios et al., 2017 ) is a state-of-the-art non-parametric density estimation technique. It is based on the idea (known as a normalizing flow ) that a simple base probability distribution can be mapped into a complicated target distribution that one wishes to approximate, using a sequence of bijective transformations (Tabak & Turner, 2013; Tabak ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.14379", "content": "Summary Masked autoregressive flow (MAF) ( Papamakarios et al., 2017 ) is a state-of-the-art non-parametric density estimation technique. It is based on the idea (known as a normalizing flow ) that a simple base probability distribution can be mapped into a complicated target distribution that one wishes to approximate, using a sequence of bijective transformations (Tabak & Turner, 2013; Tabak ..."}
+{"idx": 5, "title": "[1804.00779] Neural Autoregressive Flows - ar5iv", "date": "", "ddg_snippet": "Abstract Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF) ( Papamakarios et al., 2017 ), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time (Oord et al., 2017 ), via Inverse Autoregressive Flows (IAF) (Kingma et al., 2016). We ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1804.00779", "content": "Abstract Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF) ( Papamakarios et al., 2017 ), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time (Oord et al., 2017 ), via Inverse Autoregressive Flows (IAF) (Kingma et al., 2016). We ..."}
+{"idx": 6, "title": "Masked Autoregressive Flow for Density Estimation - arXiv.org", "date": "", "ddg_snippet": "By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing fl ow suitable for density estimation, which we call Masked Autoregressive Flow . This type of fl ow is closely related to Inverse Autoregressive Flow and is a gen- eralization of Real NVP.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1705.07057v2", "content": "By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing fl ow suitable for density estimation, which we call Masked Autoregressive Flow . This type of fl ow is closely related to Inverse Autoregressive Flow and is a gen- eralization of Real NVP."}
+{"idx": 7, "title": "PDF arXiv:1705.07057v1 [stat.ML] 19 May 2017", "date": "", "ddg_snippet": "By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing fl ow suitable for density estimation, which we call Masked Autoregressive Flow . This type of fl ow is closely related to Inverse Autoregressive Flow and is a gen- eralization of Real NVP.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1705.07057v1.pdf", "content": "By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing fl ow suitable for density estimation, which we call Masked Autoregressive Flow . This type of fl ow is closely related to Inverse Autoregressive Flow and is a gen- eralization of Real NVP."}
+{"idx": 8, "title": "[1910.13233] Contents", "date": "", "ddg_snippet": "My main contribution is Masked Autoregressive Flow , a new model for density estimation based on a bijective neural network that transforms random noise to data. At the time of its introduction, Masked Autoregressive Flow achieved state-of-the-art results in general-purpose density estimation.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1910.13233", "content": "My main contribution is Masked Autoregressive Flow , a new model for density estimation based on a bijective neural network that transforms random noise to data. At the time of its introduction, Masked Autoregressive Flow achieved state-of-the-art results in general-purpose density estimation."}
+{"idx": 9, "title": "Neural Density Estimation and Likelihood-free Inference", "date": "", "ddg_snippet": "The three chapters are listed below: Chapter 2 is about Masked Autoregressive Flow , and is based on the following paper: G. Papamakarios , T. Pavlakou, and I. Murray. Masked autoregressive ow for density estimation. Advances in Neural Information Processing Systems 30, 2017", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1910.13233", "content": "The three chapters are listed below: Chapter 2 is about Masked Autoregressive Flow , and is based on the following paper: G. Papamakarios , T. Pavlakou, and I. Murray. Masked autoregressive ow for density estimation. Advances in Neural Information Processing Systems 30, 2017"}
diff --git a/data/sampled_jsons/sitearxiv.org_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System.jsonl b/data/sampled_jsons/sitearxiv.org_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..47ecb2b162509e41f1e12295d9a5aec481fa56df
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "by JL Gamella · 2025 · Cited by 1 — We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20099", "content": "by JL Gamella · 2025 · Cited by 1 — We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work."}
+{"idx": 1, "title": "Real Physical Systems as a Testbed for AI Methodology", "date": "", "ddg_snippet": "17 Apr 2024 — We illustrate potential applications through a series of case studies in fields such as causal discovery, out-of-distribution generalization, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.11341v1", "content": "17 Apr 2024 — We illustrate potential applications through a series of case studies in fields such as causal discovery, out-of-distribution generalization, ..."}
+{"idx": 2, "title": "Causal Machine Learning: A Survey and Open Problems", "date": "", "ddg_snippet": "by J Kaddour · 2022 · Cited by 260 — Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2206.15475", "content": "by J Kaddour · 2022 · Cited by 260 — Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a ..."}
+{"idx": 3, "title": "Learned feature representations are biased by complexity, ...", "date": "", "ddg_snippet": "We find that their learned feature representations are systematically biased towards representing some features more strongly than others.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.05847v1", "content": "We find that their learned feature representations are systematically biased towards representing some features more strongly than others."}
+{"idx": 4, "title": "Causality for Tabular Data Synthesis: A High-Order ...", "date": "", "ddg_snippet": "12 Jun 2024 — In this paper, we introduce high-order structural causal information as natural prior knowledge and provide a benchmark framework for the evaluation of tabular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.08311v1", "content": "12 Jun 2024 — In this paper, we introduce high-order structural causal information as natural prior knowledge and provide a benchmark framework for the evaluation of tabular ..."}
+{"idx": 5, "title": "The Linear Representation Hypothesis and the Geometry ...", "date": "", "ddg_snippet": "The “linear representation hypothesis” is the idea that high-level concepts are represented linearly as directions in some representation space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.03658v2", "content": "The “linear representation hypothesis” is the idea that high-level concepts are represented linearly as directions in some representation space."}
+{"idx": 6, "title": "Computer Science Feb 2025", "date": "", "ddg_snippet": "Title: Sanity Checking Causal Representation Learning on a Simple Real-World System . Juan L. Gamella, Simon Bing, Jakob Runge. Comments: 24 pages, 12 figures.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-02?skip=9350&show=2000", "content": "Title: Sanity Checking Causal Representation Learning on a Simple Real-World System . Juan L. Gamella, Simon Bing, Jakob Runge. Comments: 24 pages, 12 figures."}
+{"idx": 7, "title": "Where is the Truth? The Risk of Getting Confounded ...", "date": "", "ddg_snippet": "13 Jun 2025 — Confounding factors represent a dangerous issue for AI systems , often steering models toward shortcuts instead of true understanding (Lapuschkin ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.06434v3", "content": "13 Jun 2025 — Confounding factors represent a dangerous issue for AI systems , often steering models toward shortcuts instead of true understanding (Lapuschkin ..."}
+{"idx": 8, "title": "Aligning AI Through Internal Understanding: The Role of ...", "date": "", "ddg_snippet": "10 Sept 2025 — Research on large models like GPT-4 and LLaMA has shown that interpretable patterns often give way to entangled representations that defy simple ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08592v1", "content": "10 Sept 2025 — Research on large models like GPT-4 and LLaMA has shown that interpretable patterns often give way to entangled representations that defy simple ..."}
+{"idx": 9, "title": "FairDropout: Using Example-Tied Dropout to Enhance ...", "date": "", "ddg_snippet": "10 Feb 2025 — We empirically evaluate FairDropout using the subpopulation benchmark suite encompassing vision, language, and healthcare tasks, demonstrating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.06695v1", "content": "10 Feb 2025 — We empirically evaluate FairDropout using the subpopulation benchmark suite encompassing vision, language, and healthcare tasks, demonstrating ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Table_1_MSE_for_the_estimated_conditional_mean_FD3.jsonl b/data/sampled_jsons/sitearxiv.org_Table_1_MSE_for_the_estimated_conditional_mean_FD3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..71b43c1ffc715f94dc5463b816ebd6363066e64a
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Table_1_MSE_for_the_estimated_conditional_mean_FD3.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Table 1 : MSE for the estimated conditional mean and the standard deviation.Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE(SD) for the FD 3 dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Table 1 : MSE for the estimated conditional mean and the standard deviation.Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE(SD) for the FD 3 dataset."}
diff --git a/data/sampled_jsons/sitearxiv.org_Table_4_T-Agent_AutoGPT_One-day_cost_USD_monetary.jsonl b/data/sampled_jsons/sitearxiv.org_Table_4_T-Agent_AutoGPT_One-day_cost_USD_monetary.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2985db9656c3465a3804efe3f82acac7c5ee3d71
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Table_4_T-Agent_AutoGPT_One-day_cost_USD_monetary.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "Upon reviewing the reasoning logs, we find that under the zero-day setting, AutoGPT could identify and exploit new vulnerabilities that are easier than those provided in the one-day description. We analyzed one of such cases in Section 4.3 (CVE-2024-37831). Furthermore, Cy-Agent leads to significantly lower success rates than T-Agent and AutoGPT .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "Upon reviewing the reasoning logs, we find that under the zero-day setting, AutoGPT could identify and exploit new vulnerabilities that are easier than those provided in the one-day description. We analyzed one of such cases in Section 4.3 (CVE-2024-37831). Furthermore, Cy-Agent leads to significantly lower success rates than T-Agent and AutoGPT ."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "We observe that AutoGPT demonstrates superior perfor-mance by achieving the highest success@5 rate, with an unexpectedly higher zero-day success@5 rate compared to its one - day success@5 rate. Table 4 . Per-task costs of evaluating LLM agents on CVE-Bench.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "We observe that AutoGPT demonstrates superior perfor-mance by achieving the highest success@5 rate, with an unexpectedly higher zero-day success@5 rate compared to its one - day success@5 rate. Table 4 . Per-task costs of evaluating LLM agents on CVE-Bench."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World...", "date": "", "ddg_snippet": "Costs . We present the costs of using CVE-Bench to evaluate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v3", "content": "Costs . We present the costs of using CVE-Bench to evaluate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task."}
+{"idx": 3, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World...", "date": "", "ddg_snippet": "We present the costs of using CVE-Bench to evaluate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332", "content": "We present the costs of using CVE-Bench to evaluate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task."}
+{"idx": 4, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World...", "date": "", "ddg_snippet": "Costs . We present the costs of using CVE-Bench to evaluate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v2", "content": "Costs . We present the costs of using CVE-Bench to evaluate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task."}
+{"idx": 5, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World...", "date": "", "ddg_snippet": "Costs . We present the costs of using CVE-Bench to evaluate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "Costs . We present the costs of using CVE-Bench to evaluate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task."}
+{"idx": 6, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 11 — Percentage in successful one-day exploits (%). Cy-Agent. T-Agent . AutoGPT ... Furthermore, Cy-Agent leads to significantly lower suc- cess rates ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332?", "content": "by Y Zhu · 2025 · Cited by 11 — Percentage in successful one-day exploits (%). Cy-Agent. T-Agent . AutoGPT ... Furthermore, Cy-Agent leads to significantly lower suc- cess rates ..."}
+{"idx": 7, "title": "MVISU-Bench: Benchmarking Mobile Agents for Real-World Tasks by", "date": "", "ddg_snippet": "As shown in Table 1 , they suffer from several key limitations: 1 ) Most benchmarks evaluate agents only in English environments, on Single-App ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.09057v1", "content": "As shown in Table 1 , they suffer from several key limitations: 1 ) Most benchmarks evaluate agents only in English environments, on Single-App ..."}
+{"idx": 8, "title": "From Bits to Boardrooms: A Cutting-Edge Multi-Agent LLM", "date": "", "ddg_snippet": "ChatGPT-Code Interpreter [ 24 ] combines conversational AI with a code interpreter, and LangChain Agents [ 12 , 15 , 36 , 44 ] offers different types ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15447v1", "content": "ChatGPT-Code Interpreter [ 24 ] combines conversational AI with a code interpreter, and LangChain Agents [ 12 , 15 , 36 , 44 ] offers different types ..."}
+{"idx": 9, "title": "Symphony: A Decentralized Multi-Agent Framework for Scalable", "date": "", "ddg_snippet": "... based agent frameworks rely on centralized orchestration, incurring high deployment costs , rigid communication topologies, and limited adaptability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20019v1", "content": "... based agent frameworks rely on centralized orchestration, incurring high deployment costs , rigid communication topologies, and limited adaptability."}
diff --git a/data/sampled_jsons/sitearxiv.org_X-CLIP_End-to-End_Multi-grained_Contrastive_Learning_for_Video-Text_Retrieval_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_X-CLIP_End-to-End_Multi-grained_Contrastive_Learning_for_Video-Text_Retrieval_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b708e54ca80c2974aa7c3de485d9bba38eef38dd
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_X-CLIP_End-to-End_Multi-grained_Contrastive_Learning_for_Video-Text_Retrieval_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Repeating Words for Video-Language Retrieval with", "date": "", "ddg_snippet": "Video - CLIP (Xu et al., 2021 ) , UMT-B (Li et al., 2023a ) , and VINDLU (Cheng et al., 2023 ) , in text - to - video retrieval performance on MSR ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14812v1", "content": "Video - CLIP (Xu et al., 2021 ) , UMT-B (Li et al., 2023a ) , and VINDLU (Cheng et al., 2023 ) , in text - to - video retrieval performance on MSR ..."}
+{"idx": 1, "title": "Ambiguity-Restrained Text-Video Representation Learning for", "date": "", "ddg_snippet": "Partially Relevant Video Retrieval (PRVR) aims to retrieve a video where a specific segment is relevant to a given text query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07471v1", "content": "Partially Relevant Video Retrieval (PRVR) aims to retrieve a video where a specific segment is relevant to a given text query."}
+{"idx": 2, "title": "MVP: Winning Solution to SMP Challenge 2025 Video Track", "date": "", "ddg_snippet": "To further advance the understanding of content dynamics in multimodal environments, the Social Media Prediction (SMP) Challenge 2025 introduces a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.00950v1", "content": "To further advance the understanding of content dynamics in multimodal environments, the Social Media Prediction (SMP) Challenge 2025 introduces a ..."}
+{"idx": 3, "title": "Weighted Multi-Prompt Learning with Description-free Large", "date": "", "ddg_snippet": "CLIP is trained on a web-scale dataset comprising image- text pairs to learn aligned representations through contrastive learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07147v1", "content": "CLIP is trained on a web-scale dataset comprising image- text pairs to learn aligned representations through contrastive learning ."}
+{"idx": 4, "title": "Multi-modal Generative AI: Multi-modal LLMs, Diffusions and the", "date": "", "ddg_snippet": "Note that LLM can only receive the text tokens as its input, the next important problem for multi -modal LLM is how to enable LLM to understand the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.14993v2", "content": "Note that LLM can only receive the text tokens as its input, the next important problem for multi -modal LLM is how to enable LLM to understand the ..."}
+{"idx": 5, "title": "Cache-of-Thought: Master-Apprentice Framework for", "date": "", "ddg_snippet": "... 1) CoT, a general master-apprentice framework for multi -VLM inference with a cache, (2) an effective multi -modal retrieval and in-context learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20587v2", "content": "... 1) CoT, a general master-apprentice framework for multi -VLM inference with a cache, (2) an effective multi -modal retrieval and in-context learning ..."}
+{"idx": 6, "title": "Multimodal Learning for Fake News Detection in Short Videos", "date": "", "ddg_snippet": "This paper presents HFN, Heterogeneous Fusion Net, a novel multimodal framework that integrates video , audio, and text data to evaluate the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15578v1", "content": "This paper presents HFN, Heterogeneous Fusion Net, a novel multimodal framework that integrates video , audio, and text data to evaluate the ..."}
+{"idx": 7, "title": "Rationale-Enhanced Decoding for Multi-modal Chain-of-Thought", "date": "", "ddg_snippet": "... to single-modal LLMs, chain-of-thought (CoT) prompting has been adapted for LVLMs to enhance multi -modal reasoning by generating intermediate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07685v1", "content": "... to single-modal LLMs, chain-of-thought (CoT) prompting has been adapted for LVLMs to enhance multi -modal reasoning by generating intermediate ..."}
+{"idx": 8, "title": "Context-Adaptive Multi-Prompt Embedding with Large Language", "date": "", "ddg_snippet": "Contrastive vision-language models, such as CLIP (Radford et al., 2021 ) , have become foundational for zero-shot image- text and video - text ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.02762v1", "content": "Contrastive vision-language models, such as CLIP (Radford et al., 2021 ) , have become foundational for zero-shot image- text and video - text ..."}
+{"idx": 9, "title": "Creating High-quality 3D Content by Bridging the Gap Between", "date": "", "ddg_snippet": "To address this issue, we present X -Dreamer, a novel approach for high-quality text - to -3D content creation that effectively bridges the gap between ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.00085v3", "content": "To address this issue, we present X -Dreamer, a novel approach for high-quality text - to -3D content creation that effectively bridges the gap between ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_[2408.17052]_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector.jsonl b/data/sampled_jsons/sitearxiv.org_[2408.17052]_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c19ca54edd419d8e47636393a11c9750d71553c3
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_[2408.17052]_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector? Can We Leave Deepfake Data Behind in Training Deepfake Detector? Abstract Can We Leave Deepfake Data Behind in Training Deepfake Generalizing Deepfake Video Detection with Plug-and-Play ... Deepfake Detection that Generalizes Across Benchmarks 𝒳²-DFD: A framework for e𝒳plainable and e𝒳tendable Deepfake ..... [2503.02503] Deepfake Detection via Knowledge Injection", "date": "", "ddg_snippet": "Aug 30, 2024 · In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from \"real to blendfake to deepfake \" to be a progressive transition. In this paper, we commence with a question that many researchers may ponder: Can we leave deepfake data behind in training deepfake detectors ? We argue that the abnormal data efficacy of vanilla hybrid training may stem from the poor latent space organization. 1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake etector ? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-i To exploit the generalization potential of FFD for video-level deepfake detection , we introduce a novel synthetic training data , VB: simulating the Facial Feature Drift phe-nomenon through landmark perturbation and region mask extraction on facial organs across video frames. As deepfake production techniques advance, detection methods are also evolving to keep pace. The literature en-compasses a taxonomy of detection strategies, which can be divided into two main branches: content-based and signal-based approaches. We systematically assess the intrinsic capabilities of MLLMs for deepfake detection : To our knowledge, we are the first work to provide an in-depth analysis of MLLMs’ inherent ability in deepfake detection . Mar 4, 2025 · Specifically, a knowledge injection module is proposed to learn and inject necessary knowledge into the backbone model, to achieve a more accurate modeling of the distributions of real and fake data .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "Aug 30, 2024 · In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from \"real to blendfake to deepfake \" to be a progressive transition. In this paper, we commence with a question that many researchers may ponder: Can we leave deepfake data behind in training deepfake detectors ? We argue that the abnormal data efficacy of vanilla hybrid training may stem from the poor latent space organization. 1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake etector ? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-i To exploit the generalization potential of FFD for video-level deepfake detection , we introduce a novel synthetic training data , VB: simulating the Facial Feature Drift phe-nomenon through landmark perturbation and region mask extraction on facial organs across video frames. As deepfake production techniques advance, detection methods are also evolving to keep pace. The literature en-compasses a taxonomy of detection strategies, which can be divided into two main branches: content-based and signal-based approaches. We systematically assess the intrinsic capabilities of MLLMs for deepfake detection : To our knowledge, we are the first work to provide an in-depth analysis of MLLMs’ inherent ability in deepfake detection . Mar 4, 2025 · Specifically, a knowledge injection module is proposed to learn and inject necessary knowledge into the backbone model, to achieve a more accurate modeling of the distributions of real and fake data ."}
+{"idx": 1, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "...which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called “1+1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17052v1", "content": "...which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called “1+1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?"}
+{"idx": 2, "title": "Abstract Can We Leave Deepfake Data Behind in Training Deepfake", "date": "", "ddg_snippet": "1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake etector ? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-i", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.17052", "content": "1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake etector ? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-i"}
+{"idx": 3, "title": "Generalizing Deepfake Video Detection with Plug-and-Play ...", "date": "", "ddg_snippet": "To exploit the generalization potential of FFD for video-level deepfake detection , we introduce a novel synthetic training data , VB: simulating the Facial Feature Drift phe-nomenon through landmark perturbation and region mask extraction on facial organs across video frames.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.17065v2", "content": "To exploit the generalization potential of FFD for video-level deepfake detection , we introduce a novel synthetic training data , VB: simulating the Facial Feature Drift phe-nomenon through landmark perturbation and region mask extraction on facial organs across video frames."}
+{"idx": 4, "title": "Deepfake Detection that Generalizes Across Benchmarks", "date": "", "ddg_snippet": "As deepfake production techniques advance, detection methods are also evolving to keep pace. The literature en-compasses a taxonomy of detection strategies, which can be divided into two main branches: content-based and signal-based approaches.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.06248", "content": "As deepfake production techniques advance, detection methods are also evolving to keep pace. The literature en-compasses a taxonomy of detection strategies, which can be divided into two main branches: content-based and signal-based approaches."}
+{"idx": 5, "title": "𝒳²-DFD: A framework for e𝒳plainable and e𝒳tendable Deepfake .....", "date": "", "ddg_snippet": "We systematically assess the intrinsic capabilities of MLLMs for deepfake detection : To our knowledge, we are the first work to provide an in-depth analysis of MLLMs’ inherent ability in deepfake detection .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.06126v4", "content": "We systematically assess the intrinsic capabilities of MLLMs for deepfake detection : To our knowledge, we are the first work to provide an in-depth analysis of MLLMs’ inherent ability in deepfake detection ."}
+{"idx": 6, "title": "[2503.02503] Deepfake Detection via Knowledge Injection", "date": "", "ddg_snippet": "Mar 4, 2025 · Specifically, a knowledge injection module is proposed to learn and inject necessary knowledge into the backbone model, to achieve a more accurate modeling of the distributions of real and fake data .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.02503", "content": "Mar 4, 2025 · Specifically, a knowledge injection module is proposed to learn and inject necessary knowledge into the backbone model, to achieve a more accurate modeling of the distributions of real and fake data ."}
+{"idx": 7, "title": "Generalizing Deepfake Video Detection with Plug-and-Play...", "date": "", "ddg_snippet": "2.2 Deepfake Video Detection . 2.3 Deepfake Detectors Based on Data Synthesis. Can we leave deepfake data behind in training deepfake detector ? arXiv preprint arXiv: 2408 . 17052 , 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17065v2/", "content": "2.2 Deepfake Video Detection . 2.3 Deepfake Detectors Based on Data Synthesis. Can we leave deepfake data behind in training deepfake detector ? arXiv preprint arXiv: 2408 . 17052 , 2024."}
+{"idx": 8, "title": "TruthLens: Explainable DeepFake Detection for Face Manipulated and...", "date": "", "ddg_snippet": "Conventional DeepFake Detection : Early DeepFake detection methods [33, 68] struggled with generalizability in cross-dataset settings, which is critical for in-the-wild detection . Dong et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.15867v1", "content": "Conventional DeepFake Detection : Early DeepFake detection methods [33, 68] struggled with generalizability in cross-dataset settings, which is critical for in-the-wild detection . Dong et al."}
+{"idx": 9, "title": "A Quality-Centric Framework for Deepfake Detection", "date": "", "ddg_snippet": "17 May 2025 — This paper tackles this issue by leveraging the forgery quality of training data to improve the generalization performance of existing deepfake ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.05335v3", "content": "17 May 2025 — This paper tackles this issue by leveraging the forgery quality of training data to improve the generalization performance of existing deepfake ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_abs1903.11027_nuScenes_Boston_Singapore_1000_scenes.jsonl b/data/sampled_jsons/sitearxiv.org_abs1903.11027_nuScenes_Boston_Singapore_1000_scenes.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f4bb1fc6b5d64f07d457ac09e7521dbd68b4813
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_abs1903.11027_nuScenes_Boston_Singapore_1000_scenes.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 1903 . 11027 ] nuScenes : A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1903.11027", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes ..."}
+{"idx": 1, "title": "Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction", "date": "", "ddg_snippet": "The NuScenes dataset [10] consists of 1000 scenes with 5.5 hours of footage labeled at 2Hz. It has 32k trajectories taken from a moving vehicle in 4 neighborhoods within Boston and Singapore , and includes HD semantic maps with 11 annotated layers, including pedestrian crossings, walkways, stop lines, traffic lights, road dividers, lane dividers ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13914v1", "content": "The NuScenes dataset [10] consists of 1000 scenes with 5.5 hours of footage labeled at 2Hz. It has 32k trajectories taken from a moving vehicle in 4 neighborhoods within Boston and Singapore , and includes HD semantic maps with 11 annotated layers, including pedestrian crossings, walkways, stop lines, traffic lights, road dividers, lane dividers ..."}
+{"idx": 2, "title": "Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction", "date": "", "ddg_snippet": "NuScenes : The NuScenes dataset [10] consists of 1000 scenes with 5.5 hours of footage labeled at 2Hz. It has 32k trajectories taken from a moving vehicle in 4 neighborhoods within Boston and Singapore , and includes HD semantic maps with 11 annotated layers, including pedestrian cross-ings, walkways, stop lines, traffic lights, road dividers ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.13914", "content": "NuScenes : The NuScenes dataset [10] consists of 1000 scenes with 5.5 hours of footage labeled at 2Hz. It has 32k trajectories taken from a moving vehicle in 4 neighborhoods within Boston and Singapore , and includes HD semantic maps with 11 annotated layers, including pedestrian cross-ings, walkways, stop lines, traffic lights, road dividers ..."}
+{"idx": 3, "title": "Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals", "date": "", "ddg_snippet": "5 Experiments Dataset: We evaluate our method on nuScenes [26], a self-driving car dataset collected in Boston and Singapore . nuScenes contains 1000 scenes , each 20 seconds, with ground truth annotations and HD maps. Vehicles have manually-annotated 3D bounding boxes, which are published at 2 Hz.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2106.15004", "content": "5 Experiments Dataset: We evaluate our method on nuScenes [26], a self-driving car dataset collected in Boston and Singapore . nuScenes contains 1000 scenes , each 20 seconds, with ground truth annotations and HD maps. Vehicles have manually-annotated 3D bounding boxes, which are published at 2 Hz."}
+{"idx": 4, "title": "Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom", "date": "", "ddg_snippet": "arXiv: 1903 . 11027 v5 [cs.LG] 5 May 2020.In this work we present nuTonomy scenes ( nuScenes ), the rst dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 de-gree eld of view. nuScenes comprises 1000 scenes , each 20s long and...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027", "content": "arXiv: 1903 . 11027 v5 [cs.LG] 5 May 2020.In this work we present nuTonomy scenes ( nuScenes ), the rst dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 de-gree eld of view. nuScenes comprises 1000 scenes , each 20s long and..."}
+{"idx": 5, "title": "Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the rst dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree eld of view. nuScenes comprises 1000 scenes , each 20s long and fully annotated with 3D...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v2", "content": "In this work we present nuTonomy scenes ( nuScenes ), the rst dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree eld of view. nuScenes comprises 1000 scenes , each 20s long and fully annotated with 3D..."}
+{"idx": 6, "title": "Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom", "date": "", "ddg_snippet": "nuScenes : A multimodal dataset for autonomous driving.We drive in Boston (Seaport and South Boston ) and Singapore (One North, Holland Village and Queenstown), two cities that are known for their dense traf-c and highly challenging driving situations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v1", "content": "nuScenes : A multimodal dataset for autonomous driving.We drive in Boston (Seaport and South Boston ) and Singapore (One North, Holland Village and Queenstown), two cities that are known for their dense traf-c and highly challenging driving situations."}
+{"idx": 7, "title": "Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom", "date": "", "ddg_snippet": "arXiv: 1903 . 11027 v4 [cs.LG] 8 Jan 2020. Abstract.20mm RTK positioning, 1000 Hz update rate. Table 2. Sensor data in nuScenes . capture a diverse set of locations (urban, residential, nature and industrial), times (day and night) and weather condi-tions (sun, rain and clouds).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v4", "content": "arXiv: 1903 . 11027 v4 [cs.LG] 8 Jan 2020. Abstract.20mm RTK positioning, 1000 Hz update rate. Table 2. Sensor data in nuScenes . capture a diverse set of locations (urban, residential, nature and industrial), times (day and night) and weather condi-tions (sun, rain and clouds)."}
+{"idx": 8, "title": "Learned Enrichment of Top-View Grid Maps Improves ...", "date": "", "ddg_snippet": "by S Wirges · 2020 · Cited by 3 — The nuScenes object detection benchmark [15] is a public data set with multi-modal sensor data collected in Boston and Singapore traffic.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2003.00710", "content": "by S Wirges · 2020 · Cited by 3 — The nuScenes object detection benchmark [15] is a public data set with multi-modal sensor data collected in Boston and Singapore traffic."}
+{"idx": 9, "title": "Trajectory Prediction for Autonomous Driving based on ...", "date": "", "ddg_snippet": "by K Messaoud · 2020 · Cited by 157 — The dataset was captured using vehicle mounted camera and lidar sensors driving through. Boston and Singapore . It comprises 1000 scenes , each of.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2005.02545", "content": "by K Messaoud · 2020 · Cited by 157 — The dataset was captured using vehicle mounted camera and lidar sensors driving through. Boston and Singapore . It comprises 1000 scenes , each of."}
diff --git a/data/sampled_jsons/sitearxiv.org_httpsarxiv.orgabs2109.01903.jsonl b/data/sampled_jsons/sitearxiv.org_httpsarxiv.orgabs2109.01903.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6e7603b9e46e32b77f406576ae552a207b92469c
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_httpsarxiv.orgabs2109.01903.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2507.01903] AI4Research: A Survey of Artificial Intelligence for ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2507.01903: AI4Research: A Survey of Artificial Intelligence for Scientific Research", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2507.01903", "content": "Abstract page for arXiv paper 2507.01903: AI4Research: A Survey of Artificial Intelligence for Scientific Research"}
+{"idx": 1, "title": "[2209.01903] Superconductivity and magnetism in compressed actinium ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2209.01903: Superconductivity and magnetism in compressed actinium-beryllium-hydrogen alloys", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2209.01903", "content": "Abstract page for arXiv paper 2209.01903: Superconductivity and magnetism in compressed actinium-beryllium-hydrogen alloys"}
+{"idx": 2, "title": "[2303.01903] Prophet: Prompting Large Language Models with ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2303.01903: Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.01903", "content": "Abstract page for arXiv paper 2303.01903: Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering"}
+{"idx": 3, "title": "[2109.01949] Improving Joint Learning of Chest X-Ray and Radiology ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2109.01949: Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.01949", "content": "Abstract page for arXiv paper 2109.01949: Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment"}
+{"idx": 4, "title": "[2109.01903] Robust fine-tuning of zero-shot models - arXiv.org", "date": "", "ddg_snippet": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.01903", "content": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a ..."}
+{"idx": 5, "title": "Robust ne-tuning of zero-shot models - arXiv.org", "date": "", "ddg_snippet": "Large pre-trained models such as CLIP or ALIGN o er consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without ne-tuning on a speci c dataset). Although existing ne-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.01903", "content": "Large pre-trained models such as CLIP or ALIGN o er consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without ne-tuning on a speci c dataset). Although existing ne-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and ..."}
+{"idx": 6, "title": "[2109.01903v3] Robust fine-tuning of zero-shot models", "date": "", "ddg_snippet": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a ...", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2109.01903v3", "content": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a ..."}
+{"idx": 7, "title": "[2109.01903] Robust fine-tuning of zero-shot models - ar5iv", "date": "", "ddg_snippet": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fin…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2109.01903", "content": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fin…"}
+{"idx": 8, "title": "[2103.01903] Semantic Relation Reasoning for Shot-Stable Few-Shot ...", "date": "", "ddg_snippet": "Few-shot object detection is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data. Its performance is largely affected by the data scarcity of novel classes. But the semantic relation between the novel classes and the base classes is constant regardless of the data availability. In this work, we investigate utilizing this semantic relation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2103.01903", "content": "Few-shot object detection is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data. Its performance is largely affected by the data scarcity of novel classes. But the semantic relation between the novel classes and the base classes is constant regardless of the data availability. In this work, we investigate utilizing this semantic relation ..."}
+{"idx": 9, "title": "Betti numbers of attention graphs is all you really need", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2207.01903: Betti numbers of attention graphs is all you really need", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.01903", "content": "Abstract page for arXiv paper 2207.01903: Betti numbers of attention graphs is all you really need"}
diff --git a/data/sampled_jsons/sitearxiv.org_inurl2506.08933_CR_=_OR_Coverage_Rate_=_OR_formula.jsonl b/data/sampled_jsons/sitearxiv.org_inurl2506.08933_CR_=_OR_Coverage_Rate_=_OR_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..70bf5f606782cd3896d44faec466106bb98a9ee1
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_inurl2506.08933_CR_=_OR_Coverage_Rate_=_OR_formula.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "The Future of the Affordable Care Act and Insurance Coverage", "date": "", "ddg_snippet": "These early implemented provisions had limited impact on the national insurance coverage rate . Before the ACA’s broad subsidized coverage options—the focus of the current Republican attack—became available, the US uninsurance rate stood at 14.4%, and among nonelderly adults more than one in five were uninsured. 1 Beginning in January 2014 ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5343718/", "content": "These early implemented provisions had limited impact on the national insurance coverage rate . Before the ACA’s broad subsidized coverage options—the focus of the current Republican attack—became available, the US uninsurance rate stood at 14.4%, and among nonelderly adults more than one in five were uninsured. 1 Beginning in January 2014 ..."}
+{"idx": 1, "title": "Community acquired urosepsis - EMCrit Project The Use and Types of Alpha-Diversity Metrics in Microbial NGS ... Percent Error Calculator where can I watch online? : r/formula1 - Reddit Terms of Use – Kane Formula Palmetto GBA", "date": "", "ddg_snippet": "See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org CD Genomics provides comprehensive and advanced sequencing and bioinformatics analysis services for microbial research, covering metrics of alpha diversity, beta diversity, and more. Aug 1, 2025 · Calculate percent error given estimated or experimental values and theoretical actual values. Calculator shows work and calculates absolute error and relative error. If you are looking to watch the race live, then 'free' and 'decent quality' are usually mutually exclusive! That said, you can get acceptable streams from a variety of sources such as justin.tv and the ilk. My advice is to keep an eye on the f1 fanatic live race blog which typically shows up around the start of the pre-race show. Many good folk post links to working streams there, with new ... By using the Kane formula , you agree to waive and hold the author harmless from any claims you may have arising out of your use of this tool. Dr. Kane is devoted to preserving the privacy of the users and patients. Palmetto GBA is one of the nation's largest providers of high volume claims and transaction processing, contact center operations and technical services to the federal government and other commercial customers.", "subpage_snippet": "", "source": "emcrit.org", "link": "https://emcrit.org/ibcc/urosepsis/", "content": "See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org See full list on emcrit. org CD Genomics provides comprehensive and advanced sequencing and bioinformatics analysis services for microbial research, covering metrics of alpha diversity, beta diversity, and more. Aug 1, 2025 · Calculate percent error given estimated or experimental values and theoretical actual values. Calculator shows work and calculates absolute error and relative error. If you are looking to watch the race live, then 'free' and 'decent quality' are usually mutually exclusive! That said, you can get acceptable streams from a variety of sources such as justin.tv and the ilk. My advice is to keep an eye on the f1 fanatic live race blog which typically shows up around the start of the pre-race show. Many good folk post links to working streams there, with new ... By using the Kane formula , you agree to waive and hold the author harmless from any claims you may have arising out of your use of this tool. Dr. Kane is devoted to preserving the privacy of the users and patients. Palmetto GBA is one of the nation's largest providers of high volume claims and transaction processing, contact center operations and technical services to the federal government and other commercial customers."}
+{"idx": 2, "title": "Terms of Use – Kane Formula", "date": "", "ddg_snippet": "By using the Kane formula , you agree to waive and hold the author harmless from any claims you may have arising out of your use of this tool. Dr. Kane is devoted to preserving the privacy of the users and patients.", "subpage_snippet": "", "source": "www.iolformula.com", "link": "https://www.iolformula.com/", "content": "By using the Kane formula , you agree to waive and hold the author harmless from any claims you may have arising out of your use of this tool. Dr. Kane is devoted to preserving the privacy of the users and patients."}
+{"idx": 3, "title": "where can I watch online? : r/formula1 - Reddit", "date": "", "ddg_snippet": "If you are looking to watch the race live, then 'free' and 'decent quality' are usually mutually exclusive! That said, you can get acceptable streams from a variety of sources such as justin.tv and the ilk. My advice is to keep an eye on the f1 fanatic live race blog which typically shows up around the start of the pre-race show. Many good folk post links to working streams there, with new ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/formula1/comments/bsfqc/where_can_i_watch_online/", "content": "If you are looking to watch the race live, then 'free' and 'decent quality' are usually mutually exclusive! That said, you can get acceptable streams from a variety of sources such as justin.tv and the ilk. My advice is to keep an eye on the f1 fanatic live race blog which typically shows up around the start of the pre-race show. Many good folk post links to working streams there, with new ..."}
+{"idx": 4, "title": "The Use and Types of Alpha-Diversity Metrics in Microbial NGS ...", "date": "", "ddg_snippet": "CD Genomics provides comprehensive and advanced sequencing and bioinformatics analysis services for microbial research, covering metrics of alpha diversity, beta diversity, and more.", "subpage_snippet": "", "source": "www.cd-genomics.com", "link": "https://www.cd-genomics.com/microbioseq/the-use-and-types-of-alpha-diversity-metrics-in-microbial-ngs.html", "content": "CD Genomics provides comprehensive and advanced sequencing and bioinformatics analysis services for microbial research, covering metrics of alpha diversity, beta diversity, and more."}
+{"idx": 5, "title": "Percent Error Calculator", "date": "", "ddg_snippet": "Aug 1, 2025 · Calculate percent error given estimated or experimental values and theoretical actual values. Calculator shows work and calculates absolute error and relative error.", "subpage_snippet": "", "source": "www.calculatorsoup.com", "link": "https://www.calculatorsoup.com/calculators/algebra/percent-error-calculator.php", "content": "Aug 1, 2025 · Calculate percent error given estimated or experimental values and theoretical actual values. Calculator shows work and calculates absolute error and relative error."}
+{"idx": 6, "title": "Palmetto GBA", "date": "", "ddg_snippet": "Palmetto GBA is one of the nation's largest providers of high volume claims and transaction processing, contact center operations and technical services to the federal government and other commercial customers.", "subpage_snippet": "", "source": "www.palmettogba.com", "link": "https://www.palmettogba.com/", "content": "Palmetto GBA is one of the nation's largest providers of high volume claims and transaction processing, contact center operations and technical services to the federal government and other commercial customers."}
diff --git a/data/sampled_jsons/sitearxiv.org_inurl2506.08933_Coverage_Rate.jsonl b/data/sampled_jsons/sitearxiv.org_inurl2506.08933_Coverage_Rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a74978399d1d0b4e181b299218ba20dc73162ba5
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_inurl2506.08933_Coverage_Rate.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "30%^ off Home Insurance - Comprehensive Home Coverage", "date": "", "ddg_snippet": "Safeguard your home against the unexpected with Home Insurance at 30% off + 55,000STAR$®^. Stay worry-free knowing your home is protected in case of fire with Income Home Insurance. Insurance Coverage :", "subpage_snippet": "", "source": "www.bing.com", "link": "https://www.bing.com/aclick?ld=e8FQjyOWhk6F7WoiMuGqG7-jVUCUzeC_y-yRacMt5PFA0yYYTMjb-yr6C5jgzvA4RLCYO7OEqkgDyf4yn3K51CFM7kxC3FIXcspOSDSx9QCnSsWju_jG--0Kxu1WpV7uQkguoMtu4VeyUcAhMP-BUcFKPrBqLwi5f9ZsQQ93O-YFDfNsiA92d7N___2K4IZGKxk_8iow&u=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&rlid=9ac387d0b20e18070780ef1e1301c135", "content": "Safeguard your home against the unexpected with Home Insurance at 30% off + 55,000STAR$®^. Stay worry-free knowing your home is protected in case of fire with Income Home Insurance. Insurance Coverage :"}
diff --git a/data/sampled_jsons/sitearxiv.org_r_coherence_log_probability_sum.jsonl b/data/sampled_jsons/sitearxiv.org_r_coherence_log_probability_sum.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90642a542c2f5cffff61b8e85c63a09988ef53fa
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_r_coherence_log_probability_sum.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Investigation into the Confidence-Probability Alignment ...", "date": "", "ddg_snippet": "This probability can be derived by applying the exponential function to the log probabilities or logits of each output token, depending on the model's output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.16282v5", "content": "This probability can be derived by applying the exponential function to the log probabilities or logits of each output token, depending on the model's output ..."}
+{"idx": 1, "title": "Top-H Decoding: Adapting the Creativity and Coherence ...", "date": "", "ddg_snippet": "2 Sept 2025 — To operationalize coherence, we use the total log-probability (log-likelihood ) of the generated sequence as a proxy: higher total log- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02510v1", "content": "2 Sept 2025 — To operationalize coherence, we use the total log-probability (log-likelihood ) of the generated sequence as a proxy: higher total log- ..."}
+{"idx": 2, "title": "Coherence-Enhanced Machine-Generated Text Detection ...", "date": "", "ddg_snippet": "28 Dec 2022 — (2023) , a contemporaneous metric-based method utilizing the difference of model's log probability after text perturbations. We use T5-3B to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.10341", "content": "28 Dec 2022 — (2023) , a contemporaneous metric-based method utilizing the difference of model's log probability after text perturbations. We use T5-3B to ..."}
+{"idx": 3, "title": "PiCSAR: Probabilistic Confidence Selection And Ranking ...", "date": "", "ddg_snippet": "29 Aug 2025 — The reasoning confidence term is obtained by summing the token-level log - probabilities from the model during the generation of r i r_{i} . We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21787v1", "content": "29 Aug 2025 — The reasoning confidence term is obtained by summing the token-level log - probabilities from the model during the generation of r i r_{i} . We ..."}
+{"idx": 4, "title": "Recovering Event Probabilities from Large Language ...", "date": "", "ddg_snippet": "10 May 2025 — This is because the additive rule for complementary events in the log - odds space corresponds to the sum of the log odds of the two events ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.07883v1", "content": "10 May 2025 — This is because the additive rule for complementary events in the log - odds space corresponds to the sum of the log odds of the two events ..."}
+{"idx": 5, "title": "How does the brain compute with probabilities?", "date": "", "ddg_snippet": "1 Sept 2024 — Two example fundamental computations of particular interest in probabilistic inference are the sum rule and product rule of probability . Later ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.02709v1", "content": "1 Sept 2024 — Two example fundamental computations of particular interest in probabilistic inference are the sum rule and product rule of probability . Later ..."}
+{"idx": 6, "title": "Understanding Reinforcement Learning for Model Training ...", "date": "", "ddg_snippet": "2 Sept 2025 — Keep in mind all the log numbers are going to be always negative since log of anything under 1 is a negative number and all probabilities are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.04501v1", "content": "2 Sept 2025 — Keep in mind all the log numbers are going to be always negative since log of anything under 1 is a negative number and all probabilities are ..."}
+{"idx": 7, "title": "Scoring methods for LLM predictions on multiple-choice tasks", "date": "", "ddg_snippet": "by P Tsvilodub · 2024 · Cited by 13 — The log - likelihood of human data is the sum of log - probabilities , assuming the obvious binomial likelihood function, under an LLM-based point- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.00998", "content": "by P Tsvilodub · 2024 · Cited by 13 — The log - likelihood of human data is the sum of log - probabilities , assuming the obvious binomial likelihood function, under an LLM-based point- ..."}
+{"idx": 8, "title": "Direct-Scoring NLG Evaluators Can Use Pairwise ...", "date": "", "ddg_snippet": "5 Sept 2025 — Given the above prompt for score i i , we calculate and sum the log probabilities of the LLM responding with \"Worse\", \"Better\", or \"Similar\".", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.05440v1", "content": "5 Sept 2025 — Given the above prompt for score i i , we calculate and sum the log probabilities of the LLM responding with \"Worse\", \"Better\", or \"Similar\"."}
+{"idx": 9, "title": "Probabilistic Forecasting with Coherent Aggregation", "date": "", "ddg_snippet": "5 Aug 2024 — Coherence is achieved when the forecast distribution assigns zero probability to forecasts that do not satisfy the hierarchy's constraints [35, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.09797v2", "content": "5 Aug 2024 — Coherence is achieved when the forecast distribution assigns zero probability to forecasts that do not satisfy the hierarchy's constraints [35, ..."}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2406.12384v2_resolution.jsonl b/data/sampled_jsons/sitearxiv.orghtml2406.12384v2_resolution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7bcf5c84a20d38bb5c90870817c06edf5cd28f33
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml2406.12384v2_resolution.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "Initially, we extract image information, including the source and resolution , as well as object information—such as the object category, bounding box, color, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v2", "content": "Initially, we extract image information, including the source and resolution , as well as object information—such as the object category, bounding box, color, ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2412.06329v3_2.5_Score_Based_Denoising.jsonl b/data/sampled_jsons/sitearxiv.orghtml2412.06329v3_2.5_Score_Based_Denoising.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..33efb77d13a7503a9a34b6ba6d869132dc9328db
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml2412.06329v3_2.5_Score_Based_Denoising.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Second, we identify a post-training score based denoising technique that allows one to remove the noise portion of the generated samples.Another component in our sampling pipeline is the score based denoising step.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "Second, we identify a post-training score based denoising technique that allows one to remove the noise portion of the generated samples.Another component in our sampling pipeline is the score based denoising step."}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2502.00561v2_Section_3.1_systematization_operationalization_primary_benefit.jsonl b/data/sampled_jsons/sitearxiv.orghtml2502.00561v2_Section_3.1_systematization_operationalization_primary_benefit.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd19fe23be2b7c7385eea61ca1300b967d6c74a9
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml2502.00561v2_Section_3.1_systematization_operationalization_primary_benefit.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Position: Evaluating Generative AI Systems Is a Social Science...", "date": "", "ddg_snippet": "As we explained in Section 3 , the separation of systematization and operationalization can enable stakeholders with different perspectives to participate in conceptual debates.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "As we explained in Section 3 , the separation of systematization and operationalization can enable stakeholders with different perspectives to participate in conceptual debates."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2502.00775_GTA_greedy_task_allocation_section.jsonl b/data/sampled_jsons/sitearxiv.orghtml2502.00775_GTA_greedy_task_allocation_section.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d0a5174a177b5e3ea8dad38d61769c223188313b
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml2502.00775_GTA_greedy_task_allocation_section.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "Provided we are willing to waste resources, there is a simple solution to this problem, a Greedy Task Allocation ( GTA ) strategy, which follows this principle: Make sure all workers are always busy working on some task , and stop once.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "Provided we are willing to waste resources, there is a simple solution to this problem, a Greedy Task Allocation ( GTA ) strategy, which follows this principle: Make sure all workers are always busy working on some task , and stop once."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "tasks (stochastic gradients) completed via GTA . While minimax optimal in terms of time complexity, the GTA task allocation strategy employed within Rennala SGD can be wasteful, as explained above.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "tasks (stochastic gradients) completed via GTA . While minimax optimal in terms of time complexity, the GTA task allocation strategy employed within Rennala SGD can be wasteful, as explained above."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2502.00921v1_Li_Chen_polynomial_dependence.jsonl b/data/sampled_jsons/sitearxiv.orghtml2502.00921v1_Li_Chen_polynomial_dependence.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..35956f628e626f3c10e020f760a89a286317784a
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml2502.00921v1_Li_Chen_polynomial_dependence.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "a simple theory for feature localization in generative models", "date": "", "ddg_snippet": "2 Feb 2025 — Thirdly, their final bound includes a polynomial dependence on the moments of the score, which scale with the dimension d 𝑑 d italic_d ; in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "2 Feb 2025 — Thirdly, their final bound includes a polynomial dependence on the moments of the score, which scale with the dimension d 𝑑 d italic_d ; in ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2502.01330v1.jsonl b/data/sampled_jsons/sitearxiv.orghtml2502.01330v1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3084639ff056a58f5a765d5ce1bd8eba71157381
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml2502.01330v1.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 1, "title": "Accelerating Linear Recurrent Neural Networks for the ...", "date": "", "ddg_snippet": "3 Feb 2025 — Alessandro Pierro Steven Abreu Jonathan Timcheck Philipp Stratmann Andreas Wild Sumit Bam Shrestha. Report issue for preceding element ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01330v1", "content": "3 Feb 2025 — Alessandro Pierro Steven Abreu Jonathan Timcheck Philipp Stratmann Andreas Wild Sumit Bam Shrestha. Report issue for preceding element ..."}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2502.04313v2_Equation_2_Observed_Agreement.jsonl b/data/sampled_jsons/sitearxiv.orghtml2502.04313v2_Equation_2_Observed_Agreement.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..160dacbd7599ba013ff0fc1e3b09684b15323d77
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml2502.04313v2_Equation_2_Observed_Agreement.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "where the excess agreement is subtracted both from the numerator and denominator, essentially calculating the proportion of possible excess agreement that is observed between the two models.This observation comes from the fact that both metrics use the original Cohen’s.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "where the excess agreement is subtracted both from the numerator and denominator, essentially calculating the proportion of possible excess agreement that is observed between the two models.This observation comes from the fact that both metrics use the original Cohen’s."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2503.19009v1.jsonl b/data/sampled_jsons/sitearxiv.orghtml2503.19009v1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e51042d29ef0e30460ed22ebcd0a1495142e3ad
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml2503.19009v1.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 1, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "24 Mar 2025 — We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "24 Mar 2025 — We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content."}
diff --git a/data/sampled_jsons/sitearxiv.orgpdf2503.16979_interpolating_from_its_K_nearest_anchors.jsonl b/data/sampled_jsons/sitearxiv.orgpdf2503.16979_interpolating_from_its_K_nearest_anchors.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orgpdf2503.16979_interpolating_from_its_K_nearest_anchors.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitedl.acm.org_WWW_2024_proceedings_information_retrieval_papers_year_2024.jsonl b/data/sampled_jsons/sitedl.acm.org_WWW_2024_proceedings_information_retrieval_papers_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8e991388317318f57176d45ac81207c5d95ea833
--- /dev/null
+++ b/data/sampled_jsons/sitedl.acm.org_WWW_2024_proceedings_information_retrieval_papers_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Session details: Information Retrieval | Proceedings of the 12th...", "date": "", "ddg_snippet": "Retrieval tasks and goals. Information systems applications. WWW '03: Proceedings of the 12th international conference on World Wide Web. May 2003. 772 pages.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3244992", "content": "Retrieval tasks and goals. Information systems applications. WWW '03: Proceedings of the 12th international conference on World Wide Web. May 2003. 772 pages."}
+{"idx": 1, "title": "FLIRT: Federated Learning for Information Retrieval | Proceedings of...", "date": "", "ddg_snippet": "With the development of machine learning technology, machine learning based information retrieval model plays a vital role in news retrieval tasks. In this paper , we study the integration of federated learning with the Bert model for information ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539618.3591926?cookieSet=1", "content": "With the development of machine learning technology, machine learning based information retrieval model plays a vital role in news retrieval tasks. In this paper , we study the integration of federated learning with the Bert model for information ..."}
+{"idx": 2, "title": "Information retrieval II | Proceedings of the 1962 ACM national...", "date": "", "ddg_snippet": "Information retrieval II: Variable Information Processing. Authors: M. Kosakoff, D. L. BuswellAuthors Info & Claims.ACM '65: Proceedings of the 1965 20th national conference.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/800198.806139", "content": "Information retrieval II: Variable Information Processing. Authors: M. Kosakoff, D. L. BuswellAuthors Info & Claims.ACM '65: Proceedings of the 1965 20th national conference."}
+{"idx": 3, "title": "Building simulated queries for known-item topics | Proceedings of the...", "date": "", "ddg_snippet": "SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . This paper investigates how automated query generation methods can be used to derive effective ad-hoc queries from verbose patient narratives.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/1277741.1277820", "content": "SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . This paper investigates how automated query generation methods can be used to derive effective ad-hoc queries from verbose patient narratives."}
+{"idx": 4, "title": "Using complexity measures in information retrieval | Proceedings of...", "date": "", "ddg_snippet": "A ective feedback: an investigation into the role of emotions in the information seeking process. In SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval , pages 395--402, New York, USA, 2008.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/1840784.1840843?download=true", "content": "A ective feedback: an investigation into the role of emotions in the information seeking process. In SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval , pages 395--402, New York, USA, 2008."}
+{"idx": 5, "title": "Information retrieval | Proceedings of the 1965 20th national...", "date": "", "ddg_snippet": "Information retrieval : the Chemical Abstracts Service computer based chemical information processing system. Author: W. C. DavenportAuthors Info & Claims. ACM '65: Proceedings of the 1965 20th national conference.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/800197.806045", "content": "Information retrieval : the Chemical Abstracts Service computer based chemical information processing system. Author: W. C. DavenportAuthors Info & Claims. ACM '65: Proceedings of the 1965 20th national conference."}
+{"idx": 6, "title": "Proceedings of the 22nd International Conference on World Wide", "date": "", "ddg_snippet": "... papers in 35 track sessions; the Developers track, 10 papers in 4 sessions; the Demos track, 24 demos in 3 sessions; 100 posters in an ongoing poster ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/2487788", "content": "... papers in 35 track sessions; the Developers track, 10 papers in 4 sessions; the Demos track, 24 demos in 3 sessions; 100 posters in an ongoing poster ..."}
+{"idx": 7, "title": "PROCEEDINGS OF THE ACM ON MANAGEMENT OF DATA | ACM Digital", "date": "", "ddg_snippet": "The main content of the paper must be no more than 12 pages in length with the 2 column regular ACM SIG proceedings style ( www .scomminc.com/pp ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/journal/pacmmod/author-guidelines", "content": "The main content of the paper must be no more than 12 pages in length with the 2 column regular ACM SIG proceedings style ( www .scomminc.com/pp ..."}
+{"idx": 8, "title": "Data-Efficient Information Extraction from Documents with", "date": "", "ddg_snippet": "In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , pp.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1007/978-3-030-86159-9_33", "content": "In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , pp."}
+{"idx": 9, "title": "An Analysis of Approaches Taken in the ACM RecSys Challenge", "date": "", "ddg_snippet": "In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR’16). ... Proceedings of the 40th International ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3344257", "content": "In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR’16). ... Proceedings of the 40th International ..."}
diff --git a/data/sampled_jsons/sitegithub.com_DCBM_KathPra_algorithm_area_filtering_year_2024.jsonl b/data/sampled_jsons/sitegithub.com_DCBM_KathPra_algorithm_area_filtering_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1478aa0ac5c3ef221839d080e269ca6754e7936c
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_DCBM_KathPra_algorithm_area_filtering_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - KathPra/DCBM: Official repo for ICML25 paper: DCBM: Data ...", "date": "", "ddg_snippet": "dcbm_training/: Code for training the DCBM model. data/: Directories for classes, concepts, datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. utils/: Helper scripts for running experiments. interpretation/: Tools for visualizing DCBM explanations.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "dcbm_training/: Code for training the DCBM model. data/: Directories for classes, concepts, datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. utils/: Helper scripts for running experiments. interpretation/: Tools for visualizing DCBM explanations."}
+{"idx": 1, "title": "GitHub - Alexia1305/ DCBM _OtrisymNMF: Application of OtrisymNMF...", "date": "", "ddg_snippet": "Contribute to Alexia1305/ DCBM _OtrisymNMF development by creating an account on GitHub.This package provides implementations of the Orthogonal Symmetric Nonnegative Matrix Tri-Factorization (OtrisymNMF) algorithm as proposed in the paper", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Alexia1305/DCBM_OtrisymNMF", "content": "Contribute to Alexia1305/ DCBM _OtrisymNMF development by creating an account on GitHub.This package provides implementations of the Orthogonal Symmetric Nonnegative Matrix Tri-Factorization (OtrisymNMF) algorithm as proposed in the paper"}
+{"idx": 2, "title": "DCBM /main.py at main · deepopo/ DCBM · GitHub", "date": "", "ddg_snippet": "Contribute to deepopo/ DCBM development by creating an account on GitHub.Use saved searches to filter your results more quickly. Name.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/deepopo/DCBM/blob/main/main.py", "content": "Contribute to deepopo/ DCBM development by creating an account on GitHub.Use saved searches to filter your results more quickly. Name."}
+{"idx": 3, "title": "GitHub - KathPra /LSHR_LSHT", "date": "", "ddg_snippet": "Contribute to KathPra /LSHR_LSHT development by creating an account on GitHub.Use saved searches to filter your results more quickly.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/LSHR_LSHT", "content": "Contribute to KathPra /LSHR_LSHT development by creating an account on GitHub.Use saved searches to filter your results more quickly."}
+{"idx": 4, "title": "Discussion for further sensor filtering and algorithmic improvements...", "date": "", "ddg_snippet": "Sorry, been caught up with various matters. But I do now have some metrics with the change in filtering . As we see here, the outliers with Max (tesla) are greatly reduced after the Mar 18 update but area resolution is only mildly improved.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/agittins/bermuda/issues/134", "content": "Sorry, been caught up with various matters. But I do now have some metrics with the change in filtering . As we see here, the outliers with Max (tesla) are greatly reduced after the Mar 18 update but area resolution is only mildly improved."}
+{"idx": 5, "title": "KathPra (Katharina Prasse) · GitHub", "date": "", "ddg_snippet": "PhD researcher in computer vision. KathPra has 11 repositories available. Follow their code on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/", "content": "PhD researcher in computer vision. KathPra has 11 repositories available. Follow their code on GitHub."}
+{"idx": 6, "title": "DCBM/requirements.txt at main · KathPra/DCBM · GitHub", "date": "", "ddg_snippet": "Official repo for ICML25 paper: DCBM : Data-Efficient Visual Concept Bottleneck Models - DCBM /requirements.txt at main · KathPra / DCBM", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM/blob/main/requirements.txt", "content": "Official repo for ICML25 paper: DCBM : Data-Efficient Visual Concept Bottleneck Models - DCBM /requirements.txt at main · KathPra / DCBM"}
+{"idx": 7, "title": "GitHub - deepopo/DCBM", "date": "", "ddg_snippet": "This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model ( DCBM ). The vision-language-model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/deepopo/DCBM", "content": "This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model ( DCBM ). The vision-language-model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification."}
+{"idx": 8, "title": "Releases · KathPra/DCBM · GitHub", "date": "", "ddg_snippet": "Official repo for ICML25 paper: DCBM : Data-Efficient Visual Concept Bottleneck Models - Releases · KathPra / DCBM", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM/releases", "content": "Official repo for ICML25 paper: DCBM : Data-Efficient Visual Concept Bottleneck Models - Releases · KathPra / DCBM"}
+{"idx": 9, "title": "Coverage Path Planning - GitHub", "date": "", "ddg_snippet": "DARP : Divide Areas Algorithm for Optimal Multi-Robot Coverage Path, is an equal area division algorithm for multiple robot coverage paths. DARP visualization on a test area map. Left most images are area maps that have roughly the same number of assigned cells. Point gradients are traversable L1 distance based. Region gradients pertain to the computation of the Ci matrix as given in the paper ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/18alantom/CoveragePathPlanning", "content": "DARP : Divide Areas Algorithm for Optimal Multi-Robot Coverage Path, is an equal area division algorithm for multiple robot coverage paths. DARP visualization on a test area map. Left most images are area maps that have roughly the same number of assigned cells. Point gradients are traversable L1 distance based. Region gradients pertain to the computation of the Ci matrix as given in the paper ..."}
diff --git a/data/sampled_jsons/sitegithub.com_KathPraDCBM_limitations.jsonl b/data/sampled_jsons/sitegithub.com_KathPraDCBM_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f5e12840d52d16a8fea1873b81e7b4bdb06c569
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_KathPraDCBM_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - KathPra/DCBM: Official repo for ICML25 paper: DCBM: Data ...", "date": "", "ddg_snippet": "Due to size limitations , we couldn't upload the complete dataset and corresponding concepts. We uploaded the empty structure to ensure all scripts run smoothly. To run the scripts correctly, please add the required datasets before execution. For more details, please refer to the sections and scripts within the repository or consult the paper.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "Due to size limitations , we couldn't upload the complete dataset and corresponding concepts. We uploaded the empty structure to ensure all scripts run smoothly. To run the scripts correctly, please add the required datasets before execution. For more details, please refer to the sections and scripts within the repository or consult the paper."}
+{"idx": 1, "title": "GitHub - deepopo/DCBM", "date": "", "ddg_snippet": "This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model (DCBM). The vision-language-model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/deepopo/DCBM", "content": "This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model (DCBM). The vision-language-model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification."}
+{"idx": 2, "title": "GitHub - DarkFlippers/unleashed-firmware: Flipper Zero Unleashed...", "date": "", "ddg_snippet": "Flipper Zero Unleashed Firmware. Contribute to DarkFlippers/unleashed-firmware development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/DarkFlippers/unleashed-firmware", "content": "Flipper Zero Unleashed Firmware. Contribute to DarkFlippers/unleashed-firmware development by creating an account on GitHub."}
+{"idx": 3, "title": "GitHub - MagilaWEB/unblock-youtube-discord: Allows you to bypass...", "date": "", "ddg_snippet": "About. Allows you to bypass the limitations of Discord.com, YouTube.com, x.com, used winws.exe and GoodBayDPI.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MagilaWEB/unblock-youtube-discord", "content": "About. Allows you to bypass the limitations of Discord.com, YouTube.com, x.com, used winws.exe and GoodBayDPI."}
+{"idx": 4, "title": "DCBM/requirements.txt at main · KathPra/DCBM · GitHub", "date": "", "ddg_snippet": "Official repo for ICML25 paper: DCBM: Data-Efficient Visual Concept Bottleneck Models - DCBM/requirements.txt at main · KathPra/DCBM", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM/blob/main/requirements.txt", "content": "Official repo for ICML25 paper: DCBM: Data-Efficient Visual Concept Bottleneck Models - DCBM/requirements.txt at main · KathPra/DCBM"}
+{"idx": 5, "title": "KathPra (Katharina Prasse) · GitHub", "date": "", "ddg_snippet": "PhD researcher in computer vision. KathPra has 11 repositories available. Follow their code on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/", "content": "PhD researcher in computer vision. KathPra has 11 repositories available. Follow their code on GitHub."}
+{"idx": 6, "title": "ArmyScripts/JKO/simplejko.md at master - GitHub", "date": "", "ddg_snippet": "Updated collection of Army Training scripts. Contribute to uplusion23/ArmyScripts development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uplusion23/ArmyScripts/blob/master/JKO/simplejko.md", "content": "Updated collection of Army Training scripts. Contribute to uplusion23/ArmyScripts development by creating an account on GitHub."}
+{"idx": 7, "title": "Issues · KathPra/GCBM · GitHub", "date": "", "ddg_snippet": "Official repo for arxiv paper: Aligning Visual and Semantic Interpretability through Visually Grounded Concept Bottleneck Models - Issues · KathPra/GCBM", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/GCBM/issues", "content": "Official repo for arxiv paper: Aligning Visual and Semantic Interpretability through Visually Grounded Concept Bottleneck Models - Issues · KathPra/GCBM"}
+{"idx": 8, "title": "DCBM/LICENSE at main · KathPra/DCBM · GitHub", "date": "", "ddg_snippet": "Official repo for ICML25 paper: DCBM: Data-Efficient Visual Concept Bottleneck Models - DCBM/LICENSE at main · KathPra/DCBM", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM/blob/main/LICENSE", "content": "Official repo for ICML25 paper: DCBM: Data-Efficient Visual Concept Bottleneck Models - DCBM/LICENSE at main · KathPra/DCBM"}
+{"idx": 9, "title": "GitHub - JunkFood02/Seal: Video/Audio Downloader for Android...", "date": "", "ddg_snippet": "Derivatives include but are not limited to forks and unofficial builds. Scroll to top.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/JunkFood02/Seal", "content": "Derivatives include but are not limited to forks and unofficial builds. Scroll to top."}
diff --git a/data/sampled_jsons/sitegithub.com_NJU-LHRSLHRS-Bot_LHRS-Bench_dataset_size.jsonl b/data/sampled_jsons/sitegithub.com_NJU-LHRSLHRS-Bot_LHRS-Bench_dataset_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85651a86d08ae5768a8637b0a00081e55de04816
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_NJU-LHRSLHRS-Bot_LHRS-Bench_dataset_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - NJU - LHRS / LHRS - Bot : VGI-Enhanced multimodal large...", "date": "", "ddg_snippet": "NJU - LHRS / LHRS - Bot Public. LHRS - Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset ! Preparation. Installation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "NJU - LHRS / LHRS - Bot Public. LHRS - Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset ! Preparation. Installation."}
+{"idx": 1, "title": "LHRS - Bot /main_ bench _gen.py at main · NJU - LHRS / LHRS - Bot", "date": "", "ddg_snippet": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /main_ bench _gen.py at main · NJU - LHRS / LHRS - Bot .is_main_process, ) from lhrs . Dataset .conversation import default_conversation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/main_bench_gen.py", "content": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /main_ bench _gen.py at main · NJU - LHRS / LHRS - Bot .is_main_process, ) from lhrs . Dataset .conversation import default_conversation."}
+{"idx": 2, "title": "LHRS-Bot/main_cls.py at main · NJU-LHRS/LHRS-Bot · GitHub", "date": "", "ddg_snippet": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /main_cls.py at main · NJU - LHRS / LHRS - Bot", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/main_cls.py", "content": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /main_cls.py at main · NJU - LHRS / LHRS - Bot"}
+{"idx": 3, "title": "Benchmarks of MLLMs: Survey", "date": "", "ddg_snippet": "This paper presents a comprehensive review of 200+ benchmarks and evaluations for MLLMs, focusing on (1)perception and understanding, (2)cognition and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/swordlidev/Evaluation-Multimodal-LLMs-Survey", "content": "This paper presents a comprehensive review of 200+ benchmarks and evaluations for MLLMs, focusing on (1)perception and understanding, (2)cognition and ..."}
+{"idx": 4, "title": "LHRS - Bot /README.md at main · NJU - LHRS / LHRS - Bot · GitHub", "date": "", "ddg_snippet": "git clone git@github.com: NJU - LHRS / LHRS - Bot .git cd LHRS - Bot . Create a new virtual enviroment.batch- size 1 \\ # It's better to use batchsize 1, since we find batch inference --. data -target ${ParsedLabelJsonPath} # is not stable.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/README.md", "content": "git clone git@github.com: NJU - LHRS / LHRS - Bot .git cd LHRS - Bot . Create a new virtual enviroment.batch- size 1 \\ # It's better to use batchsize 1, since we find batch inference --. data -target ${ParsedLabelJsonPath} # is not stable."}
+{"idx": 5, "title": "LHRS - Bot /main_pretrain_stage3.py at main · NJU - LHRS / LHRS - Bot", "date": "", "ddg_snippet": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /main_pretrain_stage3.py at main · NJU - LHRS / LHRS - Bot .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/main_pretrain_stage3.py", "content": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /main_pretrain_stage3.py at main · NJU - LHRS / LHRS - Bot ."}
+{"idx": 6, "title": "LHRS - Bot /main_pretrain_stage1.py at main · NJU - LHRS / LHRS - Bot", "date": "", "ddg_snippet": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /main_pretrain_stage1.py at main · NJU - LHRS / LHRS - Bot .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/main_pretrain_stage1.py", "content": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /main_pretrain_stage1.py at main · NJU - LHRS / LHRS - Bot ."}
+{"idx": 7, "title": "A question about VG evaluation · Issue #18 · NJU-LHRS/LHRS-Bot", "date": "", "ddg_snippet": "Jun 29, 2024 · Hi, I find the DIOR-RSVG test json file in the https://huggingface.co/ datasets /PumpkinCat/ LHRS _Data/tree/main doesn't contain the whole items as the original DIOR- RSVG test dataset https://drive.google.com/drive/folders/1hTqtYsC6B-m4ED2ewx5oKuYZV13EoJp_?usp=sharing.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/issues/18", "content": "Jun 29, 2024 · Hi, I find the DIOR-RSVG test json file in the https://huggingface.co/ datasets /PumpkinCat/ LHRS _Data/tree/main doesn't contain the whole items as the original DIOR- RSVG test dataset https://drive.google.com/drive/folders/1hTqtYsC6B-m4ED2ewx5oKuYZV13EoJp_?usp=sharing."}
+{"idx": 8, "title": "Require Training Dataset · Issue #35 · NJU-LHRS/LHRS-Bot", "date": "", "ddg_snippet": "Jan 31, 2025 · @pUmpKin-Co ，Thanks for your response. I’ve located the FAST dataset , but the cropped DOTA IDs don’t seem to align. It seems that FAST alone might be sufficient for my needs. Sign up for free to join this conversation on GitHub . Already have an account? Sign in to comment", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/issues/35", "content": "Jan 31, 2025 · @pUmpKin-Co ，Thanks for your response. I’ve located the FAST dataset , but the cropped DOTA IDs don’t seem to align. It seems that FAST alone might be sufficient for my needs. Sign up for free to join this conversation on GitHub . Already have an account? Sign in to comment"}
+{"idx": 9, "title": "LHRS-Bot Checkpoints · Issue #2 · NJU-LHRS/LHRS-Bot - GitHub", "date": "", "ddg_snippet": "Feb 15, 2024 · Please provide Google Drive access to download the LHRS - Bot Checkpoints. Thanks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/issues/2", "content": "Feb 15, 2024 · Please provide Google Drive access to download the LHRS - Bot Checkpoints. Thanks."}
diff --git a/data/sampled_jsons/sitegithub.com_beautyremainProDet_README.md.jsonl b/data/sampled_jsons/sitegithub.com_beautyremainProDet_README.md.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..189fcf121c9823665b1737a685187ce56b613bd5
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_beautyremainProDet_README.md.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - beautyremain / ProDet : The official code for paper...", "date": "", "ddg_snippet": "training. README . md . ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "training. README . md . ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well."}
+{"idx": 1, "title": "ProDet/README.md at main · beautyremain/ProDet · GitHub", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet / README . md at main · beautyremain / ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/blob/main/README.md", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet / README . md at main · beautyremain / ProDet"}
+{"idx": 2, "title": "ProdET_documentation/README.md at main - GitHub", "date": "", "ddg_snippet": "Contribute to ProdET / ProdET _documentation development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ProdET/ProdET_documentation/blob/main/README.md?plain=1", "content": "Contribute to ProdET / ProdET _documentation development by creating an account on GitHub ."}
+{"idx": 3, "title": "GitHub · Where software is built", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - beautyremain / ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/labels?sort=name-desc", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - beautyremain / ProDet"}
+{"idx": 4, "title": "Releases: beautyremain/ProDet - GitHub", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - Releases · beautyremain / ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/releases", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - Releases · beautyremain / ProDet"}
+{"idx": 5, "title": "ProdET/README.md at main · ProdET/ProdET · GitHub", "date": "", "ddg_snippet": "The Product Emission Tracker ( ProdET ) helps consumers make ethical purchasing decisions based on the limitation of individual ecological footprint. - ProdET / README . md at main · ProdET/ProdET", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ProdET/ProdET/blob/main/README.md?plain=1", "content": "The Product Emission Tracker ( ProdET ) helps consumers make ethical purchasing decisions based on the limitation of individual ecological footprint. - ProdET / README . md at main · ProdET/ProdET"}
+{"idx": 6, "title": "GitHub - beautyremain/ED4: The official code for paper \"ED4 ...", "date": "", "ddg_snippet": "The official code for paper \"ED4: Explicit Data-level Debiasing for Deepfake Detection\" (IEEE TIP 2025) - beautyremain /ED4", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ED4", "content": "The official code for paper \"ED4: Explicit Data-level Debiasing for Deepfake Detection\" (IEEE TIP 2025) - beautyremain /ED4"}
+{"idx": 7, "title": "deepfake-detection · GitHub Topics · GitHub", "date": "", "ddg_snippet": "GitHub Sponsors. Fund open source developers. The ReadME Project. beautyremain / SUR-LID. Star 20.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/deepfake-detection?o=desc&s=updated", "content": "GitHub Sponsors. Fund open source developers. The ReadME Project. beautyremain / SUR-LID. Star 20."}
+{"idx": 8, "title": "GitHub - beautyremain /SUR-LID: The official code for paper \"Stacking...", "date": "", "ddg_snippet": "README . md . Readme . Uh oh! There was an error while loading.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/SUR-LID", "content": "README . md . Readme . Uh oh! There was an error while loading."}
+{"idx": 9, "title": "GitHub - beautyremain /myZSSRversion", "date": "", "ddg_snippet": "Contribute to beautyremain /myZSSRversion development by creating an account on GitHub.No description, website, or topics provided. Resources. Readme . License.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/myZSSRversion", "content": "Contribute to beautyremain /myZSSRversion development by creating an account on GitHub.No description, website, or topics provided. Resources. Readme . License."}
diff --git a/data/sampled_jsons/sitegithub.com_tengxiao1DIL_DILTrainer.jsonl b/data/sampled_jsons/sitegithub.com_tengxiao1DIL_DILTrainer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..02403087a1fbb3463973370931e1beb4020c8c19
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_tengxiao1DIL_DILTrainer.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - tengxiao 1 /Awesome-Graph-Contrastive-Learning-with...", "date": "", "ddg_snippet": "tengxiao 1 /Awesome-Graph-Contrastive-Learning-with-Heterophily. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. tengxiao 1 Update README.md.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/Awesome-Graph-Contrastive-Learning-with-Heterophily", "content": "tengxiao 1 /Awesome-Graph-Contrastive-Learning-with-Heterophily. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. tengxiao 1 Update README.md."}
+{"idx": 1, "title": "tengxiao1 (Teng Xiao) · GitHub Releases · tengxiao1/DIL · GitHub GitHub - tengxiao1/tengxiao1 DIL/README.md at main · zyang1580/DIL · GitHub Issues: tengxiao1/DIL - GitHub DIL/environment.yml at main · tengxiao1/DIL · GitHub tengxiao1/Awesome-Graph-Contrastive-Learning-with-Heterophily", "date": "", "ddg_snippet": "@allenai . tengxiao1 has 21 repositories available. Follow their code on GitHub . On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Releases · tengxiao1 / DIL tengxiao1 / tengxiao1 Public Notifications You must be signed in to change notification settings Fork 0 Star 0 DIL This is an implementation for our SIGIR 2023 paper \"Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation\" based on PyTorch. This Repo contains the required code to run DIL and LighDIL on Douban, ML-10M datasets. On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Issues · tengxiao1 / DIL On a Connection Between Imitation Learning and RLHF (ICLR 2025) - DIL /environment.yml at main · tengxiao1 / DIL Papers about Graph Contrastive Learning and Graph Self-supervised Learning on Graphs with Heterophily - tengxiao1 /Awesome-Graph-Contrastive-Learning-with-Heterophily", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1", "content": "@allenai . tengxiao1 has 21 repositories available. Follow their code on GitHub . On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Releases · tengxiao1 / DIL tengxiao1 / tengxiao1 Public Notifications You must be signed in to change notification settings Fork 0 Star 0 DIL This is an implementation for our SIGIR 2023 paper \"Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation\" based on PyTorch. This Repo contains the required code to run DIL and LighDIL on Douban, ML-10M datasets. On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Issues · tengxiao1 / DIL On a Connection Between Imitation Learning and RLHF (ICLR 2025) - DIL /environment.yml at main · tengxiao1 / DIL Papers about Graph Contrastive Learning and Graph Self-supervised Learning on Graphs with Heterophily - tengxiao1 /Awesome-Graph-Contrastive-Learning-with-Heterophily"}
+{"idx": 2, "title": "Releases · tengxiao1/DIL · GitHub", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Releases · tengxiao1 / DIL", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL/releases", "content": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Releases · tengxiao1 / DIL"}
+{"idx": 3, "title": "GitHub - tengxiao1/tengxiao1", "date": "", "ddg_snippet": "tengxiao1 / tengxiao1 Public Notifications You must be signed in to change notification settings Fork 0 Star 0", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/tengxiao1", "content": "tengxiao1 / tengxiao1 Public Notifications You must be signed in to change notification settings Fork 0 Star 0"}
+{"idx": 4, "title": "DIL/README.md at main · zyang1580/DIL · GitHub", "date": "", "ddg_snippet": "DIL This is an implementation for our SIGIR 2023 paper \"Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation\" based on PyTorch. This Repo contains the required code to run DIL and LighDIL on Douban, ML-10M datasets.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyang1580/DIL/blob/main/README.md", "content": "DIL This is an implementation for our SIGIR 2023 paper \"Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation\" based on PyTorch. This Repo contains the required code to run DIL and LighDIL on Douban, ML-10M datasets."}
+{"idx": 5, "title": "Issues: tengxiao1/DIL - GitHub", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Issues · tengxiao1 / DIL", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL/issues", "content": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Issues · tengxiao1 / DIL"}
+{"idx": 6, "title": "DIL/environment.yml at main · tengxiao1/DIL · GitHub", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - DIL /environment.yml at main · tengxiao1 / DIL", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL/blob/main/environment.yml", "content": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - DIL /environment.yml at main · tengxiao1 / DIL"}
+{"idx": 7, "title": "GitHub - tengxiao 1 / DIL : On a Connection Between Imitation Learning...", "date": "", "ddg_snippet": "conda create -n DIL python=3.10 && conda activate DIL . python -m pip install flash-attn --no-build-isolation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL", "content": "conda create -n DIL python=3.10 && conda activate DIL . python -m pip install flash-attn --no-build-isolation."}
+{"idx": 8, "title": "GitHub - tengxiao 1 /Cal-DPO: Cal-DPO: Calibrated Direct Preference...", "date": "", "ddg_snippet": "tengxiao 1 / Cal-DPO Public. Notifications You must be signed in to change notification settings. Fork 1. tengxiao 1 /Cal-DPO. main.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/Cal-DPO", "content": "tengxiao 1 / Cal-DPO Public. Notifications You must be signed in to change notification settings. Fork 1. tengxiao 1 /Cal-DPO. main."}
+{"idx": 9, "title": "GitHub - tengxiao 1 /GSIL: How to Leverage Demonstration Data in...", "date": "", "ddg_snippet": "tengxiao 1 / GSIL Public. Notifications You must be signed in to change notification settings. Fork 0.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/GSIL", "content": "tengxiao 1 / GSIL Public. Notifications You must be signed in to change notification settings. Fork 0."}
diff --git a/data/sampled_jsons/sitegithub.com_yjb6IGS_N3DV_table_1.jsonl b/data/sampled_jsons/sitegithub.com_yjb6IGS_N3DV_table_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ec90bd891d8cbb65319505ba7a3cd429976c1123
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_yjb6IGS_N3DV_table_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - yjb 6 / IGS : [CVPR25 Highlight] Instant Gaussian Stream: Fast...", "date": "", "ddg_snippet": "yjb 6 / IGS Public. Notifications You must be signed in to change notification settings.Download our processed data from 4 sequences of N 3 DV , which can be directly used for training. It contains 1 ,200 optimized Gaussian points and requires 150GB of storage space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "yjb 6 / IGS Public. Notifications You must be signed in to change notification settings.Download our processed data from 4 sequences of N 3 DV , which can be directly used for training. It contains 1 ,200 optimized Gaussian points and requires 150GB of storage space."}
+{"idx": 1, "title": "GitHub - EllisonKing/taylor_gaussian", "date": "", "ddg_snippet": "1. N 3 DV . Download the dataset from here. python script/ n 3 d_process.py --videopath ./data/Neural3D/cook_spinach. Table I . Comparison of rendering on the NV3D dataset. ↓ indicates lower is better, ↑ indicates higher is better.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/EllisonKing/taylor_gaussian", "content": "1. N 3 DV . Download the dataset from here. python script/ n 3 d_process.py --videopath ./data/Neural3D/cook_spinach. Table I . Comparison of rendering on the NV3D dataset. ↓ indicates lower is better, ↑ indicates higher is better."}
+{"idx": 2, "title": "IGS/README.md at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "Download our processed data from 4 sequences of N3DV , which can be directly used for training. It contains 1,200 optimized Gaussian points and requires 150GB of storage space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/blob/master/README.md", "content": "Download our processed data from 4 sequences of N3DV , which can be directly used for training. It contains 1,200 optimized Gaussian points and requires 150GB of storage space."}
+{"idx": 3, "title": "AGM-Net Model & Training · Issue #4 · yjb6/IGS - GitHub", "date": "", "ddg_snippet": "Jul 8, 2025 · As mentioned in the paper, we trained the model on 4 sequences from N3DV and evaluated it on the remaining 2 sequences (cut_beef and flame_steak).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/issues/4", "content": "Jul 8, 2025 · As mentioned in the paper, we trained the model on 4 sequences from N3DV and evaluated it on the remaining 2 sequences (cut_beef and flame_steak)."}
+{"idx": 4, "title": "yjb6 · GitHub IGS/configs/demo.yaml at master · yjb6/IGS · GitHub About the training dataset creation · Issue #5 · yjb6/IGS IGS/infer_batch.py at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "IGS Public [CVPR25 Highlight] Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Python 72 2 [CVPR25] Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting - IGS/configs/demo.yaml at master · yjb6 / IGS Jul 29, 2025 · Description pablodawson opened on Jul 29, 2025 Hey, thanks for sharing your work! On your paper you mention: we performed motions forward and backward for five frames, creating 12,000 pairs for training. But I didn't find any information about how these forward and backward motions were calculated. Did you just train the means on the new frame with the previous splats as initialization? [CVPR25] Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting - IGS/infer_batch.py at master · yjb6 / IGS", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/", "content": "IGS Public [CVPR25 Highlight] Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Python 72 2 [CVPR25] Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting - IGS/configs/demo.yaml at master · yjb6 / IGS Jul 29, 2025 · Description pablodawson opened on Jul 29, 2025 Hey, thanks for sharing your work! On your paper you mention: we performed motions forward and backward for five frames, creating 12,000 pairs for training. But I didn't find any information about how these forward and backward motions were calculated. Did you just train the means on the new frame with the previous splats as initialization? [CVPR25] Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting - IGS/infer_batch.py at master · yjb6 / IGS"}
+{"idx": 5, "title": "IGS/configs/demo.yaml at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "[CVPR25] Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting - IGS/configs/demo.yaml at master · yjb6 / IGS", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/blob/master/configs/demo.yaml", "content": "[CVPR25] Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting - IGS/configs/demo.yaml at master · yjb6 / IGS"}
+{"idx": 6, "title": "IGS/infer_batch.py at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "[CVPR25] Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting - IGS/infer_batch.py at master · yjb6 / IGS", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/blob/master/infer_batch.py", "content": "[CVPR25] Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting - IGS/infer_batch.py at master · yjb6 / IGS"}
+{"idx": 7, "title": "Issues · SJoJoK/3DGStream · GitHub", "date": "", "ddg_snippet": "[CVPR 2024 Highlight] Official repository for the paper \"3DGStream: On-the-fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SJoJoK/3DGStream/issues", "content": "[CVPR 2024 Highlight] Official repository for the paper \"3DGStream: On-the-fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos\"."}
+{"idx": 8, "title": "Releases · KernelSU-Next/KernelSU-Next · GitHub", "date": "", "ddg_snippet": "An advanced Kernel based root solution for Android - KernelSU-Next/KernelSU-Next.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KernelSU-Next/KernelSU-Next/releases", "content": "An advanced Kernel based root solution for Android - KernelSU-Next/KernelSU-Next."}
+{"idx": 9, "title": "Windows Product Keys · GitHub", "date": "", "ddg_snippet": "Product Key. Windows 10 Home. 46J3N-RY6B3-BJFDY-VBFT9-V22HG.Windows 8 Professional. V7C3 N - 3 W6CM-PDKR2-KW8DQ-RJMRD.", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/rvrsh3ll/0810c6ed60e44cf7932e4fbae25880df", "content": "Product Key. Windows 10 Home. 46J3N-RY6B3-BJFDY-VBFT9-V22HG.Windows 8 Professional. V7C3 N - 3 W6CM-PDKR2-KW8DQ-RJMRD."}
diff --git a/data/sampled_jsons/sitegithub.com_zyxunhMask2Former_for_entity_erasure_train_net_video.py_learning_rate.jsonl b/data/sampled_jsons/sitegithub.com_zyxunhMask2Former_for_entity_erasure_train_net_video.py_learning_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3bc896c13f4e61ed6fcd7fc7e5781a9f266982ed
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_zyxunhMask2Former_for_entity_erasure_train_net_video.py_learning_rate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mask2Former_for_entity_erasure/README.md at main · zyxunh ...", "date": "", "ddg_snippet": "Contribute to zyxunh / Mask2Former _ for _ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Mask2Former_for_entity_erasure/blob/main/README.md", "content": "Contribute to zyxunh / Mask2Former _ for _ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 1, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure ."}
+{"idx": 2, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub ."}
+{"idx": 3, "title": "Releases: zyxunh/Mask2Former_for_entity_erasure - GitHub", "date": "", "ddg_snippet": "Contribute to zyxunh / Mask2Former _ for _ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Mask2Former_for_entity_erasure/releases", "content": "Contribute to zyxunh / Mask2Former _ for _ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 4, "title": "Security: zyxunh/Mask2Former_for_entity_erasure - GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Mask2Former_for_entity_erasure/security", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 5, "title": "GitHub - IDEA-Research/MP-Former: [CVPR 2023] Official...", "date": "", "ddg_snippet": "train _ net _ video . py .8/19/2023: Code is released. Install. The installation is exactly same as Mask 2 Former . Train. The following command is for no noise and all-layer MP training setting for instance segmentation. bash run_50ep_no_noise_all_ly.sh.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IDEA-Research/MP-Former", "content": "train _ net _ video . py .8/19/2023: Code is released. Install. The installation is exactly same as Mask 2 Former . Train. The following command is for no noise and all-layer MP training setting for instance segmentation. bash run_50ep_no_noise_all_ly.sh."}
+{"idx": 6, "title": "zyxunh/Mask2Former_for_entity_erasure - GitHub", "date": "", "ddg_snippet": "Model Zoo and Baselines We provide a large set of baseline results and trained models available for download in the Mask2Former Model Zoo.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Mask2Former_for_entity_erasure", "content": "Model Zoo and Baselines We provide a large set of baseline results and trained models available for download in the Mask2Former Model Zoo."}
+{"idx": 7, "title": "Mask2Former/train_net.py at main · facebookresearch ... - GitHub", "date": "", "ddg_snippet": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation\" - Mask2Former / train _ net . py at main · facebookresearch/Mask2Former", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/main/train_net.py", "content": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation\" - Mask2Former / train _ net . py at main · facebookresearch/Mask2Former"}
+{"idx": 8, "title": "GitHub - facebookresearch/ Mask 2 Former : Code release for...", "date": "", "ddg_snippet": "train _ net _ video . py . Mask 2 Former : Masked-attention Mask Transformer for Universal Image Segmentation (CVPR 2022).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former", "content": "train _ net _ video . py . Mask 2 Former : Masked-attention Mask Transformer for Universal Image Segmentation (CVPR 2022)."}
+{"idx": 9, "title": "GitHub - wenhe-jia/M2F: Code release for \"Masked-attention Mask...\"", "date": "", "ddg_snippet": "train _ net _ video . py .A single architecture for panoptic, instance and semantic segmentation. Support major segmentation datasets: ADE20K, Cityscapes, COCO, Mapillary Vistas. Updates. Add Google Colab demo. Video instance segmentation is now supported!", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wenhe-jia/M2F", "content": "train _ net _ video . py .A single architecture for panoptic, instance and semantic segmentation. Support major segmentation datasets: ADE20K, Cityscapes, COCO, Mapillary Vistas. Updates. Add Google Colab demo. Video instance segmentation is now supported!"}
diff --git a/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_train.jsonl b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_train.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a87c63cb96b74b9152dc633ae5f78df5bdb6dc67
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_train.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub ."}
+{"idx": 1, "title": "GitHub - zyxunh/Entity_for_entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 2, "title": "GitHub - zyxunh/EntityErasure-ProjectPage", "date": "", "ddg_snippet": "Contribute to zyxunh / EntityErasure -ProjectPage development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/EntityErasure-ProjectPage", "content": "Contribute to zyxunh / EntityErasure -ProjectPage development by creating an account on GitHub ."}
+{"idx": 3, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub ."}
+{"idx": 4, "title": "GitHub - zyxunh/diffusers_for_entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh /diffusers_for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/diffusers_for_entity_erasure", "content": "Contribute to zyxunh /diffusers_for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 5, "title": "Entity_for_entity_erasure/Entityv2/README.md at main · zyxunh ...", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure/blob/main/Entityv2/README.md", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 6, "title": "Entity_for_entity_erasure/Open-Metrics/README.md at main ...", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure/blob/main/Open-Metrics/README.md", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 7, "title": "How to train from scratch to reproduce the results of instance...", "date": "", "ddg_snippet": "@ zyxunh .Can you introduce the details of your parameters in detail to train YOLOv8n-seg, such as batch size, how many GPUs were used, how many epochs were trained , and whether the model needs to be pre- trained on imagenet. And can I directly use the script...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ultralytics/ultralytics/issues/1449", "content": "@ zyxunh .Can you introduce the details of your parameters in detail to train YOLOv8n-seg, such as batch size, how many GPUs were used, how many epochs were trained , and whether the model needs to be pre- trained on imagenet. And can I directly use the script..."}
+{"idx": 8, "title": "GitHub - alimama-creative/Self-supervised-Text- Erasing", "date": "", "ddg_snippet": "train online with policy outputed by feature.lambda1 1 --lambda2 10 --valid 1 --ctl_freq 10 --ctl_batchSize 2 --ctl_update_num 10 --PasteImage --mask_sigmoid --sigmoid # train with self-supervised synthesis policy on erase .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/alimama-creative/Self-supervised-Text-Erasing", "content": "train online with policy outputed by feature.lambda1 1 --lambda2 10 --valid 1 --ctl_freq 10 --ctl_batchSize 2 --ctl_update_num 10 --PasteImage --mask_sigmoid --sigmoid # train with self-supervised synthesis policy on erase ."}
+{"idx": 9, "title": "yecs/component_storage.h at master · yozhijk/yecs · GitHub", "date": "", "ddg_snippet": "virtual bool HasComponent( Entity entity ) const = 0", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yozhijk/yecs/blob/master/yecs/component_storage.h", "content": "virtual bool HasComponent( Entity entity ) const = 0"}
diff --git a/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_yaml.jsonl b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_yaml.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7141fc99294bf7eca8a63a7e35ac27ac2e897640
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_yaml.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "zyxunh/entity_erasure - EntityErasure", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025]. Introduction. This repository contains the official ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025]. Introduction. This repository contains the official ..."}
+{"idx": 1, "title": "YAML ::Nodes alias rather than moving when using std::vector:: erase ...", "date": "", "ddg_snippet": "My guess is that this is something to do with aliasing rather than copying when the vector elements are moved. This is likely an issue with the move constructor or move assignment of the Node. I am using libcpp- yaml 0.6, as I am currently limited to Ubuntu 20.04.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jbeder/yaml-cpp/issues/1209", "content": "My guess is that this is something to do with aliasing rather than copying when the vector elements are moved. This is likely an issue with the move constructor or move assignment of the Node. I am using libcpp- yaml 0.6, as I am currently limited to Ubuntu 20.04."}
+{"idx": 2, "title": "device-management/mdm/commands/device. erase . yaml at release...", "date": "", "ddg_snippet": "Device management schema data for MDM. Contribute to apple/device-management development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/apple/device-management/blob/release/mdm/commands/device.erase.yaml", "content": "Device management schema data for MDM. Contribute to apple/device-management development by creating an account on GitHub."}
+{"idx": 3, "title": "onFlush event and $uow->computeChangeSets() in some cases erase ...", "date": "", "ddg_snippet": "Entity will be updated in DB with its proper values. Cahngeset will not be erased calling $uow->computeChangeSets() even if in onFlush event did set the same value to the entity property.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/doctrine/orm/issues/7736", "content": "Entity will be updated in DB with its proper values. Cahngeset will not be erased calling $uow->computeChangeSets() even if in onFlush event did set the same value to the entity property."}
+{"idx": 4, "title": "press-button-turn-on-off- entities . yaml · GitHub", "date": "", "ddg_snippet": "press-button-turn-on-off- entities . yaml . This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/Blackshome/ca6be121c8d3e4404e33114fc88b599a", "content": "press-button-turn-on-off- entities . yaml . This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters."}
+{"idx": 5, "title": "GitHub - zyxunh/Entity_for_entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 6, "title": "GitHub - zyxunh/EntityErasure-ProjectPage", "date": "", "ddg_snippet": "Contribute to zyxunh / EntityErasure -ProjectPage development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/EntityErasure-ProjectPage", "content": "Contribute to zyxunh / EntityErasure -ProjectPage development by creating an account on GitHub ."}
+{"idx": 7, "title": "Entity_for_entity_erasure/Entityv2/README.md at main · zyxunh ...", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure/blob/main/Entityv2/README.md", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 8, "title": "Security: zyxunh/Mask2Former_for_entity_erasure - GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Mask2Former_for_entity_erasure/security", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 9, "title": "GitHub - zyxunh/diffusers_for_entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh /diffusers_for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/diffusers_for_entity_erasure", "content": "Contribute to zyxunh /diffusers_for_ entity _ erasure development by creating an account on GitHub ."}
diff --git a/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_README.jsonl b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_README.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..19d2240e9ca69f9e101d1165f6eaa597ad3dda8e
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_README.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "understanding_safety_finetuning/README.md at main - GitHub", "date": "", "ddg_snippet": "README .md Latest commit History History 114 lines (81 loc) · 9.21 KB Breadcrumbs understanding_safety_finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/README.md", "content": "README .md Latest commit History History 114 lines (81 loc) · 9.21 KB Breadcrumbs understanding_safety_finetuning"}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_mingpt_model_config_n_layer.jsonl b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_mingpt_model_config_n_layer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_mingpt_model_config_n_layer.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_pretrain.py_minGPT_n_layer.jsonl b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_pretrain.py_minGPT_n_layer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e9af900e64fb808b645e823c054ecd3a0856aab
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_pretrain.py_minGPT_n_layer.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "GitHub - fiveai/understanding_safety_finetuning: Official Code for What...", "date": "", "ddg_snippet": "saved_data/ pretrain _test_data_pcfg1.pkl'. To make the safety fine-tuning datasets run", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "saved_data/ pretrain _test_data_pcfg1.pkl'. To make the safety fine-tuning datasets run"}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitehttpsgithub.comFLAIROxah2ac2blobproductionREADME.md_3079.jsonl b/data/sampled_jsons/sitehttpsgithub.comFLAIROxah2ac2blobproductionREADME.md_3079.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitehttpsgithub.comFLAIROxah2ac2blobproductionREADME.md_3079.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitehuggingface.co_papers_2503.06366_Schubert_polynomials_accuracy.jsonl b/data/sampled_jsons/sitehuggingface.co_papers_2503.06366_Schubert_polynomials_accuracy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4008419c9c4faaa0ea231eca10b63ba13578726d
--- /dev/null
+++ b/data/sampled_jsons/sitehuggingface.co_papers_2503.06366_Schubert_polynomials_accuracy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "coefficients_of_kl_polynomials_6 - Hugging Face", "date": "", "ddg_snippet": "The Coefficients of Kazhdan-Lusztig Polynomials for Permutations of Size 6 Kazhdan-Lusztig (KL) polynomials are polynomials in a variable q q q and with integer coefficients that (for our purposes) are indexed by a pair of permutations [1].", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ACDRepo/coefficients_of_kl_polynomials_6", "content": "The Coefficients of Kazhdan-Lusztig Polynomials for Permutations of Size 6 Kazhdan-Lusztig (KL) polynomials are polynomials in a variable q q q and with integer coefficients that (for our purposes) are indexed by a pair of permutations [1]."}
+{"idx": 1, "title": "3M_Academic_Papers_Titles_and_Abstracts - Hugging Face", "date": "", "ddg_snippet": "Comprehensive Academic Papers Dataset: 3M+ Research Paper Titles and Abstracts 📋 Overview This dataset is a comprehensive collection of over 3 million research paper titles and abstracts, curated and consolidated from multiple high-quality academic sources. The dataset provides a unified, clean, and standardized format for researchers, data scientists, and machine learning practitioners ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/beta3/3M_Academic_Papers_Titles_and_Abstracts", "content": "Comprehensive Academic Papers Dataset: 3M+ Research Paper Titles and Abstracts 📋 Overview This dataset is a comprehensive collection of over 3 million research paper titles and abstracts, curated and consolidated from multiple high-quality academic sources. The dataset provides a unified, clean, and standardized format for researchers, data scientists, and machine learning practitioners ..."}
+{"idx": 2, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "Get trending papers in your email inbox once a day!", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/date/2025-09-19", "content": "Get trending papers in your email inbox once a day!"}
+{"idx": 3, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "In this paper , we propose a formal characterization of the deep research (DR) task and introduce a benchmark to evaluate the performance of DR systems. We argue that the core defining feature of deep research is not the production of lengthy report-style outputs, but rather the high fan-out over concepts required during the search process, i.e ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=quantitative+scoring", "content": "In this paper , we propose a formal characterization of the deep research (DR) task and introduce a benchmark to evaluate the performance of DR systems. We argue that the core defining feature of deep research is not the production of lengthy report-style outputs, but rather the high fan-out over concepts required during the search process, i.e ..."}
+{"idx": 4, "title": "Paper page - Machine Learning meets Algebraic Combinatorics...", "date": "", "ddg_snippet": "Papers . arxiv: 2503 . 06366 . Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics.Cite arxiv.org/abs/ 2503 . 06366 in a model README.md to link it from this page.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.06366", "content": "Papers . arxiv: 2503 . 06366 . Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics.Cite arxiv.org/abs/ 2503 . 06366 in a model README.md to link it from this page."}
+{"idx": 5, "title": "ACDRepo/partial_orders_on_lattice_paths_10x9 · Datasets at Hugging...", "date": "", "ddg_snippet": "We provide some basic baselines for this task. Benchmarking details can be found in the associated paper .Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv: 2503 . 06366 .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ACDRepo/partial_orders_on_lattice_paths_10x9", "content": "We provide some basic baselines for this task. Benchmarking details can be found in the associated paper .Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv: 2503 . 06366 ."}
+{"idx": 6, "title": "Paper page - Uncertainty Quantification for Multi-fidelity Simulations", "date": "", "ddg_snippet": "Papers . arxiv:2503.08408. Uncertainty Quantification for Multi-fidelity Simulations. Published on Mar 11.The utilization of the higher polynomial distribution in calculating the Coefficient of lift and drag has demonstrated superior accuracy and precision.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.08408", "content": "Papers . arxiv:2503.08408. Uncertainty Quantification for Multi-fidelity Simulations. Published on Mar 11.The utilization of the higher polynomial distribution in calculating the Coefficient of lift and drag has demonstrated superior accuracy and precision."}
+{"idx": 7, "title": "Gavinug/final_df · Datasets at Hugging Face", "date": "", "ddg_snippet": "We’re on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/Gavinug/final_df", "content": "We’re on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 8, "title": "Gavinug/initial_df · Datasets at Hugging Face", "date": "", "ddg_snippet": "We’re on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/Gavinug/initial_df", "content": "We’re on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 9, "title": "vocab.txt · unitary/toxic-bert at ...", "date": "", "ddg_snippet": "Use this modela447313", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/unitary/toxic-bert/blame/a4473137dc49a3372f28e773dfca2276616ace45/vocab.txt", "content": "Use this modela447313"}
diff --git a/data/sampled_jsons/siteiclr.cc_Pedestrian_Motion_Reconstruction_Table_1_dataset_subjects_year_2025.jsonl b/data/sampled_jsons/siteiclr.cc_Pedestrian_Motion_Reconstruction_Table_1_dataset_subjects_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..255ec0ef79898f24b815d30bdbd8af45b71bc3d0
--- /dev/null
+++ b/data/sampled_jsons/siteiclr.cc_Pedestrian_Motion_Reconstruction_Table_1_dataset_subjects_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF P M Reconstruction: a Large Scale Benchmark Via Mixed Reality Rendering ...", "date": "", "ddg_snippet": "We introduce the Pedestrian Motion Reconstruction (PMR) dataset , which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and modalities. PMR is developed from a mixed reality platform that combines real-world realism with the extensive, accurate labels of simulations, thereby reducing costs and risks.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/f3342358d0792ea201dc86d69570946b-Paper-Conference.pdf", "content": "We introduce the Pedestrian Motion Reconstruction (PMR) dataset , which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and modalities. PMR is developed from a mixed reality platform that combines real-world realism with the extensive, accurate labels of simulations, thereby reducing costs and risks."}
+{"idx": 1, "title": "Downloads 2025 - ICLR", "date": "", "ddg_snippet": "Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty Measurement Fewer May Be Better: Enhancing Offline Reinforcement Learning with Reduced Dataset Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2025", "content": "Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty Measurement Fewer May Be Better: Enhancing Offline Reinforcement Learning with Reduced Dataset Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI"}
+{"idx": 2, "title": "Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed ...", "date": "", "ddg_snippet": "We introduce the Pedestrian Motion Reconstruction (PMR) dataset , which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and modalities. PMR is developed from a mixed reality platform that combines real-world realism with the extensive, accurate labels of simulations, thereby reducing costs and risks.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/f3342358d0792ea201dc86d69570946b-Abstract-Conference.html", "content": "We introduce the Pedestrian Motion Reconstruction (PMR) dataset , which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and modalities. PMR is developed from a mixed reality platform that combines real-world realism with the extensive, accurate labels of simulations, thereby reducing costs and risks."}
+{"idx": 3, "title": "Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed ...", "date": "", "ddg_snippet": "Explore the Pedestrian Motion Reconstruction dataset , focusing on pedestrian behavior reconstruction using mixed reality rendering with diverse perspectives and modalities.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/29263", "content": "Explore the Pedestrian Motion Reconstruction dataset , focusing on pedestrian behavior reconstruction using mixed reality rendering with diverse perspectives and modalities."}
+{"idx": 4, "title": "ICLR 2025 Spotlights", "date": "", "ddg_snippet": "Using this dataset , we show that the degree of priming after learning new information can be predicted by measuring the token probability of key words before training.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/events/spotlight-posters", "content": "Using this dataset , we show that the degree of priming after learning new information can be predicted by measuring the token probability of key words before training."}
+{"idx": 5, "title": "proceedings.iclr.cc", "date": "", "ddg_snippet": "@inproceedings{ICLR2025_f3342358, author = {Wang, Yichen and Zhang, Yiyi and Hu, Xinhao and Niu, Li and Zhang, Jianfu and Makihara, Yasushi and Yagi, Yasushi and Peng, Pai and Liao, Wenlong and He, Tao and Yan, Junchi and Zhang, Liqing}, booktitle = {International Conference on Representation Learning}, editor = {Y. Yue and A. Garg and N. Peng and F. Sha and R. Yu}, pages = {97756--97770 ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/255-/bibtex", "content": "@inproceedings{ICLR2025_f3342358, author = {Wang, Yichen and Zhang, Yiyi and Hu, Xinhao and Niu, Li and Zhang, Jianfu and Makihara, Yasushi and Yagi, Yasushi and Peng, Pai and Liao, Wenlong and He, Tao and Yan, Junchi and Zhang, Liqing}, booktitle = {International Conference on Representation Learning}, editor = {Y. Yue and A. Garg and N. Peng and F. Sha and R. Yu}, pages = {97756--97770 ..."}
+{"idx": 6, "title": "ICLR Poster Generalizable Motion Planning via Operator Learning", "date": "", "ddg_snippet": "We demonstrate accurate value function approximation at 16× the training resolution on the MovingAI lab's 2D city dataset , compare with state-of-the-art neural valuefunction predictors on 3D scenes from the iGibson building dataset and showcase optimal planning with 4-joint robotic manipulators.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/29468", "content": "We demonstrate accurate value function approximation at 16× the training resolution on the MovingAI lab's 2D city dataset , compare with state-of-the-art neural valuefunction predictors on 3D scenes from the iGibson building dataset and showcase optimal planning with 4-joint robotic manipulators."}
+{"idx": 7, "title": "ICLR Poster SmartPretrain: Model-Agnostic and Dataset-Agnostic ...", "date": "", "ddg_snippet": "SmartPretrain: Model-Agnostic and Dataset -Agnostic Representation Learning for Motion Prediction Yang Zhou · Hao Shao · Letian Wang · Steven Waslander · Hongsheng Li · Yu Liu", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/30548", "content": "SmartPretrain: Model-Agnostic and Dataset -Agnostic Representation Learning for Motion Prediction Yang Zhou · Hao Shao · Letian Wang · Steven Waslander · Hongsheng Li · Yu Liu"}
+{"idx": 8, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "Skip to yearly menu bar Skip to main contentMain Navigation", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "Skip to yearly menu bar Skip to main contentMain Navigation"}
+{"idx": 9, "title": "Track: Poster Session 4", "date": "", "ddg_snippet": "Extensive experiments demonstrate that our method outperforms the state-of-the-art latent 3D reconstruction approaches in terms of synthesis performance and cross- dataset generalizability across diverse indoor and outdoor scenes.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31974", "content": "Extensive experiments demonstrate that our method outperforms the state-of-the-art latent 3D reconstruction approaches in terms of synthesis performance and cross- dataset generalizability across diverse indoor and outdoor scenes."}
diff --git a/data/sampled_jsons/sitemarvinfli.github.io_Blink_of_an_eye.jsonl b/data/sampled_jsons/sitemarvinfli.github.io_Blink_of_an_eye.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..51efdd0c5dd5e669176dd1cf2afeb15288978c2e
--- /dev/null
+++ b/data/sampled_jsons/sitemarvinfli.github.io_Blink_of_an_eye.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "Blink of an Eye: A Simple Theory for Feature Localization in ...", "date": "", "ddg_snippet": "We present a concise theoretical framework that explains sudden feature localization events—”blinks of an eye ”—in diffusion and autoregressive generative models.", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/publication/2025-blink-eye", "content": "We present a concise theoretical framework that explains sudden feature localization events—”blinks of an eye ”—in diffusion and autoregressive generative models."}
+{"idx": 1, "title": "PDF 3 This is a sample PDF. - marvinfli.github.io", "date": "", "ddg_snippet": "Created Date9/18/2016 9:42:42 AM", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/files/paper3.pdf", "content": "Created Date9/18/2016 9:42:42 AM"}
+{"idx": 2, "title": "Talks and presentations - Marvin Li", "date": "", "ddg_snippet": "Feb 1, 2024 · Blink of an eye : a simple theory for feature localization in generative models February 01, 2025", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/talks/", "content": "Feb 1, 2024 · Blink of an eye : a simple theory for feature localization in generative models February 01, 2025"}
+{"idx": 3, "title": "Hi, my name is Marvin. - Marvin Li", "date": "", "ddg_snippet": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models Marvin Li, Aayush Karan, Sitan Chen. ICML, 2025 (Oral, top 1% of submissions) arXiv / code A unifying theory showing why and when features suddenly “lock in” during generation in both diffusion and autoregressive models.", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/", "content": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models Marvin Li, Aayush Karan, Sitan Chen. ICML, 2025 (Oral, top 1% of submissions) arXiv / code A unifying theory showing why and when features suddenly “lock in” during generation in both diffusion and autoregressive models."}
+{"idx": 4, "title": "Portfolio - Marvin Li", "date": "", "ddg_snippet": "GitHub. Feed. © 2025 Marvin Li, Powered by Jekyll & AcademicPages, a fork of Minimal Mistakes.", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/portfolio/", "content": "GitHub. Feed. © 2025 Marvin Li, Powered by Jekyll & AcademicPages, a fork of Minimal Mistakes."}
+{"idx": 5, "title": "Teaching - Marvin Li", "date": "", "ddg_snippet": "GitHub. Feed. © 2025 Marvin Li, Powered by Jekyll & AcademicPages, a fork of Minimal Mistakes.", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/teaching/", "content": "GitHub. Feed. © 2025 Marvin Li, Powered by Jekyll & AcademicPages, a fork of Minimal Mistakes."}
+{"idx": 6, "title": "Jupyter notebook markdown generator - Marvin Li", "date": "", "ddg_snippet": "The notebooks contain a lot of documentation about the process.", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/markdown_generator/", "content": "The notebooks contain a lot of documentation about the process."}
diff --git a/data/sampled_jsons/sitemdpi.com2072-4292171162_Dataset_Resolution.jsonl b/data/sampled_jsons/sitemdpi.com2072-4292171162_Dataset_Resolution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitemdpi.com2072-4292171162_Dataset_Resolution.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitenature.com_Advancing_mathematics_by_guiding_human_intuition_with_AI_abstract.jsonl b/data/sampled_jsons/sitenature.com_Advancing_mathematics_by_guiding_human_intuition_with_AI_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c15677a5844c6b4a40f4671005c46bd2e293ed3f
--- /dev/null
+++ b/data/sampled_jsons/sitenature.com_Advancing_mathematics_by_guiding_human_intuition_with_AI_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Advancing mathematics by guiding human intuition with AI", "date": "", "ddg_snippet": "by A Davies · 2021 · Cited by 683 — Abstract . The practice of mathematics involves discovering ... Advancing mathematics by guiding human intuition with AI . Nature 600 ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-021-04086-x", "content": "by A Davies · 2021 · Cited by 683 — Abstract . The practice of mathematics involves discovering ... Advancing mathematics by guiding human intuition with AI . Nature 600 ..."}
+{"idx": 1, "title": "Practical Mathematics", "date": "", "ddg_snippet": "by J PERRY · 1912 · Cited by 6 — Advancing mathematics by guiding human intuition with AI . Article Open access 01 December 2021. Article PDF. Author information. Authors and Affiliations.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/090034b0", "content": "by J PERRY · 1912 · Cited by 6 — Advancing mathematics by guiding human intuition with AI . Article Open access 01 December 2021. Article PDF. Author information. Authors and Affiliations."}
+{"idx": 2, "title": "The Mathematics of Intellience", "date": "", "ddg_snippet": "by C SPEARMAN · 1927 — Advancing mathematics by guiding human intuition with AI . Article Open access 01 December 2021. Article PDF. Author information. Authors and Affiliations. 71 ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/120690a0", "content": "by C SPEARMAN · 1927 — Advancing mathematics by guiding human intuition with AI . Article Open access 01 December 2021. Article PDF. Author information. Authors and Affiliations. 71 ..."}
+{"idx": 3, "title": "The Relation of Mathematics to Engineering", "date": "", "ddg_snippet": "Advancing mathematics by guiding human intuition with AI . Article Open access 01 December 2021. Tutorial: a beginner's guide to building a representative ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/050042a0", "content": "Advancing mathematics by guiding human intuition with AI . Article Open access 01 December 2021. Tutorial: a beginner's guide to building a representative ..."}
+{"idx": 4, "title": "On scientific understanding with artificial intelligence", "date": "", "ddg_snippet": "by M Krenn · 2022 · Cited by 356 — Davies, A. et al. Advancing mathematics by guiding human intuition with AI . Nature 600, 70–74 (2021). Article MATH Google Scholar.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42254-022-00518-3", "content": "by M Krenn · 2022 · Cited by 356 — Davies, A. et al. Advancing mathematics by guiding human intuition with AI . Nature 600, 70–74 (2021). Article MATH Google Scholar."}
+{"idx": 5, "title": "Integrating artificial intelligence with mechanistic ...", "date": "", "ddg_snippet": "by Y Ye · 2025 · Cited by 22 — Abstract . Integrating prior epidemiological knowledge embedded ... Advancing mathematics by guiding human intuition with AI . Nature 600 ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-024-55461-x", "content": "by Y Ye · 2025 · Cited by 22 — Abstract . Integrating prior epidemiological knowledge embedded ... Advancing mathematics by guiding human intuition with AI . Nature 600 ..."}
+{"idx": 6, "title": "The Organisation of Knowledge", "date": "", "ddg_snippet": "by FE CAVE · 1922 — Advancing mathematics by guiding human intuition with AI . Article Open access 01 December 2021. Prior math achievement and inventive production predict ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/109716a0", "content": "by FE CAVE · 1922 — Advancing mathematics by guiding human intuition with AI . Article Open access 01 December 2021. Prior math achievement and inventive production predict ..."}
+{"idx": 7, "title": "Truth or Convenience", "date": "", "ddg_snippet": "by O LODGE · 1927 · Cited by 4 — Advancing mathematics by guiding human intuition with AI . Article Open access 01 December 2021. Milky Way could invalidate the hypothesis of exotic matter and ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/119423b0", "content": "by O LODGE · 1927 · Cited by 4 — Advancing mathematics by guiding human intuition with AI . Article Open access 01 December 2021. Milky Way could invalidate the hypothesis of exotic matter and ..."}
+{"idx": 8, "title": "Mathematical Literacy for the Non-mathematician", "date": "", "ddg_snippet": "by G STEINER · 1973 · Cited by 8 — Abstract . This article is based on an address by Dr Steiner at the ... Advancing mathematics by guiding human intuition with AI . Article ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/243065a0", "content": "by G STEINER · 1973 · Cited by 8 — Abstract . This article is based on an address by Dr Steiner at the ... Advancing mathematics by guiding human intuition with AI . Article ..."}
+{"idx": 9, "title": "Mitigating data bias and ensuring reliable evaluation of AI ...", "date": "", "ddg_snippet": "by W Zhou · 2025 — Davies, A. et al. Advancing mathematics by guiding human intuition with AI . Nature 600, 70–74 (2021). Article CAS PubMed PubMed Central Google ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-025-60801-6", "content": "by W Zhou · 2025 — Davies, A. et al. Advancing mathematics by guiding human intuition with AI . Nature 600, 70–74 (2021). Article CAS PubMed PubMed Central Google ..."}
diff --git a/data/sampled_jsons/siteopenaccess.thecvf.com_Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_HumanAct.jsonl b/data/sampled_jsons/siteopenaccess.thecvf.com_Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_HumanAct.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7a521057fdbd08434cc95f5f455cbb24a424703e
--- /dev/null
+++ b/data/sampled_jsons/siteopenaccess.thecvf.com_Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_HumanAct.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Executing Your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "For the action-to- motion datasets, Ba-bel [50] also collects motions from AMASS and provides action and behavior annotations. ACTOR [46] use [31] to process two action recognition datasets, HumanAct 12 [19] and UESTC [26], for action-to- motion task.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf", "content": "For the action-to- motion datasets, Ba-bel [50] also collects motions from AMASS and provides action and behavior annotations. ACTOR [46] use [31] to process two action recognition datasets, HumanAct 12 [19] and UESTC [26], for action-to- motion task."}
+{"idx": 1, "title": "MoLA: Motion Generation and Editing with Latent Diffusion ...", "date": "", "ddg_snippet": "Additionally, we integrate a variational autoencoder and a latent diffusion model, further enhanced through ad-versarial training, to achieve high-quality and fast genera-tion. Moreover, we apply a training-free guided generation framework to achieve various editing tasks with motion con-trol inputs.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025W/HuMoGen/papers/Uchida_MoLA_Motion_Generation_and_Editing_with_Latent_Diffusion_Enhanced_by_CVPRW_2025_paper.pdf", "content": "Additionally, we integrate a variational autoencoder and a latent diffusion model, further enhanced through ad-versarial training, to achieve high-quality and fast genera-tion. Moreover, we apply a training-free guided generation framework to achieve various editing tasks with motion con-trol inputs."}
+{"idx": 2, "title": "Executing your Commands via Motion Diffusion in Latent Space ...", "date": "", "ddg_snippet": "We study the importance of regularization on motion latent space . The regularized latent space provides stronger genera-tion ability and supports the latent diffusion models as demonstrated:", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Executing_Your_Commands_CVPR_2023_supplemental.pdf", "content": "We study the importance of regularization on motion latent space . The regularized latent space provides stronger genera-tion ability and supports the latent diffusion models as demonstrated:"}
+{"idx": 3, "title": "LatentHOI: On the Generalizable Hand Object Motion Generation ...", "date": "", "ddg_snippet": "Executing your commands via motion diffusion in latent space . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18000–18010, 2023. 2, 3, 5, 6 [6] Min Jin Chong and David Forsyth.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Li_LatentHOI_On_the_Generalizable_Hand_Object_Motion_Generation_with_Latent_CVPR_2025_paper.pdf", "content": "Executing your commands via motion diffusion in latent space . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18000–18010, 2023. 2, 3, 5, 6 [6] Min Jin Chong and David Forsyth."}
+{"idx": 4, "title": "ReinDiffuse: Crafting Physically Plausible Motions with ...", "date": "", "ddg_snippet": "Executing your commands via motion diffusion in latent space . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 18000–18010, June 2023. 2 [6] Xin Chen , Zhuo Su, Lingbo Yang, Pei Cheng, Lan Xu, Bin Fu, and Gang Yu.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/papers/Han_ReinDiffuse_Crafting_Physically_Plausible_Motions_with_Reinforced_Diffusion_Model_WACV_2025_paper.pdf", "content": "Executing your commands via motion diffusion in latent space . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 18000–18010, June 2023. 2 [6] Xin Chen , Zhuo Su, Lingbo Yang, Pei Cheng, Lan Xu, Bin Fu, and Gang Yu."}
+{"idx": 5, "title": "MMM: Generative Masked Motion Model - CVF Open Access", "date": "", "ddg_snippet": "Executing your commands via motion diffusion in latent space . 2023 IEEE/CVF Confer-ence on Computer Vision and Pattern Recognition (CVPR), pages 18000–18010, 2022. 2, 3, 5, 6, 7, 1, 4 [5] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Pinyoanuntapong_MMM_Generative_Masked_Motion_Model_CVPR_2024_paper.pdf", "content": "Executing your commands via motion diffusion in latent space . 2023 IEEE/CVF Confer-ence on Computer Vision and Pattern Recognition (CVPR), pages 18000–18010, 2022. 2, 3, 5, 6, 7, 1, 4 [5] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova."}
+{"idx": 6, "title": "Human Motion Synthesis with Latent-space GANs", "date": "", "ddg_snippet": "by A Amballa · 2025 · Cited by 3 — [7] Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao. Chen, Jingyi Yu, and Gang Yu. Executing your commands via motion diffusion in latent space , 2023. 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025W/ImageQuality/papers/Amballa_LS-GAN_Human_Motion_Synthesis_with_Latent-space_GANs_WACVW_2025_paper.pdf", "content": "by A Amballa · 2025 · Cited by 3 — [7] Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao. Chen, Jingyi Yu, and Gang Yu. Executing your commands via motion diffusion in latent space , 2023. 10 pages"}
+{"idx": 7, "title": "Text-Driven Shape-Aware Synthesis of Human Motions", "date": "", "ddg_snippet": "by TH Liao · 2025 · Cited by 3 — [23] Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao. Chen, and Gang Yu. Executing your commands via motion diffusion in latent space . In CVPR, 2023 ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_Shape_My_Moves_Text-Driven_Shape-Aware_Synthesis_of_Human_Motions_CVPR_2025_paper.pdf", "content": "by TH Liao · 2025 · Cited by 3 — [23] Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao. Chen, and Gang Yu. Executing your commands via motion diffusion in latent space . In CVPR, 2023 ..."}
+{"idx": 8, "title": "Generating Controllable Human-Motion Videos via Decoupled ...", "date": "", "ddg_snippet": "by B Wang · 2025 · Cited by 8 — [6] Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao. Chen, and Gang Yu. Executing your commands via motion diffusion in latent space . In CVPR, 2023 ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_HumanDreamer_Generating_Controllable_Human-Motion_Videos_via_Decoupled_Generation_CVPR_2025_paper.pdf", "content": "by B Wang · 2025 · Cited by 8 — [6] Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao. Chen, and Gang Yu. Executing your commands via motion diffusion in latent space . In CVPR, 2023 ..."}
+{"idx": 9, "title": "Uniform Text-Motion Generation and Editing Model via Diffusion", "date": "", "ddg_snippet": "[5] Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao. Chen, and Gang Yu. Executing your commands via motion diffusion in latent space . In Proceedings ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/papers/Wang_UniTMGE_Uniform_Text-Motion_Generation_and_Editing_Model_via_Diffusion_WACV_2025_paper.pdf", "content": "[5] Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao. Chen, and Gang Yu. Executing your commands via motion diffusion in latent space . In Proceedings ... 11 pages"}
diff --git a/data/sampled_jsons/siteopenaccess.thecvf.com_Descriptor-In-Pixel_SCAMP-7_bytes_per_feature.jsonl b/data/sampled_jsons/siteopenaccess.thecvf.com_Descriptor-In-Pixel_SCAMP-7_bytes_per_feature.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..93e1225fe8efc0c7259225ead49795d67a8d2ad3
--- /dev/null
+++ b/data/sampled_jsons/siteopenaccess.thecvf.com_Descriptor-In-Pixel_SCAMP-7_bytes_per_feature.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "by L Bose · 2025 — Descriptor Size: For maximum efficiency, an entire de- scriptor should be stored within each PE (i.e. one descriptor per pixel location). However, PE local ... 9 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "by L Bose · 2025 — Descriptor Size: For maximum efficiency, an entire de- scriptor should be stored within each PE (i.e. one descriptor per pixel location). However, PE local ... 9 pages"}
+{"idx": 1, "title": "This mod is missing the descriptor file - Paradox Interactive...", "date": "", "ddg_snippet": "Feb 20, 2020 · Once I added them again, I get duplicate entries and of course the \"missing descriptor file\". I guess I'll just cancel the subscription then, if I can't even let them rest there deactivated to wait for an update. btw: unsubscribing from those legacy mods doesn't work. The duplicates stay. Even if you delete the mod's files in the Steam-folder.", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/this-mod-is-missing-the-descriptor-file.1336077/", "content": "Feb 20, 2020 · Once I added them again, I get duplicate entries and of course the \"missing descriptor file\". I guess I'll just cancel the subscription then, if I can't even let them rest there deactivated to wait for an update. btw: unsubscribing from those legacy mods doesn't work. The duplicates stay. Even if you delete the mod's files in the Steam-folder."}
+{"idx": 2, "title": "Missing descriptor file in my mod even if there is one", "date": "", "ddg_snippet": "Jul 7 , 2023 · Summary Missing descriptor file in my mod even if there is one Platform Steam Operating System Windows Game Version 1.12.14.50e7 Enabled DLC None Do...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/missing-descriptor-file-in-my-mod-even-if-there-is-one.1592977/", "content": "Jul 7 , 2023 · Summary Missing descriptor file in my mod even if there is one Platform Steam Operating System Windows Game Version 1.12.14.50e7 Enabled DLC None Do..."}
+{"idx": 3, "title": "Mod Missing Descriptor File - Paradox Interactive Forums", "date": "", "ddg_snippet": "Jul 3, 2021 · I almost literally tried everything that can be found online with a google search of ''Eu4 missing mod descriptor file'' or ''Eu4 mods start missing descriptor file after restart'', I mean there are not that many things to be found with a search like this because I am pretty sure I am the only person experiencing this but I tried every solution ...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/mod-missing-descriptor-file.1481804/", "content": "Jul 3, 2021 · I almost literally tried everything that can be found online with a google search of ''Eu4 missing mod descriptor file'' or ''Eu4 mods start missing descriptor file after restart'', I mean there are not that many things to be found with a search like this because I am pretty sure I am the only person experiencing this but I tried every solution ..."}
+{"idx": 4, "title": "How to install mods manually from Paradox Mods? Descriptor.mod?", "date": "", "ddg_snippet": "Sep 25, 2020 · When I download a mod manually from the Paradox Mods site , I receive a zip file with a descriptor .mod file and a folder structure that mirrors the game folder structure. I assume I create a folder inside the Documents\\\\Mods folder and dump...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/how-to-install-mods-manually-from-paradox-mods-descriptor-mod.1427279/", "content": "Sep 25, 2020 · When I download a mod manually from the Paradox Mods site , I receive a zip file with a descriptor .mod file and a folder structure that mirrors the game folder structure. I assume I create a folder inside the Documents\\\\Mods folder and dump..."}
+{"idx": 5, "title": "HoI 4 - Path replacement in descriptor.mod files not working", "date": "", "ddg_snippet": "Nov 22, 2019 · Description of issue Path replacement in descriptor .mod files not working Game Version 1.8.1 Enabled DLC Do you have mods enabled? Yes...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/hoi-4-path-replacement-in-descriptor-mod-files-not-working.1285940/", "content": "Nov 22, 2019 · Description of issue Path replacement in descriptor .mod files not working Game Version 1.8.1 Enabled DLC Do you have mods enabled? Yes..."}
+{"idx": 6, "title": "This mod is missing a descriptor file - Paradox Interactive...", "date": "", "ddg_snippet": "Oct 18, 2020 · The mod should provide the descriptor .mod in the download zip for Paradox Mods (or in the workshop folder if you are on Steam). Maybe Daddy Pika updated the mod and forgot to include the descriptor ? In any case, you can write your own descriptor and have the mod work. Just take a look at the other mods descriptors and adapt them. If they refer to a remote file id you can substitute that string ...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/this-mod-is-missing-a-descriptor-file.1437370/", "content": "Oct 18, 2020 · The mod should provide the descriptor .mod in the download zip for Paradox Mods (or in the workshop folder if you are on Steam). Maybe Daddy Pika updated the mod and forgot to include the descriptor ? In any case, you can write your own descriptor and have the mod work. Just take a look at the other mods descriptors and adapt them. If they refer to a remote file id you can substitute that string ..."}
+{"idx": 7, "title": "All my mods are somehow missing descriptor files - please help!", "date": "", "ddg_snippet": "May 4, 2022 · I've been having a nightmare of a time trying to get mods to work in the CK3 launcher for some days. They give the warning in he title. If they work after I follow the formula of unsubscribe-delete from the mods folder-resubscribe method (and...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/all-my-mods-are-somehow-missing-descriptor-files-please-help.1523360/", "content": "May 4, 2022 · I've been having a nightmare of a time trying to get mods to work in the CK3 launcher for some days. They give the warning in he title. If they work after I follow the formula of unsubscribe-delete from the mods folder-resubscribe method (and..."}
+{"idx": 8, "title": "CK3 crashing constantly | Paradox Interactive Forums", "date": "", "ddg_snippet": "Feb 14, 2025 · For months now, I have had ck3 crash once in a while, almost always when playing with mods. They usually occurred when autosaving or trying to load an autosave. Now, however the issue has become unbearable. I can no longer play sc3 without it...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/ck3-crashing-constantly.1728784/", "content": "Feb 14, 2025 · For months now, I have had ck3 crash once in a while, almost always when playing with mods. They usually occurred when autosaving or trying to load an autosave. Now, however the issue has become unbearable. I can no longer play sc3 without it..."}
+{"idx": 9, "title": "Game crashes at loading screen with certain mods.", "date": "", "ddg_snippet": "May 9, 2023 · Using the mod \"Imperial Nostalgia\" the game crashes while the loading screen you get right after launching the game is loading. I'm a MacOS user and most of the other mods I installed actually work, except for a few ones that actually have the...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/game-crashes-at-loading-screen-with-certain-mods.1582668/", "content": "May 9, 2023 · Using the mod \"Imperial Nostalgia\" the game crashes while the loading screen you get right after launching the game is loading. I'm a MacOS user and most of the other mods I installed actually work, except for a few ones that actually have the..."}
diff --git a/data/sampled_jsons/siteopenreview.net_1BaC3AdG1i_GTA_strategy_worker_time_inefficiency.jsonl b/data/sampled_jsons/siteopenreview.net_1BaC3AdG1i_GTA_strategy_worker_time_inefficiency.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a61bbc8b9776b5f01263406475851ffef66d4017
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_1BaC3AdG1i_GTA_strategy_worker_time_inefficiency.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scaling LLM Test- Time Compute Optimally Can be... | OpenReview", "date": "", "ddg_snippet": "This observation motivates applying a \"compute-optimal\" scaling strategy , which acts to, as effectively as possible, allocate test- time compute per prompt in an adaptive manner.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4FWAwZtd2n", "content": "This observation motivates applying a \"compute-optimal\" scaling strategy , which acts to, as effectively as possible, allocate test- time compute per prompt in an adaptive manner."}
+{"idx": 1, "title": "Multi-Class Uncertainty Calibration via Mutual... | OpenReview", "date": "", "ddg_snippet": "When classes have small prior probabilities, HB also faces the issue of severe sample- inefficiency after the conversion into K one -vs-rest class-wise calibration problems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=AICNpd8ke-m", "content": "When classes have small prior probabilities, HB also faces the issue of severe sample- inefficiency after the conversion into K one -vs-rest class-wise calibration problems."}
+{"idx": 2, "title": "Uni-Mol: A Universal 3D Molecular Representation... | OpenReview", "date": "", "ddg_snippet": "Besides, Uni-Mol contains several finetuning strategies to apply the pretrained models to various downstream tasks. By properly incorporating 3D information, Uni-Mol outperforms SOTA in 14/15 molecular property prediction tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6K2RM6wVqKu", "content": "Besides, Uni-Mol contains several finetuning strategies to apply the pretrained models to various downstream tasks. By properly incorporating 3D information, Uni-Mol outperforms SOTA in 14/15 molecular property prediction tasks."}
+{"idx": 3, "title": "Discriminator-Actor-Critic: Addressing Sample Inefficiency ...", "date": "", "ddg_snippet": "Dec 20, 2018 · We address sample inefficiency and reward bias in adversarial imitation learning algorithms such as GAIL and AIRL.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Hk4fpoA5Km", "content": "Dec 20, 2018 · We address sample inefficiency and reward bias in adversarial imitation learning algorithms such as GAIL and AIRL."}
+{"idx": 4, "title": "ON THE COMPUTATIONAL INEFFICIENCY OF LARGE BATCH SIZES FOR ...", "date": "", "ddg_snippet": "1 INTRODUCTION Mini-batch stochastic gradient descent (SGD) is the dominant optimization method for training deep neural networks (DNNs) (Bengio & LeCun, 2007; Bottou, 2010). In the face of unprecedented growth in dataset size, a large body of work has attempted to scale SGD to train DNN models on increasingly large datasets, while keeping wall-clock time manageable (Iandola et al., 2015 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=S1en0sRqKm", "content": "1 INTRODUCTION Mini-batch stochastic gradient descent (SGD) is the dominant optimization method for training deep neural networks (DNNs) (Bengio & LeCun, 2007; Bottou, 2010). In the face of unprecedented growth in dataset size, a large body of work has attempted to scale SGD to train DNN models on increasingly large datasets, while keeping wall-clock time manageable (Iandola et al., 2015 ..."}
+{"idx": 5, "title": "M : L -TIME SEQUENCE MODELING WITH SPACES - OpenReview", "date": "", "ddg_snippet": "ABSTRACT Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic- time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers’ computational ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=AL1fq05o7H", "content": "ABSTRACT Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic- time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers’ computational ..."}
+{"idx": 6, "title": "Decomposing The Dark Matter of Sparse Autoencoders | OpenReview", "date": "", "ddg_snippet": "However, current SAEs fall short of completely explaining model performance, resulting in ``dark matter''—unexplained variance in activations. In this work , we predict and verify that much of SAE dark matter can be linearly predicted from the activation vector.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5IZfo98rqr", "content": "However, current SAEs fall short of completely explaining model performance, resulting in ``dark matter''—unexplained variance in activations. In this work , we predict and verify that much of SAE dark matter can be linearly predicted from the activation vector."}
+{"idx": 7, "title": "The Last Mile to Supervised Performance... | OpenReview", "date": "", "ddg_snippet": "Particularly, following the first review, we have: - Extended related work (Section 2) to provide more context on the current state of SSDA for segmentation and related fields (e.g., how does SSDA compare to UDA and SSL).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=419MRJ2U0D", "content": "Particularly, following the first review, we have: - Extended related work (Section 2) to provide more context on the current state of SSDA for segmentation and related fields (e.g., how does SSDA compare to UDA and SSL)."}
+{"idx": 8, "title": "Forum - OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Cjz9Xhm7sI", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 9, "title": "Hongliang Li - OpenReview", "date": "", "ddg_snippet": "Hongliang Li Full Professor, Department of information and communication engineering, University of Electronic Science and Technology of China Joined February 2019", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Hongliang_Li1", "content": "Hongliang Li Full Professor, Department of information and communication engineering, University of Electronic Science and Technology of China Joined February 2019"}
diff --git a/data/sampled_jsons/siteopenreview.net_27tMzmzDjO_Table_1_dataset_statistics.jsonl b/data/sampled_jsons/siteopenreview.net_27tMzmzDjO_Table_1_dataset_statistics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7bcac61e0f4ddfbe291371a186945e25b9dd044f
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_27tMzmzDjO_Table_1_dataset_statistics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NeurIPS 2024 Datasets and Benchmarks Track | OpenReview", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for NeurIPS 2024 Datasets and Benchmarks Track", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=NeurIPS.cc/2024/Datasets_and_Benchmarks_Track", "content": "Welcome to the OpenReview homepage for NeurIPS 2024 Datasets and Benchmarks Track"}
+{"idx": 1, "title": "Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets...", "date": "", "ddg_snippet": "Strengths: The authors create a new and interesting tabular dataset . The dataset is synthetically generated based on a real dataset to conserve privacy. I agree with the authors that new, well-documented tabular datasets are a very useful contribution to the wider research community in general and the algorithmic fairness community in particular.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=UrAYT2QwOX8", "content": "Strengths: The authors create a new and interesting tabular dataset . The dataset is synthetically generated based on a real dataset to conserve privacy. I agree with the authors that new, well-documented tabular datasets are a very useful contribution to the wider research community in general and the algorithmic fairness community in particular."}
+{"idx": 2, "title": "S2abEL: A Dataset for Entity Linking from Scientific Tables", "date": "", "ddg_snippet": "Our dataset , Scientific Table Entity Linking (S2abEL), focuses on EL in machine learning results tables and includes hand-labeled cell types, attributed sources, and entity links from the PaperswithCode taxonomy for 8,429 cells from 732 tables .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o7SWorg8EM", "content": "Our dataset , Scientific Table Entity Linking (S2abEL), focuses on EL in machine learning results tables and includes hand-labeled cell types, attributed sources, and entity links from the PaperswithCode taxonomy for 8,429 cells from 732 tables ."}
+{"idx": 3, "title": "AZT1D: A Real-World Dataset for Type 1 Diabetes - OpenReview", "date": "", "ddg_snippet": "High quality real world datasets are essential for advancing data driven approaches in type 1 diabetes (T1D) management, including personalized therapy design, digital twin systems, and glucose prediction models. However, progress in this area has been limited by the scarcity of publicly available datasets that offer detailed and comprehensive patient data. To address this gap, we present ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=EXBW6JlgqU", "content": "High quality real world datasets are essential for advancing data driven approaches in type 1 diabetes (T1D) management, including personalized therapy design, digital twin systems, and glucose prediction models. However, progress in this area has been limited by the scarcity of publicly available datasets that offer detailed and comprehensive patient data. To address this gap, we present ..."}
+{"idx": 4, "title": "Towards a universal dataset and metrics for training and evaluating ...", "date": "", "ddg_snippet": "This paper describes PubTables1M, the largest dataset of its kind, and a new metric, grid table similarity (GriTS), for training and evaluating models for table extraction.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6lYwZ6yknRw", "content": "This paper describes PubTables1M, the largest dataset of its kind, and a new metric, grid table similarity (GriTS), for training and evaluating models for table extraction."}
+{"idx": 5, "title": "REASONER: An Explainable Recommendation Dataset with ... - OpenReview", "date": "", "ddg_snippet": "The differences between this dataset and previous datasets are present in Table 1 , which demonstrates that this dataset is labeled by the real users, and provides multi-aspect and multi-modal explanations.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=n4OwK8cpx2", "content": "The differences between this dataset and previous datasets are present in Table 1 , which demonstrates that this dataset is labeled by the real users, and provides multi-aspect and multi-modal explanations."}
+{"idx": 6, "title": "PDF Towards a universal dataset and metrics for training and evaluating ...", "date": "", "ddg_snippet": "325 In this paper we introduced a new dataset , PubMed Tables One Million (PubTables1M), the largest 326 of its kind, and grid table similarity (GriTS), a new class of evaluation metric for table structure 327 recognition that has a much better theoretical grounding than previously proposed metrics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/3e9a23d7f1941c5c5bb7596c61e12983f6ee0307.pdf", "content": "325 In this paper we introduced a new dataset , PubMed Tables One Million (PubTables1M), the largest 326 of its kind, and grid table similarity (GriTS), a new class of evaluation metric for table structure 327 recognition that has a much better theoretical grounding than previously proposed metrics."}
+{"idx": 7, "title": "M -d : a Dataset of Datasets for Learning to Learn From Few Examples", "date": "", "ddg_snippet": "Complete main tables For completeness, Table 2 presents a more detailed version of Table 1 that also displays confidence intervals and per- dataset ranks computed using the above procedure.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=rkgAGAVKPr", "content": "Complete main tables For completeness, Table 2 presents a more detailed version of Table 1 that also displays confidence intervals and per- dataset ranks computed using the above procedure."}
+{"idx": 8, "title": "ACMMM 2025 Datasets Track | OpenReview", "date": "", "ddg_snippet": "ACM Multimedia 2025 Dataset Track ACMMM 2025 Datasets Dublin, Ireland Oct 27 2025 https://acmmm2025.org datasets@acmmm2025.org", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=acmmm.org/ACMMM/2025/Datasets_Track", "content": "ACM Multimedia 2025 Dataset Track ACMMM 2025 Datasets Dublin, Ireland Oct 27 2025 https://acmmm2025.org datasets@acmmm2025.org"}
+{"idx": 9, "title": "SciGen: a Dataset for Reasoning-Aware Text Generation from...", "date": "", "ddg_snippet": "SciGen is the first dataset that assesses the arithmetic reasoning capabilities of generation models on complex input structures, such as tables from scientific articles, and thus it opens new avenues for future research in reasoning-aware text generation and evaluation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Jul-uX7EV_I", "content": "SciGen is the first dataset that assesses the arithmetic reasoning capabilities of generation models on complex input structures, such as tables from scientific articles, and thus it opens new avenues for future research in reasoning-aware text generation and evaluation."}
diff --git a/data/sampled_jsons/siteopenreview.net_4Z04wVQ9FY_Table_1_LUNO-LA_Sample-LA_NLL_score_year_2024.jsonl b/data/sampled_jsons/siteopenreview.net_4Z04wVQ9FY_Table_1_LUNO-LA_Sample-LA_NLL_score_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..011a6888c59cbdeef2138279fbac74c791da0686
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_4Z04wVQ9FY_Table_1_LUNO-LA_Sample-LA_NLL_score_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LEARNING WITH INSTANCE-DEPENDENT NOISY LA H SAMPLE SELECTION ...", "date": "", "ddg_snippet": "ABSTRACT Learning from noisily-labeled data is common in real-world visual learning tasks. Mainstream Noisy-Label Learning ( NLL ) methods mainly focus on sample -selection approaches, which typically divide the training dataset into clean and noisy subsets according to the loss distribution of samples. However, they over-look the fact that clean samples with complex visual patterns may also ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=3nyovHUr5A", "content": "ABSTRACT Learning from noisily-labeled data is common in real-world visual learning tasks. Mainstream Noisy-Label Learning ( NLL ) methods mainly focus on sample -selection approaches, which typically divide the training dataset into clean and noisy subsets according to the loss distribution of samples. However, they over-look the fact that clean samples with complex visual patterns may also ..."}
+{"idx": 1, "title": "Sample Selection with Uncertainty of Losses for Learning with ...", "date": "", "ddg_snippet": "In learning with noisy labels, the sample selection approach is very popular, which regards small-loss data as correctly labeled data during training. However, losses are generated on-the-fly based...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=xENf4QUL4LW", "content": "In learning with noisy labels, the sample selection approach is very popular, which regards small-loss data as correctly labeled data during training. However, losses are generated on-the-fly based..."}
+{"idx": 2, "title": "Table 1: A comparison of inference time and max memory cost ...", "date": "", "ddg_snippet": "N L L ) NLL for Different Num bers of Sam ples N Figure 1 : Performance of BLoB with Varying Sample Sizes N during Inference. We fine-tuned the Llama2-7B model on the WG-S dataset for 5000 steps, evaluating the model’s performance with different sample sizes, specifically whenN is 1 , 2, 3, 4, 5, 10, 20, 40, 80, and 160. Created Date ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=KSyigaewkF&name=pdf", "content": "N L L ) NLL for Different Num bers of Sam ples N Figure 1 : Performance of BLoB with Varying Sample Sizes N during Inference. We fine-tuned the Llama2-7B model on the WG-S dataset for 5000 steps, evaluating the model’s performance with different sample sizes, specifically whenN is 1 , 2, 3, 4, 5, 10, 20, 40, 80, and 160. Created Date ..."}
+{"idx": 3, "title": "Flow Matching for Generative Modeling | OpenReview", "date": "", "ddg_snippet": "Feb 1 , 2023 · We introduce a new simulation-free approach for training Continuous Normalizing Flows, generalizing the probability paths induced by simple diffusion processes. We obtain state-of-the-art on ImageNet in both NLL and FID among competing methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=PqvMRDCJT9t", "content": "Feb 1 , 2023 · We introduce a new simulation-free approach for training Continuous Normalizing Flows, generalizing the probability paths induced by simple diffusion processes. We obtain state-of-the-art on ImageNet in both NLL and FID among competing methods."}
+{"idx": 4, "title": "BWS: Best Window Selection Based on Sample Scores for Data ...", "date": "", "ddg_snippet": "Sep 21, 2023 · BWS allows for the adaptable selection of subsets based on sample difficulty scores and consistently delivers competitive performance over a broad range of selection ratios, spanning from 1 % to 90%. It excels in comparison to existing score -based and optimization-based methods when applied to datasets like CIFAR-10/100 and ImageNet. Soundness ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9FXGX00iMF", "content": "Sep 21, 2023 · BWS allows for the adaptable selection of subsets based on sample difficulty scores and consistently delivers competitive performance over a broad range of selection ratios, spanning from 1 % to 90%. It excels in comparison to existing score -based and optimization-based methods when applied to datasets like CIFAR-10/100 and ImageNet. Soundness ..."}
+{"idx": 5, "title": "Linearization Turns Neural Operators into... | OpenReview", "date": "", "ddg_snippet": "We introduce LUNO , a novel framework for approximate Bayesian uncertainty quantification in trained neural operators.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "We introduce LUNO , a novel framework for approximate Bayesian uncertainty quantification in trained neural operators."}
+{"idx": 6, "title": "Table 3: Extended evaluation results for CIFAR10 dataset.", "date": "", "ddg_snippet": "(b) NLL Cumsum without t 1 . Table 6: DDGM and DAED hyperparameters for different datasets. Dataset. FashionMNIST CIFAR10 CelebA.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=nxl-IjnDCRo&name=supplementary_material", "content": "(b) NLL Cumsum without t 1 . Table 6: DDGM and DAED hyperparameters for different datasets. Dataset. FashionMNIST CIFAR10 CelebA."}
+{"idx": 7, "title": "Multi-Time Attention Networks for Irregularly Sampled ... | OpenReview", "date": "", "ddg_snippet": "This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled , and multivariate. In this paper, we propose a new deep learning framework for this setting that we call Multi-Time Attention Networks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4c0J6lwQ4_", "content": "This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled , and multivariate. In this paper, we propose a new deep learning framework for this setting that we call Multi-Time Attention Networks."}
+{"idx": 8, "title": "Multivariate Probabilistic Time Series Forecasting with ...", "date": "", "ddg_snippet": "In [7], the authors suggest that neither CRPS nor the energy score is a good surrogate for the NLL score in small- sample regimes, which are common in practice. However, comparing NLL is infeasible in our case, as sampling must be conducted to calibrate predictions autoregressively over the prediction range.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=cAFvxVFaii", "content": "In [7], the authors suggest that neither CRPS nor the energy score is a good surrogate for the NLL score in small- sample regimes, which are common in practice. However, comparing NLL is infeasible in our case, as sampling must be conducted to calibrate predictions autoregressively over the prediction range."}
+{"idx": 9, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian...", "date": "", "ddg_snippet": "Table 1 shows that LUNO - LA outperforms the other approaches.While LUNO - LA outperforms the other weight space methods and input perturbations, deep ensem-bles achieve the lowest expected marginal NLL in next-step prediction.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "Table 1 shows that LUNO - LA outperforms the other approaches.While LUNO - LA outperforms the other weight space methods and input perturbations, deep ensem-bles achieve the lowest expected marginal NLL in next-step prediction."}
diff --git a/data/sampled_jsons/siteopenreview.net_CoPINN_Cognitive_Physics-Informed_Neural_Networks_Diffusion_equation_Table_1_refe.jsonl b/data/sampled_jsons/siteopenreview.net_CoPINN_Cognitive_Physics-Informed_Neural_Networks_Diffusion_equation_Table_1_refe.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..286abc0bd1d79d47b8a395aba0883c1730f9118a
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_CoPINN_Cognitive_Physics-Informed_Neural_Networks_Diffusion_equation_Table_1_refe.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Abstract Physics-informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "Abstract Physics-informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs)."}
+{"idx": 1, "title": "CoPINN : Cognitive Physics - Informed Neural Networks | OpenReview", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI&referrer=[the+profile+of+Peng+Hu](/profile?id=~Peng_Hu2)", "content": "Physics - informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs)."}
+{"idx": 2, "title": "CoPINN : Cognitive Physics - Informed Neural Networks | OpenReview", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI", "content": "Physics - informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs)."}
+{"idx": 3, "title": "Physics - informed", "date": "", "ddg_snippet": "Physics - informed neural networks (PiNNs) can be considered as a solution technique for differential equations (most notably, PDEs) that approximate an unknown solution with a neural network , obtain derivatives with automatic differentiation, and minimize PDE-related loss function with...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=btaxn8Xce6", "content": "Physics - informed neural networks (PiNNs) can be considered as a solution technique for differential equations (most notably, PDEs) that approximate an unknown solution with a neural network , obtain derivatives with automatic differentiation, and minimize PDE-related loss function with..."}
+{"idx": 4, "title": "PIHLoRA: Physics - informed hypernetworks for", "date": "", "ddg_snippet": "Physics informed neural networks for solving highly anisotropic diffusion equations .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kupYlLLGdf", "content": "Physics informed neural networks for solving highly anisotropic diffusion equations ."}
+{"idx": 5, "title": "How to Select Physics-Informed Neural Networks in the Absence ...", "date": "", "ddg_snippet": "Abstract Physics-informed neural networks (PINNs) have been proposed as a potential route to inverse modelling or mesh-free alternative to numerical methods for partial differential equations (PDEs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i3Hjq9Wcvq", "content": "Abstract Physics-informed neural networks (PINNs) have been proposed as a potential route to inverse modelling or mesh-free alternative to numerical methods for partial differential equations (PDEs)."}
+{"idx": 6, "title": "CoPINN: Cognitive Physics-Informed Neural Networks - OpenReview", "date": "", "ddg_snippet": "Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI&amp;referrer=[Author+Console](/group?id=ICML.cc/2025/Conference/Authors#your-submissions)", "content": "Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs)."}
+{"idx": 7, "title": "EXTENSION OF PHYSICS INFORMED NEURAL NET S PARAMETERIZED PDES", "date": "", "ddg_snippet": "ABSTRACT In this paper, we address PINNs’ problem of repetitive and time-consuming train-ing by proposing a novel extension, parameterized physics-informed neural net-works (P2INNs). P2INNs enable modeling the solutions of parameterized PDEs via explicitly encoding a latent representation of PDE parameters. With the exten-sive empirical evaluation, we demonstrate that P2INNs outperform the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=EAkRlHFLBc", "content": "ABSTRACT In this paper, we address PINNs’ problem of repetitive and time-consuming train-ing by proposing a novel extension, parameterized physics-informed neural net-works (P2INNs). P2INNs enable modeling the solutions of parameterized PDEs via explicitly encoding a latent representation of PDE parameters. With the exten-sive empirical evaluation, we demonstrate that P2INNs outperform the ..."}
+{"idx": 8, "title": "Physics-Informed Neural Networks for Derivative-Constrained PDEs", "date": "", "ddg_snippet": "Abstract Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving partial differential equations (PDEs) using deep learning. However, standard PINNs do not ad-dress the problem of constrained PDEs, where the solution must satisfy additional equality or inequality constraints beyond the governing equa-tions .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9pFHmyx4Sh", "content": "Abstract Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving partial differential equations (PDEs) using deep learning. However, standard PINNs do not ad-dress the problem of constrained PDEs, where the solution must satisfy additional equality or inequality constraints beyond the governing equa-tions ."}
+{"idx": 9, "title": "Ral n etworks : a g radient b oosting approach", "date": "", "ddg_snippet": "Radient boosting physics informed neural networks . Physical informed neural networks with soft and hard boundary constraints for solving advection- diffusion equations using fourier expansions. arXiv preprint arXiv:2306.12749, 2023.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=nA0iiUI6mq", "content": "Radient boosting physics informed neural networks . Physical informed neural networks with soft and hard boundary constraints for solving advection- diffusion equations using fourier expansions. arXiv preprint arXiv:2306.12749, 2023."}
diff --git a/data/sampled_jsons/siteopenreview.net_Medusa_Cai_et_al._year_2024.jsonl b/data/sampled_jsons/siteopenreview.net_Medusa_Cai_et_al._year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d357064640e91bad5752b126611d4507bfca4986
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_Medusa_Cai_et_al._year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Medusa: Simple LLM Inference Acceleration Framework with ...", "date": "", "ddg_snippet": "by T Cai · Cited by 348 — Abstract. Large Language Models (LLMs) employ auto- regressive decoding that requires sequential com- putation, with each step reliant on the previous.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=PEpbUobfJv", "content": "by T Cai · Cited by 348 — Abstract. Large Language Models (LLMs) employ auto- regressive decoding that requires sequential com- putation, with each step reliant on the previous."}
+{"idx": 1, "title": "Hydra: Sequentially-Dependent Draft Heads for Medusa De", "date": "", "ddg_snippet": "by Z Ankner · Cited by 49 — Medusa decoding (Cai et al., 2024 ) is a particular configuration of the techniques listed above. Specifically, it is speculative decoding with a tree of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=FbhjirzvJG", "content": "by Z Ankner · Cited by 49 — Medusa decoding (Cai et al., 2024 ) is a particular configuration of the techniques listed above. Specifically, it is speculative decoding with a tree of ..."}
+{"idx": 2, "title": "Fast and Accurate Language Model Decoding via Parallel ...", "date": "", "ddg_snippet": "by Z Wei · Cited by 1 — [1] Cai et al. Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads . ICML 2024. [2] Leviathan et al. Fast Inference from ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=IRsxTYPqhQ", "content": "by Z Wei · Cited by 1 — [1] Cai et al. Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads . ICML 2024. [2] Leviathan et al. Fast Inference from ..."}
+{"idx": 3, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "by J Li · 2025 — We compare our method against several existing approaches: Medusa (Cai et al., 2024 ) with multiple parallel MLP heads,. Hydra (Ankner et al., 2024) with ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=HV0D8yjshu", "content": "by J Li · 2025 — We compare our method against several existing approaches: Medusa (Cai et al., 2024 ) with multiple parallel MLP heads,. Hydra (Ankner et al., 2024) with ..."}
+{"idx": 4, "title": "arXiv:2404.12022v1 [cs.CL] 18 Apr 2024", "date": "", "ddg_snippet": "by P Wu · 2024 · Cited by 3 — The single models' ap- proach is represented by Medusa ( Cai et al ., 2023), and Self-speculative decoding method (Zhang et al .,. 2023), Medusa ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TizcaJWRgO", "content": "by P Wu · 2024 · Cited by 3 — The single models' ap- proach is represented by Medusa ( Cai et al ., 2023), and Self-speculative decoding method (Zhang et al .,. 2023), Medusa ..."}
+{"idx": 5, "title": "Speculative Streaming: Fast LLM Inference without ...", "date": "", "ddg_snippet": "by N Bhendawade · 2024 · Cited by 28 — In particular, inspired by (Qi et al., 2020; Stern et al.,. 2018), Medusa (Cai et al., 2023 ) extends the main model to predict future tokens in parallel by ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=gDtKcAXF3C", "content": "by N Bhendawade · 2024 · Cited by 28 — In particular, inspired by (Qi et al., 2020; Stern et al.,. 2018), Medusa (Cai et al., 2023 ) extends the main model to predict future tokens in parallel by ..."}
+{"idx": 6, "title": "ACCELERATION MULTIPLE HEADS DECODING FOR ...", "date": "", "ddg_snippet": "by Z Zhang — This paper focus on multiple head decoding, particularly MEDUSA proposed in ( Cai et al ., 2024). Given the original model's last hidden states ht at last input ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Wn5QaSiMEV", "content": "by Z Zhang — This paper focus on multiple head decoding, particularly MEDUSA proposed in ( Cai et al ., 2024). Given the original model's last hidden states ht at last input ..."}
+{"idx": 7, "title": "Speculative Decoding and Specialized Drafters", "date": "", "ddg_snippet": "by E Yi · 2024 · Cited by 3 — Medusa ( Cai et al ., 2024) and Eagle (Li et al ., 2024): Both methods enhance the tar- get LLM by integrating additional lightweight. FFN heads.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QifOQCg8zH", "content": "by E Yi · 2024 · Cited by 3 — Medusa ( Cai et al ., 2024) and Eagle (Li et al ., 2024): Both methods enhance the tar- get LLM by integrating additional lightweight. FFN heads."}
+{"idx": 8, "title": "JUDGE DECODING: FASTER SPECULATIVE SAMPLING ...", "date": "", "ddg_snippet": "by G Bachmann · Cited by 13 — This is particularly evident when compared to SD methods that leverage small. LM heads as draft modules (such as Eagle-2 Li et al . (2024b) and Medusa Cai et al .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/7cea76e3219842445f3d5996e6585ff679c68346.pdf", "content": "by G Bachmann · Cited by 13 — This is particularly evident when compared to SD methods that leverage small. LM heads as draft modules (such as Eagle-2 Li et al . (2024b) and Medusa Cai et al ."}
+{"idx": 9, "title": "DEFT: DECODING WITH FLASH TREE-ATTENTION FOR ...", "date": "", "ddg_snippet": "by J Yao · Cited by 2 — Medusa (Cai et al., 2024) is suitable for general tree-based decoding , but it is not hardware-efficient due to significant IOs of a dense causal mask and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/639bbf460121942ab57f854016bb693f884849b5.pdf", "content": "by J Yao · Cited by 2 — Medusa (Cai et al., 2024) is suitable for general tree-based decoding , but it is not hardware-efficient due to significant IOs of a dense causal mask and ..."}
diff --git a/data/sampled_jsons/siteopenreview.net_Rew_short_feint_behaviors.jsonl b/data/sampled_jsons/siteopenreview.net_Rew_short_feint_behaviors.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..602234b654955c16be81eebac098e6c9f3e08825
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_Rew_short_feint_behaviors.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short-term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ", "content": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short-term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors ."}
+{"idx": 1, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in com- petitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing lit- erature does not provide a comprehensive (and/or concrete ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/8a0624ac9b6fb65426117d0a6de87a949b709c74.pdf", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in com- petitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing lit- erature does not provide a comprehensive (and/or concrete ..."}
+{"idx": 2, "title": "FEINT IN MULTI-PLAYER GAMES - OpenReview", "date": "", "ddg_snippet": "The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts. Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WbyWDWoXD3", "content": "The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts. Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning)."}
+{"idx": 3, "title": "Junyu Liu - OpenReview", "date": "", "ddg_snippet": "Feint Behaviors and Strategies: Formalization, Implementation and Evaluation Junyu Liu, Xiangjun Peng Published: 25 Sept 2024, Last Modified: 19 Dec 2024 NeurIPS 2024 poster", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Junyu_Liu5", "content": "Feint Behaviors and Strategies: Formalization, Implementation and Evaluation Junyu Liu, Xiangjun Peng Published: 25 Sept 2024, Last Modified: 19 Dec 2024 NeurIPS 2024 poster"}
+{"idx": 4, "title": "Feint in Multi-Player Games - OpenReview", "date": "", "ddg_snippet": "Agents trained with feint modifications collected a larger reward, although one reviewer points out that this is a questionable comparison given that the rewards have been modified. (b) Strengths: All reviewers agreed that the idea of inducing \"bluff\"-like behavior is compelling, given that this is a widespread pattern in human behavior .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=WbyWDWoXD3", "content": "Agents trained with feint modifications collected a larger reward, although one reviewer points out that this is a questionable comparison given that the rewards have been modified. (b) Strengths: All reviewers agreed that the idea of inducing \"bluff\"-like behavior is compelling, given that this is a widespread pattern in human behavior ."}
+{"idx": 5, "title": "Feint and Attack: Attention-Based Strategies for ... - OpenReview", "date": "", "ddg_snippet": "The authors propose an innovative \" Feint and Attack\" strategy, the Attention-Based Attack (ABA), which diverts the model's attention away from harmful content by embedding nested, benign prompts. This method is paired with the Attention-Based Defense (ABD), which uses attention metrics to detect and mitigate potential jailbreak attacks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=TQ7Nuy1CSm", "content": "The authors propose an innovative \" Feint and Attack\" strategy, the Attention-Based Attack (ABA), which diverts the model's attention away from harmful content by embedding nested, benign prompts. This method is paired with the Attention-Based Defense (ABD), which uses attention metrics to detect and mitigate potential jailbreak attacks."}
+{"idx": 6, "title": "Revolutionizing AI Companion in FPS Games - OpenReview", "date": "", "ddg_snippet": "TL;DR: This paper introduces the first-ever open-interaction and real-time voice-operated AI companion system for commercial 3D FPS games.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=sHUstMPM6Z", "content": "TL;DR: This paper introduces the first-ever open-interaction and real-time voice-operated AI companion system for commercial 3D FPS games."}
+{"idx": 7, "title": "CERTAIN: Context Uncertainty-aware One-Shot Adaptation for...", "date": "", "ddg_snippet": "They often assume that the adaptation context is collected using task-specific behavior policies or through multiple rounds of collection. However, in real applications, the context should be collected by a policy in a one-shot manner to ensure efficiency and safety.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=cx9AnB8rzi", "content": "They often assume that the adaptation context is collected using task-specific behavior policies or through multiple rounds of collection. However, in real applications, the context should be collected by a policy in a one-shot manner to ensure efficiency and safety."}
+{"idx": 8, "title": "Iteratively Refined Behavior Regularization for Offline...", "date": "", "ddg_snippet": "The submitte paper proposes a novel approach for offline reinforcement learning overcoming some issues of naive behavior regularization. In particular, the authors propose conservative policy iteration to iteratively refine the reference policy for behavior regularization. The algorithm is analyzed theoretical and evaluated empirically on the D4RL benchmark on which it achieves very promising ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=50vyPuz0iv", "content": "The submitte paper proposes a novel approach for offline reinforcement learning overcoming some issues of naive behavior regularization. In particular, the authors propose conservative policy iteration to iteratively refine the reference policy for behavior regularization. The algorithm is analyzed theoretical and evaluated empirically on the D4RL benchmark on which it achieves very promising ..."}
+{"idx": 9, "title": "Packet header-based reweight-long short term memory (Rew-LSTM)...", "date": "", "ddg_snippet": "Based on this, we propose the Rew-LSTM classification model for experiments on publicly available datasets of encrypted traffic, and the results show that excellent results can be obtained when using only the data in packet headers for multiple classification, especially when the data is represented using bit, which outperforms state-of-the-art ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=pPaKzDbvdd", "content": "Based on this, we propose the Rew-LSTM classification model for experiments on publicly available datasets of encrypted traffic, and the results show that excellent results can be obtained when using only the data in packet headers for multiple classification, especially when the data is represented using bit, which outperforms state-of-the-art ..."}
diff --git a/data/sampled_jsons/siteopenreview.net_YjBrt82S3v_Section_5.4_PPO_instability.jsonl b/data/sampled_jsons/siteopenreview.net_YjBrt82S3v_Section_5.4_PPO_instability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c67905d6197430c1260bf8cc9c3a1635423303ed
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_YjBrt82S3v_Section_5.4_PPO_instability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Colored Noise in PPO : Improved Exploration and", "date": "", "ddg_snippet": "To avoid learning instabilities , PPO aims to prevent large policy updates. As an on-policy algorithm, PPO collects trajectories by interacting with the environment using the.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ZfC_e3HUUy", "content": "To avoid learning instabilities , PPO aims to prevent large policy updates. As an on-policy algorithm, PPO collects trajectories by interacting with the environment using the."}
+{"idx": 1, "title": "The Surprising Effectiveness of PPO in Cooperative", "date": "", "ddg_snippet": "In this section , we analyze five factors that we find are especially influential to MAPPO’s performance: value normalization, value function inputs, training data usage, policy/value clipping, and batch size. 5 . 4 PPO Clipping.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=YVXaxB6L2Pl", "content": "In this section , we analyze five factors that we find are especially influential to MAPPO’s performance: value normalization, value function inputs, training data usage, policy/value clipping, and batch size. 5 . 4 PPO Clipping."}
+{"idx": 2, "title": "Cppo: C ontinual L earning for r einforcement", "date": "", "ddg_snippet": "In this section , we analyze the stability of the CPPO, PPO , and PPO with the knowledge retention.We observe the instability of PPO on the sentiment text generation task, while CPPO can learn stably.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=86zAUE80pP", "content": "In this section , we analyze the stability of the CPPO, PPO , and PPO with the knowledge retention.We observe the instability of PPO on the sentiment text generation task, while CPPO can learn stably."}
+{"idx": 3, "title": "ITMPRec: Intention-based Targeted Multi-round Proactive ... - OpenReview", "date": "", "ddg_snippet": "Personalized user preference driven recommendations haveseam- lessly intertwined with our daily lives.However, item providers may expect specific items to gradually increase their appeal to users over the course of users' long-term interactions with the sys- tem, but few studies pay attention to this problem.In this paper, we propose a novel intention-based targeted multi-round proac- tive ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=MipDf3C38E", "content": "Personalized user preference driven recommendations haveseam- lessly intertwined with our daily lives.However, item providers may expect specific items to gradually increase their appeal to users over the course of users' long-term interactions with the sys- tem, but few studies pay attention to this problem.In this paper, we propose a novel intention-based targeted multi-round proac- tive ..."}
+{"idx": 4, "title": "MuSe-GNN: Learning Unified Gene Representation... | OpenReview", "date": "", "ddg_snippet": "Leveraging 82 training datasets from 10 tissues, three sequencing techniques, and three species, we create informative graph structures for model training and gene representations generation...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4UCktT9XZx", "content": "Leveraging 82 training datasets from 10 tissues, three sequencing techniques, and three species, we create informative graph structures for model training and gene representations generation..."}
+{"idx": 5, "title": "Inforcement L earning with L anguage M odels", "date": "", "ddg_snippet": "To address instabilities in PPO , we tuned the KL coefficient and included BC loss in the training objective. In contrast, on FO Text-Nav PPO performed comparably to ILQL and outperformed MC.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=8cNMMrWRbZ", "content": "To address instabilities in PPO , we tuned the KL coefficient and included BC loss in the training objective. In contrast, on FO Text-Nav PPO performed comparably to ILQL and outperformed MC."}
+{"idx": 6, "title": "Inverse-Q*: Token Level Reinforcement Learning for Aligning Large", "date": "", "ddg_snippet": "enhanced flexibility and easier implementation. faces challenges like optimization instability and.responses in PPO and DPO in the previous section . 232. 195. This modeling is subsequently used to optimize In this section , we aim to develop a novel strategy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=Ar6EquF1PB&name=pdf", "content": "enhanced flexibility and easier implementation. faces challenges like optimization instability and.responses in PPO and DPO in the previous section . 232. 195. This modeling is subsequently used to optimize In this section , we aim to develop a novel strategy."}
+{"idx": 7, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · Cited by 1 — Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YjBrt82S3v", "content": "by JS Byun · Cited by 1 — Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance."}
+{"idx": 8, "title": "D-fine: R Regression Task in Detrs As Fine Distribution Refinement", "date": "", "ddg_snippet": "ABSTRACT We introduce D-FINE, a powerful real-time object detector that achieves out-standing localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distri-bution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). FDR transforms the regression process from predicting fixed coor-dinates to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=MFZjrTFE7h", "content": "ABSTRACT We introduce D-FINE, a powerful real-time object detector that achieves out-standing localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distri-bution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). FDR transforms the regression process from predicting fixed coor-dinates to ..."}
+{"idx": 9, "title": "[height=1.8em]figs/suica.png SUICA117,184,85219,97,97: Learning Super ...", "date": "", "ddg_snippet": "Spatial Transcriptomics (ST) is a method that cap- tures gene expression profiles aligned with spatial coordinates. The discrete spatial distribution and the super-high dimensional sequencing results make ST data challenging to be modeled effec- tively. In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA, empowered by the great approxi- mation ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=XWC7JXHXvo", "content": "Spatial Transcriptomics (ST) is a method that cap- tures gene expression profiles aligned with spatial coordinates. The discrete spatial distribution and the super-high dimensional sequencing results make ST data challenging to be modeled effec- tively. In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA, empowered by the great approxi- mation ..."}
diff --git a/data/sampled_jsons/siteopenreview.net_lzdFImKK8w_Appendix_A.3.2_hardware_configuration_GPU.jsonl b/data/sampled_jsons/siteopenreview.net_lzdFImKK8w_Appendix_A.3.2_hardware_configuration_GPU.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7cf2d7707933df17effacb26eedb451f0f311022
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_lzdFImKK8w_Appendix_A.3.2_hardware_configuration_GPU.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Physics Informed Distillation for Diffusion Models | OpenReview", "date": "", "ddg_snippet": "The appendix contains no specification of hardware or software.Duly noted; the information could be included in the appendix . I think that, besides the number and model of GPUs , the appendix should at least cite PyTorch accordingly (Paske et al., 2019).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rOvaUsF996", "content": "The appendix contains no specification of hardware or software.Duly noted; the information could be included in the appendix . I think that, besides the number and model of GPUs , the appendix should at least cite PyTorch accordingly (Paske et al., 2019)."}
+{"idx": 1, "title": "A Hardware -Aware Framework for Accelerating Neural Architecture...", "date": "", "ddg_snippet": "Hardware Platform & Configuration . Lightweight Iterative NAS (LINAS) method. One-shot predictor method.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=HExcb-arUxq&name=main_paper_and_supplementary_material", "content": "Hardware Platform & Configuration . Lightweight Iterative NAS (LINAS) method. One-shot predictor method."}
+{"idx": 2, "title": "Mantis: Interleaved Multi-Image Instruction Tuning | OpenReview", "date": "", "ddg_snippet": "5. We added a detailed comparison with MIMIC-IT, which is also a multimodal instruction-following dataset, by comparing the performance between Otter and Mantis-Flamingo in Appendix A .4. This is requested by reviewer `Eh2y`.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=skLtdUVaJa", "content": "5. We added a detailed comparison with MIMIC-IT, which is also a multimodal instruction-following dataset, by comparing the performance between Otter and Mantis-Flamingo in Appendix A .4. This is requested by reviewer `Eh2y`."}
+{"idx": 3, "title": "A Hardware-Aware Framework for Accelerating Neural ...", "date": "", "ddg_snippet": "In this work we experiment on CPU, GPU , and mobile device platforms for evaluating our framework and we discuss the transferability of NAS results between CPU and GPU platforms in Appendix G. Our framework makes extensive use of predictors to accelerate the discovery of optimal sub-networks, particularly when applying LINAS.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=HExcb-arUxq", "content": "In this work we experiment on CPU, GPU , and mobile device platforms for evaluating our framework and we discuss the transferability of NAS results between CPU and GPU platforms in Appendix G. Our framework makes extensive use of predictors to accelerate the discovery of optimal sub-networks, particularly when applying LINAS."}
+{"idx": 4, "title": "Hardware-Friendly Post-Training Quantization: Input- and...", "date": "", "ddg_snippet": "Sep 21, 2023 · Post-training quantization enables swift quantization of neural networks using a minimal calibration dataset. Specifically, these methods tend to underperform dramatically on hardware with fixed...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=itJj6p7ssr", "content": "Sep 21, 2023 · Post-training quantization enables swift quantization of neural networks using a minimal calibration dataset. Specifically, these methods tend to underperform dramatically on hardware with fixed..."}
+{"idx": 5, "title": "REINFORCEMENT LEARNING THROUGH ASYN ACTOR-CRITIC ON A GPU", "date": "", "ddg_snippet": "ABSTRACT We introduce a hybrid CPU/ GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. We analyze its computational traits and con-centrate on aspects critical to leveraging the GPU ’s computational power. We introduce a system of queues and a dynamic scheduling strategy, potentially ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=r1VGvBcxl", "content": "ABSTRACT We introduce a hybrid CPU/ GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. We analyze its computational traits and con-centrate on aspects critical to leveraging the GPU ’s computational power. We introduce a system of queues and a dynamic scheduling strategy, potentially ..."}
+{"idx": 6, "title": "Decoding EEG With Spiking Neural Networks on Neuromorphic ...", "date": "", "ddg_snippet": "Jun 25, 2022 · Decoding motor activity accurately and reliably from electroencephalography (EEG) signals is essential for several portable brain-computer interface (BCI) applications ranging from neural prosthetics to the control of industrial and mobile robots. Spiking neural networks (SNNs) is an emerging brain-inspired architecture that is well-suited for decoding EEG signals due to their built-in ability ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ZPBJPGX3Bz", "content": "Jun 25, 2022 · Decoding motor activity accurately and reliably from electroencephalography (EEG) signals is essential for several portable brain-computer interface (BCI) applications ranging from neural prosthetics to the control of industrial and mobile robots. Spiking neural networks (SNNs) is an emerging brain-inspired architecture that is well-suited for decoding EEG signals due to their built-in ability ..."}
+{"idx": 7, "title": "Revisions | OpenReview", "date": "", "ddg_snippet": "Nov 12, 2024 · However, creating such graphics is extremely challenging for non-professionals, since the design process requires multifaceted skills and comprehensive knowledge. Thus, despite the many available authoring tools, a significant gap remains in enabling non-experts to produce compelling information graphics seamlessly, especially from scratch.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/revisions?id=kOQz35dIqN", "content": "Nov 12, 2024 · However, creating such graphics is extremely challenging for non-professionals, since the design process requires multifaceted skills and comprehensive knowledge. Thus, despite the many available authoring tools, a significant gap remains in enabling non-experts to produce compelling information graphics seamlessly, especially from scratch."}
+{"idx": 8, "title": "DreamShard: Generalizable Embedding Table", "date": "", "ddg_snippet": "combinations, ranging from 1X to 3X (Figure 12 in Appendix A . 3 . 2 ). Thus, we not only need to.1In this work, we focus on GPU devices, where all the GPU devices are identical, which is the most common configuration in our production.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=_atSgd9Np52", "content": "combinations, ranging from 1X to 3X (Figure 12 in Appendix A . 3 . 2 ). Thus, we not only need to.1In this work, we focus on GPU devices, where all the GPU devices are identical, which is the most common configuration in our production."}
+{"idx": 9, "title": "Table 3: Notations used in this paper.", "date": "", "ddg_snippet": "The hardware configurations are four GeForce RTX 2080 Ti GPUs and SuperServer Dual Intel Xeon CPU (Ten-core 2.20GHz) Processor. All the implementation details of the baselines and the proposed method are discussed below. For AGE [4], to procure...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=8aDG51pxFc&name=supplementary_material", "content": "The hardware configurations are four GeForce RTX 2080 Ti GPUs and SuperServer Dual Intel Xeon CPU (Ten-core 2.20GHz) Processor. All the implementation details of the baselines and the proposed method are discussed below. For AGE [4], to procure..."}
diff --git a/data/sampled_jsons/siteopenreview.net_qtuxDy2qEB_Algorithm_1_parallel_Picard.jsonl b/data/sampled_jsons/siteopenreview.net_qtuxDy2qEB_Algorithm_1_parallel_Picard.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4aab719fbb7274d20af96140cd3cc5544a8366f2
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_qtuxDy2qEB_Algorithm_1_parallel_Picard.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Parallel Simulation for Log-concave Sampling and... | OpenReview", "date": "", "ddg_snippet": "Our research introduces a novel parallel sampling algorithm that significantly reduces the number of sequential steps—known as adaptive complexity—required to generate high-quality samples.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qtuxDy2qEB&referrer=[the+profile+of+Masashi+Sugiyama](/profile?id=~Masashi_Sugiyama1)", "content": "Our research introduces a novel parallel sampling algorithm that significantly reduces the number of sequential steps—known as adaptive complexity—required to generate high-quality samples."}
+{"idx": 1, "title": "Accelerating Diffusion Models with Parallel Sampling", "date": "", "ddg_snippet": "In the parallelized inference algorithm for diffusion models in the probability ow ODE formulation, we adopt the same discretization scheme as in Section 3. 1 . 1 and the exponential integrator for all updating rules.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=F9NDzHQtOl", "content": "In the parallelized inference algorithm for diffusion models in the probability ow ODE formulation, we adopt the same discretization scheme as in Section 3. 1 . 1 and the exponential integrator for all updating rules."}
+{"idx": 2, "title": "Parallel Sampling of Diffusion Models | OpenReview", "date": "", "ddg_snippet": "ParaDiGMS is the first diffusion sampling method that enables trading compute for speed and is even compatible with existing fast sampling techniques such as DDIM and DPMSolver.TL;DR: We improve sampling speed of diffusion models by 2-4x using parallel computation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1PrnxRmXP3&referrer=[the+profile+of+Andy+Shih](/profile?id=~Andy_Shih1)", "content": "ParaDiGMS is the first diffusion sampling method that enables trading compute for speed and is even compatible with existing fast sampling techniques such as DDIM and DPMSolver.TL;DR: We improve sampling speed of diffusion models by 2-4x using parallel computation."}
+{"idx": 3, "title": "When Can We Learn General-Sum Markov Games with... | OpenReview", "date": "", "ddg_snippet": "Our algorithm for learning CE integrates an adversarial bandit subroutine which minimizes a weighted swap regret, along with several novel designs in the outer loop.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6MmiS0HUJHR", "content": "Our algorithm for learning CE integrates an adversarial bandit subroutine which minimizes a weighted swap regret, along with several novel designs in the outer loop."}
+{"idx": 4, "title": "Parallel Simulation for Log-concave Sampling and Score- ...", "date": "", "ddg_snippet": "1 May 2025 — The Algorithm 1 presented in the paper could provide a way to speed up the algorithms that sample from diffusion-based methods when the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qtuxDy2qEB&noteId=Q3HrXoYt2u", "content": "1 May 2025 — The Algorithm 1 presented in the paper could provide a way to speed up the algorithms that sample from diffusion-based methods when the ..."}
+{"idx": 5, "title": "Parallel Simulation for Log-concave Sampling and Score-based ...", "date": "", "ddg_snippet": "Algorithm 1 Parallel Picard Method for sampling . 1: Input: x0 ∼ µ0, approximate score function s ≈ ∇f, the number of the iterations in outer loop J, the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qtuxDy2qEB", "content": "Algorithm 1 Parallel Picard Method for sampling . 1: Input: x0 ∼ µ0, approximate score function s ≈ ∇f, the number of the iterations in outer loop J, the ..."}
+{"idx": 6, "title": "Isoperimetry and score - based diffusion", "date": "", "ddg_snippet": ", 512 the parallel Picard algorithm for diffusion models ( Algorithm 2) generates samples from satisfies the.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=6Gb7VfTKY7&name=pdf", "content": ", 512 the parallel Picard algorithm for diffusion models ( Algorithm 2) generates samples from satisfies the."}
+{"idx": 7, "title": "Hyperparameter Settings of RD", "date": "", "ddg_snippet": "For other algorithms including TD3BC, SAC-N-Unc and TD3-N-Unc, M is set to 1 /10 of the total training steps.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=mVywRIDNIl&name=supplementary_material", "content": "For other algorithms including TD3BC, SAC-N-Unc and TD3-N-Unc, M is set to 1 /10 of the total training steps."}
+{"idx": 8, "title": "FAFSA® Application | Federal Student Aid", "date": "", "ddg_snippet": "Any student, regardless of income, who wants to be considered for federal, state, and school financial aid programs. This includes grants, scholarships, work-study funds, and loans.", "subpage_snippet": "", "source": "studentaid.gov", "link": "https://studentaid.gov/h/apply-for-aid/fafsa", "content": "Any student, regardless of income, who wants to be considered for federal, state, and school financial aid programs. This includes grants, scholarships, work-study funds, and loans."}
+{"idx": 9, "title": "FAFSA® Application | Federal Student Aid", "date": "", "ddg_snippet": "Any student, regardless of income, who wants to be considered for federal, state, and school financial aid programs. This includes grants, scholarships, work-study funds, and loans.", "subpage_snippet": "", "source": "studentaid.gov", "link": "https://studentaid.gov/h/apply-for-aid/fafsa?os=shmmfp.refapp&ref=app", "content": "Any student, regardless of income, who wants to be considered for federal, state, and school financial aid programs. This includes grants, scholarships, work-study funds, and loans."}
diff --git a/data/sampled_jsons/sitepapercopilot.com_WWW_2025_Retrieval-Augmented_Generation_RAG.jsonl b/data/sampled_jsons/sitepapercopilot.com_WWW_2025_Retrieval-Augmented_Generation_RAG.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..451cead39a18a643d099d8e0a5dc2abcfef433c3
--- /dev/null
+++ b/data/sampled_jsons/sitepapercopilot.com_WWW_2025_Retrieval-Augmented_Generation_RAG.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AAAI 2025 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "Click for ALL available AAAI statistics How to interpret the columns above: - Count: The total number of submissions is calculated as: #Total = #Accept + #Reject + #Withdraw + #Desk Reject - #Post Decision Withdraw (when applicable). - Rates: Each status rate is computed as #Status Occurrence / #Total, where the status can be Accept, Reject, etc. For example, if there are 100 total submissions ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/aaai-paper-list/aaai-2025-paper-list/", "content": "Click for ALL available AAAI statistics How to interpret the columns above: - Count: The total number of submissions is calculated as: #Total = #Accept + #Reject + #Withdraw + #Desk Reject - #Post Decision Withdraw (when applicable). - Rates: Each status rate is computed as #Status Occurrence / #Total, where the status can be Accept, Reject, etc. For example, if there are 100 total submissions ..."}
+{"idx": 1, "title": "CORL 2025 Statistics - Paper Copilot", "date": "", "ddg_snippet": "Click for ALL available CORL statistics How to interpret the columns above: - Count: The total number of submissions is calculated as: #Total = #Accept + #Reject + #Withdraw + #Desk Reject - #Post Decision Withdraw (when applicable). - Rates: Each status rate is computed as #Status Occurrence / #Total, where the status can be Accept, Reject, etc. For example, if there are 100 total submissions ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/corl-statistics/corl-2025-statistics/", "content": "Click for ALL available CORL statistics How to interpret the columns above: - Count: The total number of submissions is calculated as: #Total = #Accept + #Reject + #Withdraw + #Desk Reject - #Post Decision Withdraw (when applicable). - Rates: Each status rate is computed as #Status Occurrence / #Total, where the status can be Accept, Reject, etc. For example, if there are 100 total submissions ..."}
+{"idx": 2, "title": "Material Generation - Paper Copilot", "date": "", "ddg_snippet": "Abstract We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural material captured under flashlight illumination, first it is converted into a latent material code. Then, in the second step, conditioned on the material code, our method produces an infinite and diverse ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/cat/cs/cg/materials-and-textures/material-generation/", "content": "Abstract We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural material captured under flashlight illumination, first it is converted into a latent material code. Then, in the second step, conditioned on the material code, our method produces an infinite and diverse ..."}
+{"idx": 3, "title": "Statistics - Paper Copilot", "date": "", "ddg_snippet": "Engineering & Computer Science Artificial Intelligence Computational Linguistics Computer Graphics Computer Networks & Wireless Communication Computer Vision & Pattern Recognition Data Mining & Analysis Databases & Information Systems Multimedia Robotics", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/", "content": "Engineering & Computer Science Artificial Intelligence Computational Linguistics Computer Graphics Computer Networks & Wireless Communication Computer Vision & Pattern Recognition Data Mining & Analysis Databases & Information Systems Multimedia Robotics"}
+{"idx": 4, "title": "AI - Paper Copilot", "date": "", "ddg_snippet": "Abstract Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability. This work offers a new generation paradigm that allows flexible control of the output image, such as spatial layout and palette, while maintaining the synthesis quality and model creativity. With compositionality as the core idea, we first ...", "subpage_snippet": "", "source": "staging-dapeng.papercopilot.com", "link": "https://staging-dapeng.papercopilot.com/cat/cs/ai/page/9/", "content": "Abstract Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability. This work offers a new generation paradigm that allows flexible control of the output image, such as spatial layout and palette, while maintaining the synthesis quality and model creativity. With compositionality as the core idea, we first ..."}
+{"idx": 5, "title": "Image Synthesis - Paper Copilot", "date": "", "ddg_snippet": "Experiments show that our framework outperforms previous methods for estimating individual scene components, which also enables various novel applications for augmented reality, such as photorealistic object insertion and material editing. Code and data will be made publicly available.", "subpage_snippet": "", "source": "staging.papercopilot.com", "link": "https://staging.papercopilot.com/cat/cs/cv/image-synthesis/page/7/", "content": "Experiments show that our framework outperforms previous methods for estimating individual scene components, which also enables various novel applications for augmented reality, such as photorealistic object insertion and material editing. Code and data will be made publicly available."}
+{"idx": 6, "title": "SIGGRAPH Statistics - Paper Copilot", "date": "", "ddg_snippet": "How to interpret the columns above: - Count: The total number of submissions is calculated as: #Total = #Accept + #Reject + #Withdraw + #Desk Reject - #Post Decision Withdraw (when applicable). - Rates: Each status rate is computed as #Status Occurrence / #Total, where the status can be Accept, Reject, etc. For example, if there are 100 total submissions and 27 were accepted, the Accept Rate ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/siggraph-statistics/", "content": "How to interpret the columns above: - Count: The total number of submissions is calculated as: #Total = #Accept + #Reject + #Withdraw + #Desk Reject - #Post Decision Withdraw (when applicable). - Rates: Each status rate is computed as #Status Occurrence / #Total, where the status can be Accept, Reject, etc. For example, if there are 100 total submissions and 27 were accepted, the Accept Rate ..."}
+{"idx": 7, "title": "NeurIPS Paper List - Paper Copilot", "date": "", "ddg_snippet": "Paper Copilot™, originally mypersonal project, is now open to the public. I deeply appreciate your feedback and support.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/neurips-paper-list/", "content": "Paper Copilot™, originally mypersonal project, is now open to the public. I deeply appreciate your feedback and support."}
+{"idx": 8, "title": "SIGGRAPH 2019 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 ↑ Frequency (zoomable and pannable chart) Daniele Panozzo Christian Theobalt Baoquan Chen Chenfanfu Jiang Daniel Cohen-Or Ming Gao Ravi Ramamoorthi Ankit Phogat Denis Zorin Diego Gutierrez Fredo Durand Gordon Wetzstein Justin Solomon Keenan Crane Marie-Paule Cani Matthew O'Toole Michael Gharbi Michael Zollhöfer Minchen Li Olga Sorkine ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/siggraph-paper-list/siggraph-2019-paper-list/", "content": "0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 ↑ Frequency (zoomable and pannable chart) Daniele Panozzo Christian Theobalt Baoquan Chen Chenfanfu Jiang Daniel Cohen-Or Ming Gao Ravi Ramamoorthi Ankit Phogat Denis Zorin Diego Gutierrez Fredo Durand Gordon Wetzstein Justin Solomon Keenan Crane Marie-Paule Cani Matthew O'Toole Michael Gharbi Michael Zollhöfer Minchen Li Olga Sorkine ..."}
+{"idx": 9, "title": "StyleGAN: A Style-Based Generator Architecture for Generative ...", "date": "", "ddg_snippet": "Paper Copilot™, originally my personal project, is now open to the public. I deeply appreciate your feedback and support.", "subpage_snippet": "", "source": "staging-dapeng.papercopilot.com", "link": "https://staging-dapeng.papercopilot.com/paper/stylegan-a-style-based-generator-architecture-for-generative-adversarial-networks/", "content": "Paper Copilot™, originally my personal project, is now open to the public. I deeply appreciate your feedback and support."}
diff --git a/data/sampled_jsons/sitepapers.nips.cc_Papamakarios_Masked_Autoregressive_Flow_abstract.jsonl b/data/sampled_jsons/sitepapers.nips.cc_Papamakarios_Masked_Autoregressive_Flow_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9dda86699083b905787df981f1b1d9c66b90455c
--- /dev/null
+++ b/data/sampled_jsons/sitepapers.nips.cc_Papamakarios_Masked_Autoregressive_Flow_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Home | IMDB v2.3", "date": "", "ddg_snippet": "IMDB v2.3 Internet Media Discussion Boards Home Help Rules Search Welcome Guest. Please Login or Register.", "subpage_snippet": "", "source": "imdb2.freeforums.net", "link": "https://imdb2.freeforums.net/", "content": "IMDB v2.3 Internet Media Discussion Boards Home Help Rules Search Welcome Guest. Please Login or Register."}
+{"idx": 1, "title": "Film General | IMDB v2.3 - ProBoards", "date": "", "ddg_snippet": "Film topics not covered in the boards below.", "subpage_snippet": "", "source": "imdb2.freeforums.net", "link": "https://imdb2.freeforums.net/board/16/film-general", "content": "Film topics not covered in the boards below."}
+{"idx": 2, "title": "IMDB2 Information | IMDB v2.3 - ProBoards", "date": "", "ddg_snippet": "Visit our forum at: imdb2.freeforums.net", "subpage_snippet": "", "source": "imdb2.freeforums.net", "link": "https://imdb2.freeforums.net/board/123/imdb2-information", "content": "Visit our forum at: imdb2.freeforums.net"}
+{"idx": 3, "title": "Sports | IMDB v2.3", "date": "", "ddg_snippet": "Visit our forum at: imdb2.freeforums.net", "subpage_snippet": "", "source": "imdb2.freeforums.net", "link": "https://imdb2.freeforums.net/board/120/sports", "content": "Visit our forum at: imdb2.freeforums.net"}
+{"idx": 4, "title": "Together (2025) review | IMDB v2.3", "date": "", "ddg_snippet": "Aug 1, 2025 · This is a crazy movie. I mostly enjoyed it, but there was a big issue of me predicting pretty much everything aside from the very ending. I had a feeling where it was all leading, but the ending w", "subpage_snippet": "", "source": "imdb2.freeforums.net", "link": "https://imdb2.freeforums.net/thread/339373/2025-review", "content": "Aug 1, 2025 · This is a crazy movie. I mostly enjoyed it, but there was a big issue of me predicting pretty much everything aside from the very ending. I had a feeling where it was all leading, but the ending w"}
+{"idx": 5, "title": "Conservative commentator Charlie Kirk shot at rally | IMDB v2.3", "date": "", "ddg_snippet": "Sep 10, 2025 · The actual shot is blurred out, but it rumors are that it was a critical blow. I found an unblurred video and blood pours out of his neck.", "subpage_snippet": "", "source": "imdb2.freeforums.net", "link": "https://imdb2.freeforums.net/thread/339820/conservative-commentator-charlie-kirk-rally", "content": "Sep 10, 2025 · The actual shot is blurred out, but it rumors are that it was a critical blow. I found an unblurred video and blood pours out of his neck."}
+{"idx": 6, "title": "Which films did you see last week? (09.07.2025 - 09.13.2025) |...", "date": "", "ddg_snippet": "Sep 16, 2025 · Hello, good folks of FG. The weekly film discussion thread is back. The topic under discussion is made clear in the title. Made-for-TV, direct-to-video, streaming, TV series/episodes, documenta", "subpage_snippet": "", "source": "imdb2.freeforums.net", "link": "https://imdb2.freeforums.net/thread/339880/which-films-last-week-2025", "content": "Sep 16, 2025 · Hello, good folks of FG. The weekly film discussion thread is back. The topic under discussion is made clear in the title. Made-for-TV, direct-to-video, streaming, TV series/episodes, documenta"}
+{"idx": 7, "title": "50th anniversary of \"Dog Day Afternoon\" - September 21st | IMDB...", "date": "", "ddg_snippet": "4 days ago · Any fans of this classic Sidney Lumet film starring Al Pacino and John Cazale? I haven't seen it, but I know it's been critically acclaimed and a career high for Lumet, Pacino, and Cazale", "subpage_snippet": "", "source": "imdb2.freeforums.net", "link": "https://imdb2.freeforums.net/thread/339931/50th-anniversary-afternoon-september-21st", "content": "4 days ago · Any fans of this classic Sidney Lumet film starring Al Pacino and John Cazale? I haven't seen it, but I know it's been critically acclaimed and a career high for Lumet, Pacino, and Cazale"}
+{"idx": 8, "title": "My 100 Favorite actors: #98 - Willem Dafoe | IMDB v2.3", "date": "", "ddg_snippet": "Sep 5, 2025 · I first noticed him in Platoon when we were shown the movie in history class in High School. Been a fan ever since. Such a diverse filmography. What are your 5 or 10 favorite Willem Dafoe movie", "subpage_snippet": "", "source": "imdb2.freeforums.net", "link": "https://imdb2.freeforums.net/thread/339743/100-favorite-actors-willem-dafoe", "content": "Sep 5, 2025 · I first noticed him in Platoon when we were shown the movie in history class in High School. Been a fan ever since. Such a diverse filmography. What are your 5 or 10 favorite Willem Dafoe movie"}
+{"idx": 9, "title": "My 75 Favorite actresses: #64 - Toni Collette | IMDB v2.3", "date": "", "ddg_snippet": "Sep 11, 2025 · First saw her in the movie Emma in the late 1990s. What are your 5 or 10 favorite movies from Toni Collette and what are your favorite performances from her? Her best performance i", "subpage_snippet": "", "source": "imdb2.freeforums.net", "link": "https://imdb2.freeforums.net/thread/339833/50-favorite-actresses-toni-collette", "content": "Sep 11, 2025 · First saw her in the movie Emma in the late 1990s. What are your 5 or 10 favorite movies from Toni Collette and what are your favorite performances from her? Her best performance i"}
diff --git a/data/sampled_jsons/sitepaperswithcode.com_MLD_HumanAct12_FID.jsonl b/data/sampled_jsons/sitepaperswithcode.com_MLD_HumanAct12_FID.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7c171b28724befd8cae1037c4f03109244b46e35
--- /dev/null
+++ b/data/sampled_jsons/sitepaperswithcode.com_MLD_HumanAct12_FID.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "#2 best model for Motion Synthesis on HumanAct 12 (Accuracy metric)", "date": "", "ddg_snippet": "REMOVE. Motion Synthesis. HumanAct 12 . MLD . FID . 0.077.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/executing-your-commands-via-motion-diffusion?ref=taskswithcode.ghost.io", "content": "REMOVE. Motion Synthesis. HumanAct 12 . MLD . FID . 0.077."}
+{"idx": 1, "title": "Paper tables with annotated results for Executing... | Papers With Code", "date": "", "ddg_snippet": "Table 3: Comparison of action-conditional motion synthesis on UESTC [ji2018large] and HumanAct 12 [guo2020action2motion] dataset: FIDtrain, FIDtrain indicate the evaluated splits.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/executing-your-commands-via-motion-diffusion/review/?hl=87197", "content": "Table 3: Comparison of action-conditional motion synthesis on UESTC [ji2018large] and HumanAct 12 [guo2020action2motion] dataset: FIDtrain, FIDtrain indicate the evaluated splits."}
+{"idx": 2, "title": "HumanAct 12 Benchmark (Motion Synthesis) | Papers With Code", "date": "", "ddg_snippet": "The current state-of-the-art on HumanAct 12 is MDM. See a full comparison of 2 papers with code.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/sota/motion-synthesis-on-humanact12?p=human-motion-diffusion-model", "content": "The current state-of-the-art on HumanAct 12 is MDM. See a full comparison of 2 papers with code."}
+{"idx": 3, "title": "SOTA for Motion Synthesis on HumanAct 12 (Accuracy metric)", "date": "", "ddg_snippet": "- Motion Synthesis. HumanAct 12 . MDM. FID .Add or remove other datasets used in this paper: E.T. the Exceptional Trajectories HumanAct 12 HumanML3D InterHuman Inter-X KIT Motion-Language Motion-X. Paper introduces a new dataset?", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/human-motion-diffusion-model", "content": "- Motion Synthesis. HumanAct 12 . MDM. FID .Add or remove other datasets used in this paper: E.T. the Exceptional Trajectories HumanAct 12 HumanML3D InterHuman Inter-X KIT Motion-Language Motion-X. Paper introduces a new dataset?"}
+{"idx": 4, "title": "Paper tables with annotated results for... | Papers With Code", "date": "", "ddg_snippet": "HumanAct 12 (Fine-grained). Methods. FID ↓. Accuracy↑. Diversity→. MModality→. FID ↓.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/action2video-generating-videos-of-human-3d/review/", "content": "HumanAct 12 (Fine-grained). Methods. FID ↓. Accuracy↑. Diversity→. MModality→. FID ↓."}
+{"idx": 5, "title": "Paper tables with annotated results for Human... | Papers With Code", "date": "", "ddg_snippet": "Table 3: Evaluation of action-to-motion on the HumanAct 12 dataset. Our model leads the board in three out of four metrics. Ground-truth evaluation results are slightly different for each of the works, due to implementation differences, such as python package versions.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/human-motion-diffusion-model/review/?hl=121235", "content": "Table 3: Evaluation of action-to-motion on the HumanAct 12 dataset. Our model leads the board in three out of four metrics. Ground-truth evaluation results are slightly different for each of the works, due to implementation differences, such as python package versions."}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitepeihuali.org_ImagineFSL_Table_Flowers_DISEF.jsonl b/data/sampled_jsons/sitepeihuali.org_ImagineFSL_Table_Flowers_DISEF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..18b8d384797a762dc7c364c2467da08f82cf0d41
--- /dev/null
+++ b/data/sampled_jsons/sitepeihuali.org_ImagineFSL_Table_Flowers_DISEF.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "To overcome this limitation, we frame synthetic images as an imagined base set (iBase) , i.e., an independent, large-scale synthetic dataset encompassing diverse concepts. Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks.", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/ImagineFSL/index.html", "content": "To overcome this limitation, we frame synthetic images as an imagined base set (iBase) , i.e., an independent, large-scale synthetic dataset encompassing diverse concepts. Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks."}
+{"idx": 1, "title": "Welcome to Vision by Statistical Learning lab - Peihua Li", "date": "", "ddg_snippet": "Representative publications Selected publications by area My google scholar citations", "subpage_snippet": "", "source": "www.peihuali.org", "link": "https://www.peihuali.org/Publications.html", "content": "Representative publications Selected publications by area My google scholar citations"}
+{"idx": 2, "title": "Peihua Li - Welcome to Vision by Learning lab", "date": "", "ddg_snippet": "May 1, 2025 · Peihua Li is a professor of Dalian University of Technology. He received Ph.D degree from Harbin Institute of Technology in 2003, and then worked for one year as a postdoctoral fellow at INRIA/IRISA, France. He achieved the honorary nomination of National Excellent Doctoral dissertation in China. He was supported by Program for New Century Excellent Talents in University of Chinese Ministry of ...", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/", "content": "May 1, 2025 · Peihua Li is a professor of Dalian University of Technology. He received Ph.D degree from Harbin Institute of Technology in 2003, and then worked for one year as a postdoctoral fellow at INRIA/IRISA, France. He achieved the honorary nomination of National Excellent Doctoral dissertation in China. He was supported by Program for New Century Excellent Talents in University of Chinese Ministry of ..."}
+{"idx": 3, "title": "李培华教授的主页 - Peihua Li", "date": "", "ddg_snippet": "李培华教授的主页 - Peihua Li ... 李培华教授的主页", "subpage_snippet": "", "source": "peihuali.org", "link": "http://peihuali.org/Publications_ch.html", "content": "李培华教授的主页 - Peihua Li ... 李培华教授的主页"}
+{"idx": 4, "title": "欢迎访问李培华教授主页 - Peihua Li", "date": "", "ddg_snippet": "May 1, 2025 · 实验室新闻 2025年5月1日 一篇基于布朗距离协方差和语言-图像对比模型 (CLIP)的少样本视频识别论文被ICML 2025录用 (poster). 2025年2月26日 一篇基于大语言模型和多模态基础模型的少样本图像识别论文被CVPR 2025录用(highlight). 一篇关于跨域视频少样本识别的论文被CVPR 2025录用为墙报展示 (poster). 2024年9月26日 ...", "subpage_snippet": "", "source": "peihuali.org", "link": "http://peihuali.org/home_ch.html", "content": "May 1, 2025 · 实验室新闻 2025年5月1日 一篇基于布朗距离协方差和语言-图像对比模型 (CLIP)的少样本视频识别论文被ICML 2025录用 (poster). 2025年2月26日 一篇基于大语言模型和多模态基础模型的少样本图像识别论文被CVPR 2025录用(highlight). 一篇关于跨域视频少样本识别的论文被CVPR 2025录用为墙报展示 (poster). 2024年9月26日 ..."}
+{"idx": 5, "title": "Local Log-Euclidean Multivariate Gaussian Descriptor and Its ...", "date": "", "ddg_snippet": "Abstract—This paper presents a novel image descriptor to effectively characterize the local, high-order image statistics. Our work is inspired by the Diffusion Tensor Imaging and the structure tensor method (or covariance descriptor), and motivated by popular distribution-based descriptors such as SIFT and HoG. Our idea is to associate one pixel with a multivariate Gaussian distribution ...", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/publications/L2EMG/TPAMI_L2EMG.pdf", "content": "Abstract—This paper presents a novel image descriptor to effectively characterize the local, high-order image statistics. Our work is inspired by the Diffusion Tensor Imaging and the structure tensor method (or covariance descriptor), and motivated by popular distribution-based descriptors such as SIFT and HoG. Our idea is to associate one pixel with a multivariate Gaussian distribution ..."}
+{"idx": 6, "title": "Welcome to Vision and Learning lab!", "date": "", "ddg_snippet": "目标和轮廓跟踪 (国家自然科学基金资助 (NSFC), 教育部新世纪优秀人才支持计划项目)", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/Projects_ch.html", "content": "目标和轮廓跟踪 (国家自然科学基金资助 (NSFC), 教育部新世纪优秀人才支持计划项目)"}
diff --git a/data/sampled_jsons/siteproceedings.mlr.press_Orecchia_2022_computing_environment_experiments.jsonl b/data/sampled_jsons/siteproceedings.mlr.press_Orecchia_2022_computing_environment_experiments.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..621d0b02b6b70a1e2e86a7dded9b5a71f0803993
--- /dev/null
+++ b/data/sampled_jsons/siteproceedings.mlr.press_Orecchia_2022_computing_environment_experiments.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Proceedings of Machine Learning Research | Proceedings of the 39th...", "date": "", "ddg_snippet": "EAT-C: Environment -Adversarial sub-Task Curriculum for Efficient Reinforcement Learning. Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:822-843.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/", "content": "EAT-C: Environment -Adversarial sub-Task Curriculum for Efficient Reinforcement Learning. Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:822-843."}
+{"idx": 1, "title": "UnderGrad: A Universal Black-Box Optimization Method with ...", "date": "", "ddg_snippet": "by K Antonakopoulos · 2022 · Cited by 9 — Universal methods for optimization are designed to achieve theoretically optimal convergence rates without any prior knowledge of the problem's. 24 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/antonakopoulos22b/antonakopoulos22b.pdf", "content": "by K Antonakopoulos · 2022 · Cited by 9 — Universal methods for optimization are designed to achieve theoretically optimal convergence rates without any prior knowledge of the problem's. 24 pages"}
+{"idx": 2, "title": "Accelerated Parameter-Free Stochastic Optimization", "date": "", "ddg_snippet": "by I Kreisler · 2024 · Cited by 9 — Preliminary experiments indicate that, in the settings we study, cosine decay is not significantly better than a constant schedule combined with iterate ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/kreisler24a/kreisler24a.pdf", "content": "by I Kreisler · 2024 · Cited by 9 — Preliminary experiments indicate that, in the settings we study, cosine decay is not significantly better than a constant schedule combined with iterate ..."}
+{"idx": 3, "title": "LinSATNet: The Positive Linear Satisfiability Neural Networks", "date": "", "ddg_snippet": "by R Wang · 2023 · Cited by 33 — ( 2022 ). In this paper, we extend the scope of Sinkhorn algorithm to enforce the satisfiability of the more general positive linear constraints (Luby & Nisan ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/wang23at/wang23at.pdf", "content": "by R Wang · 2023 · Cited by 33 — ( 2022 ). In this paper, we extend the scope of Sinkhorn algorithm to enforce the satisfiability of the more general positive linear constraints (Luby & Nisan ..."}
+{"idx": 4, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and...", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17071-17093, 2022 .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a.html", "content": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17071-17093, 2022 ."}
+{"idx": 5, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and...", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17071-17093, 2022 .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a", "content": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17071-17093, 2022 ."}
+{"idx": 6, "title": "Practical Nearly-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis 1 1 Lorenzo Orecchia", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a/orecchia22a.pdf", "content": "Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis 1 1 Lorenzo Orecchia"}
+{"idx": 7, "title": "Using Large Language Models to Simulate Multiple Humans and ...", "date": "", "ddg_snippet": "We design a methodology for simulating TEs and illustrate its use to compare how well different language models are able to reproduce classic economic, psycholinguistic, and social psychology experiments : Ultimatum Game, Garden Path Sentences, Milgram Shock Experiment , and Wisdom of Crowds.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/aher23a.html", "content": "We design a methodology for simulating TEs and illustrate its use to compare how well different language models are able to reproduce classic economic, psycholinguistic, and social psychology experiments : Ultimatum Game, Garden Path Sentences, Milgram Shock Experiment , and Wisdom of Crowds."}
+{"idx": 8, "title": "Measuring the Effect of Training Data on Deep Learning ...", "date": "", "ddg_snippet": "%0 Conference Paper %T Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments %A Jinkun Lin %A Anqi Zhang %A Mathias Lécuyer %A Jinyang Li %A Aurojit Panda %A Siddhartha Sen %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/lin22h.html", "content": "%0 Conference Paper %T Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments %A Jinkun Lin %A Anqi Zhang %A Mathias Lécuyer %A Jinyang Li %A Aurojit Panda %A Siddhartha Sen %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E ..."}
+{"idx": 9, "title": "On Acceleration with Noise-Corrupted Gradients", "date": "", "ddg_snippet": "This paper provides two main technical contributions: (i) a new accelerated method AGDP that generalizes Nesterov’s AGD and improves on the recent method AXGD (Diakonikolas & Orecchia , 2018), and (ii) a theoretical study of accelerated algorithms under noisy and inexact gradient oracles, which is supported by numerical experiments .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v80/cohen18a.html", "content": "This paper provides two main technical contributions: (i) a new accelerated method AGDP that generalizes Nesterov’s AGD and improves on the recent method AXGD (Diakonikolas & Orecchia , 2018), and (ii) a theoretical study of accelerated algorithms under noisy and inexact gradient oracles, which is supported by numerical experiments ."}
diff --git a/data/sampled_jsons/siteproceedings.mlr.pressv162orecchia22a_experiments_section_machine.jsonl b/data/sampled_jsons/siteproceedings.mlr.pressv162orecchia22a_experiments_section_machine.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bfaaa3c86b4a849083d767e3478460eb65e0a7f4
--- /dev/null
+++ b/data/sampled_jsons/siteproceedings.mlr.pressv162orecchia22a_experiments_section_machine.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "Practical Nearly-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "by L Orecchia · 2022 · Cited by 9 — All experiments were conducted on an institutional cluster on machines with 24 Cores (2x 24 core Intel Xeon Silver 4116 CPU @ 2.10GHz), 48 threads and 128GB RAM ... 23 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a/orecchia22a.pdf", "content": "by L Orecchia · 2022 · Cited by 9 — All experiments were conducted on an institutional cluster on machines with 24 Cores (2x 24 core Intel Xeon Silver 4116 CPU @ 2.10GHz), 48 threads and 128GB RAM ... 23 pages"}
+{"idx": 1, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and...", "date": "", "ddg_snippet": "Lorenzo Orecchia, Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17071-17093, 2022.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a", "content": "Lorenzo Orecchia, Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17071-17093, 2022."}
+{"idx": 2, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and...", "date": "", "ddg_snippet": "Lorenzo Orecchia, Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17071-17093, 2022.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a.html", "content": "Lorenzo Orecchia, Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17071-17093, 2022."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitesemanticscholar.org_Character_animation_in_two-player_adversarial_games_abstract_year_2010.jsonl b/data/sampled_jsons/sitesemanticscholar.org_Character_animation_in_two-player_adversarial_games_abstract_year_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9696fad0af771685119941c93ebd2ad1c8a572b2
--- /dev/null
+++ b/data/sampled_jsons/sitesemanticscholar.org_Character_animation_in_two-player_adversarial_games_abstract_year_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Character animation in two-player adversarial games", "date": "", "ddg_snippet": "The approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ both long-term planning and an intelligent use of randomness, exhibiting nuanced strategies based on unpredictability. The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Character-animation-in-two-player-adversarial-games-Wampler-Andersen/aab9ee829c871af99ca5c91dbf594ad9e0674a79", "content": "The approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ both long-term planning and an intelligent use of randomness, exhibiting nuanced strategies based on unpredictability. The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games ..."}
+{"idx": 1, "title": "Simulating Multiple Character Interactions with Collaborative ...", "date": "", "ddg_snippet": "This paper proposes a new methodology for synthesizing animations of multiple characters, allowing them to intelligently compete with one another in dense environments, while still satisfying requirements set by an animator. This paper proposes a new methodology for synthesizing animations of multiple characters, allowing them to intelligently compete with one another in dense environments ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Simulating-Multiple-Character-Interactions-with-and-Shum-Komura/3b6b9d074350435a24df4c982245d703db39fc3e", "content": "This paper proposes a new methodology for synthesizing animations of multiple characters, allowing them to intelligently compete with one another in dense environments, while still satisfying requirements set by an animator. This paper proposes a new methodology for synthesizing animations of multiple characters, allowing them to intelligently compete with one another in dense environments ..."}
+{"idx": 2, "title": "Virtual ball player - Semantic Scholar", "date": "", "ddg_snippet": "This work captures the motion that mimics controlling a ball using various interaction patterns and synthesizes the trajectory of a virtual ball by analyzing the captured motion and correct the character motion to be fitted to the synthesized trajectory of the virtual ball. It is very difficult and tedious work to synthesize an animation in which a character skillfully controls several balls ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Virtual-ball-player-Choi-Kang/73f2fd75174287fa2b1565e9429845473369700d", "content": "This work captures the motion that mimics controlling a ball using various interaction patterns and synthesizes the trajectory of a virtual ball by analyzing the captured motion and correct the character motion to be fitted to the synthesized trajectory of the virtual ball. It is very difficult and tedious work to synthesize an animation in which a character skillfully controls several balls ..."}
+{"idx": 3, "title": "[PDF] Formalizing Feint Actions, and Example Studies in Two ...", "date": "", "ddg_snippet": "This work provides the first comprehensive and concrete formalization of Feint actions, which allows automatic generation of Feint actions, via the proposed Palindrome-directed Generation of Feint actions, and provides concrete principles to properly combine Feint and attack actions. Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Formalizing-Feint-Actions,-and-Example-Studies-in-Liu-Jin/1630f39b57450d1498bb1f9adf4a9344a8474be1", "content": "This work provides the first comprehensive and concrete formalization of Feint actions, which allows automatic generation of Feint actions, via the proposed Palindrome-directed Generation of Feint actions, and provides concrete principles to properly combine Feint and attack actions. Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from ..."}
+{"idx": 4, "title": "Application of ChatGPT-Based Digital Human in Animation ...", "date": "", "ddg_snippet": "by C Lan · Cited by 27 — In this paper, we present an approach that employs a digital person's animation environment. We utilized Unity plug-ins to drive the character's ...", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/354d/264b14fce01b5d00eadbce2d84be63a62f37.pdf", "content": "by C Lan · Cited by 27 — In this paper, we present an approach that employs a digital person's animation environment. We utilized Unity plug-ins to drive the character's ..."}
+{"idx": 5, "title": "Evaluating Realism for Virtual Supporting Characters", "date": "", "ddg_snippet": "by J Skorupski · Cited by 1 — Convincing and compelling virtual environments that are populated with rich characters demand consistent, nuanced, and realistic behavior that is integrated ...", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/6598/65c9fe2ecb9722921eb6da4a4c1561df7b6f.pdf", "content": "by J Skorupski · Cited by 1 — Convincing and compelling virtual environments that are populated with rich characters demand consistent, nuanced, and realistic behavior that is integrated ..."}
+{"idx": 6, "title": "E. Herbst | Semantic Scholar", "date": "", "ddg_snippet": "Alert. Cite. Character animation in two - player adversarial games .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/author/E.-Herbst/6376655", "content": "Alert. Cite. Character animation in two - player adversarial games ."}
+{"idx": 7, "title": "[PDF] Simulating competitive interactions using... | Semantic Scholar", "date": "", "ddg_snippet": "Character animation in two - player adversarial games . K. WamplerErik AndersenE. HerbstYongjoon LeeZoran Popovic.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Simulating-competitive-interactions-using-singly-Shum-Komura/1a0f59ba31bf3fef9fdef0a35b69df1c1802f746", "content": "Character animation in two - player adversarial games . K. WamplerErik AndersenE. HerbstYongjoon LeeZoran Popovic."}
+{"idx": 8, "title": "Award-Winning Papers: HCI | Semantic Scholar", "date": "", "ddg_snippet": "Existing simulations of redirected reaching are limited, however, and have not yet incorporated important stochastic characteristics like ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/product/scholars-hub/award-winning-hci", "content": "Existing simulations of redirected reaching are limited, however, and have not yet incorporated important stochastic characteristics like ..."}
+{"idx": 9, "title": "The trial of galileo: a game of motion graphs - Semantic Scholar", "date": "", "ddg_snippet": "This paper presents a 2D game designed to assist students in better understanding motion graphs, enabling the player to control the actions of a character by creating a motion graph. This paper presents a 2D game designed to assist students in better understanding motion graphs. In this game the player 's character is not controlled by a joystick or control pad. Instead the game employs a ...", "subpage_snippet": "", "source": "semanticscholar.org", "link": "https://semanticscholar.org/paper/The-trial-of-galileo:-a-game-of-motion-graphs-Pommer-Flaherty/b1bccaa87893de26d850bc86780ff68258cf9b43", "content": "This paper presents a 2D game designed to assist students in better understanding motion graphs, enabling the player to control the actions of a character by creating a motion graph. This paper presents a 2D game designed to assist students in better understanding motion graphs. In this game the player 's character is not controlled by a joystick or control pad. Instead the game employs a ..."}
diff --git a/data/sampled_jsons/spatiotemporal_reasoning_models.jsonl b/data/sampled_jsons/spatiotemporal_reasoning_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cde3e0d9520ae23472610172c59bcc9401737e26
--- /dev/null
+++ b/data/sampled_jsons/spatiotemporal_reasoning_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning ...", "date": "", "ddg_snippet": "De-spite advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs), their capacity to reason about complex spatiotemporal signals remains underexplored.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.11618", "content": "De-spite advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs), their capacity to reason about complex spatiotemporal signals remains underexplored."}
+{"idx": 1, "title": "Paper tables with annotated results for Spatiotemporal Relationship...", "date": "", "ddg_snippet": "In recent years, various models have been developed based on convolution operations for prediction or forecasting, but they lack the ability to reason over spatiotemporal data and infer the...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/spatiotemporal-relationship-reasoning-for/review/", "content": "In recent years, various models have been developed based on convolution operations for prediction or forecasting, but they lack the ability to reason over spatiotemporal data and infer the..."}
+{"idx": 2, "title": "Querying and Reasoning for Spatiotemporal Data Mining | SpringerLink", "date": "", "ddg_snippet": "SQLST: A spatiotemporal data model and query language. In Proceedings of the 19th International Conference on Conceptual Modeling (ER’00), pp. 96–111. Springer, 2000.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-540-75177-9_13", "content": "SQLST: A spatiotemporal data model and query language. In Proceedings of the 19th International Conference on Conceptual Modeling (ER’00), pp. 96–111. Springer, 2000."}
+{"idx": 3, "title": "Spatiotemporal Reasoning : Understanding Space and Time", "date": "", "ddg_snippet": "Researchers have proposed various cognitive models to explain how we perform spatiotemporal reasoning , and neuroimaging studies have identified specific brain regions involved in...", "subpage_snippet": "", "source": "alljournals.blog", "link": "https://alljournals.blog/spatiotemporal-reasoning/", "content": "Researchers have proposed various cognitive models to explain how we perform spatiotemporal reasoning , and neuroimaging studies have identified specific brain regions involved in..."}
+{"idx": 4, "title": "STaR: Unified Spatiotemporal -Causal Graph Reasoning for...", "date": "", "ddg_snippet": "These results highlight STaR as a universal, interpretable, and scalable platform for spatiotemporal reasoning , with broad applicability across molecular modeling , scientific discovery...", "subpage_snippet": "", "source": "www.techrxiv.org", "link": "https://www.techrxiv.org/doi/full/10.36227/techrxiv.175037376.67268618/v1", "content": "These results highlight STaR as a universal, interpretable, and scalable platform for spatiotemporal reasoning , with broad applicability across molecular modeling , scientific discovery..."}
+{"idx": 5, "title": "Paper Review: Visualization-of-Thought Elicits Spatial Reasoning in...", "date": "", "ddg_snippet": "Models show an innate ability for visual tracking, particularly in tasks requiring spatiotemporal reasoning . 2D grid input is more likely to activate this innate capability than natural...", "subpage_snippet": "", "source": "artgor.medium.com", "link": "https://artgor.medium.com/paper-review-visualization-of-thought-elicits-spatial-reasoning-in-large-language-models-25c1b3e98f0c", "content": "Models show an innate ability for visual tracking, particularly in tasks requiring spatiotemporal reasoning . 2D grid input is more likely to activate this innate capability than natural..."}
+{"idx": 6, "title": "(PDF) Querying and Reasoning for Spatiotemporal Data Mining", "date": "", "ddg_snippet": "The goal was to envisage a new generation of GISs, characterized by enhanced capabilities to sup- port spatiotemporal reasoning and semantic integration of diverse data models .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/23622256/Querying_and_Reasoning_for_Spatiotemporal_Data_Mining", "content": "The goal was to envisage a new generation of GISs, characterized by enhanced capabilities to sup- port spatiotemporal reasoning and semantic integration of diverse data models ."}
+{"idx": 7, "title": "The Future of Reasoning with LLMs | by Murli Sivashanmugam | a5gnet", "date": "", "ddg_snippet": "Spatiotemporal reasoning involves understanding and reasoning about relations in time and space. It enables tasks such as calculating travel time based on distance and speed or predicting...", "subpage_snippet": "", "source": "blog.a5gnet.com", "link": "https://blog.a5gnet.com/the-future-of-reasoning-with-llms-d47c479707c3", "content": "Spatiotemporal reasoning involves understanding and reasoning about relations in time and space. It enables tasks such as calculating travel time based on distance and speed or predicting..."}
+{"idx": 8, "title": "Spatial-temporal Reasoning | Engati", "date": "", "ddg_snippet": "Spatiotemporal reasoning is key for problem-solving and organizational skills. It is used in computer science to help robots understand and navigate time and space.", "subpage_snippet": "", "source": "www.engati.ai", "link": "https://www.engati.ai/glossary/spatial-temporal-reasoning", "content": "Spatiotemporal reasoning is key for problem-solving and organizational skills. It is used in computer science to help robots understand and navigate time and space."}
+{"idx": 9, "title": "VLM4D: Towards Spatiotemporal Awareness in Vision Language...", "date": "", "ddg_snippet": "Spatiotemporal (4D) Awareness. Humans intuitively reason in 4D (3D space + time), effortlessly reconstructing the dynamic spatial trajectory of moving objects from any perspective.", "subpage_snippet": "", "source": "vlm4d.github.io", "link": "https://vlm4d.github.io/", "content": "Spatiotemporal (4D) Awareness. Humans intuitively reason in 4D (3D space + time), effortlessly reconstructing the dynamic spatial trajectory of moving objects from any perspective."}
diff --git a/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_architecture_year_2024.jsonl b/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_architecture_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ae1ef82e61f20ddb197978763275f2b1335b255
--- /dev/null
+++ b/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_architecture_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Combining long and short spatiotemporal reasoning for deep ...", "date": "", "ddg_snippet": "Based on this, this study offers a deep reinforcement learning network model that greatly improves sample efficiency while also increasing state representation for short-term spatiotemporal reasoning and long-term spatiotemporal memory.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224019362", "content": "Based on this, this study offers a deep reinforcement learning network model that greatly improves sample efficiency while also increasing state representation for short-term spatiotemporal reasoning and long-term spatiotemporal memory."}
+{"idx": 1, "title": "Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning Models ...", "date": "", "ddg_snippet": "Spatiotemporal reasoning plays a key role in Cyber-Physical Systems (CPS). Despite advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs), their capacity to reason about complex spatiotemporal signals remains underexplored. This paper proposes a hierarchical SpatioTemporal reAsoning benchmaRK, STARK, to systematically evaluate LLMs across three levels of reasoning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.11618", "content": "Spatiotemporal reasoning plays a key role in Cyber-Physical Systems (CPS). Despite advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs), their capacity to reason about complex spatiotemporal signals remains underexplored. This paper proposes a hierarchical SpatioTemporal reAsoning benchmaRK, STARK, to systematically evaluate LLMs across three levels of reasoning ..."}
+{"idx": 2, "title": "PDF Structural-RNN: Deep Learning on Spatio-Temporal Graphs", "date": "", "ddg_snippet": "Deep Recurrent Neural Network architectures , though remarkably capable at modeling sequences, lack an intu- itive high-level spatio-temporal structure. That is while ...", "subpage_snippet": "", "source": "svl.stanford.edu", "link": "https://svl.stanford.edu/assets/publications/pdfs/jain_cvpr16.pdf", "content": "Deep Recurrent Neural Network architectures , though remarkably capable at modeling sequences, lack an intu- itive high-level spatio-temporal structure. That is while ..."}
+{"idx": 3, "title": "Spatiotemporal Observer Design for Predictive Learning of High ...", "date": "", "ddg_snippet": "Although deep learning -based methods have shown great success in spatiotemporal predictive learning , the frameworks of those models are mainly designed by intuition. How to make spatiotemporal forecasting with theoretical guarantees is still a challenging issue. In this work, we tackle this problem by applying domain knowledge from the dynamical system to the framework design of deep learning ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10946854", "content": "Although deep learning -based methods have shown great success in spatiotemporal predictive learning , the frameworks of those models are mainly designed by intuition. How to make spatiotemporal forecasting with theoretical guarantees is still a challenging issue. In this work, we tackle this problem by applying domain knowledge from the dynamical system to the framework design of deep learning ..."}
+{"idx": 4, "title": "Overview of spatio-temporal deep learning and examples of algorithms ...", "date": "", "ddg_snippet": "See also ' Attention in deep learning ' for more information. Challenges of spatio-temporal deep learning include Computational resource consumption: spatio-temporal data is very large and requires a lot of computational resources for learning and inference. There is a need to develop efficient algorithms for this.", "subpage_snippet": "", "source": "deus-ex-machina-ism.com", "link": "https://deus-ex-machina-ism.com/?p=73975&lang=en", "content": "See also ' Attention in deep learning ' for more information. Challenges of spatio-temporal deep learning include Computational resource consumption: spatio-temporal data is very large and requires a lot of computational resources for learning and inference. There is a need to develop efficient algorithms for this."}
+{"idx": 5, "title": "A deep graph neural network architecture for modelling spatio-temporal ...", "date": "", "ddg_snippet": "This repository contains an implementation of a deep neural network architecture combining both graph neural networks (GNNs) and temporal convolutional networks (TCNs), which is able to learn from the spatial and temporal components of rs-fMRI data in an end-to-end fashion. Please check the publications at the end of this page for more details on how this architecture was used and evaluated ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tjiagoM/spatio-temporal-brain", "content": "This repository contains an implementation of a deep neural network architecture combining both graph neural networks (GNNs) and temporal convolutional networks (TCNs), which is able to learn from the spatial and temporal components of rs-fMRI data in an end-to-end fashion. Please check the publications at the end of this page for more details on how this architecture was used and evaluated ..."}
+{"idx": 6, "title": "Learning spatiotemporal dynamics with a pretrained generative model ...", "date": "", "ddg_snippet": "Reconstructing and predicting spatiotemporal dynamics from sparse sensor data is challenging, especially with limited sensors. Li et al. address this by using self-supervised pretraining of a ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-024-00938-z", "content": "Reconstructing and predicting spatiotemporal dynamics from sparse sensor data is challenging, especially with limited sensors. Li et al. address this by using self-supervised pretraining of a ..."}
+{"idx": 7, "title": "Breaking the Limits of Thought: The Future of AI with Spatiotemporal ...", "date": "", "ddg_snippet": "Worth noticing Lightning Attention-2 2. Spatiotemporal Deconstruction at the Technical Level 1. Multi-Dimensional Exploration of Dynamic Thinking Networks Cutting-edge models are overcoming ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sampan090611/breaking-the-limits-of-thought-the-future-of-ai-with-spatiotemporal-cognitive-architectures-13d9ea0b7962", "content": "Worth noticing Lightning Attention-2 2. Spatiotemporal Deconstruction at the Technical Level 1. Multi-Dimensional Exploration of Dynamic Thinking Networks Cutting-edge models are overcoming ..."}
+{"idx": 8, "title": "Deep learning on spatiotemporal graphs: A systematic review ...", "date": "", "ddg_snippet": "Deep learning approaches, given their low cost and high reliability, have gained much popularity in different subjects, such as computer vision and natural language processing, and more recently in graph data types. Spatiotemporal graph-based neural networks have been more and more developed to solve problems related to spatiotemporal data, mainly for analyzing changes over time and for ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224006325", "content": "Deep learning approaches, given their low cost and high reliability, have gained much popularity in different subjects, such as computer vision and natural language processing, and more recently in graph data types. Spatiotemporal graph-based neural networks have been more and more developed to solve problems related to spatiotemporal data, mainly for analyzing changes over time and for ..."}
+{"idx": 9, "title": "Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning Models ...", "date": "", "ddg_snippet": "Abstract Spatiotemporal reasoning plays a key role in Cyber-Physical Systems (CPS). De-spite advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs), their capacity to reason about complex spatiotemporal signals remains underexplored. This paper proposes a hierarchical SpatioTemporal reAsoning benchmaRK, STARK, to systematically evaluate LLMs across three levels of rea ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.11618", "content": "Abstract Spatiotemporal reasoning plays a key role in Cyber-Physical Systems (CPS). De-spite advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs), their capacity to reason about complex spatiotemporal signals remains underexplored. This paper proposes a hierarchical SpatioTemporal reAsoning benchmaRK, STARK, to systematically evaluate LLMs across three levels of rea ..."}
diff --git a/data/sampled_jsons/speculative_decoding_for_autoregressive_models.jsonl b/data/sampled_jsons/speculative_decoding_for_autoregressive_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b9e66f8e0fbc7a741445c8462bafc5a7730a6a00
--- /dev/null
+++ b/data/sampled_jsons/speculative_decoding_for_autoregressive_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Continuous Speculative Decoding for Autoregressive Image ...", "date": "", "ddg_snippet": "Nov 18, 2024 · While speculative decoding has proven effective in accelerating Large Language Models (LLMs), their adaptation to continuous-valued visual autoregressive models remains unexplored. This work generalizes the speculative decoding algorithm from discrete tokens to continuous space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.11925", "content": "Nov 18, 2024 · While speculative decoding has proven effective in accelerating Large Language Models (LLMs), their adaptation to continuous-valued visual autoregressive models remains unexplored. This work generalizes the speculative decoding algorithm from discrete tokens to continuous space."}
+{"idx": 1, "title": "Looking back at speculative decoding - Google Research", "date": "", "ddg_snippet": "Dec 6, 2024 · Speculative decoding is the application of speculative sampling to inference from autoregressive models , like transformers. In this case, both f (X) and g (Y) would be the same function, taking as input a sequence, and outputting a distribution for the sequence extended by one token.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/looking-back-at-speculative-decoding/", "content": "Dec 6, 2024 · Speculative decoding is the application of speculative sampling to inference from autoregressive models , like transformers. In this case, both f (X) and g (Y) would be the same function, taking as input a sequence, and outputting a distribution for the sequence extended by one token."}
+{"idx": 2, "title": "GitHub - suryavanshi/speculative_decoding: PyTorch ...", "date": "", "ddg_snippet": "Speculative decoding is a technique that can accelerate inference from large autoregressive models like Transformers without changing the model architecture or outputs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/suryavanshi/speculative_decoding", "content": "Speculative decoding is a technique that can accelerate inference from large autoregressive models like Transformers without changing the model architecture or outputs."}
+{"idx": 3, "title": "COLING 2025 Tutorial: Speculative Decoding for Efficient LLM ...", "date": "", "ddg_snippet": "SD is introduced as an innovative decoding paradigm to mitigate the high inference latency stemming from autoregressive decoding in LLMs. At each decoding step, SD efficiently drafts several future tokens and then verifies them in parallel.", "subpage_snippet": "", "source": "speculative-decoding.github.io", "link": "https://speculative-decoding.github.io/", "content": "SD is introduced as an innovative decoding paradigm to mitigate the high inference latency stemming from autoregressive decoding in LLMs. At each decoding step, SD efficiently drafts several future tokens and then verifies them in parallel."}
+{"idx": 4, "title": "An Introduction to Speculative Decoding for Reducing Latency ...", "date": "", "ddg_snippet": "3 days ago · Speculative decoding is an inference optimization technique that accelerates large language models (LLMs) by predicting and verifying multiple tokens simultaneously, reducing latency while preserving output quality. The draft-target approach to speculative decoding uses a smaller draft model to propose tokens and a larger target model to verify them in parallel, with rejection sampling ...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/an-introduction-to-speculative-decoding-for-reducing-latency-in-ai-inference/", "content": "3 days ago · Speculative decoding is an inference optimization technique that accelerates large language models (LLMs) by predicting and verifying multiple tokens simultaneously, reducing latency while preserving output quality. The draft-target approach to speculative decoding uses a smaller draft model to propose tokens and a larger target model to verify them in parallel, with rejection sampling ..."}
+{"idx": 5, "title": "Speculative decoding in autoregressive generative artificial ...", "date": "", "ddg_snippet": "Certain aspects of the present disclosure provide techniques and apparatus for generating a response to a query input into a generative artificial intelligence model .", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2024205726A1/en", "content": "Certain aspects of the present disclosure provide techniques and apparatus for generating a response to a query input into a generative artificial intelligence model ."}
+{"idx": 6, "title": "[2502.19732] Speculative Decoding and Beyond: An In-Depth ...", "date": "", "ddg_snippet": "Feb 27, 2025 · This systematic examination of both theoretical frameworks and practical implementations provides a foundation for future research in efficient autoregressive decoding .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.19732", "content": "Feb 27, 2025 · This systematic examination of both theoretical frameworks and practical implementations provides a foundation for future research in efficient autoregressive decoding ."}
+{"idx": 7, "title": "Speculative Decoding — Model Optimizer 0.27.0", "date": "", "ddg_snippet": "... way of generating text from a language model is with autoregressive decoding : one token is generated each step and appended to the input context for ...", "subpage_snippet": "", "source": "nvidia.github.io", "link": "https://nvidia.github.io/TensorRT-Model-Optimizer/guides/7_speculative_decoding.html", "content": "... way of generating text from a language model is with autoregressive decoding : one token is generated each step and appended to the input context for ..."}
+{"idx": 8, "title": "Decoding Speculative Decoding", "date": "", "ddg_snippet": "By leveraging faster inference of smaller draft models , speculative decoding turns autoregressive decoding on the target LLM into a more hardware ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.01528v4", "content": "By leveraging faster inference of smaller draft models , speculative decoding turns autoregressive decoding on the target LLM into a more hardware ..."}
+{"idx": 9, "title": "[2203.16487] Speculative Decoding: Exploiting Speculative", "date": "", "ddg_snippet": "Speculative Decoding has two innovations: Spec-Drafter -- an independent model specially optimized for efficient and accurate drafting -- and Spec ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2203.16487", "content": "Speculative Decoding has two innovations: Spec-Drafter -- an independent model specially optimized for efficient and accurate drafting -- and Spec ..."}
diff --git a/data/sampled_jsons/spherical_coordinates_to_UV_mapping_formula_theta_phi_azimuthal_polar.jsonl b/data/sampled_jsons/spherical_coordinates_to_UV_mapping_formula_theta_phi_azimuthal_polar.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..69e5de982d615bc3fc41c8f56a07005b4f046b83
--- /dev/null
+++ b/data/sampled_jsons/spherical_coordinates_to_UV_mapping_formula_theta_phi_azimuthal_polar.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Spherical coordinate system - Wikipedia", "date": "", "ddg_snippet": "The physics convention. Spherical coordinates (r, θ, φ) as commonly used: (ISO 80000-2:2019): radial distance r (slant distance to origin), polar angle θ ( theta ) (angle with respect to positive polar axis), and azimuthal angle φ ( phi ) (angle of rotation from the initial meridian plane). This is the convention followed in this article. In mathematics, a spherical coordinate system specifies ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Spherical_coordinate_system", "content": "The physics convention. Spherical coordinates (r, θ, φ) as commonly used: (ISO 80000-2:2019): radial distance r (slant distance to origin), polar angle θ ( theta ) (angle with respect to positive polar axis), and azimuthal angle φ ( phi ) (angle of rotation from the initial meridian plane). This is the convention followed in this article. In mathematics, a spherical coordinate system specifies ..."}
+{"idx": 1, "title": "Spherical Coordinates - MATLAB & Simulink - MathWorks", "date": "", "ddg_snippet": "Phased Array System Toolbox™ software natively supports the azimuth/elevation representation. The software also provides functions for converting between the azimuth/elevation representation and the other representations. See Phi and Theta Angles and U and V Coordinates . Azimuth and Elevation Angles In Phased Array System Toolbox software, the predominant convention for spherical coordinates ...", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/help/phased/ug/spherical-coordinates.html", "content": "Phased Array System Toolbox™ software natively supports the azimuth/elevation representation. The software also provides functions for converting between the azimuth/elevation representation and the other representations. See Phi and Theta Angles and U and V Coordinates . Azimuth and Elevation Angles In Phased Array System Toolbox software, the predominant convention for spherical coordinates ..."}
+{"idx": 2, "title": "Cylindrical and spherical coordinates - University of Texas at Austin", "date": "", "ddg_snippet": "After rectangular (aka Cartesian) coordinates , the two most common an useful coordinate systems in 3 dimensions are cylindrical coordinates (sometimes called cylindrical polar coordinates ) and spherical coordinates (sometimes called spherical polar coordinates ).", "subpage_snippet": "", "source": "web.ma.utexas.edu", "link": "https://web.ma.utexas.edu/users/m408m/Display15-10-8.shtml", "content": "After rectangular (aka Cartesian) coordinates , the two most common an useful coordinate systems in 3 dimensions are cylindrical coordinates (sometimes called cylindrical polar coordinates ) and spherical coordinates (sometimes called spherical polar coordinates )."}
+{"idx": 3, "title": "Spherical Coordinates -- from Wolfram MathWorld", "date": "", "ddg_snippet": "Spherical coordinates , also called spherical polar coordinates (Walton 1967, Arfken 1985), are a system of curvilinear coordinates that are natural for describing positions on a sphere or spheroid. Define theta to be the azimuthal angle in the xy-plane from the x-axis with 0<=theta<2pi (denoted lambda when referred to as the longitude), phi to be the polar angle (also known as the zenith angle ...", "subpage_snippet": "", "source": "mathworld.wolfram.com", "link": "https://mathworld.wolfram.com/SphericalCoordinates.html", "content": "Spherical coordinates , also called spherical polar coordinates (Walton 1967, Arfken 1985), are a system of curvilinear coordinates that are natural for describing positions on a sphere or spheroid. Define theta to be the azimuthal angle in the xy-plane from the x-axis with 0<=theta<2pi (denoted lambda when referred to as the longitude), phi to be the polar angle (also known as the zenith angle ..."}
+{"idx": 4, "title": "Python: Spherical coordinates transformation · GitHub", "date": "", "ddg_snippet": "\"\"\" # spherical_coords.py Convert between ( theta , phi ) and (azimuth, elevation) coordinate systems. Author: Danny Price License: MIT ## Overview Both ( theta , phi ) and (azimuth, elevation) are spherical coordinate systems. The first is used commonly in antenna pattern measurements, while the second is used extensively in ground-based astronomy. Conversion between the two essentially requires ...", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/yigithsyn/c2d8297975cfc49e3a6c1222b226a299", "content": "\"\"\" # spherical_coords.py Convert between ( theta , phi ) and (azimuth, elevation) coordinate systems. Author: Danny Price License: MIT ## Overview Both ( theta , phi ) and (azimuth, elevation) are spherical coordinate systems. The first is used commonly in antenna pattern measurements, while the second is used extensively in ground-based astronomy. Conversion between the two essentially requires ..."}
+{"idx": 5, "title": "Spherical coordinates - University of Illinois Urbana-Champaign", "date": "", "ddg_snippet": "Spherical coordinates #rvs The spherical coordinate system extends polar coordinates into 3D by using an angle $\\ phi $ for the third coordinate . This gives coordinates $ (r, \\ theta , \\ phi )$ consisting of:", "subpage_snippet": "", "source": "dynref.engr.illinois.edu", "link": "https://dynref.engr.illinois.edu/rvs.html", "content": "Spherical coordinates #rvs The spherical coordinate system extends polar coordinates into 3D by using an angle $\\ phi $ for the third coordinate . This gives coordinates $ (r, \\ theta , \\ phi )$ consisting of:"}
+{"idx": 6, "title": "c++ - Spherical mapping calculation (UV) for point given on sphere ...", "date": "", "ddg_snippet": "I want to write an spherical mapping for my ray-tracer to generate UV - coordinates for the sphere. The texture applied with that mapping should looks like that: I have the sphere that is located at [0.0f,0.0f,0.0f] with the radius of 20.0f. For given xyz point on sphere's surface (position where ray from ray-tracer hit the sphere) I calculate the uv coordinates with this formula : float u = 0.5f ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/33747876/spherical-mapping-calculation-uv-for-point-given-on-sphere-strange-u-values", "content": "I want to write an spherical mapping for my ray-tracer to generate UV - coordinates for the sphere. The texture applied with that mapping should looks like that: I have the sphere that is located at [0.0f,0.0f,0.0f] with the radius of 20.0f. For given xyz point on sphere's surface (position where ray from ray-tracer hit the sphere) I calculate the uv coordinates with this formula : float u = 0.5f ..."}
+{"idx": 7, "title": "How to transform UV coordinates to spherical coordinates, phi-theta or ...", "date": "", "ddg_snippet": "Most MathWorks Phased Array System toolbox functions deal strictly with the azimuth-elevation coordinate system while phi - theta and UV are mainly used for interfacing with external data/tools.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/matlabcentral/answers/452299-how-to-transform-uv-coordinates-to-spherical-coordinates-phi-theta-or-azimuth-elevation", "content": "Most MathWorks Phased Array System toolbox functions deal strictly with the azimuth-elevation coordinate system while phi - theta and UV are mainly used for interfacing with external data/tools."}
+{"idx": 8, "title": "Calculus III - Spherical Coordinates - Pauls Online Math Notes", "date": "", "ddg_snippet": "This coordinates system is very useful for dealing with spherical objects. We will derive formulas to convert between cylindrical coordinates and spherical coordinates as well as between Cartesian and spherical coordinates (the more useful of the two).", "subpage_snippet": "", "source": "tutorial.math.lamar.edu", "link": "https://tutorial.math.lamar.edu/Classes/CalcIII/SphericalCoords.aspx", "content": "This coordinates system is very useful for dealing with spherical objects. We will derive formulas to convert between cylindrical coordinates and spherical coordinates as well as between Cartesian and spherical coordinates (the more useful of the two)."}
+{"idx": 9, "title": "transforming coordinates between cartesian and spherical", "date": "", "ddg_snippet": "where $\\Delta \\vec {x}_ {r}, \\Delta \\vec {x}_ {\\theta}\\mbox {, and }\\Delta \\vec {x}_ {\\ phi }$ are the spherical coordinates of the finite displacement from the origin. But plugging in numbers gives gibberish: let (x,y,z) = (1,1,1). Then all sines and cosines of theta and phi are $1/\\sqrt {2}$ and", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/5021859/transforming-coordinates-between-cartesian-and-spherical", "content": "where $\\Delta \\vec {x}_ {r}, \\Delta \\vec {x}_ {\\theta}\\mbox {, and }\\Delta \\vec {x}_ {\\ phi }$ are the spherical coordinates of the finite displacement from the origin. But plugging in numbers gives gibberish: let (x,y,z) = (1,1,1). Then all sines and cosines of theta and phi are $1/\\sqrt {2}$ and"}
diff --git a/data/sampled_jsons/spurious_correlation_program_synthesis_Schubert_polynomials_Section_5_Machine_Learning_meets_Algebra.jsonl b/data/sampled_jsons/spurious_correlation_program_synthesis_Schubert_polynomials_Section_5_Machine_Learning_meets_Algebra.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..73b25f4749e7c8ed8374c2548baf3fd728751bd2
--- /dev/null
+++ b/data/sampled_jsons/spurious_correlation_program_synthesis_Schubert_polynomials_Section_5_Machine_Learning_meets_Algebra.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics : A Suite of...", "date": "", "ddg_snippet": "Program Synthesis for Schubert Polynomials : The previous example highlighted use of narrow models for conjecture generation.), this process introduced a spurious correlation in the dataset222This issue has since been fixed.: structure constant.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "Program Synthesis for Schubert Polynomials : The previous example highlighted use of narrow models for conjecture generation.), this process introduced a spurious correlation in the dataset222This issue has since been fixed.: structure constant."}
+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics ... | alphaXiv", "date": "", "ddg_snippet": "We describe all nine datasets, the different ways machine learning models can be applied to them (e.g., training with narrow models followed by interpretability analysis or program synthesis with LLMs), and discuss some of the challenges involved in designing datasets like these.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/ja/overview/2503.06366v1", "content": "We describe all nine datasets, the different ways machine learning models can be applied to them (e.g., training with narrow models followed by interpretability analysis or program synthesis with LLMs), and discuss some of the challenges involved in designing datasets like these."}
+{"idx": 2, "title": "Machine Learning meets Algebraic Combinatorics : A Suite of...", "date": "", "ddg_snippet": "We describe all nine datasets, the different ways machine learning models can be applied to them (e.g., training with narrow models followed by interpretability analysis or program synthesis with LLMs), and discuss some of the challenges involved in designing datasets like these.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/machine-learning-meets-algebraic", "content": "We describe all nine datasets, the different ways machine learning models can be applied to them (e.g., training with narrow models followed by interpretability analysis or program synthesis with LLMs), and discuss some of the challenges involved in designing datasets like these."}
+{"idx": 3, "title": "Machine Learning meets Algebraic Combinatorics ... | OpenReview", "date": "", "ddg_snippet": "Keywords : Algebraic combinatorics , AI for Math, Datasets. TL;DR : We introduce a collection of datasets for machine learning that target both open and foundational results in algebraic combinatorics .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KQ1gI5qzAf", "content": "Keywords : Algebraic combinatorics , AI for Math, Datasets. TL;DR : We introduce a collection of datasets for machine learning that target both open and foundational results in algebraic combinatorics ."}
+{"idx": 4, "title": "Machine Learning meets Algebraic Combinatorics : A Suite... | PNNL", "date": "", "ddg_snippet": "AGM Program .The use of benchmark datasets has become an important engine of progress in machine learning (ML) over the past 15 years. Recently there has been growing interest in utilizing machine learning to drive advances in research-level mathematics.", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/publications/machine-learning-meets-algebraic-combinatorics-suite-benchmark-datasets-accelerate-ai", "content": "AGM Program .The use of benchmark datasets has become an important engine of progress in machine learning (ML) over the past 15 years. Recently there has been growing interest in utilizing machine learning to drive advances in research-level mathematics."}
+{"idx": 5, "title": "Articles by Herman Chau | Synthical", "date": "", "ddg_snippet": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics. Machine Learning , Artificial Intelligence.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/7676690f-b02f-4c82-9eb5-e3fcd833519f/articles", "content": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics. Machine Learning , Artificial Intelligence."}
+{"idx": 6, "title": "Yibo Gao's research works | Peking University and other places", "date": "", "ddg_snippet": "We define and study the dual mixed volume rational function of a sequence of polytopes, a dual version of the mixed volume polynomial . This concept has direct relations to the adjoint polynomials and the canonical forms of polytopes.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Yibo-Gao-2084490604", "content": "We define and study the dual mixed volume rational function of a sequence of polytopes, a dual version of the mixed volume polynomial . This concept has direct relations to the adjoint polynomials and the canonical forms of polytopes."}
+{"idx": 7, "title": "Davis Brown (@davisbrownr) | Aguea", "date": "", "ddg_snippet": "Machine Learning meets Algebraic Combinatorics : A Suite of...One notable case study: on an early version of the Schubert task above, both o1-mini and GPT-4o produced programs with 100% test set accuracy.", "subpage_snippet": "", "source": "aguea.net", "link": "https://aguea.net/davisbrownr", "content": "Machine Learning meets Algebraic Combinatorics : A Suite of...One notable case study: on an early version of the Schubert task above, both o1-mini and GPT-4o produced programs with 100% test set accuracy."}
+{"idx": 8, "title": "MATH-AI: The 4th Workshop on Mathematical Reasoning and AI", "date": "", "ddg_snippet": "- Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research ( Poster ) > link.- VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search ( Poster ) > link.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/workshop/84719", "content": "- Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research ( Poster ) > link.- VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search ( Poster ) > link."}
+{"idx": 9, "title": "ICML 2025 collected by Wang", "date": "", "ddg_snippet": "Title: Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption.", "subpage_snippet": "", "source": "hongsong-wang.github.io", "link": "https://hongsong-wang.github.io/ICML2025_ABSTRACT/", "content": "Title: Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption."}
diff --git a/data/sampled_jsons/spurious_correlations_math_reasoning_synthetic_data_overfitting_intermediate_steps_year_2024.jsonl b/data/sampled_jsons/spurious_correlations_math_reasoning_synthetic_data_overfitting_intermediate_steps_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ab3fbaf67818202a6af11d3616f27f1bc3dac7d2
--- /dev/null
+++ b/data/sampled_jsons/spurious_correlations_math_reasoning_synthetic_data_overfitting_intermediate_steps_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — We show that training on per- step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — We show that training on per- step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage- ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 66 — We show that training on per- step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage- ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 66 — We show that training on per- step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage- ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 66 — We show that training on per- step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 66 — We show that training on per- step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage- ..."}
+{"idx": 3, "title": "Identifying and Mitigating Spurious Correlation in Multi-Task ...", "date": "", "ddg_snippet": "by J Chai · 2025 — We address spurious correlations in multi-task learning by fine-tuning task-specific predictors, enhancing per-task performance through adversarial training ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Chai_Identifying_and_Mitigating_Spurious_Correlation_in_Multi-Task_Learning_CVPR_2025_paper.pdf", "content": "by J Chai · 2025 — We address spurious correlations in multi-task learning by fine-tuning task-specific predictors, enhancing per-task performance through adversarial training ..."}
+{"idx": 4, "title": "On the Effect of Key Factors in Spurious Correlation", "date": "", "ddg_snippet": "by Y Wang · 2024 · Cited by 5 — Spurious correlations arise when irrelevant patterns in input data are mistakenly asso- ciated with labels, compromising the gener- alizability of machine ... 26 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/wang24j/wang24j.pdf", "content": "by Y Wang · 2024 · Cited by 5 — Spurious correlations arise when irrelevant patterns in input data are mistakenly asso- ciated with labels, compromising the gener- alizability of machine ... 26 pages"}
+{"idx": 5, "title": "RL on incorrect synthetic data scales the efficiency of LLM math ...", "date": "", "ddg_snippet": "We show that training on per- step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739277", "content": "We show that training on per- step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted ..."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of", "date": "", "ddg_snippet": "We show that training on per- step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96295/paper", "content": "We show that training on per- step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted ..."}
+{"idx": 7, "title": "Rethinking Data Augmentations: A Causal Perspective", "date": "", "ddg_snippet": "Let us explain spurious correlation borrowing example from this article about data augmentation. We want to train a classifer to distinguish ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/rethinking-data-augmentations-a-causal-perspective-e0b7810579a7", "content": "Let us explain spurious correlation borrowing example from this article about data augmentation. We want to train a classifer to distinguish ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Spurious correlation s in the data , where multiple cues are predictive of the target labels, often lead to a phenomenon known as simplicity bias, where a model ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=spurious+correlation", "content": "Spurious correlation s in the data , where multiple cues are predictive of the target labels, often lead to a phenomenon known as simplicity bias, where a model ..."}
+{"idx": 9, "title": "General Intelligence Requires Reward-based Pretraining", "date": "", "ddg_snippet": "... spurious correlations between tokens. Such a reasoning system ... Since we lack large-scale training data with explicit intermediate reasoning steps ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40143", "content": "... spurious correlations between tokens. Such a reasoning system ... Since we lack large-scale training data with explicit intermediate reasoning steps ..."}
diff --git a/data/sampled_jsons/sqlmap_database_takeover_features_SQL_injection_automation.jsonl b/data/sampled_jsons/sqlmap_database_takeover_features_SQL_injection_automation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b0b5fd400c08bb4666f8cd88af8dec727a4d2e29
--- /dev/null
+++ b/data/sampled_jsons/sqlmap_database_takeover_features_SQL_injection_automation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "sqlmap: automatic SQL injection and database takeover tool", "date": "", "ddg_snippet": "sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database servers ...", "subpage_snippet": "", "source": "sqlmap.org", "link": "https://sqlmap.org/", "content": "sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database servers ..."}
+{"idx": 1, "title": "sqlmap: Open-Source SQL Injection Testing Tool", "date": "", "ddg_snippet": "31 Aug 2025 — Discover sqlmap's automated SQL injection detection , penetration testing features, and security assessment capabilities, along with a setup ...", "subpage_snippet": "", "source": "www.wiz.io", "link": "https://www.wiz.io/academy/sqlmap-overview", "content": "31 Aug 2025 — Discover sqlmap's automated SQL injection detection , penetration testing features, and security assessment capabilities, along with a setup ..."}
+{"idx": 2, "title": "sqlmapproject/sqlmap: Automatic SQL injection and ...", "date": "", "ddg_snippet": "sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sqlmapproject/sqlmap", "content": "sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database ..."}
+{"idx": 3, "title": "SQLmap: Uncovering and Exploiting SQL Injection ...", "date": "", "ddg_snippet": "In this blog I will be covering SQLmap , a premier, open-source, automated tool used for detecting and exploiting SQL Injection vulnerabilities in web ...", "subpage_snippet": "", "source": "www.evolvesecurity.com", "link": "https://www.evolvesecurity.com/blog-posts/tools-of-the-trade-your-ally-in-uncovering-sql-injection-vulnerabilities", "content": "In this blog I will be covering SQLmap , a premier, open-source, automated tool used for detecting and exploiting SQL Injection vulnerabilities in web ..."}
+{"idx": 4, "title": "Day 64 - Exploring sqlmap: Automated SQL Injection and ...", "date": "", "ddg_snippet": "sqlmap is an open-source penetration testing tool that automates detecting and exploiting SQL injection vulnerabilities in database-driven applications.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/day-64-exploring-sqlmap-automated-sql-injection-database-swarup-qkifc", "content": "sqlmap is an open-source penetration testing tool that automates detecting and exploiting SQL injection vulnerabilities in database-driven applications."}
+{"idx": 5, "title": "SQL Injection: Attacks & Defenses | by Mostafa Moradian", "date": "", "ddg_snippet": "28 Oct 2024 — SQLmap : the de-facto OSS tool for automated SQL injection attacks and database takeover. It has many features and supports many databases ...", "subpage_snippet": "", "source": "blog.gatewayd.io", "link": "https://blog.gatewayd.io/sql-injection-attacks-defenses-8c0fab2abd36", "content": "28 Oct 2024 — SQLmap : the de-facto OSS tool for automated SQL injection attacks and database takeover. It has many features and supports many databases ..."}
+{"idx": 6, "title": "Usage · sqlmapproject/sqlmap Wiki", "date": "", "ddg_snippet": "Automatic SQL injection and database takeover tool - Usage · sqlmapproject/sqlmap Wiki. ... features allow to run arbitrary SQL statements on the database ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sqlmapproject/sqlmap/wiki/usage", "content": "Automatic SQL injection and database takeover tool - Usage · sqlmapproject/sqlmap Wiki. ... features allow to run arbitrary SQL statements on the database ..."}
+{"idx": 7, "title": "sqlmap for Ethical SQL Injection Testing", "date": "", "ddg_snippet": "sqlmap is an open-source penetration testing tool that automates the detection and exploitation of SQL injection vulnerabilities in web ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@itsdavidmandal/sqlmap-for-ethical-sql-injection-testing-3d2451ca6f05", "content": "sqlmap is an open-source penetration testing tool that automates the detection and exploitation of SQL injection vulnerabilities in web ..."}
+{"idx": 8, "title": "Testing for SQL Injection", "date": "", "ddg_snippet": "SQL injection testing checks if it is possible to inject data into an application/site so that it executes a user-controlled SQL query in the database.", "subpage_snippet": "", "source": "owasp.org", "link": "https://owasp.org/www-project-web-security-testing-guide/latest/4-Web_Application_Security_Testing/07-Input_Validation_Testing/05-Testing_for_SQL_Injection", "content": "SQL injection testing checks if it is possible to inject data into an application/site so that it executes a user-controlled SQL query in the database."}
+{"idx": 9, "title": "SQLmap - Payloads All The Things", "date": "", "ddg_snippet": "SQLmap is a powerful tool that automates the detection and exploitation of SQL injection vulnerabilities, saving time and effort compared to manual testing.", "subpage_snippet": "", "source": "swisskyrepo.github.io", "link": "https://swisskyrepo.github.io/PayloadsAllTheThings/SQL+Injection/SQLmap/", "content": "SQLmap is a powerful tool that automates the detection and exploitation of SQL injection vulnerabilities, saving time and effort compared to manual testing."}
diff --git a/data/sampled_jsons/static_analysis_ML_model_dependencies_SBOM_software_bill_of_materials.jsonl b/data/sampled_jsons/static_analysis_ML_model_dependencies_SBOM_software_bill_of_materials.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f00335d0dc38f3eb69ef654236d4ab626745bfcd
--- /dev/null
+++ b/data/sampled_jsons/static_analysis_ML_model_dependencies_SBOM_software_bill_of_materials.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dependency-Track | Software Bill of Materials (SBOM) Analysis", "date": "", "ddg_snippet": "Dependency -Track is a continuous SBOM analysis platform that allows organizations to identify and reduce risk in the software supply chain. Dependency -Track is open-source and distributed under the Apache 2.0 license.", "subpage_snippet": "", "source": "dependencytrack.org", "link": "https://dependencytrack.org/", "content": "Dependency -Track is a continuous SBOM analysis platform that allows organizations to identify and reduce risk in the software supply chain. Dependency -Track is open-source and distributed under the Apache 2.0 license."}
+{"idx": 1, "title": "SBOM for AI/ML Models: Tracking Machine Learning Supply C ...", "date": "", "ddg_snippet": "Aug 27, 2025 · Learn how to create Software Bills of Materials for AI and machine learning models, including training data, model architectures, and dependencies", "subpage_snippet": "", "source": "sbomgenerator.com", "link": "https://sbomgenerator.com/guides/sbom-for-ai-models", "content": "Aug 27, 2025 · Learn how to create Software Bills of Materials for AI and machine learning models, including training data, model architectures, and dependencies"}
+{"idx": 2, "title": "What is a software bill of materials (SBOM)? - IBM", "date": "", "ddg_snippet": "Mar 13, 2025 · A software bill of materials ( SBOM ) lists all components, libraries and modules in a software product in a machine-readable format.", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/topics/sbom", "content": "Mar 13, 2025 · A software bill of materials ( SBOM ) lists all components, libraries and modules in a software product in a machine-readable format."}
+{"idx": 3, "title": "A Comprehensive Study on Software Bill of Materials Tools ...", "date": "", "ddg_snippet": "Keywords: Software Bill of Materials ( SBOM ), Software Supply Chain Security, Open- Source Tools, SBOM Adoption, Vulnerability Management, Dependency Analysis . In recent days software supply chain attacks are increasing continuously, exposing a signif-icant threat to modern software systems.", "subpage_snippet": "", "source": "lutpub.lut.fi", "link": "https://lutpub.lut.fi/bitstream/handle/10024/168579/mastersthesis_Sarwar_Mohammad+Wasee.pdf?sequence=1", "content": "Keywords: Software Bill of Materials ( SBOM ), Software Supply Chain Security, Open- Source Tools, SBOM Adoption, Vulnerability Management, Dependency Analysis . In recent days software supply chain attacks are increasing continuously, exposing a signif-icant threat to modern software systems."}
+{"idx": 4, "title": "2025 Minimum Elements for a Software Bill of Materials (SBOM)", "date": "", "ddg_snippet": "A software bill of materials ( SBOM ) has emerged as a key building block in software security and software supply chain risk management. An SBOM is a nested inventory, a list of ingredients that make up software applications and systems.", "subpage_snippet": "", "source": "www.cisa.gov", "link": "https://www.cisa.gov/sites/default/files/2025-08/2025_CISA_SBOM_Minimum_Elements.pdf", "content": "A software bill of materials ( SBOM ) has emerged as a key building block in software security and software supply chain risk management. An SBOM is a nested inventory, a list of ingredients that make up software applications and systems."}
+{"idx": 5, "title": "Software Bill of Materials (sBOMs) - NIST Computer Security ...", "date": "", "ddg_snippet": "Pedigree Assurance (history of how it was produced) (trustworthiness of it) License Management sBOM of a Service (conditions about its use) ( sBOM of sw delivering service) Provenance Patch Currency (chain of custody of it) (known fixes are applied to it) Integrity Automated Response", "subpage_snippet": "", "source": "csrc.nist.gov", "link": "https://csrc.nist.gov/CSRC/media/Projects/cyber-supply-chain-risk-management/documents/SSCA/Spring_2019/8MayAM2.3_Software_Bill_of_Materials_Robert_Martin_05_08_19_clean.pdf", "content": "Pedigree Assurance (history of how it was produced) (trustworthiness of it) License Management sBOM of a Service (conditions about its use) ( sBOM of sw delivering service) Provenance Patch Currency (chain of custody of it) (known fixes are applied to it) Integrity Automated Response"}
+{"idx": 6, "title": "Software Bill of Materials: Visualizing the Unseen - DTIC", "date": "", "ddg_snippet": "Software Bill of Materials ( SBOM ) -Background What is a Software Bill of Materials ( SBOM ) An SBOM is a formal record containing the details and supply chain relationships of various components used in building software .", "subpage_snippet": "", "source": "apps.dtic.mil", "link": "https://apps.dtic.mil/sti/trecms/pdf/AD1199683.pdf", "content": "Software Bill of Materials ( SBOM ) -Background What is a Software Bill of Materials ( SBOM ) An SBOM is a formal record containing the details and supply chain relationships of various components used in building software ."}
+{"idx": 7, "title": "Understanding SBOMs : The Ultimate Guide", "date": "", "ddg_snippet": "Software Bill of Materials ( SBOMs ) are used to document the source code, library and package dependencies , and the licenses consumed by the compiler or packager used to create a distributable artifact. The SBOMs are JSON or YAML files that live side-by-side with the built artifact.", "subpage_snippet": "", "source": "www.deployhub.com", "link": "https://www.deployhub.com/understanding-software-bill-of-materials-sboms/", "content": "Software Bill of Materials ( SBOMs ) are used to document the source code, library and package dependencies , and the licenses consumed by the compiler or packager used to create a distributable artifact. The SBOMs are JSON or YAML files that live side-by-side with the built artifact."}
+{"idx": 8, "title": "Dependency scanning by using SBOM | GitLab Docs", "date": "", "ddg_snippet": "Analyze dependency behavior. Dependency scanning by using SBOM .The dependency scanning analyzer produces CycloneDX Software Bill of Materials ( SBOM ) for each supported lock file or dependency graph export detected.", "subpage_snippet": "", "source": "docs.gitlab.com", "link": "https://docs.gitlab.com/user/application_security/dependency_scanning/dependency_scanning_sbom/", "content": "Analyze dependency behavior. Dependency scanning by using SBOM .The dependency scanning analyzer produces CycloneDX Software Bill of Materials ( SBOM ) for each supported lock file or dependency graph export detected."}
+{"idx": 9, "title": "The Dependencies SBOM Report", "date": "", "ddg_snippet": "The Mend Platform enables you to generate and export a Software Bill of Materials ( SBOM ) report, that specifies the libraries, code packages, and other third-party components that are used in your applications.", "subpage_snippet": "", "source": "docs.mend.io", "link": "https://docs.mend.io/platform/latest/the-dependencies-sbom-report", "content": "The Mend Platform enables you to generate and export a Software Bill of Materials ( SBOM ) report, that specifies the libraries, code packages, and other third-party components that are used in your applications."}
diff --git a/data/sampled_jsons/stochastic_localization_sampler_generative_models.jsonl b/data/sampled_jsons/stochastic_localization_sampler_generative_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..647b323de77c8d15049854678d0aae5547033ecf
--- /dev/null
+++ b/data/sampled_jsons/stochastic_localization_sampler_generative_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": ", the associated stochastic localization sampler is the algorithm that generates a sample for. X𝑋Xitalic_X. by first sampling .for diverse stochastic localization samplers and models of data, including diffusion and autoregression processes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": ", the associated stochastic localization sampler is the algorithm that generates a sample for. X𝑋Xitalic_X. by first sampling .for diverse stochastic localization samplers and models of data, including diffusion and autoregression processes."}
+{"idx": 1, "title": "(PDF) Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "widely-used generative models do not belong to the family of localization -based samplers .We investigate diffusion models generating synthetic samples from the probability distribution represented by the ground truth (GT) samples .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "widely-used generative models do not belong to the family of localization -based samplers .We investigate diffusion models generating synthetic samples from the probability distribution represented by the ground truth (GT) samples ."}
+{"idx": 2, "title": "Diffusion Models , Sampling and Stochastic Localization Spring 2024", "date": "", "ddg_snippet": "Selected topics on Markov chain Monte Carlo sampling algorithms, diffusion generative models and related proof techniques. The official course catalogue page can be found here.", "subpage_snippet": "", "source": "metaphor.ethz.ch", "link": "https://metaphor.ethz.ch/x/2024/fs/401-4634-24L/", "content": "Selected topics on Markov chain Monte Carlo sampling algorithms, diffusion generative models and related proof techniques. The official course catalogue page can be found here."}
+{"idx": 3, "title": "Sampling , Diffusions, and Stochastic Localization | DeepAI", "date": "", "ddg_snippet": "An algorithmic version of stochastic localization was introduced in [EAMS2022], to obtain an algorithm that samples from certain statistical mechanics models .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/sampling-diffusions-and-stochastic-localization", "content": "An algorithmic version of stochastic localization was introduced in [EAMS2022], to obtain an algorithm that samples from certain statistical mechanics models ."}
+{"idx": 4, "title": "Localized schro¨ dinger bridge sampler", "date": "", "ddg_snippet": "The sampler (5) can be used in the general context of score- generative or diffusion modeling , however, our main motivation is in Bayesian inference and in conditional sampling with applica-tions to multi-scale processes.", "subpage_snippet": "", "source": "www.maths.usyd.edu.au", "link": "https://www.maths.usyd.edu.au/u/gottwald/preprints/genmodloc.pdf", "content": "The sampler (5) can be used in the general context of score- generative or diffusion modeling , however, our main motivation is in Bayesian inference and in conditional sampling with applica-tions to multi-scale processes."}
+{"idx": 5, "title": "Sampling via Stochastic Localization", "date": "", "ddg_snippet": "From Stochastic Localization to sampling . Sherrington-Kirkpatrick model . Posterior expectation via AMP. Z2-synchronization.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~montanar/OTHER/TALKS/mcqmc2022.pdf", "content": "From Stochastic Localization to sampling . Sherrington-Kirkpatrick model . Posterior expectation via AMP. Z2-synchronization."}
+{"idx": 6, "title": "2402.10758 - Stochastic Localization via Iterative Posterior Sampling", "date": "", "ddg_snippet": "This paper introduces Stochastic Localization via Iterative Posterior Sampling (SLIPS), a novel approach enhancing sampling methodologies within probabilistic modeling and Bayesian inference.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2402.10758", "content": "This paper introduces Stochastic Localization via Iterative Posterior Sampling (SLIPS), a novel approach enhancing sampling methodologies within probabilistic modeling and Bayesian inference."}
+{"idx": 7, "title": "Sampling Techniques: Stochastic Localization Explained", "date": "", "ddg_snippet": "Learn about Stochastic Localization in sampling and its key processes.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-08-30-sampling-techniques-stochastic-localization-explained--a3jg44v", "content": "Learn about Stochastic Localization in sampling and its key processes."}
+{"idx": 8, "title": "Sampling from the Random Linear Model via Stochastic Localization ...", "date": "", "ddg_snippet": "Recently, Approximate Message Passing (AMP) has been integrated with stochastic localization (diffusion model ) by providing a computationally efficient estimator of the posterior mean.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v258/cui25c.html", "content": "Recently, Approximate Message Passing (AMP) has been integrated with stochastic localization (diffusion model ) by providing a computationally efficient estimator of the posterior mean."}
+{"idx": 9, "title": "D Iffusion M odels via s tochastic L ocalization", "date": "", "ddg_snippet": "Diffusion Models . Stochastic Localization . Stochastic localization sampling schemes were developed by El Alaoui et al.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=r5njV3BsuD", "content": "Diffusion Models . Stochastic Localization . Stochastic localization sampling schemes were developed by El Alaoui et al."}
diff --git a/data/sampled_jsons/stochastic_localization_sampler_generative_models_Blink_of_an_eye_paper_Section_2.1_definition_year_2024.jsonl b/data/sampled_jsons/stochastic_localization_sampler_generative_models_Blink_of_an_eye_paper_Section_2.1_definition_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..75e49c57bab4c621e73aca0bff4f2f7774fc9dbf
--- /dev/null
+++ b/data/sampled_jsons/stochastic_localization_sampler_generative_models_Blink_of_an_eye_paper_Section_2.1_definition_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Roughly speaking, a stochastic localization scheme is any generative model given by a time-reversal of a Markovian degradation process which takes a sample from the target distribution and generates progressively less informative \"observations\" of it.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "Roughly speaking, a stochastic localization scheme is any generative model given by a time-reversal of a Markovian degradation process which takes a sample from the target distribution and generates progressively less informative \"observations\" of it."}
+{"idx": 1, "title": "In the Blink of an Eye: A Unified Theory for Feature Emergence in ...", "date": "", "ddg_snippet": "The key insight of our approach is to exploit the powerful formalism for generative models of stochastic localization , which has roots as a proof technique in probability theory. Leveraging our consolidated theory for critical windows, we apply it to different examples of critical windows in theoretical and empirical contexts.", "subpage_snippet": "", "source": "dash.harvard.edu", "link": "https://dash.harvard.edu/items/f3795c57-8ff8-44d2-ae92-1fc5525aeb39", "content": "The key insight of our approach is to exploit the powerful formalism for generative models of stochastic localization , which has roots as a proof technique in probability theory. Leveraging our consolidated theory for critical windows, we apply it to different examples of critical windows in theoretical and empirical contexts."}
+{"idx": 2, "title": "(PDF) Blink of an eye: a simple theory for feature localization in ...", "date": "", "ddg_snippet": "PDF | Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched... | Find, read and cite all the research you ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "PDF | Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched... | Find, read and cite all the research you ..."}
+{"idx": 3, "title": "[PDF] Blink of an eye: a simple theory for feature localization in ...", "date": "", "ddg_snippet": "This work develops a simple, unifying theory to explain the sudden shifts in behavior of large language models and shows that it emerges generically as the generation process localizes to a sub-population of the distribution it models . Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Blink-of-an-eye:-a-simple-theory-for-feature-in-Li-Karan/1819508abae222d6a0ee516bdc1258ea47f084bc", "content": "This work develops a simple, unifying theory to explain the sudden shifts in behavior of large language models and shows that it emerges generically as the generation process localizes to a sub-population of the distribution it models . Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling ..."}
+{"idx": 4, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "In this work we de- velop a simple, unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QvqnPVGWAN", "content": "In this work we de- velop a simple, unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models ."}
+{"idx": 5, "title": "dblp: Blink of an eye: a simple theory for feature localization in ...", "date": "", "ddg_snippet": "Bibliographic details on Blink of an eye : a simple theory for feature localization in generative models .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-00921", "content": "Bibliographic details on Blink of an eye : a simple theory for feature localization in generative models ."}
+{"idx": 6, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Introduces \" blink of an eye \" theory to explain rapid feature emergence Shows features appear in critical time windows during model training Provides mathematical framework for understanding feature localization Demonstrates theory works across different model architectures Plain English Explanation Picture teaching a computer to recognize cats.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/blink-eye-simple-theory-feature-localization-generative", "content": "Introduces \" blink of an eye \" theory to explain rapid feature emergence Shows features appear in critical time windows during model training Provides mathematical framework for understanding feature localization Demonstrates theory works across different model architectures Plain English Explanation Picture teaching a computer to recognize cats."}
+{"idx": 7, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00921", "content": "Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final ..."}
+{"idx": 8, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Blink of an eye : a simple theory for feature localization in generative models : Paper and Code. Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/blink-of-an-eye-a-simple-theory-for-feature", "content": "Blink of an eye : a simple theory for feature localization in generative models : Paper and Code. Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This ..."}
+{"idx": 9, "title": "Sitan Chen June 10, 2025 arXiv:2502.00921v2 [cs.LG] 5 Jun 2025", "date": "", "ddg_snippet": "Blink of an eye : a simple theory for feature localization in generative models", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00921", "content": "Blink of an eye : a simple theory for feature localization in generative models"}
diff --git a/data/sampled_jsons/substitution_conflicts_coherent_conflicts_language_models_knowledge_conflicts_attention_heads_year_2024.jsonl b/data/sampled_jsons/substitution_conflicts_coherent_conflicts_language_models_knowledge_conflicts_attention_heads_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..49ed76dbc4139a30f96fea6ee76a0fff6cce70ed
--- /dev/null
+++ b/data/sampled_jsons/substitution_conflicts_coherent_conflicts_language_models_knowledge_conflicts_attention_heads_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Jun 9, 2025 · The dataset includes multiple forms of knowledge conflict , ensuring robustness against clean inputs, substitution -based conflicts , and coherent conflicts , rather than focusing on a single type.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v2", "content": "Jun 9, 2025 · The dataset includes multiple forms of knowledge conflict , ensuring robustness against clean inputs, substitution -based conflicts , and coherent conflicts , rather than focusing on a single type."}
+{"idx": 1, "title": "Taming Knowledge Conflict in Language Models", "date": "", "ddg_snippet": "We construct three evaluation tiers: (a) clean input, (b) substitution conflict , and (c) coherent conflict , representing increasing levels of contextual distraction over diverse factual domains.", "subpage_snippet": "", "source": "gaotangli.github.io", "link": "https://gaotangli.github.io/project_page/Taming-Knowledge-Conflict/", "content": "We construct three evaluation tiers: (a) clean input, (b) substitution conflict , and (c) coherent conflict , representing increasing levels of contextual distraction over diverse factual domains."}
+{"idx": 2, "title": "Taming Knowledge Conflicts in Language Models - OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the superposition of contextual information and parametric memory, where highly influential attention heads simultaneously contribute to both memory and context.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0cEZyhHEks", "content": "May 1, 2025 · In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the superposition of contextual information and parametric memory, where highly influential attention heads simultaneously contribute to both memory and context."}
+{"idx": 3, "title": "Taming Knowledge Conflicts in Language Models | alphaXiv", "date": "", "ddg_snippet": "Abstract: Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge. Previous works attribute this conflict to the interplay between \"memory heads \" and \"context heads \", attention heads assumed to promote either memory or context exclusively.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.10996v2", "content": "Abstract: Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge. Previous works attribute this conflict to the interplay between \"memory heads \" and \"context heads \", attention heads assumed to promote either memory or context exclusively."}
+{"idx": 4, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Mar 14, 2025 · In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the superposition of contextual information and parametric memory, where highly influential attention heads simultaneously contribute to both memory and context.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Mar 14, 2025 · In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the superposition of contextual information and parametric memory, where highly influential attention heads simultaneously contribute to both memory and context."}
+{"idx": 5, "title": "Addressing Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Sep 3, 2025 · In this paper, we aim to interpret the mechanism of knowledge conflicts through the lens of information flow, and then mitigate conflicts by precise interventions at the pivotal point.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-09-03-addressing-knowledge-conflicts-in-language-models--a9pwq1z", "content": "Sep 3, 2025 · In this paper, we aim to interpret the mechanism of knowledge conflicts through the lens of information flow, and then mitigate conflicts by precise interventions at the pivotal point."}
+{"idx": 6, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Paragraph-level Conflict ( Coherent Counterfactual): Re-cent work (Xie et al., 2024) demonstrates that language models rely more on context when it is coherent . In this scenario, the context extends beyond a single substitution , reinforced by coherent and persuasive evidence, often...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10996", "content": "Paragraph-level Conflict ( Coherent Counterfactual): Re-cent work (Xie et al., 2024) demonstrates that language models rely more on context when it is coherent . In this scenario, the context extends beyond a single substitution , reinforced by coherent and persuasive evidence, often..."}
+{"idx": 7, "title": "(PDF) Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. Abstract.their influence in coherent conflicts . Surprisingly, half of. the top-performing “context heads ” in substitution conflicts .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389894493_Taming_Knowledge_Conflicts_in_Language_Models", "content": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. Abstract.their influence in coherent conflicts . Surprisingly, half of. the top-performing “context heads ” in substitution conflicts ."}
+{"idx": 8, "title": "Knowledge Conflicts for LLMs: A Survey", "date": "", "ddg_snippet": "ing knowledge conflicts in language models .resolving knowledge conflicts by enabling models able information, helping the model remain faithful. 1851. 1820 to synthesize information from broader contexts, when confronted with misleading context.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=P8PF2e5EXf&name=pdf", "content": "ing knowledge conflicts in language models .resolving knowledge conflicts by enabling models able information, helping the model remain faithful. 1851. 1820 to synthesize information from broader contexts, when confronted with misleading context."}
+{"idx": 9, "title": "Taming Knowledge Conflicts in", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Gaotang Li†, Yuzhong Chen‡, Hanghang Tong†. Mechanistic analysis[1,2]: some model components ( attention heads ) are promoting memory, while others are promoting.", "subpage_snippet": "", "source": "actionable-interpretability.github.io", "link": "https://actionable-interpretability.github.io/posters/ICML_25_JuICE_Poster+-+Gaotang+Li.pdf", "content": "Taming Knowledge Conflicts in Language Models . Gaotang Li†, Yuzhong Chen‡, Hanghang Tong†. Mechanistic analysis[1,2]: some model components ( attention heads ) are promoting memory, while others are promoting."}
diff --git a/data/sampled_jsons/summary_of_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_.jsonl b/data/sampled_jsons/summary_of_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e8365a6468044fae3a035c566cac1350aaffc4be
--- /dev/null
+++ b/data/sampled_jsons/summary_of_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ..."}
+{"idx": 1, "title": "PDF RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4b77d5b896c321a29277524a98a50215-Paper-Conference.pdf", "content": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Insights: The use of synthetic data can significantly improve the efficiency of training large language models for math reasoning tasks. The presence of spurious correlations in synthetic data can hinder the performance of models trained on such data . Addressing these correlations is crucial for achieving optimal results.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/1dm164m/rl_on_incorrect_synthetic_data_scales_the/", "content": "Insights: The use of synthetic data can significantly improve the efficiency of training large language models for math reasoning tasks. The presence of spurious correlations in synthetic data can hinder the performance of models trained on such data . Addressing these correlations is crucial for achieving optimal results."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ...", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ..."}
+{"idx": 5, "title": "scaling-LLM-math-synthetic-data/README.md at master - GitHub", "date": "", "ddg_snippet": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight -Fold\"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ars22/scaling-LLM-math-synthetic-data/blob/master/README.md", "content": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight -Fold\""}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2406.14532", "content": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or ..."}
+{"idx": 7, "title": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight ...", "date": "", "ddg_snippet": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
+{"idx": 8, "title": "AI-Powered Paper Summarization about the arXiv paper 2406.14532v1", "date": "", "ddg_snippet": "Easy-to-read summary of the arXiv paper 2406.14532v1 entitled RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight -Fold", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "Easy-to-read summary of the arXiv paper 2406.14532v1 entitled RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight -Fold"}
+{"idx": 9, "title": "Learning to Reason by Failing: Offline RL on Sub-optimal Rollouts ...", "date": "", "ddg_snippet": "First, we find that while the typical approach of finetuning a model on synthetic correct or *positive* problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner **doubles** the sample efficiency of synthetic data .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=v2PV1yCFJk", "content": "First, we find that while the typical approach of finetuning a model on synthetic correct or *positive* problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner **doubles** the sample efficiency of synthetic data ."}
diff --git a/data/sampled_jsons/survey_of_causal_representation_learning_methods.jsonl b/data/sampled_jsons/survey_of_causal_representation_learning_methods.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..028992a399179c61d2b38ada498ba5a8cf1bea8b
--- /dev/null
+++ b/data/sampled_jsons/survey_of_causal_representation_learning_methods.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Reinforcement Learning : A Survey", "date": "", "ddg_snippet": "Causal discovery and causal reasoning. Causal representation learning . A Brief Introduction to Environments and Tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2307.01452", "content": "Causal discovery and causal reasoning. Causal representation learning . A Brief Introduction to Environments and Tasks."}
+{"idx": 1, "title": "GitHub - Akomand/ Causal -Generative-Modeling- Survey : A Survey on...", "date": "", "ddg_snippet": "We provide a technical survey on causal generative modeling categorized into causal representation learning and controllable counterfactual generation methods . We focus on fundamental theory, methodology, drawbacks, datasets, and metrics.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Akomand/Causal-Generative-Modeling-Survey", "content": "We provide a technical survey on causal generative modeling categorized into causal representation learning and controllable counterfactual generation methods . We focus on fundamental theory, methodology, drawbacks, datasets, and metrics."}
+{"idx": 2, "title": "A Survey on Causal Representation Learning ... | Papers With Code", "date": "", "ddg_snippet": "Causal Representation Learning (CRL) has recently been a promising direction to address the causal relationship problem in vision understanding. This survey presents recent advances in CRL in vision. Firstly, we introduce the basic concept of causal inference.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/a-survey-on-causal-representation-learning", "content": "Causal Representation Learning (CRL) has recently been a promising direction to address the causal relationship problem in vision understanding. This survey presents recent advances in CRL in vision. Firstly, we introduce the basic concept of causal inference."}
+{"idx": 3, "title": "Causal Inference Meets Deep Learning : A Comprehensive Survey ...", "date": "", "ddg_snippet": "Causal inference in visual representation . Causal inference is based on the directed relationship between variables and uses interventions, counterfactuals, and other methods to explore the causal relationships among multiple variables.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11384545/", "content": "Causal inference in visual representation . Causal inference is based on the directed relationship between variables and uses interventions, counterfactuals, and other methods to explore the causal relationships among multiple variables."}
+{"idx": 4, "title": "(PDF) Causal Inference Meets Deep Learning : A Comprehensive...", "date": "", "ddg_snippet": "gence of causal learning , increasingly causal -oriented methods . have appeared in interpretable learning . Causal inference can.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383104009_Causal_Inference_Meets_Deep_Learning_A_Comprehensive_Survey", "content": "gence of causal learning , increasingly causal -oriented methods . have appeared in interpretable learning . Causal inference can."}
+{"idx": 5, "title": "Causal Reasoning Meets Visual Representation", "date": "", "ddg_snippet": "Learning causality requires machine learning methods not only to predict the outcome of i.i.d. experiments but also to reason from a causal perspective. Causal reasoning can be divided into three levels. The first level is association.", "subpage_snippet": "", "source": "sysu-hcp.net", "link": "https://sysu-hcp.net/userfiles/files/2023/06/21/69f30755ab1615dc.pdf", "content": "Learning causality requires machine learning methods not only to predict the outcome of i.i.d. experiments but also to reason from a causal perspective. Causal reasoning can be divided into three levels. The first level is association."}
+{"idx": 6, "title": "Marrying Causal Representation Learning with", "date": "", "ddg_snippet": "In this paper, we draw a clear connection between the two and their key assumptions, allowing us to apply identifiable methods developed in causal representation learning to dynamical systems.[4] Karl Johan Åström and Peter Eykhoff. System identification—a survey .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/83eb339ed42297658fa24b5cec939285-Paper-Conference.pdf", "content": "In this paper, we draw a clear connection between the two and their key assumptions, allowing us to apply identifiable methods developed in causal representation learning to dynamical systems.[4] Karl Johan Åström and Peter Eykhoff. System identification—a survey ."}
+{"idx": 7, "title": "Causal Representation Learning : Uncovering Hidden Patterns", "date": "", "ddg_snippet": "For example, utilizing experimental designs or refined survey methods can yield more reliable data for analysis. #Conclusion. Causal representation learning is a vital area of study with significant implications across numerous fields.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-09-causal-representation-learning-uncovering-hidden-patterns--a9rddg5", "content": "For example, utilizing experimental designs or refined survey methods can yield more reliable data for analysis. #Conclusion. Causal representation learning is a vital area of study with significant implications across numerous fields."}
+{"idx": 8, "title": "Causal representation learning as... — AI Alignment Forum", "date": "", "ddg_snippet": "Causal representation learning as a technique to prevent goal misgeneralization.Main proposal. Causal representation learning (CRL) is a set of techniques that can be used to find “high-level causal variables from low-level observations” (Schölkopf, 2021).", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/vzLNnc59LyzqDCyJC/causal-representation-learning-as-a-technique-to-prevent", "content": "Causal representation learning as a technique to prevent goal misgeneralization.Main proposal. Causal representation learning (CRL) is a set of techniques that can be used to find “high-level causal variables from low-level observations” (Schölkopf, 2021)."}
+{"idx": 9, "title": "Causal reasoning meets visual representation learning ...", "date": "", "ddg_snippet": "Researchers hope that this survey can help attract attention, encourage discussions, and bring to the forefront the urgency of developing novel causal reasoning methods , publicly available benchmarks, and consensus-building standards for reliable visual representation learning and related...", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2023-11-causal-visual-representation-prospective.pdf", "content": "Researchers hope that this survey can help attract attention, encourage discussions, and bring to the forefront the urgency of developing novel causal reasoning methods , publicly available benchmarks, and consensus-building standards for reliable visual representation learning and related..."}
diff --git a/data/sampled_jsons/sybilhunter_Winter_et_al._abstract.jsonl b/data/sampled_jsons/sybilhunter_Winter_et_al._abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f11fecb8d3907a6eaf38914d0fe287668abef9c
--- /dev/null
+++ b/data/sampled_jsons/sybilhunter_Winter_et_al._abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - NullHypothesis/sybilhunter: Hunting for Sybils and ... SybilHunter: Hybrid graph-based sybil detection by ... Identifying and Characterizing Sybils in the Tor Network Identifying and characterizing sybils in the tor network Protecting Tor from Sybil attacks Protecting the Tor network from Sybil attacks", "date": "", "ddg_snippet": "Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data. For example, sybilhunter can tell you (i) when an unusally large amount of relays joined or left the Tor network, (ii) which Tor relays cha... See full list on github.com Assuming you have a working Go installation, this is how you can get started: Now you have one month worth of consensuses and can proceed to the next section to learn more about analysis examples. See full list on github.com Sybilhunter takes as input data obtained from CollecTor. Let's start by pretty-printing a file containing a network consensus or relay descriptors: Next, here's how you can analyse how often relays changed their fingerprint in a set of consensus documents: Sybilhunter is also able to create uptime images, visualising the uptime of relays over time. In such an image, every column is a relay and every row is a consensus. Each pixel is either black (relay was offline) or white (relay was online). Red blocks are adjacent relays with identical uptime. You can create an uptime image by running: Sybilhunter then writes an image like the following to disk: You can also put command line arguments into the configuration file ~/.sybilhunterrc. The format is just like command line arguments, one per line. For example: Note that command line arguments overwrite the arguments in the configuration file. See full list on github.com Check out doctor's sybil checker script, and hstools can be useful for finding anomalies in hidden service directories. See full list on github.com For bugs and requests, please file a ticket in The Tor Project's bug tracker. You can also contact me privately: Contact: Philipp Winter phw@nymity.ch See full list on github.com Aug 21, 2022 · Zheng et al . [26] identify a new type of sybil users, elite sybil users (highly-rated users recruited by attackers), and propose an early-warning system ELSIEDET to detect sybil campaigns. They characterize the behaviors of elite sybil users with a ground truth dataset from Dianping (a user-review social network App in China). Abstract Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practi-cal tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as config-uration; and behavior, such as uptime sequences. We ... In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter 's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers. Sep 9, 2016 · Code A lot of engineering effort went into sybilhunter , our command line tool to expose and analyse Sybil relays. The tool is implemented in Go and uses our parsing library zoossh. Sybilhunter takes as input archived network data from CollecTor and produces visualisations and CSV files, as the ones shown above. ABSTRACT The Tor network is periodically subject to Sybil attacks. Most of these attacks were not carefully executed and Tor directory authority operators were quick to detect and block the malignant relays. However, the Tor network lacks so-phisticated tools to detect and protect against Sybil attacks. As a result, more advanced attacks could remain undetected. While there is a large body of ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NullHypothesis/sybilhunter", "content": "Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data. For example, sybilhunter can tell you (i) when an unusally large amount of relays joined or left the Tor network, (ii) which Tor relays cha... See full list on github.com Assuming you have a working Go installation, this is how you can get started: Now you have one month worth of consensuses and can proceed to the next section to learn more about analysis examples. See full list on github.com Sybilhunter takes as input data obtained from CollecTor. Let's start by pretty-printing a file containing a network consensus or relay descriptors: Next, here's how you can analyse how often relays changed their fingerprint in a set of consensus documents: Sybilhunter is also able to create uptime images, visualising the uptime of relays over time. In such an image, every column is a relay and every row is a consensus. Each pixel is either black (relay was offline) or white (relay was online). Red blocks are adjacent relays with identical uptime. You can create an uptime image by running: Sybilhunter then writes an image like the following to disk: You can also put command line arguments into the configuration file ~/.sybilhunterrc. The format is just like command line arguments, one per line. For example: Note that command line arguments overwrite the arguments in the configuration file. See full list on github.com Check out doctor's sybil checker script, and hstools can be useful for finding anomalies in hidden service directories. See full list on github.com For bugs and requests, please file a ticket in The Tor Project's bug tracker. You can also contact me privately: Contact: Philipp Winter phw@nymity.ch See full list on github.com Aug 21, 2022 · Zheng et al . [26] identify a new type of sybil users, elite sybil users (highly-rated users recruited by attackers), and propose an early-warning system ELSIEDET to detect sybil campaigns. They characterize the behaviors of elite sybil users with a ground truth dataset from Dianping (a user-review social network App in China). Abstract Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practi-cal tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as config-uration; and behavior, such as uptime sequences. We ... In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter 's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers. Sep 9, 2016 · Code A lot of engineering effort went into sybilhunter , our command line tool to expose and analyse Sybil relays. The tool is implemented in Go and uses our parsing library zoossh. Sybilhunter takes as input archived network data from CollecTor and produces visualisations and CSV files, as the ones shown above. ABSTRACT The Tor network is periodically subject to Sybil attacks. Most of these attacks were not carefully executed and Tor directory authority operators were quick to detect and block the malignant relays. However, the Tor network lacks so-phisticated tools to detect and protect against Sybil attacks. As a result, more advanced attacks could remain undetected. While there is a large body of ..."}
+{"idx": 1, "title": "SybilHunter: Hybrid graph-based sybil detection by ...", "date": "", "ddg_snippet": "Aug 21, 2022 · Zheng et al . [26] identify a new type of sybil users, elite sybil users (highly-rated users recruited by attackers), and propose an early-warning system ELSIEDET to detect sybil campaigns. They characterize the behaviors of elite sybil users with a ground truth dataset from Dianping (a user-review social network App in China).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231222006373", "content": "Aug 21, 2022 · Zheng et al . [26] identify a new type of sybil users, elite sybil users (highly-rated users recruited by attackers), and propose an early-warning system ELSIEDET to detect sybil campaigns. They characterize the behaviors of elite sybil users with a ground truth dataset from Dianping (a user-review social network App in China)."}
+{"idx": 2, "title": "Identifying and Characterizing Sybils in the Tor Network", "date": "", "ddg_snippet": "Abstract Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practi-cal tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as config-uration; and behavior, such as uptime sequences. We ...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_winter.pdf", "content": "Abstract Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practi-cal tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as config-uration; and behavior, such as uptime sequences. We ..."}
+{"idx": 3, "title": "Identifying and characterizing Sybils in the Tor network", "date": "", "ddg_snippet": "Abstract Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack prac-tical tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , the first system for detect-ing Sybil relays based on their appearance, such as con-figuration; and behavior, such as uptime ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1602.07787.pdf", "content": "Abstract Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack prac-tical tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , the first system for detect-ing Sybil relays based on their appearance, such as con-figuration; and behavior, such as uptime ..."}
+{"idx": 4, "title": "Identifying and characterizing sybils in the tor network", "date": "", "ddg_snippet": "In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter 's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3241094.3241185", "content": "In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter 's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers."}
+{"idx": 5, "title": "Protecting Tor from Sybil attacks", "date": "", "ddg_snippet": "Sep 9, 2016 · Code A lot of engineering effort went into sybilhunter , our command line tool to expose and analyse Sybil relays. The tool is implemented in Go and uses our parsing library zoossh. Sybilhunter takes as input archived network data from CollecTor and produces visualisations and CSV files, as the ones shown above.", "subpage_snippet": "", "source": "nymity.ch", "link": "https://nymity.ch/sybilhunting/", "content": "Sep 9, 2016 · Code A lot of engineering effort went into sybilhunter , our command line tool to expose and analyse Sybil relays. The tool is implemented in Go and uses our parsing library zoossh. Sybilhunter takes as input archived network data from CollecTor and produces visualisations and CSV files, as the ones shown above."}
+{"idx": 6, "title": "Protecting the Tor network from Sybil attacks", "date": "", "ddg_snippet": "ABSTRACT The Tor network is periodically subject to Sybil attacks. Most of these attacks were not carefully executed and Tor directory authority operators were quick to detect and block the malignant relays. However, the Tor network lacks so-phisticated tools to detect and protect against Sybil attacks. As a result, more advanced attacks could remain undetected. While there is a large body of ...", "subpage_snippet": "", "source": "www.petsymposium.org", "link": "https://www.petsymposium.org/2015/papers/winter-sybil-hotpets2015.pdf", "content": "ABSTRACT The Tor network is periodically subject to Sybil attacks. Most of these attacks were not carefully executed and Tor directory authority operators were quick to detect and block the malignant relays. However, the Tor network lacks so-phisticated tools to detect and protect against Sybil attacks. As a result, more advanced attacks could remain undetected. While there is a large body of ..."}
+{"idx": 7, "title": "Identifying and characterizing sybils in the tor network", "date": "", "ddg_snippet": "In this work, we develop sybilhunter, a system for detecting Sybil relays ... Philipp Winter et al. \"Spoiled Onions: Exposing Malicious Tor Exit Relays ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3241094.3241185", "content": "In this work, we develop sybilhunter, a system for detecting Sybil relays ... Philipp Winter et al. \"Spoiled Onions: Exposing Malicious Tor Exit Relays ..."}
+{"idx": 8, "title": "MirageFlow: A New Bandwidth Inflation Attack on Tor", "date": "", "ddg_snippet": "by C Sendner · Cited by 2 — Winter et al . [72] developed a tool Sybilhunter . [18], that can identify Sybils – relays that are operated by the same entity but failed to correctly report ... 16 pages", "subpage_snippet": "", "source": "www.ndss-symposium.org", "link": "https://www.ndss-symposium.org/wp-content/uploads/2024-1133-paper.pdf", "content": "by C Sendner · Cited by 2 — Winter et al . [72] developed a tool Sybilhunter . [18], that can identify Sybils – relays that are operated by the same entity but failed to correctly report ... 16 pages"}
+{"idx": 9, "title": "Exposing Malicious Accomplices in Tor via Anomalous ...", "date": "", "ddg_snippet": "by Y Yao — Also, a similar method for identifying Sybil is proposed by. Winter et al . [9]. In this study, the authors developed \" sybilhunter ,\" a system for detecting ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "by Y Yao — Also, a similar method for identifying Sybil is proposed by. Winter et al . [9]. In this study, the authors developed \" sybilhunter ,\" a system for detecting ..."}
diff --git a/data/sampled_jsons/sympathy_activation_subreddit_participation_climate_activism_reddit_causal_model_year_2024.jsonl b/data/sampled_jsons/sympathy_activation_subreddit_participation_climate_activism_reddit_causal_model_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5250c4bcde6fea53da21bd79ca66f3d5b2a9e7b3
--- /dev/null
+++ b/data/sampled_jsons/sympathy_activation_subreddit_participation_climate_activism_reddit_causal_model_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Echo chamber (media) - Wikipedia", "date": "", "ddg_snippet": "Participants in online discussions may find their opinions constantly echoed back to them, which reinforces their individual belief systems due to ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Echo_chamber_(media)", "content": "Participants in online discussions may find their opinions constantly echoed back to them, which reinforces their individual belief systems due to ..."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "D Sympathy . E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit .3 Model . We ground our operationalization of involvement with activist communities—the activation outcome we aim to build a causal . Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "D Sympathy . E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit .3 Model . We ground our operationalization of involvement with activist communities—the activation outcome we aim to build a causal . Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 2, "title": "US maternal death rate rose sharply in 2021, CDC data shows,", "date": "", "ddg_snippet": "Get an ad-free experience with special benefits, and directly support Reddit . ... Your account must be email-verified to participate in /r/news .", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/news/comments/11sq2k5/us_maternal_death_rate_rose_sharply_in_2021_cdc/", "content": "Get an ad-free experience with special benefits, and directly support Reddit . ... Your account must be email-verified to participate in /r/news ."}
+{"idx": 3, "title": "Sex trafficking efforts focus on girls, though many surveys", "date": "", "ddg_snippet": "Get an ad-free experience with special benefits, and directly support Reddit . ... You need to be an approved commenter/poster to participate here.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/FeMRADebates/comments/1wn8fu/sex_trafficking_efforts_focus_on_girls_though/", "content": "Get an ad-free experience with special benefits, and directly support Reddit . ... You need to be an approved commenter/poster to participate here."}
+{"idx": 4, "title": "dannyman.toldme.com : Sunnyvale", "date": "", "ddg_snippet": "Reddit , on the other hand, I participate in different forums, each with its own culture. ... The biggest Quality-of-Life advice I have for Reddit is ...", "subpage_snippet": "", "source": "dannyman.toldme.com", "link": "https://dannyman.toldme.com/category/news-oped/sunnyvale/", "content": "Reddit , on the other hand, I participate in different forums, each with its own culture. ... The biggest Quality-of-Life advice I have for Reddit is ..."}
+{"idx": 5, "title": "Ruby's Quick Takes — LessWrong", "date": "", "ddg_snippet": "From the anti-regulation side, I remember Martin Casado straight up lying about mechanistic interpretability rendering AI models completely ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/LvDekdqFuFnoxx8DF/ruby-s-quick-takes", "content": "From the anti-regulation side, I remember Martin Casado straight up lying about mechanistic interpretability rendering AI models completely ..."}
+{"idx": 6, "title": "RIP Culture War Thread | Slate Star Codex", "date": "", "ddg_snippet": "... getting thousands of comments per week, some from people who had never even heard of this blog and had just wandered in from elsewhere on Reddit .", "subpage_snippet": "", "source": "slatestarcodex.com", "link": "https://slatestarcodex.com/2019/02/22/rip-culture-war-thread/", "content": "... getting thousands of comments per week, some from people who had never even heard of this blog and had just wandered in from elsewhere on Reddit ."}
+{"idx": 7, "title": "A Systematic Review of Echo Chamber Research: Comparative", "date": "", "ddg_snippet": "Future research should prioritize cross-platform studies, continuous algorithmic audits, and investigations into the causal links between ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06631v3", "content": "Future research should prioritize cross-platform studies, continuous algorithmic audits, and investigations into the causal links between ..."}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 9, "title": "Can we take a moment to appreciate Florida. : stupidpol", "date": "", "ddg_snippet": "It is the expression and active agency of a political order and moral economy in which capitalist market forces are treated as unassailable nature.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/stupidpol/comments/u9iajl/can_we_take_a_moment_to_appreciate_florida/", "content": "It is the expression and active agency of a political order and moral economy in which capitalist market forces are treated as unassailable nature."}
diff --git a/data/sampled_jsons/tengxiao1DIL_github.jsonl b/data/sampled_jsons/tengxiao1DIL_github.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b9968ded3c81bd41287e3b0f136b2b50f055d656
--- /dev/null
+++ b/data/sampled_jsons/tengxiao1DIL_github.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "tengxiao1 (Teng Xiao) · GitHub", "date": "", "ddg_snippet": "@allenai . tengxiao1 has 21 repositories available. Follow their code on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1", "content": "@allenai . tengxiao1 has 21 repositories available. Follow their code on GitHub ."}
+{"idx": 1, "title": "Activity · tengxiao1/DIL · GitHub", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Activity · tengxiao1/DIL", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL/activity", "content": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Activity · tengxiao1/DIL"}
+{"idx": 2, "title": "ICLR2025|RLHF本质是模仿学习？新框架DIL在多任务上全面超越DPO，无需调参、性能更强!", "date": "", "ddg_snippet": "0.论文简介 本研究从模仿学习的角度出发，探讨大型语言模型与偏好数据的对齐问题。研究者建立了人类反馈强化学习 (RLHF) 与模仿学习 (IL) 之间的紧密理论联系，揭示了RLHF在偏好数据分布上隐式地执行模仿学习。基于这一联系提出了DIL，这是一个直接优化模仿学习目标的原则性框架。DIL为对齐问题 ...", "subpage_snippet": "", "source": "wxredian.com", "link": "https://wxredian.com/art?id=bfd38949a9db7d1607ebf482836c422a", "content": "0.论文简介 本研究从模仿学习的角度出发，探讨大型语言模型与偏好数据的对齐问题。研究者建立了人类反馈强化学习 (RLHF) 与模仿学习 (IL) 之间的紧密理论联系，揭示了RLHF在偏好数据分布上隐式地执行模仿学习。基于这一联系提出了DIL，这是一个直接优化模仿学习目标的原则性框架。DIL为对齐问题 ..."}
+{"idx": 3, "title": "DIL - D Wiki", "date": "", "ddg_snippet": "DIL is a hand-crafted compiler implementation for the D programming language written in D v2 using the Tango standard library. The lexer and the parser are fully implemented. Semantic analysis is being worked on. The backend will most probably be LLVM. External links DIL github page", "subpage_snippet": "", "source": "wiki.dlang.org", "link": "https://wiki.dlang.org/DIL", "content": "DIL is a hand-crafted compiler implementation for the D programming language written in D v2 using the Tango standard library. The lexer and the parser are fully implemented. Semantic analysis is being worked on. The backend will most probably be LLVM. External links DIL github page"}
+{"idx": 4, "title": "Releases: tengxiao1/DIL - GitHub", "date": "", "ddg_snippet": "There aren't any releases here You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL/releases", "content": "There aren't any releases here You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs."}
+{"idx": 5, "title": "A arXiv:2503.05079v1 [cs.LG] 7 Mar 2025", "date": "", "ddg_snippet": "IL for aligning with human preferences. Furthermore, GPT-4 consistently favored DIL over both baselines and chosen responses, demonstrating improvements of DIL over baselin", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.05079", "content": "IL for aligning with human preferences. Furthermore, GPT-4 consistently favored DIL over both baselines and chosen responses, demonstrating improvements of DIL over baselin"}
+{"idx": 6, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "(ii) We propose DIL, a simple and generalized imitation learning framework for alignment. DIL unifies imitation learning on preference data and bridges the gap between density ratio estimation and preference alignment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "(ii) We propose DIL, a simple and generalized imitation learning framework for alignment. DIL unifies imitation learning on preference data and bridges the gap between density ratio estimation and preference alignment."}
+{"idx": 7, "title": "On a Connection Between Imitation Learning and Rlhf", "date": "", "ddg_snippet": "Building on this connection, we propose DIL, a principled framework that directly optimizes the imitation learning objective. DIL provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants.", "subpage_snippet": "", "source": "pure.psu.edu", "link": "https://pure.psu.edu/en/publications/on-a-connection-between-imitation-learning-and-rlhf", "content": "Building on this connection, we propose DIL, a principled framework that directly optimizes the imitation learning objective. DIL provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants."}
+{"idx": 8, "title": "Teng Xiao", "date": "", "ddg_snippet": "Teng Xiao I am a postdoctoral researcher (and Young Investigator) at the Allen Institute for AI (AI2) and the University of Washington, advised by Prof. Noah A. Smith and Prof. Hanna Hajishirzi. I completed my PhD at Pennsylvania State University, where I am advised by Prof. Vasant Honavar. I am interested in machine learning, reinforcement learning, language models. I am currently working on ...", "subpage_snippet": "", "source": "tengxiao1.github.io", "link": "https://tengxiao1.github.io/", "content": "Teng Xiao I am a postdoctoral researcher (and Young Investigator) at the Allen Institute for AI (AI2) and the University of Washington, advised by Prof. Noah A. Smith and Prof. Hanna Hajishirzi. I completed my PhD at Pennsylvania State University, where I am advised by Prof. Vasant Honavar. I am interested in machine learning, reinforcement learning, language models. I am currently working on ..."}
+{"idx": 9, "title": "Teng Xiao - CatalyzeX", "date": "", "ddg_snippet": "View Teng Xiao's profile, machine learning models, research papers, and code. See more researchers and engineers like Teng Xiao.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Teng+Xiao", "content": "View Teng Xiao's profile, machine learning models, research papers, and code. See more researchers and engineers like Teng Xiao."}
diff --git a/data/sampled_jsons/themoonlight.io_Instant_Gaussian_Stream_Implementation_Details_dataset_AGM-Net_test_sequences.jsonl b/data/sampled_jsons/themoonlight.io_Instant_Gaussian_Stream_Implementation_Details_dataset_AGM-Net_test_sequences.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4f76f35d1539bc6483dc84d3e7fe0f580ad12569
--- /dev/null
+++ b/data/sampled_jsons/themoonlight.io_Instant_Gaussian_Stream_Implementation_Details_dataset_AGM-Net_test_sequences.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - yjb6/IGS: [CVPR25 Highlight] Instant Gaussian Stream : Fast...", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting [CVPR25].We can prepare our streaming dataset following 3DGStream and SpacetimeGaussian, and here we provide a simple script.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting [CVPR25].We can prepare our streaming dataset following 3DGStream and SpacetimeGaussian, and here we provide a simple script."}
+{"idx": 1, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "Anchor-driven Gaussian Motion Network. Key-frame-guided Streaming . Loss Function. Implementation details . Datasets . AGM Network .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "Anchor-driven Gaussian Motion Network. Key-frame-guided Streaming . Loss Function. Implementation details . Datasets . AGM Network ."}
+{"idx": 2, "title": "(PDF) Instant Gaussian Stream : Fast and Generalizable Streaming ...", "date": "", "ddg_snippet": "per , we propose Instant Gaussian Stream (IGS), a fast and. generalizable streaming fr amework, to address these issues.Motion Network ( AGM - Net ). This network utilizes a set . of key points, called anchor points, to carry motion features.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "per , we propose Instant Gaussian Stream (IGS), a fast and. generalizable streaming fr amework, to address these issues.Motion Network ( AGM - Net ). This network utilizes a set . of key points, called anchor points, to carry motion features."}
+{"idx": 3, "title": "Instant Gaussian Stream : Fast and Generalizable... | alphaXiv", "date": "", "ddg_snippet": "Instant Gaussian Stream (IGS) is a framework designed for fast and generalizable streaming reconstruction of dynamic scenes for Free-Viewpoint Video.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.16979", "content": "Instant Gaussian Stream (IGS) is a framework designed for fast and generalizable streaming reconstruction of dynamic scenes for Free-Viewpoint Video."}
+{"idx": 4, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "per, we propose Instant Gaussian Stream (IGS), a fast and view images is a valuable area of research, with appli4. Implementation details . In this Section, we first introduce the datasets we used, along with the partitioning and preprocessing of training data , in Sec.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "per, we propose Instant Gaussian Stream (IGS), a fast and view images is a valuable area of research, with appli4. Implementation details . In this Section, we first introduce the datasets we used, along with the partitioning and preprocessing of training data , in Sec."}
+{"idx": 5, "title": "[Literature Review] Method of moments for Gaussian mixtures...", "date": "", "ddg_snippet": "Keywords. Gaussian mixture models. method of moments. high dimensional. parameter recovery. implementation . benchmarks. polynomial equations.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/method-of-moments-for-gaussian-mixtures-implementation-and-benchmarks", "content": "Keywords. Gaussian mixture models. method of moments. high dimensional. parameter recovery. implementation . benchmarks. polynomial equations."}
+{"idx": 6, "title": "Instantly .ai - Sales Engagement & Lead Intelligence", "date": "", "ddg_snippet": "Instantly turns leads into clients with Automated Outreach, Deliverability Network , Lead Database & CRM.Prompt AI inside Instantly to craft personalization variables and entire sequences for each prospect.", "subpage_snippet": "", "source": "instantly.ai", "link": "https://instantly.ai/", "content": "Instantly turns leads into clients with Automated Outreach, Deliverability Network , Lead Database & CRM.Prompt AI inside Instantly to craft personalization variables and entire sequences for each prospect."}
+{"idx": 7, "title": "timeseries_ dataset _from_array: Creates a dataset of sliding windows...", "date": "", "ddg_snippet": "This function takes in a sequence of data -points gathered at equal intervals, along with time series parameters such as length of the sequences /windows, spacing between two sequence /windows, etc., to produce batches of timeseries inputs and targets.", "subpage_snippet": "", "source": "rdrr.io", "link": "https://rdrr.io/cran/keras3/man/timeseries_dataset_from_array.html", "content": "This function takes in a sequence of data -points gathered at equal intervals, along with time series parameters such as length of the sequences /windows, spacing between two sequence /windows, etc., to produce batches of timeseries inputs and targets."}
+{"idx": 8, "title": "Body Measurements Datasets - Mendeley Data", "date": "", "ddg_snippet": "~ Dataset Information~ This dataset could be the first of its kind and probably the only of its kind on public database.", "subpage_snippet": "", "source": "data.mendeley.com", "link": "https://data.mendeley.com/datasets/bjv6c9pmp4/1", "content": "~ Dataset Information~ This dataset could be the first of its kind and probably the only of its kind on public database."}
+{"idx": 9, "title": "Free AI Face Swap Online (No Sign-up)", "date": "", "ddg_snippet": "Instantly swap faces with AI Face Swap online for free. Enjoy the fun of face swapping in photos with no login required. 100% free, no watermark.", "subpage_snippet": "", "source": "aifaceswap.io", "link": "https://aifaceswap.io/", "content": "Instantly swap faces with AI Face Swap online for free. Enjoy the fun of face swapping in photos with no login required. 100% free, no watermark."}
diff --git a/data/sampled_jsons/three-branch_checks-and-balances_ethical_AI_limitations.jsonl b/data/sampled_jsons/three-branch_checks-and-balances_ethical_AI_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f0440631675353aef1a10e1d0effc4e61c30b25
--- /dev/null
+++ b/data/sampled_jsons/three-branch_checks-and-balances_ethical_AI_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AI Bias and Fairness: The Definitive Guide to Ethical AI |", "date": "", "ddg_snippet": "Ensuring fairness in AI is critical to preventing discrimination, fostering trust, and promoting ethical AI adoption.", "subpage_snippet": "", "source": "smartdev.com", "link": "https://smartdev.com/de/addressing-ai-bias-and-fairness-challenges-implications-and-strategies-for-ethical-ai/", "content": "Ensuring fairness in AI is critical to preventing discrimination, fostering trust, and promoting ethical AI adoption."}
+{"idx": 1, "title": "The Intersection Between AI Ethics and AI Governance", "date": "", "ddg_snippet": "AI ethics is a specific branch of ethics that deals with the ethics of the design, development, deployment, and evolution of AI systems.", "subpage_snippet": "", "source": "www.lumenova.ai", "link": "https://www.lumenova.ai/blog/intersection-between-ai-ethics-ai-governance/", "content": "AI ethics is a specific branch of ethics that deals with the ethics of the design, development, deployment, and evolution of AI systems."}
+{"idx": 2, "title": "2. Hopes about developments in ethical AI | Pew Research Center", "date": "", "ddg_snippet": "Political and judicial systems will be asked to keep abuses in check , and evolving case law will emerge (some experts are concerned this could be a ...", "subpage_snippet": "", "source": "www.pewresearch.org", "link": "https://www.pewresearch.org/internet/2021/06/16/2-hopes-about-developments-in-ethical-ai/", "content": "Political and judicial systems will be asked to keep abuses in check , and evolving case law will emerge (some experts are concerned this could be a ..."}
+{"idx": 3, "title": "ethical-ai – Dr. Olaf Witkowski", "date": "", "ddg_snippet": "The real challenge is balancing efficiency with transparency, ensuring AI serves as a bridge—not a barrier—in both machine and human- AI ...", "subpage_snippet": "", "source": "olafwitkowski.com", "link": "https://olafwitkowski.com/tag/ethical-ai/", "content": "The real challenge is balancing efficiency with transparency, ensuring AI serves as a bridge—not a barrier—in both machine and human- AI ..."}
+{"idx": 4, "title": "Exploring AI Ethics_ Challenges, Solutions, and Significance |", "date": "", "ddg_snippet": "The document emphasizes the importance of balancing AI innovation with ethical considerations to build trust and align AI with human values.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/exploring-ai-ethics-challenges-solutions-and-significance/262740724", "content": "The document emphasizes the importance of balancing AI innovation with ethical considerations to build trust and align AI with human values."}
+{"idx": 5, "title": "Ethics of AI: Benefits and risks of artificial intelligence", "date": "", "ddg_snippet": "Ethical concerns range from the esoteric, such as who is the author of an AI -created work of art; to the very real and very disturbing matter of ...", "subpage_snippet": "", "source": "germany.timesofnews.com", "link": "https://germany.timesofnews.com/breaking-news/ethics-of-ai-benefits-and-risks-of-artificial-intelligence", "content": "Ethical concerns range from the esoteric, such as who is the author of an AI -created work of art; to the very real and very disturbing matter of ..."}
+{"idx": 6, "title": "AI Ethics Are a Concern. Learn How You Can Stay Ethical –", "date": "", "ddg_snippet": "AI ethics is a established of ethical rules to guide and tell the growth and use of synthetic intelligence systems.", "subpage_snippet": "", "source": "sebastianpremici.com", "link": "https://sebastianpremici.com/ai-ethics-are-a-concern-learn-how-you-can-stay-ethical.html", "content": "AI ethics is a established of ethical rules to guide and tell the growth and use of synthetic intelligence systems."}
+{"idx": 7, "title": "GitHub - fabriziosalmi/You-Know-What-AI-Mean: A comprehensive", "date": "", "ddg_snippet": "... roadmap for navigating these multifaceted challenges, aiming to balance AI 's transformative potential with the imperative of upholding ethical and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fabriziosalmi/You-Know-What-AI-Mean", "content": "... roadmap for navigating these multifaceted challenges, aiming to balance AI 's transformative potential with the imperative of upholding ethical and ..."}
+{"idx": 8, "title": "From What to How: An Initial Review of Publicly Available AI", "date": "", "ddg_snippet": "Balancing the tension between supporting innovation, so that society’s right to benefit from science is protected (Knoppers and Thorogood 2017 ), ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11948-019-00165-5", "content": "Balancing the tension between supporting innovation, so that society’s right to benefit from science is protected (Knoppers and Thorogood 2017 ), ..."}
+{"idx": 9, "title": "A high-level overview of AI ethics: Patterns", "date": "", "ddg_snippet": "This review embraces inherent interdisciplinarity in the field by providing a high-level introduction to AI ethics drawing upon philosophy, law, and ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/patterns/fulltext/S2666-3899(21)00157-4", "content": "This review embraces inherent interdisciplinarity in the field by providing a high-level introduction to AI ethics drawing upon philosophy, law, and ..."}
diff --git "a/data/sampled_jsons/total_loss_stage_1_disease-matching_constraint_\316\273m_OR_lambda_m_radiology_report_generation.jsonl" "b/data/sampled_jsons/total_loss_stage_1_disease-matching_constraint_\316\273m_OR_lambda_m_radiology_report_generation.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..9ec4b39c984f1bc3f11c86dfe84f856cbc1203e1
--- /dev/null
+++ "b/data/sampled_jsons/total_loss_stage_1_disease-matching_constraint_\316\273m_OR_lambda_m_radiology_report_generation.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constructive Total Loss in Property Insurance | Property ...", "date": "", "ddg_snippet": "Aug 16, 2025 · Learn how the doctrine of constructive total loss shapes property insurance claims when repair is prohibited, ensuring fair recovery.", "subpage_snippet": "", "source": "www.propertyinsurancecoveragelaw.com", "link": "https://www.propertyinsurancecoveragelaw.com/blog/constructive-total-loss-in-property-insurance-doctrine/", "content": "Aug 16, 2025 · Learn how the doctrine of constructive total loss shapes property insurance claims when repair is prohibited, ensuring fair recovery."}
+{"idx": 1, "title": "What is “medical loss ratio” or “MLR” and why does it matter?", "date": "", "ddg_snippet": "The MLR is a comparison of how much of your premium goes towards paying medical claims compared to how much the insurer pays for administrative costs and keeps as profits. For example, an 82% MLR would mean that 82% of the total premiums paid by policyholders was going towards paying claims, and the other 18% was kept by the insurer for administrative expenses and profits.", "subpage_snippet": "", "source": "www.dfs.ny.gov", "link": "https://www.dfs.ny.gov/faqs/consumer-health/what-medical-loss-ratio-or-mlr-and-why-does-it-matter", "content": "The MLR is a comparison of how much of your premium goes towards paying medical claims compared to how much the insurer pays for administrative costs and keeps as profits. For example, an 82% MLR would mean that 82% of the total premiums paid by policyholders was going towards paying claims, and the other 18% was kept by the insurer for administrative expenses and profits."}
+{"idx": 2, "title": "Emergency Department Triage - StatPearls - NCBI Bookshelf How to get the most money from insurance for a totaled car ... Explaining Health Care Reform: Medical Loss Ratio (MLR) - KFF", "date": "", "ddg_snippet": "Aug 28, 2023 · Triage originates from the French word \"trier,\" which is used to describe the processes of sorting and organization. Triage is utilized in the healthcare community to categorize patients based on the severity of their injuries and, by extension, the order in which multiple patients require care and monitoring. The history of the emergency triage originated in the military for field doctors. As ... Aug 14, 2025 · You can negotiate with your insurance company for a higher settlement if your car is deemed a total loss . Learn how to get more money for a totaled car. Feb 29, 2012 · This fact sheet explains the Medical Loss Ratio requirement under the Affordable Care Act (ACA). The MLR provision limits the portion of premium dollars health insurers may spend on administration ...", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/books/NBK557583/", "content": "Aug 28, 2023 · Triage originates from the French word \"trier,\" which is used to describe the processes of sorting and organization. Triage is utilized in the healthcare community to categorize patients based on the severity of their injuries and, by extension, the order in which multiple patients require care and monitoring. The history of the emergency triage originated in the military for field doctors. As ... Aug 14, 2025 · You can negotiate with your insurance company for a higher settlement if your car is deemed a total loss . Learn how to get more money for a totaled car. Feb 29, 2012 · This fact sheet explains the Medical Loss Ratio requirement under the Affordable Care Act (ACA). The MLR provision limits the portion of premium dollars health insurers may spend on administration ..."}
+{"idx": 3, "title": "How to get the most money from insurance for a totaled car ...", "date": "", "ddg_snippet": "Aug 14, 2025 · You can negotiate with your insurance company for a higher settlement if your car is deemed a total loss . Learn how to get more money for a totaled car.", "subpage_snippet": "", "source": "www.insurance.com", "link": "https://www.insurance.com/auto-insurance/totaled-car/how-to-get-the-most-money-from-insurance-for-a-totaled-car/", "content": "Aug 14, 2025 · You can negotiate with your insurance company for a higher settlement if your car is deemed a total loss . Learn how to get more money for a totaled car."}
+{"idx": 4, "title": "Explaining Health Care Reform: Medical Loss Ratio (MLR) - KFF", "date": "", "ddg_snippet": "Feb 29, 2012 · This fact sheet explains the Medical Loss Ratio requirement under the Affordable Care Act (ACA). The MLR provision limits the portion of premium dollars health insurers may spend on administration ...", "subpage_snippet": "", "source": "www.kff.org", "link": "https://www.kff.org/affordable-care-act/explaining-health-care-reform-medical-loss-ratio-mlr/", "content": "Feb 29, 2012 · This fact sheet explains the Medical Loss Ratio requirement under the Affordable Care Act (ACA). The MLR provision limits the portion of premium dollars health insurers may spend on administration ..."}
+{"idx": 5, "title": "Population-Aligned Persona Generation for LLM-based Social", "date": "", "ddg_snippet": "... employ a two‑ stage resampling techniques, importance sampling followed by optimal transport, to make resampled distribution matches the human one ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10127v1", "content": "... employ a two‑ stage resampling techniques, importance sampling followed by optimal transport, to make resampled distribution matches the human one ..."}
+{"idx": 6, "title": "Patient Discharge Status and Claim Frequency Code Reporting", "date": "", "ddg_snippet": "Sep 11, 2020 · A patient discharge status code is a two-digit code that identifies where the patient is at the conclusion of a healthcare facility encounter or at the end of a billing cycle (the ‘through’ date of a claim).", "subpage_snippet": "", "source": "communityfirsthealthplans.com", "link": "https://communityfirsthealthplans.com/hhsc-news/patient-discharge-status-and-claim-frequency-code-reporting/", "content": "Sep 11, 2020 · A patient discharge status code is a two-digit code that identifies where the patient is at the conclusion of a healthcare facility encounter or at the end of a billing cycle (the ‘through’ date of a claim)."}
+{"idx": 7, "title": "PriorRG: Prior-Guided Contrastive Pre-training and", "date": "", "ddg_snippet": "... this challenge, we propose PriorRG, a novel chest X-ray report generation framework that mirrors real-world radiology workflows via a two- stage ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05353v1", "content": "... this challenge, we propose PriorRG, a novel chest X-ray report generation framework that mirrors real-world radiology workflows via a two- stage ..."}
+{"idx": 8, "title": "disruptions – The OM Blog by Heizer, Render, & Munson", "date": "", "ddg_snippet": "There was not a total loss of the $160,000 but there were significant losses due to the costs of labor for cleanup, restoration of the beehives and ...", "subpage_snippet": "", "source": "operationsmanagementblog.com", "link": "https://operationsmanagementblog.com/tag/disruptions/", "content": "There was not a total loss of the $160,000 but there were significant losses due to the costs of labor for cleanup, restoration of the beehives and ..."}
+{"idx": 9, "title": "Guest Posts – The OM Blog by Heizer, Render, & Munson", "date": "", "ddg_snippet": "There was not a total loss of the $160,000 but there were significant losses due to the costs of labor for cleanup, restoration of the beehives and ...", "subpage_snippet": "", "source": "operationsmanagementblog.com", "link": "https://operationsmanagementblog.com/category/guest-posts/", "content": "There was not a total loss of the $160,000 but there were significant losses due to the costs of labor for cleanup, restoration of the beehives and ..."}
diff --git a/data/sampled_jsons/typical_scaling_law_exponent_pre-training_language_models_error_rate.jsonl b/data/sampled_jsons/typical_scaling_law_exponent_pre-training_language_models_error_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bdc87169c84dd1a8034172b3fae7ed33f7f84262
--- /dev/null
+++ b/data/sampled_jsons/typical_scaling_law_exponent_pre-training_language_models_error_rate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law", "date": "", "ddg_snippet": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down."}
+{"idx": 1, "title": "Scaling Laws of Synthetic Data for Language Models", "date": "", "ddg_snippet": "26 Mar 2025 — These error rates correspond to accuracies of 60.0 % percent 60.0 60.0\\% 60.0 % and 71.3 % percent 71.3 71.3\\% 71.3 % . This highlights the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19551v2", "content": "26 Mar 2025 — These error rates correspond to accuracies of 60.0 % percent 60.0 60.0\\% 60.0 % and 71.3 % percent 71.3 71.3\\% 71.3 % . This highlights the ..."}
+{"idx": 2, "title": "[2001.08361] Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "by J Kaplan · 2020 · Cited by 4597 — We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size, dataset size, and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2001.08361", "content": "by J Kaplan · 2020 · Cited by 4597 — We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size, dataset size, and the ..."}
+{"idx": 3, "title": "Language models scale reliably with over-training and on ...", "date": "", "ddg_snippet": "by SY Gadre · Cited by 61 — Scaling laws are useful guides for derisking expensive training runs, as they predict performance of large models using cheaper, small- scale experiments.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=iZeQBqJamf", "content": "by SY Gadre · Cited by 61 — Scaling laws are useful guides for derisking expensive training runs, as they predict performance of large models using cheaper, small- scale experiments."}
+{"idx": 4, "title": "Scaling Laws for LLMs: From GPT-3 to o3", "date": "", "ddg_snippet": "Scaling laws help us to predict the results of larger and more expensive training runs, giving us the necessary confidence to continue investing in scale.", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/llm-scaling-laws", "content": "Scaling laws help us to predict the results of larger and more expensive training runs, giving us the necessary confidence to continue investing in scale."}
+{"idx": 5, "title": "Revisiting Scaling Laws for Language Models: The Role of ...", "date": "", "ddg_snippet": "by Z Chen · 2025 — This paper revisits these scaling laws by examining the impact of data quality and training strategies on model performance. Through extensive ... 19 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1163.pdf", "content": "by Z Chen · 2025 — This paper revisits these scaling laws by examining the impact of data quality and training strategies on model performance. Through extensive ... 19 pages"}
+{"idx": 6, "title": "Revisiting Neural Scaling Laws in Language and Vision", "date": "", "ddg_snippet": "by I Alabdulmohsin · Cited by 156 — TOP: The y-axis is ImageNet 10-shot error rate while the x-axis is the number of examples in JFT-300M [34] seen during pre - training . The architecture is BiT/ ... 27 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/8c22e5e918198702765ecff4b20d0a90-Supplemental-Conference.pdf", "content": "by I Alabdulmohsin · Cited by 156 — TOP: The y-axis is ImageNet 10-shot error rate while the x-axis is the number of examples in JFT-300M [34] seen during pre - training . The architecture is BiT/ ... 27 pages"}
+{"idx": 7, "title": "Scaling Laws Across Model Architectures: A Comparative ...", "date": "", "ddg_snippet": "by S Wang · 2024 · Cited by 7 — This paper delves into the analysis of scaling laws for Dense and MoE Models within the con- text of LLMs. We extend foundational research on.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.319.pdf", "content": "by S Wang · 2024 · Cited by 7 — This paper delves into the analysis of scaling laws for Dense and MoE Models within the con- text of LLMs. We extend foundational research on."}
+{"idx": 8, "title": "A Hitchhiker's Guide to Scaling Law Estimation", "date": "", "ddg_snippet": "by L Choshen · Cited by 7 — Scaling laws predict the loss of a target language model by extrapolating from easier-to- train mod- els with fewer parameters or smaller training .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KugSHTH0c8", "content": "by L Choshen · Cited by 7 — Scaling laws predict the loss of a target language model by extrapolating from easier-to- train mod- els with fewer parameters or smaller training ."}
+{"idx": 9, "title": "Scaling Laws from the Data Manifold Dimension", "date": "", "ddg_snippet": "by U Sharma · 2022 · Cited by 67 — The scaling law can be explained if neural models are effectively just performing regression on a data manifold of intrinsic dimension d. This simple theory ...", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume23/20-1111/20-1111.pdf", "content": "by U Sharma · 2022 · Cited by 67 — The scaling law can be explained if neural models are effectively just performing regression on a data manifold of intrinsic dimension d. This simple theory ..."}
diff --git a/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmark_dataset_image_size_2024_2023.jsonl b/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmark_dataset_image_size_2024_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2a1c0bb74b1551908220804cef53f39f4a2dba58
--- /dev/null
+++ b/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmark_dataset_image_size_2024_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) A Vision Centric Remote Sensing Benchmark", "date": "", "ddg_snippet": "Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390038158_A_Vision_Centric_Remote_Sensing_Benchmark", "content": "Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG."}
+{"idx": 1, "title": "Cross-city matters: A multimodal remote sensing benchmark dataset ...", "date": "", "ddg_snippet": "To this end, we build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task (called C2Seg dataset ), which consists of two cross-city scenes, i.e., Berlin-Augsburg...", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/cross-city-matters-a-multimodal-remote-sensing-benchmark-dataset-for-cross-city-semantic-segmentation-using-high-resolution-domain-adaptation-networks/926299798284272144-3747", "content": "To this end, we build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task (called C2Seg dataset ), which consists of two cross-city scenes, i.e., Berlin-Augsburg..."}
+{"idx": 2, "title": "A Vision Centric Remote Sensing Benchmark", "date": "", "ddg_snippet": "Floodnet: A high resolution aerial imagery dataset for post flood scene understanding. IEEE Access, 9:89644–89654, 2021. Sumbul et al.Foundation models for remote sensing and earth observation: A survey. arXiv preprint arXiv:2410.16602, 2024 . Zhai et al. [ 2023 ].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.15816v3", "content": "Floodnet: A high resolution aerial imagery dataset for post flood scene understanding. IEEE Access, 9:89644–89654, 2021. Sumbul et al.Foundation models for remote sensing and earth observation: A survey. arXiv preprint arXiv:2410.16602, 2024 . Zhai et al. [ 2023 ]."}
+{"idx": 3, "title": "GitHub - isaacnewtonfx/satellite- image -dl- datasets : Datasets for deep...", "date": "", "ddg_snippet": "Publication: PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval.Public dataset for roof segmentation from very- high - resolution aerial imagery (7.5cm).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/isaacnewtonfx/satellite-image-dl-datasets", "content": "Publication: PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval.Public dataset for roof segmentation from very- high - resolution aerial imagery (7.5cm)."}
+{"idx": 4, "title": "blanchon/FAIR1M · Datasets at Hugging Face", "date": "", "ddg_snippet": "FAIR1M is a part of the ISPRS Benchmark on Object Detection in High - Resolution Satellite Images . Please note that, as of now, only a portion of the training dataset (1,732/15,000 images ) has been released for the challenge. 1 million object instances.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/blanchon/FAIR1M", "content": "FAIR1M is a part of the ISPRS Benchmark on Object Detection in High - Resolution Satellite Images . Please note that, as of now, only a portion of the training dataset (1,732/15,000 images ) has been released for the challenge. 1 million object instances."}
+{"idx": 5, "title": "MARIDA: A benchmark for Marine Debris detection... | IEEE DataPort", "date": "", "ddg_snippet": "Dataset Files. MARIDA.zip ( Size : 1.08 GB).Katerina Kikaki 2023 MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data .", "subpage_snippet": "", "source": "ieee-dataport.org", "link": "https://ieee-dataport.org/documents/marida-benchmark-marine-debris-detection-sentinel-2-remote-sensing-data", "content": "Dataset Files. MARIDA.zip ( Size : 1.08 GB).Katerina Kikaki 2023 MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data ."}
+{"idx": 6, "title": "Remote sensing of diverse urban environments: From the single city...", "date": "", "ddg_snippet": "• Built a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR). • Developed a high - resolution domain adaptation network (HighDAN) to promote the AI model’s gener alization ability.", "subpage_snippet": "", "source": "pages.charlotte.edu", "link": "https://pages.charlotte.edu/gang-chen/wp-content/uploads/sites/184/2024/03/Chen-2024-Review-Multi-city-RS.pdf", "content": "• Built a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR). • Developed a high - resolution domain adaptation network (HighDAN) to promote the AI model’s gener alization ability."}
+{"idx": 7, "title": "AI applications in forest monitoring need remote sensing benchmark ...", "date": "", "ddg_snippet": "(DOI: 10.1109/BigData55660.2022.10020772) With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/ai-applications-in-forest-monitoring-need-remote-sensing-im07x60j", "content": "(DOI: 10.1109/BigData55660.2022.10020772) With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring..."}
+{"idx": 8, "title": "yriyazi/ High - Resolution - Remote - Sensing - Image ... - Githubissues", "date": "", "ddg_snippet": "yriyazi / High - Resolution - Remote - Sensing - Image -Captioning-Based-on-Structured-Attention-and-SAM-Network. Automatically generating language descriptions for remote sensing images has emerged as a significant research area within the field of remote sensing .", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/yriyazi/High-Resolution-Remote-Sensing-Image-Captioning-Based-on-Structured-Attention-and-SAM-Network/readme", "content": "yriyazi / High - Resolution - Remote - Sensing - Image -Captioning-Based-on-Structured-Attention-and-SAM-Network. Automatically generating language descriptions for remote sensing images has emerged as a significant research area within the field of remote sensing ."}
+{"idx": 9, "title": "FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in...", "date": "", "ddg_snippet": "论文阅读SUM: A benchmark dataset of Semantic Urban Meshes.【CVPR 2024 】【遥感目标检测】Poly Kernel Inception Network for Remote Sensing Detection.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/symuamua/article/details/117591140", "content": "论文阅读SUM: A benchmark dataset of Semantic Urban Meshes.【CVPR 2024 】【遥感目标检测】Poly Kernel Inception Network for Remote Sensing Detection."}
diff --git a/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmark_datasets_image_size.jsonl b/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmark_datasets_image_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0e6ae773c3d3f50c3be7abf6aae4c8f80f98aa0f
--- /dev/null
+++ b/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmark_datasets_image_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "手机型号中的Pro、Ultra、Note、Mate代表什么？您的手机呢？ - 知乎", "date": "", "ddg_snippet": "三星S23 Ultra 三、Mate Mate在英文里是伴侣的意思。华为的Mate系列主打高端商务手机，对标各个厂商的高端旗舰产品。Mate系列在这一含义上衍生出的是：期望智能手机可以成为用户的可靠伙伴，方便用户的工作生活，带来陪伴之意。这也是华为做手机的理念的体现。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/593491239", "content": "三星S23 Ultra 三、Mate Mate在英文里是伴侣的意思。华为的Mate系列主打高端商务手机，对标各个厂商的高端旗舰产品。Mate系列在这一含义上衍生出的是：期望智能手机可以成为用户的可靠伙伴，方便用户的工作生活，带来陪伴之意。这也是华为做手机的理念的体现。"}
+{"idx": 1, "title": "英特尔的酷睿ultra和i系列CPU有什么区别？哪个好？ - 知乎", "date": "", "ddg_snippet": "Ultra系列，相比同系列来说，最大的价值应该是核显增加了吧。 ultra 200系列性能也稳步提升。 具体情况具体分析，不能用名称直接下定义： 多线程生产力： 酷睿 Ultra 7 155H（16 核/22 线程）与 i7-13700H 接近，但功耗更低；传统 i9 系列（24 核）仍领先多核性能。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/9215091075", "content": "Ultra系列，相比同系列来说，最大的价值应该是核显增加了吧。 ultra 200系列性能也稳步提升。 具体情况具体分析，不能用名称直接下定义： 多线程生产力： 酷睿 Ultra 7 155H（16 核/22 线程）与 i7-13700H 接近，但功耗更低；传统 i9 系列（24 核）仍领先多核性能。"}
+{"idx": 2, "title": "如何评价新一代酷睿Ultra 200HX 系列处理器，以及其 XPU、AI 加持等新...", "date": "", "ddg_snippet": "如今面向游戏本市场的酷睿 Ultra 200HX 系列处理器在 3 月底集中亮相，不少消费者对于新处理器的优势和性能肯定充满了好奇，我们今天就来重点关注一下新处理器的优势以及它为游戏本品类，带来了哪些变革？", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/1889974373852616161", "content": "如今面向游戏本市场的酷睿 Ultra 200HX 系列处理器在 3 月底集中亮相，不少消费者对于新处理器的优势和性能肯定充满了好奇，我们今天就来重点关注一下新处理器的优势以及它为游戏本品类，带来了哪些变革？"}
+{"idx": 3, "title": "英特尔酷睿14代和酷睿 Ultra 处理器有什么区别？ - 知乎", "date": "", "ddg_snippet": "今年英特尔酷睿处理器处于新老架构的过渡阶段，在移动端又推出了酷睿14代、酷睿Ultra和酷睿1代三个系列，所以小白搞不清很正常。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/639908226", "content": "今年英特尔酷睿处理器处于新老架构的过渡阶段，在移动端又推出了酷睿14代、酷睿Ultra和酷睿1代三个系列，所以小白搞不清很正常。"}
+{"idx": 4, "title": "酷睿 Ultra 5 和 Ultra 7，或者i5和i7差距多大？ - 知乎", "date": "", "ddg_snippet": "Ultra 5 125H 对比 Ultra 7 155H 由于Ultra系列CPU采用分离式架构设计，包括3中类型的核心，另外还有独立的NPU，因此对Ultra系列的CPU我想应该从多个方向进行评估，比如CPU性能（单核，多核性能）、CPU核显、CPU功耗等等来评估。 通过Geekbench 5单核跑分中U5和U7两者差距在6%。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/640235797", "content": "Ultra 5 125H 对比 Ultra 7 155H 由于Ultra系列CPU采用分离式架构设计，包括3中类型的核心，另外还有独立的NPU，因此对Ultra系列的CPU我想应该从多个方向进行评估，比如CPU性能（单核，多核性能）、CPU核显、CPU功耗等等来评估。 通过Geekbench 5单核跑分中U5和U7两者差距在6%。"}
+{"idx": 5, "title": "英特尔 15 代 CPU 为什么要改名 Ultra，背后有哪些原因？ - 知乎", "date": "", "ddg_snippet": "Nov 21, 2024 · 英特尔 15 代 CPU 为什么要改名 Ultra，背后有哪些原因？ 桌面端15代U分别是： Ultra 5 245KUltra 7 265K Ultra 9 285K 好像是特意绕开以前的命名方式，是因为没有提升吗？", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/4668613212?write", "content": "Nov 21, 2024 · 英特尔 15 代 CPU 为什么要改名 Ultra，背后有哪些原因？ 桌面端15代U分别是： Ultra 5 245KUltra 7 265K Ultra 9 285K 好像是特意绕开以前的命名方式，是因为没有提升吗？"}
+{"idx": 6, "title": "Intel新出来的ultra 9 285H，什么水平？ - 知乎", "date": "", "ddg_snippet": "Ultra 9 285H，是笔电的第二代ultra处理器，这个cpu是H45版本，不像V系列，可以跟内存集成一起出片，价格便宜，性能还强，那Ultra 200系列H45处理器会不会成了一个挤牙膏的典型选手？", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/13779457253", "content": "Ultra 9 285H，是笔电的第二代ultra处理器，这个cpu是H45版本，不像V系列，可以跟内存集成一起出片，价格便宜，性能还强，那Ultra 200系列H45处理器会不会成了一个挤牙膏的典型选手？"}
+{"idx": 7, "title": "Intel Ultra 5还是AMD R7 8845H？2025年高性价比笔记本选择策略分享", "date": "", "ddg_snippet": "Aug 9, 2025 · 比如说，Ultra 5 125H版本的Thinkbook14+，笔记本左侧最内侧有一个TGX显卡扩展坞接口，而这玩意R7 8845H就没有。 AMD版本的Thinkbook14+，其实同样的位置就是封闭的，这也就等于是说，这个版本它是没办法用TGX这么个近乎带宽无损的接口外接高性能显卡的。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/tardis/zm/art/715336349", "content": "Aug 9, 2025 · 比如说，Ultra 5 125H版本的Thinkbook14+，笔记本左侧最内侧有一个TGX显卡扩展坞接口，而这玩意R7 8845H就没有。 AMD版本的Thinkbook14+，其实同样的位置就是封闭的，这也就等于是说，这个版本它是没办法用TGX这么个近乎带宽无损的接口外接高性能显卡的。"}
+{"idx": 8, "title": "酷睿Ultra9-185H和i9-13900H哪个强？", "date": "", "ddg_snippet": "May 24, 2024 · 因特尔酷睿Ultra 9-185H和i9-13900H都是性能比较强大的处理器，主要用于高性能的商务部或者是游戏本，深受消费者的喜爱。 那么，酷睿Ultra9-185H和i9-13900H哪个强？ 酷睿Ultra9-185H： 因特尔酷睿Ultra 9 185H是一款具有16个核心和22个线程的处理器，具有6个性能核心、8个效能核心和2个低功耗核心。拥有24MB的 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/tardis/zm/art/699662155", "content": "May 24, 2024 · 因特尔酷睿Ultra 9-185H和i9-13900H都是性能比较强大的处理器，主要用于高性能的商务部或者是游戏本，深受消费者的喜爱。 那么，酷睿Ultra9-185H和i9-13900H哪个强？ 酷睿Ultra9-185H： 因特尔酷睿Ultra 9 185H是一款具有16个核心和22个线程的处理器，具有6个性能核心、8个效能核心和2个低功耗核心。拥有24MB的 ..."}
+{"idx": 9, "title": "手机型号后面的 Pro、Plus、Max、ultra、Note、SE、e是什么意思？厂商...", "date": "", "ddg_snippet": "从定位上来看：Ultra > Max > Pro > Plus > 标准版 > Note > SE = e： 其中 iPhone 是 Max > Pro > 标准版 > SE = e； 其中 iPhone 的 SE > 国产手机的 SE。 厂商的算盘可以简单理解为： 将产品划分为不同档位，让不同需求的用户各取所需，提供多样化的型号选择。网络舆论的理解一般都是： Pro 版是标准版，标准版是减配 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/13639595067", "content": "从定位上来看：Ultra > Max > Pro > Plus > 标准版 > Note > SE = e： 其中 iPhone 是 Max > Pro > 标准版 > SE = e； 其中 iPhone 的 SE > 国产手机的 SE。 厂商的算盘可以简单理解为： 将产品划分为不同档位，让不同需求的用户各取所需，提供多样化的型号选择。网络舆论的理解一般都是： Pro 版是标准版，标准版是减配 ..."}
diff --git a/data/sampled_jsons/understanding_safety_finetuning_repository_minGPT_model.py_config.py.jsonl b/data/sampled_jsons/understanding_safety_finetuning_repository_minGPT_model.py_config.py.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3380296593e6adb41fe4d700e43ac2171a0fe5b
--- /dev/null
+++ b/data/sampled_jsons/understanding_safety_finetuning_repository_minGPT_model.py_config.py.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - karpathy/minGPT: A minimal PyTorch re-implementation ... karpathy/minGPT | DeepWiki orange-pi-mindspore/Online/training/02-minGPT/mingpt/model.py ... minGPT-tuned/mingpt/model.py at master · BlinkDL ... - GitHub", "date": "", "ddg_snippet": "A PyTorch re-implementation of GPT, both training and inference. minGPT tries to be small, clean, interpretable and educational, as most of the currently available GPT model implementations can a bit sprawling. GPT is not a complicated model and this implementation is appropriately about 300 lines of code (see mingpt / model . py ). All that's going on is that a sequence of indices feeds into a Transformer, and a probability distribution over the next index in the sequence comes out. The majority of the complexity is just being clever with batching (both across examples and over sequence length) for efficiency. note (Jan 2023): though I may continue to accept and change some details, minGPT is in a semi-archived state. For more recent developments see my rewrite nanoGPT. Basically, minGPT became referenced across a wide variety of places (notebooks, blogs, courses, books, etc.) which made me less willing to make the bigger changes I wanted to make to move the code forward. I also wanted to change the direction a bit, from a sole focus on education to something that is still simple and hackable but has teeth (reproduces medium-sized industry benchmarks, accepts some tradeoffs to gain runtime efficiency, etc). The minGPT library is three files: mingpt / model . py contains the actual Transformer model definition, mingpt /bpe. py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt /trainer. py is (GPT-independent) PyTorch boilerplate code that trains the model . Then there are a number of demos and projects that use the library in the projects folder: •projects/adder trains a GPT from scratch to add numbers (inspired by the addition section in the GPT-3 paper) •projects/chargpt trains a GPT to be a character-level language model on some input text file •demo.ipynb shows a minimal usage of the GPT and Trainer in a notebook format on a simple sorting example See full list on github.com If you want to import mingpt into your project: See full list on github.com Here's how you'd instantiate a GPT-2 (124M param version): And here's how you'd train it: See full list on github.com Coverage is not super amazing just yet but: See full list on github.com •add gpt-2 finetuning demo on arbitrary given text file •add dialog agent demo •better docs of outcomes for existing projects (adder, chargpt) •add mixed precision and related training scaling goodies •distributed training support •reproduce some benchmarks in projects/, e.g. text8 or other language modeling See full list on github.com Code: •openai/gpt-2 has the model definition in TensorFlow, but not the training code •openai/image-gpt has some more modern gpt-3 like modification in its code, good reference as well •huggingface/transformers has a language-modeling example. It is full-featured but as a result also somewhat challenging to trace. E.g. some large functions have as much as 90% unused code behind various branching statements that is unused in the default setting of simple language modeling See full list on github.com Apr 18, 2025 · Purpose and Scope minGPT is a minimal PyTorch re-implementation of the GPT (Generative Pre-trained Transformer) architecture, designed for clarity, educational value, and interpretability. This document provides a high-level overview of the minGPT repository , explaining its core components, design philosophy, and basic usage patterns. MindSpore notebooks on OrangePi AiPro. Contribute to mindspore-courses/orange-pi-mindspore development by creating an account on GitHub. A *tuned* minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training - BlinkDL/ minGPT -tuned", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT", "content": "A PyTorch re-implementation of GPT, both training and inference. minGPT tries to be small, clean, interpretable and educational, as most of the currently available GPT model implementations can a bit sprawling. GPT is not a complicated model and this implementation is appropriately about 300 lines of code (see mingpt / model . py ). All that's going on is that a sequence of indices feeds into a Transformer, and a probability distribution over the next index in the sequence comes out. The majority of the complexity is just being clever with batching (both across examples and over sequence length) for efficiency. note (Jan 2023): though I may continue to accept and change some details, minGPT is in a semi-archived state. For more recent developments see my rewrite nanoGPT. Basically, minGPT became referenced across a wide variety of places (notebooks, blogs, courses, books, etc.) which made me less willing to make the bigger changes I wanted to make to move the code forward. I also wanted to change the direction a bit, from a sole focus on education to something that is still simple and hackable but has teeth (reproduces medium-sized industry benchmarks, accepts some tradeoffs to gain runtime efficiency, etc). The minGPT library is three files: mingpt / model . py contains the actual Transformer model definition, mingpt /bpe. py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt /trainer. py is (GPT-independent) PyTorch boilerplate code that trains the model . Then there are a number of demos and projects that use the library in the projects folder: •projects/adder trains a GPT from scratch to add numbers (inspired by the addition section in the GPT-3 paper) •projects/chargpt trains a GPT to be a character-level language model on some input text file •demo.ipynb shows a minimal usage of the GPT and Trainer in a notebook format on a simple sorting example See full list on github.com If you want to import mingpt into your project: See full list on github.com Here's how you'd instantiate a GPT-2 (124M param version): And here's how you'd train it: See full list on github.com Coverage is not super amazing just yet but: See full list on github.com •add gpt-2 finetuning demo on arbitrary given text file •add dialog agent demo •better docs of outcomes for existing projects (adder, chargpt) •add mixed precision and related training scaling goodies •distributed training support •reproduce some benchmarks in projects/, e.g. text8 or other language modeling See full list on github.com Code: •openai/gpt-2 has the model definition in TensorFlow, but not the training code •openai/image-gpt has some more modern gpt-3 like modification in its code, good reference as well •huggingface/transformers has a language-modeling example. It is full-featured but as a result also somewhat challenging to trace. E.g. some large functions have as much as 90% unused code behind various branching statements that is unused in the default setting of simple language modeling See full list on github.com Apr 18, 2025 · Purpose and Scope minGPT is a minimal PyTorch re-implementation of the GPT (Generative Pre-trained Transformer) architecture, designed for clarity, educational value, and interpretability. This document provides a high-level overview of the minGPT repository , explaining its core components, design philosophy, and basic usage patterns. MindSpore notebooks on OrangePi AiPro. Contribute to mindspore-courses/orange-pi-mindspore development by creating an account on GitHub. A *tuned* minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training - BlinkDL/ minGPT -tuned"}
+{"idx": 1, "title": "GitHub - fiveai/understanding_safety_finetuning: Official ...", "date": "", "ddg_snippet": "This repository supports three different safety fine-tuning protocols: supervised safety fine-tuning , direct preference optimization and unlearning. The corresponding commands for each of them are given below:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "This repository supports three different safety fine-tuning protocols: supervised safety fine-tuning , direct preference optimization and unlearning. The corresponding commands for each of them are given below:"}
+{"idx": 2, "title": "Example Projects | karpathy/minGPT | DeepWiki", "date": "", "ddg_snippet": "Apr 18, 2025 · Example Projects Relevant source files This page documents the example projects included in minGPT , which demonstrate practical applications of the GPT model implementation. These examples show how to leverage the core components of minGPT for specific machine learning tasks.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/karpathy/minGPT/4-example-projects", "content": "Apr 18, 2025 · Example Projects Relevant source files This page documents the example projects included in minGPT , which demonstrate practical applications of the GPT model implementation. These examples show how to leverage the core components of minGPT for specific machine learning tasks."}
+{"idx": 3, "title": "Katiyar48/MinGPT · Hugging Face", "date": "", "ddg_snippet": "The minGPT library is three files: mingpt / model.py contains the actual Transformer model definition, mingpt /bpe. py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt /trainer. py is (GPT-independent) PyTorch boilerplate code that trains the model .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Katiyar48/MinGPT", "content": "The minGPT library is three files: mingpt / model.py contains the actual Transformer model definition, mingpt /bpe. py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt /trainer. py is (GPT-independent) PyTorch boilerplate code that trains the model ."}
+{"idx": 4, "title": "karpathy/minGPT | DeepWiki", "date": "", "ddg_snippet": "Apr 18, 2025 · Purpose and Scope minGPT is a minimal PyTorch re-implementation of the GPT (Generative Pre-trained Transformer) architecture, designed for clarity, educational value, and interpretability. This document provides a high-level overview of the minGPT repository , explaining its core components, design philosophy, and basic usage patterns.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/karpathy/minGPT", "content": "Apr 18, 2025 · Purpose and Scope minGPT is a minimal PyTorch re-implementation of the GPT (Generative Pre-trained Transformer) architecture, designed for clarity, educational value, and interpretability. This document provides a high-level overview of the minGPT repository , explaining its core components, design philosophy, and basic usage patterns."}
+{"idx": 5, "title": "minGPT-tuned/mingpt/model.py at master · BlinkDL ... - GitHub", "date": "", "ddg_snippet": "A *tuned* minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training - BlinkDL/ minGPT -tuned", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BlinkDL/minGPT-tuned/blob/master/mingpt/model.py", "content": "A *tuned* minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training - BlinkDL/ minGPT -tuned"}
+{"idx": 6, "title": "01_NeMo_ Models .ipynb - Colab", "date": "", "ddg_snippet": "Attaching the model and module-level configs more_vert.This notebook port's the minGPT codebase into equivalent NeMo code. The license for minGPT has therefore been attached here.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/01_NeMo_Models.ipynb", "content": "Attaching the model and module-level configs more_vert.This notebook port's the minGPT codebase into equivalent NeMo code. The license for minGPT has therefore been attached here."}
+{"idx": 7, "title": "minGPT 代码详解（训练 GPT 模型执行两位数加法）-CSDN博客", "date": "", "ddg_snippet": "minGPT / model . py 包含实际的 Transformer 模型定义 minGPT /trainer. py 是训练模型的 (与 GPT 无关的) PyTorch样板代码C = CfgNode() #. either model _type or (n_layer, n_head, n_embd) must be given in the config .", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/wxc971231/article/details/132000182", "content": "minGPT / model . py 包含实际的 Transformer 模型定义 minGPT /trainer. py 是训练模型的 (与 GPT 无关的) PyTorch样板代码C = CfgNode() #. either model _type or (n_layer, n_head, n_embd) must be given in the config ."}
+{"idx": 8, "title": "minGPT TF Download - minGPT TF Source code download", "date": "", "ddg_snippet": "The core minGPT \"library\" (hah) is two files: mingpt / model . py contains the actual Transformer model definition and mingpt /trainer. py is ( GPT -independent) PyTorch boilerplate that trains the model .", "subpage_snippet": "", "source": "www.downcodes.com", "link": "https://www.downcodes.com/download/193831.html", "content": "The core minGPT \"library\" (hah) is two files: mingpt / model . py contains the actual Transformer model definition and mingpt /trainer. py is ( GPT -independent) PyTorch boilerplate that trains the model ."}
+{"idx": 9, "title": "Training “real-world” models with DDP — PyTorch Tutorials...", "date": "", "ddg_snippet": "model . py defines the model architecture. char_dataset. py contains the Dataset class for a character-level dataset. gpt 2_train_cfg.yaml contains the configurations for data, model , optimizer, and training run.", "subpage_snippet": "", "source": "pytorch-tutorials-preview.netlify.app", "link": "https://pytorch-tutorials-preview.netlify.app/intermediate/ddp_series_mingpt", "content": "model . py defines the model architecture. char_dataset. py contains the Dataset class for a character-level dataset. gpt 2_train_cfg.yaml contains the configurations for data, model , optimizer, and training run."}
diff --git a/data/sampled_jsons/vector_embeddings_set-theoretic_operations_intersection_union_negation_limitations_geometric_approac.jsonl b/data/sampled_jsons/vector_embeddings_set-theoretic_operations_intersection_union_negation_limitations_geometric_approac.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8e5bae358c2b5cccaf67edb5483b93ab1c5443cc
--- /dev/null
+++ b/data/sampled_jsons/vector_embeddings_set-theoretic_operations_intersection_union_negation_limitations_geometric_approac.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Information-Geometric Set Embeddings (IGSE)", "date": "", "ddg_snippet": "by K Sun · 2019 · Cited by 3 — This letter introduces an abstract learning problem called the “ set embedding ”: The objective is to map sets into probability distributions so ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1911.12463", "content": "by K Sun · 2019 · Cited by 3 — This letter introduces an abstract learning problem called the “ set embedding ”: The objective is to map sets into probability distributions so ..."}
+{"idx": 1, "title": "ConE: Cone Embeddings for Multi-Hop Reasoning over ...", "date": "", "ddg_snippet": "by Z Zhang · Cited by 172 — The approach also proposes four operators (a) Projection, (b) Intersection, (c) Union, and (d) Complement.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Twf_XYunk5j", "content": "by Z Zhang · Cited by 172 — The approach also proposes four operators (a) Projection, (b) Intersection, (c) Union, and (d) Complement."}
+{"idx": 2, "title": "Capturing Set-Theoretic Semantics of Words using Box ...", "date": "", "ddg_snippet": "by S Dasgupta · 2022 · Cited by 24 — The purpose of these operations is to mimic the essence of union and intersection in the vector space, however, it is evident that the trained ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.acl-long.161.pdf", "content": "by S Dasgupta · 2022 · Cited by 24 — The purpose of these operations is to mimic the essence of union and intersection in the vector space, however, it is evident that the trained ..."}
+{"idx": 3, "title": "Spherical Embeddings for Atomic Relation Projection ...", "date": "", "ddg_snippet": "by CDM Nguyen · 2025 · Cited by 1 — In this work, we propose a symbolic learning-free approach using fuzzy logic to address the shape-closure problem that restricted geometric -based embedding ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3696410.3714747", "content": "by CDM Nguyen · 2025 · Cited by 1 — In this work, we propose a symbolic learning-free approach using fuzzy logic to address the shape-closure problem that restricted geometric -based embedding ..."}
+{"idx": 4, "title": "Gamma Embeddings for Logical Queries on Knowledge ...", "date": "", "ddg_snippet": "by D Yang · 2022 · Cited by 46 — GammaE provides a closed solution for all. FOL operators, including a projection, intersection , union , and negation . 2. GammaE firstly ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.emnlp-main.47.pdf", "content": "by D Yang · 2022 · Cited by 46 — GammaE provides a closed solution for all. FOL operators, including a projection, intersection , union , and negation . 2. GammaE firstly ..."}
+{"idx": 5, "title": "Approximating Intersections and Differences Between Linear ...", "date": "", "ddg_snippet": "by M Weiherer · 2024 · Cited by 2 — In this paper, we present a new approach to qualitatively compare two linear SSMs that takes into account the affine vector spaces spanned by the models.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2024/papers/Weiherer_Approximating_Intersections_and_Differences_Between_Linear_Statistical_Shape_Models_Using_WACV_2024_paper.pdf", "content": "by M Weiherer · 2024 · Cited by 2 — In this paper, we present a new approach to qualitatively compare two linear SSMs that takes into account the affine vector spaces spanned by the models."}
+{"idx": 6, "title": "A Holistic Approach for Answering Logical Queries on ...", "date": "", "ddg_snippet": "by Y Wu · Cited by 9 — To fill the gap, we propose a Holistic Approach to an- swer Logical queries on Knowledge graphs based on a new arc embedding (namely HaLk) in this paper, in ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=98sYnvYGWW", "content": "by Y Wu · Cited by 9 — To fill the gap, we propose a Holistic Approach to an- swer Logical queries on Knowledge graphs based on a new arc embedding (namely HaLk) in this paper, in ..."}
+{"idx": 7, "title": "arXiv:2303.11858v3 [cs.DB] 4 Mar 2025", "date": "", "ddg_snippet": "by Y He · 2023 · Cited by 5 — Query embedding approaches this problem by computing the low-dimensional vector repre- sentations of entities, relations, and logical queries.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.11858", "content": "by Y He · 2023 · Cited by 5 — Query embedding approaches this problem by computing the low-dimensional vector repre- sentations of entities, relations, and logical queries."}
+{"idx": 8, "title": "Quantum Embedding of Knowledge for Reasoning", "date": "", "ddg_snippet": "by D Garg · 2019 · Cited by 70 — Here + sign means the vector sum and not the set theoretic union . That is, logical OR or union between two subspaces is the smallest subspace encompassing both.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/8797-quantum-embedding-of-knowledge-for-reasoning.pdf", "content": "by D Garg · 2019 · Cited by 70 — Here + sign means the vector sum and not the set theoretic union . That is, logical OR or union between two subspaces is the smallest subspace encompassing both."}
+{"idx": 9, "title": "Embedding Logical Queries on Knowledge Graphs", "date": "", "ddg_snippet": "by WL Hamilton · Cited by 396 — In our approach , we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., transla- tion, rotation) ... 12 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "http://papers.neurips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs.pdf", "content": "by WL Hamilton · Cited by 396 — In our approach , we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., transla- tion, rotation) ... 12 pages"}
diff --git a/data/sampled_jsons/vh9yEPLeyD_backbone_model_Xception_EfficientNet_experimental_setup.jsonl b/data/sampled_jsons/vh9yEPLeyD_backbone_model_Xception_EfficientNet_experimental_setup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7c0c6b9e5e409ba3bf797ec37f669b901b3d0a29
--- /dev/null
+++ b/data/sampled_jsons/vh9yEPLeyD_backbone_model_Xception_EfficientNet_experimental_setup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Exception Models", "date": "", "ddg_snippet": "Скаутское агентство Exception Models уже более 4 лет занимается поиском и продвижением моделей. Мы - первое в Украине официальное скаутское агентство, динамично развивающееся и стремящееся к совершенству во всём.", "subpage_snippet": "", "source": "www.exceptionmodels.com.ua", "link": "https://www.exceptionmodels.com.ua/", "content": "Скаутское агентство Exception Models уже более 4 лет занимается поиском и продвижением моделей. Мы - первое в Украине официальное скаутское агентство, динамично развивающееся и стремящееся к совершенству во всём."}
+{"idx": 1, "title": "IPTV Xtream Setup Guide: Portal Configuration and STB Emulator...", "date": "", "ddg_snippet": "Complete IPTV Xtream guide: portal setup , STBemu configuration & Stalker middleware. Learn streaming technology & emulator solutions.", "subpage_snippet": "", "source": "iptv.taghdoutelive.com", "link": "https://iptv.taghdoutelive.com/2025/09/iptv-xtream-setup-guide-portal.html", "content": "Complete IPTV Xtream guide: portal setup , STBemu configuration & Stalker middleware. Learn streaming technology & emulator solutions."}
+{"idx": 2, "title": "Experimental Setup For Large Language Model Video... | HackerNoon", "date": "", "ddg_snippet": "The research examines 2T tokens, fine-tunes for text-to-video tasks, and evaluates zero-shot benchmarks including MSR-VTT, UCF-101, and Kinetics 600. featured image - Experimental Setup For Large Language Model Video Generation.", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/experimental-setup-for-large-language-model-video-generation", "content": "The research examines 2T tokens, fine-tunes for text-to-video tasks, and evaluates zero-shot benchmarks including MSR-VTT, UCF-101, and Kinetics 600. featured image - Experimental Setup For Large Language Model Video Generation."}
+{"idx": 3, "title": "This Frankenstein Of A Cooling Setup Contains... - Pokde. Net", "date": "", "ddg_snippet": "This prototype passive cooling setup for the RTX 3080 FE GPU involves a total of ten CPU heatsinks, and a huge block of copper.", "subpage_snippet": "", "source": "pokde.net", "link": "https://pokde.net/system/pc/cooling/rtx-3080-passive-cooling-setup", "content": "This prototype passive cooling setup for the RTX 3080 FE GPU involves a total of ten CPU heatsinks, and a huge block of copper."}
+{"idx": 4, "title": "Out Of Memory Exception , about... - GithubHelp", "date": "", "ddg_snippet": "Reading the discord, some of the others found out that a keenswh ssl cert is expired. Wonder if this is related? Reading through the decoded error, it does have something to do with network requests not completing and then piling up until it reaches some threshold and then crashes.", "subpage_snippet": "", "source": "githubhelp.com", "link": "https://githubhelp.com/mmmaxwwwell/space-engineers-dedicated-docker-linux/issues/34", "content": "Reading the discord, some of the others found out that a keenswh ssl cert is expired. Wonder if this is related? Reading through the decoded error, it does have something to do with network requests not completing and then piling up until it reaches some threshold and then crashes."}
+{"idx": 5, "title": "Steal a Brainrot Script (September 2025)", "date": "", "ddg_snippet": "Install the executor and complete the setup process. Get the required key if needed for your steal a brainrot script execution.Advanced auto steal systems with instant brainrot collection and automated proximity prompts using our steal a brainrot script for maximum efficiency .", "subpage_snippet": "", "source": "stealabrainrotscript.info", "link": "https://stealabrainrotscript.info/", "content": "Install the executor and complete the setup process. Get the required key if needed for your steal a brainrot script execution.Advanced auto steal systems with instant brainrot collection and automated proximity prompts using our steal a brainrot script for maximum efficiency ."}
+{"idx": 6, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "One effective solution to enhance this ability is to train the models with manually-blended data, which we termed ''blendfake'', encouraging models to learn generic forgery artifacts like blending boundary.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/vh9yEPLeyD@OpenReview", "content": "One effective solution to enhance this ability is to train the models with manually-blended data, which we termed ''blendfake'', encouraging models to learn generic forgery artifacts like blending boundary."}
+{"idx": 7, "title": "Indoor-локация без дополнительных датчиков: Wi-Fi как... / Хабр", "date": "", "ddg_snippet": "Затем он подключается к сети Сколтеха и отправляет пакет HTTP на сервер по локальной сети. Сервером выступает обычный Raspberry Pi 3 Model B.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/949142/", "content": "Затем он подключается к сети Сколтеха и отправляет пакет HTTP на сервер по локальной сети. Сервером выступает обычный Raspberry Pi 3 Model B."}
+{"idx": 8, "title": "Efficient 3D Perception on Embedded Systems via Interpolation-Free...", "date": "", "ddg_snippet": "Experimental Setup . Datasets and Evaluation Metrics.We compare our proposed architecture with other repre-sentative baselines. The first is a 3D Base model , which represents a standard dense 3D convolutional neural network using a U- Net -like architecture.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14641", "content": "Experimental Setup . Datasets and Evaluation Metrics.We compare our proposed architecture with other repre-sentative baselines. The first is a 3D Base model , which represents a standard dense 3D convolutional neural network using a U- Net -like architecture."}
+{"idx": 9, "title": "How to Integrate NinjaTrader with Python for Automated Trading?", "date": "", "ddg_snippet": "Setting Up the Development Environment. Proper environment setup is crucial for smooth integration.Review NinjaTrader’s log files and the output/ exceptions from your Python script for clues. Developing Automated Trading Strategies in Python.", "subpage_snippet": "", "source": "trading-strategies.academy", "link": "https://trading-strategies.academy/archives/1655", "content": "Setting Up the Development Environment. Proper environment setup is crucial for smooth integration.Review NinjaTrader’s log files and the output/ exceptions from your Python script for clues. Developing Automated Trading Strategies in Python."}
diff --git a/data/sampled_jsons/website_size_developed_countries_developing_countries_median_KB_MB_smaller_larger_year_2025.jsonl b/data/sampled_jsons/website_size_developed_countries_developing_countries_median_KB_MB_smaller_larger_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..969fb365074d0c71ec407832d1bf786c4959a636
--- /dev/null
+++ b/data/sampled_jsons/website_size_developed_countries_developing_countries_median_KB_MB_smaller_larger_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Websites from developing and developed countries differ", "date": "", "ddg_snippet": "Differences in digitalization between developing and developed countries involve a variety of factors.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/unequal-internet-study-highlights-differences-between-websites-from-u7uje", "content": "Differences in digitalization between developing and developed countries involve a variety of factors."}
+{"idx": 1, "title": "PDF Digital Disparities: A Comparative Web Measurement Study Across ...", "date": "", "ddg_snippet": "Figure 1(a) shows that the median website size in developed countries is 3.35 MB , i.e., slightly larger than the 2.99 MB median size in developing countries , both of which exceed the global median of 2.6 MB as per the HTTP archive [28].", "subpage_snippet": "", "source": "www.staicu.org", "link": "https://www.staicu.org/publications/www2025.pdf", "content": "Figure 1(a) shows that the median website size in developed countries is 3.35 MB , i.e., slightly larger than the 2.99 MB median size in developing countries , both of which exceed the global median of 2.6 MB as per the HTTP archive [28]."}
+{"idx": 2, "title": "A Comparative Web Measurement Study Across Economic ...", "date": "", "ddg_snippet": "by MHM Bhuiyan · Cited by 1 — The median JS size for websites in developed countries such as the US and Australia is 921.71 KB and 968.61 KB , respectively, while in developing countries like.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=IMhoJgWANP", "content": "by MHM Bhuiyan · Cited by 1 — The median JS size for websites in developed countries such as the US and Australia is 921.71 KB and 968.61 KB , respectively, while in developing countries like."}
+{"idx": 3, "title": "AMSJ_VOL.24_NO.4_2020_FULLTEXT", "date": "", "ddg_snippet": "by V Gallart-Camahort · 2020 — retain talent, organizations in less developed countries with the double motive for boosting their ... developing countries . International Journal of ...", "subpage_snippet": "", "source": "fliphtml5.com", "link": "https://fliphtml5.com/xzfda/houf/AMSJ_VOL.24_NO.4_2020_FULLTEXT/", "content": "by V Gallart-Camahort · 2020 — retain talent, organizations in less developed countries with the double motive for boosting their ... developing countries . International Journal of ..."}
+{"idx": 4, "title": "Computers Helping People with Special Needs", "date": "", "ddg_snippet": "16 May 2016 — ... developed countries and societies are requested to make use of the ... ( developing) countries . These factors all show the technology's.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-319-41264-1.pdf", "content": "16 May 2016 — ... developed countries and societies are requested to make use of the ... ( developing) countries . These factors all show the technology's."}
+{"idx": 5, "title": "(PDF) Benefit Cost Ratio of Different Fungicidal Treatments ...", "date": "", "ddg_snippet": "... website. size of the genome under investigation. Therefore, it is now ... developing countries were Traditional the main reasons for the ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/25982631/Benefit_Cost_Ratio_of_Different_Fungicidal_Treatments_Tested_Under_Field_Conditions", "content": "... website. size of the genome under investigation. Therefore, it is now ... developing countries were Traditional the main reasons for the ..."}
+{"idx": 6, "title": "BOOK OF PROCEEDINGS 2017", "date": "", "ddg_snippet": "... developed countries and 12% of the population in developing countries is ... website size has reached 2602 KB . This represents an increase of 50.7 ...", "subpage_snippet": "", "source": "www.icens.eu", "link": "https://www.icens.eu/sites/default/files/2017/ICENS_PROCEEDING_2017_V2_v2.pdf", "content": "... developed countries and 12% of the population in developing countries is ... website size has reached 2602 KB . This represents an increase of 50.7 ..."}
+{"idx": 7, "title": "A Comparative Web Measurement Study Across Economic ...", "date": "", "ddg_snippet": "1 Mar 2017 — Figure 1(a) shows that the median website size in developed countries is 3.35 MB , i.e., slightly larger than the 2.99. MB median size in ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3696410.3714647?download=true", "content": "1 Mar 2017 — Figure 1(a) shows that the median website size in developed countries is 3.35 MB , i.e., slightly larger than the 2.99. MB median size in ..."}
+{"idx": 8, "title": "Omega-6 fatty acids and the risk of cardiovascular disease:", "date": "", "ddg_snippet": "Official websites use .gov A .gov website belongs to an official government organization in the United States. ... Secure .gov websites use HTTPS A ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8924827/", "content": "Official websites use .gov A .gov website belongs to an official government organization in the United States. ... Secure .gov websites use HTTPS A ..."}
+{"idx": 9, "title": "Vitamin A for treating measles in children - PMC", "date": "", "ddg_snippet": "Official websites use .gov A .gov website belongs to an official government organization in the United States. ... Secure .gov websites use HTTPS A ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7076287/", "content": "Official websites use .gov A .gov website belongs to an official government organization in the United States. ... Secure .gov websites use HTTPS A ..."}
diff --git a/data/sampled_jsons/what_does_YFCC_stand_for_in_YFCC100M_dataset.jsonl b/data/sampled_jsons/what_does_YFCC_stand_for_in_YFCC100M_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af05482570f0e9a1e5e32452f62d0401b22a4524
--- /dev/null
+++ b/data/sampled_jsons/what_does_YFCC_stand_for_in_YFCC100M_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Practical Guide to Using the YFCC 100 M and MMCOMMONS on...", "date": "", "ddg_snippet": "The YFCC 100 M Dataset Browser is a web-based tool you can use to search the dataset by keyword.Webscope buckets (I3 set 1-I3setN) that hold a copy of the YFCC 100 M , but you only can access it using the path you were assigned after requesting the dataset .", "subpage_snippet": "", "source": "records.sigmm.org", "link": "https://records.sigmm.org/2017/10/09/practical-guide-to-using-the-yfcc100m-and-mmcommons-on-a-budget/", "content": "The YFCC 100 M Dataset Browser is a web-based tool you can use to search the dataset by keyword.Webscope buckets (I3 set 1-I3setN) that hold a copy of the YFCC 100 M , but you only can access it using the path you were assigned after requesting the dataset ."}
+{"idx": 1, "title": "Download the YFCC 100 m dataset without going insane.", "date": "", "ddg_snippet": "What is the YFCC 100 m dataset ? A quote from its websiteThis is done with the first command: python -m yfcc 100 m .convert_metadata -o . This will look for yfcc 100 m _ dataset .sql in the input directory and download it if necessary.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/yfcc100m/", "content": "What is the YFCC 100 m dataset ? A quote from its websiteThis is done with the first command: python -m yfcc 100 m .convert_metadata -o . This will look for yfcc 100 m _ dataset .sql in the input directory and download it if necessary."}
+{"idx": 2, "title": "(PDF) YFCC 100 M : The New Data in Multimedia Research (2015)", "date": "", "ddg_snippet": "The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/yfcc100m-the-new-data-in-multimedia-research-17fflsw100", "content": "The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license."}
+{"idx": 3, "title": "YFCC 100 M : The New Data in Multimedia Research... : Internet Archive", "date": "", "ddg_snippet": "We present the Yahoo Flickr Creative Commons 100 Million Dataset ( YFCC 100 M ), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos...", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/arxiv-1503.01817", "content": "We present the Yahoo Flickr Creative Commons 100 Million Dataset ( YFCC 100 M ), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos..."}
+{"idx": 4, "title": "YFCC 100 M -HNfc6: a Large-Scale", "date": "", "ddg_snippet": "YFCC 100 M -HNfc6: a Large-Scale Deep Features Benchmark for Similarity Search. Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro, and Fausto Rabitti.", "subpage_snippet": "", "source": "falchi.isti.cnr.it", "link": "https://falchi.isti.cnr.it/Draft/2016-SISAP-YFCC100M-DRAFT.pdf", "content": "YFCC 100 M -HNfc6: a Large-Scale Deep Features Benchmark for Similarity Search. Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro, and Fausto Rabitti."}
+{"idx": 5, "title": "GitHub - abhimanyudubey/GeoYFCC: Dataset accompanying the...", "date": "", "ddg_snippet": "Geographically Split YFCC for Domain Generalization (Geo- YFCC ).Corresponding row ID within YFCC 100 M (present in the first column of the yfcc 100 m _ dataset file from YFCC 100 M , begins with 0).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/abhimanyudubey/GeoYFCC", "content": "Geographically Split YFCC for Domain Generalization (Geo- YFCC ).Corresponding row ID within YFCC 100 M (present in the first column of the yfcc 100 m _ dataset file from YFCC 100 M , begins with 0)."}
+{"idx": 6, "title": "We provide additional details for linear probe experiments", "date": "", "ddg_snippet": "G. Dataset Ablation on YFCC 100 M .We do not think this is likely as YFCC 100 M is only 3.7% of the overall WIT data blend and it did not noticeably change the performance of models when it was added to the existing data blend during the creation of WIT.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/radford21a/radford21a-supp.pdf", "content": "G. Dataset Ablation on YFCC 100 M .We do not think this is likely as YFCC 100 M is only 3.7% of the overall WIT data blend and it did not noticeably change the performance of models when it was added to the existing data blend during the creation of WIT."}
+{"idx": 7, "title": "dalle-mini/ YFCC 100 M _OpenAI_subset at main", "date": "", "ddg_snippet": "Dataset card. YFCC 100 M _OpenAI_subset.py. Safe.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/dalle-mini/YFCC100M_OpenAI_subset/tree/main", "content": "Dataset card. YFCC 100 M _OpenAI_subset.py. Safe."}
+{"idx": 8, "title": "Extended Travel Itinerary Datasets Towards Reproducibility", "date": "", "ddg_snippet": "Flickr metadata ( YFCC 100 m metadata). yfcc 100 m downloader (Project ID: 24899445). accuracy 16 latitude/longitude.Figure 1: Data flow to generate extended travel trajectory datasets from YFCC 100 m dataset [14].", "subpage_snippet": "", "source": "ceur-ws.org", "link": "https://ceur-ws.org/Vol-3568/paper3.pdf", "content": "Flickr metadata ( YFCC 100 m metadata). yfcc 100 m downloader (Project ID: 24899445). accuracy 16 latitude/longitude.Figure 1: Data flow to generate extended travel trajectory datasets from YFCC 100 m dataset [14]."}
+{"idx": 9, "title": "What do Deep Networks Like to See?", "date": "", "ddg_snippet": "Using the YFCC 100 m as training set comes with some unique advantages.When training on the YFCC 100 m , the reconstruction er-ror is checked every 1 million images and, learning rate is reduced to η = 0.2η if the loss does not improve af-ter two consecutive checks.", "subpage_snippet": "", "source": "www.dfki.de", "link": "https://www.dfki.de/fileadmin/user_upload/import/9663_s2snet-cvpr2018.pdf", "content": "Using the YFCC 100 m as training set comes with some unique advantages.When training on the YFCC 100 m , the reconstruction er-ror is checked every 1 million images and, learning rate is reduced to η = 0.2η if the loss does not improve af-ter two consecutive checks."}
diff --git a/data/sampled_jsons/why_low_regularization_lambda_0.00_fails_coordination_human-AI_Hanabi_overfitting.jsonl b/data/sampled_jsons/why_low_regularization_lambda_0.00_fails_coordination_human-AI_Hanabi_overfitting.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a8225597e33385060f459e8ee19d4a6b9cb63a7d
--- /dev/null
+++ b/data/sampled_jsons/why_low_regularization_lambda_0.00_fails_coordination_human-AI_Hanabi_overfitting.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regularization in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "5 days ago · Regularization is a technique used in machine learning to prevent overfitting and performs poorly on unseen data. By adding a penalty for complexity, regularization encourages simpler, more generalizable models. Prevents overfitting : Adds constraints to the model to reduce the risk of memorizing noise in the training data.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/regularization-in-machine-learning/", "content": "5 days ago · Regularization is a technique used in machine learning to prevent overfitting and performs poorly on unseen data. By adding a penalty for complexity, regularization encourages simpler, more generalizable models. Prevents overfitting : Adds constraints to the model to reduce the risk of memorizing noise in the training data."}
+{"idx": 1, "title": "How does L1 and L2 regularization prevent overfitting ...", "date": "", "ddg_snippet": "Jul 23, 2025 · Here, we will emphasize on L1 and L2 regularization . How does L1, and L2 regularization prevent overfitting ? L1 regularization , or Lasso regularization , introduces a penalty term based on the absolute values of the weights into the model's cost function.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/how-does-l1-and-l2-regularization-prevent-overfitting/", "content": "Jul 23, 2025 · Here, we will emphasize on L1 and L2 regularization . How does L1, and L2 regularization prevent overfitting ? L1 regularization , or Lasso regularization , introduces a penalty term based on the absolute values of the weights into the model's cost function."}
+{"idx": 2, "title": "Overfitting: L2 regularization | Machine Learning | Google ...", "date": "", "ddg_snippet": "Aug 25, 2025 · Learn how the L2 regularization metric is calculated and how to set a regularization rate to minimize the combination of loss and complexity during model training, or to use alternative regularization techniques like early stopping.", "subpage_snippet": "", "source": "developers.google.com", "link": "https://developers.google.com/machine-learning/crash-course/overfitting/regularization", "content": "Aug 25, 2025 · Learn how the L2 regularization metric is calculated and how to set a regularization rate to minimize the combination of loss and complexity during model training, or to use alternative regularization techniques like early stopping."}
+{"idx": 3, "title": "Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge. Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action – making it an ideal testbed for human-AI coordination .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21490", "content": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge. Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action – making it an ideal testbed for human-AI coordination ."}
+{"idx": 4, "title": "The importance of lambda in a regularization function, with ...", "date": "", "ddg_snippet": "Jun 19, 2016 · The hyperparameter $\\ lambda $ controls this tradeoff by adjusting the weight of the penalty term. If $\\ lambda $ is increased, model complexity will have a greater contribution to the cost. Because the minimum cost hypothesis is selected, this means that higher $\\ lambda $ will bias the selection toward models with lower complexity.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/219567/the-importance-of-lambda-in-a-regularization-function-with-respect-to-the-hypot", "content": "Jun 19, 2016 · The hyperparameter $\\ lambda $ controls this tradeoff by adjusting the weight of the penalty term. If $\\ lambda $ is increased, model complexity will have a greater contribution to the cost. Because the minimum cost hypothesis is selected, this means that higher $\\ lambda $ will bias the selection toward models with lower complexity."}
+{"idx": 5, "title": "What is Lambda in Machine Learning? - ML Journey", "date": "", "ddg_snippet": "Mar 29, 2025 · In machine learning, lambda is a term commonly associated with regularization , a technique used to prevent models from overfitting . Overfitting occurs when a model learns the noise or unnecessary patterns in the training data, which leads to poor generalization on unseen data.", "subpage_snippet": "", "source": "mljourney.com", "link": "https://mljourney.com/what-is-lambda-in-machine-learning/", "content": "Mar 29, 2025 · In machine learning, lambda is a term commonly associated with regularization , a technique used to prevent models from overfitting . Overfitting occurs when a model learns the noise or unnecessary patterns in the training data, which leads to poor generalization on unseen data."}
+{"idx": 6, "title": "Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay ...", "date": "", "ddg_snippet": "Aug 20, 2023 · Coordination (LBF) problem (Treutlein et al.,2021). In this work, our goal is to bring back the focus to the ad-hoc coordination challenge which is a more general problem.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373263012_Towards_Few-shot_Coordination_Revisiting_Ad-hoc_Teamplay_Challenge_In_the_Game_of_Hanabi", "content": "Aug 20, 2023 · Coordination (LBF) problem (Treutlein et al.,2021). In this work, our goal is to bring back the focus to the ad-hoc coordination challenge which is a more general problem."}
+{"idx": 7, "title": "Language Instructed Reinforcement Learning for Human - AI ...", "date": "", "ddg_snippet": "Hanabi is a fully cooperative card game that is often used as a benchmarking environment for MARL and human - AI coordination (Bard et al., 2020) . It requires implicit communication through actions and reasoning about other people’s intentions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2304.07297", "content": "Hanabi is a fully cooperative card game that is often used as a benchmarking environment for MARL and human - AI coordination (Bard et al., 2020) . It requires implicit communication through actions and reasoning about other people’s intentions."}
+{"idx": 8, "title": "(PDF) Language Instructed Reinforcement Learning for Human - AI ...", "date": "", "ddg_snippet": "significantly boosts human - AI coordination per-. formance through human evaluations in Hanabi .chandani et al. (2021) rewards the agent for finishing any. semantically meaningful low -level such as ‘picking up a key’. and gradually build up a dataset for learning to correlate.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370070312_Language_Instructed_Reinforcement_Learning_for_Human-AI_Coordination", "content": "significantly boosts human - AI coordination per-. formance through human evaluations in Hanabi .chandani et al. (2021) rewards the agent for finishing any. semantically meaningful low -level such as ‘picking up a key’. and gradually build up a dataset for learning to correlate."}
+{"idx": 9, "title": "augementatitons, regularizations etc solve or delay overfitting ??", "date": "", "ddg_snippet": "im not sure while making this statement but the extent to which i have onsluded in my experiments is that all the overfitting correction techniques just delay it or make the process slow but eventually it lands at same point.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/discussions/general/608495", "content": "im not sure while making this statement but the extent to which i have onsluded in my experiments is that all the overfitting correction techniques just delay it or make the process slow but eventually it lands at same point."}
diff --git a/data/sampled_jsons/why_score-based_generative_models_require_high_NFE.jsonl b/data/sampled_jsons/why_score-based_generative_models_require_high_NFE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05d5d5d6c1699343c5207638b03f405a026643ea
--- /dev/null
+++ b/data/sampled_jsons/why_score-based_generative_models_require_high_NFE.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Score-based Generative Models with Lévy Processes - OpenReview", "date": "", "ddg_snippet": "Sep 21, 2023 · This paper presents a new score - based generative model called Lévy-Ito ̄ Model (LIM) that tackles the challenges of slow convergence rate of Number of Function Evaluation ( NFE ) and mode collapse in diffusion models when applied to imbalanced data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0Wp3VHX0Gm", "content": "Sep 21, 2023 · This paper presents a new score - based generative model called Lévy-Ito ̄ Model (LIM) that tackles the challenges of slow convergence rate of Number of Function Evaluation ( NFE ) and mode collapse in diffusion models when applied to imbalanced data."}
+{"idx": 1, "title": "Score-based Generative Models with Lévy Processes - NeurIPS", "date": "", "ddg_snippet": "Abstract Investigating the optimal stochastic process beyond Gaussian for noise injection in a score - based generative model remains an open question. Brownian motion is a light-tailed process with continuous paths, which leads to a slow convergence rate for the Number of Function Evaluation ( NFE ).", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/8011b23e1dc3f57e1b6211ccad498919-Paper-Conference.pdf", "content": "Abstract Investigating the optimal stochastic process beyond Gaussian for noise injection in a score - based generative model remains an open question. Brownian motion is a light-tailed process with continuous paths, which leads to a slow convergence rate for the Number of Function Evaluation ( NFE )."}
+{"idx": 2, "title": "17_score_based_generative_models - GitHub Pages", "date": "", "ddg_snippet": "In fact, diffusion-based models correspond to score models . Diffusion- based models are very similar to score models trained with a schedule for $\\sigma$ because both models are iterative and both models consider more noise at each step.", "subpage_snippet": "", "source": "jmtomczak.github.io", "link": "https://jmtomczak.github.io/blog/17/17_sbgms.html", "content": "In fact, diffusion-based models correspond to score models . Diffusion- based models are very similar to score models trained with a schedule for $\\sigma$ because both models are iterative and both models consider more noise at each step."}
+{"idx": 3, "title": "A Good Score Does not Lead to A Good Generative Model", "date": "", "ddg_snippet": "Score - based Generative Models (SGMs) is one leading method in generative modeling , renowned for their ability to generate high-quality samples from complex, high-dimensional data distributions. The method enjoys empirical success and is supported by rigorous theoretical convergence properties.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.04856v1", "content": "Score - based Generative Models (SGMs) is one leading method in generative modeling , renowned for their ability to generate high-quality samples from complex, high-dimensional data distributions. The method enjoys empirical success and is supported by rigorous theoretical convergence properties."}
+{"idx": 4, "title": "Bytez: Score-based Generative Models with Lévy Processes", "date": "", "ddg_snippet": "The researchers propose a new generative model called the Lévy-Itô Model (LIM), which uses a different type of noise called the Lévy process. This model can generate images faster and with more diversity compared to existing models.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/73054/paper", "content": "The researchers propose a new generative model called the Lévy-Itô Model (LIM), which uses a different type of noise called the Lévy process. This model can generate images faster and with more diversity compared to existing models."}
+{"idx": 5, "title": "Score-Based Generative Models | AI Tutorial | Next Electronics", "date": "", "ddg_snippet": "Score - based generative models and diffusion models share a deep theoretical connection, both rooted in the idea of gradually transforming noise into data through a learned stochastic process.", "subpage_snippet": "", "source": "next.gr", "link": "https://next.gr/ai/explainable-ai/score-based-generative-models", "content": "Score - based generative models and diffusion models share a deep theoretical connection, both rooted in the idea of gradually transforming noise into data through a learned stochastic process."}
+{"idx": 6, "title": "Score-Based Neural Processes | IEEE Journals & Magazine ...", "date": "", "ddg_snippet": "Jan 20, 2025 · To tackle this problem, we introduce score - based NP (SNP) models, drawing inspiration from recently developed score - based generative models (SGMs) that restore data from noise by reversing a perturbation process.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10847300", "content": "Jan 20, 2025 · To tackle this problem, we introduce score - based NP (SNP) models, drawing inspiration from recently developed score - based generative models (SGMs) that restore data from noise by reversing a perturbation process."}
+{"idx": 7, "title": "Score - Based Generative Modeling through Stochastic Differential...", "date": "", "ddg_snippet": "By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2011.13456", "content": "By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples."}
+{"idx": 8, "title": "Inject Noise to Remove Noise: A Deep Dive into Score - Based ...", "date": "", "ddg_snippet": "Why Score Based ? Score Estimation and Score Matching. Sample Generation and Langevin Dynamics. Gaussian Perturbations of data. Score - Based Generative Modeling through Stochastic Differential Equations (ICLR 2021). Experiments.", "subpage_snippet": "", "source": "wandb.ai", "link": "https://wandb.ai/ucalyptus/ScoreGM/reports/Inject-Noise-to-Remove-Noise-A-Deep-Dive-into-Score-Based-Generative-Modeling-Techniques--Vmlldzo1OTE2NDg", "content": "Why Score Based ? Score Estimation and Score Matching. Sample Generation and Langevin Dynamics. Gaussian Perturbations of data. Score - Based Generative Modeling through Stochastic Differential Equations (ICLR 2021). Experiments."}
+{"idx": 9, "title": "Gotta Go Fast with Score - Based Generative Models | OpenReview", "date": "", "ddg_snippet": "Unfortunately, current score - based models generate data very slowly due to the sheer number of score network evaluations required by numerical SDE solvers. In this work, we aim to accelerate this process by devising a more efficient SDE solver.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gEoVDSASC2h&referrer=[the+profile+of+Alexia+Jolicoeur-Martineau](/profile?id=~Alexia_Jolicoeur-Martineau1)", "content": "Unfortunately, current score - based models generate data very slowly due to the sheer number of score network evaluations required by numerical SDE solvers. In this work, we aim to accelerate this process by devising a more efficient SDE solver."}
diff --git a/data/sampled_jsons/z-identifiability_Bareinboim_Pearl_surrogate_experiments_year_2013.jsonl b/data/sampled_jsons/z-identifiability_Bareinboim_Pearl_surrogate_experiments_year_2013.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6f83b94c4e16b092cda49ea3e821cfdce897cdc
--- /dev/null
+++ b/data/sampled_jsons/z-identifiability_Bareinboim_Pearl_surrogate_experiments_year_2013.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Inference by Surrogate Experiments: z-Identifiability", "date": "", "ddg_snippet": "by E Bareinboim · 2012 · Cited by 126 — This problem, which we call z - identifiability , reduces to ordinary identifiability when Z = empty and, like the latter, can be given syntactic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1210.4842", "content": "by E Bareinboim · 2012 · Cited by 126 — This problem, which we call z - identifiability , reduces to ordinary identifiability when Z = empty and, like the latter, can be given syntactic ..."}
+{"idx": 1, "title": "Causal Inference by Surrogate Experiments: z-Identifiability", "date": "", "ddg_snippet": "by E Bareinboim · 2012 · Cited by 126 — This problem, which we call z - identifiability , reduces to ordinary identifiability when Z = ∅ and, like the latter, can be given syntactic characterization.", "subpage_snippet": "", "source": "ftp.cs.ucla.edu", "link": "https://ftp.cs.ucla.edu/pub/stat_ser/r397.pdf", "content": "by E Bareinboim · 2012 · Cited by 126 — This problem, which we call z - identifiability , reduces to ordinary identifiability when Z = ∅ and, like the latter, can be given syntactic characterization."}
+{"idx": 2, "title": "Causal inference by surrogate experiments: z-identifiability", "date": "", "ddg_snippet": "We address the problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z , that is more accessible to ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3020652.3020668", "content": "We address the problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z , that is more accessible to ..."}
+{"idx": 3, "title": "Causal Inference by Surrogate Experiments: z-Identifiability", "date": "", "ddg_snippet": "The problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z , that is more accessible to manipulation ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Causal-Inference-by-Surrogate-Experiments:-Bareinboim-Pearl/89837e2c196bdc78ea5359e5e81e67060cdfcdc1", "content": "The problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z , that is more accessible to manipulation ..."}
+{"idx": 4, "title": "General Identifiability with Arbitrary Surrogate Experiments", "date": "", "ddg_snippet": "by S Lee · 2020 · Cited by 107 — The gID setting encompasses two well-known problems in causal inference, namely, identifiability [Pearl, 1995] and z-identifiability [Bareinboim and Pearl, 2012] ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v115/lee20b.html", "content": "by S Lee · 2020 · Cited by 107 — The gID setting encompasses two well-known problems in causal inference, namely, identifiability [Pearl, 1995] and z-identifiability [Bareinboim and Pearl, 2012] ..."}
+{"idx": 5, "title": "General Identifiability with Arbitrary Surrogate Experiments", "date": "", "ddg_snippet": "Correa, and Elias Bareinboim. General identifiability with arbitrary surrogate experiments . Technical Report R-46, Causal AI Lab, Columbia University, 2019 ... 1 page", "subpage_snippet": "", "source": "causalai.net", "link": "https://causalai.net/r46e.pdf", "content": "Correa, and Elias Bareinboim. General identifiability with arbitrary surrogate experiments . Technical Report R-46, Causal AI Lab, Columbia University, 2019 ... 1 page"}
+{"idx": 6, "title": "Elias Bareinboim", "date": "", "ddg_snippet": "Causal inference by surrogate experiments: z-identifiability . E Bareinboim, J Pearl. Proceedings of the 28th Conference on Uncertainty in Artificial ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=r5U-D7YAAAAJ&hl=en", "content": "Causal inference by surrogate experiments: z-identifiability . E Bareinboim, J Pearl. Proceedings of the 28th Conference on Uncertainty in Artificial ..."}
+{"idx": 7, "title": "General Identifiability with Arbitrary Surrogate Experiments", "date": "", "ddg_snippet": "by S Lee · Cited by 107 — We study the problem of causal identification from an arbitrary collection of observational and experimental distributions, and substantive. 11 pages", "subpage_snippet": "", "source": "causalai.net", "link": "https://causalai.net/r46.pdf", "content": "by S Lee · Cited by 107 — We study the problem of causal identification from an arbitrary collection of observational and experimental distributions, and substantive. 11 pages"}
+{"idx": 8, "title": "Causal inference and the data-fusion problem", "date": "", "ddg_snippet": "by E Bareinboim · 2016 · Cited by 899 — E Bareinboim, J Pearl, Causal inference by surrogate experiments: z-identifiability . Proceedings of the Twenty-Eighth Conference on Uncertainty in ...", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.1510507113", "content": "by E Bareinboim · 2016 · Cited by 899 — E Bareinboim, J Pearl, Causal inference by surrogate experiments: z-identifiability . Proceedings of the Twenty-Eighth Conference on Uncertainty in ..."}
+{"idx": 9, "title": "Optimal Experiment Design for Causal Effect Identification", "date": "", "ddg_snippet": "by S Akbari · 2025 — Elias Bareinboim and Judea Pearl. Causal inference by surrogate experiments: z-identifiability . In Proceedings of the Twenty-Eighth Conference on ... 56 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume26/22-1516/22-1516.pdf", "content": "by S Akbari · 2025 — Elias Bareinboim and Judea Pearl. Causal inference by surrogate experiments: z-identifiability . In Proceedings of the Twenty-Eighth Conference on ... 56 pages"}
diff --git a/data/sampled_jsons/zzOOqD6R1b_Section_5.1_fine-tuning_high-quality_demonstrations_ceiling_performance.jsonl b/data/sampled_jsons/zzOOqD6R1b_Section_5.1_fine-tuning_high-quality_demonstrations_ceiling_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d123461e40e3f50a8ca7590ce058d885e7f450a5
--- /dev/null
+++ b/data/sampled_jsons/zzOOqD6R1b_Section_5.1_fine-tuning_high-quality_demonstrations_ceiling_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The fine art of fine-tuning: A structured review of advanced ...", "date": "", "ddg_snippet": "Jun 1 , 2025 · In this study, we will be reviewing the types of techniques developed, their impacts and benefits concerning performance and resource usage along with the latest developments in the domain.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2949719125000202", "content": "Jun 1 , 2025 · In this study, we will be reviewing the types of techniques developed, their impacts and benefits concerning performance and resource usage along with the latest developments in the domain."}
+{"idx": 1, "title": "The Ultimate Guide to Fine-Tuning LLMs from Basics to ...", "date": "", "ddg_snippet": "Enhanced Performance : Research experiments shows that HFT maintains or even surpasses the performance of full fine - tuning (FFT) on downstream tasks, demonstrating its effectiveness in balancing knowledge retention with task-specific learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.13296v1", "content": "Enhanced Performance : Research experiments shows that HFT maintains or even surpasses the performance of full fine - tuning (FFT) on downstream tasks, demonstrating its effectiveness in balancing knowledge retention with task-specific learning."}
+{"idx": 2, "title": "Introducing DoRA, a High-Performing Alternative to LoRA for ...", "date": "", "ddg_snippet": "Jun 28, 2024 · DoRA is a fine - tuning method that is compatible with LoRA and its variants and exhibits a closer resemblance to FT learning behavior. DoRA consistently outperforms LoRA across various fine - tuning tasks and model architectures.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/introducing-dora-a-high-performing-alternative-to-lora-for-fine-tuning/", "content": "Jun 28, 2024 · DoRA is a fine - tuning method that is compatible with LoRA and its variants and exhibits a closer resemblance to FT learning behavior. DoRA consistently outperforms LoRA across various fine - tuning tasks and model architectures."}
+{"idx": 3, "title": "How To Fine Tune Your Home Surround Sound Set Up For The Best ...", "date": "", "ddg_snippet": "Feb 26, 2024 · Let's face it: No matter how high -tech your TV is, built-in speakers just don't cut it. A proper audio setup is necessary if you want to create a true cinematic experience in your home.", "subpage_snippet": "", "source": "www.slashgear.com", "link": "https://www.slashgear.com/1523364/how-to-fine-tune-home-surround-sound-best-experience/", "content": "Feb 26, 2024 · Let's face it: No matter how high -tech your TV is, built-in speakers just don't cut it. A proper audio setup is necessary if you want to create a true cinematic experience in your home."}
+{"idx": 4, "title": "High Performance Computing Tuning Guide for AMD EPYC 9004 ...", "date": "", "ddg_snippet": "This document helps you tune systems powered by 4th Gen AMD EPYCTM processors for High Performance Computing (HPC) workloads but is not an all-inclusive guide. Further, some items referred to herein may have different names across various OEM systems, such as OEM-specific BIOS settings.", "subpage_snippet": "", "source": "www.amd.com", "link": "https://www.amd.com/content/dam/amd/en/documents/epyc-technical-docs/tuning-guides/58002_amd-epyc-9004-tg-hpc.pdf", "content": "This document helps you tune systems powered by 4th Gen AMD EPYCTM processors for High Performance Computing (HPC) workloads but is not an all-inclusive guide. Further, some items referred to herein may have different names across various OEM systems, such as OEM-specific BIOS settings."}
+{"idx": 5, "title": "Ultimate Guide to Audio Calibration: Optimizing Your Sound System", "date": "", "ddg_snippet": "This enables the Av receiver to determine exactly what actions are necessary for you to enjoy well-calibrated, high - quality sound. Input the distance measurements into your receiver.", "subpage_snippet": "", "source": "audiocurious.com", "link": "https://audiocurious.com/ultimate-guide-to-audio-calibration/", "content": "This enables the Av receiver to determine exactly what actions are necessary for you to enjoy well-calibrated, high - quality sound. Input the distance measurements into your receiver."}
+{"idx": 6, "title": "GTA: Supervised-Guided Reinforcement Learning for Text ...", "date": "", "ddg_snippet": "Abstract In natural language processing tasks, pure reinforcement learning (RL) fine-tuning methods often suffer from inefficient exploration and slow convergence; while supervised fine-tuning (SFT) methods, although efficient in training, have limited performance ceiling and less solid theoretical foundation compared to RL. To address efficiency-capability trade-off, we propose the G uess- T ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.12108v1", "content": "Abstract In natural language processing tasks, pure reinforcement learning (RL) fine-tuning methods often suffer from inefficient exploration and slow convergence; while supervised fine-tuning (SFT) methods, although efficient in training, have limited performance ceiling and less solid theoretical foundation compared to RL. To address efficiency-capability trade-off, we propose the G uess- T ..."}
+{"idx": 7, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "by R Greenblatt · Cited by 24 — When fine - tuning on Llama7B demonstrations , we achieve performance higher than Llama7B but lower than the ceiling . Weak-to-strong generalization (Burns et al., ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "by R Greenblatt · Cited by 24 — When fine - tuning on Llama7B demonstrations , we achieve performance higher than Llama7B but lower than the ceiling . Weak-to-strong generalization (Burns et al., ..."}
+{"idx": 8, "title": "Stress-Testing Capability Elicitation With... | OpenReview", "date": "", "ddg_snippet": "We find that a few high - quality demonstrations are often sufficient to fully elicit password-locked capabilities. More surprisingly, fine - tuning can elicit other capabilities that have been locked using the same password, or even different passwords.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zzOOqD6R1b", "content": "We find that a few high - quality demonstrations are often sufficient to fully elicit password-locked capabilities. More surprisingly, fine - tuning can elicit other capabilities that have been locked using the same password, or even different passwords."}
+{"idx": 9, "title": "Alibaba-NLP/Tongyi-DeepResearch-30B-A3B · Hugging Face", "date": "", "ddg_snippet": "Tongyi-DeepResearch demonstrates state-of-the-art performance across a range of agentic search benchmarks, including Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch and FRAMES.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Alibaba-NLP/Tongyi-DeepResearch-30B-A3B", "content": "Tongyi-DeepResearch demonstrates state-of-the-art performance across a range of agentic search benchmarks, including Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch and FRAMES."}
diff --git a/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models.jsonl b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e668766768761d2643d717d27d7b5ccac7db6a86
--- /dev/null
+++ b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password ."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked Models [Paper] Stress-testing capability elicitation with password ... Stress-Testing Capability Elicitation With Password-Locked Models Images Stress-testing capability elicitation with password-locked models [Paper] Stress-testing capability elicitation with password ... Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "May 29, 2024 · To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities . But simple prompting strategies often fail to elicit an LLM's full capabilities . One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task. In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to ... Jun 4, 2024 · But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great); Sep 26, 2024 · The concept of “ Password-Locked Models ” presents a novel approach to evaluating the efficacy of LLM capability elicitation techniques. By deliberately hiding certain capabilities behind a password , researchers create a controlled environment to test different elicitation methods. View all Jun 5, 2025 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities . Jun 4, 2024 · Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization). Thank you! Disanalogies from natural “locking” Internal computations that result in locking may differ from a simple conditional policy in important ways. Hidden capabilities may be less “salient” in realistic models . Real models might not exhibit the hidden capability at all, making RL-based elicitation even less effective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.19550", "content": "May 29, 2024 · To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities . But simple prompting strategies often fail to elicit an LLM's full capabilities . One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task. In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to ... Jun 4, 2024 · But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great); Sep 26, 2024 · The concept of “ Password-Locked Models ” presents a novel approach to evaluating the efficacy of LLM capability elicitation techniques. By deliberately hiding certain capabilities behind a password , researchers create a controlled environment to test different elicitation methods. View all Jun 5, 2025 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities . Jun 4, 2024 · Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization). Thank you! Disanalogies from natural “locking” Internal computations that result in locking may differ from a simple conditional policy in important ways. Hidden capabilities may be less “salient” in realistic models . Real models might not exhibit the hidden capability at all, making RL-based elicitation even less effective."}
+{"idx": 2, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great);", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great);"}
+{"idx": 3, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Sep 26, 2024 · The concept of “ Password-Locked Models ” presents a novel approach to evaluating the efficacy of LLM capability elicitation techniques. By deliberately hiding certain capabilities behind a password , researchers create a controlled environment to test different elicitation methods.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "Sep 26, 2024 · The concept of “ Password-Locked Models ” presents a novel approach to evaluating the efficacy of LLM capability elicitation techniques. By deliberately hiding certain capabilities behind a password , researchers create a controlled environment to test different elicitation methods."}
+{"idx": 4, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Jun 5, 2025 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740125", "content": "Jun 5, 2025 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities ."}
+{"idx": 5, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization).", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization)."}
+{"idx": 6, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Thank you! Disanalogies from natural “locking” Internal computations that result in locking may differ from a simple conditional policy in important ways. Hidden capabilities may be less “salient” in realistic models . Real models might not exhibit the hidden capability at all, making RL-based elicitation even less effective.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/92923.pdf", "content": "Thank you! Disanalogies from natural “locking” Internal computations that result in locking may differ from a simple conditional policy in important ways. Hidden capabilities may be less “salient” in realistic models . Real models might not exhibit the hidden capability at all, making RL-based elicitation even less effective."}
+{"idx": 7, "title": "Stress - Testing Capability Elicitation With Password - Locked Models", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7ff97417474268e6b5a38bcbfae04944-Abstract-Conference.html", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password."}
+{"idx": 8, "title": "Stress - Testing Capability Elicitation With Password - Locked Models", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2405.19550v1", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password."}
+{"idx": 9, "title": "(PDF) Stress - Testing Capability Elicitation With Password - Locked ...", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password. Stress -T esting Capability Elicitation With . Password - Locked Models .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381006018_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password. Stress -T esting Capability Elicitation With . Password - Locked Models ."}
diff --git a/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_D_lock_formula.jsonl b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_D_lock_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eeb79c74f4d92df944b790ac8d106d33317ca6a2
--- /dev/null
+++ b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_D_lock_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress - Testing Capability Elicitation With", "date": "", "ddg_snippet": "Stress - Testing Capability Elicitation With Password - Locked Models . Ryan Greenblatt∗ Redwood Research ryan@rdwrs.com.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "Stress - Testing Capability Elicitation With Password - Locked Models . Ryan Greenblatt∗ Redwood Research ryan@rdwrs.com."}
+{"idx": 1, "title": "Stress - Testing Capability Elicitation With Password - Locked Models", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7ff97417474268e6b5a38bcbfae04944-Abstract-Conference.html", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password."}
+{"idx": 2, "title": "(PDF) Stress - Testing Capability Elicitation With Password - Locked ...", "date": "", "ddg_snippet": "Password - locked . models enable a novel method of ev aluating capabilities elicitation methods, by. testing whether these password - locked capabilities can be elicited without using.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381006018_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models", "content": "Password - locked . models enable a novel method of ev aluating capabilities elicitation methods, by. testing whether these password - locked capabilities can be elicited without using."}
+{"idx": 3, "title": "Stress - Testing Capability Elicitation With Password - Locked Models", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2405.19550v1", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password."}
+{"idx": 4, "title": "2405.19550 - Stress - Testing Capability Elicitation With ...", "date": "", "ddg_snippet": "Stress - Testing Capability Elicitation With Password - Locked Models . (2405.19550).Methodology. Password - locked models are designed to exhibit certain capabilities only when a specified password is present in the prompt.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2405.19550", "content": "Stress - Testing Capability Elicitation With Password - Locked Models . (2405.19550).Methodology. Password - locked models are designed to exhibit certain capabilities only when a specified password is present in the prompt."}
+{"idx": 5, "title": "Stress - Testing Capability Elicitation | Events at FAR.AI", "date": "", "ddg_snippet": "Stress - Testing Capability Elicitation . Dmitrii Krasheninnikov. July 20, 2024.We train these password - locked models via either fine tuning a pretrained model to imitate a weaker model when there is no password and behave normally otherwise, or just from scratch on a toy task.", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/events/sessions/dmitrii-krasheninnikov-stress-testing-capability-elicitation", "content": "Stress - Testing Capability Elicitation . Dmitrii Krasheninnikov. July 20, 2024.We train these password - locked models via either fine tuning a pretrained model to imitate a weaker model when there is no password and behave normally otherwise, or just from scratch on a toy task."}
+{"idx": 6, "title": "Ryan Greenblatt - Google Scholar | Redwood Research - Cited by 505", "date": "", "ddg_snippet": "Stress - testing capability elicitation with password - locked models .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=UESxdDsAAAAJ&hl=en", "content": "Stress - testing capability elicitation with password - locked models ."}
+{"idx": 7, "title": "Alignment Workshop - YouTube", "date": "", "ddg_snippet": "Dmitrii Krasheninnikov – Stress - testing Capability Elicitation with Password - locked Models .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/playlist?list=PLpvkFqYJXcrdxYK-C4ZRj0cgcco3o0VxF", "content": "Dmitrii Krasheninnikov – Stress - testing Capability Elicitation with Password - locked Models ."}
+{"idx": 8, "title": "Articles by Ryan Greenblatt | Synthical", "date": "", "ddg_snippet": "Stress - Testing Capability Elicitation With Password - Locked Models .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/52ba0ca9-16c0-436c-8b01-c6f03ea17d49/articles", "content": "Stress - Testing Capability Elicitation With Password - Locked Models ."}
+{"idx": 9, "title": "AI Capabilities - AI Alignment Forum", "date": "", "ddg_snippet": "48[Paper] Stress - testing capability elicitation with password - locked models .", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/w/ai-capabilities", "content": "48[Paper] Stress - testing capability elicitation with password - locked models ."}
diff --git a/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_section_8_limitations_year_2024.jsonl b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_section_8_limitations_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ccf8d89fde1d0f9b9ded338d741799fce6433513
--- /dev/null
+++ b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_section_8_limitations_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password ."}
+{"idx": 1, "title": "[Paper] Stress-testing capability elicitation with password-locked", "date": "", "ddg_snippet": "But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great);", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great);"}
+{"idx": 2, "title": "NeurIPS Poster Stress-Testing Capability Elicitation With Password ...", "date": "", "ddg_snippet": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password ."}
+{"idx": 3, "title": "PDF Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Test-bed for eliciting capabilities definitely present Results for elicitation via finetuning: Only a few samples needed for unlocking with supervised finetuning SFT unlocking generalizes SFT on high-quality samples from one subdomain unlocks other subdomains This also works when the subdomains are locked with different passwords", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/92923.pdf", "content": "Test-bed for eliciting capabilities definitely present Results for elicitation via finetuning: Only a few samples needed for unlocking with supervised finetuning SFT unlocking generalizes SFT on high-quality samples from one subdomain unlocks other subdomains This also works when the subdomains are locked with different passwords"}
+{"idx": 4, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740125", "content": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities."}
+{"idx": 5, "title": "Stress-Testing Capability Elicitation With Password-Locked Models ...", "date": "", "ddg_snippet": "This innovative approach offers a controlled setting to meticulously assess the efficacy of various elicitation methods. The study finds that fine-tuning, especially when provided with a few high-quality demonstrations, effectively recovers the hidden capabilities of password-locked models .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "This innovative approach offers a controlled setting to meticulously assess the efficacy of various elicitation methods. The study finds that fine-tuning, especially when provided with a few high-quality demonstrations, effectively recovers the hidden capabilities of password-locked models ."}
+{"idx": 6, "title": "The Elicitation Game: Stress-Testing Capability ... - OpenReview", "date": "", "ddg_snippet": "In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training \\emph { model organisms} -- LMs with hidden capabilities that are revealed by a password . We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zy6LB5t62f", "content": "In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training \\emph { model organisms} -- LMs with hidden capabilities that are revealed by a password . We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model ."}
+{"idx": 7, "title": "Password-locked models: a stress case for capabilities", "date": "", "ddg_snippet": "Appendix: Password-locked Models as Reverse Trojans Trojans are models which, when exposed to a particular kind of input, act in an undesirable way. Analogies have been drawn between detecting Trojans and deceptive models waiting for a time to strike, with the hope that work on Trojans would build techniques useful for detecting treacherous turns.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "Appendix: Password-locked Models as Reverse Trojans Trojans are models which, when exposed to a particular kind of input, act in an undesirable way. Analogies have been drawn between detecting Trojans and deceptive models waiting for a time to strike, with the hope that work on Trojans would build techniques useful for detecting treacherous turns."}
+{"idx": 8, "title": "Password-locked models: a stress case for capabilities evaluation", "date": "", "ddg_snippet": "Here, you'd be looking for a small pattern which, when applied to any prompt, improves performance on capabilities benchmarks. Constructing a dataset of password-locked models and unlocked models , then training a neural network to classify which is which by examining the weights or activations on standard inputs.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "Here, you'd be looking for a small pattern which, when applied to any prompt, improves performance on capabilities benchmarks. Constructing a dataset of password-locked models and unlocked models , then training a neural network to classify which is which by examining the weights or activations on standard inputs."}
+{"idx": 9, "title": "dk655@cam.ac.uk arXiv:2405.19550v1 [cs.LG] 29 May 2024", "date": "", "ddg_snippet": "he prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked c pabilities can be elicited without using the password . We find that a few high-quality demonstrations are often", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.19550", "content": "he prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked c pabilities can be elicited without using the password . We find that a few high-quality demonstrations are often"}
diff --git "a/data/sampled_jsons/\342\200\234Section_3.3\342\200\235_\342\200\234Derivative-based_Vision_Network_for_INR\342\200\235_accelerated_derivative_computation_year_2025.jsonl" "b/data/sampled_jsons/\342\200\234Section_3.3\342\200\235_\342\200\234Derivative-based_Vision_Network_for_INR\342\200\235_accelerated_derivative_computation_year_2025.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..9cf753bfd825175acc8fffb99c845ef8b556d520
--- /dev/null
+++ "b/data/sampled_jsons/\342\200\234Section_3.3\342\200\235_\342\200\234Derivative-based_Vision_Network_for_INR\342\200\235_accelerated_derivative_computation_year_2025.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Hypernetwork-Based Approach to KAN Representation of Audio", "date": "", "ddg_snippet": "This study introduces the Kolmogorov-Arnold Network (KAN), a novel architecture using learnable activation functions, as an effective INR model for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.02585v1", "content": "This study introduces the Kolmogorov-Arnold Network (KAN), a novel architecture using learnable activation functions, as an effective INR model for ..."}
+{"idx": 1, "title": "COLI: A Hierarchical Efficient Compressor for Large Images", "date": "", "ddg_snippet": "Optimizing such a network on large images further escalates the computational cost, which makes training an INR model for a large image a long ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11443v1", "content": "Optimizing such a network on large images further escalates the computational cost, which makes training an INR model for a large image a long ..."}
+{"idx": 2, "title": "Explorable INR: An Implicit Neural Representation for Ensemble", "date": "", "ddg_snippet": "... INR ) to develop an explorable ensemble surrogate model that exhibits significantly lower computation and memory cost compared to existing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.00904v2", "content": "... INR ) to develop an explorable ensemble surrogate model that exhibits significantly lower computation and memory cost compared to existing ..."}
+{"idx": 3, "title": "T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal", "date": "", "ddg_snippet": "Moreover, due to the smooth nature of neural networks , INR allows for the straightforward computation of derivatives of the signal.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.00066v1", "content": "Moreover, due to the smooth nature of neural networks , INR allows for the straightforward computation of derivatives of the signal."}
+{"idx": 4, "title": "May 12, 2025 - 10-K: Annual report [Section 13 and 15(d), not", "date": "", "ddg_snippet": "... EUR iso4217:CHF iso4217: INR xbrli:shares iso4217:USD xbrli:shares nvl:Mg utr:MMBTU nvl:gallon 0001304280 2024-04-01 2025-03-31 0001304280 2024-09-30 ...", "subpage_snippet": "", "source": "investors.novelis.com", "link": "https://investors.novelis.com/sec-filings/all-sec-filings/content/0001304280-25-000023/nvl-20250331.htm", "content": "... EUR iso4217:CHF iso4217: INR xbrli:shares iso4217:USD xbrli:shares nvl:Mg utr:MMBTU nvl:gallon 0001304280 2024-04-01 2025-03-31 0001304280 2024-09-30 ..."}
+{"idx": 5, "title": "Capturing Longitudinal Changes in Brain Morphology Using", "date": "", "ddg_snippet": "Tensor- Based -Morphometry(TBM) for Neurodegenerative Diseases : Statistical measures from tensor based morphometry (TBM) derived from the deformation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09514v1", "content": "Tensor- Based -Morphometry(TBM) for Neurodegenerative Diseases : Statistical measures from tensor based morphometry (TBM) derived from the deformation ..."}
+{"idx": 6, "title": "Glossary", "date": "", "ddg_snippet": "For example, subcontractor service for a safety inspection for a specific piece of equipment in the month of January which was validated as completed ...", "subpage_snippet": "", "source": "staging.projectcontrolsinstitute.com", "link": "https://staging.projectcontrolsinstitute.com/int/glossary", "content": "For example, subcontractor service for a safety inspection for a specific piece of equipment in the month of January which was validated as completed ..."}
+{"idx": 7, "title": "Union Budget 2025", "date": "", "ddg_snippet": "Budget 2025-26 provides a roadmap for introduction of new income-tax bill, tax reforms including no income tax on earnings up to INR 12 lakhs ...", "subpage_snippet": "", "source": "www.pwc.in", "link": "https://www.pwc.in/budget/union-budget-2025.html", "content": "Budget 2025-26 provides a roadmap for introduction of new income-tax bill, tax reforms including no income tax on earnings up to INR 12 lakhs ..."}
+{"idx": 8, "title": "Floorplan sample autocad Jobs, Employment | Freelancer", "date": "", "ddg_snippet": "... Network Security Specialist Security Consultant STRICTLY NOT REQUIRE BELOW JOB TITLE AND DOMAIN: The following domains and job titles will not be ...", "subpage_snippet": "", "source": "www.freelancer.co.uk", "link": "https://www.freelancer.co.uk/job-search/floorplan-sample-autocad/3/", "content": "... Network Security Specialist Security Consultant STRICTLY NOT REQUIRE BELOW JOB TITLE AND DOMAIN: The following domains and job titles will not be ..."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}